ISIpedia

The Working Group II contribution to the Sixth Assessment Report synthesizes the knowledge on observed impacts of climate change in Chapter 16. While the synthesis text is well accessible from the report itself, the broad literature collection rests on three large supplementary tables that are difficult to grasp in their original form. The tables cover:

climate attribution (Table 16.21, indicated in grey)
impact attribution (Table 16.22, indicated in orange)
weather sensitivity (Table 16.23, indicated in blue)

This article makes these tables interactively accessible through Figure 16.2 of the IPCC report. You can filter for an impact category like Marine ecosystems and a world region through the dropdowns above the figure, and click on the figure icons to reach specific categories like phenology shifts in marine ecosystems. Click on the small rectangles next to each icon to scroll directly to a specific world region for a specific impact category. Please note the legend below the figure and mind the color coding!

The content of this article is taken directly from the IPCC report and is therefor also subject to copy-editing, corrigenda and trickle backs.

Figure 16.2: Impact of Climate Change or Weather Fluctuations.

Table SM16.21: Detection and Attribution of observed changes in the climate-related systems ("climate attribution", references for grey symbols in Figure 16.1). Assessment statements related to "climate attribution", i.e., identification and potential quantification of the contribution of anthropogenic forcing to the observed changes.
Region	Observed change in the climate-related system + attribution to anthropogenic climate forcing	Reference	Synthesis statement: direction (and strength) of the anthropogenic forcing, level of confidence	Underlying mechanism
	Atmosphere - Increase in CO2 concentration
	Observations Atmospheric CO2 concentrations are increasing by 1.6 ppm/yr from 1960-2019.	IPCC AR6 WGI Chapter 2 (Gulev et al., 2021), rate calculated from Annex III information	very high confidence in the growth of CO2;
	Last time concentrations were as high as today was 2 million years ago, and current rates of change (since 1850) are unprecedented in the last 800ka.	IPCC AR6 WGI Chapter 2 (Gulev et al., 2021)	high confidence
	The amplitude of the seasonal cycle of atmospheric CO2 concentrations has increased	IPCC AR6 WGI Chapter 3 (Eyring et al., 2021)
	Attribution The increase reflects the net balance of fossil fuel and cement production and uptake in oceans and terrestrial vegetation. Increased plant activity because of CO2 fertilization responsible for the increase in the amplitude of the seasonal cycle	IPCC AR6 WGI Chapter 2 (Gulev et al., 2021) and Chapter 5 (Canadell et al., 2021)	Anthropogenic CO2 emissions are the main driver of the observed increase in atmospheric CO2 concentrations, virtually certain (***)	Fossil fuel burning and cement production
	Atmosphere - Warming
	Observations Global surface air temperatures (GSAT) have increased since the late 19th century. Total estimated warming from 1850-1900 is 0.85°C (1995-2014), 1.09°C (2011-2020), 1.03°C (2006-2019)	IPCC AR6 WGI Chapter 2 (Gulev et al., 2021), Cross Chapter Box 2.3	virtually certain	dominated by GHG emissions
	Attribution Best estimate of human induced warming since pre-industrial is approximately equal to the observed warming	IPCC AR6 WGI Chapter 3 (Eyring et al., 2021) major contribution of anthropogenic emissions of climate forcers to observed global warming	virtually certain (***)
	S05a Atmosphere - Mean rainfall (annual or seasonal mean)
Global	Observations Global land precipitation has likely increased since the middle of the 20th century, with faster increase since 1980. 1901-2019: Global averaged land precipitation shows a significant increase in two out of three available datasets (GPCCv2020 and GHCNv4, no significant trend in CRU TS 4.04). 1980-2019: General increase in annual precipitation over land, significant in 3 out of 4 available precipitation datasets.	IPCC AR6 WGI Chapter 2 (Gulev et al., 2021), Ren et al. (2013) (1900-2010), Adler et al. (2017) (1979-2014)	Medium confidence in increasing global averaged land precipitation
	Attribution It is likely that human influence has contributed to large scale precipitation changes since 1950. 1900-2010: After removing aerosol effects the effect of anthropogenic GHG on global mean precipitation has been quantified with 2.3 % K-1 respectively 0.071 mm day-1 century-1 (0.072 mm day-1 century-1 over ocean and 0.069 mm day-1 century-1 over land)	IPCC AR6 WGI Ch 3 (Eyring et al., 2021), Gu and Adler (2015) (1900-2010)	Anthropogenic GHG emissions are the main drivers of the observed increase in global mean precipitation, high confidence (***), while aerosol emissions may have dampened the global mean increase.	The increase in global mean precipitation does not follow the increase in atmospheric water vapor but is constrained by the global energy budget leading to a slower increase per degree of global warming.
	Despite large decadal variability, there is medium confidence that rainfall over the wet regions of the tropics has increased due to enhanced greenhouse gas forcing. Yet, there is also growing evidence and medium confidence that this tropical precipitation increase has been partly muted by anthropogenic aerosols.	Gu and Adler (2013) (1988-2010), Gu et al. (2016) (1979-2012)
Africa	Observations The Horn of Africa has experienced a significant long-term decrease in rainfall in the rain seasons from March to May, while rainfall intensity throughout the rainy season increased in the Sahel region during the period 1980-2010.	IPCC AR6 WGI Atlas (Gutiérrez et al., 2021)	high confidence
Africa	Attribution Enhanced rainfall intensity over the Sahel in the last two decades is associated with anthropogenic climate forcing, while East African drying is associated partly with decadal natural variability in SSTs over the Pacific Ocean and partly with anthropogenic-forced rapid warming of Indian Ocean SSTs.	IPCC AR6 WGI Atlas (Gutiérrez et al., 2021)	moderate long-term increase in rainfall intensity (over the Sahel) to minor reduction in rainfall (over East Africa) induced by anthropogenic climate forcing compared to effects of internal climate variability, low confidence (*)
Asia	Observations Mean precipitation has shown significant trends in many parts of Asia, especially increases across higher latitudes, however, spatial variability remains high.	IPCC AR6 WGI Ch 12 (Ranasinghe et al., 2021)	medium
	Summers in eastern China have become wetter in the south but drier in the north in the past 50 years.	Zhou et al. (2021)
	Attribution Aerosols play an important role in weakening (monsoonal) precipitation intensity, as opposed to greenhouse gases that have an enhancing effect on precipitation. Another driver of the observed changes is natural variability. Changed circulation patterns due to anthropogenic forcing can explain a large part of the precipitation trends over eastern China.	IPCC AR6 WGI Ch 12 (Ranasinghe et al., 2021), Ch 3 (Eyring et al., 2021) & Atlas (Gutiérrez et al., 2021), Zhou et al. (2021)	Moderate increases and decrease in precipitation induced by anthropogenic climate forcing depending on the regions, low confidence (*)
Australasia	Observations Australia: Northern Australian rainfall has increased since the 1970s, while April-October rainfall has decreased 16% since the 1970s in south-western Australia and 12% from 2000-2019 in south-eastern Australia. Australian-average rainfall was lowest on record in 2019.	Chapter 11, Table 11.2a, Dey et al. (2019) BoM and CSIRO (2020), BoM (2020)	Medium
	New Zealand: From 1960-2019, almost half of the 30 sites had an increase in annual rainfall (mostly in the south) and 10 sites (mostly in the north) had a decrease. Rainfall increased by 2.8%/decade in Whanganui, 2.1%/decade in Milford Sound and 1.3%/decade in Hokitika. Rainfall decreased by 4.3%/decade in Whangarei and 3.2%/decade in Tauranga.	Chapter 11, Table 11.2b MfE (2020)
	Attribution Decreased rainfall over south-western and south-eastern Australia is partly attributable to anthropogenic climate change. Increased rainfall over north-western Australia is partly attributable to an increase in anthropogenic aerosols rather than greenhouse gases.	Delworth and Zeng (2014),Timbal and Drosdowsky (2013) [IS1], Post et al. (2014), Hope et al. (2017), Dey et al. (2019)	moderate reduction in rainfall due to anthropogenic greenhouse gases in south-western and south-eastern Australia, medium confidence (*), moderate increase in rainfall due to anthropogenic aerosols in north-western Australia, low confidence ()
Central and South America	Observations Precipitation trends are diverse in CSA with small increasing trend in Central America, decreasing trend in southeast Brazil and increasing trend in southern South America	IPCC AR6 WGI Chapter 12 (Ranasinghe et al., 2021)
Central and South America	Attribution Positive rainfall trends over southern South America have been attributed to ozone depletion and GHG.	IPCC AR6 WGI Chapter 3 (Eyring et al., 2021)	Moderate contribution of anthropogenic climate forcing to increasing rainfall over Southern South America, low confidence (*) no assessment elsewhere
Europe	Observations Precipitation has generally increased in Northern and decreased in Southern Europe.	IPCC AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	high confidence
Europe	Attribution This pattern in mid to high latitude land precipitation over the northern hemisphere has been attributed to human influence.	IPCC AR6 WGI Chapter 3 (Eyring et al., 2021)	moderate increase in northern and moderate decrease in southern Europe induced by anthropogenic climate forcing, medium confidence (**)
North America	Observations There is evidence of long-term declines in precipitation over northwestern Mexico and the southwestern United States. Significant increases in rainfall are observed in the northern portions of the continent.	IPCC AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	medium
North America	Attribution The precipitation observing network is spatially inadequate and temporally inconsistent over some regions of North America, so that detection and attribution of multidecadal trends is difficult.	IPCC AR6 WGI Atlas (Gutiérrez et al., 2021)	inconsistent findings
Small Islands	Observations Observation datasets have revealed no significant long-term trends in rainfall in the Caribbean over the 20th century when analysed at seasonal and inter-decadal timescales. Over the western Pacific, recent analysis of station data showed spatial variations in the mostly decreasing annual total rainfall from 1961-2011, trends mostly non-significant.	IPCC AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	low
Small Islands	Attribution no dedicated studies		no assessment
	S05b Atmosphere - Heavy precipitation
Global	Observations Over the last three decades the number of record-breaking events has significantly increased in the global mean. Globally, this increase has led to 12% more record-breaking rainfall events over 1981-2010 compared to those expected in stationary time series. From 1951 -1999 observations of heavy precipitation show an overall increasing trend in the total Northern Hemisphere, with 65% and 61% of the total data-covered areas having positive trends for both daily maximum precipitation and five-day consecutive precipitation.	Lehmann et al. (2015), Zhang et al. (2013)
Global	Attribution On global scale the increase can be explained by the warming of air and associated increasing water holding capacity. Whilst the number of rainfall record-breaking events can be related to natural multi-decadal variability over the period from 1901 to 1980, observed record-breaking rainfall events significantly increased afterwards consistent with rising temperatures. Climate model simulations indicate that global warming primarily induced by anthropogenic forcing has increased the probability of heavy precipitation events (daily rainfall > 99.9 percentile of pre-industrial distribution) by about 18% (= Fraction of Attributable Risk). Anthropogenic climate forcing is estimated to have intensified annual maximum 1 day precipitation in sampled Northern Hemisphere locations by 3.3% (1.1% to 5.8%, >90% confidence interval) on average.	Lehmann et al. (2015), Fischer and Knutti (2015), Zhang et al. (2013)	moderate contribution of anthropogenic climate forcings to the observed increase in the occurrences of extreme precipitation events, medium confidence (**)	Atmospheric warming increases the water holding capacity of the air and therefore the potential for more intense precipitation events. While changes in global average precipitation are constrained by the global energy budget, levels of extreme precipitation are expected to increase at a higher rate in line with the Clausius-Clapeyron relationship. In addition to these thermodynamic changes heavy precipitation events are subject to dynamic changes depending on the regions. Dynamic circulation changes include the weakening of the large-scale monsoon circulation, the modulation of regional monsoon circulation by changes in land-sea thermal contrast and the SST patterns, and changes in tropical synoptic disturbances such as monsoon depression and tropical cyclones.
Africa	Observations Precipitation extremes show non-homogenous trends over the African continent where data are available. Over the Sahara and Sub-Saharan Africa, increases in the frequency and intensity of extreme precipitation have been observed. A larger percentage of stations shows increases than decreases. Significant upward trends for extreme precipitation-related indices are identified.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021)	There is insufficient data to assess trends in heavy precipitation over the continent but medium confidence in increased heavy precipitation in Southern Africa.
Africa	Attribution Individual heavy precipitation extremes have not been attributed in West Africa and Southern Africa where studies exist.	Lawal et al. (2016), Parker et al. (2017), Fučkar et al. (2020)	minor contribution of anthropogenic climate forcing where studies exist (West Africa, Southern Africa), low confidence (*), largely missing assessments
Asia	Observations There is an observed increase in precipitation extremes over Central Asia, most of South Asia, the southern and northern Tibetan Plateau, the northwest Himalaya, the Indochina and east-central Philippines, Jakarta, the eastern and northwestern China, Japan and Korea.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021)	Heavy precipitation likely increased.
	Attribution Studies on extreme precipitation events mostly found that anthropogenic climate forcing has increased the observed probability or magnitude of observed precipitation events. A study on the 2018 summer persistent heavy rainfall in central western China found that anthropogenic forcing has reduced the probability of persistent rainfall, but increased that of daily extremes.	Burke et al. (2016) (China), Sun and Miao (2018) (China), Zhou et al. (2017) (China) Yuan et al. (2018), Sun et al. (2019), Li et al. (2017a) (daily precipitation extremes over China), Ma et al. (2016) (shift from light to heavy precipitation over eastern China), Zhang et al. (2020b) (western China daily precipitation), Rimi et al. (2019) (probability of pre-monsoon extreme rainfall events in Bangladesh), Kawase et al. (2020) (heavy rain event of 2018 in Japan),	anthropogenic climate forcing has contributed to the observed increase in heavy precipitation, high confidence (***)
	In other cases a contribution of climate could not be identified	Zhou et al. (2013) (2012 extreme rainfall in North China), van Oldenborgh et al. (2016) (heavy precipitation event of 2015 in India)
Australasia	Observations Australia: Daily rainfall associated with thunderstorms has increased from 1979-2016, particularly in northern Australia. Daily rainfall intensity increased in the northwest from 1950-2005 and in the east from 1911-2014, and decreased in the south-west and Tasmania from 1911-2010. Hourly extreme rainfall intensities increased by 10-20% in many locations between 1966-1989 and 1990-2013.	IPCC AR6 WGI Ch 11 (Seneviratne et al., 2021), Table 11.2a, Dey et al. (2019) Alexander and Arblaster (2017) MfE (2020) Donat et al. (2016), Dunn et al. (2020a), Evans et al. (2017), Dowdy (2020) Guerreiro et al. (2018)	There is limited evidence for an increase in heavy precipitation for the continent as a whole, but medium confidence for an increase in heavy precipitation in Northern Australia.
	New Zealand: The number of days with extreme rainfall increased at 14 of 30 sites and decreased at 11 sites from 1960-2019. Most sites with increasing annual rainfall had more extreme rainfall and most sites with decreasing annual rainfall had less extreme rainfall.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Table 11.2b, MfE (2020)	low confidence
	Attribution Australia: Anthropogenic greenhouse gas influence on extreme rainfall events in southern and eastern Australia is highly uncertain. In southeast Australia ENSO has a much stronger influence on extreme precipitation than anthropogenic climate change. In northwest Australia, the extreme rainfall increase since 1950 can be related to increased monsoonal flow due to increased anthropogenic aerosol emissions, but cannot be attributed to an increase in greenhouse gases.	Christidis et al. (2013) King et al. (2013) Lewis and Karoly (2015) Dey et al. (2019) Tozer et al. (2020) Lewis et al. (2017)	mostly minor contribution of anthropogenic climate forcing to observed changes in extreme precipitation, low confidence (*)
	New Zealand: The risk of an extreme 5-day July rainfall event over Northland, New Zealand, such as was observed in early July 2014, has likely increased due to anthropogenic influence on climate.	Rosier et al. (2015) MfE (2017)
Central and South America	Observations Extreme precipitation has increased in South America, though trends (both upward and downward) in precipitation indices are not statistically significant at most stations.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021)	There is insufficient data to assess trends in heavy precipitation for the whole region, but high confidence for increase in Southeastern South America.
	Attribution Attributable increase in extreme rainfall in most parts of South America, apart from SWS.	Li et al. (2020c)	Moderate contribution of anthropogenic climate forcing to increase in extreme rainfall, low confidence (*)
	In the Uruguay River Basin anthropogenic climate change has increased the risk of the April-May 2017 extreme rainfall by at least 2 times.	de Abreu et al. (2019)
	The extremely wet March of 2017 in Peru is partly attributable to anthropogenic climate change, which made such an event at least 1.5 times more likely than under baseline conditions.	Christidis et al. (2019)
Europe	Observations There is an observed increase in extreme precipitation events over Europe as a whole with strong regional differences even at local scales. Over northern Europe, rainfall extremes in winter have increased.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021)	Likely intensification of heavy precipitation.
Europe	Attribution Some studies found that climate change mostly increased the probability or magnitude of observed extreme precipitation events including European winters. Wet summer of 2012 not attributable to climate change.	extreme winter rainfall: Otto et al. (2018a) Schaller et al. (2016) Vautard et al. (2016) high summer rainfall: Otto et al. (2015d) (wet summer 2012) Schaller et al. (2014) (heavy precipitation in May-June 2013 in the upper Danube and Elbe basins) Wilcox et al. (2018) (European summer of 2012) IPCC AR6 WG1 Chapter 11 (Seneviratne et al., 2021)	Anthropogenic climate forcing has contributed to the observed intensification of heavy precipitation, high confidence (*) in particular in Northern European winter but not in the summer, medium confidence () (robust evidence)
North America	Observations Precipitation extremes have increased throughout North America since 1950, especially in the eastern half of the United States.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Hall and Kossin (2019) (for rainfall associated with hurricanes)	high confidence Likely intensification of heavy precipitation
North America	Attribution Some studies found an increasing effect of anthropogenic climate forcing on the probability or magnitude of observed extreme precipitation events for parts of the US for individual events. Results depend on framing. Studies of hurricane Harvey severely affecting the Houston metropolitan area in August 2017 found a 15%-38% increase in storm total precipitation attributable to global warming. Anthropogenic climate forcing is estimated to have increased the precipitation associated with Hurricane Katrina severely affecting the Gulf Coast of the USA in August 2005 by 4%-9%, and by 6% for Irma affecting the Florida Keys in 2017. Increased rainfall associated with hurricanes may partly be induced by increased stalling near the coast.	Knutson et al. (2014) Szeto et al. (2015) Emanuel (2017) Van Oldenborgh et al. (2017), Risser and Wehner (2017), Wang et al. (2018) (all three on hurricane Harvey) Patricola and Wehner (2018) (hurricane Katrina, Irma, Maria) Trenberth et al. (2015) (Boulder floods, 2013), Eden et al. (2016) (Boulder floods, 2013), Wang et al. (2015) (Extreme precipitation over the southern Great Plains, May 2015) IPCC WG1 Chapter 11, SPM (Seneviratne et al., 2021)	Anthropogenic climate forcing has contributed to the observed increase in frequency or intensification of heavy precipitation, medium confidence (), high confidence with regard to hurricanes (*)
Small Islands	Observations Record-breaking rainfall associated with hurricane Maria over Puerto Rico in 2017 (highest total averaged precipitation of 129 storms that have impacted the island since 1956). Heavy precipitation associated with hurricane Irma heavily affecting the Virgin Islands.	Keellings and Ayala (2019)
Small Islands	Attribution The probability of precipitation of Maria's magnitude is estimated to have increased due to long-term climate changes by about a factor of 5 (best estimate). Anthropogenic climate forcing is estimated to have increased the rainfall associated with Maria and Irma by 9% and 6%, respectively.	Keellings and Ayala (2019), Patricola and Wehner (2018)	moderate increase in precipitation associated with tropical cyclones, high confidence (***) no assessment elsewhere
	S05c Atmosphere - Drought
Global	Observations There is medium / high confidence in increasing drought conditions in several parts of the world, such as the Mediterranean, West Africa, the Caribbean and Central Asia, while drought conditions have become less severe in Northern Europe, central North America and North-West Australia.	Chapter 4.2.5.1	There is medium confidence that drought conditions have increased moderately in several regions on all continents.
Global	Attribution Anthropogenic climate forcing has increased the frequency and the severity of droughts over the last decades in the Mediterranean, western North America, south-western Australia ,southern Africa, and southwestern South America. Event attribution studies suggest that anthropogenic climate change increased the likelihood of several major drought events that had substantial societal impacts in North America, South America, Africa and Asia.	Chapter 8.3.1.6	Anthropogenic climate forcing has contributed to increasing drought conditions in the Mediterranean, western North America, south-western Australia, southern Africa, and southwestern South America, medium to high confidence () and reduced of drought conditions in northern Europe, medium confidence ()	Human influence has contributed to changes in water availability during the dry season over land areas, including decreases over several regions due to increases in evapotranspiration (medium confidence). The increases in evapotranspiration have been driven by increases in atmospheric evaporative demand induced by increased temperature, decreased relative humidity and increased net radiation over affected land areas (high confidence). WGI Ch11, 11.6.4.5
Africa	Observations In West Africa, increases in drought duration and intensity, as well as the duration and intensity of drought in southern Africa are observed. In Eastern Africa an increase in meteorological drought has been observed. Cape Town, South Africa experienced a serious drought during 2015-2017 where annual mean rainfall fell below 50% of long term mean in some areas.	Kasei et al. (2009) (West Africa), Masih et al. (2014) (entire continent), Otto et al. (2018c) (Cape Town)
Africa	Attribution The increased drying in West Africa is attributable to climate change over 1901-2010 and 1951-2010 time frames, but there is an unexplained trend reversal for 1981-2010. A relatively large number of attribution studies on drought in Eastern Africa found no significant change due to anthropogenic climate change. However, in Southern Africa recent meteorological droughts have been attributed to anthropogenic climate change. Cape Town drought has become three times more likely due to anthropogenic climate forcing. Further high impact drought events that have been attributed to anthropogenic climate forcing (increased likelihood): East Africa, 2017, Southern Africa 2016, East Africa, 2014	AR6 WGI Table 11.6 (West Africa) (Seneviratne et al., 2021), Funk et al. (2018), Otto et al. (2018a), Philip et al. (2017), Uhe et al. (2018) (all on East Africa), Otto et al. (2018c) (Cape Town drought), Bellprat et al. (2015) (South Africa), AR6 WG1 Chapter 8 (8.3.1.6) (Douville et al., 2021) Chapter 4.2.5, Table 4.5	Minor contribution of anthropogenic climate forcing to increasing drought conditions in East and West Africa, low confidence (), moderate contribution of anthropogenic climate forcing to increasing drought conditions in southern Africa, medium confidence (*)
Asia	Observations China: Since the 1950s some regions of China have experienced a drying trend and more intense and longer droughts, in particular in Northern China, but in some (sub-)regions, droughts have become less frequent, less intense, or shorter.	Qin et al. (2015a) (Southwestern China), Zhao and Dai (2016) Qin et al. (2015b) Liu et al. (2015) (all three on Northern China), Dai and Zhao (2017), Zhang et al.) Li et al. (2020b) IPCC AR6 WG1 Chapter 11 (Seneviratne et al., 2021)	medium confidence in increase in drought observed in West Central Asia, East Central Asia, and East Asia
	South Asia: Drought frequency is increasing in some areas, whereas it is decreasing in the Tibetan Plateau.		medium confidence in increase in drought observed in West Central Asia, East Central Asia, and East Asia
	Middle East: Between 2006/07 and 2010/11, the Fertile Crescent region experienced the most severe drought in the instrumental record. Persistent drought in southern Levant during 2014 rainy season.	Hoerling et al. (2012): Gleick (2014), Kelley et al. (2015), Bergaoui et al. (2015) (Middle East)	medium confidence in increase in drought observed in West Central Asia, East Central Asia, and East Asia
	Attribution There is evidence that drought occurrence, severity, and regime have changed as a result of anthropogenic climate forcing. In some regions in South East Asia droughts have been attributed to El Nino, but not climate change. Middle East: There is a long-term winter drying trend in the Mediterranean and Levant region that can only be explained taking into account anthropogenic emissions-related forcings. The magnitude of the 2006/07 to 2010/11 drought would have been highly unlikely without this trend. The 2014 drought was also made more likely by anthropogenic emissions-related forcings	Chen and Sun (2017b), Chen and Sun (2017a) (China), McBride et al. (2015) (no link to climate change for the 2015 drought in Singapore/ Malaysia), King et al. (2016) (Drought in Indonesia made more likely by El Niño and climate change) (South East Asia), Hoerling et al. (2012), Gleick (2014), Kelley et al. (2015), Bergaoui et al. (2015)(Middle East)	minor contribution (e.g. South East Asia) of anthropogenic climate forcing to increasing drought conditions, low confidence (), to strong contribution of anthropogenic climate forcing to increasing drought conditions (e.g. Middle East), medium confidence (*)
	China: Anthropogenic climate forcing has increased the likelihood of some high impact droughts in China: Yunnan, south-western China, 2019, Southwestern China, 2019, South China, 2019, South China, 2018. Anthropogenic climate forcing has decreased the likelihood of the high-impact drought in the middle and lower reaches of the Yangtze River, China.	Chapter 4.2.5, Table 4.5
	Thailand: Anthropogenic climate forcing has increased the likelihood of the high impact drought in Thailand, 2016	Chapter 4.2.5, Table 4.5
Australasia	Observations Australia: Major Australian droughts occurred in 1895-1902, 1914-1915, 1937-1945, 1965-1968, 1982-1983, 1997-2009 and 2017-2019. The 2019 annual mean precipitation set a dryness record (since 1900) for Australia as a whole and southeastern Australia where it was associated with extreme wildfires. Fewer droughts have occurred across most of northern and central Australia since the 1970s, more droughts in the south-west since the 1970s, and mixed drought trends in the south-east since the late 1990s. An increase in extreme fire weather and in the length of the fire season since the 1950s, especially in southern Australia. More dangerous conditions for extreme pyro convection events since 1979, particularly in southern and eastern Australia. Far southwest Western Australia has a statistically significant increase in drought intensity and southeast Australia has shown a significant increase in the average length of droughts.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Table 11.2a, BoM (2021), Kirono et al. (2020), Gallant et al. (2013), Delworth and Zeng (2014), Alexander and Arblaster (2017), Knutson and Zeng (2018), Dey et al. (2019), Dunn et al. (2020a), Spinoni et al. (2019), Rauniyar and Power (2020), Dai and Zhao (2017), Dowdy and Pepler (2018), BoM and CSIRO (2020)	Medium confidence in decrease in agricultural drought in Northern Australia and increase in Southern Australia.
	New Zealand: Inconsistent trends. Drought frequency increased at 13 of 30 sites from 1972-2019 and decreased at 9 sites. Drought intensity increased at 14 sites, 11 of which are in the north, and decreased at 9 sites, 7 of which are in the south.	Table 11.2b, MfE (2020)
	Attribution Australia: Reduced drought frequency in northern Australia and increased drought frequency in south-western Australia have been partly attributed to anthropogenic climate change. Extreme fire weather in southeastern Australia in 2019-20 was 30% more likely due to anthropogenic climate change. New Zealand: A study of the 2013 North Island New Zealand drought found a 0.2-0.4 fraction of attributable risk to anthropogenic climate change.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Delworth and Zeng (2014), Dey et al. (2019), Knutson and Zeng (2018), Harrington et al. (2014) (New Zealand), van Oldenborgh et al. (2021) (fire weather), IPCC AR6 WG1 Chapter 8 (8.3.1.6) (Douville et al., 2021)	anthropogenic climate forcing has partly contributed to the reduction in drought probability in northern Australia, low confidence () anthropogenic climate forcing has contributed to the increase in drought conditions in southwestern Australia, medium to high confidence (*)
Central and South America	Observations There is medium to high confidence in a mostly upward trend in meteorological droughts in 4 out of 9 AR6 regions of Central and South America (mixed signals elsewhere, low confidence).	IPCC AR6 WGI Table 11.15 (Seneviratne et al., 2021)	There is medium to high confidence for an increase in droughts in several regions.
	Mega-Drought in Central Chile, 2010: The region has experienced an uninterrupted sequence of dry years since 2010 with mean rainfall deficits of 20-40%. The Mega Drought (MD) is the longest event on record and with few analogues in the last millennia.	Garreaud et al. (2020)
	Attribution In South America attribution of two droughts in North East Brazil found no role of climate change: For the droughts in the larger Sao Paulo area in 2014 and 2016 factors other than climate change have been found to be the main drivers of the drought.	Otto et al. (2015c), Martins et al. (2018)	minor (North East Brazil) to moderate (southwestern South America) increase of drought probability / intensity induced by anthropogenic climate forcing, medium confidence (**)
	Mega-Drought in Chile, 2010: Event has been partly attributed to anthropogenic greenhouse gas emissions and ozone depletion. There is medium confidence that drying in central Chile can be attributed to human influence.	IPCC AR6 WG1 Chapter 8 (8.3.1.6) (Douville et al., 2021)
Europe	Observations In Europe there are overall small changes observed with respect to droughts, and they depend on the drought metric, the season and region. For the Mediterranean, there is medium confidence in an observed drying trend. There is an increase in the probability and intensity of agricultural and ecological droughts caused by an increase in atmospheric evaporative demand and increase of hydrological droughts in Southern and South Eastern Europe.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), CCP4	There is medium confidence in an increase in drought in the Mediterranean and decrease in Northern Europe.	Increased evapotranspiration
	Mediterranean France: The Canadian Fire Weather Index, an indicator based only on climate, increased from 1958 to 2017.	Barbero et al. (2020)
	Attribution Anthropogenic influences on increased drought in the Mediterranean.	SR1.5 (Hoegh-Guldberg et al., 2018), IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Chapter 8.3.1.6, Douville et al. (2021)	Anthropogenic climate forcing has increased in drought conditions in the mediterranean region and reduced drought conditions in northern regions, medium to high confidence (**)
	Mediterranean France: Anthropogenic climate change accounting for nearly half the increase in fire risk index.	Barbero et al. (2020)
North America	Observations Droughts have become less frequent, less intense, or shorter in Central North America since 1950. There are inconsistent trends in other regions, although some heat waves have reached record intensity in western regions of the United States.	IPCC AR6 WGI Ch 11 (Seneviratne et al., 2021), Chapter 8 (8.3.1.6) (Douville et al., 2021)	There is medium confidence in an increase in ecological and agricultural drought in Western North America.
	California 21st century drought conditions: Southwestern North America has been anomalously dry and warm in the 21st century (2000 to 2018) relative to the 20th century with reduced river flow and lake levels and declines in groundwater availability.	Williams et al. (2020), Xiao et al. (2018), (Colorado river flow), Rodell et al. (2018), Faunt et al. (2016) (both on groundwater reduction)
	Attribution Anthropogenic climate change accounts for half the severity of the strong soil moisture deficits in the last two decades in western North America. Increased atmospheric evaporative demand played a dominant role in the intensification of the 2014 drought in California: Anthropogenic climate forcing has increased the probability of co-occurring warm-dry conditions like those that have created the 2012-14 drought. Even in the absence of trends in mean precipitation - or trends in the occurrence of extremely low-precipitation events - the risk of severe drought in California has increased due to extremely warm conditions induced by anthropogenic global warming.	IPCC AR6 WGI Chapter 11 (Seneviratne et al., 2021), Chapter 8 (8.3.1.6) (Douville et al., 2021) Diffenbaugh et al. (2015), Williams et al. (2015)	Anthropogenic forcing has contributed to recent droughts and drying trends in western North America, medium to high confidence (**) no broad assessment elsewhere
	Anthropogenic climate forcing has increased the probability of the following high-impact droughts: USA, Northern Great Plains, 2017, USA, Washington state, 2015	Chapter 4.2.5, Table 4.5, Lee et al. (2021)
Small Islands	Observations In the Caribbean, the Palmer Drought Severity Index (scPDSI) calculated for the years from 1950 to 2016 shows a clear drying trend in the region and shifts to increasing dry conditions. The trend is -0.09 / decade (p < 0.05, negative values indicate drought conditions, while 0 indicates normal conditions) The most severe drought was recorded in the 2013-2016 period.	Herrera and Ault (2017), Herrera et al. (2018)
Small Islands	Attribution Anthropogenic climate forcing has contributed ~15-17% to the severity and ~7% to the spatial extent of the 2013-2016 drought.	Herrera et al. (2018)	anthropogenic climate forcing has contributed to drought conditions in the Caribbean, low confidence (*) no assessment elsewhere
	Ocean - Warming
	Observations More than 90% of the Earth’s energy imbalance between 1971 and 2010 has been stored as heat in the ocean and has warmed its water-masses	IPCC AR6 WGI 7.2.2.2 (Forster et al., 2021)	high confidence
	Sea Surface Temperatures (SSTs) have risen at global scales, at a rate of 0.15°C per decade over the satellite era (1980-2020), and at an average rate of 0.09°C per decade for the period 1900-2020. SST has warmed in all ocean sectors, with the exception of some regions such as the eastern Pacific Ocean, the North Atlantic Ocean, and the Southern Ocean, which have warmed slowly or cooled owing to regional ocean dynamics	IPCC AR6 WGI 9.2.1.1, Figure 9.3 (Fox-Kemper et al., 2021)	high confidence
	Since the early 1970 ocean has been warming at average rates of >0.1-C per decade in the upper 75 m and 0.015°C per decade at 700 m depth.	Reid (2016) (ocean heat uptake), AR5 (ocean warming)
	Observations Strong warming in the Southern Ocean, warming in the North Atlantic and cooling throughout most of the Indo-Pacific over the 21st century at 2000-4000m.	Desbruyères et al. (2017)	medium confidence
	Observations Warming is detectable throughout the Southern Ocean basin, with a global mean trend of 0.53°C per year from 1990 to 2010.	IPCC AR6 WGI Chapter 9.2.2.1, Purkey and Johnson (2010) Desbruyères et al. (2016)
	Attribution Basin-scale temperature changes in the upper 0-700m of the ocean since 1955 are induced by anthropogenic forcing.	IPCC AR6 WGI Chapter 9.2.2.1 (Fox-Kemper et al., 2021)	Strong contribution of anthropogenic climate forcing in ocean warming, virtually certain (***)
	Improved Southern Ocean observations have revealed that large-scale 0-2000m temperature changes can be attributed to mainly anthropogenic greenhouse gases.	IPCC AR6 WGI Chapter 9.2.2.1 (Fox-Kemper et al., 2021)	medium confidence
	S06 Marine ecosystems - Marine heatwaves
Global	Observations From 1925 to 2016, marine heat waves have become more frequent (+34%) and longer (+17%), resulting in an increase in the number of marine heat wave days at the global scale over the same period (+54 %). From 1981 to 2017 the frequency, duration, and intensity of large marine heat waves increased: In the first decade 27 large marine heat waves occurred with an average duration of 32 days, an average peak temperature anomaly of 4.8°C. By contrast, 172 large marine heat waves occurred in the past decade, with an average duration of 48 days, an average peak temperature anomaly of 5.5°C.	Bond et al. (2015), Zhou and Wu (2016), Jackson et al. (2018) Oliver et al. (2017) Oliver et al. (2018a), Oliver et al. (2018b) Benthuysen et al. (2018), Kim and Han (2017) (Holbrook et al., 2019), Smale et al. (2019), Smale et al. (2015), Frölicher et al. (2018), Frölicher and Laufkötter (2018), Munari (2011) Laufkötter et al. (2020), Oliver et al. (2021)	very high confidence in increasing frequency and intensity of marine heatwaves
Global	Attribution These trends in marine heat waves are explained by an increase in ocean mean temperatures and 87% of marine heat waves are attributable to human-induced warming. Examples relevant for impact attribution below:	IPCC AR6 WGI Cross-Chapter Box 9.2 (Fox-Kemper et al., 2021), Frölicher et al. (2018), Oliver et al. (2018b) Laufkötter et al. (2020)	major contribution of anthropogenic climate forcing to the observed increase in the frequency and duration of marine heat waves, very high confidence (***)
Polar seas	Observations Marine heatwave in the Northwest Atlantic 2012 (partly polar partly temperate) with an intensity of 2.15°C (temperature anomaly above the baseline climatology averaged across the area of the heatwave and its duration) and a duration of 57 days (number of days where temperatures are above the 99.9th percentile of the baseline climatology), Marine heatwaves in the Southern Ocean 2016 with an intensity of 1-C and a duration of 183 days.	Laufkötter et al. (2020)
Polar seas	Attribution Marine heatwave in the Northwest Atlantic 2012: Likelihood of an event of this intensity occurring now is estimated to be more than 30 times higher than under pre-industrial conditions, likelihood for an event of this duration has increased 25-fold. Marine heatwaves in the Southern Ocean 2016: No significant effect of climate change on the likelihood for the occurrence of an event of this intensity or duration.	Laufkötter et al. (2020)	major to minor increase of occurrence probabilities of some recent major marine heatwaves induced by anthropogenic climate forcing, low confidence (*)
Temperate oceans	Observations Marine heatwave in Western Australia, 2011 (partly temperate, partly tropical) with an intensity of 2.26-C (temperature anomaly above the baseline climatology averaged across the area of the heatwave and its duration) and a duration of 101 days (number of days where temperatures are above the 99.9th percentile of the baseline climatology), Marine heatwave in the Northwest Atlantic 2012 (partly polar, partly temperate, discussed above), Marine heatwave in the Northeast Pacific from 2013 to 2015 with an intensity of 1.56-C and a duration of 367 days, Marine heatwave in the Tasman Sea 2015 and 2016 with an intensity of 1.49°C and a duration of 175 days, Marine heatwave in the Southwest Atlantic 2017 with an intensity of 1.96-C and a duration of 82 days,	Laufkötter et al. (2020) Oliver et al. (2021)
Temperate oceans	Attribution Marine heatwave in Western Australia, 2011: Likelihood of an event of this duration occurring now is estimated to be about 5 times higher than under pre-industrial conditions, but the change is not significant, Marine heatwave in the Northwest Atlantic 2012: see above, Marine heatwave in the Northeast Pacific from 2013 to 2015: Likelihood of an event of this intensity and an event of this duration has increased more than 100-fold, Marine heatwave in the Tasman Sea 2015 and 2016: Likelihood for an event of this intensity and an event of this duration has increased about 50-fold and more than 100-fold, respectively. Marine heatwave in the Southwest Atlantic 2017: Likelihood of an event of this intensity and an event of this duration has increased more than 100-fold.	Laufkötter et al. (2020)	moderate to major increase of occurrence probabilities of some recent major marine heatwaves induced by anthropogenic climate forcing, low confidence (*)
Tropical oceans	Observations Marine heatwave in Western Australia, 2011 (partly temperate, partly tropical, discussed above). Marine heatwave in the Indo-Australian Basin 2016 with an intensity of 1.67°C (temperature anomaly above the baseline climatology averaged across the area of the heatwave and its duration) and a duration of 90 days (number of days where temperatures are above the 99.9th percentile of the baseline climatology). The 2015-16 North Australian Marine heat wave was the most intense and the second longest in the 35-year long record of satellite measurements. Marine heat wave in tropical Indian Ocean in 2015: Heat stress exceeded the threshold for coral bleaching for > 60% of reefs. Marine heatwaves in the tropical Atlantic in 2005, 2010 and 2016: 39-61% of reefs under heat stress exceeding bleaching thresholds.	Laufkötter et al. (2020) Park et al. (2017) Oliver et al. (2018b), Donner et al. (2007), Oliver et al. (2021)
Tropical oceans	Attribution Marine heatwave in Western Australia, 2011 (discussed above), Marine heatwave in the Indo-Australian Basin 2016: Likelihood of an event of this intensity occurring now is estimated to be more than 100 times higher than under preindustrial conditions, Marine heat wave in tropical Indian Ocean in 2015: Event attribution analysis finds that the probability of the sea surface temperature anomalies were increased at least 7 times by anthropogenic forcing. Marine heatwave in the tropical Atlantic in 2005: Anthropogenic climate forcing is estimated to have increased the probability of the 2005 heat wave by at least an order of magnitude. The level of heat stress measured in the eastern Caribbean in 2005 would have been a 1 in 1,000-year event absent anthropogenic forcing.	Park et al. (2017), Laufkötter et al. (2020), Donner et al. (2007)	moderate to major increase of occurrence probabilities of some recent major marine heatwaves induced by anthropogenic climate forcing, low confidence (*)
	Ocean - Changes in salinity patterns
	Observations Near-surface ocean salinity has changed since the 1950s, with regional enhancement of salinity contrasts: fresh regions freshening and salty regions becoming saltier	IPCC AR6 WGI Chapter 9.2.2.2 (Fox-Kemper et al., 2021)	extremely likely	Ocean freshening (decreased salinity) is induced by enhanced precipitation relative to evaporation and is exacerbated by sea ice melt.
	Attribution Patterns of basin-scale salinity changes are extremely likely to result from anthropogenic forcing in the 0-700m ocean since the mid-20th century.	IPCC AR6 WGI Chapter 9.2.2.2 (Fox-Kemper et al., 2021)	Strong contribution of anthropogenic climate forcing to salinity changes, extremely likely (***)
	Ocean composition - Acidification
	Observations Over the period 1991-2011, mean ocean pH declined by 0.018 ± 0.004 units per decade in 70% of ocean biomes, with the largest declines in the Indian Ocean, eastern Equatorial Pacific and the South Pacific subtropical biomes, with slightly lower rates of change in the Atlantic and Southern Oceans. The decline is accompanied by a fall in concentration and the saturation states of various calcium carbonates. The depths at which spontaneous dissolution of calcium carbonate minerals (aragonite and calcite saturation depths) vary considerably between and within oceans. In some locations of the western and equatorial Atlantic Ocean, saturation depth has risen by ~300 m	SROCC, Chapter 5: Lauvset et al. (2015) Sulpis et al. (2018) (rise of saturation depth), Negrete-García et al. (2019) (rise of saturation depth)	decline in ocean pH virtually certain
	Attribution Over the last two decades the ocean has sequestered about 25% of the CO2 released by anthropogenic activities, which drives the decline of ocean pH.	SROCC, Chapter 5, Quéré et al. (2018)	Anthropogenic CO2 emissions are the main driver of the observed global scale ocean acidification with only local exceptions, high confidence (***)	The rise in atmospheric CO2 causes ocean acidification. Locally surface acidification can be induced by high fertilizer inputs.
	Increased fertilizer run-off and atmospheric deposition of anthropogenic nitrogen and sulphur has enhanced surface ocean acidification as shown for the Gulf of Mexico and the East China Sea	Doney et al. (2007), Doney et al. (2009), Cai et al. (2011)
	S08a Coastal systems - Mean sea levels Global mean changes in sea level are measured as increase or decrease in the volume of the ocean divided by the ocean surface area. Regional attribution assessments refer to relative sea levels, i.e. relative to the land at a particular location as measured at tide gauges. There is no attribution statement provided for Figure 16.1 if attribution studies only refer to changes in absolute (eustatic) regional sea levels, i.e. the distance from the center of the earth to the sea surface.
Global	Observations Global mean sea level rose at a rate of 1.35 [0.78 to 1.92] mm yr-1 for the period 1901-1990 and 3.25 [2.88 to 3.61] mm yr -1 for 1993-2018.	IPCC AR6 WGI Chapter 2, Palmer et al. (2021)	The rate of global mean sea level rise since the 20th century is faster than over any preceding century in at least the last three millennia, high confidence (***)
	Attribution Today climate change is the main driver of global mean sea level rise. At least 70% of the combined change in ice mass loss from glaciers and ice sheets, and thermal expansion since 1970 can be attributed to anthropogenic forcing. The percentage has increased over the course of the 20th century.	SROCC, IPCC AR6 WGI Chapter 3 (Eyring et al., 2021), Marcos and Amores (2014) (thermal expansion), Slangen et al. (2014), Slangen et al. (2016) (total across glaciers, Antarctic and Greenland ice sheet surface mass balance and thermal expansion),	Anthropogenic climate forcing is the dominant driver of observed increase in global mean sea level at least since 1970, high confidence (***)	Global mean sea level rise is driven by thermal expansion, mass loss of mountain glaciers and the Greenland and Antarctic ice sheet as well as changes in land water storage.
	The direct anthropogenic influence on global sea level through land water storage (-0.21 mm yr-1) is small compared to ice mass loss and thermal expansion (1.52 mm yr-1) over the period 1900-2018. The dominance of the latter terms increases for the periods 1957-2018 and 1993-2018. Anthropogenic alteration of land water storage peaks with dam construction in the 1970s and strongly declines thereafter.	Frederikse et al. (2020)
Africa	Observations Over 1900-2018 basin-mean relative sea-level rose 1.3 ± 0.5 mm yr -1 in the Indian Ocean-South Pacific, 2.1 ± 0.7 mm yr -1 in the South Atlantic and 1.1 ± 0.3 mm yr -1 in the subpolar North Atlantic. Over 1993-2018 basin-mean relative sea-level rose 3.9 ± 0.6 mm yr -1 in the Indian Ocean-South Pacific, 3.9 ± 1.4 mm yr -1 in the South Atlantic and 2.7 ± 0.5 mm yr -1 in the subpolar North Atlantic.	Dangendorf et al. (2019) Frederikse et al. (2020) (source for basin estimates), AR6 WGI Chapter 12	High confidence in magnitude and direction of change on ocean-basin scale (***).
Africa	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Indian Ocean-South Pacific and South Atlantic over 1957-2018 and in the subpolar North Atlantic over 1993-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers play a minor role. See global assessment for details.	Frederikse et al. (2020), AR6 WGI Chapter 3 (Eyring et al., 2021)	Anthropogenic climate forcing is the dominant cause of sea level rise around Africa over 1993-2018, low confidence (*)	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In addition to absolute local sea levels, relative ones are subject to vertical land movements induced by subsidence or changes in sedimentation.
Asia	Observations Over 1900-2018 basin-mean relative sea-level rose 1.3 ± 0.5 mm yr -1 in the Indian Ocean-South Pacific and 1.7± 0.4 mm yr -1 in the Northwest Pacific. Over 1993-2018 basin-mean relative sea-level rose 3.9 ± 0.6 mm yr -1 in the Indian Ocean-South Pacific and 2.8 ± 0.7 mm yr -1 in the Northwest Pacific.	Dangendorf et al. (2019), Frederikse et al. (2020) (source for basin estimates), AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	High confidence in magnitude and direction of change on ocean-basin scale.
	Along the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea (the “China Seas”) coastline, records from 18 tide gauge stations show an average increase in relative sea levels of 2.6 ± 0.5 mm/yr (1950-2016) and 3.7 ± 0.8 mm/yr (1980-2016).	Qu et al. (2019)	high confidence in increasing relative sea levels along China’s seas.
	In the Bay of Bengal, satellite altimetry-based absolute sea level data show a positive trend of 3.11 ± 0.44 mm yr -1 over the period 1993-2010 in line with the trend in global mean sea level. At 4 out of 5 available tide gauges no significant trends in relative annual mean sea levels are detected. One gauge station shows a significant increase in annual mean relative sea level.	Ghosh et al. (2018) Antony et al. (2016)
	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Indian Ocean-South Pacific and Northwest Pacific over 1957-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020), AR6 WGI Chapter 3 (Eyring et al., 2021)	Anthropogenic climate forcing is the dominant cause of sea level rise around Asia since the 1970s, low confidence (*).	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels, relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
Asia	Observations China’s Seas: Tide gauge stations along the China Seas, in particular in cities, have undergone significant subsidence due to groundwater extraction. During the satellite altimetry data period 1993-2016, contributions from vertical land movement to relative sea level changes range from -4.5 ± 1.0 mm/yr to 1.4 ± 1.3 mm/yr across the stations. After removing the effect of vertical land movement (assuming that the trend due to vertical land movement found during 1993-2016 is the same over longer periods), absolute sea level is estimated to rise at a rate of 3.2 ± 1.1 mm/yr (1993-2016), 2.9 ± 0.8 mm/yr (1980-2016) and 1.8 ± 0.5 mm/yr (1950-2016) when averaging over all available tide gauge records. The contribution of thermal expansion and salinity changes is estimated to reach up to 0.9 ± 0.3 mm/yr (1950-2016). Glaciers and ice sheets are estimated to reach up to 1.1 ± 0.1 mm/yr (1950-2016). Given the dominant influence of anthropogenic climate forcing on the latter components on a global scale since 1970 anthropogenic climate forcing is considered to provide a major contribution to the observed increase in regional relative sea levels along China’s seas.	Qu et al. (2019)	Major contribution of anthropogenic climate forcing to regional sea level rise along China’s Seas, low confidence (*)
Asia	Bay of Bengal: The 1993-2010 trend in absolute sea level is in line with the trend in global mean sea level. The missing trend in 4 out of 5 tide gauges records indicate that vertical land movements may play an important role regarding changes in relative sea levels. However, the limited number of stations does not allow for a systematic assessment.	Ghosh et al. (2018) Antony et al. (2016)
Australasia	Observations Over 1900-2018 basin-mean relative sea-level rose 1.3 ± 0.5 mm yr -1 in the Indian Ocean-South Pacific. Over 1993-2018 basin-mean relative sea-level rose 3.9 ± 0.6 mm yr -1 in the Indian Ocean-South Pacific.	Dangendorf et al. (2019) Frederikse et al. (2020) (source for basin estimates), AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	High confidence in magnitude and direction of change on ocean-basin scale.
	Australia: For the periods 1966 to 2009 and 1993 to 2009, the average trends of relative sea level around the coastline are 1.4 ± 0.3 mm yr-1 and 4.5 ± 1.3 mm yr-1, which become 1.6 ± 0.2 mm yr-1 and 2.7 ± 0.6 mm yr-1 after removal of the signal correlated with ENSO. Relative sea-level rise varied between 1.9 ± 1.6 mm/y 2.5 ± 1.1 mm/y for the four stations with records exceeding 75 years in length. The 1992-2019 average trend in sea surface height around Australia observed by satellites was 3.4mm/yr.	White et al. (2014), Watson (2020)
	New Zealand: Relative sea levels increased by 1.8 mm/year from 1900-2018, 1.2 mm/year from 1900-1960 and 2.4 mm/year from 1961-2018. A 20th century trend of 1.7 mm/yr has been reconstructed using records at 10 tide gauges along the coast covering different periods.	Bell and Hannah (2018), Hannah and Bell (2012), Denys et al. (2020)
	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Indian Ocean-South Pacific over 1957-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020), AR6 WGI Chapter 3	Anthropogenic climate forcing is the dominant cause of sea level rise around Australasia since the 1970s, low confidence (*)	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
	Australia: Vertical land motion contributes about 40% to relative sea level rise for the four Australian stations exceeding 75 years in length.	Watson (2020)
	New Zealand: The contribution from vertical land movement to relative sea level rise is small but significant (0.2mm/yr).	Denys et al. (2020)
Central and South America	Observations Over 1900-2018 basin-mean relative sea-level rose 1.3 ± 0.5 mm yr -1 in the Indian Ocean-South Pacific, 2.1 ± 0.7 mm yr -1 in the South Atlantic and 1.2 ± 0.4 mm yr -1 in the Eastern Pacific. Over 1993-2018 basin-mean relative sea-level rose 3.9 ± 0.6 mm yr -1 in the Indian Ocean-South Pacific, 3.9 ± 1.4 mm yr -1 in the South Atlantic and 1.8 ± 0.7 mm yr -1 in the Eastern Pacific.	Dangendorf et al. (2019) Frederikse et al. (2020) (source for basin estimates), AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	High confidence in magnitude and direction of change on ocean-basin scale.
	Caribbean: Within the basin, large spatial variability in the trends is identified. In the period 1908-2009 of available tide gauge records, relative sea levels have been rising with a rate between -2.0 mm/yr and 10.7 mm/yr.	Torres and Tsimplis (2013)
	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Indian Ocean-South Pacific, South Atlantic and Eastern Pacific over 1957-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020)	Anthropogenic climate forcing is the dominant cause of sea level rise around Central and South America rise since the 1970s, low confidence (*).	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels, relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
	Caribbean: The influence from glacial isostatic adjustment ranges between -0.3 and 0.5 mm/yr, but the large local variability in relative sea level trends indicates that the influence from unknown vertical land movements cannot be ruled out.	Torres and Tsimplis (2013)	no individual quantification of contribution of anthropogenic climate forcing in context of other drivers
Europe	Observations Over 1900-2018 basin-mean relative sea-level rose 1.1 ± 0.3 mm yr -1 in the subpolar North Atlantic. Over 1993-2018 basin-mean relative sea-level rose 2.7 ± 0.5 mm yr -1 in the subpolar North Atlantic.	Dangendorf et al. (2019), Frederikse et al. (2020) (source for basin estimates), IPCC AR6 WGI Chapter 12	High confidence in magnitude and direction of change on ocean-basin scale.
	Baltic Sea: In the study period 1950-2015, relative sea level trends at tide gauge stations in the Baltic Sea range from a sinking of 7.49 mm/yr in the north (Ratan, Sweden) to a rise of sea levels of 2.04 mm/yr in the south (Klaipeda, Lithuania).	Gräwe et al. (2019)
	Southern Europe: The five tide gauge records that span most of the 20th century show positive relative sea level trends between 1.2 and 1.5 ± 0.1 mm yr-1. Trends obtained from the 21 longest records (>35 yr) are smaller in the Mediterranean (0.3 to -0.7 mm yr-1) than in the neighbouring Atlantic sites (1.6 to -1.9 mm yr-1) for the period 1960-2000. The strongest trend is reported for Vigo, Spain (2.5 mm yr-1).	Marcos and Tsimplis (2008)
	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the subpolar North Atlantic over 1993-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020)	Anthropogenic climate forcing is the dominant cause of sea level rise around Europe over 1993-2018, low confidence (*)	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels, relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
	Baltic Sea: The contribution of vertical land movement caused by glacial isostatic adjustment ranges from -12 mm/yr in the northern Gulf of Bothnia to 1 mm/yr along the coast of Denmark. In the study period 1950-2015, absolute sea level rise contributed between 1.71 ± 0.51 mm/yr in the southwest and 2.34 ± 1.05 mm/yr in the northeast to relative sea level changes. The effect of absolute sea level rise is clearly outweighed by vertical land movements in most regions. The trends in absolute sea levels of around 2 mm/yr are in line with global mean sea level rise mainly driven by anthropogenic climate forcing. Minor regional variations in derived absolute sea level rise are due to meteorological variations of uncertain origin.	Gräwe et al. (2019)	minor contribution of anthropogenic climate forcing to the observed decrease in the northern regions, strong contribution of anthropogenic climate forcing to the observed increase in the southern regions, low confidence (*)
North America	Observations Over 1900-2018 basin-mean relative sea-level rose 2.5 ± 0.6 mm yr -1 in the Subtropical North Atlantic, 1.2 ± 0.3 mm yr -1 in the subpolar North Atlantic and 1.2 ± 0.4 mm yr -1 in the Eastern Pacific. Over 1993-2018 basin-mean relative sea-level rose 4.0 ± 1.2 mm yr -1 in the Subtropical North Atlantic, 2.7 ± 0.5 mm yr -1 in the subpolar North Atlantic and 1.8 ± 0.7 mm yr -1 in the Eastern Pacific.	Dangendorf et al. (2019), Frederikse et al. (2020) (source for basin estimates), IPCC AR6 WGI Ch 12 (Ranasinghe et al., 2021)	High confidence in magnitude and direction of change on ocean-basin scale.
North America	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Eastern Pacific and subtropical North Atlantic over 1957-2018 and in the subpolar North Atlantic over 1993-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020)	Anthropogenic climate forcing is the dominant cause of basin-mean sea level rise around North America over 1993-2018, low confidence (*).	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels, relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
Small Islands	Observations Over 1900-2018 basin-mean relative sea-level rose 1.3 ± 0.5 mm yr -1 in the Indian Ocean-South Pacific, 1.7 ± 0.4 mm yr -1 in the Northwest Pacific, 1.8 ± 0.7 mm yr -1 in the Eastern Pacific and 2.5 ± 0.6 mm yr -1 in the Subtropical North Atlantic. Over 1993-2018 basin-mean relative sea-level rose 3.9 ± 0.6 mm yr -1 in the Indian Ocean-South Pacific, 2.8 ± 0.7 mm yr -1 in the Northwest Pacific, 1.8 ± 0.7 mm yr -1 in the Eastern Pacific and 4.0 ± 1.2 mm yr -1 in the Subtropical North Atlantic.	Dangendorf et al. (2019), Frederikse et al. (2020) (source for basin estimates), AR6 WGI Chapter 12 (Ranasinghe et al., 2021)	High confidence in magnitude and direction of change on ocean-basin scale.
	For the period 1993-2017, altimetry measurements show an increase in mean absolute sea level in coastal zones (within 50 km distance from coast) of small islands. However, trends are heterogeneously distributed with the highest increases in Oceania and lowest in North America.	Li et al. (2019a)
	Solomon Islands: A rapid rise in sea levels in the Solomon Islands between 1994 and 2014 of about 15 cm (average of 7 mm yr -1 between 1994 and 2014) is indicated, which is above the as compared to a longer term trend of 3 mm yr-1 for the western equatorial Pacific.	Albert et al. (2016)
	Indian Ocean: from 1950 to 2009 relative sea level rose by 1.8 ± 0.3 mm/yr at Cocos Island and Diego Garcia, 2.8 ± 0.1 mm/yr at Malé Hulule in the Maldives and 2.6 ± 0.4 at Le Tampon in the Reunion Island.	Palanisamy et al. (2014)
	Attribution Ice mass loss and thermal expansion are the main contributors to relative sea level rise in the Indian Ocean-South Pacific, the Northwest Pacific, the Eastern Pacific and the Subtropical North Atlantic over 1957-2018. Anthropogenic forcing is the main driver of these contributors since the 1970s. Non-climate related drivers are minor. See global assessment for details.	Frederikse et al. (2020)	Anthropogenic climate forcing is the dominant cause of sea level rise around Small Islands since the 1970s, low confidence (*)	Absolute local sea level changes are driven by patterns induced by changing ocean dynamics in addition to global sea level change processes. In contrast to absolute sea levels, relative ones are subject to local changes in land surface and bathymetry induced by subsidence or changes in sedimentation.
	Solomon Islands: Increased sea level rise since the 1990s is partly due to stronger trade winds, related to ENSO and the PDO.	Albert et al. (2016)	Strong contribution of anthropogenic climate forcing to observed increase in relative sea level in the considered sites on Solomon Islands, low confidence (*)
	Indian Ocean: More than 50% of the relative sea level rise at Malé Hulule in the Maldives and Le Tampon in the Reunion is due to land subsidence.	Palanisamy et al. (2014)
	S08b Coastal systems - Extreme water levels
Global	Observations Extreme water levels as observed by tide gauges have increased along most of the global coastlines with a median 165% increase in high-tide flooding between 1960-1980 and 1995-2014.	IPCC AR6 WGI 9.6.4.1(Fox-Kemper et al., 2021)	high confidence
	Attribution Relative sea level change (as opposed to changes in tide and surge) is the primary driver of changes in extreme coastal water levels at most locations.	IPCC AR6 WGI, Ch 9 (Fox-Kemper et al., 2021)	major contribution of anthropogenic climate forcing to increase in extreme water levels, medium confidence (**)	In addition to the drivers of long term sea levels discussed above, changes in extreme sea levels also depend on changes in storm activity including tropical cyclones.
	Observed changes in extreme sea level can very likely be attributed to global warming, because long-term relative sea level change can be attributed to global warming.	IPCC AR6 WGI 9.6.4.1 (Fox-Kemper et al., 2021)
Africa	Observations Missing studies
Africa	Attribution Missing studies		no assessment
Asia	Observations Northwest Pacific/South China Sea: Maximum annual sea levels as well as 90th, 99th and 99.9th percentile sea levels exhibit significant increasing trends in 12 of 15 gauges.	Feng et al. (2015), Feng et al. (2018)	high confidence in increasing extreme water levels along Northwest Pacific/South China Sea
	East India/Bay of Bengal: 4 out of 5 available tide gauges show no significant trends in the high water percentiles. One gauge station shows a significant increasing trend in all high water percentiles.	Antony et al. (2016)	low confidence	Indian ocean sea level variability is influenced by Indian ocean dipole events are a driver of Indian ocean sea level variability and thus influence extremes.
	Attribution While extreme surges are driven by tropical cyclones in the Northwest Pacific and by monsoonal winds in the South China Sea, all observable trends in extreme water levels are strongly related to changes in relative sea level. As anthropogenic climate forcing is a major driver of long-term relative sea level rise (see section above on sea level rise), the observed trends in extreme sea level can very likely be attributed to global warming, too.	Pham et al. (2019)	Strong contribution of anthropogenic climate forcing to positive trends in extreme water levels along China’s seas, low confidence (*), no attribution in other regions (such as Indian Ocean).
	Observed changes in extreme water levels seem to be in line with the associated changes in annual mean relative water levels. However, as the contributions of the individual drivers of annual mean water levels have not been quantified (see sea level section above) changes in extreme water levels cannot be attributed either.	Antony et al. (2016)
Australasia	Observations no assessment
Australasia	Attribution no assessment		no assessment
Central and South America	Observations Caribbean: Extreme water levels (at the 50th, 90th, 95th, 99th, and 99.9th percentile) increase in all of the five available long-term (more than 20 years of data) gauge records at rates between 1.3 and 9.9 mm/yr.	Torres and Tsimplis (2014)
	South America: There is no systematic assessment of changes in extreme coastal water levels but only observations of extreme absolute water levels (reanalysis and satellite altimetry) showing a positive trend over the last 5 decades.	Losada et al. (2013)	medium confidence
	Attribution Caribbean: The increase in extreme water levels is consistent with the trend in mean relative sea level since the rates of the upper sea level percentiles are in line with mean sea level trends at each gauge station. South America: Only in the Río de la Plata the dominant driver is storm surge, in all other areas along the coast of South America, it is sea level rise. As there is no quantification of mean sea level changes induced by climate forcing, there is no quantification of its contribution to extreme water levels either.	Torres and Tsimplis (2014)	no separate assessment
Europe	Observations Mediterranean Sea: Significant increasing linear trends of annual 99.9th percentile of hourly water levels between the mid 20th century and the year 2000 (exact years depending on data availability at each station) are observed in the Atlantic and Adriatic regions (5 out of 11 available long-term records for the Mediterranean Sea). There are no significant trends in the remaining records.	Marcos et al. (2009) (Mediterranean Sea)
	Atlantic, North Sea: According to tide gauge records, the intensity and number of occurrences of extremes (skew surges as well as high percentiles) has increased over the last 100 years.	Dangendorf et al. (2014) (North Sea), Haigh et al. (2016) (UK), Marcos and Woodworth (2017) (Atlantic),
	Baltic Sea: The 12 tide gauge stations in the study show varying trends in the annual 99th percentile of monthly relative sea levels ranging from -5.64 mm/yr in the north to 1.71 mm/yr in the south.	Ribeiro et al. (2014), Barbosa (2008) (Baltic sea)
	Attribution Mediterranean Sea: No attribution assessment available.		minor (Baltic Sea) to major (Atlantic, North Sea) contribution (increase in extreme water levels) by anthropogenic climate forcing, low confidence (*)
	Atlantic, North Sea: Changes in extreme water levels are significantly correlated to both local absolute mean sea level rise and changes in the Northern Atlantic Oscillation. Most of the observed changes in extreme sea level can very likely be attributed to global warming, because long-term MSL change as well as changes to the Northern Atlantic Oscillation can be attributed to global warming.	Marcos and Woodworth (2017)
	Baltic Sea: While the main driver of sea level change in this region is vertical land movement due to glacial isostatic adjustment (see section about mean sea level), the extreme water levels are also influenced by global MSL rise and changed weather patterns. Model simulations only accounting for observed global mean sea level rise and observed changes in weather patterns indicated that both processes lead to an increase in absolute extreme water water levels (1.5 to 10 mm/yr over the period 1961-2005). As changes in both drivers are dominated by anthropogenic climate forcing changes in absolute extreme water levels are dominated by anthropogenic climate forcing, too. However, in terms of coastal water levels the effects are overcompensated by the dampening effect of the upward vertical land movement. That is why the contribution of anthropogenic climate forcing to local changes in extreme water levels are rated “minor”.	Pindsoo and Soomere (2020)
North America	Observations In the New York harbour region, eight of the twenty highest recorded water levels recorded since 1927 have occurred since 1990 and process-based model simulations indicate that a ~2.25-m flood height which was a 1 in 500 years event in 850-1800 the pre-anthropogenic era has become a 1 in ~25 years event in 1970-2005 the anthropogenic era.	Talke et al. (2014) Reed et al. (2015)
	Upward trends in extreme water levels (annual 99th percentiles) are significant at 70% of the stations along the coasts of the North Atlantic and the Gulf of Mexico.	Marcos and Woodworth (2017)
	The probability of nuisance level flood events has increased at almost all 45 tide gauges along the US east and west coast between 1950 and 2012 with the only exception in the northwest where return periods remained the same.	Sweet (2014)
	Significant increasing trends exist in extreme sea level records (annual 99.5th percentile of hourly records) at 18 of 20 tide gauges along the Atlantic and Pacific coast for the 1929-2013 period varying between 0 and 7 mm/yr.	Wahl and Chambers (2015)
	Attribution New York: Observed changes in extreme water levels for New York are dominated by sea level rise compared to changes in hurricane activity. However, a large part of the local relative sea level rise has been due to postglacial adjustment, i.e. a natural cause (see discussion of mean sea level changes in the section above).	Talke et al. (2014) Reed et al. (2015)	Moderate contribution of anthropogenic warming to increase in extreme water levels, medium confidence (**)
	Relevance of hurricane activity along the Atlantic coast: In addition, the frequency of large surge events at tide gauges along the Atlantic coast, modeled using a threshold surge index, exhibits a statistically significant increasing trend in the period 1923-2008. This is highly correlated with most measures of TC activity. Increasing trends in TC activity can partly be attributed to anthropogenic warming (see section on tropical cyclones below).	Grinsted et al. (2012)
	Relevance of increasing mean sea levels along Atlantic and Pacific coastlines: The increasing trends in extreme sea levels along the Atlantic and Pacific coast largely follow relative sea level rise which is only in part due to anthropogenic warming (see mean sea level section).	Wahl and Chambers (2015)
Small Island	Observations Central North Pacific/Hawaii: The number of extreme sea level events recorded at one gauge station is increasing over the past 60 years.	Aucan et al. (2012) (for Midway, North Pacific),
Small Island	Attribution Central North Pacific/Hawaii: The increasing frequency of extreme water level events is estimated to be induced by changes Pacific Decadal Oscillation. The observed change in the Pacific Decadal Oscillation (PDO) has not been attributed to anthropogenic climate forcing.		minor contribution of anthropogenic climate forcings to increasing extreme water levels in Central North Pacific/Hawaii, low confidence (*), no assessment elsewhere
	S07 Coastal systems - Tropical cyclone activity
	Observations Identifying past trends in tropical cyclone (TC) metrics remains a challenge due to the heterogeneous character of the historical instrumental data. There is substantial literature that finds positive trends in intensity-related metrics during the 'satellite period' limited to the past 40 years. For example, the likelihood that a TC will be at major TC intensity (Cat. 3-5) and the precipitation associated with TCs has increased. However, there is evidence that the ~40-year period of highest quality post-satellite era data is shorter than the timescale required for TC intensity trends to emerge from the noise.	AR6 WGI Chapter 11.7.1.2 (Seneviratne et al., 2021)	low to medium confidence	The genesis, development, and tracks of TCs depend on conditions of the large-scale circulation of the atmosphere (e.g. wind shear) and ocean. The sea surface temperature distribution together with the thermodynamic condition of the ocean mixed layer directly affect TC activities.
	North Atlantic TC activity has increased since the 1970s.	AR6 WGI 11.7.1.4 (Seneviratne et al., 2021)	virtually certain
	Poleward migration of the location of peak tropical cyclone intensity in the western North Pacific lies outside the range of natural variability.	AR6 WGI 11.7.1.4 (Seneviratne et al., 2021)	low-to-medium confidence
	Category 5 tropical cyclones have only recently emerged in the South Indian Ocean. Since 1989, their frequency of occurrence has increased. This increase poses a heightened risk of storm damage for the South Indian Ocean Island States.	Fitchett (2018)	low confidence
	TC translation speed has slowed over the USA since 1900, which is expected to increase local rainfall amounts as well as coastal and inland flooding.	AR6 WGI 11.7.1.2 (Seneviratne et al., 2021)
	The frequency of rapidly intensifying TCs has increased globally over the past 40 years.	AR6 WGI 11.7.1.2 (Seneviratne et al., 2021)
	Attribution As tropical cyclone (TC) activities are affected by atmosphere-ocean coupling and associated multi-decadal modes, detection of anthropogenic effects from natural variabilities of these modes is generally difficult and particularly constrained by heterogeneity in long term observational data. In addition, SST patterns are also affected by aerosol forcing.	Lackmann (2014) (Hurricane Sandy, 2012), Takayabu et al. (2015) (Typhoon Haiyan, 2013), Patricola and Wehner (2018) (Hurricane Katrina, Maria, and Irma)	minor influence of anthropogenic climate forcing on TC activity, low confidence (), except for a moderate increase of TC activity induced by reduced aerosol forcing in the North Atlantic, medium confidence (*)
	A reduction in aerosol forcing has contributed at least in part to the observed increase in tropical cyclone intensity in the North Pacific, in the Arabian basin, and most prominently in the North Atlantic since the 1970s.	AR6 WGI 11.7.1.4 (Seneviratne et al., 2021)
	While the slowdown of TC translation speed over the USA has contributions from anthropogenic forcing, and the poleward migration of TCs in the western North Pacific cannot be explained entirely by natural variability, there is only limited evidence for anthropogenic effects on rapid TC intensifications.	IPCC AR6 WGI 11.7.1.4 (Seneviratne et al., 2021)
	Impacts of tropical-cyclone storm-surge flooding is exacerbated through sea-level rise and intensifying rainfall. Anthropogenic climate change is the dominant driver of sea level rise (see sea level section of this table above) and anthropogenic climate forcing is also affecting the occurrence of heavy rainfall events (see associated section of this table below).	IPCC AR6 WGI Chapter 11-(Seneviratne et al., 2021)
	S01 Cryosphere - Sea ice extent
	Observations Arctic sea ice retreated and seasonal area declined consistently since 1979. Arctic summer sea-ice area during the last decade was the lowest since at least 1850.	IPCC AR6 WGI Chapter 9 (Fox-Kemper et al., 2021)	virtually certain
	No significant trend in overall Antarctic sea ice is observed from 1979 to 2020.	IPCC AR6 WGI Chapter 9 (Fox-Kemper et al., 2021)
	Attribution Anthropogenic climate forcing has very likely caused at least half of Arctic summer sea ice loss since 1979. A robust linear relationship between observed September sea-ice area and cumulative carbon dioxide (CO2) emissions implies a sustained loss of 3 ± 0.3 square meters of September sea-ice area per metric ton of CO2 emission. The record low sea ice extent observed in 2012 has been shown to be consistent with a scenario including anthropogenic influence and to be extremely unlikely in a scenario excluding anthropogenic influence. Slight Antarctic regional increases or decreases in ice area result from regional wind forcing (medium confidence),	IPCC AR6 WGI Chapter 9 (Fox-Kemper et al., 2021), Notz and Stroeve (2016) Kirchmeier-Young et al. (2017a)	major contribution of anthropogenic climate forcing to Arctic sea ice retreat, minor contributions to changes in Antarctic sea ice, high confidence (***)
	S02 Cryosphere - Glacier mass
Global	Observations With few exceptions, glaciers have retreated since the second half of the 19th century and continued to retreat with increased rates since the 1990s (very high confidence), this behaviour is unprecedented in at least the last 2000 years (medium confidence). Global total glacier mass loss 1961-2016: 9625 Gt, 27 ± 22 mm. Ca. 30% increase of global glacier mass loss of 2006-2015 compared to 1986-2005. Glaciers are estimated to have contributed 0.027±0.022 m to sea level rise between 1961 and 2016.	AR6 WGI Chapter 2 (Gulev et al., 2021), Zemp et al. (2019), IPCC SROCC, AR6 WGI Chapter 9 (Fox-Kemper et al., 2021)	Very high confidence in global scale retreat (the exact numbers bear some uncertainty as indicated), medium confidence in unprecedentedness
Global	Attribution Climate change has become the major driver of glacier retreat since the 1990s: The fraction of the total cumulative global glacier mass loss attributable to anthropogenic climate change since the preindustrial era varies among the few available studies, between 25% and close to 100%. For the last 3 decades there is consensus of a high contribution of anthropogenic climate change, from about 70 to 100%. Global glacier mass loss since the last decades of the 20th century cannot be explained without human induced warming (high confidence).	AR6 WGI Chapter 9 (Fox-Kemper et al., 2021), Marzeion et al. (2014), Slangen et al. (2016), SROCC, Roe et al. (2021)	Anthropogenic climate change has become the major driver of recent glacier mass loss, high confidence (***)	Increasing temperatures are clearly the main driver of global glacier loss. Locally to regionally, other climatic variables such as precipitation or humidity, or soot, can also play a role as drivers.
Africa	Observations Persistent glacier mass loss since 1980. On Kilimanjaro, between 1912 and 2011 85% of the area lost, with a relatively constant retreat rate. On Mt Kenya glacier area loss is estimated to have reached 0.6-0.1 km2 from late 19th century to 2010.	Mölg et al. (2012) Pepin et al. (2014) Cullen et al. (2013) Prinz et al. (2016)
Africa	Attribution While climate change clearly is the main driver of the observed losses it is not yet fully clear how much of the observed climate change is due to anthropogenic forcing or induced by natural variability. On Kilimanjaro land-cover change is estimated to be responsible for 7-17% of glacier decline.	Mölg et al. (2012), Pepin et al. (2014), Cullen et al. (2013), Prinz et al. (2016)	moderate to strong impact of anthropogenic climate change on observed glacier loss, low to medium confidence (*)	precipitation and moisture increasing ablation by drying of summit climate of Kilimanjaro, lower humidity, less cloud cover, more solar radiation, in addition there is less accumulation due to reduced precipitation.
Asia	Observations Highest glacier mass loss in the eastern Himalayas (cumulative change 1960-2016: -8m water equivalent), Karakoram being the only region in the world with some mass gain still recently (cumulative change 1960-2016: +5m water equivalent), Central Asia moderate mass loss since 1980, stronger mass loss in North Asia. In Caucasus and Middle East cumulative changes from 1960-2016 close to -20m water equivalent.	Zemp et al. (2019) Hock et al. (2019) (SROCC), Bolch et al. (2019)
Asia	Attribution For the regions with higher losses there is a good correlation between mass loss and increasing temperatures. In combination with current process-understanding anthropogenic climate change provides a good explanation for the losses. However, for the Karakoram region the relation to anthropogenic climate change is unclear.	Marzeion et al. (2014), Roe et al. (2021), Hock et al. (2019) (SROCC), Bolch et al. (2019)	Strong impact of anthropogenic climate change in regions with high mass losses, medium confidence (**), role of climate change for mass gain in Karakoram unresolved	Increasing (annual average) temperatures as the main driver of glacier change for most of the region. Smaller scale and local conditions in precipitation and temperature drive spatial variability of glacier response. Length and area change of debris-covered glaciers imply slower response to climatic changes.
Australasia	Observations Since 1970 consistent mass loss on New Zealand glaciers, with an increasing negative trend. From 1978-2016, the area of 14 glaciers in the Southern Alps declined 21%. The end-of-summer snowline elevation for 50 glaciers rose 300m from 1949-2019. Cumulative changes 1960-2016 have reached about -15m water equivalent. From 1977 to 2018, NZ glacier ice volume decreased from 26.6 km3 to 17.9 km3 (a loss of 33%).	Zemp et al. (2019) Hock et al. (2019) (SROCC), Chinn and Chinn (2020) Salinger et al. (2019) Baumann et al. (2021), Salinger et al. (2021)
Australasia	Attribution NZ glacier retreat partly attributed to climate change. The signal of anthropogenic climate change has strongly increased since the 1990s. Event attribution indicates that anthropogenic climate forcing has increased the probability of particularly high mass-losses as observed in 2011 and 2018 across New Zealand’s Southern Alps by a factor of six (2011) and a factor of ten (2018) compared to conditions without anthropogenic climate forcing.	Vargo et al. (2020), Marzeion et al. (2014)	Strong contribution of anthropogenic climate change to today’s glacier mass loss in New Zealand, high confidence since 2000 (***)
Central and South America	Observations Very strong glacier mass loss, especially in the Southern Andes (cumulative loss of -40m water equivalent for 1960-2016). In the tropical Andes cumulative loss from 1960-2016 has reached -30m water equivalent. For the whole region increasing trend of mass loss since 1980.	Zemp et al. (2019) Hock et al. (2019) SROCC), Dussaillant et al. (2019) Reinthaler et al. (2019)
	Palcaraju glacier, Peru: The Palcaraju glacier feeding Lake Palcacocha (Cordillera Blanca, Peru) retreated by 1.8km since 1850.	Stuart-Smith et al. (2021)
	Attribution The strong, regionally consistent signal of glacier mass loss can only be explained by increasing (annual average) temperatures (the rate of temperature change is varying across the region).	Marzeion et al. (2014), Dussaillant et al. (2019) Huggel et al. (2020)	Strong reduction of glacier mass induced by anthropogenic climate change, high confidence (***)	Loss mainly driven by temperature increase, while precipitation changes play a minor role. Inter-annual variability strongly influenced by ENSO.
	The overall retreat of Palcaraju glacier is entirely attributable to the observed temperature trend, of which 85 to 105% is attributable to human greenhouse gas emissions. It is virtually certain that the observed retreat of Palcaraju glacier could not have occurred due to natural variability alone.	Stuart-Smith et al. (2021)
Europe	Observations From 2000 - 2014 a rapid glacier retreat across the European Alps (-39 km² a-1) with ice thickness changes of -0.5 to -0.9m a-1 has been observed. Strongest downwasting in the Swiss Glarus and Lepontine Alps. For the entire Alps a mass loss of 1.3 ± 0.2 Gt a-1 is estimated. Since 1980 increasing mass loss. Cumulatively close to 20m water equivalent loss for 1960-2016. In Scandinavia glaciers gained mass in the 1980s and 1990s. Since 2000 they have lost mass (ca -1000 kg/m2/yr). Cumulative change from 1960-2016 has reached -13m water equivalent.	Sommer et al. (2020) Zemp et al. (2019) Hock et al. (2019) SROCC
Europe	Attribution Due to long-term, detailed, high-quality climate and glacier monitoring networks high confidence of attribution of glacier mass loss to anthropogenic climate change. Especially for the last 3 decades anthropogenic warming is the dominant cause of and contributor to (70-100%) glacier retreat.	Marzeion et al. (2014) Roe et al. (2021)	Strong contribution of anthropogenic climate change on glacier mass loss, high confidence (***)	Increasing (annual average) temperatures, including repeated hot temperature extremes since the 2000-s are the main driver of glacier loss. Precipitation variability is not a main driver across the region and decadal times scales.
North America	Observations Increasing glacier mass loss since 1960, with close to -30 m water equivalent loss for 1960-2016 for western Canada and mainland USA. Alaska has also experienced strong glacier mass loss. For Arctic Canada glacier mass loss is less strong and with more uncertainties.	Zemp et al. (2019) Hock et al. (2019) (SROCC)
North America	Attribution For Alaska and western Canada there is high confidence in attribution to anthropogenic climate change, at least for the observation period from the 1990s to present.	Marzeion et al. (2014) Roe et al. (2021)	Strong contribution of anthropogenic climate change to glacier mass loss, high confidence (***)
Small Islands	Observations no studies
Small Islands	Attribution no studies		no separate assessment
	S03 Cryosphere - Permafrost
Global	Observations Although permafrost persists in areas of the Northern Hemisphere where it was absent prior to 3000 years ago, increases in temperatures in the upper 30 m over the past three to four decades (start of the observational programs) have been widespread. Since at least the early 1980s permafrost has been warming across the Arctic. During 2007-2016, ground temperatures near the depth of zero annual amplitude increased globally by 0.29 ±0.12°C (0.39 ±0.15°C and 0.20 ±0.10-C in the continuous and discontinuous permafrost zone, respectively).	AR6 WGI Section 2.3.2.5 (Gulev et al., 2021), AR6 WGI Section 9.5.2 (Fox-Kemper et al., 2021), Biskaborn et al. (2019)	high confidence in temperature increase in the upper 30m over the last three to four decades.
Global	Attribution There is a clear physical link between ground temperatures (and thus permafrost) and surface air temperatures whose increase have been clearly attributed to human influence in the Arctic. The increase in ground temperatures in the continuous permafrost zone is physically consistent with surface air temperature increases (incl. the Arctic amplification). In the discontinuous zone, permafrost temperatures have warmed less because of latent heat effects and less strong warming.	IPCC AR6 WGI 9.5.2.1 (Fox-Kemper et al., 2021), Biskaborn et al. (2019)	moderate contribution of anthropogenic climate forcing to permafrost thawing in the Arctic continuous and the discontinuous permafrost zone, medium confidence (**)	High Arctic: Polar amplification of global air temperature increase. Subarctic: Earlier snow season and increase of snow thickness can lead to ground warming, versus latent heat effect near 0-C. Variability in permafrost temperature trends are often related to local conditions such as snow cover, topography (especially in mountains), surface type, or ice content.
Asia	Observations Warming trend of mean annual ground temperatures observed in all permafrost regions with strongest increases in Central Asia (see below).	Hock et al. (2019) SROCC, Biskaborn et al. (2019)
Asia	Attribution see regional assessments below	Hock et al. (2019) SROCC, Biskaborn et al. (2019)	Strong contribution of anthropogenic climate change to permafrost warming, medium-high confidence (*/)
Central Asia	Observations Warming trend of mean annual ground temperatures in Tien Shan reaching 0.3-0.6-C/decade, 1974-2011. Increasing thickness of active layer (season freezing) reaching 19 cm/dec, 1992-2011.	Hock et al. (2019) SROCC
Central Asia	Attribution	Hock et al. (2019) SROCC	Strong impact of anthropogenic climate change on permafrost warming, medium-high confidence (**)
Tibet	Observations Warming trend of mean annual ground temperatures reaching 0.08-0.24°C/decade, 2002-2012. Increasing thickness of the active layer with seasonal freezing reaching 15-67 cm/dec, 2002-2014.	Hock et al. (2019) SROCC
Tibet	Attribution	Hock et al. (2019) SROCC	Strong contribution of anthropogenic climate change to permafrost warming, medium-high confidence (**)
Mongolia	Observations Warming trend of mean annual ground temperatures reaching 0.2-0.3°C/decade, 2000-2009.	Hock et al. (2019) SROCC
Mongolia	Attribution	Hock et al. (2019) SROCC	Strong contribution of anthropogenic climate change to permafrost warming, medium-high confidence (**)
Australia	Observations
Australia	Attribution		no assessment
Europe	Observations Positive trend in permafrost mean annual ground temperatures observed in the European Alps as well as Scandinavian permafrost regions with stronger trends in the Alps (see below). In both regions the thickness of the active layer (seasonal freezing) is increasing with stronger trends in the Alps, too.	Hock et al. (2019) SROCC Dunn et al. (2020b) Biskaborn et al. (2019)
Europe	Attribution	Hock et al. (2019) SROCC, Biskaborn et al. (2019)	Strong contribution of anthropogenic climate forcing to permafrost warming, medium-high confidence (/*)
European Alps	Observations Warming of mean annual ground temperatures (0-2 to 0.6-C, depending on depth, for debris since 1987, and up to 1-C/decade for bedrock, since 2008. Thickness of seasonally thawed active layer is increasing with a rate of 10-100 cm/decade (2000-2020).	Hock et al. (2019) SROCC, Dunn et al. (2020b) PERMOS (2021)
European Alps	Attribution	Hock et al. (2019) SROCC	Strong contribution of anthropogenic climate forcing to permafrost warming, high confidence (***)
Scandinavia	Observations Warming trend of mean annual ground temperatures (0.1 to 0.5°C per decade for 2000-2019, with up to 0.2°C/decade for debris and up to 0.5°C/decade for bedrock). Increasing thickness of active layer of seasonal freezing (7-20 cm per decade, 1978-2006)	(Richter-Menge et al., 2020) Hock et al. (2019) SROCC
Scandinavia	Attribution		Strong contribution of anthropogenic climate forcing to permafrost warming, medium-high confidence (***)
North America	Observations Mean annual ground temperature increased more in Arctic regions with continuous permafrost than in those with discontinuous permafrost (ca. 0.3 to 0.8 versus ca. 0.1 to 0.3 °C per decade for the last 4 decades) Active layer thickness (ALT) increased continuously over the past 25 years in the Alaska Interior region but for Alaska North Slope and NW Canada ALT changes are less uniform.	(Richter-Menge et al., 2020) O’Neill et al. (2019) AR6 WGI 9.5.2. (Fox-Kemper et al., 2021)
North America	Attribution Physically consistent lines of evidence between increase inground (permafrost) temperatures and surface air temperatures	(Richter-Menge et al., 2020) AR6 WGI 9.5.2. (Fox-Kemper et al., 2021)	Strong contribution of anthropogenic climate change to permafrost warming, medium confidence (**)	Latent heat effects related to melting ground ice influences differences between continuous and discontinuous permafrost. Observed changes in ALT relate to shorter-term fluctuations in climate and are especially sensitive to changes in summer air temperature and precipitation
	S04 Atmosphere - Heatwaves
Global	Observations A measure of cumulative heat shows significant increases almost everywhere since the 1950s, mainly driven by heatwave days. Trends in heatwave frequency, duration and cumulative heat have accelerated since the 1950s	Perkins-Kirkpatrick and Lewis (2020)
Global	Attribution See assessments below		strong contribution of anthropogenic forcing to increase in intensity and frequency of heatwaves, medium to high confidence (**) in most regions
Africa	Observations In Africa, heat waves, regardless of the definition, have been becoming more frequent, longer lasting and hotter over more than three decades in Africa. The reporting is still heavily constrained.	AR6 WGI Chapter 11 (Seneviratne et al., 2021), Harrington and Otto (2020)	high confidence that heat waves have increased over the whole continent.
Africa	Attribution There are currently only attribution studies for heat waves in North and East Africa which show a strong influence of human-induced climate change.	Bergaoui et al. (2015) (heat wave in Levante 2014), Otto et al. (2015b) Philip et al. (2017) (East Africa)	major contribution by anthropogenic climate forcing in North and East Africa, high confidence (***), no assessment elsewhere
Asia	Observations Over most parts of Asia, daily high temperature extremes have increased during the last decades, including the Himalaya and Tibetan Plateau.	AR6 WGI Table 11.7 (Seneviratne et al., 2021)	high confidence
	India: Historical observations show that the likelihood of heatwaves has not increased or even decreased in some parts while it has increased in others.	van Oldenborgh et al. (2018)
	Attribution Attribution studies focus on Japan and China and consistently find human-induced climate change plays an important role.	Lu et al. (2018), Lu et al. (2016) Yin et al. (2017a) (China), Wehner et al. (2016)	regional mixed impacts ranging from strong contribution of anthropogenic climate forcing to increasing occurrence of heatwaves, high confidence (**) to regional decrease in annual maxima of daily maximum temperature in some parts of India, low confidence ()
	India: Model simulations indicate that aerosol emissions or the effects of irrigation may prevent an increase in probabilities associated with greenhouse gas emissions. Both mechanisms are considered “anthropogenic climate forcing”, too.	van Oldenborgh et al. (2018)
Australasia	Observations Temperature extremes and heatwaves have increased. Australia: Multi-day heatwave events have increased in frequency and duration across many regions since 1950. In 2019, the national average maximum temperature exceeded the 99th percentile on 43 days (more than triple the number in any of the years prior to 2000) and exceeded 39°C on 33 days (more than the number observed from 1960 to 2018 combined). New Zealand: the number of warm days (over 25°C) increased at 19 of 30 sites, and the number of heatwave days increased at 18 of 30 sites during 1972-2019. Increase in the frequency of hot February days exceeding the 90th percentile between 2010-2019 and 1980-1989, with some regions showing more than a five-fold increase.	AR6 WGI, BoM and CSIRO (2020), Perkins-Kirkpatrick and Lewis (2020), Trancoso et al. (2020) MfE (2020) Harrington (2021)	high confidence (***)
Australasia	Attribution A large number of studies attribute recent heatwaves in Australia to anthropogenic climate change. For example, it is very likely (with 90% confidence) that anthropogenic climate change increased the likelihood of the October 2015 heatwave breaking the previous Southern Australian temperature record by at least 400%. Human influence increased the likelihood of the January 2014 heat wave in Adelaide by 186% and Melbourne by 89%. 7-day heatwave in Dec 2019 was at least twice as likely due to anthropogenic climate change.	King et al. (2015a) (extreme Brisbane heat during November 2014), Dittus et al. (2014) (trends in maximum temperatures), Lewis and Karoly (2013) Lewis et al. (2017) Gallant and Lewis (2016) (Record-breaking Australian spring temperatures in 2013 and 2014), Black and Karoly (2016) (Record-breaking heat in southern Australia in October 2015), Black et al. (2015) (Adelaide and Melbourne heatwaves of January 2014), Perkins and Gibson (2015) (May 2014 heatwave), Hope et al. (2016) (record October heat in 2015), Perkins-Kirkpatrick and Lewis (2020), van Oldenborgh et al. (2021)	major contribution of anthropogenic climate forcing to observed increase in temperature extremes, high confidence (***)
Central and South America	Observations The number of warm days and nights has increased, and the number of cold days and nights have decreased in the last decades, except over South East South America (SES) where hot extremes have decreased during austral summer.	AR6 WGI 11.3.2 (Seneviratne et al., 2021)	medium-to-high confidence
Central and South America	Attribution There is only one heat wave attribution study in Central and South America for a heat wave in Argentina. An attribution study of a cold wave in Peru showed that climate change made it less likely to occur.	Hannart et al. (2015) (heatwave), Otto et al. (2018b) (coldwave).	no assessment
Europe	Observations Maximum temperatures and the frequency of heat waves has increased.	AR6 WGI Chapter 11 (Seneviratne et al., 2021)	high confidence
Europe	Attribution There are few attribution studies on Scandinavia but over Britain and Central Europe as well as the Mediterranean extreme heat in summer and decrease in cold extremes can be attributed to climate change.	Sippel et al. (2018), Sippel et al. (2017), Sippel and Otto (2014) Wilcox et al. (2018) King et al. (2015b) (England), Roth et al. (2019) (England), Kew et al. (2019) (Mediterranean), Stott et al. (2004) (European heatwave 2003), Otto et al. (2012) (European heatwave 2010), Vautard et al. (2020)	major contribution of anthropogenic climate forcing to observed increased frequency and intensity of heat waves, high confidence (***)
North America	Observations In North America, an increase in the number of warm days and nights and decrease in the number of cold days and nights, also over central North America and the eastern United States, albeit with changes smaller than elsewhere in North America.	AR6 WGI Chapter 11 (Seneviratne et al., 2021)	high confidence
North America	Attribution Heat wave attribution studies are sparse over North America, but those that do exist find an attributable signal.	Wang et al. (2017) Shiogama et al. (2014) Philip et al. (2018) (June 2013 and 2015 heat wave in Western US)	major contribution of anthropogenic climate forcing to increased intensity and frequency of heatwaves where studies exist, medium confidence (**), limited regional coverage
Small Islands	Observations
Small Islands	Attribution		no assessment
There are no rows for your selection in this table.

Table SM16.22: Detection and Attribution of observed changes in natural, human and managed systems to long-term changes in climate related systems ("impact attribution", references for dark red symbols in Figure 16.1). The titles of the (partly) dark red symbols in Figure 16.1 and associated numbers can be found here as titles of associated subsection.
Region	Observed change in natural, human and managed system + attribution to long-term changes in climate-related systems	Reference	Synthesis statement: direction (and strength) of impact induced by changes in climate-related systems, level of confidence	Underlying mechanism
	S09 Marine ecosystems - Phenology shift
Global	Observations Spring aggregation of plankton and fish: Spring aggregation occurs consistently earlier. Rates of observed shifts in phenology are comparable to, or greater, than those for terrestrial systems. Based on time-series of observational data/surveys:	Chapter 3: Poloczanska et al. (2013)	high confidence
	High consistency (81.5%, n=71) in earlier spring aggregation of phytoplankton (-5.2 days per decade, n=68), albeit with limited evidence from the Southern Hemisphere	Chapter 3, 3.4.3.2	High confidence
	High consistency (81.1%, n=80) in earlier spring aggregation of zooplankton (-4.4 days per decade, n=55)	Chapter 3, 3.4.3.2	High confidence
	High consistency (88.0%, n=64) in earlier spring aggregation of meroplankton (those species that are only temporarily in the plankton) (-5.9 days per decade, n=52)	Chapter 3, 3.4.3.2	High confidence
	Other seasonal events. High consistency (80.2%, n=8) in phenological changes in benthic invertebrates (-6.1 days per decade, n=7)	Chapter 3, 3.4.3.2	Medium confidence (limited evidence, high agreement)
	High consistency (75.5%, n=177) in earlier occurrences of seasonal events of fish (-4.5 days per decade, n=71)	Chapter 3, 3.4.3.2	High confidence
	Seasonal changes for marine mammals and seabirds are equivocal	Chapter 3, 3.4.3.2	Low confidence, limited agreement
	High consistency (80.1%, n=8) in earlier occurrences of seasonal events for marine reptiles (-6.1 days per decade, n=7)	Chapter 3, 3.4.3.2	Medium confidence (limited evidence, high agreement)
	Attribution There is high confidence that the observed changes are primarily driven by climate change (particularly earlier warming of sea waters). Attribution is largely provided by the general agreement of signals relative to expectations under climate change across multiple independent data sources/time series (as per Parmesan et al. (2013)). No alternative driver is expected to induce a similarly consistent global pattern of change	Chapter 3, 3.4.3.2	strong contribution of climate change to observed changes in the phenology of ocean taxa, high confidence (***)
Polar seas	Observations Earlier timing of Arctic plankton blooms in spring and summer, occurrence of novel fall phytoplankton blooms in the Arctic	Kahru et al. (2011); Kahru et al. (2016); (Ardyna et al., 2014) Ardyna et al. (2017)	high confidence
Polar seas	Attribution Shifts in timing in phytoplankton bloom in the Arctic are closely linked to decreasing in early summer (June) ice concentration. Secondary bloom is induced by delayed freeze-up allowing for wind-driven vertical mixing promoting primary production	Kahru et al. (2011); Kahru et al. (2016); (Ardyna et al., 2014); Ardyna et al. (2017)	strong impact of climate change on changes in the timing of phytoplankton bloom (expansion of vegetation period), high confidence in causal link between sea ice retreat and timing of plankton blooms (***)	biomass events mainly occur in response to changes in light and nutrients driven by the seasonal cycles of radiation, temperature and water column stability.
Temperate oceans	Observations Zooplankton: Earlier occurrence of peaks in zooplankton abundance in temperate zones. Evidence of shifts in phenology of zooplankton is strongest in the Northeast Atlantic. Phytoplankton: Based on time series from surveys, evidence of changes in phenology of phytoplankton is strongest in the North Atlantic and the Baltic Sea	Chapter 3 Edwards and Richardson (2004), (North Sea); McGinty et al. (2011) Persson et al. (2012) Chevillot et al. (2017) (zooplankton, Northeast Atlantic); Chivers et al. (2020) (phytoplankton, North Atlantic) Scharfe and Wiltshire (2019) Wasmund et al. (2019) (phytoplankton, Baltic Sea);	high confidence
Temperate oceans	Attribution Zooplankton + Phytoplankton: Attribution is provided by the general agreement of signals relative to expectations under climate change across multiple independent data sources/time series (as per Parmesan et al. 2013) Zooplankton: Changes in the timing of summer plankton maxima could be largely explained by SSTs in the North Sea in particular for dinoflagellates and meroplankton. Fluctuations in zooplankton phenology and age structure in the White Sea are partly driven by temperature changes	Edwards and Richardson (2004) (North Sea); McGinty et al. (2011) Persson et al. (2012)	Strong to moderate impact of climate change on changes in timing of seasonal events in phytoplankton and zooplankton, respectively; high confidence (***)
Tropical oceans	Observations In the northern Red Sea winter phytoplankton blooms have shorter duration during warm conditions as they start later and end earlier	Gittings et al. (2018)
	Published reports documenting phenological changes are predominately for the temperate or polar oceans, with 96% of reports since SROCC from the Northern Hemisphere	Chapter 3, 3.4.3.2
	Attribution Changes in winter phytoplankton bloom in northern Red Sea directly linked to thermal stratification and changes in air-sea heat fluxes	Gittings et al. (2018)	major contribution of climate change to the observed change in phytoplankton phenology, low confidence (*), no assessment elsewhere
	S10 Marine ecosystems - Range reduction or shift
Global	Observations Poleward range shifts of marine taxa. Marine taxa shifting at higher velocities than terrestrial species (expansion of cold limit by 72km per decade versus 6km per decade, respectively)	Poloczanska et al. (2016); Poloczanska et al. (2013)	High confidence
	Evidence from deep-sea sediment records of zooplankton indicate community-level polewards shifts at an average rate of 40 km per decade (range: 3-170 km per decade)	Jonkers et al. (2019)	High confidence
	Evidence for vertical range shifts into deeper waters is also present typically at rates slower than 10 m decade-1; however, this may be possible for only a limited number of taxa because of oxygen limitation and associated effect on suitable habitat	Chapter 3.4.3.1, Dulvy et al. (2008) Currey et al. (2015) Brown and Thatje (2015)	Medium confidence (limited evidence, high agreement)
	Attribution Of the species responding to climate change, rates of distribution shifts were, on average, consistent with those required to track ocean surface temperature changes	Poloczanska et al. (2016); Molinos et al. (2017)	Strong impact of climate change on range expansion of warm-affiliated species, high confidence (*) Strong contribution of climate change to the observed contraction of ranges of polar fish species, high confidence () (see polar seas) Strong impact of climate change on development of novel species assemblages, with increasing representation by warm-affiliated species, high confidence (**)
Polar seas	Observations Ranges of Arctic fish species are declining and contracting, while those of arcto-boreal or boreal fish species are expanding and increasing	Frainer et al. (2017) Kjesbu et al. (2014) Fossheim et al. (2015) Hedges et al. (2017)	high confidence	temperature, sea ice melt
Polar seas	Attribution Functional trait distribution changed rapidly, especially in the Arctic, concurrent with the observed increase in sea bottom temperature and reduction in sea ice coverage. Boreal communities have expanded northwards at a pace reflecting the local climate velocities	Frainer et al. (2017) Kjesbu et al. (2014) Fossheim et al. (2015) Hedges et al. (2017)	Strong contribution of climate change to the observed contraction of ranges of polar fish species and strong expansion of ranges of arcto-boreal or boreal fish, high confidence (***)
Temperate oceans	Observations The observed expansion of marine communities from warmer regions into colder ones (thermophilization); effectively leads to novel communities and biotic interactions, with concomitant changes in ecosystem functioning and servicing. Observed expansion of the range of tropical fish into subtropical and temperate regions for tuna and tropical herbivores (tropicalization)	WGII Ch3 3.4.4.1.3, Burrows et al. (2019) Kumagai et al. (2018) Fossheim et al. (2015) (thermophilization) Zarco-Perello et al. (2017) Pecuchet et al. (2020) Vergés et al. (2019) Peleg et al. (2020) Nagelkerken et al. (2020) Erauskin-Extramiana et al. (2019) (tuna); Vergés et al. (2019) (tropical herbivores)	high confidence
	Shift in mackerel stocks: Rapid change in the distribution of the northeast Atlantic mackerel stock after 2007. Atlantic mackerel became more abundant in northern Atlantic waters with ranges progressively expanding as far as Icelandic and south Greenlandic waters in the west, and Spitzbergen in the north	ICES (2013); Nøttestad et al. (2015)
	Attribution Poleward range shift of many species, including zooplankton (eastern North Atlantic, Mediterranean Sea), tuna (all temperate oceans), tropical herbivores (Australian coast) and bony and cartilaginous species (NW Atlantic), have tracked changes in temperature. In some cases, competitive interactions between species have been influenced by warming (cod and haddock in the North Atlantic)	Section 3.4.3.1; Fredston-Hermann et al. (2020); Erauskin-Extramiana et al. (2019) (tuna), Villarino et al. (2020) Durant et al. (2020) Vergés et al. (2019) (tropical herbivores), Monllor-Hurtado et al. (2017); Mach et al. (2019) (tuna)	Strong contribution of climate change to the observed expansion of ranges of warm-affiliated species into colder regions, high confidence (*) Strong impact of climate change on development of novel species assemblages, with increasing representation by warm-affiliated species in colder regions, high confidence (*)
Tropical oceans	Observations Decline in species richness around the equator since the 1970’s, especially pelagic fish	Chaudhary et al. (2021) (pelagic fish)	medium confidence in loss of marine species diversity around the equator
	Loss of living coral from mass coral bleaching events has led to changes in reef fish species assemblages, including loss of specialist species, e.g., in the Great Barrier Reef (Australia) and the Seychelles	Section 3.4.2.1; Richardson et al. (2018) Robinson et al. (2019)	High confidence
	Attribution Decline in species richness around the equator followed the climate induced shift of climatically suitable range of sea surface temperature	Chaudhary et al. (2021)	Moderate loss of species richness induced by climate change, low confidence (*)
	Change in reef fish species assemblages occur within 1-3 years of coral bleaching events, which can be attributed to marine heat waves	Section 3.4.2.1; Richardson et al. (2018) Robinson et al. (2019)
	S11 Marine ecosystems - Coral bleaching and associated effects
Global	Observations no global assessment as warm water corals are mainly located in the tropical ocean
Global	Attribution no global assessment as warm water corals are mainly located in the tropical ocean		no assessment
Tropical oceans	Observations First mass coral bleaching events were observed in the 1980s. Since then the frequency of mass coral bleaching and mortality events has increased in concert with anomalous ocean temperatures such that the return time between events has been reduced to less than the time needed for full recovery	Donner et al. (2017); Hughes et al. (2018); Sully et al. (2019) Section 3.4.2.1	very high confidence
Tropical oceans	Attribution Process-understanding, experiments and observations show that coral bleaching is induced by unusually high water temperatures. Mass bleaching events follow the trend in the occurrence of marine heat waves	(Hoegh-Guldberg et al., 2014); AR5 regarding temperature sensitivity of corals (Chapter. 6); IPCC Working Group 2 Section 3.4.2.1	Strong contribution of climate change to observed bleaching-induced loss of warm water corals, virtually certain (***)	Bleaching occurs when the density of algal symbionts, or zooxanthellae (Symbiodinium spp.) is lost due to changes in the environmental conditions, including increases in temperature.
Tropical Atlantic	Observations The Caribbean and western tropical Atlantic have reported more bleaching events since the 1980s at 2-3 times the rate of any other region. Widespread bleaching was observed in 2005, 2010 and 2016, with 50-90% of locations surveyed each year experiencing at least moderate bleaching. Most Mexican Caribbean coral reefs are no longer dominated by hard corals, as coral cover declined from 26% to 16% from 1978 to 2016	Donner et al. (2017); Hughes et al. (2018); Contreras-Silva et al. (2020) (Mexican Caribbean, 1978-2016)	very high confidence
Tropical Atlantic	Attribution Periods of locally anomalous warm temperatures are well understood as a trigger for mass coral bleaching. The spatial extent and the timing of available bleaching observations match the occurrence of heat stress in the historical record, including the 2005, 2010 and 2016 events. Other drivers of bleaching (cold water, fresh water, tidal exposure) or non-climate hazards cannot explain the increase in bleaching observations or the timing or broad spatial extent of those observations. At minimum the event in 2005 can be attributed to anthropogenic climate change as the underlying marine heatwave has been attributed (see section on marine heatwaves in Table SM16.20)	Donner et al. (2017); Eakin et al. (2010); Hughes et al. (2017)	major contribution of climate change to increased frequency of bleaching and associated mortality, high confidence (***)
Tropical Indian Ocean	Observations Mass bleaching was observed in 1998, 2010 and 2015/16. In 2016, bleaching intensity exceeded 20% in surveyed locations in the western Indian Ocean, eastern Indian Ocean and western Indonesia. In Huvadhoo atoll in southern Maldives, the 2016 event contributed to a decline in reef complexity and exceptionally low juvenile coral densities, below recovery thresholds identified for other reefs in the regions	McClanahan et al. (2019), Chapter 15, Perry and Morgan (2017) (Maldives)	very high confidence
Tropical Indian Ocean	Attribution As in other regions, the seasonal timing and spatial extent of bleaching matches with periods of warm season heat stress. Peak temperature values and timing of hot and cold peaks best explain the spatial pattern of bleaching in the 2015 event (in the Indian Ocean and SW Pacific). At least the 2015 event can be attributed to anthropogenic climate change as the underlying marine heatwave has been attributed (see section on marine heatwaves in Table SM16.20)	Park et al. (2017) McClanahan et al. (2019)	major contribution of climate change to increased frequency of bleaching, high confidence (***)
Tropical Pacific	Observations Climate-driven mass bleaching has been reported in all countries in the region, with the most bleaching reports coinciding with 2014 through 2017 marine heatwaves. 50% of coral within shallow-water reefs of the northern and central two-thirds of the GBR were killed in 2015/16. Subsequent coral recruitment in 2018 was reduced to only 11% of the long-term average, representing an unprecedented shift in the ecology of the northern and middle sections of the reef system to a highly degraded state	Chapter 3; Chapter 11; Chapter 15 Hughes et al. (2018); Hughes et al. (2019)	very high confidence
Tropical Pacific	Attribution As in other regions, observed mass warm season bleaching events are correlated with periods of warm season heat stress. For example, the spatial pattern and severity of mass bleaching on the GBR in 1998, 2002, and 2016 were determined by marine heatwaves. The likelihood of 2016 heat stress has been strongly increased by climate change (see marine heatwave attribution section in Table SM16.20)	Chapter 3, Chapter 11, Chapter 15, Hughes et al. (2017)	strong adverse impact of climate change, high confidence (***)
	S12 Marine ecosystems - Kelp forest distribution
Global	Observations Kelp populations have declined in 38% of the ecoregions examined and increased or remained stable in 68% of ecoregions	Krumhansl et al. (2016) Smale et al. (2019)	very high confidence
Global	Attribution Warming is driving range contraction and local extinction at the warm end of species ranges, and range expansions at the cold end of species ranges. Kelp forests have decreased due to the direct effects of increased temperature associated with both chronic gradual warming and acute extreme warming events exceeding species’ thermal limits		major gains (at the cold end of the species range) and moderate losses (at the warm end) leading to an overall gain in kelp forests induced by climate change, medium confidence (**)	Temperatures outside species’ thermal limits experience physiological stress, leading to tissue damage, reduced growth and reduced productivity. Chronic gradual warming generally leads to range shifts, whereas acute heat stress can lead to mortality, decreases in size, decreases in abundance, and local extinction
Polar seas	Observations Although observations to date are rare, available data from Greenland and Kongsfjorden, Svalbard report increased kelp abundance and productivity		medium confidence
Polar seas	Attribution Increasing abundances can be explained by decreased ice cover leading to increased light and substrate availability: Seasonal sea ice cover has been identified as principal driver of spatial and temporal variation in depth extension and annual production of kelp in Greenland and a combination of melting landfast sea-ice, increased open-water light period and increased sedimentation can explain the observed changes in kelp distributions in Kongsfjorden, Svalbard	Krause-Jensen et al. (2012) (Greenland); Bartsch et al. (2016) (Svalbard)	Major contribution of climate change to minor gains in kelp abundance; medium confidence (**)
Temperate oceans	Observations Long-term declines in kelp area and acute kelp loss have been observed across temperate oceans, particularly at the warm edge of species. Local declines in kelp and other canopy-forming seaweeds have caused shifts to systems dominated by turf, urchins or tropical species like corals	Filbee-Dexter and Wernberg (2018) Smale (2019)	very high confidence
Temperate oceans	Attribution The timing and spatial extent of both long-term and acute kelp losses correspond with warming trends reducing species’ thermal habitat and MHWs exceeding species’ thermal thresholds. A well-documented abrupt loss event was triggered by the marine heatwave in Western Australia in 2011, which may be attributed to anthropogenic climate change (fraction of attributable risk ~0.8 but not significantly greater than zero, see Table SM16.20). Even independent of the attribution to anthropogenic climate forcing the event clearly documents the sensitivity of the system to heat	see below	Major contribution of anthropogenic climate change to kelp forest decline and shifts in species’ ranges, including minor gains and major losses, high confidence (***)
North Atlantic	Observations NW Atlantic: Decline in mean kelp biomass by 85-99% over the past 4-6 decades in Nova Scotia, Canada, and in sugar kelp cover (Saccharina latissima) by 80% since 1972 in Rhode Island, USA	Filbee-Dexter et al. (2016) (Canada); Filbee-Dexter et al. (2020) (USA)
North Atlantic	Attribution The timing of kelp decline in Nova Scotia and Rhode Island correlates with gradual warming and the timing of MHWs, including the 2012 NW Atlantic heatwave which has been attributed to anthropogenic climate change (see Table SM16.20)	Filbee-Dexter et al. (2016) (Canada); Filbee-Dexter et al. (2020) (USA) Laufkötter et al. (2020)	major contribution of ocean warming to the observed loss of kelp forests along the North American east coast, medium confidence (**)
North Atlantic	Observations Poleward expansion of kelp forests, loss of species along the Iberian Peninsula; and loss of cold-water species along Ireland and Norway	Smale et al. (2015) (southwest UK); Schoenrock et al. (2019) (western Ireland), Rinde et al. (2014) (Norway); Simkanin et al. (2005) (Ireland) Casado-Amezúa et al. (2019) (Spain, Portugal); Piñeiro-Corbeira et al. (2016) (Spain); Díez et al. (2012) (Spain); Fernández (2011) (Spain); Voerman et al. (2013) (Spain)
	Attribution Poleward expansion of kelp forests follows poleward expansion of the thermal niche, i.e. abundance rapidly increased at the poleward leading range edge	Smale et al. (2015) (southwest UK); Schoenrock et al. (2019) (western Ireland)	the impact of climate change ranges from major gain, to major losses, high confidence (***)
	Observed long-term declines are induced by gradual warming: Loss of species at their thermal maximum	Rinde et al. (2014) (Norway); Simkanin et al. (2005) (Ireland) Casado-Amezúa et al. (2019) (Spain, Portugal); Piñeiro-Corbeira et al. (2016) (Spain); Díez et al. (2012) (Spain); Fernández (2011) (Spain); Voerman et al. (2013) (Spain)
North Pacific	Observations Japan: Long term decline in species abundances. North America : Long-term decline in species abundance at sites in British Columbia, California and northern Mexico since the early 1990s, including a 90% decline in bull kelp (Nereocystis luetkeana) in northern California and accompanying phase shift to urchin-dominated systems, and loss of giant kelp (Macrocystis pyrifera) at the warm edge of the species range in Mexico	Tanaka et al. (2012) (Japan); Rogers-Bennett and Catton (2019) (California), Starko et al. (2019) (Canada); Arafeh-Dalmau et al. (2019) (Mexico); McPherson et al. (2021)
North Pacific	Attribution Japan: Declines are induced by gradual warming: Loss of species occurs at their thermal maximum. North America : Timing of decline in abundance of multiple species (British Columbia), bull kelp decline and ecological phase shifts (California), and giant kelp loss (Mexico) are linked with 2013-2015 MHW (‘the Blob’), chronic warming, and local ecological stressors (California) The 2013-2015 MHW has been attributed to anthropogenic climate change (see Table SM16.20)	Tanaka et al. (2012) (Japan); Rogers-Bennett and Catton (2019) (California), Starko et al. (2019) (Canada); Arafeh-Dalmau et al. (2019)	major to moderate contribution of climate change to observed reduction of kelp forests, medium confidence (**)
Australasia	Observations Long-term decline in kelp species observed in southeast Australia and South Island of New Zealand, including localized extinction of bull kelp (Durvillaea spp.) in New Zealand	Babcock et al. (2019) (Australia); Carnell and Keough (2019) (Australia); Thomsen et al. (2019) (New Zealand)	high confidence (***)
Australasia	Attribution Decline in common kelp species (Ecklonia radiata) in southeast Australia at the warm edge of species range since 1997, and increase in urchin abundance and distribution, corresponding with ocean warming and marine heatwaves. Extent and timing of loss and local extinction of bull kelp in New Zealand correlated with a 2017-2018 MHW demonstrating the sensitivity of the system to high temperatures	Babcock et al. (2019) (Australia); Carnell and Keough (2019) (Australia); Thomsen et al. (2019) (New Zealand)	Moderate to major impact contribution of climate change to observed loss of kelp forest, medium confidence (**)
Temperate Indian Ocean	Observations Loss of 43% of kelp forests in southwest Australia and abrupt decline (>90%) of a common kelp species (Ecklonia radiata) during 2011 Western Australia MHW. Localized extinction and tropicalization of kelp ecosystems observed at the warm edge of species’ range	Babcock et al. (2019) Wernberg et al. (2016)	very high confidence (***)
Temperate Indian Ocean	Attribution Timing and spatial pattern of kelp loss, local extinction and tropicalization correspond with 2011 Western Australia MHW, which has been attributed to anthropogenic climate change (see Table SM16.20)	Babcock et al. (2019) Wernberg et al. (2016) Laufkötter et al. (2020)	Major impact of climate change on kelp loss and local extinction, high confidence (***)
Tropical oceans	Observations Kelp forests are extremely rare in the tropics.
Tropical oceans	Attribution		no assessment
	S13 Marine ecosystems - Seagrass distribution
global	Observations In areas with available data, 29% of seagrass meadows were lost between 1879 and 2006, with rates of loss accelerating from 0.9% per year before 1980 to 7% per year after 1990	Waycott et al. (2009)	medium confidence (**)
global	Attribution Historically, non-climate hazards, including coastal development and reduced water quality, have been primary drivers of seagrass loss. Since 1990, warming and marine heatwaves, together with storm-driven turbidity and structural damage, have contributed to seagrass die-off and shift in species assemblages	Section 3.2.4.5; Waycott et al. (2009); Nowicki et al. (2017)	moderate decline in seagrass induced by climate change, low confidence (*)
Polar seas	Observations Very rare in polar regions.
Polar seas	Attribution		no assessment
Temperate oceans	Observations Specific cases of seagrass loss in the temperate ocean include. 38% loss of seagrass cover in Langebaan Lagoon, South Africa between 1960 and 2007	Pillay et al. (2010); Mvungi and Pillay (2019)
	Loss of one-third of dense seagrass cover in Shark Bay, Western Australia from 2010 through 2016.	Strydom et al. (2020) Serrano et al. (2021)
	Attribution Mainly attributed to direct human impact, eutrophication impacts exacerbated by warming	Mvungi and Pillay (2019)	moderate contribution of climate change to observed loss of seagrass in South Africa, low confidence (), major impact of climate change on seagrass loss in Western Australia, high confidence (**)	Warming due to climate change exacerbates eutrophication, and fouling by microalgae
	Timing of Western Australia seagrass decline corresponds with 2011 Western Australia MHW, during which sea surface temperatures exceeded the thermal optima for key species. The MHW has been attributed to anthropogenic climate change (see Table SM16.20) and the decline of seagrass appears as part of a long-term change of the ecosystem from a dominance of temperate species to a dominance of tropical species	Laufkötter et al. (2020) Strydom et al. (2020) Serrano et al. (2021)		Marine heatwaves lead to direct warming induced loss.
Tropical oceans	Observations For the large majority of sites a decline in seagrass communities has been documented (e.g., tropical USA and Australia, Bermuda and Colombia), while for some sites an increase has been reported (e.g., USA, Australia, Cayman Islands, US Virgin Island); large data gap for tropical Indo-Pacific regions	Chapter 15, Waycott et al. (2009) (review of sites across globe)
Tropical oceans	Attribution As in other regions, attribution to climate change is complicated by increasing non-climate hazards, including direct physical damage, coastal erosion linked to development and decline in local water quality	Section 3.4.2.5; Short et al. (2016); Mach et al. (2019)	minor contribution of climate change to observed loss of seagrass at several Small Islands’ coasts, low confidence (*);no assessments elsewhere
	S21 Terrestrial ecosystems - Range reduction or shift
Global	Observations Poleward and up-elevation range shifts tend to lead ultimately to reduced range sizes. Shifts observed in about 50% of terrestrial taxa. 47% of 976 species examined showed local extinctions at warm boundaries, higher in tropics (55%) than temperate (39%) latitudes, higher for animals (50%) than plants (39%), highest in freshwater (74%), then marine (51%) then terrestrial (46%) realms. Observed range shifts for temperate zone species typically lag far behind those predicted by recent warming (1). In contrast, observed shifts among tropical species roughly match those predicted by climate shifts (12)	Chapter 2, Wiens (2016) Sheldon (2019); Scheffers et al. (2016) Parmesan and Yohe (2003) (50%, 460 of 920 species) Root et al. (2003) (52%, 483 of 926 species) Wiens (2016) (47%, 460 of 976 species)
Global	Attribution Poleward and up-elevation range shifts mainly due to local extinction at warm boundaries with some colonization beyond previously cold temperature limited boundaries. Global warming trends can be quantitatively linked to observed range shifts, elevational shifts parallel latitudinal shifts	Wiens (2016) Parmesan and Yohe (2003) Root et al. (2003), Rosenzweig et al. (2008) Anderegg et al. (2019)	Major contribution of climate change to the observed large scale shifts to higher altitudes and colder latitudes, high confidence (***) It is to be noted that there exists very wide variation and idiosyncrasy in individual species range responses to climate change, and this discounts the likelihood that ecological communities would shift range as coherent entities	Warming exceeds tolerance limits of species warm range edges, and increases habitability of cold range edges, thus prompting local extinction and colonization in each habitat respectively, extinction rate at “trailing edges” is often lagging colonization rate at “leading edges”
Africa	Observations Ethiopian wolf: Reduction and upward shift (2500 m.a.s.l in early 20th century, to 3000 m.a.s.l. currently) of the range of the rare and Africa’s most endangered Ethiopian wolf (Canis simensis)	Gottelli et al. (2013)
	Afro-palearctic species: Population declines in migratory Afro-palearctic species (1981-2012), species wintering in southern Africa and some in the northern Sahel had the greatest mean declines per species	Beresford et al. (2019)
	Attribution Ethiopian wolf: Numbers of Ethiopian wolves declined naturally with natural global warming 18000 years ago, as Afroalpine habitats contracted at higher elevations; since the 20th century, unique habitats that supported this species are becoming both more dessicated and prone to human disturbance. Direct human influences on the habitat appear to be the dominant driver in contrast to climate change	Gottelli et al. (2013) (Ethiopian wolf)	minor contribution of climate change to the observed contraction of range and populations assessed so far, low confidence (*), no assessment elsewhere
	Afro-palearctic species: For Afro-palearctic birds, impacts appear to occur via climate effects on vegetation activity, with senescence (aging and dying of leaves) more important in Sahel and green-up more so in southern Africa. The observed changes in population patterns suggest that species’ populations increased more (or decreased less) where senescence is delayed	Beresford et al. (2019)
Asia	Observations Range expansions and neutral range shifts observed in Larix species. In Russia, on average 83% of tree lines advanced northward, mainly L. cajanderi (92% of sites). In China, range reduction of L. olgensis was concentrated in north-eastern China (67% of sites). In Mongolia, L. sibirica range shifts were evenly split between advance (45%) and recessions (45% of sites)	Mamet et al. (2019)
	Species range of black-billed Capercaille declined by 35.50% between 1970 and 2000	Zhang et al. (2020a) Yang et al. (2018)
	Attribution Using Larix species bioclimatic niches of ~5-16°C and ~100-390 mm for GST and GSP, the direction that niche centroids moved between observation periods (1944-1979 and 1980-2015) was evaluated. Siberian species tracked temperature increases, while increased trends in precipitation were also significant. L. potaninii tracked drier and L. griffithiana tracked wetting conditions, but tracking of changes in precipitation and temperature trends was variable among many species	Mamet et al. (2019)	Moderate contribution of climate change to range reduction, medium confidence (**)	Larix (larch) distributions may be particularly sensitive to climatic change due to deciduous habit, fast growth and maturation.
	Black-billed Capercaillie: Ensemble range simulations using historical observed data to assess the relative impact of climate change and anthropogenic disturbance between 1970s and 2000s show that T. urogalloides is sensitive to climate change. The observed decline in species range between 1970 and 2000 is dominated by human disturbances other than climate change (29%) while climate change has contributed, too (6.5%)	Zhang et al. (2020a) Yang et al. (2018)
Australasia	Observations
Australia	Observations Bramble Cay Melomys (rodent): 100% range loss (global extinction) in 2014	Waller et al. (2017) (Bramble Cay Melomys)
	Canopy tree species in Australian Alps: Local extirpations and replacement of dominant canopy tree species and replacement by woody shrubs due to seeders having insufficient time to reach reproductive age (Alpine Ash) or vegetative regeneration capacity is exhausted (Snow Gum woodlands) in southern and south-western Australia	Bowman et al. (2014); Fairman et al. (2016) Harris et al. (2018) Zylstra (2018)
	Vertebrate species: Decline in distribution area of the koala in south-eastern New South Wales; population decline and range contraction of the tawny dragon lizard, mass mortality of wildlife species (flying foxes, freshwater fish), decline in distribution area and population size of possums and birds in the Australian Wet Tropics rainforests	Lunney et al. (2014) (koala), Walker et al. (2015) (lizard), Ratnayake et al. (2019) (flying fox), Vertessy et al. (2019) (freshwater fish) Hoffmann et al. (2019) (possum, bird)
	Alpine vegetation: Shifts in dominant vegetation with a decline in grasses and other graminoids and an increase in forb and shrub cover in Bogong High Plains, Victoria, Australia	Wahren et al. (2013)
New Zealand	Observations Endemic forest birds: Range contraction and population decline in bird species with large body size, those nesting in tree cavities and/or have poor dispersal capabilities due to increased predation pressure by invasive species	Walker et al. (2019)
New Zealand	Attribution see individual assessments below		Strong reduction in ranges caused by climate change, high confidence (***)
Australia	Attribution Extinction of Bramble Cay Melomys: Sea-level rise and storm surges in Torres Strait resulted in habitat loss and direct mortality of this rodent species	Waller et al. (2017)
	Canopy tree species in Australian Alps: Multiple wildfires in short succession resulting from increased fire risk conditions induced by declining winter rainfall and increasing hot days	Fairman et al. (2016) Harris et al. (2018) Zylstra (2018)
	Vertebrate species: Droughts and increase in temperatures as a major driver of the decline in distribution area of the koala in south-eastern New South Wales. Higher body temperatures and declining rainfall led to population decline and range contraction of the tawny dragon lizard. Extreme heat caused mass die-offs of flying foxes. Extreme hot and dry climate conditions caused fish deaths. In the wet tropics rainforest, warming and increasing length of dry season led to decline in possum and bird populations	Lunney et al. (2014) (koala), Walker et al. (2015) (lizard), Ratnayake et al. (2019) (flying fox), Vertessy et al. (2019) (freshwater fish) Hoffmann et al. (2019) (possum, bird)
	Alpine vegetation: Severe drought, warming and climate-induced biotic interactions led to shifts in dominant vegetation	Wahren et al. (2013)
New Zealand	Attribution Endemic forest birds: Climate warming leads to range expansion of invasive species and increase in predation pressure on endemic forest birds that retreat to higher elevations of forested mountains	Walker et al. (2019)
Central and South America	Observations Andean tree genera: A study of 38 Andean tree genera monitored in repeated (2003/04-2007/08) censuses of 14 1-ha forest inventory plots spanning an elevational gradient from 950 to 3400m in Manu National Park in south-eastern Peru show an upward shift of the mean distributions of most species upslope (2.5-3.5 vertical metres upslope per year). Abundances of tree genera previously distributed at lower elevations increased in the majority of study plots	Feeley et al. (2011)
	Documentation mountaintop extirpations in tropical bird community, Peru: Comparison of survey data from 1985 and 2017 on bird species shows significant upward shifts of most species. The low elevation limit of mountaintop species (historically found only above 1,300 m) has generally shifted upslope and squeezed high-elevation populations into smaller total areas. Nearly all mountaintop species have declined in abundance and 8 of the 16 species could not be found anymore. In contrast, low-elevation species seem to be benefitting from temperature increases. The upper elevational limits of species found at the lowest elevations (i.e., at the Palatoa River) during the historic survey have shifted upward even as these species continue to exist at the bottom of the transect. As a consequence, lowland species have increased in elevational distribution by an average of 71 m and also increased in available area	Freeman et al. (2018)
	Attribution Andean tree genera: The observed changes support the hypothesis that rising temperatures are one important driver of upward movement where the movement is slower than expected based on observed temperature change. However, overall the study period (4 years) is still too short to consider the observed changes an ‘observed impact of long term climate change’, but rather a hint how sensitive the system may be to changing climate. So far it cannot be excluded that the observed changes may be in response to an isolated climatic event	Feeley et al. (2011)	strong reduction of the range and abundance of mountaintop bird species and range expansion of low-elevation birds induced by warming, low confidence (*) as only based on one study (summarized in Figure 16.2 as range reduction in mountaintop birds) no broader assessment
	Mountaintop extirpations in tropical bird community, Peru: The observed reduction in range and abundances matches the expected shifts induced by warming, the area is not affected by other direct influences such as land use changes	Freeman et al. (2018)
Europe	Observations Systematic shifts towards higher elevation and upstream were found for 32 stream fish species in France	Comte et al. (2013)
	For British taxa, mean northwards range margin change 1966-1975 to 1986-1995 was 23 km per decade (N = 13 taxonomic groups) and from 1986-1995 to 2001-2010 was 18 km per decade (N = 16 taxonomic groups)	Mason et al. (2015)
	37 of 48 sampled butterfly species in Finland shifted range margins northward (average of 59.9km, 1992-1996 compared to 2000-2004), 9 species’ ranges retracted southward, 2 species’ ranges did not change	PÖYRY et al. (2009)
	Mountain butterflies (37 species), eastern Alps: Significant shifts of distributions towards higher elevations (1965-2015). While the highest altitudinal maxima were mostly observed in more recent years, observations at the respective lowest altitudes are restricted to the period prior to 1980. Average altitudinal distributional shift of more than 300 m uphill across six decades	Rödder et al. (2021)
	Increase in plant species richness on 87% of the 302 sampled mountain tops over 145 years. This trend was consistent across all nine geographical regions that were sampled. Rate of species increase was 1.1 species per decade in 1957-1966, and increased to 5.4 species per decade in 2007-2016	Steinbauer et al. (2018)
	Attribution The shifts follow geographic variation in climate change. For British data, range shifts were associated with warming of 0.21 and 0.28 °C per decade. For the data from Finland, mean temperature of the summer months increased by 0.45-1.8°C during the study period and isotherms of growing degree days shifted by 200-300 km northward. Increase in plant species richness on mountain tops was positively related to temperature increase across all 302 time series. Mountain butterflies (37 species), eastern Alps: Climate change is identified as a main driver of the shifts as observations are basically in line with species distribution models accounting for temperature and precipitation changes in addition to topographic variables	Comte et al. (2013) Mason et al. (2015) PÖYRY et al. (2009); Steinbauer et al. (2018) Rödder et al. (2021)	Strong shifts to higher elevation and reduction of ranges of different plant and animal taxa induced by climate change; high confidence (***).
North America	Observations Bull trout: For the bull trout (Salvelinus confluentus) 11-20% (8-16% decade-1) of headwater stream breeding habitat was lost in Idaho, and a reduction in the number of occupied sites was documented in a watershed in Montana (site abandonment probabilities (0.36) higher than colonization probabilities (0.13))	Isaak et al. (2010) Eby et al. (2014) (bull trout in Montana)
	Stonefly: Range of stonefly (Zapada glacier) retreated upstream to higher and cooler sites in alpine streams in Montana. Of the 6 streams sampled in 1963-1979 the stonefly was detected in only 1stream in 2011-2013 and at 2 new sites	Giersch et al. (2015)
	Breeding ranges of birds: 84% of species shifted their elevational distribution in the Sierra Nevada Mountains, USA (n=77 sites, 223 bird species) over 80-100 years. Of those 51% shifted upslope (161 to 1320 m for lower limits, 218 to 2503 m for upper limits), the others downslope (ward shifts ranged from 113 to 1557 m for lower limits and 127 to 1567 m for upper limits)	Tingley et al. (2012)
	Populations of the endemic Eastern Massasauga snake (Sistrurus catenatus) are declining	Pomara et al. (2014)
	Attribution Bull trout: Largest losses are occurring in coldest habitats and have been attributed to increasing temperatures induced by anthropogenic climate forcing and to increased wildfire that reduced vegetation shading streams	Isaak et al. (2010) (bull trout in Idaho), Eby et al. (2014) (bull trout in Montana)	Moderate contribution of climate change to observed shifts in species ranges, medium confidence (**) no broad assessment	Spawning and early juvenile rearing appear to be adversely affected by warming water, but study calls for further work to understand mechanisms better
	Stonefly: Upstream retreat of the stonefly was attributed to increase of water temperature (0.67-1.00°C from 1960-2012) and decrease in glacial surface area (35% reduction from 1966-2005)	Giersch et al. (2015)
	Breeding ranges of birds: Upslope shifts of bird ranges associated with increase in temperatures, downslope shift with increase in precipitation	Tingley et al. (2012)
	Demographic models for 189 population locations based on observational data for 1950 to 2008 predicted known extant and extirpated populations well (AUC = 0.75), and allowed association of population declines to increasing extreme event frequency and severity (note models combining climate change and land use change factors performed better than models incorporating these drivers independently)	Pomara et al. (2014)
Small Islands	Observations Birds: Island extinction rates are two to three orders of magnitude higher than continental rates for birds and mammals. In Papua New Guinea upslope shifts of bird ranges averaged 113 m (Mt. Karimui) and 152 m (Karkar Island) for upper limits and 95 m (Mt. Karimui) and 123 m (Karkar Island) for lower limits over 47 (Mt. Karimui) and 44 (Karkar Island) years, respectively	Freeman and Freeman (2014) (birds and other species, New Guinean birds), Koide et al. (2017) (vascular plants, Hawaii), Taylor (2016) (review across different species, Pacific region) Taylor (2016) Loehle and Eschenbach (2012) (bird island extinction rate)
	Puerto Rico: 8 of 20 forest bird species (38%) changed elevational distribution significantly, of those, distribution expanded for 6 species (upslope for 5 of those), distribution contracted for 2 species	(Campos-Cerqueira et al., 2017: author-year)
	Reptiles and amphibians: Upslope distribution movement of 30 species, representing five families of reptiles and amphibians in the Tsaratanana massif, Madagascar (overall mean shifts in elevational midpoint from 1993 to 2003 of 19-51m upslope; mean lower elevation limit 29-114m; mean upper elevation limit 8 to 53m)	Raxworthy et al. (2008) (reptiles and amphibians, Madagascar)
	Vascular plants: Significant mean upward range shift across 69 vascular plant species on island of Hawaii (65.2m from 1970 and 2010). In the subgroup of native species, the upper elevation limit did not change, but the lower elevation limit shifted significantly upward by 94.1m (insignificant range contraction). In contrast, the subgroup of non-native species displayed a different pattern of a significant upward shift in both their upper and lower elevation limits, by 126.4 and 81.6 m, respectively (no range contraction)	Koide et al. (2017) (vascular plants, Hawaii)
	Marion Island: Earlier breeding increased the number of invasive mice on Marion Island by 430.0% between 1979-1980 and 2008-2011	McClelland et al. (2018)
	Attribution While generally reductions in species ranges have often been dominated by direct human interventions (land use changes, pollution, distribution of invasive species), the observed upward range shifts appear to be dominated by climate trends. Bird ranges in Papua New Guinea shifted upward by 32 and 27.5 m/0.1°C while regions are not disturbed by other direct human influences. Observed range shifts linked to local temperature trends, including a high warming rate of 2.2°C in 17 years in Puerto Rico. Observed shifts in tropical montane species across very different taxa (e.g. trees, plants, birds, lizards, and moths) seem to match observed shifts in temperature ranges more closely than the range shifts of temperate montane species whose response may lag behind for a longer time. Climate change also at least appears as a very plausible explanation for the upward shift of vascular plants. Range shifts of reptiles and amphibians in Madagascar are well in line with the shift of temperature ranges from 1993 to 2003	Taylor and Kumar (2016), Freeman and Freeman (2014) (New Guinean birds and re-evaluation of surveys across other taxa), Campos-Cerqueira et al. (2017) (Puerto Rico), Koide et al. (2017) (vascular plants, Hawaii), Raxworthy et al. (2008) (reptiles and amphibians, Madagascar), McClelland et al. (2018) (invasive mice in Marion Island)	Climate change is a dominant driver of the observed upward range shifts in mountain regions, medium confidence (**)	Warming directly affects the thermal performance of sensitive species Warming trends reduce habitat suitability within small, limited terrain at low elevation and increase suitability at higher elevations, range sizes become further restricted at higher elevations due to decreasing availability of land area with altitude
	S22 Terrestrial ecosystems - Net primary production (NPP) NPP = amount of atmospheric carbon fixed by plants and accumulated as biomass = photosynthesis by plants - plant energy use through respiration. Note: The synthesis in Figure 16.2 refers to the effects of climate change, not CO2 fertilization
Global	Observations Global: Global terrestrial NPP has exceeded land use emissions since the early 2000s, making terrestrial ecosystems a net carbon sink	Friedlingstein et al. (2020) (terrestrial carbon sink), Nemani et al. (2003) (NPP changes), Zhao and Running (2010) (drought impacts), Huang et al., 2016 (NDVI), Yuan et al. (2019) (NPP decrease since 1999); Pan et al. (2018) (expansion of browning areas, 1982-2013), Zhang et al. (2021) (reversal of greening trend, NDVI, 1981- 2015) Chapter 2 (2.4.4.5.1) Zhao and Running (2010)
	Global terrestrial NPP increased 6% from 1982 to 1999, then decreased 1% from 2000 to 2009. From 1999 to 2015, Normalized Difference Vegetation Index (NDVI) declined globally, particularly in semi-arid ecosystems, indicating widespread decreases in NPP and expansion of browning areas. Between 2000 and 2009, NPP increased on 65% of vegetated land area in the northern hemisphere, while it decreased on 70% of vegetated land in the southern hemisphere (based on remotely sensed vegetation properties)	Friedlingstein et al. (2020) (terrestrial carbon sink), Nemani et al. (2003) (NPP changes), Zhao and Running (2010) (drought impacts), Huang et al., 2016 (NDVI), Yuan et al. (2019) (NPP decrease since 1999) Pan et al. (2018) (expansion of browning areas, 1982-2013), Zhang et al. (2021) (reversal of greening trend, NDVI, 1981- 2015) Chapter 2 (2.4.4.5.1) Zhao and Running (2010)
	From 2000 to 2014, 29 of 53 countries together representing 90% of global NPP showed increasing NPP trends. Russia, Argentina, Peru and several countries in southeast Asia showed a marked decrease in NPP (~15 g C/m2/y)	Peng et al. (2017)
	Spatially varying trends in satellite derived biomass (vegetation optical depth, VOD) and nine site-based NPP measurements	Murray-Tortarolo et al. (2016)
	Structurally intact tropical forests: The carbon gains through tree growth and newly recruited trees (here considered the NPP component) have increased (1983-2011)	Hubau et al. (2020) (Africa and Amazonian), Brienen et al. (2015) (Amazon), Qie et al. (2017) (Southeast Asia)
	Grassland ecosystems: Globally, 49.25% of grassland ecosystems experienced degradation (2000-2010). Slight increase in NPP in Asia and North America, slight decrease in NPP in Africa, Oceania, South America and Europe	Gang et al. (2014)
	Arid regions: Of 44.5 mio km global area of dryland vegetation, 2.70 mio km (6%) showed a significant negative NPP trend (desertification), 41% showed significant positive NPP trend (greening), and 53% showed no significant change between 1982 and 2015	Burrell et al. (2020) Murray-Tortarolo et al. (2016)
	Attribution Global terrestrial NPP increase from 1982 to 1999 is attributed to increasing temperature and increased solar radiation in the Amazon from decreased cloud cover. The decrease of global NPP from 2000 to 2009 has been shown to be induced by droughts in the southern hemisphere. The observed decline in NPP (derived from NDVI) from 1999 to 2015 is induced by increased aridity	Nemani et al. (2003) (climate-related increase); Zhao and Running (2010) (drought induced reduction 2000-2009); Yuan et al. (2019) (both on declines from 1999-2015), Zhang et al. (2021) (water availability controlling vegetation trends, NDVI, 1981- 2015), Peng et al. (2017) Chapter 2 (2.4.4.5.1)	Climate change increased NPP in the observational period into the 2000s, medium confidence (**), findings for later period still inconclusive Increasing CO2 concentrations have been an overarching factor facilitating increased NPP, and have partly compensated for some climate change-related adverse impacts on NPP.
	Structurally intact tropical forests: The increase in NPP is linked to CO2 increase (effect about equally strong in Africa as in Amazonia) which has compensated for negative effects of droughts and temperature increase. A weaker climate-induced reduction in Africa seems to be driven by slower warming, fewer or less extreme droughts, lower forest sensitivity to droughts, and overall lower temperatures (African forests are on average ~1.1 °C cooler than Amazonian forests, because they typically grow at higher elevations of ~200 metres above sea level)	Hubau et al. (2020) (Africa and Amazonian)
	Arid regions: Globally, CO2 fertilization was the largest absolute driver of NPP change in 44.1% of areas, followed by land use practices (28.2%), climate variability (14.6%), and then climate change (13.1%). The main driver of significant NPP decline (desertification) was land use (79.9%), followed by climate change and climate variability	Burrell et al. (2020); Donohue et al. (2013)
	Using a combination of statistical and modelling approaches, variations in temporal trends in NPP were attributed to variations in temporal trends in dry season intensity and length in dry regions when the opposing positive effect of CO2 fertilization was removed	(Murray-Tortarolo et al., 2016)
	Modelling studies have identified a consistent overarching positive effect of fertilization by increased atmospheric CO2 in enhancing NPP (1961 to 2010)	Li et al. (2017b); Martínez Martínez et al. (2019) Liu et al. (2019)
Africa	Observations Positive NPP trends observed in tropical Africa, and negative trends observed in subtropical southern Africa 2000 - 2009	Zhao and Running (2010)
	The Central African Republic is observed to have the highest positive NPP trend globally (23 g C/m2/y) between 2000 and 2014	Peng et al. (2017)
	Intact African tropical forests: Analysis of live aboveground biomass of 244 plots spanning 11 countries show a stable carbon sink (no trend) in live aboveground biomass for the three decades to 2015. The NPP term (=tree growth and newly recruited trees) shows a positive trend (0.008 Mg C ha-1 yr -2, 1983-2011)	Hubau et al. (2020) (244 plots)
	Sahel Greening: Major increase in NPP (greening) observed across the Sahel between 1981-2015. Homogeneous greening in the Northern sub-Saharan Africa is the major contribution to the zonally averaged greening in the region of 5°N-15°N which is strongest and long-lasting in comparison to other latitudes	Burrell et al. (2020); Zhang et al. (2021) (NDVI, 1981- 2015)
	Attribution Increase in net primary productivity in Africa was attributed to decrease in vapor pressure deficit and increase in rainfall. Model simulations indicate that the positive trend from 1980-2009 can be explained by an increase in precipitation, increasing CO2 (accounts for 29% of NPP variation), and increasing nitrogen deposition (accounts for 28% of NPP variation)	Zhao and Running (2010); Hoscilo et al. (2015) Pan et al. (2018)	Recent climate change has reduced NPP (tropical forests), medium confidence () and strongly increased NPP (in the Sahel zone), high confidence (*) Increasing CO2 concentrations partly compensated for the climate induced decline in NPP (e.g. in the tropical forests) and contributed for the increase in the Sahel zone	Warmer and wetter climatic conditions, together with elevated atmospheric CO2 concentration and nitrogen deposition, have resulted in a significant increase in African terrestrial NPP during 1980-2009
	African tropical forests: The long-term positive trend in carbon gains from tree growth and newly recruited trees (NPP) derived from census data of intact forests, is mainly induced by CO2 fertilization that has compensated for negative effects induced by droughts and rising temperatures (3.7% increase due to CO2 fertilization, 0.5% reduction due to droughts, 0.1% reduction due to temperature increase, 2000-2015). Another study of satellite measurements (2000-2009) indicates that the identified increase in NPP is mainly induced by decreased vapor pressure deficit (VPD) in line with the model simulations mentioned above	Hubau et al. (2020) (census data), Zhao and Running (2010) (satellite measurements)
	Sahel Greening: Greening in the Sahel zone is related to recovery from severe droughts in the 1970s and early 1980s causing a low baseline for vegetation productivity; and an increase in precipitation from later 1980s onwards allowing for strong vegetation growth in the water-limited ecosystem. CO2 fertilization additionally increases NPP	Burrell et al. (2020); Zhang et al. (2021) (NDVI, 1981- 2015), Kaptué et al. (2015); Piao et al. (2020) (review of characteristics, drivers and feedbacks of global greening)
Asia	Observations Asian rainforest: Between 2000-2009 NPP decreased at -0.562 Pg C per decade across Asian rainforests. Across Borneo, intact forests gained 0.43 Mg C ha-1 per year between 1988-2010 (n=49 plots), and edge forests lost 0.28 Mg ha-1 per year (n=22 plots)	Zhao and Running (2010) (Asia), Qie et al. (2017) (Borneo)
	Grasslands: Compared globally, Asia had the largest areas of both, grassland degradation and restoration. 41.31% of grassland showed an increase in NPP	Gang et al. (2014)
	Case studies: In the Source Region of Yangtze River total NPP increased by 0.18 TgC per year (2000-2014). NPP (aboveground) also increased at four sites of ungrazed grassland of the Tianshan mountains in central Asia (1985-2016)	Yuan et al. (2021) (Source Region of Yangtze River, 2000-2014), Li et al. (2020a) (Tianshan mountains, 1985-2016)
	India and China: Overall strong positive contribution of India and (eastern) China to global average greening (NPP increase). China accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area (2000-2017). In India the contribution to overall greening comes from forests (42%) and croplands (32%), while in India it is mainly induced through croplands (82%)	Zhang et al. (2021) (NDVI, 1981- 2015), Chen et al. (2019a)
	Attribution Arid and semiarid China: Model simulations for NPP across arid + semiarid China showed that NPP was positively correlated with precipitation. The reduction in NPP due to reduction in precipitation was compensated by the CO2 fertilization effect, resulting in a slight increase of NPP	Fang et al. (2017b)	Climate change seems to play a minor role in the region’s overall contribution to global NPP (greening) which seems to be dominated by direct human influences in India and China. Regionally varying positive and negative effect of climate change on NPP, low confidence (*) CO2 fertilization partly compensated for climate induced declines.
	Asian grassland: Observational data from 2000-2010 showed that 38.94 % of grassland degradation across Asia was caused by human activities, 30.67% were caused by climate change and 30.38% by combined effect of human activity and climate change. 36.12% of grassland restoration was caused by human activities, 35.12% by combined effects and 28.76% by climate change. Model simulation of NPP across Central Asia identified climate change (especially decrease in precipitation) as the main driver of NPP decline between 1982-1999. After 1999, human activities were mostly responsible for NPP decline. In both time periods, climate change led to an increase in NPP in certain regions (e.g., in Tajikistan)	Gang et al. (2014) (observational data), Chen et al. (2019b) (model simulation)
	Asian rainforest: Decreased NPP across 2000-2009 caused by decrease in solar radiation	Zhao and Running (2010)		Growth of rainforests constrained by solar radiation due to strong cloudiness.
	Arid regions: Decrease in NPP (desertification) across central and western Asia caused mostly by land use change, and to a lesser extent by climate change. Greening in India caused by land-use change, CO2 and climate variability, and greening in China caused by land-use change	Burrell et al. (2020)
	India and China: The greening in China and India is mainly due to land management (conservation and expansion of forests in China, intensification of agriculture)	Chen et al. (2019a); Piao et al. (2020) (review), Burrell et al. (2020)
	Case studies: Temperature and precipitation increase seem to be the main drivers of NPP increase in most of the Source Region of Yangtze River and the high elevation grassland sites in Tianshan mountains in central Asia. Direct human influences have played a more important role in the downstream region where they have induced a decline in NPP	Yuan et al. (2021) (2000-2014), Li et al. (2020a)
Australasia	Observations Increase in NPP (greening) between 1981-2015, with southern Australia contributing strongly to total greening, while NPP decreased in northern Australia	Zhang et al. (2021) (1981-2015)
	Arid regions: Increase in NPP (greening) across arid regions in Australia from 1982-2015, especially southern and northeastern Australia	Burrell et al. (2020)
	Grassland: Between 2000-2010 significant decline in grassland NPP across central and eastern part of the state Western Australia	Gang et al. (2014)
	Attribution Increase in precipitation identified as driver of NPP increase across Australia	Zhang et al. (2021) (1981-2015)	Climate change seems to play a minor role in the region’s overall contribution to global NPP (greening) which seems to be dominated by direct human influences in India and China. Regionally varying positive and negative effect of climate change on NPP, low confidence (*) CO2 fertilization partly compensated for climate induced declines.
	Arid regions: NPP increase driven by CO2, land use and climate variability. Model simulation driven by observational climate data estimated for Australian grazing land that increased rainfall from 1931-1970 to 1971-2010 led to an increase in NPP	Burrell et al. (2020) (arid regions, 1982-2015), Liu et al. (2017) (model simulation)
Central and South America	Observations Tropical forests: Average NPP across the forest area decreased by 2 gC m-2yr-1 per year in Central and South America (2001-2013) and spatially aggregated NPP has declined by 0.424 Pg C per decade in the Amazon rainforest (2000-2009)	Yin et al. (2017b) (satellite data, 2001-2013), Zhao and Running (2010) (satellite data, 2000-2009)
	Analysis of live aboveground biomass of 321 plots of intact tropical forest shows a decline of the overall carbon sink in live aboveground biomass. However, the loss is dominated by tree mortality while the NPP term (=tree growth and newly recruited trees) shows a slight positive trend (0.014 Mg C ha-1 yr-2, 1983-2011)	Hubau et al. (2020) (analysis of 321 plots, 1983-2011), Brienen et al. (2015) (census data)
	Arid regions: Significant NPP decline in Caatinga forest in Brazil	Burrell et al. (2020) (1982-2015)
	Southern South America: Widespread browning in Southern South America	Zhang et al. (2021) (NDVI, 1981- 2015)
	Attribution A weak positive trend in carbon gains from tree growth and newly recruited trees (NPP) derived from census data of intact forests, is mainly induced by CO2 fertilization (3.7%) while drought effects reduced carbon gains by 2.7% and temperature increase further reduced gains by 1.1% (2000 -2015). The estimated negative effect of climate change aligns with the analysis of satellite data indicating that the identified reduction in NPP from 2000-2009 was caused by increasing air temperature, which greatly increased autotrophic respiration, and by a slight drying trend, especially a severe drought in 2005	Hubau et al. (2020) (analysis of 321 plots), Brienen et al. (2015) (census data)	Climate change seems to play a minor role in the region’s overall contribution to global NPP (greening) which seems to be dominated by direct human influences in India and China. Regionally varying positive and negative effect of climate change on NPP, low confidence (*) CO2 fertilization partly compensated for climate induced declines.
	Arid regions: NPP decline in north-eastern Brazil caused by both, climate change and land-use change. Negative impacts of decreased rainfall over semi-arid Caatinga forest of Brazil has amplified the effects of widespread deforestation and grazing intensification	Burrell et al. (2020)
Europe	Observations Overall strong positive contribution of Europe to global trend in greening (1981 to 2015)	Zhang et al. (2021) (NDVI)
	Forest stands in Central Europe: Analysis of oldest existing experimental forest plots in Central Europe indicate that the dominant tree species Norway spruce (36 plots) and European beech (22 plots) exhibit significantly faster tree growth (32 to 77%) and stand volume growth (10 to 30%) than in 1960. The increase in growth rates mean, that stands achieved defined sizes, standing stock and stand development stages significantly earlier under recent conditions than in the past	Pretzsch et al. (2014)
	Summer 2003 and 2010: Particularly low average NPP across Europe (annual NPP deviation of about -100 TgC from 2000 and 2011 average)	Ciais et al. (2005); Bastos et al. (2014)
	Grassland: Compared globally, the least grassland degradation was observed for Europe, but the NPP trend was still negative	Gang et al. (2014)
	Attribution Agricultural intensification may have contributed considerably to greening over agricultural lands in Eastern Europe. In addition, model simulations indicate that the afforestation and forest regrowth have contributed to the greening in parts of Europe	Piao et al. (2020) (review), Kondo et al. (2018)	climate change has increased NPP in some sites (forest stands in Central Europe), low confidence (), but has decreased NPP in individual events attributable to anthropogenic climate forcing, medium confidence (*)
	Rising atmospheric CO2 concentrations have contributed to the overall greening (increase in NPP)	Burrell et al. (2020) (on arid land),
	Forest stands in Central Europe: Observed acceleration in growth is primarily due to rises in temperature and expansion of growing seasons. The impacts are particularly strong on sites that are not constrained by the nutrient supply	Pretzsch et al. (2014)
	Summer 2003 and 2010: Observed strong reductions in NPP were induced by the 2003 and 2010 heatwaves in Europe that have been attributed to anthropogenic climate forcing (see Table SM16.20). In the 2003 heatwave, both moisture deficits and high temperatures drove the extreme response of vegetation, while for the 2010 event very high temperatures appear to be the sole driver of very low productivity	Ciais et al. (2005) Bastos et al. (2014)
	Grassland: NPP decline in 47.5% of grasslands caused by combined effect of climate and land-use change. 51.8% of NPP increase in restored grasslands caused by human activities	Gang et al. (2014)
North America	Observations Strong greening in southeastern North America (1981-2015)	Zhang et al. (2021) (NDVI)
	Temperate forest plots, Maryland, US: Tree biomass data collected over the past 22 years from 55 temperate forest plots with known land-use histories and stand ages ranging from 5 to 250 years show that recent biomass accumulation greatly exceeded the expected growth caused by natural recovery	McMahon et al. (2010)
	Attribution The observed greening in southeastern North America seems to be mainly driven by plant regrowth due to land use change, and CO2 fertilization, the relative quantification of these effects is uncertain	Burrell et al. (2020) (western part, CO2 effect), Kondo et al. (2018) (1960-2010)	climate change has increased NPP in some sites (forest stands in Central Europe), low confidence (), but has decreased NPP in individual events attributable to anthropogenic climate forcing, medium confidence (*)
	Temperate forest plots, Maryland, US: Ruling out other explanations, long-term temperature increase, longer growing seasons, and CO2 fertilization are the most likely drivers of the observed increase in recent rates of biomass gain above their long-term trend	McMahon et al. (2010)
Small Islands	Observations Negative trends in NPP on Java island (2001 - 2011), the Philippines (-9 g C m-2y-1, 2000 - 2014), Papua New Guinea (large areas with trends below -30 g C m-2y-1 2000-2014), and Indonesia (-14 g C m-2y-1 2000-2014)	Indiarto and Sulistyawati (2014) (Java), Peng et al. (2017) (Philippines, Papua New Guinea, and Indonesia)
	Island of Cephalonia in western Greece: A study of the endemic Greek fir (Abies cephalonica) from Ainos Mountain shows a decline in productivity of A. cephalonica which was replaced by a positive productivity trend after 1988. The conifer Pinus halipensis has shown declining productivity from the early 1900s to early 2000s	Koutavas (2013), Sarris et al. (2011)
	Attribution Java island: Statistical analysis used to conclude strong relationship between NPP and climatic parameters, but not between NPP and land-cover related parameters	Indiarto and Sulistyawati (2014)	minor to moderate contribution of climate change to observed reduction in NPP, low confidence (*) One case of an apparent ameliorating effect of CO2 fertilization	Java island: High precipitation values decreased solar radiation and photosynthesis.
	Indonesian islands: NPP and drought (SPEI) linked statistically, distinctly from the contribution of land cover change to NPP change	Peng et al. (2017)
	Island of Cephalonia in western Greece: Statistical analysis and inference suggests that the limiting effect of drought on productivity of A. cephalonica has been replaced by a positive productivity trend due to CO2 fertilization since the late 1980s. Linear regression was used to attribute declining productivity in P. halipensis to declining rainfall	Koutavas (2013) (Greek fir), Sarris et al. (2011) (P. halipensis)
	S23 Terrestrial ecosystems - Structural change
Global	Observations Biome shifts :latitudinal and elevational biome shifts recorded at 19 sites in boreal, temperate and tropical ecosystems, further upslope or latitudinal biome shifts over periods of 24 to 210 years have been recorded at numerous sites	AR5 Chapter 2; Gonzalez et al. (2010) Chapter 2
	13% to 14% of the spatial extent of all natural and semi-natural vegetation assessed (representing all African biomes) have changed biome state (as defined by biome structure and function) between 1981 and 2012	Higgins et al. (2016)
	Woody encroachment: Global trend of woody encroachment established prior to the 1980s, woody encroachment recorded widely in tropical and temperate grasslands	Chapter 2; Stevens et al. (2017)
	Satellite observations, analysed to remove the effect of variations in precipitation, show that shrub cover across environments where water is the dominant limit to vegetation growth has increased by 11%	Donohue et al. (2013)
	Widespread increase of shrub cover in arid ecosystems globally, with some areas showing increased grass dominance	Chapter 2
	Attribution Upslope and latitudinal shifts have been attributed to, or found to be consistent with observed warming trends	AR5 Chapter 2; (Gonzalez et al., 2010) Chapter 2	Moderate global shifts in vegetation structure overall, due to warming, moisture and growing season changes, high confidence (*) Additional moderate moderate contribution of rising CO2 concentrations to observed thickening of shrublands and woodlands and expansion of woodlands and forests, high confidence (*)	Warming, moisture and growing season changes, induce shifts in vegetation height and activity, with rising atmospheric CO2 increasing water use efficiency, permitting greater leaf area development in arid systems, and faster recovery from wildfire disturbance converting grass-dominated to woodland systems
	Global analysis of biome state shifts based on remote sensed data for vegetation productivity and height identified temperature, moisture, growing season duration and seasonality changes as consistent with observed biome shifts	Higgins et al. (2016)
	Arid ecosystem grass dominance changes attributed to changing rainfall amounts and seasonality, with shrub cover increase also attributed to CO2 fertilization, woody encroachment of temperate grasslands attributed to warming	Chapter 2
	Mechanisms incorporated into modelling approaches link a substantive fraction of the observed woody encroachment and increase in leaf area index to rising atmospheric CO2 concentration	Chapter 2
Africa	Observations When accounting for land use, African savannas have a mean rate of annual woody cover increase 2.5 times that of Australian savannas. In Africa, encroachment occurs across multiple land uses and is accelerating over time	Chapter 9, Stevens et al. (2017)
	Woody vegetation cover over sub-Saharan Africa increased by 8% over the past three decades. Woody cover loss was prevalent in parts of the Sahel, East Africa and much of Madagascar, but woody plant encroachment dominated the central-interior of Africa. Countries exhibiting a mean fractional increase >30% were Cameroon, Central African Republic, South Sudan, and Uganda	Venter et al. (2018)
	African Sahel has experienced widespread increases in vegetation cover and above-ground biomass	Zhu et al. (2016) Liu et al. (2016a) Brandt et al. (2017)
	Woody cover increase and shift of mesic species into arid areas in Namib Desert over 97 years	Rohde et al. (2019)
	Central inland arid Nama-Karoo shrub-grasslands have increased in grass cover, shifting grass/shrub balance over 10s to 100s of kms westwards	Masubelele et al. (2015); du Toit and O’Connor (2014)
	Woody encroachment in Nama-Karoo valley bottoms	Masubelele et al. (2015) Ward et al. (2014a); Hoffman et al. (2018)
	Attribution Namib woody increase associated with increased fog days and westward expansion of convective rainfall, increase in number of extreme rainfall events and ~30% rise in atmospheric CO2 in the last 97 years	Rohde et al. (2019)	Moderate contribution to expansion and thickening of woodlands, roughly equally due to climate and CO2 change, high confidence (***)	Rising atmospheric CO2 increases water use efficiency permitting greater leaf area development in arid systems, and increases carbon storage in woody plants that permits faster recovery from disturbance; however this effect is likely to interact with multiple drivers
	Nama-Karoo grass increase associated with warming and rainfall trends, enhanced by fire regime	du Toit and O’Connor (2014); du Toit et al. (2015); du Toit and O’Connor (2017)
	Boosted regression tree approach used to attribute Sub-Saharan African woody vegetation cover increase of 8% over the past three decades, with 18% and 19% of these trends attributable to rainfall and temperature changes respectively (together with warming and rainfall trends, land use and disturbance trends explained up to 78% of the spatial variation in the overall woody cover increasing trend)	Venter et al. (2018)
Asia	Observations Woodland and forest spatial shifts: In Central Siberia, structural shifts to woodland vegetation associated with poleward shift of southern taiga biome ecotone	Brazhnik et al. (2017)
	Low montane vegetation (tundra and steppe) replaced by woodland and forest as treeline position in Asian mountains has shifted broadly in Central and Northern Asia	Chapter 10; Shiyatov and Mazepa (2015); Zolotareva and Zolotarev (2017): Moiseev et al. (2018); Sannikov et al. (2018); Gaisin et al. (2020); Schickhoff et al. (2014)
	Attribution Site-specific interaction of a positive effect of warming on tree growth, which offsets drought stress. Part of the change can also be attributed to land use change, especially grazing	Chapter 2, Liang et al. (2014); Tiwari et al. (2017); Tiwari and Jha (2018)	Moderate impact of warming on structural shifts from low tundra and steppe vegetation to woodland vegetation in montane regions of central and northern Asia, high confidence (***)
Australasia	Observations Low rates of woody plant encroachment in savanna systems	Stevens et al. (2017)
	Declines in grass and graminoid cover, and replacement by shrubs and forbs in Bogon High Plains, Victoria	Hoffmann et al. (2019)
	Canopy dieback across a range of forest and woodland types	Hoffmann et al. (2019)
	Replacement of dominant canopy tree species by woody shrubs	Bowman et al. (2014) Fairman et al. (2016) Harris et al. (2018) Zylstra (2018)
	Widespread mortality of fire sensitive trees species	(Hoffmann et al., 2019)
	Attribution In Australia, changes in rainfall and warming trends are linked to drought stress and enhanced wildfire regime, the observed increase in foliage cover in warm arid environments is in line with predictions of direct responses to rising CO2	Stevens et al. (2017); Donohue et al. (2013)	Moderate changes in vegetation structure due to warming, drought and related impacts via wildfire, comprising decline of fire-and drought-sensitive woodland and forest species, and encroachment of woody shrubs and forbs, medium confidence (**)
Central and South America	Observations Woody cover is increasing rapidly in the remaining uncleared savannas of South America	Stevens et al. (2017)
	Structural change due to losses of standing biomass observed in tropical forest (Amazon)	Brienen et al. (2015); Feldpausch et al. (2016); Phillips et al. (2009); Zuleta et al. (2017)
	Alpine paramo vegetation increased vegetation cover and height at its upward election limit in the Chimborazo, while structural change above the upper forest limit is occurring at a lower rate, or not observed yet	Morueta-Holme et al. (2015); Harsch et al. (2009); Rehm and Feeley (2015)
	Extensive dieback in several Nothofagus species, with evidence of one species decline beginning from the 1940s	Suarez et al. (2004), Rodríguez-Catón et al. (2016)
	Attribution In South America woody encroachment is most likely due to fire suppression and land fragmentation	Stevens et al. (2017); Donohue et al. (2013)	Moderate impacts due to warming and rainfall change on structural changes in multiple ecosystems in locations ranging from tropical forest to temperate low-stature vegetation (from arid lowlands to high elevations), medium confidence (**) Some evidence of CO2 fertilization-linked woody encroachment in savanna	Land use and fire suppression effects my overwhelm effect of increases in atmospheric CO2, these systems have been subjected to significant human land use
	Biomass loss attributed to drought events causing at least temporary losses and structural change	Brienen et al. (2015) Feldpausch et al. (2016) Zuleta et al. (2017) Phillips et al. (2009)
	Nothofagus decline attributed to impacts of several droughts after the 1940s	Rodríguez-Catón et al. (2016)
Europe	Observations 867 vegetation samples (764 vascular plant species) above the treeline from 60 summit sites in all major European mountain systems show that generally more cold-adapted species decline while warm-adapted species increase (covered time period: 2001-2008)	Gottfried et al. (2012)
Europe	Attribution The general patterns of change in mountain plant communities mirrors the degree of recent warming and is more pronounced in areas where the temperature increase has been higher. This general pattern indicates that climate change is an important driver of the observed changes	Gottfried et al. (2012)	Strong contribution of climate change to the observed change in mountain vegetation structure, low confidence (*) given the relatively short observational time series no assessment elsewhere
North America	Observations Boreal, subalpine and arctic vegetation: Structural changes with decreases in Evergreen Forest area (-14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome, and structural switch of tundra to herbaceous and shrub vegetation (+7.4 ± 2.0%) in the Arctic biome	Wang et al. (2020)
	Alpine meadows structural change to conifer forest	Lubetkin et al. (2017)
	Alaska: Boreal forest biome shifting into tundra grassland at poleward edge and undergoing replacement by grassland and temperate forest at equatorward edge	Beck et al. (2011) (Alaska); Myers-Smith et al. (2019)
	Western US arid lands: Dominant Chihuahuan Desert grasses expanding into great plains. Woody encroachment of Chihuahuan and Sonoran Deserts by Prosopis spp. with increased cover of 3% per decade, woody encroachment of great plains grasslands, at 8% per decade, by Propsis and Juniper species. Invasive alien grasses expanding in sagebrush steppes	Collins and Xia (2015); Rudgers et al. (2018); Chambers et al. (2014) Archer S.R. et al. (2017)
	Western US: Limited tree regeneration following fires in recent decades particularly acute in low-elevation forests	Roccaforte et al. (2012); Rother and Veblen (2016)
	Vegetation type conversions: Oak forest decline in south-central USA; Oak forest structural shifts in Californian Oak woodlands; Forest conversion western North America (wildfire driven)	Bendixsen et al. (2015); McIntyre et al. (2015); Coop et al. (2020); O’Connor et al. (2020)
	Attribution Western US arid lands: Chihuahuan grass expansion into great plains associated with increased aridity and inter-annual rainfall variation. Great plains encroachment is multifaceted, but includes elements of climate and atmospheric CO2. Invasive grass expansion in sage steppes associated with warmer growing season temperatures	Rudgers et al. (2018); Chambers et al. (2014); Hufft and Zelikova (2016). Reviews by Archer S.R. et al. (2017)	Moderate impacts due to warming and rainfall change on structural changes in multiple ecosystems in locations ranging from tropical forest to temperate low-stature vegetation (from arid lowlands to high elevations), medium confidence (**) Some evidence of CO2 fertilization-linked woody encroachment in savanna	Invasive release from natural enemies, combined with CO2 effects, are likely interacting to support grass encroachment
	Western US: Wildfires act like a trigger of abrupt vegetation changes enabled by long term climate change: After-fire regeneration of low-elevation ponderosa pine and Douglas-fir forests shows a distinct threshold for recruitment based on vapor pressure deficit, soil moisture, and maximum surface temperature. At dry sites, seasonal to annual climate conditions over the past 20 years have crossed these thresholds	Davis et al. (2019); Tepley et al. (2017)		Wildfire can catalyse vegetation change by killing adult trees that could otherwise persist in climate conditions no longer suitable for seedling establishment and survival.
	Alaska: Boreal forest biome shift attributable via trends in satellite estimates of primary productivity and tree-ring data that show consistency between warming trends and growth increases since 1982 at the boreal-tundra ecotone, and decreases at the boreal-temperate ecotones	Juday et al. (2015)		Warming extends growing season allowing boreal forest expansion at poleward edge, and drought and thermal stress induces declines at equatorward edge
Small Islands	Observations Pinus brutia natural tree stands (>80 years old) on Samos Island (Eastern Aegean) experienced lethal desiccation of Pinus brutia natural tree stands in late summer of 2000. The evergreen sclerophyllous shrub vegetation (macchia) of the region was also damaged with Juniperus phoenicea and Phillyrea latifolia affected the most	Sarris et al. (2007) Körner et al. (2005)
Small Islands	Attribution Correlative analysis used to attribute tree ring growth reductions to precipitation reductions	Sarris et al. (2007); Körner et al. (2005)	Minor contribution of decreasing precipitation to changes in ecosystem structure, low confidence (*)
	S24 Terrestrial ecosystems - Phenology shift
Global	Observations On average (5 continents), animals (15 classes including insects, mammals, reptiles and birds) have advanced their spring phenological events significantly by about 3 days per decade. In freshwater systems, earlier spring phytoplankton and zooplankton development and fish spawning with growing season extension have occurred. A study on 677 species found advancement of spring phenology for 62%, no trend for 27%, and a delay for 9%, with a mean advancement of 2.3 days/decade. Trends were documented for earlier arrival of migrant birds and butterflies, frog breeding, bird nesting, first flowering, and tree budburst	Chapter 2, Cohen et al. (2018) Parmesan and Yohe (2003) (n=677 species)
	Breeding phenology of amphibia: In 35% of the n=59 studies a statistically significant change in phenology has been observed; mean advance 6.1±1.65 days per decade (range: 17.5 days per decade delay to 41.9 days per decade advance); higher latitudes advanced more	While and Uller (2014) (meta-analysis of 59 studies)
	Breeding phenology of seabirds: Global study of seabirds (n=209 time series, 145 breeding populations) showed no adjustment of breeding season (-0.020 days/year) and no response to rise in sea surface temperature (-0.272 days/°C) from 1952-2015	Keogan et al. (2018)
	Herbarium records reveal consistent phenology shifts over decades to centuries	Willis et al. (2017)
	Northern hemisphere deciduous trees: Delayed autumn senescence averaged 0.33 days/year and 1.2 days per degree warming; more delay at low latitudes across N-hemisphere	Gill et al. (2015)
	Complex phenological responses are being observed, with vernalization signals conflicting with retardation by extended growing season length in some species	Chapter 2, Cook et al. (2012)
	Attribution Temperature increases have been identified as the primary driver of changes in animal phenology at mid-latitudes with precipitation being more important at lower latitudes. In freshwater systems, rising temperatures are linked to growing season extension	Cohen et al. (2018) (synthesis of hundreds of published time series, mainly from Europe, North America and Eastern Australia), Root et al. (2005) (synthesis of hundreds of published time series, mainly from Europe, North America and Eastern Australia)	Strong impact of climate change on changes in phenological behaviours, high confidence (***)	In freshwater systems rising water temperatures, ice cover reductions and prolonged thermal stratification drive changes
	Breeding phenology of amphibia: In 65% (30 out of 47) of studies, a significant relationship is shown between breeding phenology and temperature, supporting causality	While and Uller (2014)
	Northern hemisphere deciduous trees: Warmer temperatures allow leaves to retain functionality later in the season, but delay constrained due to daylength limits at higher latitudes	Gill et al. (2015)
Africa	Observations Terrestrial vegetation: Significant trends in Land Surface Phenology have been observed in the remote sensing record for Africa as a whole, mainly indicating delays in the start of season and end of season dates, concentrated in the Sudano-Sahelian and Sudanian regions, after controlling for land-cover change. An estimated 9% of the entire continent had delayed start-of-season	Adole et al. (2018)
	Migratory birds: Comparing arrival and departure dates for barn swallows in South Africa for the periods 1987-1991 and 2007-2011, showed that swallows departed 8 days earlier in northern parts of South Africa, thus shortening their stay. Swallows did not shorten their stay in other parts of South Africa	Altwegg et al. (2012)
	In the central Highveld of South Africa, arrival and departure timing shifted consistently for 9 Palearctic species but not for 7 intra-African migratory species	Bussière et al. (2015)
	Spatially variable and no coherent shifts for Afro-palearctic migrant species	Beresford et al. (2019)
	Attribution Spearman’s non-parametric rank correlation analysis on magnitude and direction of temporal trends in seasonal timing data (95% confidence level), climate attribution not made	Adole et al. (2018)	Moderate impacts of changing seasonality on growing season timing and length (mainly in subtropical savanna and woodlands), and timing of migratory phenology of some migrant bird species, low confidence (*)
	Non-linear curve-fitting methods based on effort-adjusted reporting rate data from bird atlas data, comparing data between two atlas periods: 1987-1991 and 2007-2012. Significant changes identified mainly driven by waterbirds, similar analysis conducted for barn swallows	Altwegg et al. (2012); Bussière et al. (2015)
	Geographically weighted regression (GWR) models testing for relationship between climate and vegetation phenology trends and Afro-palearctic bird population trends found spatially variable, and no consistent migration-route related associations between climate or vegetation phenology and bird population trends	Beresford et al. (2019)
Asia	Observations Reproductive phenology of freshwater Gymnocypris selincuoensis advanced 2.9 days per decade on average from the 1970s to 2000s	Tao et al. (2018)
	Lengthening flowering season in 3 species of alpine ginger Roscoaea, advanced by 22 days or delayed by 8-30 days between 1913 and 2011	Mohandass et al. (2015)
	Advance in fruit ripening of Myrica esculenta by 10 to 15 days against historical record	Alamgir et al. (2014)
	Asian grassland: In general advances in spring greenup and autumn senescence	Huang et al. (2020)
	Siberian boreal forest plants advanced their early season (leaf out and flowering) and mid-season (fruiting) phenology by -2.2, -0.7 and -1.6 days/decade, and delayed the onset of senescence by 1.6 days/decade during 1976-2018. The growing season in the Siberian boreal forests has extended by approximately 15 days. (Results are based on >15,000 phenological records of 67 common Siberian plant species)	Rosbakh et al. (2021)
	Attribution Warming observed in parallel with shifts in phenology due to springtime warming during the 1970s and 1990s, and delayed onset of winter in the 2000s		Moderate impacts of changing seasonality on growing season timing and length (mainly in subtropical savanna and woodlands), and timing of migratory phenology of some migrant bird species, low confidence (*)	Warming lengthens growing season by advancing the initiation of growth and retarding the cessation of growth, lengthening flowering and fruiting seasons,
	Asian grassland: Complex responses to multiple climate indicators. In general warming advances growth initiation in spring and completion of life cycles, while water availability interacts to enhance or retard the response	Huang et al. (2020)
	Siberian plant species: Advancement of early season (leaf out and flowering) and mid-season (fruiting) phenology has primarily been caused by (significantly) increasing spring and summer air temperatures	Rosbakh et al. (2021)
Australasia	Observations Advancement of flowering time for 5 of 20 orchid species from 1950 to 2007, for one species flowering was delayed; study of two donkey orchid species showed advancement of flowering time for Diuris orientis but no changes in flowering time for sympatric Diuris behrii	Gallagher et al. (2009) (orchids), MacGillivray et al. (2010) (donkey orchids), Hoffmann et al. (2019)
	Phenological advance in the emergence time of common brown butterflies by 1.5 days per decade over a 65-year period	Kearney et al. (2010)
	Phenological shifts in behaviour and life stages of multiple bird and insect species in several ecosystems, and plant phenology observed to have advanced an average of 9.7 d decade-1 (n = 390; 95% credible interval of 7.3-12.1 d decade-1)	Beaumont et al. (2015)
	Attribution Changes in orchid flowering times coincided with increase in mean annual temperature of 0.74°C between 1950 and 2007, but with some influences of shifts from cold La Nina phase to a warm El Nino phase,	Gallagher et al. (2009)	moderate impact of climate change on phenology of multiple animal species in several taxonomic groups (mainly due to warming, but interactions with timing of rainfall in some cases), and warming impacts on plant phenology, high confidence (***)
	Physiologically based model of climatic influences on development and statistical analyses of climate data was used to attribute the response of the brown butterfly to anthropogenic warming	Kearney et al. (2010)
	Synthetic assessment applied to attribute phenological shifts in behaviour and life stages of multiple bird and insect species in several ecosystems, and meta-analysis used to reveal plant phenology advances	Beaumont et al. (2015)
Central and South America	Observations
Central and South America	Attribution		no assessment
Europe	Observations Across Austria, Germany, Switzerland, for the time period 1951-2018, 90% of 63,667 time-series datasets showed advanced leaf unfolding and flowering (-0.240 ± 0.002 days/year), and 57% of 5,971 datasets showed delayed leaf colouring (0.036 ± 0.007 days/year). For farmed areas, 84% of 12,285 datasets showed) lengthening of the growing season (mean trend of +0.261 ± 0.008 days/year) with 87% spring trends advance and 57% autumn trends delay	Menzel et al. (2020) (Austria, Germany, Switzerland)
	Mediterranean region: For 17 common amphibian species, earlier first spring appearances were observed from 1983 to 1997, but for most species the trend stopped or reversed in most species between 1998 and 2013	Prodon et al. (2017)
	Terrestrial and freshwater plants, UK: high consistency (84%, n=25,532 time series, 726 taxa) in advancement of phenological trends (terrestrial plants 93% advancing, mean 5.8 days/decade, freshwater plants 62% advancing, mean 2.3 days/decade)	Thackeray et al. (2010)
	Attribution Attribution followed the methods proposed by (Rosenzweig et al., 2007; Rosenzweig et al., 2008) to positively link phenological responses to observed climatic change by frequency analysis of expected (based on climate trends) versus observed responses	Menzel et al. (2020)	Strong advancement in spring phenology and delay in autumn phenology due to warming, medium confidence (**)	Warming lengthens the growing season by advancing the initiation of growth and retarding the cessation of growth.
	Linear mixed effects models used to distinguish rates of change among taxonomic groups, attribution to warming inferred	Thackeray et al. (2010)
	Generalized additive modelling and generalised linear modelling approaches used to identify relationships between phenological shifts and climate variables	Prodon et al. (2017)
North America	Observations Earlier egg laying by 7 shorebird and passerine bird species comparing 1984-1986 with 2007-2009	Grabowski et al. (2013)
	In rivers in Newfoundland and Labrador (n=13) median return time of Atlantic salmon (Salmo salar) advanced by 12 days from 1978 to 2012	Dempson et al. (2017)
	Attribution Earlier egg laying correlated with snowmelt and earlier resource availability. Salmon return time advancement associated with warmer climatic conditions	Grabowski et al. (2013) (birds), Dempson et al. (2017) (fish)	Observed shifts in phenology mainly due to historical warming, low confidence (*)	no assessment of other processes
Small Islands	Observations
Small Islands	Attribution		no assessment due to insufficient evidence
	S25 Terrestrial ecosystems - Burned areas Note: This is exclusively about attribution of changes in burned areas and not about the attribution of changes in purely weather based fire risk indices
Global	Observations Globally, 4.2 million km2 of land per year (about the size of the European Union) burned on average from 2002 to 2016 with the highest fire frequencies in the Amazon rainforest, African deciduous forests and savannas, and northern Australia. On average (2003-2016), the largest fires were found in Australia (17.9 km2), boreal North America (6.0 km2) and Northern Hemisphere Africa (5.1 km2), while Central America (1.7 km2), equatorial Asia (1.8 km2) and Europe (2.0 km2) had the smallest average fire sizes	Giglio et al. (2018) Earl and Simmonds (2018); Andela et al. (2017); Andela et al. (2019)	high confidence in global decline (***) based on independent satellite products
	Burned area decreased by 1.4 ± 0.5% yr-1 (GFED4, 1998-2015), 0.7% yr-1 (insignificant, GFED4, 1996-2015) and 0.7% yr-1 (insignificant, FireCCI50, 2001-2015). Decrease is dominated by reductions in regions with low or intermediate tree cover (tropical savannas of South America and northern hemispheric Africa, grasslands across the Asian steppe). Positive trends in closed-canopy forests	Andela et al. (2017) (GFED4, 1998-2015) Forkel et al. (2019) (GFED4, 1996-2015; FireCCI50, 2001-2015)	medium confidence in decline (**)
	Attribution The overall decline is primarily driven by agricultural expansion and intensification. Based on empirical relationships between variations in precipitation and burned areas climate change may have induced a decline in burned areas in addition to the effects of the other drivers	Andela et al. (2017)	minor climate induced decrease of burned areas on global average, low confidence (*)	Climate change is expected to induce heterogeneous responses in terms of burned areas depending on vegetation types: Climate influences wildfire potential primarily by modulating fuel availability in fuel-limited environments, and by modulating fuel aridity in flammability-limited environments
Africa	Observations 70% of the global burned area lies in Africa. Burned area for the continent as a whole decreased 1-2% per year from 2002 to 2016. The average decrease is dominated by the reduction in northern-hemispheric Africa (from 2002-2016 80% of the decline occurred in the northern hemisphere.). Cropland burning shows a steeper negative trend than natural land covers in both hemispheres (2002-2015). Crop land accounts for about one third of the overall decline in burned areas (2002-2015). Burned area regionally increased in extensive forest areas in west, central, and southern Africa but positive trends were often not significant	van der Werf et al. (2010) Andela and Van Der Werf (2014) Zubkova et al. (2019) Wei et al. (2020); Wilson et al. (2010)
Africa	Attribution The negative trend in burned areas in northern hemispheric burned areas is shown to be partly due to direct human forcing (agricultural expansion and changes in management, populations density) but observed changes in climate are estimated to have had a similar to higher contribution (2001-2012 2002-2016), partly induced by indirect effects of climate change on net primary productivity. Direct human forcings seem to have a smaller contribution to the trends in southern hemispheric Africa where climate drivers seem to dominate but the overall trend is less clear and more dependent on the considered time window	Andela and Van Der Werf (2014) (2001-2012); Zubkova et al. (2019) (2002-2016); Earl and Simmonds (2018) Wei et al. (2020) Forkel et al. (2019)	moderate to strong climate induced reduction of burned areas in Africa particularly driven by changes in northern hemispheric Africa, medium confidence (**) Overall the observational records are still relatively short which means that observed trends in climate drivers may partly be induced by climate oscillations such as ENSO. In particular the trends in southern hemispheric Africa are quite dependent on the considered time window.
Asia	Observations Arctic tundra and boreal forest: Since the end of the twentieth century, Siberia has seen an increase in the area of forest fires. In extreme fire years (2002, 2003, 2012, 2019) the area burned by fire reached 10-12 x 106 ha. Catastrophic fires have also been observed in earlier times, but with much lower frequency. During the last decade the area burned increased by approximately two-fold or even more depending on the region and considered time interval. In the Siberian Arctic, wildfires are migrating northward. Wildfires in Eastern Siberia have reached the Arctic Ocean shore	Ponomarev et al. (2016) Nitze et al. (2018) Kharuk et al. (2021) (review)
Asia	Attribution Extreme wildfires coincided with years of anomalously high air temperatures. The northern boundary of fires in Western Siberia is correlated with temperature anomalies. Overall the evidence on the sensitivity of burned areas to weather conditions (air temperature anomalies, increasing climate aridity and drought events (see Table SM16.22)) and the observed changes in climate indicate a contribution of climate change to the observed increase in burned areas. However, no attribution analyses have been conducted on relative influences of climate and non-climate factors	Kharuk et al. (2021) (review)	Climate change has contributed to the observed increase in burned areas, medium confidence (**)
Australia	Observations The fraction of vegetated area burned increased significantly in eight of the 32 bioregions in southeastern Australia from 1975 to 2009, up to 500%, but decreased significantly in three bioregions. In the southeastern state of Victoria, burned area increased significantly between the periods 1950-2002 and 2003-2020, with the area burned in the 2019-2020 fires the highest in the record. Insignificant trends in burned areas derived from satellite data and aggregated across Australia but significant trends in certain regions (see below)	Andela et al. (2017) Bradstock et al. (2014); van Oldenborgh et al. (2020)
	Mostly positive trends in burned areas in forested areas along the south eastern coast (1975-2009). Mostly no change in relatively dry, interior woodland bioregions	Bradstock et al. (2014)
	Record 2019-20 bushfires: Unprecedented bushfire activity across South Eastern Australia (Queensland, New South Wales, Victoria, South Australia, Western Australia and Australian Capital Territory) in 2019/20. Estimates of burned area range from 5.8 to >12 mio hectares, resulting in the loss or displacement of nearly 3 billion animals. More than 23% of the temperate forests in southeastern Australia were burnt in the 2019/20 fire season, making the scale of these forest fires unprecedented both in an Australian and global context	van Oldenborgh et al. (2021); Lindenmayer and Taylor (2020), Chapter Box 11.1, Boer et al. 2020[SH1], Ward et al. 2020[SH2], Godfree et al. 2021[SH3]
	Attribution see below		moderate to strong contribution of climate change to increase in forested areas burned in south-eastern Australia and increased probability of the Extreme fires in 2019/20, low confidence (*), missing analyses elsewhere
	Changes in fire risk indices are shown to contribute to positive trends in burned areas in the forested areas along the south-eastern coast. There were no instances of change (increases or decreases) in areas burned across different vegetation types that occurred completely independently of change in the climatic predictors. Extreme fires in 2019/20: Anthropogenic climate change has increased the likelihood of extreme fire weather conditions by 30% and purely weather-based fire risk indices show a relatively high correlation with burned areas (~40% in summer) indicating an impact of climate change on the areas burned that however, has not been quantified	Chapters 2 and 11; Bradstock et al. (2014); van Oldenborgh et al. (2020)	moderate contribution of climate change to increases in burned areas in primarily forested areas along the south-eastern coast, low confidence (*)
Central and South America	Observations Amazonia: Burned areas mainly distributed across the southern boundary of the Amazon basin in Am (tropical monsoon) and Aw (tropical winter dry, ~93% of the mean annual burned areas, 1982-2017, 94% in the dry season, August-October). Annual total burned area (178.5 ± 65.4 × 103 km2) in Am and Aw was relatively stable but expanded mainly in the transition season between May and August (mean fraction of burned area in Am increased from 3.7 ± 3.7% (1982-2000) to 5.4 ± 3.4% (2001-2017)). Burned area increased mainly across the southern boundary of the Amazon basin at “Arc of Deforestation”. Burned area in Acre, Brazil, increased 36-fold from 1984 to 2016	Xu et al. (2020) Garreaud 2018, Nobre et al. (2016) Marengo et al. (2018) Silva et al. (2018) (Acre)
	Chile: In contrast to the number of fires, the size of the burnt areas does not show a statistically significant change in the period 1984-2016	Úbeda and Sarricolea (2016) (1984-2016), Urrutia-Jalabert et al. (2018) (Central and south central Chile, 1976-2013)
	Attribution Amazonia: Burning in the Amazon coincides spatially and temporally with deforestation and forest degradation from agricultural expansion. Deforestation fragments the rainforest and increases the dryness and flammability of vegetation. From 1981 to 2018, the Amazon forest cover loss reduced moisture inputs to the lower atmosphere, increasing drought and fire in a self-reinforcing feedback. In the Amazon, deforestation exerts an influence on wildfire that can be stronger than climate change. Chile: While the interannual variability of burned areas is associated with weather conditions (see Table SM16.22), the impact of climate change on long term changes in burned areas is unclear	van Marle et al., 2017; da Silva et al., 2018, Xu et al., 2020; Alencar et al. (2015)	Moderate contribution of climate change to observed increases in burned areas in Amazonia, medium confidence (**).
Europe	Observations Burned area for Mediterranean Europe as a whole decreased from 1985 to 2011	Turco et al. (2016); Turco et al. (2017); Turco et al. (2018)
	Portugal: Insignificant increase in burned areas, 1980-2017. Record extent of burned areas in 2017	Turco et al. (2019)
	Spain: Burned area for Spain as a whole did not show significant long-term trends from 1968 to 2010. Negative trend in burned areas in North Eastern Spain 1970-2007	Moreno et al. (2014), Turco et al. (2014)
	Attribution While burned area in Mediterranean Europe was correlated to summer drought, fire suppression exerted a stronger influence	Turco et al. (2013); Turco et al. (2017); Pereira et al. (2013)	minor contribution of climate change to observed negative trends in NE Spain to moderate contribution of climate change to positive trend that is reduced by other direct human influences in Portugal, low confidence (*)
	Portugal: In Portugal variations in drought conditions and time under extremely high temperatures can explain more than 60% of the observed annual variability of burned areas. Based on this empirical relationship climate change has increased burned areas compared to a baseline with stationary drought and heat conditions. The observed trend is weaker (and not significant) than the purely climate driven trend, an effect that may be due to a general decline in forest areas. Extreme extent of burned areas in 2017 is in line with the model accounting for climate change and cannot be explained by the model assuming stationary fire weather conditions	Turco et al. (2019)	minor contribution of climate change to observed negative trends in NE Spain to moderate contribution of climate change to positive trend that is reduced by other direct human influences in Portugal, low confidence (*)
	North Eastern Spain: Pure climate change may have induced a slightly negative trend in burned areas as the higher fuel flammability induced by climate change is counterbalanced by the negative effect of rising extreme temperatures on fuel availability and fuel connectivity. Improved fire prevention and fire extinction are considered a main cause for the observed decline in burned areas	Turco et al. (2014); Moreno et al. (2014)	minor contribution of climate change on observed negative trends in burned areas, low confidence (*)
North America	Observations No significant change in total burned areas across North America as a whole, but significant trends in individual regions (see below)	Andela et al. (2017)	low confidence in trends in total burned areas
North America	Attribution Identification of climate impacts on burned areas is focussed on positive trends in forested Western North America (see below). In some of these regions a strong impact has been identified with high confidence, while in other regions (in particular in regions with negative trends) an assessment is not possible as there are no studies	see below	strong contribution of climate change to positive trends in burned areas, high confidence (***) in certain regions, missing evidence in others
Western United States (excluding Alaska)	Observations Strong increases in burned areas: Area burned in large (> 400ha) forest fires increased significantly (1973-2012) with 123000 ha (~390% of the average of the first decade); per decade and to a lesser extent in large non-forest wildfires (40585 ha per decade, ~65% of the average of the first decade)	Westerling (2016) Holden et al. (2018) Abatzoglou and Williams (2016); Williams and Abatzoglou (2016)	high confidence in strong increase
Western United States (excluding Alaska)	Attribution Climate change is a major driver of the observed trends. Based on a robust close relationship between weather related fire risk indicators and burned areas anthropogenic climate change is estimated to have doubled the cumulative area burned from 1984-2015 compared to a situation without anthropogenic climate change. The positive trend in potential evapotranspiration accounts for ~78 % of the positive trend in burned area from 1884-2015	Holden et al. (2018) Abatzoglou and Williams (2016); Williams and Abatzoglou (2016)	strong contribution of climate change to increase in burned areas; high confidence (***)	Increased temperature has increased vapor pressure deficit, which increases fuel aridity. Anthropogenic climate change accounted for 55% of the increases in fuel aridity, 1979-2015
California (as subregion of western US)	Observations Fivefold increase in annual burned area from 1972-2018 mainly induced by an eightfold increase in summer forest-fire extent (1972-2018); Weaker signal in autumn burned area increased by ~40% per decade during 1984-2018	Williams et al. (2019) Goss et al. (2020)	high confidence in strong increase (***)
California (as subregion of western US)	Attribution Climate change strongly increased the burned areas. Robust interannual relationships between purely weather-based fire risk indices and summer forest-fire area in California indicate that 70% and more of the observed trends summer forest-fire area can be explained by climate change. Climate change effects were less evident in nonforested lands and in fall	Williams et al. (2019) Goss et al. (2020)	strong adverse impact of climate change on summer forest-fire area, high confidence (***)	Increased summer vapor pressure deficit, caused by increased temperatures, and decreases in autumn precipitation. In autumn changes in wind events and delayed onset of winter precipitation contribute to increased weather-based fire risk.
Alaska, USA	Observations Burned area in 2015 was the second highest in the 1940-2015 record	Partain Jr et al. (2016)
Alaska, USA	Attribution Climate model simulations indicate climate change has increased the likelihood of fuel conditions at 2015’s levels or higher by 34%-60%. A systematic assessment of the correlation between the annual fluctuations of the indicator of fuel conditions and total burned areas is missing, i.e. the contribution of climate change to the extent of burned areas has not been quantified but seems to exist based on independent studies indicating linkages between fire weather indices and actually burned areas	Partain Jr et al. (2016) Ziel et al. (2015)	Unquantified but positive contribution of climate change to burned areas, medium confidence (**)	High temperatures and low relative humidity caused high fuel aridity
Canada	Observations Burned area in British Columbia (BC) in 2017 was the highest in the 1950 - 2017 record, 40% greater than the previous record in 1958. Positive trends in burned areas in BC (1950-2017) and Canada as a whole (1959-1999)	(BC Wildfire Service, 2017) Kirchmeier-Young et al. (2019); Gillett et al. (2004)	high confidence in positive trends
Canada	Attribution Anthropogenic climate change has induced a positive trend in weather-based fire risk indices. Based on the observed correlation between fire weather indices and annual variations in burned areas it is estimated that climate change has contributed to the positive trend in burned areas across Canada and can explain the trends in BC. Anthropogenic climate change has increased burned areas 7 to 11 times over the area of natural burning, comparing the period 2011-2020 to the period 1961-1970	Kirchmeier-Young et al. (2017b); Kirchmeier-Young et al. (2019); Gillett et al. (2004)	Major contribution of temperature positive trends to trend in burned areas; medium confidence (**); high confidence that observed temperature trend is induced by anthropogenic emissions of climate forcers	Trend in fire risk are primarily driven by temperature increase
Small Islands	Observations
Small Islands	Attribution		no assessment
	S16 Water distribution - Reductions in water availability + induced damages and fatalities So this is not about droughts themselves that are considered climatic events and therefore addressed in Table SM16.20 on climate attribution. This section does not address the direct impacts of drought on crop yields, conflict, malnutrition etc. that are addressed in individual dedicated sections.
Global	Observations Changes in low flow indicators vary across regions and trends often also depend on the considered time period. Strong reductions in low flow indicators are observed in Southern Australia and North-East Brazil from 1971-2010, but weak opposite trends from 1961-2000 and 1951-1990, respectively. A persistent negative trend is observed in the European Mediterranean region (strong) and Southern Africa (weaker). Increases in observed low flow indicators are strongest in East Asia (mainly data from Japan) and weak but relatively persistent in Central and Northern Europe, Central and Western North America, and Southeastern South America	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
	Large economic losses and food insecurity induced by limited water availability (listed in the regional sections below)	Chapter 4.2.5, Table 4.5
	Attribution Multi-model historical simulations forced by observed climate show that changes in climate can explain a considerable part of the observed spatial pattern of trends in the annual 10th percentile of daily discharge. Effects of historical water and land management only have a minor effect on the simulated trends. Historical hydrological simulations forced by simulated historical climate accounting for natural and anthropogenic radiative forcing also basically reproduce the observed spatial pattern of trends that does not emerge in simulations forced by simulated preindustrial climate and only accounting for historical changes in land use and water management. That indicates that external climate forcing acts as a causal driver of the general spatial patterns of trends in the annual 10th percentile of daily average river discharge at the global scale (1971-2010) that is not explained by direct human influences	Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)	The general spatial pattern of trends in low river flows can largely be explained by observed changes in weather conditions, i.e. climate change is a major driver of observed increases and decreases in low flows, low confidence () as based on one study anthropogenic climate forcing has increased likelihood of a range of large-impact droughts, medium confidence (*)
Africa	Observations Due to limited data availability regional trends in the annual 10th percentile of daily discharge are only estimated for Southern Africa where trends have been negative in the early period (1951-2000) but particularly weak and not significant from 1971-2010	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
	Cape Town water crisis: Water shortage in Cape Town reached a peak at the beginning of 2018 when Cape Town was expected to run out of water in March 2018. To avoid the ‘day zero’ extreme restrictions on water usage were implemented including completely ceasing irrigation. That induced significant losses in agriculture but finally avoided the ‘day zero’. The direct cost of the water crisis induced by reduced water revenue, losses in agricultural jobs and production and indirect costs such as a drop in tourism have been estimated to reach more than 2.5 billion South African rands (US$181 million). Farmers lost R14bn	Otto et al. (2018b) Muller (2018)
	East Africa, 2014: Some isolated food security crises	Chapter 4.2.5, Table 4.5
	East Africa, 2017: Food insecurity approaching near famine conditions in East Africa, 2017	Chapter 4.2.5, Table 4.5
	Southern Africa, 2016: Millions of people were affected by famine, disease, and water shortages. In addition, a 9-million ton cereal deficit resulted in 26 million people in need of humanitarian assistance	Chapter 4.2.5, Table 4.5
	Attribution Multimodel hydrological simulations forced by observed climate change and direct human influences show stronger negative trend in Southern Africa than the observed trend in the annual 10th percentile of daily discharge that is mainly induced by direct human influences while climate forcing alone does not induce a trend in the considered regional low flow indicator. This may indicate that climate change has had a minor influence on the low flow indicator but the discrepancy between the simulated (climate + direct human forcing) and observed trends means that our understanding of the obserservations is not yet sufficient to explain the observations and allow for attribution on the considered regional level		anthropogenic climate forcing has contributed to individual severe regional water restrictions and associated consequences, medium confidence (**) no assessment elsewhere
	Cape Town water crisis: The water crisis occurred at the end of three consecutive years of below average rainfall from 2015-2017 leading to a prolonged drought that has been attributed to anthropogenic climate forcing (see Table 16.1). However, at the same time Cape Town’s population continues to grow, such that increased water demand and withdrawal over the year leading to the crisis could provide another explanation for the extreme water shortage reached in the reservoirs beginning of 2018. Currently there are no published hydrological simulations quantifying the effect of recent changes in demand on 2018 reservoir levels. However, Cape Town has been internationally recognised for having stabilised water demand growth to around 2% per annum indicating that the drought was the major driver of the observed water limitations and associated losses in 2018. The assessment explicitly does not include any statement about the management measures that could have avoided the crisis as according to the definition climate impact attribution is comparing the observed state of the system to the state of the same system without climate change	Otto et al. (2018b)
	East Africa 2014: Region affected by drought conditions that have been attributed to anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
	East Africa, 2017: Tanzania, Ethiopia, Kenya, and Somalia were affected by an extensive drought that has been attributed to anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
	Southern Africa, 2016: Region was affected by drought conditions that have been attributed to anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
Asia	Observations Due to limited data availability regional trends in the annual 10th percentile of daily discharge are only estimated for India and Japan where trends have been slightly positive from 1971-2010 (no trend or very weak trend in India and slightly stronger positive trend in Japan)	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
	Yunnan, south-western China, 2019: Water scarcity affected nearly 7 million residents and resulted in crop failure over at least 1.35 × 104 km2 cropland (Fig. 1). More than 94% of the total area in the province was drought-stricken, and around 2 million people faced drinking water shortages, with a direct economic loss of about 6.56 billion RMB	Chapter 4.2.5, Table 4.5
	Southwestern China, 2019: Over 640,100 hectares of crops with rice, corn, and potatoes extensively damaged. Over 100 rivers and 180 reservoirs dried out. Over 824,000 people and 566,000 head of livestock having a severe lack of drinking water, with a direct economic loss of 2.81 billion Chinese Yuan ($400 million)	Chapter 4.2.5, Table 4.5
	South China, 2019: A lightning-caused forest fire in Muli County killed 31 firefighters and burned about 30 ha of forest	Chapter 4.2.5, Table 4.5
	South China, 2018: Shrinking reservoirs, water shortages. Area and yield for early rice reduced by 350 thousand hectares and 1.28 million tons relative to 2017	Chapter 4.2.5, Table 4.5
	Middle and lower reaches of the Yangtze River, China, Yangtze River, China, 2019: Reduced agriculture productivity and increased load on power system supplies and transportations, and on human health	Chapter 4.2.5, Table 4.5
	Thailand 2016: Loss of crops, such as rice and sugarcane, losses in the agricultural production of about half a billion U.S. dollars	Chapter 4.2.5, Table 4.5
	Attribution Multimodel hydrological simulations forced by observed climate change and direct human influences well reproduce the positive trend in Japan and slightly overestimate it in India. Simulated trends are barely influenced by direct human drivers in Japan and only very weakly reduce the positive trend in the annual 10th percentile of daily discharge in India. So climate change is estimated to have contributed to the observed (weak) increase in river low flow across 1971-2010. Where only in Japan a weak increase in the low flow indicators also occurs in hydrological simulations forced by an ensemble of historical climate model simulations accounting for anthropogenic and natural forcings indicating that part of the weak increase in annual 10th percentile of daily discharge may be due to external forcing rather than internal climate variability	Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)	anthropogenic climate forcing has contributed to individual severe regional water restrictions and associated consequences, medium confidence (**) no assessment elsewhere
	Yunnan, south-western China, 2019, Southwestern China, 2019, South China, 2018, 2019: Events induced by droughts whose likelihood has been increased by anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
	Middle and lower reaches of the Yangtze River, China, Yangtze River, China, 2019: Event induced by drought whose likelihood has been decreased by anthropogenic climate forcing	Chapter 4.2.5, Table 4.5
	Thailand, 2016: Losses induced by drought affecting 41 Thai provinces, likelihood has been increased by anthropogenic climate forcing	Chapter 4.2.5, Table 4.5
Australasia	Observations Due to limited data availability a regional trend in the annual 10th percentile of daily discharge is only estimated in Southern Australia where it is significantly negative from 1971-2010 while slightly and not significantly positive from 1961-2000	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
	In New Zealand, the 2007/08 drought cost 2017-NZ$3.2 billion and the 2012/13 drought cost 2017-NZ$1.6 billion (total costs estimated by an economic model)	Frame et al. (2020a)
	Attribution The observed strong negative trend is well reproduced by multimodel hydrological simulations forced by observed climate change and direct human influence where the direct human influences barely affect the simulated trends. This indicated that the observed climate change has been a major driver of the observed reduction of the annual 10th percentiles of daily discharge (1971-2010). The observed trend is in contrast not reproduced by hydrological simulations forced by an ensemble of historical climate model simulations accounting for natural and human forcings. This indicates that the observed changes in low discharge may be induced by internal climate variability rather than by external climate forcings	Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)	anthropogenic climate forcing has contributed to individual severe regional water restrictions and associated consequences, medium confidence (**) no assessment elsewhere
	New Zealand, damages induced by droughts: Anthropogenic climate forcing has increased the probability of circulation patterns like those associated with the droughts with the fraction of attributable (FAR) risks reaching 15% in 2007/08 and 20% in 2012/13 (FAR = 1 - P0/P1 with P0 and P1 being the probabilities under natural and observed climate forcing, respectively). By increasing the likelihood of the drought anthropogenic climate forcing has also increased the likelihood of the associated damages. However, there is no estimate of the fraction of the damages that can be attributed to a potential anthropogenic intensification of the droughts	Frame et al. (2020a)
Central and South America	Observations Observed trends in the annual 10th percentile of daily discharge are very weak in Southeastern South America and the Amazon region while a strong decline is observed in North-East Brazil from 1971-2010	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
Central and South America	Attribution The observed changes in annual 10th percentiles of daily discharge are not well reproduced by multimodel hydrological simulations forced by observed climate change and direct human influence except for an also negative but much weaker trend North-East Brazil. Direct human influences seem to only very slightly contribute to the simulated reduction. So there is some indication that observed climate change has contributed to the strong reduction in the annual 10th percentiles of daily discharge but our overall understanding of the observed changes does not allow for a comprehensive attribution	Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)	minor impact of climate change on water crisis in Sao Paulo 2015, low confidence (*), no assessment elsewhere
Europe	Observations The annual 10th percentile of daily discharge has increased in Northern Europe, decreased Southern Europe and barely changed in Central Europe (1971-2010). In particular the reduction in Southern Europe is strong and persistent across different 40 year time windows from 1951-2010	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
Europe	Attribution The observed pattern of changes in the annual 10th percentile of daily discharge is very well reproduced by multimodel hydrological simulations forced by observed climate change and direct human influence where the direct human influences barely affect the simulated trends. This indicated that the observed changes in climate have been a major driver of the observed changes in the annual 10th percentiles of daily discharge. In addition the observed pattern of changes in low flows is also reproduced by hydrological simulations forced by an ensemble of historical climate model simulations accounting for natural and human forcings. This indicates that the observed changes in low flows are induced by external climate forcing rather than internal climate variability	Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)	climate change has been a major driver of the observed reduction in the annual 10th percentiles of daily discharge in Southern Europe and the increase in annual low flows in Northern Europe (1971-2010), low confidence (*) as based on only one study
North America	Observations From 1971-2010 the annual 10th percentile of daily discharge has decreased in Eastern North America while changes have been minor in Western and Central North America	Gudmundsson et al. (2019) (annual minimum of daily discharge, annual 10th percentile of daily discharge across three 40 year periods from 1951-2010), Gudmundsson et al. (2021) (annual 10th percentile of daily discharge, 1971-2010)
	USA, Northern Great Plains, 2017: “Billion-dollar disaster”; widespread wildfires (one of Montana’s worst wildfire seasons on record) compromised water resources, destruction of property, livestock sell-offs, reduced agricultural production, agricultural losses of $2.5 billion	Chapter 4.2.5, Table 4.5
	Washington state, USA, 2015: The US$335 million loss for the agricultural industry	Chapter 4.2.5, Table 4.5
	California drought 2012: Beginning in 2012 California experienced acute water shortages, groundwater overdraft, critically low streamflow, and enhanced wildfire risk. The water shortage has led to water use restrictions, fallowed agricultural fields, and ecological disturbances such as large wildfires and tree mortality	Diffenbaugh et al. (2015) Williams et al. (2015)
	Attribution The observed pattern of changes in the annual 10th percentile of daily discharge (1971-2010) is to some degree reproduced by multimodel hydrological simulations forced by observed climate change and direct human influence (very weak trends in Western and Central America, but also very weak reduction in Eastern North America where the observed reduction is much stronger). This indicates that climate change only had a minor impact on trends in annual low flows in Western and Central North America but observations in Eastern North America are not yet understood well enough to allow for an attribution		Anthropogenic climate had only a minor impact on annual low flows in Western and Central North America (1971-2010), low confidence () Anthropogenic climate forcing has increased likelihood of individual severe drought-induced regional water restrictions and associated consequences, medium confidence (*)
	USA, Northern Great Plains, 2017: Losses induced by drought whose likelihood has been increased by anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
	Washington state, USA, 2015: Losses induced by drought whose likelihood has been increased by anthropogenic climate forcing (see Table SM16.20)	Chapter 4.2.5, Table 4.5
	California drought 2012: Anthropogenic climate forcing has increased the probability of co-occurring warm-dry conditions like those that have created the 2012-14 drought conditions leading to the acute water shortages (see ‘Atmosphere - Drought’, Table SM16.20’). The impact of other direct human drivers is not explicitly assessed such that the impact on climate change can not be set into perspective and	Diffenbaugh et al. (2015) Williams et al. (2015)
Small Islands	Observations Caribbean 2013-2016: Over 50% of crop production lost. More than two million people were pushed into food insecurity with Haiti particularly affected (one million people (~10% of its population)) severely affected by food insecurity and required immediate assistance)	FAO (2016), OCHA (2015) Herrera et al. (2018)
Small Islands	Attribution Losses induced by the pan-Caribbean drought whose intensity and extent has been increased by anthropogenic climate forcing (see Table SM16.20)	Herrera et al. (2018)	Anthropogenic climate forcing has increased drought-induced regional water limitations and associated consequences in 2013-2016, low confidence (*) no assessment elsewhere
	S15a Water distribution - Flood hazards (peak discharge, flood volume and flooded areas)
Global	Observations Observations Global annual maximum flood volume: Heterogeneous trends (negative trends in South Europe, Western North America, South Australia, North-East Brazil; positive trends in Northwestern Europe, eastern North America, parts of Southeastern South America). More stations (out of 9213) with significantly negative trends than significantly positive trends. Flood frequency: Significant increase in the occurrence of moderate- and long-duration floods on global and latitudinal level. From before to after the year 2000 the frequencies of long-duration floods increased by a factor of 4 and 2.5 events per year across the tropics and northern midlatitudes, respectively. Changes were not monotonic, but frequencies reached a maximum in 2003 and decreased afterwards. No significant trend in short term flood events. Flood duration: Significant monotonic increase in median flood durations at global scale from 4 days in 1995 to 10 days in 2015. Similar patterns at latitudinal bands. Timing and level of maximum discharge: Across 1744 catchments without major dams in Australia, Brazil, Europe and the United States, the occurrence of the largest daily discharge within 1980-2009 more often fell into the second 15 year than in the first 15 year and the maximum levels reached in the first 15 years were on average smaller than the maximum levels reached in the second period. However, the trend in occurrence frequencies is non-monotonic but reaches a peak in 1995 and may strongly depend on the considered time window	Do et al. (2017) (annual maximum streamflow, analysis of 9213 stations across the globe), Blöschl et al. (2019) (annual maximum discharge, Europe, 3,738 stations, 1960-2010) Gudmundsson et al. (2019) (maximum annual discharge, over 30,000 stations around the world, 1951-2010); Najibi and Devineni (2018) (frequency and duration, 1985-2015); Berghuijs et al. (2017) (time of occurrence of maximum); Wasko and Sharma (2017)	Variations of maximum discharge often depend on low frequency climate oscillations making trends depending on the considered time periods. Confidence in long-term changes strongly depend on the considered regions (see individual regional assessments)
Global	Attribution Model simulations forced by observed weather variations excluding changes in other direct human forcings show a purely climate induced increase in annual maximum flood volume (1990-2010), but no significant trend between 1980-2010. Changes in historical radiative forcing (including natural and anthropogenic) act as a causal driver of the general spatial patterns of trends in the annual 90th percentile of daily average river discharge at the global scale (1971-2010) that cannot be explained by direct human influences. Multi-model historical simulations forced by observed climate can explain a considerable part of the observed spatial pattern of trends in high river flows while the simulated effects from historical water and land management are minor. Flooded areas: Model simulations forced by observed weather variations excluding changes in other direct human forcings show a purely climate induced positive trend (0.15% per year from 1960 to 2013). Timing and level of maximum discharge: The restriction to largely unmanaged catchments indicate that the observed variation in the timing and level of maximum discharge is primarily climate induced. Flood frequency: After adjusting for the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO) on global scale and AMO and Pacific Decadal Oscillation (PDO) in the tropics, there is no significant trend in flood frequencies anymore, i.e. the trend across the globe and tropics can be largely explained by decadal and multidecadal climate variability. However, as in the considered time period AMO is highly correlated with global mean temperature change that does not rule out the alternative explanation that trends are forced by anthropogenic warming. Flood duration: The positive monotonic trend is not explained by climate oscillations, though in a minority of basins flood duration is correlated with ENSO. The long-term trends could be induced by long-term monotonic changes in climate but also by direct human influences such as land use changes	Jongman et al. (2015) (flood volume, 1990-2010 and 1980-2010); Gudmundsson et al. (2021) (7250 stations around the world, annual 90th percentile of daily average river discharge, 1971- 2010), Tanoue et al. (2016) (changes in flooded areas, 1960 to 2013); Ward et al. (2014b) (Influence of El Niño-Southern Oscillation (ENSO) on maximum annual discharge, 1958-2000); Ward et al. (2016); Berghuijs et al. (2017) (timing of the occurrence of the largest observed daily flow rate during the period 1980-2009, 1744 catchments located in Australia, Brazil, Europe and the United States), Do et al. (2017) (effects from historical water and land management), Najibi and Devineni (2018) (association of changes in flood frequency or duration and climate oscillation such as the Atlantic Multidecadal Oscillation, North Atlantic Oscillation, and Pacific Decadal Oscillation, 1985-2015)	minor increase of global average annual maximum discharge and flooded areas induced by climate change, medium confidence (**) Variations in flood frequencies and the timing of maximum discharge in the historical period are strongly influenced by climate variations (high confidence) which means that global and regional trends partly depend on the considered time window. However, the general spatial pattern of trends (1971-2010) in high river flows can largely be explained by observed changes in weather conditions.
Africa	Observations Flood volume: Annual maximum flood volume derived from in situ observations show both positive and negative trends in the Sahel region. Of the entire continent of Africa, only Southern Africa (SAF) has more than 50 stations allowing for a regional analysis of long-term trends. Weak decline in regional average maximum annual discharge (trend is significant from 1961-2000 and insignificant from 1971-2010), nearly no trend in 90th percentile of daily discharge, heterogeneous trends on station level. Declining annual maximum discharge in western Africa in data sets of at least 30 years within 1955-2014, but number of stations and the covered time period is too limited to draw strong conclusions. Annual maximum discharge and peak over threshold discharge show positive trend in 3 Saherian basins for 1970-2010. No significant signal was found in the 8 basins in West Africa during the same period	Do et al. (2017), Gudmundsson et al., 2019 (South Africa, 1951-1990, 1961-2000, and 1971-2010); Aich et al. (2014); Aich et al. (2015) Nka et al. (2015) (Sahel region)	Weak regional reduction in maximum annual discharge in South Africa, low confidence due to low agreement across subregions and time windows.
Africa	Attribution Flood volume: Model simulations show that both climate and land use change contributed to observed increase in maximum annual discharge in the Sahel Zone. Models are not yet able to fully reproduce and explain the observed changes in observed trends in maximum annual discharge. Minor or weakly negative trends in high flow indicators in Southern Africa are associated with drying conditions in the region and might have been intensified through human water and land management. However, hydrological simulations forced by observed climate and direct human influences show a stronger decline in the 90th percentile of daily average river discharge than observed (1971-2010)	Aich et al. (2015) (Sahel region), Gudmundsson et al. (2021); Gudmundsson et al. (2019) (90th percentile of daily average river discharge)	inconclusive because of limited agreement between modelled and simulated discharge or no assessment because of missing data
Asia	Observations Flood volume: In North Asia (NAS), the annual 90th percentile of daily discharge increases significantly from 1951-1990 and 1961-2000 but annual maximum discharge does not. In East Asia, observations show significant increases in annual maximum flows over the time period 1961-2000 but decrease in annual maximum daily discharge in Yellow river for 1950-2010. In South Asia a significant decline in annual maximum discharge and the annual 90th percentile of daily discharge has been identified from 1971-2010. An analysis of individual station data with more than 30 years of record in 1955-2014 show a smaller number of stations with increasing trends than stations with decreasing trends, where maximum annual discharge tended to increase in the far eastern region of Russia. However, data coverage is too limited to draw solid conclusions. Along the East River three of four stations have recorded a decline of annual maximum stream-flow from 1954-2009	Do et al. (2017); Gudmundsson et al. (2019); Bai et al. (2016) (Yellow river), Zhang et al. (2015) (East Asia)
	Attribution There are only very limited studies attributing the observed changes. The decrease in annual maximum daily discharge in the Yellow rivers seems to be driven by a decrease of precipitation extreme upstream and human activities in midstream and downstream without a quantification of the individual contributions. Changes of annual maximum stream-flow along the East River are mostly driven by natural climate variability and the water reservoirs with only a minor contribution of climate chang, Overall, there is only limited observational discharge data available to cover the entire region. High flow indicator (90th percentile of daily discharge) averaged across India shows a relatively strong decline that is however not well reproduced by historical hydrological simulations accounting for observed changes in climate and direct human influences which means that we do not have a sufficient understanding of the observed changes to attribute them. In Japan there are only very minor changes in high river flows in the observations and the simulations	Bai et al. (2016), Zhang et al. (2015) Gudmundsson et al. (2021) (India, Japan, 1971-2010)	Anthropogenic climate had only a minor impact on annual low flows in Western and Central North America (1971-2010), low confidence () Anthropogenic climate forcing has increased likelihood of individual severe drought-induced regional water restrictions and associated consequences, medium confidence (*)
	The probability for the occurrence of the 2018 extreme flooding over the upper Yellow River basin decreased by 34% due anthropogenic climate change, which causes a decrease in surface runoff due to reduced seasonal precipitation	Ji et al. (2020)
Australasia	Observations In Southern Australia and New Zealand strong and significant negative regional trends of high flows (maximum annual discharge and annual 90th percentile of daily discharge) have been recorded from 1971-2010. Negative trends in annual maximum discharge in South Eastern Australia supported by analysis of individual station data. In non-urban areas, where the flood response is also dependent on antecedent catchment conditions, there is no empirical evidence of increasing flood magnitudes in Australia except for the most extreme events.	Do et al. (2017) Gudmundsson et al. (2019) Frame et al. (2020a) Johnson et al. (2016); Sharma, (2018) Ishak, (2013); Zhang et al. (2016); Bennett, (2018); Wasko, (2019)
Australasia	Attribution Strong negative trend in annual 90th percentiles of daily discharge in South Australia have also been found in multi-model simulations forced by observed weather (1971-2010). Human intervention such as river engineering or urbanisation seem to play a minor role. The decline in the annual 90th percentiles of daily discharge in South Australia is not reproduced by hydrological simulations forced by historical climate generated by an ensemble of climate model simulations accounting for historical radiative forcing (including natural and anthropogenic). This could be a hint that the observed trends in climate may be introduced by internal variability rather than external forcing.	Gudmundsson et al. (2021) (annual 90th percentile of daily discharge)	Strong contribution of observed climate change to observed reduction in high river flows in Southern Australia, low confidence (*), no assessment in other regions due to data constraints
Central and South America	Observations Flood volume: In the Amazon region observed high river flows have increased significantly from 1971-2010. In North-East Brazil discharge indices show a significantly increasing regional trend in the 1951-1990 period, but have significantly declined in the 1971-2010 period. From 1951- 2010 annual maximum discharge has increased in Southeastern South America.	Berghuijs et al. (2017) Do et al. (2017) Gudmundsson et al. (2019)	low confidence in regional trend because of limited data availability and strong dependence of trends on the considered time period
	Lake Palcacocha, Peru: The lake grew in area from 0.08 km2 in 1995 to 0.49 km2 in 2018.	Stuart-Smith et al. (2021)
	Attribution Multi-model hydrological simulations forced by observed historical climate and human management are in line with the observed increase in high river flows in Southeastern South America (1971-2010). The simulations suggest that the influence of land and river management on the regional trends is minor. Hydrological simulation forced by simulated historical climate do not reproduce the observed increase in the high flows in that area which may be a hint that the observed changes in climate are partly due to internal variability.	Gudmundsson et al. (2021) (annual 90th percentile of daily discharge)	observed climate change has increased high river flows in Southeastern South America, low confidence (*) no assessment elsewhere because of insufficient observational data or low agreement between simulated and observed trends in high flows.
	Lake Palcacocha, Peru: The Lake Palcacocha area expanded due to the rapid retreat of the Palcaraju glacier. This retreat is entirely attributable to the observed temperature trend, of which 85 to 105% is attributable to human greenhouse gas emissions. The increase in lake area enhances the risk of flooding. The study does not enter the synthesis statement for the Figure as it does attribute an observed flood event or trends in peak discharge, flood volume or flooded area. Nevertheless, it attributes an observed change in a physical hazard indicator for flooding and is therefore mentioned.	Stuart-Smith et al. (2021)		observed climate change has increased high river flows in Southeastern South America, low confidence (*) no assessment elsewhere because of insufficient observational data or low agreement between simulated and observed trends in high flows.
Europe	Observations Flood volume: A range of different studies confirm a general pattern of increasing annual maximum discharge in North and Central Europe (northwestern Europe: +2.3% per decade, 1960-2010; northern UK: +6.6% per decade, 1960-2010) but decreasing trends in Mediterranean region (southern Europe: -5% per decade, 1960-2010) and Eastern Europe (-6% per decade, 1960-2010). Across Europe, occurrence frequencies and magnitudes of station-specific maximum 1980-2009 discharge increase with time.	Blöschl et al. (2019) (trends in maximum annual discharge, medium to large catchments, 1960-2010); Mediero et al. (2015); Mediero et al. (2014) Mangini et al. (2018) Berghuijs et al. (2017) (timing of occurrence of maximum 1980-2009 discharge values); Gudmundsson et al. (2019) Hodgkins et al. (2017) (changes in 25-, 50- and 100 year events)	Spatially heterogeneous trends in maximum annual discharge, high confidence	Floods in the considered medium and large catchments are produced by long-duration synoptic storms, while small catchments are more affected by local short duration convective storms with high intensities as well as soil compaction, abandoned terraces and land-cover changes. Therefore, flood trends in small catchments may differ from the ones described here.
	Flooded areas: Using reported flood events (river and coastal flooding) and assuming that associated flood extents correspond to simulated inundation areas associated with 100-year flood events, there is a positive trend in inundated areas from 1870 to 2016 even after correcting for potential underreporting of events (~1.5% per year).	Paprotny et al. (2018b)	low confidence (limited observations of flooded area, remaining events gap-filled using reports on which subnational administrative units were affected and simulated 100-year flood extents)
	Attribution In northwestern Europe increasing maximum annual discharge is mainly induced by increasing autumn and winter rainfall; decreasing in precipitation and increasing evaporation have led to observed decrease in annual maximum discharge in Southern Europe, while changes in eastern Europe are due to decreasing snow cover and snowmelt, resulting from warmer temperatures. Increasing trends in high flows in Northern and Central Europe as well as negative trends in the Mediterranean have also been identified in observation driven hydrological simulations. Simulations show a minor effect of historical land and river management. Hydrological simulations forced by historical simulations from an ensemble of climate models also show positive trends in high flows in Northern Europe and the negative trends in the Mediterranean (1971-2010) and indicate that the observed trends can also be traced back to external climate forcing. Extreme flooding in the UK in autumn 2000: Anthropogenic greenhouse gas emissions are estimated to have increased the probability for river runoff of the observed magnitude or higher by more than 20%.	Blöschl et al. (2019); Mediero et al. (2015) (Spain), Gudmundsson et al. (2021) (annual 90th percentile of daily discharge); Pall et al. (2011) (Extreme flooding in the UK in autumn 2000)	strong contribution of observed climate change to observed positive trends in high flow indices in northern Europe and negative trends in Southern Europe, medium confidence (**)	The occurrence of major flood events is affected by long-term climate oscillations. Annual maximum discharge in Europe is least affected by ENSO compared to other world regions. Also PDO only plays a minor role in Europe annual maximum discharge has been shown to be correlated to NAO and AMO variations. As, since about 1975, the AMO shows an increase very similar to global mean temperature it cannot be decided yet whether the observed changes are due to anthropogenic GHG emissions or a long term climate oscillation. Thus, the assessment explicitly refers to attribution “to long-term climate change”, not necessarily to “anthropogenic climate change”.
North America	Observations Stark regional patterns of changing flood frequencies between 1985 and 2015: increases in flood risk around the upper Midwest/ Great Lakes region and decreases on the Gulf Coastal Plain, the southeastern United States, and California. The magnitude of 100-year flood events has increased by 20% in the Mississippi river in the period 1897-2015 compared to 1500-1800. From 1962-2011 observations indicate significant increases in the frequency but not in the magnitude of flood events in the central USA. For basins across USA and Canada there is no clear systematic pattern of changes in the probability of exceeding certain threshold levels of discharge from 1931 - 2010 and 1961-2010.	The annual 90th percentile of daily discharge has decreased in Eastern and Western North America (1971-2010).	Slater and Villarini (2016) (USA, 1985-2015) Munoz et al. (2018), (Mississippi, 1500-2000); Mallakpour and Villarini (2015) (1962-2011) (central US); Hodgkins et al. (2017) (changes in 25-, 50- and 100 year events, 1931-2010 and 1960 and 2010) Gudmundsson et al. (2018) (1971-2010)
North America	Attribution Between 1971-2010, decreasing high flows in Western and Eastern North America are also found in hydrological simulations forced by observed changes in climate and direct human influences where the direct human influences only play a minor role. Hydrological simulations derived from an ensemble of historical climate model simulations also show negative trends in high flows in these regions. This indicates that the observed declines in high river flows are at least partly attributable to external climate forcing. However, in the highly managed Mississippi basin 75 % of the increase in river discharge is attributed to river engineering. Changes in flood behaviour along rivers across the central US (1962-2011) can be largely attributed to concomitant changes in rainfall and temperature. The occurrence probability of major floods (changes in 25-, 50- and 100 year events) seems to be dominated by multidecadal variability, i.e. may be induced by long-term trends in climate that are difficult to attribute to external forcing or internal variability given the length of the observational period.	Gudmundsson et al. (2021) Munoz et al. (2018) (Mississippi) (1500-2000), Mallakpour and Villarini (2015) (1962-2011) (central US), Hodgkins et al. (2017) (changes in 25-, 50- and 100 year events)	Observed climate change has contributed to the decline in high river flows in Western and Eastern US (1971-2010), low confidence (*) The observed trends may be partly due to internal climate variability. no assessments for Alaska, Canada, Greenland and Iceland due to insufficient data coverage.
Small Islands	Observations
Small Islands	Attribution		no assessment
	S15b Water distribution - Flood-induced fatalities:
Global	Observations The annual total number of reported fatalities from flooding shows a positive trend (1.5% per year from 1960-2013). The number of fatalities has declined in high-income countries but increased in lower middle-income countries. No clear trend across low-income countries. A substantial interannual variability of reported fatalities is observed.	Jongman et al. (2015) (Munich RE’s NatCatSERVICE, 1980-2010); Kundzewicz et al. (2014) Tanoue et al. (2016) (EM-DAT, 1960-2013) Formetta and Feyen (2019) (Munich RE’s NatCatSERVICE, 1980-2016)	medium confidence in trends in fatalities because of potential reporting biases in particular before 1980.
Global	Attribution The increase in the number of fatalities on global scale is most likely not due to climate change as there only is a much smaller increase in the areas affected by flooding derived from observational weather data (0.15% per year from 1960 to 2013). Increasing exposure has probably contributed to the trend in fatalities as modeled exposed population derived from observed weather data and observed changes in population patterns shows a much stronger trend than affected areas (1.5% per year). The exposure-driven increase in risk has been dampened by a reduction in vulnerability across income groups. Thus, the number of fatalities in high-income countries has declined, even though the number of potentially exposed people has not. In lower middle-income countries, the number of fatalities has been rising, but not as rapidly as the number of potentially exposed people.	Ward et al. (2014c) (effects of ENSO) Jongman et al. (2015) Kundzewicz et al. (2014) Tanoue et al. (2016) (change in mortality rates induced by population shifts) Formetta and Feyen (2019) (1980-2016)	minor impact of climate change on observed increase in fatalities induced by river floods, medium confidence (**)
Africa	Observations
Africa	Attribution		White no separate assessment
Asia	Observations Flood-related fatalities from large floods in south-eastern Asia, corrected for population growth, had no significant trend for 1985-2018. Decrease in flood fatalities relative to population in Turkey 1930-2020. For all Asia, a slight decrease in deaths (unadjusted) reported for 1980-2019, with increase in Western and South-Eastern Asia, and decrease in China and India; decline in flood fatalities in Japan (1925-2007).	(Chen et al., 2020a) (DFO dataset); (Haltas et al., 2021) (compilation for Turkey); (Wang et al., 2021) (EM-DAT); (Huang, 2014) (Japan)
Asia	Attribution No attribution of trends made other than population growth or decrease in vulnerability due to economic growth.		no assessment (not enough evidence for the region)
Australasia	Observations
Australasia	Attribution		White no separate assessment
Central and South America	Observations
Central and South America	Attribution		White no separate assessment
Europe	Observations Flood-related fatalities declined by 1.4% per year since 1870 and 4.3% since 1950 until 2016. Stronger decline was reported for flash floods compared to large-scale river floods. No significant trend in the Mediterranean region for fatalities (1980-2015).	Paprotny et al. (2018b) (river floods + coastal flooding); (Petrucci et al., 2019) (MEFF: Mediterranean Flood Fatalities database)
Europe	Attribution The observed decline is mainly driven by a reduction in vulnerability. Climate related hazards (affected areas, see above) show a positive trend. In addition, changes in population patterns have induced a positive trend in affected people estimated from reported flood events and associated 100-year flood extents in contrast to the observed decline in the number of fatalities.	Paprotny et al. (2018b) (river floods + coastal flooding)	minor impact of climate change on observed negative trends in the number of fatalities, medium confidence (**)
North America	Observations
North America	Attribution	White no separate assessment
Small Islands	Observations
Small Islands	Attribution		no assessment
	S15c Water distribution - Flood induced economic damages
Global	Observations Recorded economic losses increased by 6.3% of the annual mean from 1960-2013, from 1980-2010 a significant positive trend of 4.4%/year, compared to the baseline annual average damage (1980-1995), was observed, and also over the extended time period 1980-2016, global damages show a significant increase. In both high- and low-income countries absolute losses have increased in the period 1990-2010 compared to 1980-1990. In areas where discharge maxima show rising trends over the period 1971-2010 (major parts of Europe and Asia and Latin America) damage trends have significantly increased by 8.7% from 1980-2010, but also in areas where discharge maxima have decreased (major parts of Africa, North-America and Australasia) damages have increased significantly.	Jongman et al. (2015); (Neumayer and Barthel, 2011) (1980-2009), (Tanoue et al., 2016) (1960-2013), (Sauer et al., 2021) (1980-2010), (Formetta and Feyen, 2019) (1980-2016)	Medium confidence in positive trends (reporting biases especially in early time periods may contribute to upwards trends in reported losses but confidence increases with improved reporting after 1980).
Global	Attribution Direct human influences are major drivers of these increases. After removing trends in population patterns and economic growth no significant trends in losses can be observed across income groups on global scale. However, after regional disaggregation according to regions with positive or negative trends in maximum annual discharge, an impact of climate change on damages can be quantified: Assuming fixed 2010 socio-economic conditions, model simulations suggest that climate change has significantly increased the expected median damage in 2010 by 84.3% of the baseline damage compared to 1971 in regions with positive discharge trends, and decreased damages insignificantly by -46.8% in regions with negative discharge trends. The confidence in the assessment is limited, the full model accounting for changes in exposure, vulnerability and climate change can explain at least 45% of the observed variance of annual damages in regions with positive discharge trends. In regions with negative discharge trends, the model cannot sufficiently explain the observed variance of annual damages. A mainly exposure driven increase in potential damages has been dampened by a global reduction in vulnerability: The global loss rate (recorded damage/exposed GDP) declined significantly in all studied time periods and across all income groups . There is a tendency of convergence in vulnerability between low- and high-income countries.	(Neumayer and Barthel, 2011); (Ward et al., 2014a) (effects of ENSO on damages) Jongman et al., 2015; Kundzewicz et al. 2014; Formetta and Feyen 2020, Tanoue et al., 2016; (Sauer et al., 2021)	minor contribution of climate change to observed damages on global scale, moderate contribution (compared to other drivers) in subregions with increasing discharge trends, low confidence (*)	Annual variations in damages appear to be affected by large-scale climate oscillations. In the considered regions long term trends may be induced by the AMO which, during the study period, cannot be separated from a potential influence of global mean temperature change. Thus we explicitly only attribute to long-term climate change (including AMO) instead of anthropogenic climate change.
Africa	Observations In South and Sub-Saharan Africa, economic damages have significantly increased by 2.2%/year, compared to the baseline annual average damage (1980-1995), from 1980 to 2010. In areas where maximum discharge has increased (mainly the Sahel region) economic damages have increased significantly by 11.3% derived from trend analysis.	(Sauer et al., 2021), Munich RE’s NatCatSERVICE, 1980-2010
Africa	Attribution Across the entire South and Sub-Saharan Africa (SSA) region and the North Africa + Middle East (NAF) region total damages induced by river floods cannot yet be well explained by model simulations accounting for observed weather fluctuations and changes in exposure and vulnerability. For this reason we do not provide attribution assessments.		still insufficient understanding
Asia	Observations In Eastern Asia damages have increased significantly by 4.8 %/year between 1980 and 2010 compared to annual average damages from 1980-1995. In areas of Eastern Asia with positive discharge trends from 1971-2010, damages increased by 7.6%/year compared to the baseline. In Central Asia and Russia no significant changes in damage trends have been observed from 1980-2010. In South and South-Eastern Asia observed damages have increased significantly by 4.8 % in areas where discharge maxima increased between 1980-2010. These areas encompass basically parts of South-Western India and Pakistan, great parts of Indonesia, Thailand, Vietnam and Cambodia. Annual average flood losses recorded in the period 1984-2018 in China reached $19.2 billion (normalized to 2015 values), which accounted for 0.5% of the national GDP in China and 54% of the total national direct economic losses due to climate and weather.	Sauer et al. (2021); Munich RE’s NatCatSERVICE, 1980-2010; (Jiang et al., 2020)
Asia	Attribution Eastern Asia: In Eastern Asia as well as in its associated subregion of increasing trends in annual maximum discharge, damage records can be well explained by model simulations accounting for historical weather fluctuations and changes in exposure and vulnerability (55% to 70% explained variance). For entire Eastern Asia, model simulations assuming fixed 2010 socio-economic conditions indicate that climate change has increased the median damage in 2010 by 67.8% (106% in areas with positive discharge trends) since 1971. However, this effect is still much smaller than the effect of a significant increase in exposure that has only been compensated by a strong decrease in vulnerability. South, South Eastern Asia and Central Asia: In South and South Eastern Asia and Central Asia damage records cannot yet be well explained by model simulations accounting for historical weather fluctuations and changes in exposure and vulnerability and therefore we do not provide an associated assessment. Only in the subregion with positive discharge trends of South and South Eastern Asia the full model accounting for changing climate and changes in exposure and vulnerability explains over 50% of the annual variability of observed damage. Model simulations assuming fixed 2010 socio-economic conditions indicate that climate change has increased the median damage in 2010 significantly by 302.3% since 1980. However, the climate contribution is not significant for the time period 1971-2010.	Sauer et al. (2021)	Strong adverse impact of climate change in some regions with increasing trends in discharge, low confidence (*); no assessment in parts of Asia
Australasia	Observations In Australasia an insignificant increase in damages of 0.8 %/year has been observed from 1980-2010. Areas with decreasing trends in discharge maxima, mainly Southern and Eastern Australia, show also an insignificant increase of 0.75% has been observed. No significant trend in insured flood losses in Australia between 1966 and 2017 found after correcting for growth in exposure.	Sauer et al. (2021), Munich RE’s NatCatSERVICE (1980-2010), McAneney et al. (2019) (insured losses)
Australasia	Attribution Across entire Australasia and the subregion of decreasing hazards interannual variability and trends in damage records can be well explained by model simulations accounting for historical weather fluctuations and changes in exposure and vulnerability (60-70%), in other subregions observed damages cannot be well explained. Therefore we do not provide an associated attribution assessment for the subregion with increasing discharge trends. Model simulations assuming fixed 2010 socio-economic conditions indicate that climate change has decreased the expected median damage risk in 2010 by 9.7% since 1971 in the subregion with decreasing discharge trends, for the entire region the risk reduction is not significant. The increase in observed damages appears to be mainly attributable to increasing vulnerability.	Sauer et al. (2021)	moderate climate change induced decline in damages in areas with decreasing maximum annual discharge in Australia, and strong increase in damages in New Zealand induced by anthropogenic climate change, low confidence (*)
New Zeland	Observations From mid-2007 to mid-2017, 12 major extreme rainfall events caused NZ$472 million in insured flood losses in New Zealand.	Frame et al. (2020a)	moderate climate change induced decline in damages in areas with decreasing maximum annual discharge in Australia, and strong increase in damages in New Zealand induced by anthropogenic climate change, low confidence (*)
New Zeland	Attribution In New Zealand, NZ$140 million of the insured NZ$472 million damages could be attributed to anthropogenic climate change, where the estimate is based on the strong assumption that damage attributable to anthropogenic climate forcing can be approximated by multiplying the actual damage by the “fraction of attributable risk (FAR)” of the observed droughts, where FAR = 1 - P0/P1 with P0 and P1 being the probabilities of an event of this magnitude under natural and observed climate forcing, respectively.	Frame et al. (2020a)	moderate climate change induced decline in damages in areas with decreasing maximum annual discharge in Australia, and strong increase in damages in New Zealand induced by anthropogenic climate change, low confidence (*)
Central and South America	Observations In Latin America an insignificant damage reduction of -0.2%/year, compared to annual average losses from 1980-1995, was observed 1980-2010. Across the subregion of increasing maximum annual discharge (mainly areas of the Amazonian region, Southeastern South America and areas of Central America and the Caribbean) economic damages show an insignificant increase of 0.2%/year from 1980 and 2010.	Sauer et al. (2021) Munich RE’s NatCatSERVICE, 1980-2010)
Central and South America	Attribution Across Latin America and the subregion of increasing hazards, interannual variability and trends in damage records can partly be explained by model simulations accounting for historical weather fluctuations and changes in exposure and vulnerability (explained variances ~30%). Across the area of increasing maximum annual discharge (Amazonian areas, Southeastern South America and areas of Central America and the Caribbean) model simulations assuming fixed 2010 socio-economic conditions indicate that climate change has increased the median damage in 2010 significantly by 69.3% compared to expected median damages in 1971. The minor changes in observed damages seem to be due to an increase in exposure that is counterbalanced by a reduction in vulnerability. In the entire region and the regions with increasing trends in annual maximum discharge climate is estimated to only have had a minor insignificant effect on damages. For the subregion with decreasing discharge trends, no assessment can be made, due to a limited explanatory power of model simulations.	Sauer et al. (2021)	minor impact of climate change on flood induced damages aggregated across Latin America as a whole and the subregion of increasing maximum annual discharge, low confidence (), but anthropogenic climate forcing is estimated to have increased the probability of individual high-impact events, low confidence () no assessment elsewhere because of insufficient process understanding
Europe	Observations Records of reported flood events that have caused severe damages show phases of flood-rich and flood-poor periods since 1500, with 1990-2016 being the strongest since 1840-1870 across Europe (except Spain and Scandinavia). There is some evidence of a positive trend in flood losses (corrected for inflation) in Europe between 1970-2010, although the trends are not significant and may partly be induced by reporting biases. From 1980-2010 a significant increasing trend of 3.5%/year, compared to the baseline annual damage from 1980-1995, was observed in Europe. In areas where discharge maxima show rising trends over the period 1971-2010 (basically North-Western Europe) damage trends have significantly increased, while in areas where discharge maxima have decreased (mainly Mediterranean areas) no significant trend in damages was detected.	Hall et al. (2014); Montanari (2012) Nobre et al. (2017) Barredo (2009) Barredo et al. (2012) Stevens et al. (2016) Paprotny et al. (2018b) (river floods + coastal flooding), Sauer et al. (2021)(Munich RE’s NatCatSERVICE, 1980-2010),; Schaller et al. (2016) (UK winter flood in 2013/14), Blöschl et al. (2020) (flood poor/rich periods)	medium confidence in increasing flood induced damages since 1980In Southern England a series of storms in the winter of 2013/14 caused severe floods and £451 million insured losses in the Thames basin.
	Attribution Flood-rich and poor phases are strongly linked to long-term memory of hydrological processes and large-scale climate variabilities, such as ENSO, NAO, and the East Atlantic pattern. NAO was significantly correlated with economic loss in most of North-Western and Central Europe (1950-2018). Changes in population patterns, increasing per capita real wealth and asset values are the main drivers of the observed increase in losses. After correcting for increasing exposure due to population and economic growth, no significant trends remained in economic losses (Europe 1900-2016, Spain 1971-2008, Switzerland 1972-2016). These purely empirical findings could be supported by a semi-empirical approach building on process-based simulations of flooded areas: The observed interannual fluctuations in damages from 1980 to 2010 can be well explained by observed weather fluctuations and changes in exposure and vulnerability (explained variance ~30%). Model simulations assuming fixed 2010 socio-economic conditions indicate that climate change has only insignificantly decreased the expected median damage risk in 2010. Increasing exposure and slightly decreasing vulnerability basically explain the recorded damage increases, while climate contributions remain insignificant. In the subregions of increasing or decreasing maximum annual discharge the explanatory power of the model is not sufficient to allow for attribution. Thus, minor impacts of climate on damages in Europe as a whole may be due to heterogeneous trends in hazards whose effects on damages cancel out in regional aggregation.	(Hall et al., 2014) (Montanari, 2012), (Nobre et al., 2017) (Barredo, 2009) (Barredo et al., 2012) (Stevens et al., 2016) (Paprotny et al., 2018b) (river floods + coastal flooding), (Sauer et al., 2021) (Munich RE’s NatCatSERVICE, 1980-2010), (Andres and Badoux, 2019) (Zanardo et al., 2019)	minor impact of climate change on flood induced damages in Europe as a whole, medium confidence (**), no assessment in subregions with increasing or decreasing hazards and Europe due to insufficient process understanding
	For events similar to the 2013/14 flood in the UK, about 1,000 more properties are placed at risk of fluvial flooding and potential losses have been increased by £24 million relative to a climate without anthropogenic GHG emissions (best estimates). However, large uncertainty is found in the climate model ensemble.	(Schaller et al., 2016) (Kay et al., 2018) (ensemble simulation of 2013/2014 event)
North America	Observations In North America damages induced by river floods increased insignificantly from 1980 to 2010 in the region as a whole (0.5 %/year, compared to annual average losses from 1980-1995)
	Attribution The observed interannual fluctuations in damages from 1980 to 2010 can be well explained by model simulations accounting for observed weather fluctuations and changes in exposure and vulnerability (explained variance > 80% ). Model simulations assuming fixed 2010 socio-economic conditions suggest that climate change has increased the expected median damage risk in 2010 in the region as a whole by 8.3% since 1971, though the effect is insignificant.	(Sauer et al., 2021) (Munich RE’s NatCatSERVICE, 1980-2010)	minor to strong increase in flood induced damages due to climate change, medium confidence (**) in the sign of the effect, but low agreement on the order of the effect.
	In the US, around one-third of the cumulative economic flood damage from 1988 to 2017 was caused by precipitation change. Climate models show that anthropogenic climate forcing has increased the probability of exceeding precipitation thresholds at the upper intensity quantiles causing most of the damage.	(Davenport et al., 2021)
Small Islands	Observations
Small Islands	Attribution		no assessment
	S17 Water distribution - Water-borne diseases
Global	Observations
Global	Attribution There is no attribution of changes to climate change but only ‘detection of weather sensitivity’ (see part 3 of this table)		no assessment
	S18 Food system - Crop yields
Global	Observations Crop yields are improving in most areas. Among maize, wheat, soy and rice, recent rates of global mean yield growth were highest for maize and lowest for wheat. However, a stagnation or decline in yields is observed on 30% (maize), 39% (wheat), 37% (rice), and 24% (soy) of harvested areas (data from 1961 to 2008). Stagnation of yields affects high-yield systems for rice in East Asia (China, Republic of Korea and Japan), wheat in Northwest Europe (United Kingdom, France, Germany, The Netherlands, Denmark) and India, and maize in South Europe (Italy and France) but stagnation or very low rates of yield increases are also observed in low-yield systems such as maize in east Africa.	(Iizumi et al., 2018b) (maize, rice, wheat and soybeans, 1980-2008); (Ray et al., 2012) (maize, rice, wheat and soybeans, 1961-2008) (Ray et al., 2013) (maize, rice, wheat, and soybean, 1961-2008), (Grassini et al., 2013) (wheat, rice, maize, 1965-2011)	high confidence in mostly increasing maize, wheat, rice and soy yields
	Attribution Effects of climate change: The considered estimates of global average impacts of historical climate change are subject to some important limitations: Current process-based estimates are forced by simulated historical climate in comparison to simulated pre-industrial climate conditions and miss a clear evaluation to what degree models are able to reproduce observed yields when forced by observed climate conditions (Iizumi et al., 2018). The applied empirical models considered on global scale do not explicitly account for the impacts of extreme events (Ray et al., 2019, Lobell et al., 2011 based on growing season average temperature and precipitation) although the relevance of extremes has been demonstrated in regional studies (e.g. Butler et al., 2017 for maize in the US). In addition, studies are constrained by only fragmented information about historical growing season adjustments. Wheat: Empirical models indicate a purely climate induced reduction in global average wheat (0.9% reduction of recent yields compared to counterfactual situation not accounting for climate trends from 1974 to 2008, 5.5% reduction of average 1980-2008 yields induced by climate trends over the same period, ~3% reduction in 2002 yields induced by climate trends since 1981). Reduction is supported by a process-based model estimating a purely climate induced reduction in wheat yields by comparing yield simulations forced by simulated historical climate to simulations forced by a pre-industrial reference climate (about 5% loss of 1981-2010 yields not accounting for CO2 fertilization); mostly (20 out of 30 representative global sites) negative responses to historical warming (1980-2010) derived from the median of 30 process-based models that has been shown to reproduce temperature responses of field experiments. Maize: Empirical estimates of historical climate induced yield changes range from no change of recent yields by climate trends from 1974 to 2008, to about 2% reduction in 2002 yields induced by climate trends since 1981, and 3.5% reduction of average 1980-2008 yields induced by climate trends over that period. Process-based simulations indicate a ~6% reduction of 1981 to 2010 yields when comparing yields derived from simulated historical climate to yields under simulated pre-industrial climate not accounting for CO2 fertilization. Rice: Minor reduction in yields (0.3% reduction of recent yields compared to counterfactual situation not accounting for climate trends from 1974 to 2008; 0.1% reduction of average 1980-2008 yields induced by climate trends over the same period; less than 0.5% reduction in 2002 yields induced by climate trends since 1981, and ~2% reduction of 1981 to 2010 yields compared to counterfactual pre-industrial climate conditions) derived from three empirical models and one process-based model simulations comparing simulation forced by simulated historical climate to simulations forced by simulated pre-industrial climate. Soy: Empirical estimates of historical climate induced yield changes range from a 3.5% increase of recent yields by climate trends from 1974 to 2008, to 1% increase in 2002 yields induced by climate trends since 1981, and 1.7% reduction of average 1980-2008 yields induced by climate trends over that period. Processed-based model simulations estimate a 7% loss of yields induced by simulated historical climate change compared to pre-industrial climate (not accounting for CO2 fertilization). Others: Empirical estimates indicate that climate change from 1974 to 2008 has reduced recent yields of barley (-7.9%), cassava (-0.5%), oil palm (-13.4%) and increased yields of rapeseeds (0.5%), sorghum (2.1%), and sugarcane (1.0%).	(Iizumi et al., 2018b) (process-based crop model simulations forced by simulated historical versus pre-industrial climate); (Asseng et al., 2015) (wheat at 30 representative global locations), (Ray et al., 2019) (Lobell and Field, 2007) (empirical estimates).	mixed global scale responses of yields to climate change across different crops. Moderate decline of global average wheat yields induced by climate change (medium confidence (**); mostly inconclusive for other crops because (limited evidence and agreement)
	Effects of increasing atmospheric CO2 concentrations: Process understanding represented in crop models and empirical evidence indicate a general positive effect of rising atmospheric CO2 on crop yields. However, global average process-based model simulations indicate that the effect has not compensated for the negative effects of climate change induced by anthropogenic emissions of climate forcers on maize and soybean yields while it may have for wheat and rice. An empirical model fit to national yield statistics (aggregated indicator across a range of crops) shows mostly positive trends induced by increasing CO2 concentrations except for a range of countries in Europe and only a few individual countries in Africa, Asia, and South America. However, the model does not allow for a separation of trends induced by CO2 fertilization and climate change. Instead CO2 concentrations are considered a proxy for the combined effect adjusted for additional climate related predictors (El Niño-southern oscillation, Palmer drought severity index, and geopotential height anomalies).	(Iizumi et al., 2018a) (Najafi et al., 2018)	moderate positive effect of increasing atmospheric CO2 concentrations, medium confidence (**)
Africa	Observations Large areas of stagnation or decline in maize production with the exception of some West African countries and individual countries in Southern Africa showing increasing trends across different data sets. Divergent or missing information about trends of soy, wheat or rice yields in large areas of the continent. Estimated trends show relatively strong national patterns. Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase in yields” are estimated to be 0% (maize in South Africa), the crop for which South Africa belongs to the top ten producers.	(Iizumi et al., 2018a)	low confidence in trends in wheat, soy and rice yields; medium confidence in declining maize yields in large parts of Central Africa.
Africa	Attribution Effects of climate change: Wheat, Sub-Saharan-Africa: An empirical study comparing present day yield levels to the ones estimated for a counterfactual climate not accounting for trends from 1974-2008 indicates weak losses in wheat yields by the historical change in climate (2.3%). A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by simulated pre-industrial climate support a mostly negative combined impact of historical climate change and increasing atmospheric CO2 concentration on wheat. Northern Africa: Empirical estimates indicate a moderate positive effect of climate change on wheat yields from 1974-2008 (present day yields are estimated to be 12% higher than compared to a counterfactual world without the historical trend in climate). In contrast, process-based crop model simulations forced by simulated historical climate versus simulated pre-industrial climate indicate no significant combined impact of climate change and CO2 fertilization on wheat yields. Maize, Sub-Saharan Africa: Technological advances are empirically estimated to explain most of the observed trend in maize yields from 1962 to 2014 (13 kg/ha per year of the overall 15 kg/ha per year increase in maize yields), where the estimated trend in time could also be partly driven by increasing atmospheric CO2 concentrations. Climate variables are estimated to have played a comparatively small role where increasing temperatures are estimated to have had a negative effect. An empirical study comparing present day yield levels to the ones estimated for a counterfactual climate not accounting for trends from 1974-2008, maize yields are estimated to have been reduced by about 5.8% by climate change. A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by simulated pre-industrial climate indicate a mostly negative combined impact of climate change and increasing atmospheric CO2 concentrations on maize yields. Northern Africa: In an empirical study comparing present day yield levels to the ones estimated for a counterfactual climate not accounting for trends from 1974-2008, maize yields are estimated to have been reduced by 4.3% by climate change. Process-based crop model simulations forced by simulated historical climate versus simulated pre-industrial climate support mostly negative impacts of climate change on maize yields. Soy, Sub-Saharan Africa: An empirical study comparing present day yield levels to the ones estimated for a counterfactual climate not accounting for trends from 1974-2008 indicates yield losses of 1.6% induced by climate change. A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by simulated pre-industrial climate supports a mostly negative impact of climate change on soybean yields in Sub-Saharan Africa that is not even compensated by increasing atmospheric CO2 concentrations. Northern Africa: Empirical estimates indicate a moderate positive effect of climate change on soy yields from 1974-2008 (present day yields are estimated to be 11% higher than compared to a counterfactual world without a trend in climate). In contrast, process-based crop model simulations forced by simulated historical climate versus simulated pre-industrial climate show a mostly negative but regionally very confined combined impact of climate change and increasing CO2 concentrations on soy yields. Rice, Sub-Saharan Africa: Empirical estimates indicate a negative effect of climate change on rice yields from 1974-2008 (1.3% reduction of present day yields compared to a counterfactual world without a trend in climate). A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by simulated pre-industrial climate indicates mixed positive and negative combined impacts of climate change and increasing atmospheric CO2 concentrations on rice yields. Northern Africa: Empirical estimates indicate a negative effect of climate change on rice yields from 1974-2008 (1.3% reduction of present day yields compared to a counterfactual world without a trend in climate). A comparison of process-based crop model simulations forced by simulated historical climate to simulations based on pre-industrial climate indicates regionally confined and mixed combined impacts of climate change and CO2 fertilization on rice yields. Others, Sub-Saharan Africa: Technological advances are empirically estimated to explain most of the observed trend in groundnut and sorghum yields from 1962 to 2014 (2.8 kg/ha per year of the overall 3.7 kg/ha per year increase in groundnut yields, and 3.2 kg/ha per year of the overall 3.8 kg/ha per year increase in sorghum yields), where the estimated trend in time could also be partly driven by increasing atmospheric CO2 concentrations. Climate variables are estimated to have played a comparatively small role. Increasing temperatures are estimated to have reduced yields. A minor effect of historical climate change on sorghum (0.7% increase of present day yields) is supported by an empirical study comparing present day yield levels to the ones estimated for a counterfactual climate not accounting for observed trends from 1974-2008. The same empirical study finds minor negative effects of historical climate change on barley (0.6% reduction) and slightly higher losses of sugarcane yields (3.9%), but no, minor or strong positive effects on oil palm (0%), cassava (1.7%), and rapeseeds (24.9%), respectively. Northern Africa: Empirical estimates indicate a negative effect of climate change on barley and sugarcane yields from 1974-2008 (6.8 and 5.1% reduction of present day yields compared to a counterfactual world without a trend in climate), but also show moderate positive effects of climate change on cassava, sorghum (18% increase in present day yields compared to a counterfactual situation without the historical trend in climate). West Africa: The comparison of process-based crop model simulations forced by simulated historical climate versus pre-industrial climate over the period 2000-2009 show stronger impacts of climate change on millet than on sorghum. Averaged across West Africa one model not accounting for CO2 fertilization simulates yield losses of 17.7% for millet and 15.0% for sorghum. A second model accounting for CO2 fertilization shows weaker losses (10.9% for millet and 5.9% for sorghum).	(Hoffman et al., 2018) (empirical model, maize, sorghum, and groundnut in sub-Saharan Africa), Iizumi et al. (2018b) (process-based); Sultan et al. (2019) (process-based, millet and sorghum in West Africa), Ray et al. (2019) (empirical model)	mixed impacts of climate change on crop yields in Africa ranging from strong reductions in millet and sorghum yields in West Africa to moderately positive impacts on cassava, sorghum, soybean and wheat in Northern Africa, low confidence (*)
Asia	Observations Mostly increasing maize, wheat, rice, and soybean yields, with the exception of maize in north-eastern China, soybean in parts of China, rice in parts of China and Central Asia, and larger parts of the wheat production area in China and India where yields are stagnating or decreasing. Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase in yields'' are estimated to have reached 52.2% (China), 37% (India), 2% (Indonesia) of harvested areas of maize; 55.5% (China), 70% (India), 13% (Pakistan), and 64% (Turkey) of harvested areas of wheat; 79% (China), 37% (India), 81% (Indonesia), 0% (Bangadesh), 2% (Vietnam), 2.6% (Thailand), 0% (Myanmar), 12% (Philippines), and 19% (Japan) of harvested areas of rice; 58% (China), 51% (India), and 59% (Indonesia) of harvested areas of soy. Results are only listed for the crops (out of maize, wheat, rice and soy) for which the countries belong to the top ten producers.	Grassini et al. (2013), (Iizumi et al., 2018a)	low confidence regarding regions of stagnation (differences between individual datasets);
	Attribution Impacts of climate change: Maize: A comparison of process-based model simulations for 1981-2010 forced by simulated historical climate and pre-industrial climate indicates climate induced losses of maize yields over central and southern parts of Asia (between 5-10% in China and India not accounting for changes in CO2 fertilization) and some gains in higher latitude. A loss of about 7% of 1980-2008 average yields induced by climate change is also estimated empirically for China, but the study also shows a slight increase in yields for India. Another empirical study also indicates gains in maize yields (5.1% and 1% increase in recent yields in comparison to yields under counterfactual climate conditions not accounting for trends from 1974 to 2008 for Central / Eastern Asia and Western / Southern / South-eastern Asia, respectively). Other process-based simulations explicitly accounting for changes in growing seasons and cultivars enabled by historical climate change estimate an associated 7-17 % gain in yields per decade in Northern China from 1980-2009. A similar study based on process based model simulations for the Loess Plateau in the center of China also shows historical climate change may have had a negative impact on maize yields but also enabled the adjustments of cultivars that may have compensated for the climate induced losses assuming early cultivars. However, the simulated trends in yields are not compared to the observed ones and not based on reported adjustments of cultivars. Nepal: An empirical study of maize yields in the Koshi River basin, shows strongly varying impacts of climate change in terms of the considered region and elevation.	(Iizumi et al., 2018a) (process-based model, global gridded, maize, wheat, rice, soy), (Ray et al., 2019) (empirical model, world regions, 10 most important crops), Lobell et al., 2011 (empirical, major producers, maize, wheat, rice, soy), (Meng et al., 2014) (process-based, China, maize), (Bu et al., 2015) (process-based, China, maize), (Bhatt et al., 2014) (Nepal, 1967-2008)	minor impact of climate change on rice yields, medium confidence (**), mostly inconsistent findings for other crops
	Wheat: Process-based model simulations forced by simulated historical versus pre-industrial climate show moderate purely climate driven reductions in Russia (about 5% purely climate induced loss of 1981-2010 yields compared to pre-industrial climate conditions, potentially compensated by CO2 fertilisation). An empirical study indicates gains and minor losses in wheat yields in larger areas (4.5% increase and ~1% loss in recent yields compared to yields under counterfactual climate conditions not accounting for trends from 1974 to 2008 for Central / Eastern Asia and Western / Southern / South-eastern Asia, respectively). China: Process-based model simulations forced by simulated historical versus pre-industrial climate show a moderate reduction of wheat yields in China (about 5% purely climate driven loss of 1981-2010 yields compared to pre-industrial climate conditions). Empirical estimates indicate a slightly positive effect of climate change on average Chinese wheat yields (recent yields compared to yields under counterfactual climate conditions not accounting for trends from 1974 to 2008). Further empirical evidence derived from 120 agricultural meteorological stations accounting for growing season adjustments also estimated that changes in growing season average temperature, precipitation and solar radiation increased wheat yields in northern China by 1-13% and reduced wheat yield in southern China by 1-10% over the period from 1981-2009. From 1981 to 2009, climate trends were associated with a ≤30% (or ≤1.0% per year) wheat yield increase at 23 stations in eastern and southern parts of Huang-Huai-Hai Plain, western China and a ≤30% (or ≤1.0% per year) reduction at 11 other stations. Increases have been associated with increases in daily minimum temperatures supported by additional studies of field data across major Chinese winter wheat regions and Tibet. India: Process-based model simulation forced by simulated historical versus pre-industrial climate show a strong reduction of wheat yields in India (about 20% lower average yields in 1981-2010 compared to pre-industrial climate conditions). An empirical study finds slightly negative impacts of climate change on wheat yields in India (recent yields compared to yields under counterfactual climate conditions not accounting for trends from 1974 to 2008). A panel regression of yield data from 208 districts and growing seasons average daily minimum and maximum temperatures, solar radiation, and total precipitation indicates that wheat yields have been 5.2% lower than they would have been if temperatures had not increased during the study period (1981-2009). Nepal: An empirical study of wheat yields in the Koshi River basin shows strongly varying impacts of climate change in terms of the considered region and elevation.	(Iizumi et al., 2018b) (process-based model, global gridded, maize, wheat, rice, soy), Ray et al. (2019) (empirical model, world regions, 10 most important crops), Lobell et al., 2011 (empirical, major producers, maize, wheat, rice, soy), (Tao et al., 2014) (wheat, comparison northern and southern China, 1981-2009), (Tao et al., 2017) (wheat, Huang-Huai-Hai Plain), (Zheng et al., 2017) (wheat, China, 1980 and 2015), (Zheng et al., 2016) (wheat, Tibet, China, 1988-2012), Gupta 2017 (wheat, India, 1981-2009), (Bhatt et al., 2014) (Nepal, 1967-2008)	minor impact of climate change on rice yields, medium confidence (**), mostly inconsistent findings for other crops
	Rice: Mainly small reductions (<5%) in rice yields induced by historical climate change in China, India, Indonesia, Bangladesh and Vietnam derived from process-based simulations comparing 1981-2010 yields under historical climate forcing to associated yields under pre-industrial climate conditions. Empirical estimates show relatively small changes in rice yields induced by historical climate change (0.9% gain and 0.8% loss of recent yields in comparison to yields under counterfactual climate conditions not accounting for trends from 1974 to 2008 for Central / Eastern Asia and Western / Southern / South-eastern Asia, respectively). Process-based model simulations across 9 provinces in China indicate that observed temperature increase has had a minor non-significant impact on trends in rice yields from 1961 to 2003. Observed positive trends are estimated to have been clearly dominated by increasing N inputs and technological progress such as genetic improvement. Another empirical study indicates that potential negative effects of climate change on national crop production in China during 1945-2015 have been counterbalanced by northwards movements of production. If the spatial distribution of rice areas had not changed after 1949-1951, country level rice yield would have been 162 kg ha-1 lower than the actual yield in 2011-2015. The joint effects of temperature increase, changes in precipitation and radiation is estimated to have increased yields of early- and late- rice by 0.5% and 2.8%, respectively (1980-2012, Southern China). Nepal: An empirical study of rice yields in the Koshi River basin shows strongly varying impacts of climate change in terms of the considered region and elevation.	(Iizumi et al., 2018b) (process-based model, global gridded, maize, wheat, rice, soy), (Ray et al., 2019) (empirical model, world regions, 10 most important crops), (Lobell et al., 2011) (empirical, major producers, maize, wheat, rice, soy), (Liu et al., 2016b) (rice, Southern China, 1980-2012), Wang and Hijmans (2019) (empirical model, rice, China), Sawano et al. (2015) (China, rice), Bhatt et al. (2014) (Nepal, 1967-2008)	minor impact of climate change on rice yields, medium confidence (**), mostly inconsistent findings for other crops
	Soy: Process-based crop model simulations forced by simulated historical climate versus pre-industrial climate estimate mostly negative effects of historical anthropogenic climate change on soy yields in South-eastern Asia with an about 7% purely climate induced reduction in China (1981-2010). An empirical study has found non-significant slight negative effects of climate change on soy yields in China (1980-2008). In contrast a slight increase found in an empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends. The same statistical approach shows minor effects of climate change in Central and Eastern Asia, and a 3.2% reduction of current yields compared to yields under counterfactual climate conditions in Western, Southern + South-eastern Asia.	(Iizumi et al., 2018b) (process-based historical simulations, not based on observed yields, 1981-2010), Ray et al. (2019) (empirical model, 1974-2008), (Lobell et al., 2011) (empirical, 1980-2008)	minor impact of climate change on rice yields, medium confidence (**), mostly inconsistent findings for other crops
	Others: An empirical study of yields in Western + Southern + South Eastern and Central + Eastern Asia comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends has found largest yield losses induced by climate change for oil palm in the Western + Southern + South Eastern regions (~16%) and largest climate induced gains for rapeseed in the Central + Eastern Asia region (~6%).	Ray et al. (2019) (empirical, 1974-2008)	minor impact of climate change on rice yields, medium confidence (**), mostly inconsistent findings for other crops
	Impacts of CO2 fertilization: Empirical estimates indicate that CO2 fertilization has increased 2002-2006 Chinese soy yields by 5.10% compared to assuming 1980 levels of CO2. Process-based estimates indicate that the effect of CO2 fertilization may have compensated for climate induced losses in soy and wheat yields and even slightly overcompensated the losses in rice yields in China (1981-2010). Process-based rice yields simulations for nine provinces in China also indicate a slight increase from 1961 to 2003 overcompensating small non-significant negative effects of temperature increase but still minor compared to the effects of change in fertilizer inputs and technological progress.	Sakurai et al. (2014) (soy, China, 2002-2006), Iizumi et al., 2018 (rice, wheat, soy, process-based, China 1981-2010), Sawano et al. (2015) (China, rice, 1961 and 2003)	high confidence on positive effects of CO2 fertilization
Australasia	Observations Wheat: Australian wheat yields have increased steadily from 1900 to 1990 but growth appears to have stalled since 1990 with no significant trend from 1990 to 2015. Based on census data from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached 60% of harvested areas, the crop for which Australia belongs to the top ten producers. Maize, soy, rice: Inconsistent trends or missing information.	(Iizumi et al., 2018b) Hochman et al. (2017); (Ray et al., 2012)	medium confidence in negative wheat trends, low confidence in trends of the other crops
	Attribution Maize: A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by pre-industrial climate show mixed effects of historical anthropogenic climate change and CO2 fertilization on 1981-2010 maize yields in Australia and mostly positive impacts in New Zealand. A slight purely climate induced reduction was found in an empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends (1.2%).	(Iizumi et al., 2018b) (process-based, 1980-2010); Ray et al. (2019)	inconsistent estimates of climate induced trends in wheat yields and mostly minor effects on other crops (e.g. maize), low confidence (*).
	Rice: A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by pre-industrial climate show mostly nonsignificant changes in 1981-2010 rice yields induced by climate change and CO2 fertilization. A purely climate induced increase in rice yields was found in an empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends (4.1% increase with respect to the 1974-2008 average).:(Iizumi et al., 2018b) (process-based, 1980-2010); Ray et al. (2019) (empirical, 1974-2008)	(Iizumi et al., 2018b) (process-based, 1980-2010), Ray et al. (2019) (empirical, 1974-2008)
	Wheat: A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by pre-industrial climate show mostly nonsignificant changes in 1981-2010 wheat yields induced by climate change and CO2 fertilization. Some negative effects in Northern Australia and positive effects in Southern Australia and New Zealand. A purely climate induced reduction in wheat yields was found in an empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends (5.8% loss with respect to the 1974-2008 average). Empirical modelling supports that climate trends increased wheat yields in four subregions of New South Wales by 8.5 to 21.2% from 1922 to 2000. Highly detailed process-based crop model simulations forced by station based daily weather data at 50 sites representative for Australia’s agro-ecological zones and of soil types in the national grain zone indicate a strong decline in water-limited yield potential by 27% from 1990 to 2015. This is mainly driven by trends in rainfall that dominate over negative effects of increasing daily maximum temperatures. The decline in potential yields does not translate into declines in actual yields that tripled from 1990 to 2015 mostly driven by technological progress closing the gap between potential and actual yields from 40% in 1990 to 55% in 2015.	(Iizumi et al., 2018b) (process-based, 1980-2010) Wang et al. (2015) (New South Wales, 1922 to 2000), Hochman et al. (2017) (potential water limited wheat yields); Ray et al. (2019) (empirical, 1974-2008)
	Soy: A comparison of process-based crop model simulations forced by simulated historical climate versus simulations forced by pre-industrial climate show mostly nonsignificant changes in 1981-2010 soy yields induced by climate change and CO2 fertilization. Some negative effects in Northern Australia and positive effects in Southern Australia. A purely climate induced reduction in soy yields was found in an empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends (6.3% loss with respect to the 1974-2008 average).	(Iizumi et al., 2018b) (process-based, 1980-2010), Ray et al. (2019) (empirical, 1974-2008)
	Others: An empirical study comparing 1974-2008 yields to yields under counterfactual climate conditions not accounting for the recent 1974-2008 trends has found largest yield losses induced by climate change for Sorghum (~30.5%) and largest climate induced gains for rice (~4%).
	Impact of CO2 fertilization: The effect of CO2 fertilization is estimated to have prevented a further 4% reduction in potential wheat yields relative to 1990 yields over 1990-2015.	Hochman et al. (2017)
Central and South America	Observations General increase in maize, wheat, soy and rice yields in South America. Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached 19% (Brazil) and 5% (Argentina) of the harvested area of maize; 21% (Brazil) of the harvested areas of rice; 14% (Brazil), 12% (Argentina), 98% (Paraguay), and nearly 100% (Bolivia) of the harvested area of soy. Results are only listed for the crops (out of maize, wheat, rice and soy) for which the countries belong to the top ten producers.	(Iizumi et al., 2018b) (The PLOS ONE Staff, 2017), (Ray et al., 2012)
	Attribution Impacts of climate change: Maize: Estimates of climate induced changes in maize yields in Brazil range from about 10% reduction in 1980-2010 yields derived from process-based simulations forced by simulated historical versus pre-industrial climate; a purely climate induced gain of about 6% derived from an empirical model forced by climate data accounting for 1974-2008 trends and counterfactual data not accounting for the trends (2.7% gain for the Caribbean and South America as a whole); and an empirically derived climate induced loss of 1980-2008 yields about 7.5% in Brazil. An empirical study of 33 counties in the Pampas region of Argentina indicates climate induced yield losses of 5.4% compared to a situation without trends in climate from 1971-2012.	(Iizumi et al., 2018b) (1980-2010), Ray et al. (2019) (1974-2008), (Lobell et al., 2011) (1980-2008), Verón et al. (2015) (county level wheat, maize and soy yields at the county level in the Pampas region of Argentina, 1971 - 2012)	moderate mostly negative impacts of climate change on wheat yields, medium confidence (**), inconsistent findings or inconclusive findings for other maize, soy and rice.
	Rice: The comparison of 1980-2010 yields derived from process-based simulations forced by simulated historical versus pre-industrial climate indicates primarily positive combined impacts of climate change and CO2 fertilization on rice yields. A minor purely climate induced loss of about 1% derived from an empirical model forced by climate data accounting for 1974-2008 trends and counterfactual data not accounting for the trends.		moderate mostly negative impacts of climate change on wheat yields, medium confidence (**), inconsistent findings or inconclusive findings for other maize, soy and rice.
	Wheat: The comparison of 1980-2010 yields derived from process-based simulations forced by simulated historical versus pre-industrial climate indicates primarily negative impacts of climate change on wheat yields even though additionally accounting for the effect of CO2 fertilization. An empirical model also indicates a climate induced reduction in wheat yields (1.6%) in South America + Caribbean induced by climate change from 1974-2008. An empirical study of 33 counties in the Pampas region of Argentina indicates climate induced yield losses of 5.1% compared to a situation without trends in climate from 1971-2012.	moderate mostly negative impacts of climate change on wheat yields, medium confidence (**), inconsistent findings or inconclusive findings for other maize, soy and rice.
	Soy: The comparison of process-based simulations forced by simulated historical climate and simulated pre-industrial climate indicate a purely climate-induced reduction of 1980-2010 yields of 5-10% in Brazil and Argentina and even slightly stronger losses in Paraguay. In contrast, empirical models indicate a 5% gain in soy yields in South America + Caribbean induced by climate change from 1974-2008 and gains (~2.5%) in soy yields in Argentina (1980-2008), while an empirical study of 33 counties in the Pampas region of Argentina indicates climate induced yield losses of 2.6% compared to a situation without trends in climate from 1971-2012.		moderate mostly negative impacts of climate change on wheat yields, medium confidence (**), inconsistent findings or inconclusive findings for other maize, soy and rice.
	Impacts of CO2 fertilization: Process-based CO2 fertilization is estimated to have increased 2002-2006 soy yields in Brazil by 7.57% compared to assuming 1980 levels of CO2. Positive effects of a similar order are supported by process-based crop model simulations.	Sakurai et al. (2014)
Europe	Observations Wheat: After decades of increasing yields wheat yields have reached a plateau in northwest Europe. Over the time period from 1961-2014 wheat yields seem to have reached a plateau in Germany, France and the UK while being affected by a deceleration in Belgium. Inconstrast yields increased linearly in Spain and Italy. Based on census data from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached about 80% of harvested areas in France and Germany, both belonging to the top ten producers globally. Maize: In aggregate maize yields seem to be increasing (e.g. linear increases in Germany and Spain, 1961-2014) but may have reached a plateau in Italy and Belgium and be affected by deceleration in France (1961-2014). Based on census data from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached 10.5% of harvested areas in France and 59.2% in Italy, both countries belonging to the top ten producers globally.	(Grassini et al., 2013), (Iizumi et al., 2018b) (The PLOS ONE Staff, 2017), Agnolucci and De Lipsis (2019) (wheat and maize)	high confidence
	Attribution Impacts of climate change:Maize: Estimated impacts of observed climate change on maize yields are still inconsistent. The comparison of process-based simulations forced by simulated historical climate and simulated pre-industrial climate indicate a purely climate-induced increase of 1981-2010 yields of ~10% in France and even more than 15% when additionally accounting for CO2 fertilization. The same study estimates a primarily positive combined effect of climate change and CO2 fertilization on 1981-2010 in most of Europe except for negative impacts in the southern Mediterranean region. These findings are in line with an empirical approach indicating that in Italy about 20% of the long-run increase in maize yields over time (1961-2014) has been cancelled out by the adverse impact of weather (about 5% in Spain). While climate change has had a positive contribution on the long term-trends in Belgium (1.6%), France (5%), and Germany (3.2%). Additional support for the general spatial pattern and the direction of the impact of climate change is provided by another empirical study of 1989-2009 crop yields indicating a minor positive effect of climate change on overall European maize yields (0.3%) but significant negative impacts on yields in Spain, Italy, and Portugal. However, another empirical study finds purely climate induced reductions in 1974-2008 maize yields of ~6% in Wester + Southern Europe and ~25% in Eastern + Northern Europe. Wheat: Estimated impacts of observed climate change on wheat yields are still inconsistent. The comparison of process-based simulations forced by simulated historical climate and simulated pre-industrial climate indicate a purely climate-induced increase of 1981-2010 yields of ~10% in France and even more than 15% when additionally accounting for CO2 fertilization. Accounting for climate change and CO2 fertilization the same study finds mostly insignificant or positive impacts on 1981-2010 wheat yields in most of Europe. These findings are in contrast to an empirical approach indicating that in Germany about 30% of the long-run increase in wheat yields over time (1961-2014) has been cancelled out by the adverse impact of weather (about 9% in Belgium, 20% in France, 11% in Italy, and 7% in Spain). In contrast, climate change is estimated to have increased the UK's long-run growth rate by two thirds. Large scale negative impacts of climate change on European wheat yields are additionally supported by another empirical study of 1989-2009 crop yields (2.5% reduction of recent yields compared to a situation without climate change) and an empirical study of 1974-2008 wheat yields indicating a purely climate induced reduction of yields in Wester + Southern Europe (~9%) and Eastern + Northern Europe (2.1%). Rice: Mostly positive combined impact of climate change and CO2 fertilization derived from process-based model simulations forced by simulated historical climate and simulated pre-industrial climate (1981-2010). Empirically derived negative impacts (3.2% purely climate induced reduction of 1974-2008 yields in Western + Southern Europe, minor 0.4% reduction in Eastern + Northern Europe). Soy: Mostly positive combined impact of climate change and CO2 fertilization derived from process-based model simulations forced by simulated historical climate and simulated pre-industrial climate (1981-2010). Empirically derived negative impacts (21% purely climate induced reduction of 1974-2008 yields in Western + Southern Europe, 3.8% reduction in Eastern + Northern Europe).	Moore and Lobell (2015) (wheat, maize, barley, and sugar beet, 1989-2009, empirical), Ray et al. (2019) (empirical, 1974-2008), (Iizumi et al., 2018b) (process-based building on simulated historical versus pre-industrial climate, 1981-2010), Agnolucci and De Lipsis (2019) (empirical analysis of wheat and maize yields, 1961-2014)	mixed impacts of climate change across different crop types ranging from negative impacts on overall wheat yields and primarily positive but weaker effects on maize yields, medium confidence (*) because of limited agreement of studies. Low consistency of findings and limited evidence for other crops
	Others: Empirical analysis indicates that long-term temperature and precipitation trends since 1989 have reduced continent-wide barley yields by 3.8%, i.e. climate change can explain 10% of the observed slowdown in barley yields. Negative impacts on barley yields supported by an additional empirical study (16% purely climate induced reduction of 1974-2008 yields in Western + Southern Europe, 9% reduction in Eastern + Northern Europe). In addition, climate change is estimated to have slightly increased sugar beet yields (would have been 0.2% lower without trend in growing season temperature and precipitation).	(Moore and Lobell, 2015) (barley, and sugar beet, 1989-2009, empirical) Ray et al. (2019) (empirical, 1974-2008)
	Impacts of CO2 fertilization: Based on process-based model simulation the increase in CO2 from pre-industrial levels to 1980-2010 has led to an increase in maize and wheat yields in France by 5-10%.	(Iizumi et al., 2018b) (process-based building on simulated historical versus pre-industrial climate)
North America	Observations General increase in maize, wheat, soy and rice yields in North America. For example, maize yields have increased by 1.28 tonnes/ha per decade across the Midwest since 1981. However, regionally yields also show a stagnation or decline. US: In the period 1936-2009 yields for maize, wheat, rice, soy, barley, and oats grew by more than 1.0% per year, with particularly high growth rate of maize yields (2.99% yr-1). However, growth rates appear to have slowed down. i.e. higher rates in 1936-1990 (1.81% yr-1, average across six crops) than in 1990-2009 (1.17% yr-1). The slowdown is strongest for maize (from 3.43% yr-1 to 1.75% yr-1), wheat (from 2.09% yr-1 to 0.62% yr-1), barley (from 2.14% yr-1 to 1.39% yr-1), and oats (1.73% yr-1 to 0.62% yr-1) and less severe for rice (from 1.64% yr-1 to 1.31% yr-1) and soy (from to 1.61% yr-1 to 1.34% yr-1). Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase” in yields are estimated to have reached 7.6% (maize), 36% (wheat) and 9% (soy) of harvested areas, the three crops for which the US belongs to the top ten producers. Canada: While yields are mostly increasing they seem to have reached a plateau or show a decline in some areas in recent years. Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached 0.8% (wheat) and 0% (soy), the two crops for which Canada belongs to the top ten producers. Mexico: While yields are mostly increasing they seem to have reached a plateau or show a decline in some areas in recent years. Based on census data for maize, wheat, rice and soy from 1961-2008 areas affected by “no (further) increase in yields” are estimated to have reached 31% (maize), the crop for which Mexico belongs to the top ten producers.	Butler et al. (2018) Iizumi et al. (2018b) Ray et al. (2019) Ray et al. (2012); Andersen et al. (2018)	High confidence in increasing wheat, maize, rice and soy yields (high agreement between individual data sets)
	Attribution Impacts of climate change vary from region to region and across crops (see individual assessments below).	Ray et al. (2019) (all empirical); Iizumi et al. (2018b) (process-based)	mixed estimated impacts of climate change on yields across different crops and regions ranging from positive effects (e.g. maize in the US) to moderate negative effects (e.g. wheat in the US), low confidence (*) because of divergent estimates in the US and only limited evidence elsewhere
	US: There is no clear attribution of the observed slow down of yields from 1936-1990 to 1990-2009 as climate attribution studies mostly only cover the recent decades from about 1980 and partly focus on specific regions rather than the US as a whole.	Iizumi et al. (2018b) (wheat, maize, rice, soy, based on simulated historical climate compared to pre-industrial climate, process-based)
	Maize: Weather conditions have affected maize yields by i) increasing mean temperatures and decreased exposure to extreme heat assuming fixed growing seasons and ii) allowing for adjustments of growing seasons and prolonged grain filling phases. The combination of both effects is estimated to have contributed about 25-28% to the observed positive trend in maize yield (1980-2017), with about equal contributions of both components. Positive effects of an expansion of the grain filling period (by 0.37 days from 2000 to 2015 and probably induced by variety renewal) are supported by an independent study of county-level data and are estimated to account for roughly one-quarter (23%) of the positive trend in yields reported for 2000-2015. The remaining positive trend may be partly due to CO2 fertilization and technological progress, in particular the adoption of genetically engineered varieties after 1996. In the Midwestern states the latter effect is estimated to have increased growth rates of maize from 0.94% yr-1 before 1996 to 1.59% yr-1 afterwards. The positive effect of observed changes in weather conditions assuming fixed growing seasons is partly due to reduced exposure to extremely high temperatures. Only minor climate induced gains in maize yields (0.1% wrt current levels, 1974-2008) found by an independent empirical study may be due to not accounting for extreme temperatures. Process-based simulations based on simulated historical climate only accounting for anthropogenic emissions of climate forcers do not account for indirect effects of agricultural management on temperatures and show a purely climate driven reduction in 1981-2010 US maize yields by about 5% compared to pre-industrial climate conditions.	Butler et al. (2018) (empirical, combined effect of changes of weather within growing seasons and growing season adjustments 1980-2017), Zhu et al. (2019) (effect of expansion of grain filling period, maize, 2000-2015); Ortiz-Bobea and Tack (2018) (estimated yield increase induced by technological development, maize, county-level data, 1981-2015), Iizumi et al. (2018b) (wheat, maize, rice, soy, based on simulated historical climate compared to pre-industrial climate, process-based, 1981-2010), Ray et al. (2019) (10 crops, empirical, 1974-2008)		Maize: While the favourable increase in growing season average temperature is assumed to be due to anthropogenic emissions of climate forcers, the reduction in exposure to extreme heat is assumed to be directly induced by increased evapotranspiration driven by agricultural management changes.
	Wheat: Based on a simple statistical model accounting for annual and growing season average temperature and precipitation variations, wheat yields are estimated to have been reduced by climate change (1.4% w.r.t. current levels, 1974-2008). Process-based simulations forced by simulated historical climate and simulated pre-industrial climate conditions indicate a loss of about 7% of 1981-2010 yields induced by anthropogenic climate forcing not accounting for CO2 fertilization.	Ray et al. (2019) (empirical, 1974-2008); (Iizumi et al., 2018b) (based on simulated historical climate compared to pre-industrial climate, process-based, 1981-2010)
	Soy: Based on a simple statistical model accounting for annual and growing season average temperature and precipitations variations, soybean yields are estimated to have increased due to climate change (+3.7% w.r.t current levels, 1974-2008); accounting for within-season variations of temperature and precipitation, climate change is estimated to have had a negative effect on trends in soy yields from 1994-2013 (without climate change soybean yield trends might have been 30% higher than the observed one); process-based crop model simulations support a negative effect of simulated historical climate change on observed soy yields (1980-2010 average yields accounting for climate change are about 6% smaller than yields derived from pre-industrial climate conditions not accounting for CO2 fertilization).	Ray et al. (2019) (empirical, 1974-2008); Mourtzinis et al. (2015) (soy, empirical, 1994- 2013); (Iizumi et al., 2018b) (based on simulated historical climate compared to pre-industrial climate, process-based, 1981-2010)
	Rice: Based on a statistical model accounting for annual and growing season average variations in temperature and precipitation, rice yields have been reduced (minor effect) by climate change (0.3% w.r.t. current levels, 1974-2008). Mostly insignificant effects of climate change + CO2 fertilizations on 1981-2010 yields also found in process-based crop model simulations forced by simulated historical climate and simulated pre-industrial climate.	Ray et al. (2019) (empirical, 1974-2008); (Iizumi et al., 2018b) (based on simulated historical climate compared to pre-industrial climate, process-based, 1981-2010)
	Others: Empirical estimates indicate that climate change from 1974 to 2008 has reduced yields of barley (2.8%), and increased yields of sorghum (12.7%) and sugarcane (6.5%).	Ray et al. (2019) (empirical, 1974-2008);
	Effects of increasing atmospheric CO2 concentrations: Maize: Process-based model simulations indicate that the effect of CO2 fertilization may have compensated for about half of the simulated historical climate induced reduction of 1981-2010 maize yields. Wheat: Process-based model simulations indicate that the effect of CO2 fertilization may have compensated for the simulated climate induced reduction of 1981-2010 wheat yields. Soy: CO2 fertilization is estimated to have increased 2002-2006 US soy yields by 4.34% compared to assuming 1980 levels of CO2. Process-based model simulations indicate that the effect may have compensated for the climate induced reduction of 1981-2010 soy yields.	Sakurai et al. (2014) (effect of CO2 fertilization on 2002-2006 soy yields), (Iizumi et al., 2018b) (wheat, maize, rice, soy, based on simulated historical climate compared to pre-industrial climate, process-based)
	Canada: Based on a statistical model accounting for annual and growing season average temperature and precipitation variations the following impacts of climate change on 1974-2008 yields are estimated: maize: +6.0%, wheat: -1.5%, barley: -5.1%, rapeseed: -0.7%.	Ray et al. (2019) (empirical, 1974-2008)
	Mexico: Based on a statistical model accounting for annual and growing season average temperature and precipitation variations the following impacts of climate change on 1974-2008 yields are estimated: maize: +4.9%, wheat: -8.5%, rice: +5.1%, soy: +19.1%, barley: +5.6%, oilpalm: +34.3%, sorghum: -2.2%, sugarcane: +7%.	Ray et al. (2019) (10 crops, empirical);
Small Islands	Observations missing studies
Small Islands	Attribution missing studies		no assessment
	S19 Food system - Food prices
Global	Observations
Global	Attribution There is no attribution of changes in food prices to climate change but only a ‘detection of weather sensitivity’ (see Table 16.3)		no assessment
	S20 Food system - Malnutrition
Global	Observations
Global	Attribution There is no attribution of changes in malnutrition to climate change but only a ‘detection of weather sensitivity’ (see part 3 of this table)		no assessment
	S28 Other societal impacts - Heat-related mortality
Global	Observations Heat-related mortality: Most evidence on temporal trends in heat-related excess mortality stems from developed countries in North America, Europe, Australasia and East Asia. Majority of studies find that attributable fractions (percent deaths attributable to heat exposure out of total deaths) have declined over recent decades, with notable exceptions in countries where unprecedented heat waves occurred recently. Studies considering the entire 20th century mostly find declining trends in heat-related excess mortality over time, with some indication of a slowing trend in more recent decades. Cold-related mortality: Inconclusive evidence on temporal trends in the fraction of deaths associated with cold exposure, with only few locations (e.g., in Asia, and Australasia) showing decreasing trends, and several countries/regions reporting no or even increasing trends in cold-related mortality fractions.	Arbuthnott et al. (2016) (review); Kinney (2018) (review; heat); Sheridan and Allen (2018) (review); Vicedo-Cabrera et al. (2018) (multi-country assessment)	Trends in the overall heat and cold death burden are determined by shifts in the temperature distributions, changes in the susceptibility to heat and cold, and demographic parameters (population, age structure). If climate change was the sole driver of changes in temperature-related excess mortality one would have expected rising heat-related excess mortality and declining cold-related excess mortality over recent decades.
Global	Attribution Heat-related mortality: It is generally understood that alterations in the vulnerability to heat have dominated the temporal change in the fraction (or number) of deaths associated with heat exposure. Thus, where no or decreasing trends in heat-related excess mortality have been observed, decreases in vulnerability have outpaced the impact of climate change, which alone would have caused an increase in heat-related excess mortality. Empirical temperature-mortality relationships show that a significant relative risk persists at high temperatures despite reductions in vulnerabilities. Independently, studies clearly indicate a contribution of GHG emission to increasing temperatures and rising frequency and intensity of heat waves (see “heat waves” in part 1 of the table). This implies that the observed heat-related excess mortality would have been much lower without climate change. Accordingly, a recent study estimates around 37.0% (range 20.5 to 76.3%) of the average heat-related excess mortality in 1991-2018 across 43 countries to be attributable to anthropogenic climate change. Cold-related mortality: No existing evidence in a multi-country setting	Vicedo-Cabrera et al. (2018) (trends, cold and heat, 10 countries); Vicedo-Cabrera et al. (2021) (heat, 732 locations in 43 countries); Sera et al. (2021) (multi-country assessment; heat)	minor to strong impact of climate change on fractions (number) of deaths associated with heat exposure, medium confidence (**)
Africa	Observations No evidence on temporal trends in heat- and cold-related excess mortality in Africa. Regarding average temperature-mortality associations estimated for African countries see entries on weather sensitivity in part 3 of the table.
Africa	Attribution Heat-related mortality: A recent study finds that 43.8% of current heat-related mortality (1991-2018) in South Africa can be attributed to human-induced climate change. Cold-related mortality: No conclusive evidence.	Vicedo-Cabrera et al. (2021) (South Africa)	strong increase in heat related-mortality due to climate change, low confidence (*)
Asia	Observations Heat-related mortality - temporal trends: Studies on trends in heat induced excess mortality only exist for East Asian countries where they generally indicate reductions in mortality fractions/heat risks over time, albeit one study finds recent increases in heat-related excess mortality in Japan. Cold-related mortality - temporal trends: Mixed evidence on trends in susceptibility to cold exposure/cold attributable mortality fractions in East Asia, with a tendency of observing declining cold-related excess mortality in most locations.	Lee et al. (2018) (South Korea, Japan, Taiwan Province of China), Vicedo-Cabrera et al. (2018) (Japan, South Korea)
	Heat-related mortality - individual extreme events: Upwards of 2200 excess deaths are estimated to have occurred in the Indian heatwave 2015 and at least 700 alone in the megacity of Karachi during the Pakistani heatwave of the same year.	Masood et al. (2015) Ratnam et al. (2016)
	Attribution Heat-related mortality: Studies on the vulnerability towards heat tend to show reductions over recent decades.. However, in general, sensitivity of mortality to heat does not disappear over time. In combination with the observed increase in probability and intensity of heatwaves (see part 1 of this table) these results imply that climate change has induced excess mortality. The only existing quantitative attribution study including data on Asia finds that the proportion of heat-related excess mortality (averaged over 1991-2018) attributable to human-induced climate change is on the order of 21.3% (China) to 67.7% (Kuwait) across the 9 Asian countries studied. With regard to specific extreme events, deadly heat waves in India and Pakistan in 2015 were found to be exacerbated by anthropogenic climate change (see part 1 of the Table). Thus, their impacts in terms of excess mortality are considered attributable to anthropogenic climate change, too. Cold-related mortality: No conclusive evidence	Gasparrini et al. (2015a) (South Korea, Japan); Chung et al. (2018) (Japan); Kim et al. (2019) (South Korea); Chung et al. (2017) (South Korea, Japan, Taiwan); Vicedo-Cabrera et al. (2021) (only heat; 1991-2018; Iran, Kuwait, South Korea, Japan, China, Thailand, Vietnam, Philippines, Taiwan); Vicedo-Cabrera et al. (2018) (Japan, South Korea);	moderate to strong impacts of climate change on heat-related mortality, low confidence (*)
Australasia	Observations Heat-related mortality: Evidence for decreasing heat-associated deaths during the course of the 20th century. By contrast, over recent decades heat-related mortality fractions tend to increase, at least in major Australian cities. In Australia, during 1987-2016, natural disasters caused an estimated 971 deaths, of which more than 50% were associated with heatwaves in cities. Cold-related mortality: Limited evidence points to decreasing trends in cold-related mortality fractions since the late 1980s in Australia	Coates et al. (2014) (Australia); Gasparrini et al. (2015b) (3 Australian cities); Vicedo-Cabrera et al. (2018) (3 Australian cities); Deloitte (2017)
	Attribution Heat-related mortality: Qualitative evidence that increasing temperatures have contributed to the trend of increasing heat-related mortality fractions in Australian cities. The impact of climate change may even have been amplified by increasing sensitivities. A recent attribution study suggests that between 1991 and 2018, 35-36% of average heat-related mortality in Brisbane, Sydney and Melbourne was attributable to climate change, amounting to about 106 deaths a year on average	Vicedo-Cabrera et al. (2018); Vicedo-Cabrera et al. (2021)	moderate to strong impact of climate change on heat related excess mortality, medium confidence (**)
	Cold-related mortality: Increase in the ratio of summer versus winter death over 40 years can be related to rising temperatures due to climate change. Evidence suggests that the shift in ratio is largely due to decreasing winter deaths. However, studies indicate a non-climate related decrease in susceptibility to cold that makes it difficult to quantify the pure climate induced contribution to the effect.	Bennett et al. (2014)	unquantified decrease in cold related mortality induced by climate change, low confidence (*)
Central and South America	Observations Only evidence on mortality trends stems from Brazil during 1996-2011: Heat-related mortality: No temporal trend in heat-attributable mortality fractions. Cold-related mortality: Decreasing trend in cold-related mortality fractions.	Vicedo-Cabrera et al. (2018) (Brazil)
Central and South America	Attribution Heat-related mortality: Between approximately 20.5% (Argentina) and 76.6% (Ecuador) of current heat-related excess mortality (1991-2018) across 13 Latin American countries is estimated to be attributable to human-induced climate change. No conclusive evidence regarding attribution of temporal trends. Cold-related mortality: No conclusive evidence.	Vicedo-Cabrera et al. (2021) (Guatemala, Mexico, Panama, Puerto Rico, Colombia, Paraguay, Costa Rica, Peru, Ecuador, Chile, Uruguay, Brazil, Argentina)	moderate to strong impact of climate change on heat related excess mortality, low confidence (*)
Europe	Observations Heat-related mortality - temporal trends: Evidence on decreasing trends in heat attributable mortality during recent decades (since approximately 1980s/1990s) in most European countries, with some exceptions. Tendency of southern, warmer locations showing decreasing trends and northern, cooler locations showing increasing trends in heat-related excess mortality. Notable differences in trends exist between cause-specific mortality categories (all-cause, cardiovascular, respiratory), sex and age groups. Studies investigating heat-related excess mortality since the early 20th century in Europe generally find more pronounced decreasing trends than studies focusing on recent decades. Cold-related mortality temporal trends: Mixed evidence on trends in cold-attributable mortality fractions over time, some countries showing decreasing trends, other countries stable or even increasing trends.	Arbuthnott et al. (2016) (review; citing studies from UK, Sweden, Netherlands, Austria, Czech Republic, France), De’Donato et al. (2015) (only heat; 9 European cities), Achebak et al. (2018); Achebak et al. (2019) (Spain), Vicedo-Cabrera et al. 2018 (Switzerland, Spain, UK, Ireland), Åström et al. (2018) (Sweden); Díaz et al. (2019) (only cold, Spain)
	Heat-related mortality - extreme events: Significant heat-related mortality (with up to 70,000 excess deaths) observed during 2003 European summer heat wave and 11,000 excess deaths in Moscow alone reported during 2010 Russian heat wave.	Robine et al. (2008) D'Ippoliti et al. (2010); Schewe et al. (2019) Muthers et al. (2017) Shaposhnikov et al. (2014)
	Attribution Heat-related mortality: Formal detection and attribution studies suggest that rising temperatures from climate change alone would have increased heat-related mortality during recent decades. However, where no or decreasing trends in heat-related mortality over time have been observed, decreasing susceptibility to heat have outweighed the impacts of climate change. Considering the average heat-related mortality in 1991-2018, a recent study finds that the proportion of heat-related deaths attributable to human-induced climate change is on the order of 20% to 45% across 17 European countries studied. This is in accordance with another study from Sweden which finds that mortality related to heat extremes in the period 1980-2009 in Stockholm has doubled compared to what would have been observed under the climate of the early 20th century. Another study looking at the 2003 summer heat wave in London and Paris found that 20% and 70%, respectively, of the heat-related excess summer deaths are attributable to anthropogenic climate change. For the UK, it has been estimated that around 50% of the excess deaths registered during the 2003 and 2018 heat wave can be attributed to man-made climate change. The contribution of anthropogenic climate change to the European summer heat wave 2003 and the Russian heat wave 2010 has been firmly established (see part 1 of this Table). Therefore, at least part of the associated heat-related excess mortality is attributable to climate change.	Christidis et al. (2010) (1976-2005; UK); Vicedo-Cabrera et al. (2021) (only heat; 1991-2018; 17 European countries), Åström et al. (2013) (1980-2009, Sweden); Mitchell et al. (2016) (2003 heat wave; UK, France); Clarke et al. (2021) (2003 and 2018 heat waves; UK)	moderate to strong impact of climate change on heat related excess mortality, medium confidence (**)
	Cold-related mortality: Very little research has addressed the contribution of climate change to observed temporal trends in cold-related mortality in a quantitative manner. One study finds that anthropogenic climate change has contributed to a decrease in cold-related mortality observed in the UK. Another study from Sweden finds increased mortality attributable to extreme cold due to a higher frequency of cold spells in recent decades compared to early 20th century climate.		Minor to moderate impact of climate change on cold-related mortality; contradictory in terms of adverse versus beneficial; low confidence (*)
North America	Observations Heat-related mortality: Consistent evidence for North America that heat-related excess mortality (and vulnerability to heat) has decreased over time, with strongest decline observed since the 1960s. Declining trends also found in Canada. Cold-related mortality: Mixed evidence on trends in cold-related excess mortality, with some studies finding no change and some even positive trends.	Gasparrini et al. (2015b); Kinney (2018) Petkova et al. (2014) Nordio et al. (2015); Barnett (2007)
North America	Attribution Heat-related mortality: Increasing prevalence of central air conditioning has been shown to explain some of the decline in heat-related excess mortality in the USA, yet not all studies agree on this finding. Changes in the susceptibility to heat have largely determined trends in heat-related mortality since the 1980s. However, the reduction of vulnerability does not mean that societies get insensitive to heat. Excess mortality is still observed at high temperatures, i.e. comparing the observed declining heat related-mortality to a counterfactual baseline where vulnerability to heat declines over time according to observations but climate does not change would still show a purely climate driven change in heat-related mortality. Accordingly, in a study that considers average vulnerability during recent decades(1991-2018) 34.7% (USA) and 38.5% (Canada) of present-day heat-related excess mortality have been attributed to human-induced climate change. Cold-related mortality: No conclusive evidence	Barreca et al. (2016) Bobb et al. (2014) Vicedo-Cabrera et al. (2018); (Vicedo-Cabrera et al., 2021) Sera et al. (2021)	moderate impact of climate change on heat related excess mortality, medium confidence (**).
Small Islands	Observations No evidence on temporal trends in heat- and cold-related excess mortality in Small Island states. Regarding average temperature-mortality associations estimated for Small Islands see entries on weather sensitivity in part 3 of the table.
Small Islands	Attribution According to a recent study, 51.9% of heat-related mortality over 1991-2018 in the Caribbean island of Puerto Rico are attributable to human-induced climate change.	Vicedo-Cabrera et al. (2021)	Strong impact of climate change on heat related excess mortality, low confidence (*)
	S27 Other societal impacts - Vector-borne diseases
Global	Observations Dengue: Globally, dengue cases have increased over 8-fold during the last two decades.	WHO (2021); Zeng et al. (2021)
	Malaria: The incidence and endemicity of malaria overall is declining but is expanding in areas experiencing socio-political challenge and highland areas that were previously malaria-free.	Gething et al. (2010) Colón-González et al. (2021)
	Attribution Malaria: Expansion of malaria into highland areas is a result of warmer temperatures allowing for transmission at higher altitudes. Where malaria has declined, this has predominantly been driven by improved healthcare access, surveillance, control and treatment.	Watts et al. (2021); Feachem et al. (2019)	Climate change has increased malaria transmission in highland areas; medium confidence () Climate change has expanded the distribution of dengue transmission to more temperate latitudes; medium confidence ()	Malaria: Malaria is a disease caused by Plasmodium parasites, the most common being P. falciparum and P. vivax. Malaria is transmitted by infected female Anopheles mosquitoes. Dengue: Dengue fever is a disease caused by the dengue virus, and transmitted by Aedes mosquitoes. Both malaria and dengue are sensitive to climatic conditions, which influence various life-history traits of both the mosquito vectors and the malaria parasite and dengue virus. For example, the extrinsic incubation period (time taken for the parasite or virus to develop inside the mosquito) is influenced by temperature, as is mosquito development and longevity. In general, a combination of climate-associated expansion in the geographic range of vector species and non-climatic factors such as globalization, increased levels of international travel and trade, urbanization, poor environmental hygiene and ineffective vector control measures, has driven observed vector-borne disease trends.
	Dengue: Increases in dengue are mostly dominated by socio-economic changes (i.e. urbanisation, sanitation, travel, reporting, protective measures). Increased urbanization and population mobility are thought to be the main drivers of dengue expansion in the last 20 years. However, climate change is an important driver allowing for the observed expansion to higher latitudes. Climate suitability for the dengue mosquito vector increased by 15% between 1950-2018.	Ryan et al. (2019); Watts et al. (2021)
Africa	Observations Malaria: Although malaria cases have declined since the late 1990s, more of sub-Saharan Africa is experiencing year-round malaria transmission. In particular, there has been substantial geographical expansion of malaria in the East African highlands, with more frequent outbreaks in these areas.	Pascual et al. (2006) (malaria, highlands); Ryan et al.) (malaria, Africa)
	Dengue and chikungunya: Reported dengue and chikungunya cases have increased in sub-Saharan Africa. Between 1960-2017, more than 20 dengue epidemics were reported in over 20 African countries.	Tjaden et al. (2017) (chikungunya); Simo et al. (2019) (dengue, Africa); Amarasinghe et al. (2011) (dengue epidemics, Africa); Humphrey et al. (2016); Pabalan et al.)
	Attribution Malaria: Higher temperatures and changes in rainfall have shifted the distribution of malaria vectors in sub-Saharan Africa, allowing vectors to colonize at higher altitudes. Using data between 1950-2002, the warming trend in four highland sites was associated with an increase in incidence of malaria in the East African highlands since the end of the 1970s. There is also further evidence of malaria distribution shifting to higher altitudes in Ethiopia between 1993-2005, during warmer years. A study estimated that recent climate change (1990-2000) has contributed to an increase of more than 21% in the number of malaria cases across African countries, including Algeria, Malawi and the Central African Republic. Between 2015-2019 suitability for malaria transmission in highland areas was 38.7% higher in the African region compared to a 1950s baseline. Warming trends between 1950-2002 have been associated with increases in epidemic malaria in the African highlands in four sites in Kenya, Uganda, Burundi and Rwanda. Despite some controversy associated with the lack of high quality long-term data, and socioeconomic and biological factors, such as drug resistance that may have amplified malaria increases due to warming, there is medium confidence that climate change has increased the number of malaria cases in African highlands.	Pascual et al. (2006) (malaria, four highland sites 1950-2002); Siraj et al. (2014) (Ethiopia 1993-2005) Egbendewe-Mondzozo et al. (2011) (multiple African countries 1990-2000); Watts et al. (2021) (2015-2019 transmission suitability)	moderate contribution of climate change to the observed increase in malaria cases in African highlands; medium confidence (*); moderate contribution of climate change to the observed increase in dengue cases in Africa; low confidence ()	Warmer temperatures increase transmission of malaria by speeding up the development of Anopheles mosquitoes, and replication of the parasite inside the mosquito. Elevated temperatures as a result of climate change allows Anopheles species to colonize higher altitudes at the edges of their historical ranges. Rainfall provides an important Anopheles mosquito breeding habitat, in the form of stagnant pools of water that allow for the aquatic lifecycle stage to be completed.
	Dengue: Urbanisation and globalisation (increased mobility) are the dominant drivers of the observed increase in dengue epidemics in sub-Saharan Africa but climate change has added to this positive trend by expanding suitable conditions for the vector.	Tong et al. (2021) (dengue, African region)		Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance. Urbanization and population density increase the abundance of arbovirus vectors that breed in artificial containers inside or near to urban dwellings.
Asia	Observations Dengue: Incidence has risen dramatically in southeast Asia (46% increase in cases 2015-2019), with high increases in urban areas and recent spread to rural areas. In Singapore dengue cases between 1978-1999 increased 10-fold from 384 to 5,285	WHO (2008); WHO (2021) (dengue, Singapore)
	Malaria and Japanese encephalitis: Other vector-borne diseases, including malaria and Japanese encephalitis have expanded to non-endemic mountainous areas. There has been an increase in the number of seasonal malaria epidemics observed in highland areas of Nepal with an increase in the proportion of Plasmodium falciparum malaria cases recorded. However, in southeast Asia between 2000-2017 cases declined by 8%.	Dhimal et al. (2015) Dhimal et al. (2014) Battle et al. (2019)
	Attribution Dengue: The expansion of dengue is predominantly a result of rapid urbanization and increases in population density, but may have been facilitated by warming temperatures that allow for year-round suitability in transmission. However, consistent evidence linking climate change and dengue outbreaks in Asia is lacking. Outbreak risk of mosquito-borne disease, including dengue in southeast Asia was shown to peak at the highest monthly temperatures of 33.5°C. Due to climate change, these high monthly temperatures now occur in previously colder areas, allowing dengue transmission to shift polewards. In Singapore, increases in dengue cases between 1978-1999 have been attributed to warming annual temperatures, which increased by 1.5°C during the same period.	Servadio et al. (2018) (dengue, southeast Asia); WHO (2008) (dengue, Singapore)	minor increase in dengue in southeast Asia induced by climate change; low confidence () moderate increase in malaria at higher altitudes due to climate change; medium confidence (*)	Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance. Urbanization and population density increase the abundance of arbovirus vectors that breed in artificial containers inside or near to urban dwellings.
	Malaria: The increasing number of warmer days and rising temperature trends observed at higher altitudes of Nepal allow malaria vectors to persist, which has contributed to increased malaria outbreaks. Using data for 1999-2008, mean annual temperature increases were correlated with an increase in malaria cases in an endemic district of Nepal.	Bhandari et al. (2013) Dhimal et al. (2014) (malaria, Nepal)
Australasia	Observations Dengue: Arbovirus transmission including dengue has shifted further south and outbreaks are becoming more common.	Ryan et al. (2019) Hanna et al.)
	Chikungunya: Outbreaks of vector-borne diseases are becoming more common in Australia. For example, chikungunya cases in northern Australia have increased between 2008-2017, with the largest number of annual imported cases of 134 occuring in 2013.	Tjaden et al. (2017) Huang et al. (2019)
	West Nile Fever: West Nile outbreaks are becoming more common in Australia, particularly between 2009-2011 and West Nile mosquito vectors are expanding into southern regions.	Prow (2013)
	Attribution Chikungunya: Evidence shows that increasing temperatures have shifted the mosquito vector further south in Australia and have expanded its range. Increasing trends in arboviruses can be explained by a 13.7% increase in vectorial capacity of the Aedes aegypti mosquito from 1950s to 2016.	Hill et al. (2014) Zhang et al. (2018b)	Moderate contribution of climate change to increase in dengue, chikungunya and West Nile fever vectors (Aedes and Culex) in Southern Australia; low confidence (*)	Chikungunya: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. The range limits of the Aedes vector are being expanded as a result of increasinging climate suitability in areas previously inhospitable.
	West Nile Fever: Peak and total Culex abundance has changed due to warming temperatures, with the peak occurring earlier in the year with populations maintained for longer, increasing the risk of West Nile transmission. Extensive flooding has also been shown to promote the Culex mosquito life cycle. Climate change and warmer temperatures have shifted the distribution of arbovirus vectors to cooler southern regions of Australia, where summer temperatures are more suitable for West Nile virus amplification.	Frost et al. (2012); Prow (2013) Paz (2015)		West Nile Fever: Temperature speeds up the vectorial capacity of Culex mosquitoes as well as viral replication of West Nile virus. Abundance of Culex mosquitoes is sensitive to rainfall, with large amounts flushing out habitats and drought conditions bringing hosts and mosquito vectors into close contact due to water storage practices.
Central and South America	Observations Dengue: Over the last four decades, the incidence of dengue has increased from 1.5 million cumulative cases between 1980-1989 to 16.2 million between 2010-2019, with an estimated 500 million people in the Americas now at risk. Dengue epidemics are also increasing in frequency and magnitude, the transmission season has lengthened and dengue has expanded into more temperate areas at the southern fringes. There is evidence that mosquito vectors of dengue have expanded into higher altitude regions of Mexico.	Robert et al. (2019) PAHO: Puntasecca et al. (2021), (Equihua et al., 2017) (Mexico)
	Malaria: The malaria transmission season has lengthened and expanded into higher altitude areas in Colombia. In Venezuela and neighbouring countries malaria incidence has dramatically increased.	Siraj et al. (2014) (malaria, Colombia); Grillet et al. (2019) (malaria, Venezuela), WHO (2020)
	Mayaro: Outbreaks of Mayaro virus in urban areas are increasing in frequency across Latin and South America and in particular in central and northern Brazil.	Caicedo et al. (2021) Esposito and Fonseca) Acosta-Ampudia et al. (2018)
	Attribution Dengue and other arboviruses: The expansion of the primary dengue vector Aedes aegypti into temperate areas and to higher elevations, such as South Brazil, northern Argentina and Mexico, is a result of warmer temperatures that favour establishment and increased transmission.	Barcellos and Lowe (2014) (dengue, Brazil); Robert et al. (2019) (dengue, Argentina); Lozano-Fuentes et al. (2012) (dengue, México)	moderate contribution of climate change to the observed increased dengue cases, medium confidence (); moderate contribution of climate change to the increasing number of malaria cases in higher altitudes, medium confidence ()	Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
	Zika: The rapid spread of Zika in Brazil has primarily been attributed to rapid urbanization and population mobility, although may have been facilitated by increasing temperatures that allow mosquito vectors to persist in favourable climatic conditions.	Paz and Semenza (2016) (Zika, Brazil)		Zika: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
	Malaria: Increased malaria cases have been observed at higher altitudes areas during warmer years in Colombia, as a result of climate change allowing for range expansion of mosquito vectors. Recent increases in cases of malaria in some areas including Venezuela and Brazil are a result of socioeconomic factors, including political instability and population mobility.	Siraj et al. (2014) (malaria, Colombia); Grillet et al. (2019) (malaria, Venezuela)		Malaria: Warmer temperatures increase transmission of malaria, by speeding up the development of Anopheles mosquitoes, and replication of the parasite inside the mosquito. Warmer temperatures at higher altitudes allow Anopheles mosquitoes to persist at higher elevations and expand their range limits.
Europe	Observations Lyme disease and tick-borne encephalitis: Expanded from western Europe northwards to Sweden, Norway and the Russian Arctic and to higher elevations in Austria and the Czech Republic over the last few decades.	Jaenson et al. (2012) (Sweden); Jore et al. (2014) (Norway); Medlock et al. (2013) (Europe review); Tokarevich et al.) (Russian Arctic); Daniel et al.) (high altitudes, central Europe); Heinz et al.) (Austria)
	Chikungunya: Transmission of arboviruses such as dengue and chikungunya have expanded in southern Europe, including France, Italy and NE Spain.	Roiz et al. (2015)
	Attribution Lyme disease and tick-borne encephalitis: The increased abundance of ticks has been attributed to high availability of tick maintenance hosts (particularly deer) during the last three decades, as well as a warmer climate with milder winters and a prolonged growing season that permits greater survival and proliferation over a larger geographical area of both the tick itself and deer.		moderate contribution of climate change to the observed increase in tick-borne diseases (Lyme and tick-borne encephalitis) in Europe, medium confidence (**)	Lyme: Warmer temperatures shorten the lifecycle and increase abundance of Ixodes ticks that carry Lyme disease. High temperatures also expand the distribution and range of rodent and deer hosts, as well as their activity, increasing human exposure to Lyme disease. Dry conditions can leave tick larval nymphs susceptible to desiccation, which can lead to subsequent decreases in Lyme disease.
	Dengue: There is evidence of increased climate suitability for mosquito vectors due to warmer winter temperatures in western Europe. Vectorial capacity for the Aedes aegypti vector in Europe has increased by 25.8% compared to a 1950’s baseline.	Caminade et al. (2012) Watts et al. (2021) Salami et al. (2020)		Dengue and chikungunya: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
North America	Observations Lyme and tick-borne disease: In temperate regions of the US, Lyme and tick-borne diseases such as tick-borne encephalitis have expanded northwards. Lyme disease incidence has been increasing since the 1980s with annual infections tripling between 2004-2016.	Kugeler et al.); Bisanzio et al. (2020); Lin et al. (2019) Schwartz et al. (2017) Rosenberg et al. (2018); Rochlin et al. (2019b)
	West Nile Fever: West Nile was the most common vector-borne disease reported in the US between 2004-2016. Larger outbreaks of West Nile Fever are being recorded with a gradual northward spread.	Ronca et al. (2021) Nelson et al. (2015) Rochlin et al. (2019b) Rosenberg et al. (2018)
	Dengue: Increase in outbreak intensity and spatial distribution of dengue in the USA, with an increased number of outbreaks in the southern US including Florida, Texas and Hawaii.	Braithwaite et al. (2016) Brathwaite Dick et al. (2012) Bouri et al. (2012); Brady and Hay (2020) Whitehorn and Yacoub (2019)
	Attribution Lyme and tick-borne disease: Increasing incidence of Lyme disease has been associated with warming annual temperatures, as has the northward range expansion of the tick vector Ixodes scapularis in North America and Canada. Non-climatic factors however are also important in explaining increasing trends of Lyme, due to increased awareness and surveillance, as well as host mobility. Increased human outdoor activity as a result of shorter winters and longer summer conditions also contributes to increased exposure to ticks and Lyme infection. Overall, even without robust quantification of the contribution of other drivers, there is moderate confidence that climate change has contributed to the observed increase in Lyme disease incidence.	Clow et al. (2017); Clow et al.) McPherson et al. (2017); Kilpatrick et al. (2017) Ostfeld and Brunner (2015) Scott and Scott (2018); Couper et al. (2020)	moderate contribution of climate change to increased tick borne disease; high confidence (**) minor impact of climate change on increased arbovirus incidence; low confidence ()	Lyme: Warmer temperatures shorten the lifecycle and increase abundance of Ixodes ticks that carry Lyme disease. Higher temperatures also expand the distribution and range of rodent and deer hosts, as well as their activity, increasing human exposure to Lyme disease. Dry conditions can leave tick larval nymphs susceptible to desiccation, which can lead to subsequent decreases in Lyme disease.
	Dengue: Expansion patterns have mainly been driven by trade and travel, although transmission potential has increased. This is due to increased climate suitability (warmer temperatures) for the mosquito vectors at range limits.	Butterworth et al. (2017) Robert et al. (2019)		Dengue:Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
Small Islands	Observations Dengue: Observed increase in the number of outbreaks of dengue and Zika in the Caribbean islands, and the Pacific, including in the Marshall Islands, Fiji and Micronesia over the last decade.	Lowe et al. (2020); Arima et al.); Cao-Lormeau and Musso)
	Mayaro virus: In Trinidad, mosquitoes tolerant to brackish water that cause the Mayaro virus are now found in coastal mangrove ecosystems where they never occurred before.	Ali et al. (2019);Mohammed and van Oosterhout (2020)
	Attribution Dengue: There is evidence of a link between drought and dengue outbreaks in Barbados. Tidal inundation from sea level rise is providing more permanent aquatic breeding habitats for dengue mosquitoes. Disruption from tropical cyclones is increasing population mobility, making it easier for mosquitoes to transmit dengue and other vector-borne diseases. However, there is limited assessment of the role of climate change in driving observed outbreaks in arboviruses in the Pacific.	Lowe et al. (2018b) Leal Filho et al. (2019)	moderate contribution of climate change to increases in vector borne disease in small islands; medium confidence (**)	Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
	Mayaro virus: The new occurrence of the Mayaro virus in mangrove ecosystems of Trinidad is a result of consistent rises in coastal temperatures and sea-level rise that provides new breeding habitat.	Ali et al. (2019); Mohammed and van Oosterhout (2020)
	S14 Coastal systems - Damages
Global	Observations No assessment on global scale
Global	Attribution No assessment on global scale		white not enough studies available for assessment.
Africa	Observations Coastal Erosion: Shoreline retreat rates along the Palmarin peninsula (Senegal) have been sharply increasing in recent decades (1982-2018), destroying buildings and tourist camps.	Enríquez-de-Salamanca (2020)
Africa	Attribution Coastal erosion: In absence of significant human factors, sea level rise has been identified as the most reasonable explanation for the observed increase in shoreline retreat rates along the Palmarin peninsula.	Enríquez-de-Salamanca (2020)	strong contribution of sea level rise to shoreline retreat along the Palmarin peninsula, low confidence (*) as based on only one study. no assessment elsewhere
Asia	Observations No clear trend in damage from individual tropical cyclones is found in China.	Chen et al. (2013)
	Concerning more general coastal disasters in China, including rough seas, storm surges, sea ice, and algal blooms, no significant trend is observed in the period 1989-2014 for economic losses; fatalities declined. Algal bloom events cause minor economic impact compared to the other disaster types, but they only have started to occur in the Yellow and East China Sea since the 2000s, causing threat to the overall health of coastal ecosystems.	Fang et al. (2017a)
	A statistically significant decrease in fatalities caused by storms and floods including TCs is identified for Japan for the period 1968 to 2014.	USHIYAMA (2017) Lee et al. (2020)
	Normalized cost of damages caused by TCs has increased in the Philippines since 1971 while there were no statistically significant trends reported in the frequency, intensity and landfall of TCs. Typhoon Haiyan in 2013 caused damages with socio-economical cost of 2 billion USD due to a devastating storm surge, reaching twice the level of the second largest damage event in the historical record.	Cinco et al. (2016) Lee et al. (2020)
	Attribution Coastal erosion: Most areas along the Sindh coastline (Pakistan) have experienced significant erosion in the period 1989-2018, positively correlated with sea level rise.	Kanwal et al. (2020)	sea level rise has increased coastal damages in Pakistan, low confidence (*) as based on individual studies only No assessment elsewhere
Australasia	Observations A swell-dominated ocean beach in Tasmania shows an abrupt change of long-term shoreline position variability circa 1980, from episodic erosion and accretion since at least 1947 to persistent recession with no recovery up to the present. The frequency of occurrence of coastal nuisance flooding has been doubling every 20 years in Brisbane, Australia since 1977 and every 34 years in Townsville, Australia since 1959.	Sharples et al. (2020) Hague et al. (2020)
Australasia	Attribution Recent sea-level rise and increasing winds driving increased wave-setup have sufficient explanatory power to account for the observed changes. No formal attribution statement for Brisbane and Townsville Nuisance flooding.	Sharples et al. (2020) Hague et al. (2020)	locally minor to moderate increase in damages induced by (relative) sea level rise, low confidence (*), missing studies elsewhere
Central and South America	Observations No studies available
Central and South America	Attribution No studies available		white not enough studies available for assessment.
Europe	Observations Local government decides to no longer defend a village and relocate its residents over the next 50 years (Fairbourne, Wales, UK).	Gwynned Councyl (2019), Williams et al. (2018)
	Catalan coast: Storm-induced coastal damage has increased at a rate of about 40% per decade during the last 50 years along the Catalan coast.	Jiménez et al. (2012)
	Attribution Rising sea levels leads to decision of abandonment for coastal village (Fairbanks, Wales, UK)	Gwynned Councyl (2019), Williams et al. (2018)	locally no to moderate increase in damages induced by storm related hazards and (relative) sea level rise, respectively, low confidence (*), missing studies elsewhere
	European coastal wetlands and small beaches may have initiated a shift toward erosion in the 1990s, suggesting influence of sea level rise.	Le Cozannet et al. (2020)
	Catalan coast: The increase in damages has been attributed to urban growth along the coastal fringe and the generalized erosive behavior of beaches while no temporal trend in storm related hazards has been detected.	Jiménez et al. (2012)
North America	Observations Tropical cyclones. Three of the five costliest storms in US history were in 2017: Harvey, Irma, and Maria. The other two are Katrina and Sandy, which flooded New Orleans in 2005 and New York in 2012 (non-normalized costs). In September 2017 hurricane Harvey hit the south of the US (in particular Texas and Louisiana) causing an estimated direct damage of US$ 85bn to 125bn.	Frame et al. (2020b); Wehner and Sampson (2021)
	Direct economic losses unadjusted for inflation and economic growth increased from 1900 - 2018.	Grinsted et al. (2019) Weinkle et al. (2018)
	Nuisance flooding. Across 27 locations in the United States, the number of nuisance flood days (defined by the National Weather Service as the flood level at which minor impacts start to occur) has risen from an average of 2.1 days per year during 1956-1960 to 11.8 during 2006-2010. In Annapolis the number threshold of nuisance floods has been exceeded on 63 days in 2017 and led to reduced visits to the historic downtown by 1.7%, loss of city businesses equivalent to 0.7 to 1.4% of their potential revenue without flood occurrence.	Sweet et al. (2018) (27 locations across the United States); Hino et al. (2019) (Annapolis, Maryland)
	Alaska: 184 of 213 of Alaska Native villages are subject to flooding and erosion affecting infrastructure such as schools and health clinics and residents’ livelihoods as traditional modes of transport and hunting practices become increasingly untenable. In Kivalina, an Inupiaq Inuit community on Alaska’s western coast, the frequency of high-damage storms is reported to have increased with 80% of reported storms since 1970 having occurred in the last 15 years.	GAO (2009) Smith and Sattineni (2016) Fang et al. (2017a) (Kivalina), Albert et al. (2018)
	Herschel Island, Yukon Territory, Canada (UNESCO World Heritage candidate site): Study area is characterized by widespread erosion. Mean coastal retreat decreased from -0.6 m·a-1 to -0.5 m·a-1, for 1952-1970 and 1970-2000, respectively, and increased to -1.3 m·a-1 in the period 2000-2011.	Radosavljevic et al. (2016)
	Land loss, Louisiana: Louisiana lost approximately 4833 square kilometers of coastal wetlands between 1932 and 2016, amounting to a decrease of approximately 25 percent.	Couvillion et al. (2017)
	Attribution Tropical cyclones. Sea level rise has contributed to the increase in damages of hurricane Sandy by $11.1B and the number of people affected by 100000. Flood modeling with precipitation counterfactuals attribute 13$billion of damages from Harvey to anthropogenic climate change. The fraction of attributable risk for the extreme rainfall associated with Harvey (contribution of anthropogenic climate forcing to occurrence probability) is estimated 0.75 (0.67-0.9 range). Assuming that Harvey's heavy rainfall was a major driver of the damages, the fraction of attributable risk of the observed damages is also considered 0.75. Long-term increases in reported damages due to US landfalling hurricanes since 1900 can be explained, at least in part, by increases in exposure and the value of the assets at risk. Depending on the definition of exposure and the normalization of asset values over time, an unexplained trend remains that is or is not consistent with climate change. Due to lacking data, changes in vulnerability are not quantified separately in these studies. Nuisance flooding. In Annapolis, nuisance floods in 2017 could almost entirely be attributed to sea level rise (almost 1 foot since 1950) that has increased the number of flood hours from about 1h per year in 1950-1965 to more than 40h in 2010-2017 (63h in 2017). Hino et al. (2019) (Annapolis, Maryland)	Strauss et al. (2021) (hurricane Sandy); Wehner and Sampson (2021), Frame et al. (2020b) (Hurricane Harvey) Estrada et al. 2015[IS4], Weinkle et al. 2018, Grinsted et al. 2019, Pielke 2020[IS5], Botzen et al. 2020[IS6]	Individual case studies (hurricanes Harvey and Sandy, nuisance flooding in Annapolis, and coastal erosion in Alaska) indicate a strong adverse impact of relative sea level rise, increased precipitation associated with tropical cyclones, and sea ice retreat on coastal human systems, medium confidence (**) Relative sea level rise in New York, Louisiana and Alaska is partly induced by subsidence not related to anthropogenic climate forcing but its influence has been decreasing over time such that today an increasing fraction is related to sea level contributors driven by anthropogenic climate change (see global assessment). Changes in sea ice retreat and permafrost thawing are dominated by anthropogenic climate forcing (see part 1 of this table). no dedicated attribution of damages elsewhere.
	Alaska: Relative sea level rise, permafrost thawing, and loss of coastal protection against storms due to sea ice loss are considered possible drivers of the observed erosion. Permafrost thawing and sea ice retreat are mainly driven by anthropogenic climate forcing (see ‘permafrost’ and ‘sea ice section’ in the first part of this table). Relative sea level rise is partly induced by vertical land movements not related to anthropogenic climate forcing but its influence has been decreasing over time. A detailed study in Kivalina indicates that sea ice retreat may be a critical climate-related driver: Whie there is no significant trend in the timing of the first day of the open-water season, the autumn freeze-up has been delayed by 4.7 days/decade 1979-2015 (> two weeks over the entire observation period). Each high-damage storm event occurred during the open water season for that year.	Smith and Sattineni (2016) Fang et al. (2017a) (Kivalina), Albert et al. (2018)
	Herschel Island, Yukon Territory, Canada: There is no explicit attribution of erosion to individual drivers. In the absence of other known drivers, sea level rise, higher waves through a lengthened open water season, and permafrost thaw through warmer coastal surface waters are plausible drivers of erosion linked to climate change.	Radosavljevic et al. (2016)
	Land loss, Louisiana: The loss of coastal wetlands in Louisiana is due to relative sea level rise, including local, regional, and global factors such as global mean sea level rise, subsidence, oil and gas extraction, or altered hydrology.	Couvillion et al. (2017)
Small Islands	Observations The base of the beach predominantly exhibited retreat in a high mountain tropical island (Tubuai, French Polynesia) over 1982-2014. The coastline of Ouani experienced degradation and retreat in recent decades (Anjouan, Comores).	Salmon et al. (2019) Ratter et al. (2016)
	Solomon Islands: Loss of five vegetated reef islands (1-5 ha in size) by permanent inundation, further six islands subject to severe shoreline recession leading to destruction of two villages that have existed since at least 1935 and associated community relocations at two sites. Fiji: Vunidogoloa village relocated in early 2014 to reduce their vulnerability to encroaching sea level and inundation events that regularly devastated the community.	Albert et al. (2016) McNamara and Jacot Des Combes (2015)
	TC Maria 2017: Significant disturbance across 50% of Puerto Rico. 1000-3000 excess deaths in Puerto Rico in the six months after the event. Mortality was higher in lowest developed municipalities. Considered as the worst natural disaster on record in Dominica and Puerto Rico; large impact on the vegetation of these islands. TC Irma 2017: Most powerful hurricane that struck the northern Caribbean over the last 100 years. In Saint Martin Island approximately 80% of the mangrove area was damaged by the hurricane.	de Beurs et al. (2019) (TC Maria), Hu and Smith (2018) (TC Maria), Walcker et al. (2019) Santos-Burgoa et al.) (TC Irma)
	Attribution The observed damages in Solomon Islands and Fiji are driven by relative sea level rise. In the Solomon Islands this can be traced back to anthropogenic climate forcing as the influence of non-climate driven vertical land movement is minor and absolute sea level rise in the Indo Pacific is majorly induced by anthropogenic climate change (see assessment of mean sea level rise). Fiji: For the observed damages in Fiji there is no assessment of the potential local contributions of non-climate driven vertical land movement.	Albert et al. (2016) McNamara and Jacot Des Combes (2015)	Strong contribution of relative sea level rise to observed damages on Fiji and combined with wave dynamics on Solomon Islands, contribution of increased rainfall to damages induced by TC Irma and Maria, 2017, medium confidence (**). In the Solomon Islands relative sea level rise is dominated by anthropogenic climate forcing (medium confidence). no assessment elsewhere.
	TC Irma and Maria: Climate change is estimated to have increased the amount of rainfall associated with the hurricanes Irma and Maria by 6% and 9%, respectively (see ‘climate attribution: Heavy rainfall’, Table SM16.20). There is no process-based simulation translating the additional amount of rainfall into the additional area flooded and associated damages, but assuming that higher rainfall is increasing the damage, anthropogenic climate has contributed to the observed damage induced by both hurricanes.	Clarke et al. (2021)
	S31a Overarching impacts - Macroeconomic output
Global	Observations Over the period 1980-2010 global annual GDP growth was 0.255 percentage points on average.	World Development Indicator database of the World bank (Nominal GDP per cap in constant 2011 USD), Diffenbaugh and Burke (2019)
	Attribution Climate change (increasing temperatures) have lowered global annual GDP growth by 0.002 percentage points on average. Median losses exceed 25% (relative to a world without anthropogenic forcing) over large swaths of the tropics and subtropics. Median gains can be at least as large in the high latitudes.	Carleton and Hsiang (2016); Diffenbaugh and Burke (2019)	Impacts range from a moderate increase in GDP (in high latitudes) to strong reduction (in subtropics and tropics) induced by climate change; low confidence (*) (studies are not independent but based on the same empirical relationship)	Estimates are based on an empirical relationship between annual national temperature fluctuations and production observed by Burke et al, 2015.
	Since 2000, warming has already cost both the US and the EU at least $4 trillion in lost output, and tropical countries are >5% poorer than they would have been without this warming.	Burke and Tanutama (2019)		In contrast to Burke et al. 2015, estimates are derived from sub-national data. Study verifies the inverse u-shaped relationship between production and annual temperature fluctuations observed by Burke et al, 2019.
Africa	Observations By the second half of the 1970s, the average pace of growth of African economies began to slow down and by the 1980s even resulted in economic contraction.	Barrios et al. (2010)
Africa	Attribution Rainfall had a significant effect on economic growth in Sub-Saharan Africa (SSA) If rainfall in SSA had remained at its high 1955-1960 level or at its lower average levels across 1901-1959, the gap in GDP per capita between SSA and non-SSA developing countries would have been about 40% or 15% less than what was observed in in actuality at the end of the 19th century (1997).	Barrios et al. (2010)	Decline in long-term rainfall trends caused a strong reduction of GDP growth in SSA countries; low confidence (*) (hypothesis needs support by independent studies)	Agriculture and hydro-energy supply are considered the main channels through which rainfall is likely to have reduced GDP growth in SSA countries.
Asia	Observations observations
Asia	Attribution		no assessment
Australasia	Observations observations
Australasia	Attribution		no assessment
Central and South America	Observations
Central and South America	Attribution no studies		no assessment
Europe	Observations
Europe	Attribution no studies		no assessment
North America	Observations
North America	Attribution no studies		no assessment
Small Islands	Observations
Small Islands	Attribution no studies		no assessment
	S31b Overarching societal impacts - Between country inequality
Global	Observations Globally between-country inequality has decreased over the past half century (1961-2010). The ratio between the population-weighted 90th percentile and the 10th percentile country-level per capita GDP (‘90:10’) decreased from about 150 to 50 within this period.
Global	Attribution Based on the inverse U-shape relationship between national annual temperature fluctuations and GDP, it is estimated that anthropogenic climate change has slowed down the observed reduction of inequality. Global economic inequality (measured by ‘90:10’ ratio) is estimated to be about 25% larger than in a counterfactual scenario without anthropogenic forcing. Without calculating its long-term effect, independent studies support an inverse U-shape relationship between GDP growth and annual temperature fluctuations derived from historical data. The relationship implies that warming increases GDP growth in countries whose long term average temperature lies below critical threshold temperature, while warming decreases GDP growth above this threshold. Such a relationship would have increased inequality between developed and developing countries since in most developed (developing) countries annual mean temperatures are below (above) the threshold.	Diffenbaugh and Burke (2019) (estimation of impact of long-term historical temperature rise); Pretis et al. (2018); Kalkuhl and Wenz (2020) (sensitivity of annual national GDP growth on temperature variations)	Strong increase of between countries inequality induced by climate change, low confidence (*)	Dependence of GDP growth and annual temperature has been found to have followed an inverse U-shape with growth rate increases and decreases below and above a critical threshold temperature (see ‘Overarching impacts - Sensitivity of economic growth to variations in weather conditions’).
Africa	Observations Until the early 1970s there was little difference between the growth performance of African and other developing countries. By the second half of the 1970s, however, the average pace of growth of African economies began to slow down and by the 1980s even resulted in economic contraction.	Barrios et al. (2010)
Africa	Attribution Rainfall had a significant effect on economic growth in Sub-Saharan Africa in contrast to the group of other developing countries. If rainfall in SSA had remained at its high 1955-1960 level (or at its lower average levels across 1901-1959), the gap in GDP per capita between SSA and non-SSA developing countries would have been about 40% (or 15% less) than what was observed in in actuality at the end of the 19th century (1997).	Barrios et al. (2010)	Decline in long-term rainfall trends in SSA caused a strong increases of inequality between SSA countries and other developing countries low confidence (*) (hypothesis needs support by independent studies)	Agriculture and hydro-energy supply are considered the main channels through which rainfall is likely to have reduced GDP growth in SSA countries.
Asia:	Observations
Asia:	Attribution no studies		no assessment
Australasia	Observations
Australasia	Attribution no studies		no assessment
Central and South America	Observations
Central and South America	Attribution no studies		no assessment
Europe	Observations
Europe	Attribution no studies		no assessment
North America	Observations
North America	Attribution no studies		no assessment
Small Islands	Observations
Small Islands	Attribution		no assessment
	S30 Overarching societal impacts - Within country inequality
Global	Observations
Global	Attribution No studies		no assessment
Africa	Observations
Africa	Attribution		no assessment
Asia	Observations
Asia	Attribution		no assessment
Central and South America	Observations
Central and South America	Attribution		no assessment
Europe	Observations
Europe	Attribution		no assessment
North America	Observations
North America	Attribution		no assessment
Small Islands	Observations
Small Islands	Attribution		no assessment
	S26 Overarching societal impacts - Social Conflict
Global	Observations
Global	Attribution		no assessment on global scale
Africa	Attribution Long-term local temperature growth is associated with increased prevalence of conflict events across Africa, 2003-17. Effect is non-linear with declining conflict likelihood at high end of T change	van Weezel (2020)	Moderate increase and decrease (non-linear response) in occurrence of armed conflict events induced by temperature rise, low confidence (*) (inconclusive evidence, limited number of independent studies)	High temperature (warming)
Asia	Observations Persistent armed conflicts in Western and Central Asia over recent decades; a notable decline in conflict occurrence in East Asia since the 1970s. Middle East: Civil unrest across most countries in the Middle East and North Africa region during the winter of 2010-11. In Syria, protest events and armed response by state security forces in March 2011 gradually escalated into a state of civil war. Outbreak of civil war in Syria in 2011 as part of the ‘Arab Spring’ uprisings	Gleditsch et al. (2002); Pettersson and Öberg (2020) (UCDP dataset)
Asia	Attribution Middle East: The civil war was preceded by a' long, severe drought in the north of the country which was amplified by anthropogenic global warming (see section on ‘increase in drought conditions’ in the climate attribution part of this table); this lead to unusually large rural-to-urban migration. The role of climate-driven migration in accentuating social grievances and sparking initial protest is disputed and poorly documented (see separate case study of the Syrian civil war in the main text).	Gleick (2014) Kelley et al. (2015) Kelley et al. (2017) Werrell et al. (2015) Selby (2019) Selby et al. (2017); Ide (2018); Ash and Obradovich (2020) Eklund and Thompson (2017)	Minor contribution of climate change to occurrence of civil war in Syria, low confidence (*, low agreement); no assessments elsewhere in the modern era
Australasia	Observations
Australasia	Attribution		No study of climate change impact on armed conflict in the modern era
Central and South America	Observations
Central and South America	Attribution		No study of climate change impact on armed conflict in the modern era
Europe	Observations International fisheries conflict increased between 1974 and 2016. The geographical point of gravity of these conflicts has shifted from Europe and North America to Asia. Climate change is affecting the distribution and potential yield of marine species through altered water temperatures, ocean currents, and coastal upwelling patterns.	Spijkers et al. (2019); Jones and Cheung (2018) Pinsky et al. (2018)
Europe	Attribution The range of the northeast Atlantic mackerel has shifted markedly in recent years, resulting in a spatial mismatch between the areas for which multilateral stock management policies existed, and the actual fishing grounds, which ensued ongoing dispute between the countries involved. The contributions of climate variability and climate change to the range shift are not yet understood.	Spijkers and Boonstra (2017) Gänsbauer et al. (2016)	Moderate contribution of climate change to occurrence of fishing disputes, low confidence (*)	Ocean warming
North America	Observations
North America	Attribution		No study of climate change impact on armed conflict in the modern era
Small Islands	Observations
Small Islands	Attribution		No study of climate change impact on armed conflict in the modern era
	S29 Other societal impacts - Displacement and migration
Global	Observations
Global	Attribution There is no attribution of changes in displacement and migration to climate change but only a ‘detection of weather sensitivity’ (see part 3 of this table)		no assessment
Asia	Observations
Asia	Attribution		no assessment
North America	Observations From 184 Alaska Native communities generally affected, 31 face imminent threats from flooding and erosion and three of them (Shishmaref, Newtok and Kivalina) are forced to relocate. Shishmaref experienced ten flooding events between 1973 and 2013, seven of them declared state emergencies and three federal emergencies. Since 1969 Sarichef, the island where Shishmaref is located, has lost about 60m of land (AECOM Technical Services 2016). Erosion has undermined buildings and infrastructure, causing several structures to collapse into the sea. As protection measures have turned out to be ineffective the community has voted for relocation in 2002, 2007, and 2016 which however has not been realized so far.	Smith and Sattineni (2016); Albert et al. (2018)
	Within Louisiana’s coastal parishes, only a small part of the population moved landward since 1940 compared to seaward population movements in the same period.	Hauer et al. (2019)
	Attribution Alaska: Relative sea level rise, permafrost thawing, and loss of coastal protection against storms due to sea ice loss are considered possible drivers of the observed erosion. Permafrost thawing and sea ice retreat are mainly driven by anthropogenic climate forcing (see ‘permafrost’ and ‘sea ice section’ in Table SM16.20). Relative sea level rise is partly induced by vertical land movement not related to anthropogenic climate forcing but its influence has been decreasing over time. A detailed study in Kivalina indicates that sea ice retreat may be a critical climate-related driver: While there is no significant trend in the timing of the first day of the open-water season, the autumn freeze-up has been delayed by 4.7 days/decade 1979-2015 (> two weeks over the entire observation period). Each high-damage storm event occurred during the open water season for that year.	Smith and Sattineni (2016), Fang et al. (2017a) (Kivalina), Albert et al. (2018)	Minor impact of relative sea level rise on population distribution in Louisiana, low confidence (), to strong impact of observed relative sea level rise, permafrost thawing and sea ice retreat on decision for relocation, medium confidence (*) Relative sea level rise is partly induced by subsidence from glacial isostatic adjustment, but its influence is decreasing over time such that an increasing fraction is related to sea level contributors driven by anthropogenic climate change (see global assessment). Changes in sea ice retreat and permafrost thawing are dominated by anthropogenic climate forcing (see part 1 of this table). no assessment elsewhere
	Louisiana: The lack of landward population movement within coastal parishes suggests that observed shoreline encroachment since 1940 due to relative sea level rise does not translate into a movement of the population yet.	Hauer et al. (2019)
Small Islands	Observations Solomon Islands: Shoreline recession at two sites has destroyed villages that have existed since at least 1935, leading to community relocation. Fiji: Vunidogoloa village relocated in early 2014 to reduce their vulnerability to encroaching sea level and inundation events that regularly devastated the community. Micronesia: Reef-edge islands around Pohnpei have disappeared within living memory or drastically reduced in size in the past decade.	Albert et al. (2018); Albert et al. (2016) McNamara and Jacot Des Combes (2015); Nunn et al. (2017)
Small Islands	Attribution The observed displacement in Solomon Islands and Fiji is driven by relative sea level rise. In the Solomon Islands this can be traced back to anthropogenic climate forcing as the influence of non-climate driven vertical land movement is minor and absolute sea level rise in the Indo Pacific is majorly induced by anthropogenic climate change (see assessment of mean sea level rise). Fiji: For the observed displacement in Fiji there is no assessment of the potential local contributions of non-climate driven vertical land movement. Micronesia: Reef-edge island erosion around Pohnpei over the last few decades can mostly be explained by recent sea-level rise.	Albert et al. (2016) McNamara and Jacot Des Combes (2015); Nunn et al. (2017)	Strong contribution of relative sea level rise to observed displacement on Fiji and combined with wave dynamics on Solomon Islands, medium confidence (**). In the Solomon Islands this is dominated by anthropogenic climate forcing (medium confidence). no assessment elsewhere.
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Table SM16.23: Attribution of variations and disturbances in natural, human, and managed systems to fluctuations or extremes in climate or climate-related systems (‘Identification of weather sensitivity’, references for blue symbols in Figure 16.1) The titles of the (partly) blue symbols in Figure 16.1 and associated numbers can be found here as titles of associated subsections.
Region	Observed variations or disturbance of the natural, human or managed systems + attribution to fluctuations in climate or climate related systems	Reference	Synthesis statement (Strength of influence, level of confidence)	Underlying mechanism
	S16 Water distribution - Reductions in water availability + induced damages and fatalities
Global	Observations
	Attribution EM-DAT attributes $247.8 billion (2010 US$) in direct economic damages and 2.2 million fatalities to droughts between 1960 and 2020. Droughts cause ~60% of all fatalities from all meteorological, climatological and hydrological hazards, making it the deadliest category of weather-related disasters in the database. On the other hand, only about 7% of global direct damages induced by all meteorological, climatological and hydrological disasters are due to droughts.	CRED and Guda-Sapir (2021)	high sensitivity, medium confidence (**) We explicitly note that this rating is focussed on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.	Our assessment largely builds on direct economic losses and fatalities reported in ‘The International Disaster Database’ (EM-DAT, CRED and Guda-Sapir (2021)). The damages considered here refer to the amount of damage to property, crops, and livestock. For each disaster, the registered figure corresponds to the direct damage value at the moment of the event and does not include damages that unfold over following years. The database is made up of information from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies, with priority given to data from UN agencies, governments, and the International Federation of Red Cross and Red Crescent Societies. The entries are constantly reviewed for inconsistencies, redundancy, and incompleteness. However, there may be differences in estimated damages across sources (see e.g. estimated damages induced by the 2012 US drought in the North America section). We uniformly assume a ‘medium confidence’ associated with the drought assessments based on EM-DAT assuming a more difficult assignment of damages and fatalities droughts than to tropical cyclones.
	Globally, for the period 1981-2010, the utilization rate of hydropower and thermoelectric power was reduced by 5.2% and 3.8% respectively during drought years compared to the long-term average values	Van Vliet et al. (2016)
Africa	Observations
Africa	Attribution EM-DAT attributes $10.2 billion (2010 US$) in direct economic damages and ~697,000 fatalities to droughts between 1960 and 2020. According to the EM-DAT numbers, 95% of all deaths induced by meteorological, climatological and hydrological hazards (1960-2010) have been induced by droughts, a share far higher than for any other category. Even though the drought-induced economic damages, given in 2010 US$, are lower than in any other region, they still make up 27% of all damages due to meteorological, climatological and hydrological extreme events.	CRED and Guda-Sapir (2021)	high sensitivity, medium confidence (**) We explicitly note that this rating is focussed on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
Asia	Observations
Asia	Attribution EM-DAT attributes $27.3 billion (2010 US$) in direct economic damages and 1.5 million fatalities to droughts between 1960 and 2020. More than half (56%) of all fatalities induced by weather related extreme events in Asia stem from drought, while drought-induced economic damages have a share of about 6% of overall damages from weather-related disasters in the region.	CRED and Guda-Sapir (2021)	high sensitivity, medium confidence (**) We explicitly note that this rating is focused on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
Australasia	Observations
Australasia	Attribution EM-DAT attributes $27 billion (2010 US$) in direct economic damages and 600 fatalities to droughts between 1960 and 2020.28% of all fatalities induced by weather related extreme events in Australasia (1960-2010) stem from droughts. In terms of damages, however, droughts have the highest share compared to the other world regions: Drought damages in Australasia make up 30% of all damages induced by weather-related disasters in the region (1960-2010).	CRED and Guda-Sapir (2021)	high sensitivity, medium confidence (**) We explicitly note that this rating is focused on specific immediate damages induced by droughts while other impacts of wildfires, droughts on e.g. conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
Central and South America	Observations Sao Paulo water crisis in 2015: In January 2015 the metropolitan region of Sao Paulo, the largest megacity in South America experienced a severe water shortage with main reservoirs reaching storage levels of only 5% of their capacity. In order to reduce leakages from the pipes, that amount to 30%-40% of the water, SABESP reduced the water pressure which left millions “for hours and even days” without water.	Otto et al. (2015a) Nobre et al. (2016)
	Brazil, 2016: Três Marias, Sobradinho, and Itaparica reservoirs reached 5% of volume capacity. (Ceará), registered 39 (of 153) reservoirs empty in Ceará. Another 42 reached inactive volume. 96 (of 184) Ceará municipalities experienced water supply interruption.	Chapter 4, 4.2.5, Table 4.5
	Attribution EM-DAT attributes $31.5 billion (2010 US$) in direct economic damages and 85 fatalities to droughts between 1960 and 2020. The number of fatalities represents a share of 0.7% of all fatalities from all meteorological, climatological and hydrological hazards.18% of all damages induced by weather-related disasters in the region were due to drought.	CRED and Guda-Sapir (2021)	Individual droughts can have severe consequences. However, measured in terms of economic damages and fatalities the influence of weather extremes on water availability is relatively small compared to the impact of weather extremes through infrastructure destruction and injuries as e.g. induced by flooding or tropical cyclones and mortality induced by heat. Therefore the sensitivity is rated moderate, medium confidence (**) here. We explicitly note that this rating is focussed on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
	Drought in the Brazilian Pantanal, 2019-2020: Due to the prolonged drought river levels reached extremely low values and transportation had to be restricted in some parts of the river. Low water levels affect mobility of people and shipping of soybeans and minerals to the Atlantic Ocean by the Paraná-Paraguay Waterway inducing considerable economic losses.	Marengo et al. (2021)
	Sao Paulo water crisis in 2015: Since the austral summer of 2014 southeastern Brazil has been experiencing one of the most severe droughts in decades. The water crisis can be partly explained by the drought but is also partly induced by growing water demand of an increasing population. There is no evidence that the drought conditions have become more prevalent as a result of anthropogenic climate forcing. However, water use may have increased not only because of population growth but also because of the warm summer. Currently there are no model simulations comparing the influences of the different drivers.	Otto et al. (2015a), Nobre et al. (2016)
	Water crisis, Brazil 2016: The severe water restrictions have been introduced by drought conditions that have however not been attributed to anthropogenic climate forcing	Chapter 4, 4.2.5, Table 4.5
Europe	Observations
	Attribution EM-DAT attributes $41.6 billion (2010 US$) in direct economic damages and 2 fatalities to droughts between 1960 and 2020. Lowest number of fatalities due to drought compared to other world regions. In terms of damages, the share remains below 10% (9%) of the total damages induced by all weather-related extreme events.	CRED and Guda-Sapir (2021)	Individual droughts can induce widespread overall losses. However, measured in terms of economic damages and fatalities the influence of weather extremes on water availability is relatively small compared to the impact of weather extremes through infrastructure destruction as e.g. induced by flooding or mortality induced by heat. Therefore the sensitivity is rated moderate, medium confidence (**) here. We explicitly note that this rating is focused on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections
	United Kingdom: On extreme high temperature days (~3 days in a year), almost 50% of freshwater thermal capacity is lost, causing losses in the range of average GBP 29-66 million/year, and in case of ~20% of particularly vulnerable power plants, these losses could go up to GBP 66-95 million/year annualized over a 30 year period	Byers et al. (2020)
	The costs of agricultural droughts in Italy 2003 are estimated to be 1.75 billion Euros (-0.1 % of the GDP), 0.92 billion Euros (-0.05 % GDP) in 2006 and 0.56 billion Euros (-0.03% of the GDP) in 2011.	García-León et al. (2021)
North America	Observations
	Attribution EM-DAT attributes $ 64.6 billion (2010 US$) in direct economic damages and 45 fatalities to droughts between 1960 and 2020. In North America, 45 fatalities due to drought were reported (1960-2020). This amounts to a low share of 2%. Also in terms of damages, droughts are on the lower end for the North American continent, amounting to 5% of all damages.	CRED and Guda-Sapir (2021)	Individual droughts can induce widespread overall losses. However, measured in terms of economic damages and fatalities the influence of weather extremes on water availability is relatively small compared to the impact of weather extremes by infrastructure destruction as e.g. induced by flooding and tropical cyclones or mortality induced by heat, respectively. Therefore the sensitivity is rated moderate, medium confidence (**) here. We explicitly note that this rating is focused on specific immediate damages induced by droughts while other impacts of droughts on e.g. wildfires, conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
	In U.S. social costs of water scarcity between 2001-2012 was estimated to be US$330,000 (at 2015 value) per month for every power plant that experienced water scarcity	Eyer and Wichman (2018)
	The US drought of 2012 was a multi-billion disaster. NCEI estimates for the 2012 drought indicate that losses, largely agricultural, topped $30 billion.	Rippey (2015)
Small Islands	Observations Marshall Islands: In 2013, 11000 inhabitants of the Marshall Islands suffered from water shortages. In 2016 strict water rationing had to be applied.	Barkey and Bailey (2017)
	St. Lucia, 2010: 80% of the population of St. Lucia had to cope with limited water supply.	Cashman (2014)
	Attribution EM-DAT attributes $554.6 million (2010 US$) in direct economic damages and 84 fatalities to droughts between 1960 and 2020. With 84 out of a total of ~25000 reported fatalities (0.3 %) induced by weather-related disasters, droughts are not the most dangerous threat to the inhabitants of small islands. Similarly, droughts contribute 0.4% to damages from all meteorological, climatological and hydrological hazards, the lowest number in all regions.	CRED and Guda-Sapir (2021)	Individual droughts can induce widespread overall losses. However, measured in terms of associated economic damages and fatalities the influence of weather extremes on water availability is relatively small compared to the impact of weather extremes through infrastructure destruction and injuries as e.g. induced by flooding and tropical cyclones or mortality induced by heat, respectively. Therefore the sensitivity is rated moderate, medium confidence (**) here. We explicitly note that this rating is focussed on specific immediate damages induced by droughts while other impacts of droughts on e.g. conflict, displacement and migration, crop yields, malnutrition are addressed in other individual sections.
	Water rationing Marshall Islands: The water shortage in 2013 and the 2016 water rationing were induced in response to droughts. The 2016 drought was one of the strongest droughts on record. The rationing has become necessary as rainwater in rooftop catchment systems was depleted and groundwater wells were brackish.	Barkey and Bailey (2017)
	St. Lucia: The constraints on water availability were induced by Hurricane Tomás that caused a landslide that damaged electricity supply and pumping facilities.	Cashman (2014)
	S18 Food system - Crop yields
Global	Observations
	Attribution General impact of weather fluctuations: In many of the main producing countries there is a clear signal of weather fluctuations in reported national fluctuation of maize, wheat, rice, and soy yields demonstrated. However, explained variances vary from country to country reaching more than 50% in some important production countries. Missing explanatory power may be due to reporting errors, management changes, effects of diseases or pests that may be dominant in some countries. Even on subnational level weather fluctuations explain ~1 third of yield variability on average with higher contribution in substantial areas of the global breadbaskets (>60%). Simple climate indicators (precipitation, maximum, and minimum daily temperature averaged over the growing season) can explain about one third and more of global annual fluctuations of wheat (41%), rice (29%), maize (47%), soy (52%), barely (65%), and sorghum (29%) yields over 1961-2002.	Müller et al. (2017) (wheat, rice, maize, soy, national) Frieler et al. (2017) (wheat, rice, maize, soy, national) Ray et al. (2015) (wheat, rice, maize, soy, subnational, 1979-2008) Lobell and Field (2007) (global, wheat, rice, maize, soybeans, barley and sorghum, 1961-2002)	contributions of weather fluctuations to observed fluctuations in crop yields range from strong (e.g. maize in Australia), high confidence (**), to minor in other regions and of other crops, minor confidence () Sensitivity to heat often depends on water availability and can partly be reduced by irrigation.	Variations seem to be largely driven by water availability (Frieler et al., 2017)
	Impacts of weather extremes: Across 1964-2007 heat waves and droughts have significantly reduced annual national cereal yields (7.6% and 5.1%, respectively). Drought-induced reductions of cereal yields were highest in the more technically developed agricultural systems of North America, Europe and Australasia (~16%, significant) and insignificant reductions (< 5%) in Asia, Africa, and Latin America + Caribbean. Maize: Significant annual national yield reductions by extreme heat (~12%) and drought (~3%). Global-average drought induced yield loss estimated from gridded data reaches 7% per drought (average deviation from long term mean across all years with positive drought index, 1983-2009). Wheat: Insignificant reductions in annual national yields induced by droughts and extreme heat (1964-2007). Global-average drought induced wheat yield loss estimated from gridded data reaches 8% per drought (average deviation from long term mean across all years with positive drought index, 1983-2009). Soy: Global-average rought induced yield loss estimated from gridded data reaches 7% per drought (average deviation from long term mean across all years with positive drought index, 1983-2009). Rice: Global-average drought induced yield loss estimated from gridded data reaches only 3% per drought probably due to widespread irrigation (average deviation from long term mean across all years with positive drought index, 1983-2009).	Lesk et al. (2016), (national yields losses induced by drought and extreme heat, 1964-2007), Kim et al. (2019) (subnational drought induced yields losses, 1983-2009)		Variations seem to be largely driven by water availability (Frieler et al., 2017)
	Interactions between heat and drought conditions: On global level heat was shown to be more damaging in dry than in normal conditions for maize and wheat (1961-2014). Temperature effects were not significant in wet conditions for maize, soybeans, and wheat. That observed damaging effects of heat unfold through effects on water-deficits is supported by a range of earlier observational and modelling studies.	Matiu et al. (2017), Schlenker and Roberts (2009) (US, observational data), Troy et al. (2015) (US, observational data), Jägermeyr and Frieler (2018) (global, model simulations), Lobell et al. (2013) (US, model simulations)		Variations seem to be largely driven by water availability (Frieler et al., 2017)
	Model simulations only forced by observational weather data confirm the assumption that the observed yield losses are indeed induced by the underlying weather conditions. Extreme events such as droughts and heat waves significantly contribute to yield variations with 18-43% of the observed subnational variations in maize, soy, rice, and spring wheat explained by observed variations in extreme weather indicators. A combined indicator of heat and drought conditions can explain about 40% of the observed wheat yield variations (deviations from long term mean, 1980-2010).	Jägermeyr and Frieler (2018) (national) Vogel et al. (2019) (subnational), Zampieri et al. (2017) (wheat, 1980-2010)		Even under heat wave conditions the losses appear to be primarily driven by water deficits (Lobell et al., 2013. Jägermeyr and Frieler, 2018. Schlenker and Roberts 2009. Schauberger et al., 2016)
Africa	Observations
Africa	Attribution Maize: In South Africa, more than 50% of the variance of national annual maize yields can be explained by observed weather fluctuations. High sensitivity of maize yields to droughts (in terms of correlation between drought indicator and yield variations) in particular in Southeast Africa. Rice: Low sensitivity of rice yields to droughts. Wheat: Mostly low sensitivity of wheat yields to droughts except for Kenya. Soy: Mostly low sensitivity of soy yields to droughts except for South Africa.	Frieler et al. (2017) (maize, wheat, rice, soy, 1980-2010), Kim et al. (2019) (subnational drought induced yields losses)	low (rice and wheat) to high (maize) contribution of weather fluctuations to reported crop yield fluctuations, low confidence (*)
Asia	Observations
	Attribution Maize: Mostly low sensitivity of maize yields to drought except for parts of Indonesia and northeast China (in terms of correlation between drought indicator and yields). A separate study of Liaoning province in northeast China shows maize yield losses up to 25.8% when severe drought occurred in June-July. Under the ten main producers, India shows the highest risk of maize yield reduction under droughts (~88% probability that maize production falls below its long-term average when experiencing an exceptional drought). Wheat: Under the main producers globally, Syria and Iran are the Asian countries where a relatively large part of the variance of annual wheat yields could be explained by weather fluctuations (~35%-50%). Mostly low sensitivity of wheat yields to droughts except for parts of Kazakhstan, Turkey, Iran and Syria. Under the ten main producers globally, Russia shows a relatively high risk of wheat yield reduction under droughts (~75% probability that wheat production falls below its long-term average when experiencing an exceptional drought). In Nepal, Koshi River basin, a unit change in growing season maximum, minimum and mean temperatures is estimated to induce a yield changes (compared with 2008 yields) of -5 to 10 %, -5 to 2 % and -4 to 11 %, respectively, depending on the locations and elevations of the area.	Kim et al. (2019) (subnational correlation of yields to drought indicator, maize, rice, soy, and wheat from 1983 to 2009), Leng and Hall (2019) (probability of below average yields under drought conditions, wheat, maize, rice and soybeans, 1961-2016); Frieler et al. (2017) (maize, wheat, rice, soy, 1980-2010), Chen et al. (2016) (drought effects on maize, rice, sorghum, soybean, and millet in Liaoning province, China, 1960-2015) Bhatt et al. (2014) (rice, maize, wheat, Nepal, 1967 - 2008)	major contribution of weather fluctuations to annual fluctuation of rice yields, mostly low sensitivity elsewhere, regionally the sensitivity to droughts shows an opposite pattern with lower sensitivities of rice yields because of irrigation and higher sensitivities of other crops, low confidence (*).
	Rice: In Japan and South Korea more than 50% of the variance of annual national rice yields can be explained by observed weather fluctuations. Mostly low sensitivity of rice yields to droughts (e.g. in Liaoning province, China) except Indochina (measured by correlation between drought index and yields in dry years). Among the ten main producers rice yields are most vulnerable to droughts in Vietnam and Thailand when measured as the probability of below long-term average yields under exceptional drought (86% and 76%, respectively). Climatic variations are estimated to have accounted for 40.04% and 29.72% of yield variability for early- and late-rice, respectively (1980-2012, Southern China). In Nepal, Koshi River basin, a unit change in growing season maximum, minimum and mean temperatures is estimated to induce a yield changes (compared with 2008 yields) of -7 to 4 %, -9 to 11 % and -6 to 16 %, respectively, depending on the locations and elevations of the area. Soy: Under the main producers, less than 20% of the variance of annual national soy yields in China and India can be explained by observed weather fluctuations. Mostly low sensitivity of soy yields to droughts except for in northeast China when measured by correlation between national yields and a drought index in dry years. However, under the ten main producers India and Russia belong to the five most vulnerable ones to droughts when measured by the probability of below long term average yields under exceptional drought conditions (75% and 80%, respectively). In Nepal, Koshi River basin, a unit change in growing season maximum, minimum and mean temperatures is estimated to induce a yield changes (compared with 2008 yields) of -9 to 4 %, -12 to 1 % and -12 to 3 %, respectively, depending on the locations and elevations of the area. Most consistent negative correlation compared to maize and rice.	Liu et al. (2016b) (rice, Southern China, 1980-2012)
Australasia	Observations Wheat: Australian wheat yield is highly variable from year to year across major production regions. For example, in New South Wales (NSW) annual wheat production ranged between 2477 and 10 488 kt across 2003-2013, where harvested area varied from 2995 to 4322 kha and yields from 0.62 to 2.75 t ha-1.	Wang et al. (2015) ABARES (2021)
Australasia	Attribution Maize: Partly high sensitivity of maize yields to droughts in eastern Australia. Wheat: More than 50% of the variance of annual Australian wheat yields can be explained by observed weather fluctuations. In New South Wales explained variance reaches 40%. High sensitivity of wheat yields to droughts in southeast Australia and southwest Australia. Soy: Low sensitivity of soy yields to droughts in Australia. Rice: Low sensitivity of rice yields to weather fluctuations in Australia.	Kim et al. (2019) (subnational drought induced yields losses), Frieler et al. (2017) Wang et al. (2015) (wheat in New South Wales)	major contribution of weather fluctuations (in particular variations in rainfall) to annual fluctuation of wheat yields, high confidence (**) low sensitivity of reported rice and soy yields to weather fluctuations, low confidence ()
Central and South America	Observations
Central and South America	Attribution Maize: Mostly minor sensitivity of maize yields to droughts except for high sensitivity in the Pampas of South America. In this region annual fluctuations of growing season average temperatures, diurnal temperature ranges, and growing season total precipitation can explain about 40% of the variance of maize yields reported for 33 counties (1971-2012). Wheat: Mostly minor sensitivity of wheat yields to droughts except for higher sensitivities found in some parts of the Pampas of South America (subregion with significant drought induced yields losses is smaller than for maize). In the Pampas annual fluctuations of growing season average temperatures, diurnal temperature ranges, and growing season total precipitation can explain about 30% of the variance of wheat yields reported for 33 counties (1971-2012). Soy: In Japan and South Korea more than 50% of the variance of annual national soy yields can be explained by observed weather fluctuations. Mostly minor sensitivity of soy yields to droughts except for high sensitivities found in the Pampas of South America. In the Pampas annual fluctuations of growing season average temperatures, diurnal temperature ranges, and growing season total precipitation can explain about 47% of the variance of soy yields reported for 33 counties (1971-2012). Rice: Mostly low sensitivity to droughts except for high sensitivities in some parts of northeast Brazil.	Kim et al. (2019) (subnational drought induced yields losses), Frieler et al. (2017) Verón et al. (2015) (county level wheat, maize and soy yields at the county level in the Pampas region of Argentina, 1971 - 2012)	major contribution of weather fluctuations to annual fluctuation of soy yields, low confidence () minor sensitivity of reported wheat, maize, and rice yields to weather fluctuations, low confidence ()
Europe	Observations
Europe	Attribution Wheat: In Spain, Hungary, and Romania more than 50% of the variance of annual national wheat yields can be explained by observed weather fluctuations. High sensitivity of wheat yields to droughts found in South and Eastern Europe, no sensitivities detected elsewhere. A separate spatial explicit analysis of wheat yields dependence on a combined drought + heat indicator in France shows that the index can explain about 25% of the observed yields variability especially in the central-northern part where the main wheat producing areas are located. A more detailed assessment of the sign of the scaling coefficient shows that wheat yields in Mediterranean France is more sensitive to drought while the northern part are more sensitive to water excess. Maize: In Romania, France, Hungary, Germany, and Italy more than 50% of the variance of annual national maize yields can be explained by observed weather fluctuations. High sensitivity of maize yields to droughts found in southern Europe, no detection of sensitivities elsewhere. Rice: No detection of sensitivity of rice yields to droughts. Soy: No detection of sensitivity of soy yields to droughts.	Kim et al. (2019) (subnational drought induced yields losses), Frieler et al. (2017) Zampieri et al. (2017) (France, wheat, 1986-2014)	major contribution of weather fluctuations to annual fluctuation of wheat and maize yields in some European countries, medium confidence (*) minor contributions in other regions and of soy and rice yields, low confidence ()
North America	Observations Maize: Wheat:
North America	Attribution Wheat: In Canada more than 50% of the variance of annual national wheat yields can be explained by observed weather fluctuations. High sensitivity of wheat yields to droughts in the Great Plains of North America, low sensitivities found elsewhere. Observed national crop yields show that there is a 80% probability that wheat production falls below its long-term average when experiencing an exceptional drought, especially in the US and Canada. Analysis of wheat variety field trial outcomes (Kansas, 1985-2013) show highest sensitivities of wheat yields to freezing temperatures in the Fall and extreme heat events in the Spring (negative effects of exposure). Maize: In the US more than 50% of the variance of annual national maize yields can be explained by observed weather fluctuations. Effects of excessive rainfall on US maize yields has been shown to be comparable to the effects of droughts. High sensitivity of maize yields to droughts in the Great Plains of North America, low sensitivities found elsewhere. Soy: High sensitivity of soy yields to droughts in the Great Plains of North America, low sensitivities found elsewhere. Also, under the ten main producers, the US belongs to the three ones most vulnerable to droughts when measured by the probability of below long term average yields under exceptional drought conditions (75%). Rice: No sensitivity of rice yields to droughts found in North America.	Kim et al. (2019) (subnational drought induced yields losses), Leng and Hall (2019) (probabilistic drought effects), Frieler et al. (2017), Li et al. (2019b) (effects of excessive precipitation on maize yields in the US), Tack et al. (2015) (wheat, field trial data)	major contribution of weather fluctuations to annual fluctuation of wheat and maize yields in Canada and the US, respectively, high confidence (**). minor sensitivity of rice yields in general and soy yields in specific regions, low confidence ()
Small Islands	Observations
Small Islands	Attribution		no assessment
	S19 Food system - Food prices
Global	Observations International food prices spiked in 2007-2008 and 2010-2011 and domestic food prices increased substantially in most countries during these periods.	FAO et al. (2011)	High confidence
	Attribution Research on the identification of drivers of food price peaks in 2007/08 and 2010/11 is still debated. Different effects have been suggested as the main drivers for the recent price spikes. These include: biofuel production and oil price, speculation and ethanol production, supply-demand dynamics in combination to trade policies, and export restrictions. Dependence on internal supply-demand dynamics in principle allows for weather conditions being responsible for the price peaks to the extent weather affects production. The pure effect of weather extremes on supply and associated price responses compared to a counterfactual “regular” situation has not been estimated so far. However, given the explanatory power of the alternative drivers the impact is estimated to be minor.	Headey (2011) (export restrictions), Schewe et al. (2017) (supply-demand dynamics) Lagi et al. (2015) (speculation and ethanol production), To and Grafton (2015) (biofuel production and oil price), Bren d’Amour et al. (2016) (teleconnected supply-shocks)	minor influence of weather conditions on observed price peaks on the global market, low confidence () minor sensitivity of domestic price levels to ENSO events - medium confidence (*) In some cases the sensitivity of prices to ENSO may have been higher but as there is low confidence in this finding we decided for the above synthesis.
	Both extreme events, El Niño and La Niña, have been shown to increase international food price volatility of maize during Spring-Summer and Autumn-Winter seasons and of soybean, during the Spring-Summer.	Peri (2017)	moderate sensitivity of wheat, maize and soy price to ENSO events (soy price sensitivity is slightly lower), low confidence (*)
	A statistically significant linkages between ENSO events in the Niño3.4 region and agricultural commodity price levels have been revealed. For instance, the relationship wrt. wheat prices is nonlinear. Wheat prices increase (by up to 6%) after La Niña events, and decrease after El Niño events.	Ubilava (2018) (prices of agricultural commodities), Ubilava (2017) (wheat prices) Ubilava and Holt (2013)
Africa	Observations
Africa	Attribution Attribution: Developing countries in general: During the recent period of food price spikes, external international grain prices have been found to only weakly influence the domestic price movements, when internal drivers are also considered. Domestic weather anomalies and variation in agricultural productions are identified as important driver for domestic price movements: Across all developing countries 20% of markets are affected by domestic weather and only 9 % of markets are affected by changes in international prices. East Africa: In Tanzania two thirds of local price variations come from domestic influences including weather but also harvest cycles and trade policies. In Uganda variations in demand seem to have a stronger effect on food prices than supply shocks, but potentially weather driven variations in production are estimated to have a stronger effect than the pass-thought of international price.	Brown and Kshirsagar (2015) (developing countries) Baffes et al. (2019) (Tanzania) Mawejje (2016) (Uganda), Hill and Fuje (2020) (Ethiopia) Nsabimana and Habimana (2017) (Rwanda)	moderate sensitivity of food prices to weather variations in East Africa, medium confidence (**) no assessment elsewhere
Asia	Observations Russia: Grain production in Russia was characterised by extreme weather events in 2010/11 and 2012/13 with grain production falling 30% below the average of the three preceding years in both years (even more than 60% below average in some regions). While in 2010/11 an export ban was implemented, trade stayed freely possible in 2012/13. In addition, in 2007/08 export restrictions were implemented in response to raising global grain prices without Russia being explicitly affected by production shortages (wheat production 7% higher than in the average across the three preceding years). Domestic (and global) wheat prices rose in all three cases but stayed lowest in 2010/11 compared to 2012/13 (highest level) and 2007/08.	Götz et al. (2016) (Russia)
	Malaysia: Food prices have been steadily increasing over the recent years (2010 - 2017)	Wong et al. (2019) (Malaysia)
	Attribution Russia: A comparison of the three situations allowed to estimate the impact of weather induced production shortages and on domestic prices and the role of export restrictions in dampening associated price peaks induced by supply shortages or/ and global grain prices. The analysis indicates an influence of domestic supply shortages on domestic prices that are not only following global prices and a strong dampening of this effect by export restrictions.	Götz et al. (2016) (Russia)	moderate sensitivity of domestic prices to extreme weather condition, low confidence (*), limited evidence no assessment elsewhere
	Malaysia: Further, when La Niña and El Niño events occur in a given year, crude palm oil production decreases by 3.37%, palm oil stock level decreases by 2.5%, while they lead to an increase in crude palm oil price by 10.2%.	Rahman et al. (2013)
Australasia	Observations
Australasia	Attribution no dedicated studies		no assessment
Central and South America	Observations
Central and South America	Attribution Weather has been shown to have affected the Colombian consumer food inflation growth (i.e. an increase in the food price index) during El Niño. On the contrary, there are reductions in the inflation in the La Niña phase.	Abril-Salcedo et al. (2016) Abril-Salcedo et al. (2020)	moderate sensitivity of ENSO on food prices in Colombia, low confidence (*), no assessment elsewhere
Europe	Observations Ukraine: The Ukraine implemented direct or indirect export restrictions during all three recent peaks of global wheat prices (2007/08, 2010/11 and 2012/13) and domestic grain production was about 20% lower than 2005-2013 average in all three years 2007, 2010 and 2012.	Götz et al. (2016) (Ukraine)
Europe	Attribution Ukraine: As in contrast to the Russian situation (see above) there was no period of domestic supply shortages but no trade restriction, the impacts of supply shortages on domestic prices could not be quantified separately.	Götz et al. (2016) (Ukraine)	no assessment
North America	Observations no dedicated studies
North America	Attribution		no assessment
Small Islands	Observations no dedicated studies
Small Islands	Attribution		no assessment
	S25 Terrestrial ecosystems - Burned areas Note: This is exclusively about the sensitivity of burned areas to weather fluctuations.
Global	Observations Strong year to year fluctuations in areas burned by wildfires by orders of magnitude.	Jolly et al. (2015) (global)
	Attribution In forested lands fluctuation in purely weather-based measures of fuel aridity can explain large amounts of variations in annual burned areas with weaker correlations in drier regions. By contrast, in non-forested regions, cumulative precipitation antecedent to the fire season shows positive correlations with annual burned areas. Here, correlations are stronger in drier regions. Overall, climate variability explains about one-third of the interannual variability in burned areas. Spatial analysis of monthly burned areas show highly significant decreases in burnt area with soil moisture, increases with dry days and maximum temperature while controlling for spatial variations in NPP, diurnal temperature range, grazing land, rass/shrub cover, cropland area, human population.	Abatzoglou et al. (2018) Bistinas et al. (2014) (spatial analysis of monthly burned areas from 2000-2005)	mostly strong to moderate sensitivity of burned areas to weather fluctuations, medium confidence (**)
	A spatial empirical analysis of gridded satellite products of the fractional area burned per year and monthly burned areas indicates that increasing population density (comparison across grid cells not time) mainly reduces burned areas.	Knorr et al. (2014) (annual data), Bistinas et al. (2014) (monthly data)
Africa	Observations
	Attribution Seasonally anomalous weather had a statistically demonstrable impact on fire probability, warmer/drier seasons show higher fire probability. The state of the Antarctic Ocean Oscillation (AAO) is an important large-scale influence linked to global circulation. Fire probability increases when AAO is in positive phases. However, accumulated precipitation ending 14 months prior to the fire season, 12-month accumulated precipitation ending 2 months prior to the fire season, a Fire Weather Index, and climatic water deficit over the fire season individually only explain relative small fractions of the annual variation in burned areas.	Abatzoglou et al. (2018)	Mostly minor to moderate sensitivity of observed area burned to weather fluctuations, low confidence (*) Anomalously dry and warm weather enhances vegetation dryness, flammability and wildfire risk
	In arid regions of Southern Africa, large fires typically followed La Niña periods (e.g., 2011 and 2012), when increased rainfall and productivity increase fuel connectivity. Significant reductions in burned areas in Southern Africa during El Niño years compared to La Niña year but relatively small effect when normalized by all year mean differences (1997-2016).	Andela et al. (2019) Chen et al. (2017)
Asia	Observations
Asia	Attribution Within season weather-based fuel-aridity measures and annual precipitation prior to the fire season can independently explain 30% to more than 50% of the observed variance in burned forest areas in maritime Southeast Asia. Similar levels are reached by within-season weather-based fuel-aridity measures in forested North Asia. Siberian Larch forests: Inter-annual variations in burned areas (1996 - 2015) are moderately to highly correlated with variations in drought intensity measured by the Standardized Precipitation Evapotranspiration Index, (SPEI, r = -0.4) and temperatures (r = 0.5 for the entire fire season to 0.7 in June-July). Results are supported by similar studies summarized in the review paper. Starting from an El Niño phase in the Pacific Ocean, atmospheric blocking in summer 2019 led to highest recorded temperatures and low precipitation over Siberia, and causing burned area 4x above the 2001-2019 average burned area.	Ponomarev et al. (2016) Abatzoglou et al. (2018) (Siberian Larch forests), Kharuk et al. (2021) (Siberia, review), Bondur et al. (2020) (2019 wildfire in Siberia)	Strong sensitivity of burned areas to weather fluctuations in parts of Asia, medium confidence (**) no assessment elsewhere
Australasia	Observations
Australasia	Attribution High correlations between fire season fuel-aridity measures and burned forest areas in South-Eastern Australia on ecoregion level, depending on the measure, explained variance can reach more than 50%. Forests in the Australian Alps in South-Eastern Australia may be subject to a positive feedback of fire, i.e. post-fire stands are more likely to burn than mature stands.	Abatzoglou et al. (2018), van Oldenborgh et al. (2021) Zylstra (2018)	Strong sensitivity of burned areas to weather fluctuations in South-Eastern Australia, medium confidence (*), mostly strong sensitivity in other regions too, low confidence ()
Central and South America	Observations In January and February 2017, over 500,000 ha were burned in Central Chile, the largest burned area across the previous 40 yr.
	Attribution Variations in a fire weather and water deficit index can individually explain 50% and more of the variation in burned areas in parts of the forested land, in most other forested regions it is 30-50%. On most not forested lands at least one of both indicators reaches an explained variance of 50% or more.	Abatzoglou et al. (2018)	moderate to strong sensitivity of burned areas to weather fluctuations in forested land, strong sensitivity of burned areas to fire weather and water deficits in non-forested land, low confidence (*)
	Amazonia: Extreme droughts that occur during warm phases of the El Niño-Southern Oscillation and the Atlantic Multidecadal Oscillation can drive extreme fire years. The impacts of deforestation are greater under drought conditions, as fires set for forest clearance can become uncontrollable and burn larger areas, especially forests that have been previously logged	Aragão et al. (2018) Silva et al. (2018) Alencar et al. (2015) Marengo et al. (2018) Chen et al. (2011)
	Central Chile: Fire activity in central Chile was mainly associated with above-average precipitation during winter of the previous year and with dry conditions during spring to summer. In addition, burned areas are correlated with maximum temperature. R2 between climate indicators and burned areas reaches about 20-50%. The central Chile mega-fire in January 2017 occurred during the warmest summer ever recorded in Central Chile but were also preceded by a relatively warm and dry spring identified as increasing fire risk in the correlation analysis. More than half of the area burnt in Chile were forest plantations with fast growing, high-density forest stands that are highly flammable and favor the development of large and severe wildfires.	Urrutia-Jalabert et al. (2018) (Central and south central Chile, 1976-2013), De la Barrera et al. 2018, Holz et al., 2017 (‘fire activity’ measured by tree ring data not necessarily having a direct translation into ‘burned areas’), Gómez-González 2018 (2017 megafires in Chile)
Europe	Observations
Europe	Attribution Within season weather-based fuel-aridity measures can explain 30% to more than 50% of the observed variance in burned forest areas in South-Eastern and Northern Europe. Summer droughts and high temperatures are primary determinants of the interannual variability of fires in Southern Europe. Averaged across the Mediterranean Europe drought indices can explain about 40% of the observed variations in burned areas. In Spain, Portugal, and Latvia fire weather season length can explain about 50% of the annual variability in observed burned areas with even slightly higher values in Italy and lower but still significant values > 20% in Greece and France (1980-2013).	Abatzoglou et al. (2018) Turco et al. (2017) Trigo et al. (2016) Jolly et al. (2015) (fire weather season length)	moderate to strong sensitivity of burned area to weather conditions, medium confidence (**)
North America	Observations Anaktuvuk River Fire in Arctic tundra in 2007: Single fire burned a 1039-km2 area, more than doubling the total area burned during the previous 60 years in the region.	Hu et al. (2010a) Chipman et al. 2015
	Attribution United States: In forested (primarily flammability limited) regions at least 30% of the variance in annual fluctuations in macroscale burned areas can be explained by variations in purely weather based within-season fire risk indicators, with more than 60% reached in Pacific Northwest, Northern Rockies, the Eastern Great Basin, Rocky Mountain and Southwest. Climate-driven increases in lightning-ignitions and meteorological conditions favouring high fire spread led to extreme fires in the northern treeline ecotone. Explained variances in non-forested regions are usually lower but still reach 30% and more when antecedent fire risk indicators are included. Fire weather season lengths can explain about 50% of the variations in burned areas in the United States (1979-2013, 1992-2013), correlations are lowe in Canada.	Seager et al. (2015) (US) Abatzoglou and Kolden (2013), and others also used for ‘impact attribution’ (Table 16.2) Jolly et al. (2015) (fire weather season length), Veraverbeke et al. (2017)	Within-season weather conditions have a strong influence on interannual variability in burned areas in forested land and Arctic tundra, high confidence in forested land (***)	Weather conditions regulate vegetation productivity and fuel moisture. While rainfall during the dry season suppresses fire activity, vegetation build-up during wet years in more fuel-limited arid areas can increase burned area in subsequent years. Burned area is mostly limited by a combination of above-average air temperatures and drought, often caused by lightning in high-northern latitudes.
	Arctic tundra, Alaska: Average temperature and total precipitation in June-August alone can explains ~90% of the variance in annual area burned from 1950-2009 in Alaska, with thresholds at ~11°C and ~150 mm (minimal sensitivities below the temperature and above the precipitation threshold, but strong increases in burned areas when crossing the thresholds). Exceptionally warm and dry conditions facilitated the Anaktuvuk River Fire in 2007.	Hu et al. (2010a) Hu et al. (2015), Jones et al. (2009) (Anaktuvuk River Fire)
Small Islands	Observations
	Attribution Pacific, Big Island Hawaii: Excess rainfall for the year prior to fire occurrence increases vegetation productivity increased fire risk across grasslands, and thus overall fire probability, more so than drought the year that fire occurred. Prior year rainfall anomalies can explain about 14% of the variations in the observed probability of fire, but the effect on burned areas is not explicitly quantified.	Trauernicht (2019)	moderate sensitivity of burned areas to weather fluctuations in individual Island in the Caribbean and Pacific, low confidence (*) because of limited number of studies, no assessment elsewhere
	Caribbean, Puerto Rico: Relative weather indicators (average across a certain number of days before the event divided by the long term average) are shown to have a high predictive power when classifying local units into fire occurrence versus no fire occurrence. Absolute weather indicators (average across a certain number of days before the event) were most important for the classification of large versus small fires. The classification of the extent is more difficult but the climate indicator still provided substantially better performance than random assignment. For the occurrence problem precipitation and minimum temperatures were most important while in the extent problem, after precipitation, maximum temperature and wind speed were most important.	Van Beusekom et al. (2018)
	S20 Overarching societal impacts - Malnutrition
Global	Observations More than 1 in 3 children in many developing countries are affected by stunting - a result of long-term nutritional deprivation. Moreover, as of 2019 more than 750 million people worldwide (i.e. almost 1 in 10) were severely food insecure.	UNICEF WHO (2019) Joint child malnutrition estimates, FAO et al. (2020)
	Attribution 53 developing countries: In the recent past, periods of drought were associated with lower body height in children under 5 years old, in the study countries. The severity of stunting increased monotonically with drought severity.	Cooper et al. (2019)	moderate (as partly hard to quantify) sensitivity of stunting to weather fluctuations (in particular droughts) in developing countries and in context of subsidence farming, medium confidence (**) no assessment elsewhere
	Drought (measured by 24-month SPEI) Subsistence farmers in low- and middle-income countries: Significant but variable link between weather variables, e.g., rainfall, extreme weather events (floods/droughts), seasonality, and temperature, and childhood stunting at the household level (12 of 15 studies, 80%)	Phalkey et al. (2015) (review)		rainfall variability and intensity, droughts, floods
	Evidence from 19 low- and middle-income countries and more than 107 000 children shows that long-term (over 30 years) temperature levels and low precipitation in the year prior to the surveys are associated with decreases in overall child diet diversity.	Niles et al. (2021)
Africa	Observations Sub-Saharan Africa contained 17% of the world’s children under 5 but a third of all undernourished children in 2015.	UN (2015)
	Attribution In Sub-Saharan Africa high temperatures and low precipitation have been shown to reduce child weight. High temperatures are also shown to lead to wasting. The largest weight loss is monitored in rural areas, likely due to a loss of agricultural yields caused by excess heating during the growing season.	Baker and Anttila-Hughes (2020) Thiede and Strube (2020) Davenport et al. (2020) Grace et al. (2015) Randell et al. (2020) Saronga et al. (2016)	major sensitivity of malnutrition to weather in Sub Saharan Africa, high confidence (***)
	In rural Nigeria, a negative rainfall shock has been shown to decrease agricultural productivity and hence decrease household consumption by 37%.	Amare et al. (2018)
	Ethiopia: Greater rainfall during the rainy seasons in early life is linked to greater height for age. Higher temperatures in utero and more rainfall are positively associated with severe stunting.	Randell et al. (2020)
	Tanzania: Droughts and floods have been shown to lead to food shortages in rural Tanzania.	Saronga et al. (2016) (qualitative)
Asia	Observations Bangladesh: Bangladesh was hit by the worst flood in over a century in the summer of 1998.	del Ninno and Lundberg (2005)
	Mongolia: The severe winter in 2009-10 caused catastrophic damage and resulted in the death of 10.3million livestock.	Groppo and Kraehnert (2016)
	Indonesia: In Indonesia, about a third of children under age 5 are stunted among the highest rates in Asia and the Pacific.	UNICEF (2019)
	India: The prevalence of underweight among children in India is the highest worldwide.	Kumar et al. (2016)
	Japan: The mean national height-for-age z-scores of Japanese primary school children in the 1930, the period that Ogasawara and Yumitori (2019) look at (see below), was close to the scores in rural areas in low- and middle-income countries today.	Ogasawara and Yumitori (2019)
	Attribution Bangladesh: The nutritional status of children in households that were more severely exposed to the flood deteriorated. Evidence from a three round panel data suggests that children exposed to the flood were adversely affected by the shock to their health and did not recover within the survey period.	del Ninno and Lundberg (2005)	moderate sensitivity of nutrition status (of children) and consumption to weather conditions in Bangladesh, China, Japan, India, Indonesia and Mongolia, medium confidence in India (*) and in remaining countries low confidence as based on individual studies ()
	Mongolia: Identification of a causal impact of the weather shock on children’s height by exploiting exogenous variation in the intensity of the shock across time and space by two waves of a panel survey. The shock significantly slowed the growth trajectory of exposed children from herding households. This negative effect is persistent, remaining observable in both panel waves, three and four years after the shock. The effect is driven by children who experienced the shock in utero.	Groppo and Kraehnert (2016)
	Indonesia: Evidence from panel data regression analyses shows that delays in the monsoon are systematically associated with poor child health outcomes in Indonesia. Delays in monsoon onset during the prenatal period are linked to a reduced child height among children age 2-4 years. Delays in the most recent monsoon season adversely affect weight of young (<2 years) children.	Thiede and Gray (2020)
	India: Evidence from rural India shows that children exposed to a drought in utero or at birth have a lower weight-for-age z-score, a higher probability of being underweight and severely underweight, and a higher probability of dying before age 1. Further, adverse weather events have been shown to aggravate inequality by reducing consumption of poor farming households	Kumar et al. (2016) (drought impacts) Sedova and Kalkuhl (2020) (farming households)
	Dry shocks (absolute deviation of rainfall below its long-run mean) have been shown to have a statistically significant and negative effect on household nutrition. A median dry shock corresponds to a drop in households spending by 1 percent per capita per month on food and a drop in calories, protein, and fat consumption by up to 1.4 percent.	Carpena (2019)
	In rural Eastern India, flooding is associated with child undernutrition.	Rodriguez-Llanes et al. (2016)
	Japan: In Japan, exposure to cold waves in early-life was associated with stunting. In the coldest regions, the stunting effects of cold weather shocks on the boys and girls are estimated to be approximately 0.8 and 0.6 cm, respectively.	Ogasawara and Yumitori (2019)
	China: The prenatal exposure to heat waves has been shown to have stronger negative effects than exposure to cold spells on surviving births. Between the 1980s and the 1990s, the population-weighted total number of days with a mean temperature above 28°C increased from 233 days to 261 days and have on average caused 1.4% (46.5 g) additional damage to birth weight.	Chen et al. (2020b)
Australasia	Observations
Australasia	Attribution		no assessment
Central and South America	Observations Brazil: Amazonian societies in Brazil were exposed to severe floods between 2009 and 2014 and major droughts in 2005, 2010 and 2015, leading to disruption to flows of essential goods and public services.	de Oliveira et al. (2021)
	Ecuador. Severe floods hit Ecuador during the 1997-1998 El Niño phenomenon, leading to socio-economic losses that reached 13% 13% of Ecuador’s GDP in 1996. Losses in agriculture accounted for approximately 6% of the GDP, whereby approximately 30% of Ecuador's crops were destroyed. Damages in Ecuador’s infrastructure and transportation corresponded to 3.7% of the GDP.	Rosales-Rueda (2018)
	Peru: Peru experiences one of the highest rates of stunting in Latin America, affecting roughly one in four children.	Nicholas et al. (2021)
	Attribution Brazil: Focusing on Amazonian populations in Brazil, a study shows that prenatal exposure to extremely intense rainfall is associated with preterm birth, restricted intrauterine growth and lower mean birth weight (≤ -183 g). Adverse birth outcomes are also a consequence of non-extreme intense rainfall (40% higher odds of low birth weight), drier conditions than seasonal averages (-39 g mean birth weight) and conception in the rising-water season (-13 g mean birth weight).	Chacón-Montalván et al. (2021)	strong sensitivity of nutrition status of children to weather in Brazil, Columbia, Ecuador and Peru, medium confidence in Brazil (*), in remaining countries low confidence as based on individual studies ()	Rainfall variability
	Another study on impacts of in utero exposure to Hurricane Catarina in Brazil finds that the adverse effects of the exposure are concentrated among babies born to mothers in age 15-24: birth weight decreased by 82 g, the probability of being born low birth weight increased by 3.4 pp, and fetal deaths increased by about 17 per 1,000 live births and fetal deaths.	de Oliveira et al. (2021)		Extreme events (Hurricane Catarina)
	Negative rainfall shocks in the Brazilian semiarid are correlated with higher infant mortality, lower birth weight, and shorter gestation periods.	Rocha and Soares (2015)		Rainfall variation
	Colombia: Exposure to moderate heat waves during the third trimester of pregnancy leads to a reduction in infants’ birth weight by about 4.1 g. Exposure to moderate cold shocks during the first and second trimesters of pregnancy leads to a reduction in the length at birth by 0.014-0.018 cm.	Andalón et al. (2016)		Extreme heat and cold
	Ecuador: An analysis of extreme floods in Ecuador during the 1997-1998 El Niño shows that children exposed to severe floods in utero, in particular during the third trimester, were more likely to be born with low birth weight and are shorter in stature five and seven years later. The mechanisms behind can be attributed to households’ decline in income, total consumption, and food consumption in the aftermath of the shock.	Rosales-Rueda (2018)		Extreme events (El Niño-related floods)
	Peru: Rural, indigenous children at age 0-1 experience a reduction in height-for-age associated with prenatal excess rainfall, which can also be observed at age 4-5.	Nicholas et al. (2021)		Rainfall variation
Europe	Observations
Europe	Attribution		no assessment
North America	Observations An estimated 10.5% of households in the USA were food insecure at least some time during 2019, i.e. they lacked access to food sufficient for an active, healthy life for all household members.	U.S. Department of Agriculture Coleman-Jensen et al. (2020)
	Attribution Following disasters (i.e. Hurricane Katrina, Hurricane Harvey) socio-economically vulnerable groups were more at risk to be food insecure.	Clay et al. (2018) (Hurricane Katrina) Fitzpatrick et al. (2020) (Hurricane Harvey)	moderate sensitivity of nutrition status to weather events, medium confidence (**)
	In the USA, exposure to cold and hot exposure during pregnancy increased low birthweight risk.	Ha et al. (2017) Molina and Saldarriaga (2017)
Small Islands	Observations The reliance on food imports and processed foods as well as a decline in consumption of locally produced foods - all of which are linked to malnutrition - have an increasing trend in Pacific Island Countries.	Iese et al. (2021)
Small Islands	Attribution In the aftermath of the Tropical Cyclone (TC) Harold in April 2020 crops were destroyed and livestock killed in Tonga, Solomon Islands, Vanuatu and Fiji. This led to increased hardships by reducing production, income and food for households in the affected countries.	Iese et al. (2021)	no assessment because of missing additional studies
	S14 Coastal systems - Damages
Global	Observations Coastal erosion: The surface of eroded land in 1984-2015 is approximately 28,000 km² according to satellite imagery, twice the surface of gained land.	Mentaschi et al. (2018)
	Salinization: An estimated 500 million people worldwide are adversely affected by the salinization of freshwater and soils. This includes impacts on food and water security, agricultural livelihoods, and human health.	Rahman et al. (2019)
	Attribution Coastal erosion: While the dominant cause for land loss is planned exploitation of coastal resources, the installment of dams, or the clearing of mangrove forests, another important driver of coastal erosion are natural disasters such as extreme storms, as well as relative sea level rise.	Mentaschi et al. (2018)	high sensitivity to tropical cyclones, high confidence (***)
	Salinization: Local drivers such as groundwater abstraction, improper maintenance of sea defense infrastructure, and saline aquaculture are compounded with global sea level rise and changes in storm surge.	Rahman et al. (2019), Bayabil et al. (2020) Eswar et al. (2021) Mukhopadhyay et al. (2021)
	Tropical cyclones: Between 1960 and 2020, more than 2,000 landfalling TCs caused almost $1.4 trillion (2010 US$) in direct economic damages and approximately 910,000 fatalities, the most destructive being Hurricane Katrina (2005) with $150 billion in direct economic damages. The single most fatal TC was the 1970 Bhola cyclone with 300,000 reported fatalities. Coastal floods: Apart from TC-related flooding, EM-DAT attributes $14.6 billion (2010 US$) in direct economic damages and 3,269 fatalities to the remaining types of coastal flood disasters between 1960 and 2020 (hydrological disasters, especially not including Tsunamis). Even though coastal areas make up only a small fraction of global land area, 40% of all damages and 25% of all fatalities in the EM-DAT disaster groups of meteorological, climatological and hydrological disasters are due to coastal disasters (TCs and coastal floods).	CRED and Guda-Sapir (2021)		The considered data from ‘The International Disaster Database’ (EM-DAT, CRED and Guda-Sapir (2021)) refer to the amount of damage to property, crops, and livestock at the moment of the event and do not include damages that unfold over following years. The database is made up of information from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies, with priority given to data from UN agencies, governments, and the International Federation of Red Cross and Red Crescent Societies. Entries are constantly reviewed for inconsistencies, redundancy, and incompleteness. We uniformly assume ‘high confidence’ associated with the tropical cyclone assessments based on EM-DAT assuming an easier assignment of damages and fatalities to tropical cyclones than to droughts.
Africa	Observations
	Attribution Cyclone Idai: Reported 602 deaths, 1600 injuries, 1.85 million people in need, damages and losses of US $3.2 billion in Mozambique. Total damages between US$548 million and US$622 million in Zimbabwe. Between 1900 and 2018, at least 334 major flood events occurred in the Western Cape, South Africa, with the number of flooding events per year increasing over time.	Nhamo and Chikodzi (2021) Charrua et al. (2021) Dube et al. (2021)	high sensitivity to tropical cyclones, high confidence (***)
	Total TC impacts: Over the period 1960-2020, more than 5,500 fatalities and almost $10 billion (2010 US$) in direct economic damages are associated with TCs in Africa (EM-DAT). The share of economic damages due to coastal hazards among all meteorological, climatological and hydrological hazards (according to EM-DAT) in Africa is 25%, which is lower than the global average, but still high for a region with generally low TC activity.	CRED and Guda-Sapir (2021)		see discussion of EM-DAT data in the global section.
Asia	Observations
	Attribution Tropical cyclones: According to EM-DAT, of the 910,000 recorded deaths due to TCs worldwide between 1960 and 2020, a total of 847,000 were due to TCs in Asia, while only $385 of the total $1,402 billion (2010 US$) in direct economic damages are associated with TCs in Asia. Coastal floods: Apart from TC-related flooding, EM-DAT attributes $12.3 billion (2010 US$) in direct economic damages and 2,060 fatalities to the remaining types of coastal flood disasters between 1960 and 2020 in Asia (hydrological disasters, especially not including Tsunamis). Within Asia, the share of economic damages as well as fatalities due to coastal hazards among all meteorological, climatological and hydrological hazards (according to EM-DAT) is more than 31% each, which is lower than the global average for economic damages, but higher in terms of deaths.	CRED and Guda-Sapir (2021)	high sensitivity to tropical cyclones, high confidence (***)	see discussion of EM-DAT data in the global section.
	Cyclones Sidr (2007) and Aila (2009): The prevalence of diarrhoea, skin diseases, hepatitis and other infectious diseases has increased after the events, and the mental health of people in coastal areas of Bangladesh has degraded.	Kabir (2014)
	Saline contamination of soils: Extreme coastal water levels lead to saline intrusion and subsequent agricultural income losses in Bangladesh.	Chen and Mueller (2018) Sherin et al. (2020)
Australasia	Observations
	Attribution Damages were recorded for 38 of all 154 landfalling tropical cyclones in Australia since 1970. On average, each landfalling TC (i.e. including the ones not inducing damages) caused damages of about 11 million Australian dollars in the last two decades of the twentieth century. Cyclone Tracy (1974) caused insured damages of $4090 million (normalized to 2012). Cyclone Yasi (2011) caused a $300 million loss to agricultural production in Queensland and insured damages of $1412 million.	Seo (2014) Handmer et al. (2018)	coastal damages induced by individual tropical cyclones can be very high but overall they were lower than in other regions which has led us to the assessment: moderate sensitivity, high confidence (***)
	Total TC impacts: Over the period 1960-2020, 236 fatalities and more than $20 billion (2010 US$) in direct economic damages are associated with TCs in Australasia (EM-DAT). The share of economic damages due to coastal hazards among all meteorological, climatological and hydrological hazards (according to EM-DAT) in Australasia is 22%, which is lower than the global average.	CRED and Guda-Sapir (2021)		iscussion of EM-DAT data in the global section.
Central and South America	Observations
	Attribution Hurricanes Eta and Iota (2020) caused more than 400 deaths and damages above $6 billion (2010 US$) in Central America, mainly due to extreme rainfall. Hurricane Mitch (1998): According to EM-DAT, 3.2 million people from 8 countries were affected by this storm, almost 19,000 lost their lives. The direct economic damages amounted to $8.88 billion (2010 US$). Total hurricane impacts: Over the period 1960-2020, more than 35,000 fatalities and almost $64 billion (2010 US$) in direct economic damages are associated with hurricanes in Central and South America (EM-DAT). The share of economic damages due to coastal hazards among all meteorological, climatological and hydrological hazards (according to EM-DAT) in Central and South America is 36%, which is slightly lower than the global average, but still high for a region with generally low TC activity. For fatalities, the share of 33% is clearly above-average.	CRED and Guda-Sapir (2021)	high sensitivity to tropical cyclones, high confidence (***)	see discussion of EM-DAT data in the global section.
	Hurricanes in Mexico: Between 1990 and 2011, hurricanes caused an estimated 1,600 deaths in terms of mortality displacement. Due to underreporting, this exceeds the number of 989 officially reported deaths in EM-DAT.	Pugatch (2019)		see discussion of EM-DAT data in the global section.
Europe	Observations
	Attribution The storm-induced North Sea flood of 1953 caused damages of 0.5 billion in Belgium, more than 1 billion in the UK and almost 5 billion in the Netherlands inflation-adjusted to 2011 EUR. Flooding from the Vincinette winter storm caused damages of almost 5 billion in Hamburg in 1962 inflation-adjusted to 2011 EUR. Flooding from cyclone Xynthia 2010 caused damages of 1.3 billion in coastal France inflation-adjusted to 2011 EUR.	Paprotny et al. (2018a)	coastal damages induced by individual storms can be high but overall events were rare and the overall damage much lower that in other regions which has led us to the assessment: moderate sensitivity, high confidence (***)
	Winter storm Xaver (2013): The financial impact of the coastal flooding was estimated to be more than EUR 1.5 billion.	Horsburgh et al. (2017)
	Hurricanes: Even though Europe is clearly outside the typical realm of TC activities, remnants of North Atlantic hurricanes occasionally hit European coastlines, as in the case of Hurricane Charley (1986). EM-DAT associates 176 deaths and $3.7 billion (2010 US$) in economic damages to hurricanes that reached European countries 22 times between 1960 and 2020. Clearly, this is not a major disaster type in Europe, where economic damages from other weather-related disasters amount to almost $450 billion.	CRED and Guda-Sapir (2021)
North America	Observations
North America	Attribution Total hurricane impacts: Over the period 1960-2020, more than 4,300 fatalities and $770 billion (2010 US$) in direct economic damages are associated with hurricanes along the western and eastern coasts of North America (EM-DAT). For North America, hurricanes are clearly the single most devastating disaster category. The share of economic damages due to hurricanes among all meteorological, climatological and hydrological hazards (according to EM-DAT) in North America is almost 55%.	CRED and Guda-Sapir (2021)	high sensitivity to hurricanes, high confidence (***)
Small Islands	Observations Exemplary damages induced by individual tropical cyclones (TC): TC Pam 2015: Destruction is considered one of the worst natural disasters in the history of Vanuatu. The cumulative reduction in economic activity in affected Pacific islands within the first 5 months following the event (as indicated by nightlight activity) amounted to as much as 111%. TC Fantala 2016: In addition to land loss in uninhabited islets, up to 28% of land area, the category 5 storm spared only 4 of the 50 buildings in the Farquhar Atoll (Seychelles) from severe damages, leaving 19 buildings completely destroyed. TC Winston 2016: US$0.9 billion destruction in Fiji equivalent to 20% of GDP Damages to agriculture: USD 56.5 million through TC Pam in Vanuatu and 255 million through TC Winston in Fiji.	Magee et al. (2016) (TC Pam), Mohan and Strobl (2017) (TC Pam), (UNITAR, 2016) Duvat et al. (2017) (TC Fantala), (Shultz et al., 2019) (TCs in 2017), Mansur et al. (2017) (TC Winston)	coastal damages induced by individual storms can be high but overall events were rare and the overall damage much lower that in other regions which has led us to the assessment: moderate sensitivity, high confidence (***)
	During 2017, 22 of the 29 Caribbean SIDS were affected by at least one named storm and multiple SIDS experienced extreme damage. Mangrove habitats throughout the Caribbean severely damaged by hurricanes.	Taillie et al. (2020) Walcker et al. (2019)
	Major floods in the Maldives led to serious damage to the entire Archipelago (1987) and affected 1650 people and more than 500 housing units (2007) The giant swell 1996 from a Major floods affected a large area in French Polynesia (> 20% flooded or destroyed homes). A stronger but shorter event in 2011 caused less damage.	Wadey et al. (2017) Canavesio (2019)
	Attribution The damages are clearly associated with the cyclones (wind speed, rainfall, coastal flooding). In several cases the intensity of the extremes (rainfall associated with the tropical cyclones) could be attributed to anthropogenic climate forcing. However, as there is no translation of the attributable increase in intensity to the associated increase in damage, the description of the total damages induced by the TCs only documents a sensitivity to weather extremes but does not represent an attribution of damages to climate change. For SIDS, TCs are very clearly the single most devastating disaster category: The share of economic damages due to TCs among all meteorological, climatological and hydrological hazards (according to EM-DAT) in SIDS is more than 97%.	CRED and Guda-Sapir (2021)
	Swells from Southern Ocean storms were a main driver of flood damages in the Maldives for 1987 and 2007 events. Relation to climate change is unclear. Similar swells drove the flood events in French Polynesia in 1996 and 2011. The role of climate change for such swell-driven floods is not quantified.	Wadey et al. (2017) Canavesio (2019)
	S26 Other societal impacts - Social conflict
Global	Observations Armed conflict (measured as outbreak or incidence of militarized conflict between organized actors over political incompatibilities that causes at least 25 battle fatalities per calendar year) exhibits considerable inter-annual, inter-decadal, and inter-regional variation in frequency and trends. Global frequency of armed conflict 2014-19 has been at the highest level since the early 1990s. Almost all contemporary armed conflicts are intrastate (civil) conflicts. In 2019, 50 of the 54 active state-based armed conflicts were located in Africa or Asia. Global severity of armed conflict (measured as battle-related deaths) has declined steeply on average since 1950, despite an uptick in recent years. Around 51,000 were killed in state-based armed conflict in 2019, only around 200 of which were casualties of interstate conflicts. Non-state (inter-group) conflict has increased markedly since 1989 and caused around 20,000 deaths in 2019. One-sided violence including terrorism shows a downward trend. UCDP estimates that around 5000 civilians were killed in one-sided events in 2019. Other data sources (e.g., Correlates of War, ACLED) corroborate these trends.	Gleditsch et al. (2002) Pettersson and Öberg (2020) (UCDP dataset)	Very likely
	Attribution Climate (variability, hazards, trends) affects prevalence of armed conflict within states, primarily in countries that harbor multiple non-climatic risk factors. The climate effect is judged to be small relative to leading non-climatic factors.	Mach et al. (2019),Buhaug et al. (2014) Hsiang and Burke (2014) Theisen (2017) Koubi (2019) von Uexkull and Buhaug (2021)	Sensitivity of armed conflict to climate conditions increases from low to moderate conditional on non-climate risk factors such as high population, ethnopolitical exclusion, agricultural dependence or socio-economic development, medium confidence (**).	All relevant dimensions of the climate system
	Weather- and climate-related disasters and extremes increase prevalence of social unrest and civil conflict in countries marked by high populations, ethnopolitical exclusion, agricultural dependence, and low socioeconomic development.	Eastin (2016) Schleussner et al. (2016) Ide et al. (2020) Abel et al. (2019) Buhaug et al. (2020) Ghimire and Ferreira (2016) Ide et al. (2021)	Sensitivity of armed conflict to climate conditions increases from low to moderate conditional on non-climate risk factors such as high population, ethnopolitical exclusion, agricultural dependence or socio-economic development, medium confidence (**).	Drought, meteorological, climatological, and hydrological disasters
	Climatic hazards increase the prevalence of armed conflict via forced migration and human displacement.	Abel et al. (2019) Bhavnani and Lacina (2015) Bosetti et al. (2020) Ghimire et al. (2015) Burrows and Kinney (2016) AR6 WGII Ch. 7.4 & Box 7.1 FOD	Minor sensitivity of armed conflict to weather-induced migration, low confidence	Forced migration in response to climatic hazards
	Climate-induced variation in transboundary freshwater resources increases international cooperation, although effect is sensitive to the institutional context. Some evidence also suggests that weather anomalies increase prevalence of water-related interstate disputes	Bernauer and Böhmelt (2020) Dinar et al. (2015) Dinar et al. (2019) Link et al. (2016) Petersen-Perlman et al. (2017) Schmidt et al. (2021)	Minor to moderate sensitivity of international water cooperation to weather conditions, depending on context, low confidence (some concerns about data quality)	Variation in transboundary river flows
	Drought and rainfall loss increase the prevalence of civil conflict through loss of livelihood and income, notably for agriculturally dependent and politically excluded social groups (medium confidence). Some concerns about sample selection bias in the literature.	Bell and Keys (2018) Buhaug et al. (2015) Harari and Ferrara (2018) Owain and Maslin (2018) von Uexkull (2014) Von Uexkull et al. (2016)	Sensitivity of violent conflict to weather fluctuations (including extreme events) increases from minor to moderate (e.g. social unrest to weather-affected food price shocks) depending on societal context (e.g. political exclusion, agricultural dependence), medium confidence (**, some concerns about sample selection bias in the literature)	Precipitation deficit and drought
Africa	Observations
	Attribution Drought and rainfall loss increase prevalence of non-state (communal) violence and individual support for use of violence, especially among marginalized and agriculturally dependent groups (medium confidence). Some concerns about sample selection bias in the literature	Bagozzi et al. (2017) Detges (2017) Linke et al. (2018) Nordkvelle et al. (2017) van Weezel (2019) Vestby (2019) von Uexkull et al. (2020) Yeeles (2015)	Sensitivity of violent conflict to weather fluctuations (including extreme events) increases from minor to moderate (e.g. social unrest to weather-affected food price shocks) depending on societal context (e.g. political exclusion, agricultural dependence), medium confidence (**, some concerns about sample selection bias in the literature)	Precipitation deficit and drought
	High temperature anomalies increase prevalence of civil and non-state conflict (low confidence, low agreement) some concerns about sample selection bias in the literature.	Bollfrass and Shaver (2015) Landis (2014) Maystadt et al. (2015) O’Loughlin et al. (2014) Yeeles (2015)		High temperature
	Weather-affected rising food prices or food insecurity increase the likelihood of social unrest, especially in states marked by poor governance and inefficient markets. Moderate to strong sensitivity of social unrest to weather-affected food price shocks, depending on context, (low confidence). Limited quantification of weather effect.	Smith (2014) Raleigh et al. (2015) Jones et al. (2017) McGuirk and Burke (2020) Koren et al. (2021)		Rising food prices in response to drought
Asia	Observations
	Attribution Drought increases the prevalence of civil conflict for agriculturally dependent and politically excluded social groups in poor countries (low confidence, limited number of independent studies). Drought effects on lower levels of water-related conflict (in MENA region) are similarly highly context dependent.	Von Uexkull et al. (2016) Wischnath and Buhaug (2014) Ide et al. (2021)	Minor to moderate sensitivity of violent conflict to weather conditions (particularly drought) depending on social context (e.g. exclusion and agricultural dependence), medium confidence (**)	Drought
	Drought and rainfall loss increase prevalence of non-state (communal) violence and individual support for use of violence, especially among marginalized and agriculturally dependent groups in poor countries (medium confidence, some concerns about sample selection bias in the literature)	Bagozzi et al. (2017) Nordkvelle et al. (2017) Yeeles (2015)		Precipitation deficit and drought
	High temperature anomalies increase prevalence of civil and non-state conflict (medium confidence, some concerns about sample selection bias in the literature).	Bollfrass and Shaver (2015) Caruso et al. (2016) Landis (2014) Yeeles (2015)		High temperature
Australasia	Observations
Australasia	Attribution		No study of weather sensitivity of armed conflict
Europe	Observations
Europe	Attribution		No study of weather sensitivity of armed conflict
Central and South America	Observations
Central and South America	Attribution		No study of weather sensitivity of armed conflict
North America	Observations
North America	Attribution		No study of weather sensitivity of armed conflict
Small Islands	Observations
Small Islands	Attribution		No study of weather sensitivity of armed conflict
	S29 Other societal impacts - Displacement and migration
Global	Observations Internal displacement: Since reporting started in 2008, every year between 15 million and 42 million people worldwide were displaced within their countries in relation to disasters.	IDMC, Global Report on Internal Displacement, IDMC (2020)
	International migration: Millions of people emigrate from their countries of origin each year, becoming international migrants as reported by the UN’s International Organization for Migration. Migrants make up roughly 3% of the global population.	IOM Global Migration Indicators 2018 Abel and Sander (2014) Abel (2018)
	Internal migration/ urbanization: Increasing urban population: In 2000, there were 371 cities with 1 million inhabitants or more worldwide. By 2018, this figure increased to 548 cities	UN (2018)
	International refugees: Millions of people every year flee their countries of origin, becoming international refugees or asylum seekers as reported by UNHCR. Global trend in forced displacement has been rising every year since 2011.	UNHCR Global Trends in Forced Displacement 2020 UNHCR (2020)
	Attribution Sensitivity of internal displacement and international migration to (extreme) weather is different (see below). For the Figure we summarized the finding as described in the orange cell.		Sensitivity assessments range from High confidence (*) in strong sensitivity of displacement to high confidence (*) in minor sensitivity of migration in low and high income groups
	Internal displacement: Weather extremes are the dominant driver of internal displacement. On average, 87% of disaster-induced displacement is due to weather-related disasters, mainly floods (51%) and storms (35%), only 13% are due to geophysical hazards such as earthquakes. The close temporal and spatial proximity between disaster and displacement occurrence allows a simple attribution of displacement to disasters, however, vulnerability to weather-induced displacement is not uniform and not well understood.	IDMC, Global Report on Internal Displacement. IDMC, Disaster displacement: A global review, 2008-2018, (IDMC, 2019) IDMC (2020) Kakinuma et al. (2020)	Strong sensitivity of displacement to weather fluctuations, high confidence (***)	Primary drivers: Fluvial, pluvial, and coastal flooding, tropical cyclones, and other storms. Other drivers: Droughts, wildfires, landslides, extreme temperatures.
	International migration: Effects of temperature or precipitation anomalies on international migration have been found in multiple studies using panel datasets including large numbers of countries. The sign and magnitude of the effect are not universal but depend on factors such as income levels in the country of origin. Generally, positive temperature anomalies and both negative and strongly positive precipitation anomalies have been found to increase emigration from middle-income countries. In poor countries, such anomalies may have no effect or even suppress migration, as people cannot afford to migrate (poverty traps).	Coniglio and Pesce (2015) (migration to rich OECD countries) Cattaneo and Peri (2016) (low income countries, poverty traps) Beine and Parsons (2017) (poverty traps) Nawrotzki and DeWaard (2016)	moderate to minor sensitivity, depending on income levels and the importance of the agriculture sector, high confidence (***)	inter-annual or inter-decadal anomalies in temperature or precipitation
	Climate (temperature, precipitation) anomalies influence international out-migration via agricultural productivity and employment in the agricultural sector. This effect was found for countries such as Mexico or India, and generally for countries with large importance of the agricultural sector. The effect is mediated by the income in the origin country: No climate effect was found in the poorest countries - likely because of poverty constraining migration - and in rich countries.	Cai et al. (2016) Nawrotzki et al. (2015) Viswanathan and Kavi Kumar (2015) Falco et al. (2019)		Weather-induced variations in agricultural productivity
	Internal migration/Urbanization: Multi-stage regression analyses as well as recent meta-regression analyses provide further evidence of an indirect effect of climate variability on migration, operating through urbanization: Greater variability in temperature and rainfall is associated with increased rural-urban movement, particularly within middle-income countries, and increased urbanization in turn is associated with higher out-migration rates to international destinations.	Šedová et al. (2021), Marchiori et al. (2012), Maurel and Tuccio (2016) Castells-Quintana et al. (2020) Peri and Sasahara (2019) Hoffmann et al. (2020)
	Natural hazards are found to affect out-migration particularly from middle-income countries, however, studies combine weather-related and geophysical hazards into composite indices, making it impossible to discern the effects of weather events alone.	Beine and Parsons (2017) Gröschl and Steinwachs (2017)		Natural hazards (both weather-related and geophysical)
	International refugees: Drought and temperature anomalies in countries of origin are overall weakly associated with increased flows of irregular international migrants and asylum seekers, with direction, shape, and strength of relationship varying between studies, regions, and over time.	Missirian and Schlenker (2017) Abel et al. (2019) Cottier and Salehyan (2021) Schutte et al. (2021)	Mostly minor sensitivity (moderate for some countries and periods), medium confidence (**)	Drought, measured by Standardized Precipitation-Evaporation Index (SPEI), temperature anomaly
Africa	Observations Displaced people in East Africa crossing borders and becoming refugees/increasing urbanization rates in Sub-Saharan Africa	Owain and Maslin (2018)
	Attribution During 1963-2014 in East Africa, severe droughts contributed to refugees crossing international borders	Owain and Maslin (2018)	Magnitude of sensitivity cannot be assessed with study design and therefore rated moderate, medium confidence (**) locally high sensitivity to flood-induced displacement
	In Sub-Saharan Africa, regions where cities are likely to be manufacturing centers, drier conditions increase urbanization.	Henderson et al. (2017)
	Flood-induced displacement is high under low to moderate exposure to flooding in low-income countries such as Nigeria and Zimbabwe.	Kakinuma et al. (2020)
Asia, internal and international migration, urbanisation, rural-to-urban migration	Observations internal migration/urbanization/rural-to-urban migration in Indonesia, India, Bangladesh, Vietnam and other countries.
	Attribution Temperature had a nonlinear effect on migration within Indonesia	Bohra-Mishra et al. (2014)	moderate sensitivity in Indonesia and India and Vietnam, low confidence () in India and Indonesia as only based on one study, medium confidence in Vietnam (*) no assessment elsewhere	annual temperature anomalies
	Natural disasters, notably flood hazards, increase urbanization, i.e., domestic rural-to-urban migration in Bangladesh and Vietnam, whereas drought reduces migration (Vietnam).	Petrova (2021) Koubi et al. (2016)		Natural hazards (floods, drought)
	An analysis of household-level permanent migration (internal and international) in coastal Bangladesh (populations surveys, 2003-2011) establishes a link from soil salinity to crop production and then on migration indicating that soil salinity may be a more important driver of migration than direct flooding. However, salinity is only measured at two times, 2000 and 2009. Changes in sea levels are a plausible driver of salinity changes, but the paper does not explicitly establish this connection.	Chen and Mueller (2018) (Bangladesh)
	Adverse weather shocks drive rural-urban migration to different states in India. Approximately 8% of urbanization between 2005 and 2012 can be attributed to weather.	Sedova and Kalkuhl (2020)		Temperature and precipitation anomalies
	Several studies using econometric analyses show that in Vietnam, sudden onset events such as typhoons or floods can be attributed to migration to urban areas.	Gröger and Zylberberg (2016) Koubi et al. (2016) Nguyen et al. (2015)		Weather related natural hazards
Australasia	Observations Migration and temporary relocation in Australia
Australasia	Attribution In Australia, sudden onset hazards (e.g. floods, cyclones and wildfires) have been shown to lead to temporary relocation. Slow onset climate change impacts (sea-level rise, temperature or precipitation extremes) have more sustained effects on long-term mobility.	Bakar and Jin (2018) (case study in the Murray-Darling Basin, Australia) Zander and Garnett (2020)	moderate sensitivity in Australia, low confidence (*) as conclusions only derived from studies on Australia	Weather related natural hazards
Central and South America	Observations An increase in migration in Central and South America
	Attribution Droughts and hurricanes have driven migration in Northern Latin America, Central America and the Caribbean. For instance, an estimated 4 more residents aged 15-25 per 1000 will move outside of the province capital as a result of a 1 standard deviation increase in drought intensity.	Baez et al. (2017) (Northern Latin America and the Caribbean), Chort and de la Rupelle (2016) (Mexico), Spencer and Urquhart (2018) (migration from Central American and Caribbean to the United States)	moderate to strong sensitivity e.g. in terms of dependence on agriculture, medium confidence (**)	Weather related natural hazards
	Mexico-US migration: Emigration from Mexico (to the US) is sensitive to Mexican climate conditions. The sensitivity seems to be transmitted through the impact of weather conditions on agriculture as it appears particularly high in rural areas. An analysis of annual migration data from 1970-2009 show that decadal minimum precipitation is associated with particularly high emigration as soon as agriculture is broadly affected, too.	Murray-Tortarolo and Salgado (2021) (drought effects on migration, 1970-2009) Feng et al. (2010) (effect of weather induced fluctuations of crop yields, 1995-2005) Nawrotzki et al. (2015) (comparison of urban versus rural sensitivities, 1986-1999)
Europe	Observations Internal migration in the Netherlands
Europe	Attribution Adverse climatic conditions have been shown to affect historical internal population movements in the Netherlands.	Jennings and Gray (2015)	moderate sensitivity to weather variability in the Netherlands, low confidence (*) as based on one study only, no assessment elsewhere	Temperature- and precipitation-related
North America	Observations An increase in migration and displacement
North America	Attribution In the USA, severe natural disasters such as tornadoes, hurricanes or floods have led to both displacement and migration. Specifically, from 1920 to 2010, severe disasters (a composite indicator capturing disasters associated with 25 or more deaths) have increased out-migration rates at the county level by 1.5 percentage points.	Raker (2020) Boustan et al. (2020)	moderate sensitivity to weather variability, medium confidence (**)	Weather related natural hazards
Small Islands	Observations Puerto Rico: After September 2017 Puerto Rico witnessed a “depopulation” of 14% in only 2 years as a result of emigration to the US mainland.	Melendez and Hinojosa (2017)
	Grenada: 150-200 persons emigrated from Carriacou, Grenada per year since the end of World War II. In sum, this is more than its current population.	Cashman and Yawson (2019)
	Attribution Puerto Rico: The migration appears as a direct consequence of Hurricane Maria	Melendez and Hinojosa (2017) Alexander et al. (2019)	Strong sensitivity, medium confidence (**), no assessment elsewhere
	Grenada: The significant droughts in the mid-1930s to mid-1940s, mid-1960s to mid-1970s, and recently in 2000-2001 coincided with population dips in Carriacou, Grenada after the drought events. The dips are mainly due to out-migration.	Cashman and Yawson (2019)
	S31a Other societal impacts - Macroeconomic output
Global	Observations Per capita GDP growth rates (in real as we as in nominal terms) in nearly all countries have been subject to substantial annual fluctuations including frequent changes of sign since 1960.	World Bank’s World Development Indicator Database		According to World Development Indicator database of the World bank also employed by original studies (Nominal and Real GDP per cap in constant 2011 USD)
	Attribution Economic performance is shown to be sensitive to temperature fluctuations and weather extremes. The individual assessments are provided below and summarized here.		Moderate sensitivity of economic performance to temperature fluctuations but high sensitivity of output growth to extreme weather conditions, high confidence (***, based on high agreement and robust evidence from several independent studies)
	Effects of temperature fluctuations: Nonlinear dependence of economic production and thus real per capita GDP growth rates on annual temperature variability. Dependence has inverse U-shape with growth rate increases and decreases below and above a critical threshold temperature, respectively. Threshold temperature not country dependent.	Burke et al. (2015) Kalkuhl and Wenz (2020) Burke and Tanutama (2019) Pretis et al. (2018)	Moderate sensitivity of GDP to annual temperature anomalies, high confidence based on high agreement, robust evidence from several independent studies (***)	driver: annual temperature variability, understanding of underlying processes still lacking
	Effect of extreme weather events: Severe tropical cyclones and fluvial floods have negative short-term (immediate and up to 5 years after shock) and long-term (up to 10-15 years after shock) impacts on economic growth. Moderate floods may have positive short-term impacts on economic growth but impacts of severe floods are always negative.	Felbermayr and Gröschl (2014) Panwar and Sen (2019) (tropical cyclones, short term effects) Hsiang and Jina (2014) Berlemann and Wenzel (2018), Krichene et al. (2020) (tropical cyclones, long term effects) Loayza et al. (2012) Felbermayr and Gröschl (2014) Fomby et al. (2013) (fluvial floods, short term effects) Krichene et al. (2021) (fluvial floods, long term effects)	Strong sensitivity of GDP to severe tropical cyclones and fluvial floods. Impact on GDP are strongly negative in the short term, high confidence based on high agreement and robust evidence from several independent studies (**). Long-term responses in GDP are moderately negative, low to medium confidence based on medium agreement and limited evidence ()
	Extreme temperature anomalies, droughts, and wildfires have significant negative short-term impacts on economic growth (in the year of and in the year following the disaster), storms, floods and wet-mass movements also affect economic growth significantly negatively in the long-term (more than five years of the disaster)	Klomp and Valckx (2014) Kousky (2014)	Strong sensitivity of economic growth to extreme events. High confidence (***, high agreement and robust evidence from more than 25 independent studies).
Africa	Observations Per capita GDP growth rates have been subject to substantial fluctuations including changes in sign in Sub-Sahran Africa in the period 1970-2009.	World Bank’s World Development Indicators and African Development Indicators databases
Africa	Attribution In SSA countries, there is an inverted U-shaped relationship between annual mean temperature and economic growth. If a threshold temperature of 24.9 °C is exceeded a percentage increase in temperature significantly reduces economic performance in SSA by approximately 0.13 %. Below the threshold an increase in average temperature by one degree increases economic performance by 3%. Study is based on panel data for 1970-2009 and for 18 SSA countries.	Alagidede et al. (2016)	Strong sensitivity of GDP to annual temperature anomalies, low confidence (*)
Asia	Observations Chinese total factor productivity (TFP) in the manufacturing sector and per capita GDP in this sector has been growing substantially over the period 1998-2007 driving increases in total per-capita GDP due to the large importance of the manufacturing sector for the Chinese economy.	Penn World Tables v. 9.1
Asia	Attribution Inverse U-shaped relationship between output in the manufacturing sector and local daily mean temperatures are mainly caused by a U-shaped dependence of the firm level TFP on temperature for labour and capital-intensive firms. A day with temperature above 90°F decreases TFP by 0.56%, relative to a day with temperature between 50-60°F. Uses data from a half million Chinese firms over 1998-2007.	Zhang et al. (2018a)	Strong sensitivity of GDP and TFP in the manufacturing sector to daily temperature anomalies in China, no assessment elsewhere, low confidence (*) (needs verification by independent studies)
Australasia	Observations
Australasia	Attribution no studies		no assessment
Central and South America	Observations Per capita GDP growth rates(in real as we as in nominal terms) of countries in Central America have been subject to substantial annual fluctuations including some changes of sign since 1960.	World Bank’s World Development Indicator Database		According to World Development Indicator database of the World bank also employed by original studies (Nominal and Real GDP per cap in constant 2011 USD)
Central and South America	Attribution Tropical cyclone strikes reduce output growth on average by 0.83 percentage point in the year of the disaster.	Strobl (2012) (Central America and Caribbean)	Strong sensitivity of output growth to hurricane strikes in the year of the event in 5 Central American countries and 26 Caribbean Small Island States, no assessment elsewhere, low confidence (*, needs verification by independent studies)	Destruction caused by strong winds and extreme rainfall from hurricanes.
Europe	Observations
Europe	Attribution no studies		no assessment
North America	Observations Per capita GDP growth rates (in real as we as in nominal terms) of the US have been subject to annual fluctuations 1960.	World Bank’s World Development Indicator Database		According to World Development Indicator database of the World bank also employed by original studies (Nominal and Real GDP per cap in constant 2011 USD)
	Attribution Dependence on variations in global mean temperature: The combined value of market and nonmarket damage across analysed sectors (agriculture, crime, coastal storms, energy, human mortality, and labour) in the US increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. The meta analysis is based on individual studies demonstrating the sensitivity of the individual systems to regional fluctuations in temperature and precipitation.	Carleton and Hsiang (2016) Hsiang et al. (2017) (Meta analysis based on multiple peer-reviewed studies: Labour (1), mortality (2), crime (2), agriculture (3), coastal impacts (1), energy costs (1))	Strong sensitivity of economic performance to global mean temperature fluctuations in the US, medium confidence (**)
	Sensitivity to hurricanes: Hurricanes reduce annual GDP growth rates of affected coastal counties by at least 0.45 per- centage points in the year of the shock, no significant longer-term impacts, negligible effect on state and national level	Strobl (2011)	Moderate sensitivity of economic performance to hurricane strikes in affected coastal counties in the US, low confidence (*, would need support from independent studies) no assessment elsewhere	28% of the negative growth effects of a hurricane are due to relatively richer people moving away from affected counties in response to the hurricane
Small Islands	Observations Per capita GDP growth rates (in real as we as in nominal terms) in Small Islands States have been subject to substantial annual fluctuations including frequent changes of sign since 1960	World Bank’s World Development Indicator Database		According to World Development Indicator database of the World bank also employed by original studies (Nominal and Real GDP per cap in constant 2011 USD)
Small Islands	Attribution Tropical cyclone strikes substantially reduce economic activity in the 0.5-1.5 years following the disaster. Non significant or even positive impacts are observed after this period. Strong differences in the response dynamics of the various sectors. Immediate impacts in the year of the tropical cyclone strike: Estimates of loss in income growth induced by hurricanes range from 0.83 (across 26 Caribbean Small Islands States + 5 Central American Countries, Strobl, 2012) to 1.5 percentage points (21 Caribbean Small Islands States, Bertinelli et al. 2013) in the year of the disaster. In individual cases the losses can be reach much higher values (Mohan et al., 2017 on the impact of Cyclone Pam on South Pacific Small Island States Ishizawa et al. 2019 on Dominican Republic) Long term effects: Inconsistent findings ranging from now effects beyond the year of the strike (Bertinelli et al. 2013) to negative impacts up to 15 months after the strike (Ishizawa et al. 2019) and even longer lasting impacts (2-3 years after the event) such as significant reduction of exports or consumption (Mohan et al., 2018). The different timing and different directions of the impacts on individual components of national GDP may hide the signal in overall GDP.	Strobl (2012) (26 Caribbean Small Islands States + 5 Central American Countries), Bertinelli and Strobl (2013) (21 Caribbean Small Islands States), Ishizawa et al. (2019) (Dominican Republic) Mohan and Strobl (2017) (Impact of Cyclone Pam on South Pacific Small Island States) Mohan et al. (2018) (21 Caribbean Small Islands States)	Strong sensitivity of economic activity to hurricane strikes in the Caribbean and South Pacific Islands, (high confidence ***, high agreement across studies using different methods) no assessment elsewhere
	S30 Other societal impacts - Within country inequality
Global	Observations Since 1980, within- country income inequality has increased in nearly all countries, but at different speeds. It has increased rapidly in North America, China, India, and Russia and moderately in Europe. Average inequality within developing countries has been slowly rising, though staying fairly flat since 2000.	Ravallion (2014) Alvaredo et al.) UNDP (2013)
	Attribution Based on Data for 92 Countries, extreme weather events have contributed to an increase in within-country inequality because poor people suffer relatively higher well-being losses.	Chapter 8, Hallegatte and Rozenberg (2017) Hallegatte et al. (2016) Hallegatte et al. (2017)	Low to moderate sensitivity of within-country inequality to extremes, low confidence (*, high agreement but more independent studies needed since all studies are based on World Bank’s Decomposing World Income Distribution Database and share same Methodology)	Poorer people are more strongly exposed to climate extremes and have lower coping capacities. Their consumption losses (relative to their lost assets) are therefore larger than for richer parts of the populations
	Based on data from 86 countries from 1965 to 2004, among fluvial and flash floods and tropical cyclones, only floods have been found to negatively impact within-country income inequality. The impact is observed only in the very short-term (up to 5 years after disaster) and doesn’t persist in the long-term (5 to 10 years after disaster).	Yamamura (2015)		As risk of extreme weather events can be predicted, rich people tend to reside in less risky areas. Moreover, poor people are less able to invest in disaster-prevention measures.
Africa	Observations Sub-Saharan Africa has the second-highest average within- country income inequality of all world regions, although with no clear trend since 1980. North Africa has seen steadily falling inequality in this period. Ethiopia: Since 1980, Ethiopia has seen large increases in average income but income inequality has prevailed. Kenya: Inequality in Kenya has been decreasing since 1990. However, the country remains unequal where the top 1% of households have 15% of total income compared to 14% of total income for the bottom 50% of households.	World Inequality Database, World Inequality Report World Inequality Lab (2017)
	Attribution There is evidence that low rainfall has increased within country inequality in Ethiopia and Kenya, see individual statements below.		Moderate sensitivity of within-country inequality to droughts in Ethiopia and Kenya, low confidence (*) no assessment elsewhere
	Ethiopia: Ethiopian drought of 1998-2000 had regressive effects on household income. Poverty traps exist below a certain post-shock asset threshold.	Carter et al. (2007), (1998-2000 drought in Ethiopia), (Dercon et al., 2005)	Moderate sensitivity of within-country inequality to droughts in Ethiopia, low confidence (*, hypothesis would need support from other independent studies)	Poorer household have less financial means to recover and a higher risk to fall below the post-shock asset threshold marking the poverty trap
	Kenya: Negative annual rainfall anomalies have significantly reduced income of households and calories consumption in rural Kenya, and pushed additional people into poverty.	Wineman et al. (2017)	Moderate sensitivity income and calories consumption to negative annual rainfall anomalies. Low confidence (*, needs support by other independent studies)	Among four weather extremes (high rainfall, low rainfall, heat and high winds), households in rural Kenya are mostly affected by low rainfall, as they rely on rain-fed agriculture and livestock.
Asia	Observations Trends in consumption inequality between 1990 and early 2010s have been heterogeneous across the region with pronounced inequality increases in China and India and strong inequality reductions in the Russian Federation. Individual cases for which there is research on the sensitivity of within country inequality to variations in weather conditions: Myanmar: In the period 2005-2010, average real household consumption expenditures remained stagnant, but measured poverty incidence and inequality both declined significantly in Myanmar.	World Inequality Report 2018
	India: Consumption inequality in India has been rising at a moderate pace since the early 1990s. The increase accelerated between 1993/4-2004/5 and then slowed down	India’s National Sample Surveys
	Attribution Myanmar after cyclone Nargis in 2008: Increased inequality within affected regions but decreased inequality between affected and non-affected regions. Overall measured inequality declined because between-region reduction exceeded the within-region increase. Low sensitivity of within country consumption inequality to major cyclone Nargis in Myanmar.	Warr and Aung (2019) (Myanmar)	Sensitivity of within country inequality to weather fluctuations ranges from low (impact of individual tropical cyclones) to high (impact of rainfall variability and drought conditions in rural areas) in Vietnam, India and Myanmar, low confidence (*, would need further independent studies) no assessment elsewhere (not indicated in the Figure in favor of highlighting the range of findings given that only two types of rating can be displayed)
	India: Poor households in Rural India respond more strongly to (seasonal) temperature changes: An increase in spring temperature by 1°C increases consumption of the non-poor by 6% and reduces consumption of the poor by almost 22%. In the cold rabi season poor households profit more strongly from higher temperatures than non-poor households. Less precipitation is harmful to the poor in the monsoon kharif season and beneficial in the winter and spring seasons. Adverse weather aggravates inequality by reducing consumption of the poor farming households. Strong sensitivity of within country consumption inequality to seasonal temperature anomalies in rural India.	Sedova and Kalkuhl (2020)	Sensitivity of within country inequality to weather fluctuations ranges from low (impact of individual tropical cyclones) to high (impact of rainfall variability and drought conditions in rural areas) in Vietnam, India and Myanmar, low confidence (*, would need further independent studies) no assessment elsewhere (not indicated in the Figure in favor of highlighting the range of findings given that only two types of rating can be displayed)	Agriculture is a main impact channel for consumption inequality.
	Vietnam: Higher rainfall variability, higher intensity of floods and meteorological droughts negatively impact growth of household consumption in Vietnam. Consumption reduction is higher among poor households increasing within-country inequality: Weather extremes increase the share of the population living under the poverty threshold in Vietnam from 18.9% to 21.6%, and cause a rise of the inequality (Gini) index by 0.2%. Estimates based on 2008 household survey. Strong sensitivity of within-country inequality to weather extremes in Vietnam. Sensitivity on both rural and urban areas, mainly to rainfall variability and meteorological droughts.	Bui et al. (2014)	Sensitivity of within country inequality to weather fluctuations ranges from low (impact of individual tropical cyclones) to high (impact of rainfall variability and drought conditions in rural areas) in Vietnam, India and Myanmar, low confidence (*, would need further independent studies) no assessment elsewhere (not indicated in the Figure in favor of highlighting the range of findings given that only two types of rating can be displayed)	Higher rainfall variability and meteorological droughts mainly impacts the consumption of poor people in rural areas whereas floods more strongly affect urban areas.
	Vietnam: Households living in communes with steeper slope, higher annual rainfall and temperature variability and flood and drought hazards have significantly lower consumption. Poor households in communes with higher annual rainfall variability and drought hazards have significantly lower consumption growth. Household level, 3-waves, panel data 2010, 2012, 2014	Narloch and Bangalore (2018)	Moderate sensitivity of regional consumption differences to exposure to annual rainfall & temperature variability, and flood & drought hazards
North America	Observations Since 1980, within-country income inequality has increased rapidly in the US.	Ravallion (2014) Alvaredo et al.) UNDP (2013) Alvaredo et al. (2018)
North America	Attribution Historical US hurricanes have increased inequality in affected states. For every 100 billion US dollars in hurricane economic damages there is an increase in income inequality by 5.4 % as measured by Gini coefficient	Miljkovic and Miljkovic (2014) (State level panel data 1910-200) Boustan et al. (2020) (County level panel-data 1920-2010)	Strong sensitivity of income inequality in the US to hurricane strikes, medium confidence due to high agreement of different longitudinal studies but partially conflicting findings for individual hurricanes, e.g., Katrina (Shaughnessy, 2010) (**) no assessment elsewhere	Richer people are moving out There are conflicting findings for individual hurricanes, e.g., Katrina (Shaughnessy et al., 2010)
Small Islands	Observations
Small Islands	Attribution Impact of tropical cyclone Evan on Samoan households. Poorer households suffered higher and more persistent losses with regard to their livelihoods than middle and high income households	Le De et al. (2015)	Low sensitivity of income inequality in Samoa to major tropical cyclone Evans, low confidence (*) no assessment elsewhere	Middle and high income households receive remittances which push up their recovery. The lack of remittances for the poor in Samoa increases with-country inequality.
	S31b Other societal impacts - Between country inequality
Global	Observations Annual per capita GDP growth rates were similar in developing countries and industrialized countries in the period 1960 - 2000. From 2000 onwards, GDP growth rates were substantially higher in developing than in industrialized countries.	World Bank’s World Development Indicators
	Attribution Extreme weather events and temperature fluctuations dampen the convergence of economic development:		Sensitivity of between country inequality to variations in weather conditions range from moderate (e.g., substantial impact on economic productivity in developing compared to non significant impacts in developed countries) to low (e.g., GDP growth in developing economies where the agricultural sector is important is more strongly reduced by droughts than in developed economies where the agricultural sector is only from minor importance), high confidence (***, good agreement across many independent studies)
	Positive annual temperature anomalies dampen the convergence of economic development. They lead to a stronger reduction in economic growth rates in developing countries than in industrialized countries. Probably because the agricultural sector, which suffers the largest growth rate losses, is more important for developing than for industrialized countries.	Dell et al. (2012), Felbermayr and Gröschl (2014), Letta and Tol (2019)	Sensitivity of between country inequality to variations in weather conditions range from moderate (e.g., substantial impact on economic productivity in developing compared to non significant impacts in developed countries) to low (e.g., GDP growth in developing economies where the agricultural sector is important is more strongly reduced by droughts than in developed economies where the agricultural sector is only from minor importance), high confidence (***, good agreement across many independent studies)	driver: annual temperature variability, Strongest growth rate losses in agricultural sector. Poor countries have a much larger share of their GDP in this sector.
	Increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth. An extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction)	Kotz et al. (2021)	Sensitivity of between country inequality to variations in weather conditions range from moderate (e.g., substantial impact on economic productivity in developing compared to non significant impacts in developed countries) to low (e.g., GDP growth in developing economies where the agricultural sector is important is more strongly reduced by droughts than in developed economies where the agricultural sector is only from minor importance), high confidence (***, good agreement across many independent studies)
	Tropical cyclones have a relatively stronger adverse impact on long-term economic growth in low-income countries than in industrialized countries. Panel data 1960-2002 (low confidence).	Berlemann and Wenzel (2018)	Sensitivity of between country inequality to variations in weather conditions range from moderate (e.g., substantial impact on economic productivity in developing compared to non significant impacts in developed countries) to low (e.g., GDP growth in developing economies where the agricultural sector is important is more strongly reduced by droughts than in developed economies where the agricultural sector is only from minor importance), high confidence (***, good agreement across many independent studies)	Impact channels: Long-term growth losses in low-income countries increased by i) lack of protection measures, ii) increase in net fertility, and iii) decrease in educational efforts in disaster aftermath
	Droughts affect short-term (year of drought or year after drought) as well as long-term (up to 5 years after disaster) GDP growth rate in poorer countries more strongly than in OECD countries (low confidence)	short-term: Panwar and Sen (2019), Fomby et al. (2013) long-term (up to 5 years after disaster): Berlemann and Wenzel (2016)	Sensitivity of between country inequality to variations in weather conditions range from moderate (e.g., substantial impact on economic productivity in developing compared to non significant impacts in developed countries) to low (e.g., GDP growth in developing economies where the agricultural sector is important is more strongly reduced by droughts than in developed economies where the agricultural sector is only from minor importance), high confidence (***, good agreement across many independent studies)	Impact channels: Long-term growth losses due to i) lower education levels, ii) lower saving rates and iii) higher fertility in aftermath of droughts
	Moderate fluvial floods have stronger positive short-term effect on GDP growth in developing than in industrialized countries. (Extreme floods have negative impact on GDP growth rate in developing and industrialized economies)	Fomby et al. (2013), Panwar and Sen (2019) Cunado and Ferreira (2014)	medium confidence (**, high agreement, hypothesis would need support from other independent studies)	Share of agricultural GDP larger in developing countries than in industrialized countries
	Fluvial floods increase between-country income inequality in the short-term (up to 5 years after disaster), this effect disappears in the long-term term (within 10 years after disaster)	Panel data 1965-2004 for 83 countries Yamamura (2015)	low confidence (*, hypothesis would need support from other independent studies)	Poor people are more dependent upon strongly affected agricultural sector and are less mobile.
	Meta-analysis of extreme events impact: Climatic (extreme temperatures, droughts, wildfires, etc.) and hydro-meteorological (floods, storms, wet-mass movements) disasters have relatively stronger adverse impact on economic growth of developing countries than of developed countries.	Klomp and Valckx (2014) Kousky (2014)	Low sensitivity of between country inequality to weather extremes. High confidence (***, results are derived from meta-analysis of 25 independent studies, and more than 750 regressions).	As developing countries rely more on agriculture, climatic and hydro-meteorological extreme events have a larger negative impact on their economies.
	S28 Other societal impact, heat-related mortality
Global	Observations Excess human mortality
Global	Attribution Ample evidence that exposure to high and low ambient temperatures is associated with excess mortality around the world, across climatic zones. Notable geographical heterogeneity in the shape of the relationship, with warmer climates showing higher optimal temperatures where the minimum mortality risk is observed. Most research is based on data from developed countries in temperate climates, but more research on the topic is recently originating from developing countries and (sub-)tropical climates (see below).	Gasparrini et al. (2015b), Guo et al. (2014) Son et al. (2019) Ryti et al. (2016) Mora et al. (2017), Green et al. (2019) Carleton et al. (2020), Chapter 7	strong sensitivity, high confidence (***)	A number of (patho-) physiological pathways have been identified that link exposure to heat and cold with increased mortality risk, predominantly involving cardiorespiratory disease patterns.
Africa	Observations
Africa	Attribution .Higher mortality rates on days with raised temperatures have been documented in a number of African countries (Ghana, Burkina Faso, Kenya, South Africa, Tanzania, Tunisia) with largest risks among children and the elderly. Involved causes of deaths are cardiovascular diseases, respiratory diseases, suicide, and other non-communicable diseases.	Scovronick et al. (2018) (South Africa) Wichmann (2017) (South Africa) Azongo et al. (2012) (Ghana) Diboulo and Si (2012) (Burkina Faso) for more references see Chapter 9.	where assessments available strong sensitivity, low confidence (*) Many African countries without assessment (not indicated in Figure 16.1 in favor of showing the results of the available studies)
Asia	Observations
Asia	Attribution Exposure to heat and cold increases relative risks of mortality in the tropical/subtropical locations in the Middle-East, South-East and South Asia studied, similarly to what is known for East-Asian regions.. In addition to all-cause mortality, increases in mortality across a wide range of causes (cardiovascular, respiratory, diabetic, infectious diseases) have been associated with high temperatures, and especially heatwaves, in Asia.	Gholampour et al. (2019) (Iran) Alahmad et al. (2019) (Kuwait), Seposo et al. (2015) (Philippines), Dang et al. (2016) (Vietnam), Ingole et al. (2017) (rural India), Fu et al. (2018) (India), Mazdiyasni et al. (2017) (India), for more references, covering East-Asian regions see Chapter 10	Strong sensitivity, high confidence (***)
Australasia	Observations
Australasia	Attribution Exposure to non-optimal ambient temperature is associated with excess mortality/life shortening in Australasia. There is also evidence that increased temperature variability at an hourly time-scale increases mortality risk. Evidence of temperature-mortality associations in Australasia stems from studies considering all-cause mortality and specific causes (e.g., out-of-hospital cardiac arrest).	Huang et al. (2012) (Brisbane), Cheng et al. (2017) (5 Australian capital cities), Doan et al. (2021) (Brisbane), Nitschke et al. (2011) (Adelaide), Hales et al. (2000) (Christchurch, NZ), Chapter 11	Strong sensitivity, high confidence (***)
Central and South America	Observations
Central and South America	Attribution Evidence that mortality is associated with heat and cold with notable differences in the contribution of cold versus heat to total temperature-related mortality depending on prevailing climate (dry, temperate, tropical). Population characteristics, such as age, gender, education level, housing conditions have been shown to explain heterogeneity in observed risks in Brazil.	(Péres et al., 2020) (Brazil), Son et al. (2016) (Brazil Rodrigues et al. (2019)), (Brazil), Chapter 12	strong sensitivity, medium confidence (**)
Europe	Observations
Europe	Attribution Ample evidence points to the sensitivity of human mortality in Europe to high and low ambient temperature.. This encompasses a wide range of mortality causes, especially with regard to heat. Specific pathways such as the association of hot nights with increased mortality risk have also been documented. Besides age and gender, individual fitness has also been shown to modify susceptibility to heat. At the level of city characteristics, population density, air pollution levels and green area coverage have been identified as important modifiers of the heat effect.	Recent European scale analysis: (Martínez-Solanas et al., 2021), Urban et al. (2021) Cause-specific mortality : Gasparrini et al. (2012), Hot nights: Royé et al. (2021) Risk factors: Schuster et al. (2017) Sera et al. (2019) see also Chapter 13	Strong sensitivity, high confidence (***)
North America	Observations
North America	Attribution High and low temperatures increase mortality, with impacts varying by age, gender, location, and socioeconomic factors. Temperature-mortality associations in the USA and Canada have been intensively researched, with earliest publications on the subject stemming from the early 20th century.	Barreca (2012) Martin et al. (2012) Nordio et al. (2015), Weinberger et al. (2019), Lay et al. (2021) Chapter 14	Strong sensitivity, high confidence (***)
Small Islands	Observations
Small Islands	Attribution Elevated summer temperatures are associated with increased non-accidental mortality in Puerto Rico. Stroke and cardiovascular diseases are primary underlying causes of death.	Méndez-Lázaro et al. (2018), Chapter 15	Strong sensitivity, low confidence (*) Many Small Island countries without assessment (not indicated in Figure 16.1 in favor of showing the results of the available studies)
	S17 Water distribution - Water-borne diseases
Global	Observations Waterborne diseases encompass a wide range of pathogens and water-associated transmission pathways. The focus here lies on gastrointestinal infections/diarrheal diseases, which are typically associated with waterborne transmissions and constitute a large proportion of the global burden of diseases. It is important to note that a clear distinction between waterborne and foodborne transmission is not possible for some of the pathogens causing gastrointestinal infections (e.g., Campylobacter), which were included in the assessment.
Global	Attribution Positive associations between ambient temperatures and all-cause diarrhea are reported from low- middle-, and high-income countries. Recent meta-analyses find, as their central estimates, 3% and 7% increase in all-cause diarrhea per 1°C rise in ambient temperatures, respectively. Underlying this temperature sensitivity of enteric infections are however critical differences between involved pathogens. While bacterial diarrhea generally shows positive associations with temperature, the risk of viral diarrhea decreases with higher temperatures. There is also evidence that the incidence of diarrheal diseases increases after heavy rainfall and flooding events, especially in areas with low hygiene and sanitation standards. No conclusive evidence exists regarding the effect of droughts on diarrheal disease outbreaks with results dependent on location-specific settings.	Chua et al. (2021) Carlton et al. (2016) Levy et al. (2016)	low to high sensitivity depending on the sanitary conditions, medium confidence (**)	High ambient temperatures, high precipitation and flooding events are associated with increased incidences of water-borne diseases. The strength of the association is modulated by socio-economic determinants (in particular hygiene and sanitation standards), environmental factors (such as flowing or standing water as sources of drinking water)
Africa	Observations
Africa	Attribution Studies in several African countries (Ethiopia, Senegal, South Africa, Mozambique) provide evidence that both high ambient temperatures and high precipitation show positive associations with diarrheal cases. Specifically, for cholera outbreaks the influence of interannual variability in temperature and rainfall, as linked to the ENSO cycle, has been shown, albeit socioeconomic determinants also play a role. There is, e.g., evidence on the critical role of human mobility related to a mass gathering that took place during the initial phase of the 2005 cholera outbreak in Senegal, in addition to the role of rainfall in driving disease transmission. Similarly, there is evidence that the spread of intestinal schistosomiasis in Burkina Faso has been linked to human mobility and water development projects (dam construction), besides climatic factors implicated in the disease transmission. There is also evidence from Africa on positive associations between ambient temperature and gastrointestinal diseases linked to protozoan parasites (Cryptosporidium, Giardia).	Chapter 7, Finger et al. (2016) Perez-Saez et al. (2015)	Strong sensitivity, medium confidence (**)
Asia	Observations
Asia	Attribution Weather sensitivity of diarrheal diseases has been found in several Asian countries (e.g., Bangladesh Cambodia, China, Philippines, Vietnam, India). Climatic variables associated with increased incidences of diarrhea are high temperatures, high humidity and high cumulative rainfall. There is also evidence that diarrheal disease incidences (including leptospirosis and typhoid fever) rise following heavy rainfall events and flooding, modulated by the presence/absence of functioning sanitation systems. For seasonal cholera and shigellosis outbreaks in Bangladesh, the influence of interannual climate variability linked to the ENSO cycle has also been demonstrated.	Cash and Rod (2014) (cholera, shigellosis, Bangladesh), Phung et al. (2015) (all-cause diarrhea, Vietnam), Mertens et al. (2019) (all-cause diarrhea in children, India), Zhang et al. (2019) (infectious diarrhoea, China), Chapters 7 and 10	Strong sensitivity, medium confidence (**)
Australasia	Observations
Australasia	Attribution Partly inconsistent evidence exists for the associations between water-borne enteric diseases and climatic variables in Australia and New Zealand. While positive associations of temperature and rainfall have been found for some diseases (cryptosporidiosis, giardiasis) in specific locations, other studies were not able to confirm the associations or found inconsistent results across different locations studied. For Campylobacter, case associations with temperatures were generally insignificant, yet a 2016 outbreak in New Zealand could be linked to contamination of a local water supply following heavy rainfall. Clearer evidence exists regarding the increased incidence of viral diarrhea (rotavirus) during periods of lower temperature.	Hales (2019) (review), Milazzo et al. (2017) (campylobacter), Bi et al. (2008) (campylobacter), Britton et al. (2010) (cryptosporidium, giardia), D'SOUZA et al. (2008) (rotavirus), Lal et al. (2013) (cryptosporidium, giardia, campylobacter), (Gilpin et al.) (campylobacter) (Milazzo et al., 2017) (campylobacter) Bi et al. (2008) (campylobacter), (Britton et al., 2010) (cryptosporidium, giardia), (D'SOUZA et al., 2008) (rotavirus), Lal et al. (2013) (cryptosporidium, giardia, campylobacter), Gilpin et al. 2020)[SH1] (campylobacter)	Moderate sensitivity, low () to medium (*) confidence
Central and South America	Observations
Central and South America	Attribution Research from Ecuador showed that heavy rainfall events were linked to increased diarrheal incidences only if they followed a dry period, whereas after a wet period heavy rainfall events had a protective effect. For the cholera outbreaks starting 1991 in Peru and 2010 in Haiti, recent studies point to the role of climatic factors (especially rainfall) in determining seasonal dynamics of the ongoing epidemic. By contrast, most evidence shows that climate factors were not responsible for the initial emergence of the disease. In particular for Peru there is evidence that ENSO influenced the resurgence of cholera in 1998, but did not impact the emergence in 1991. In Haiti, recent studies demonstrate that the pathogen strain of Vibrio cholerae initiating the cholera epidemic was introduced through foreign emergency aid troops sent to Haiti after the earthquake. Non-climatic factors such as failing sanitation systems, and the immunity status and mobility of susceptible populations were also shown to be critical drivers of disease transmission.	Carlton et al. (2014) (diarrhea incidences), Ramrez (2015) (cholera, Peru), Ramrez and Grady (2016) (cholera, Peru), Rinaldo et al. (2012) (cholera, Haiti), Orata et al. (2014) (cholera, Haita)	Strong sensitivity, medium confidence (**)
Europe	Observations
Europe	Attribution Ample evidence exists for the association of ambient temperatures with waterborne/gastrointestinal disease occurrence in Europe. Waterborne disease outbreaks have also been linked to heavy rainfall events, albeit not all studies are consistent in this finding. There is evidence from France that heavy rainfall and associated flush events increased acute gastrointestinal outcomes only if preceded by a dry period. Where heavy rainfall was identified as a driver of waterborne disease outbreaks, single household water supplies were identified as particularly vulnerable.	Kuhn et al. (2020) (campylobacter), Suk et al. (2020) (review flooding-related infectious disease outbreaks), Morral-Puigmal et al. (2018) (gastrointestinal infections), Atchison et al. (2010) (rotavirus), (2016) (aetiology-unspecific waterborne outbreaks), Semenza et al. (2012) (review), Setty et al. (2018) (gastrointestinal infections)	Moderate sensitivity, medium confidence (**)
North America	Observations
North America	Attribution In the USA and Canada, waterborne disease outbreaks have been found to be often preceded by heavy rainfall events. For Massachusetts, USA, it was shown that emergency room visits for gastrointestinal illness were associated with heavy rainfall events only in areas with combined sewer overflows, where stormwater runoff and sewage is jointly released into drinking water sources. High temperatures, and especially extreme heat, has also been associated with increased hospitalizations due to gastrointestinal infections in New York State, USA.	Levy et al. (2018) Jagai et al. (2015), Jagai et al. (2017)	Moderate sensitivity, medium confidence (**)
Small Islands	Observations
Small Islands	Attribution Weather sensitivity of waterborne diseases are well documented for small island states. One study finds positive associations between temperature, rainfall and gastrointestinal infections in the Federal States of Micronesia. The exceptional ENSO-associated severe drought of 2011 in Tuvalu was found to be linked with a large diarrhea outbreak, with underlying risks especially high where the drought severely lowered the water availability of household tanks and decreased hand washing frequency. There is also clear evidence of substantially increased morbidity and mortality in small island states hit by recent category 4-5 tropical cyclones (TCs). For example, TC Maria affecting Puerto Rico in 2017 was associated with up to 3000 excess deaths in the 5 months following the event. TC Winston hitting Fiji in 2016 induced a large burden of enteric infections, with 30% of the registered surveillance cases after the event reporting acute watery diarrhoea. In the Cook Islands and French Polynesia it has been demonstrated that the incidence of Ciguatera fish poisoning (CFP) is associated with sea surface temperature anomalies.	McIver et al. (2015), McIver et al. (2016) Emont et al. (2017), Santos-Burgoa et al. (2018) Zheng et al. (2020), Chapters 7 and 15 Zheng et al. (2020)	Strong sensitivity, medium confidence (**)
	S27 Other societal impacts - Vector-borne diseases
Global	Observations
Global	Attribution There is no analysis of the contribution of weather fluctuations to the observed fluctuations of vector-borne disease incidence at the global scale. Attribution of variation in incidence has only been done at the regional level (see below). However, based on these individual assessments, the sensitivity of vector-borne disease incidence to weather fluctuations is rated moderate at global scale (medium confidence).		moderate sensitivity, medium confidence (**)
Africa	Observations Malaria: Malaria epidemics are highly seasonal in some parts of Africa (e.g. in Sahelian countries such as Nigeria and Burkina Faso), in others (mainly Central Africa) transmission is not restricted to specific seasons but year-round. Outbreak in Sudan, 2019: There was a large outbreak of malaria in Sudan in 2019, with malaria accounting for 12.4% of all diseases surveyed, and 30% increase in mortality compared to the previous year. An earlier outbreak in 2013 led to an increase in the number of severe malaria cases from 18.4% in 2012 to 22.5% in 2013. Outbreak in Ugandan highlands 1998: An epidemic of malaria in south western Uganda occurred in 1998. The epidemic occurred in a highland area with normally moderate to low malaria transmission. Dengue: Dengue outbreaks in Africa are predominantly confined to urban areas.	Sinka et al. (2020), Lindblade et al. (1999) (Uganda outbreak 1998), OCHA (2019) (Sudan 2019), Elsanousi et al. (2018) (Sudan 2013)
	Attribution Malaria: Intensity and duration of malaria outbreaks across Africa are primarily determined by interventions such as vector control, prophylaxis and anti-malarial availability, as well as sociopolitical stability. More recent outbreaks are partly attributed to drug resistance and resistance to insecticides used for control, and the arrival of urban-adapted mosquito vectors. Outbreaks in Ugandan (1998) highlands and Sudan (2013 and 2019) have been associated with extreme flooding. Higher than average rainfall resulting from the 1997 El Nino event was shown to be associated with a malaria epidemic in south-west highland Uganda. Specifically, increased rainfall before and during the outbreak was positively correlated with Anopheles vector density one month later. South Africa: Malaria outbreaks in South Africa are positively associated with La Niña induced extreme rainfall events and sea surface temperature anomalies in the Indian Ocean. Botswana, Kenya, Tanzania: Temporal patterns of outbreaks in Botswana, particularly in 1996 (1982-2002), Kenya (1982-2000) and Tanzania (1997-98) are highly related to seasonal patterns of rainfall, and are also associated with the Indian Ocean Dipole and abnormal rainfall following drought conditions, which support populations of breeding mosquito vectors via water storage in and around the home. Malawi: A study of variations in malaria burden in Malawi, between 2004-2017 indicated that a unit increase in rainfall three months prior to case detection was associated with a 3% increase in childhood malaria burden, and every 1°C increase in temperature was associated with a 3% increase.	Boyce et al. (2016), Adeola et al. (2017) Elsanousi et al. (2018) (Sudan, flooding 2013), Hashizume et al. (2009)(malaria outbreaks, Kenya), Mabaso et al. (2007) (South Africa, La Nina), Behera et al. (2018) (SSTs, South Africa), Thomson et al. (2006) (malaria outbreaks, Botswana), Jones et al. (2007) (malaria outbreaks, Tanzania), Chirombo et al. (2020) (malaria, Malawi), Lindblade et al. (1999) (Uganda outbreak 1998)	high sensitivity of malaria incidence to extreme rainfall events in Uganda and Sudan, medium confidence (). moderate sensitivity of malaria incidence to ENSO fluctuations in South Africa, high confidence () low sensitivity of dengue outbreaks to rainfall and temperature, low confidence () Summary for Figure 16.2: Weather influence is ranging from high regarding the outbreaks in Uganda (1998) and Sudan (2019), medium confidence (*), to low for dengue outbreaks, low confidence ()	Malaria: The dominant malaria vector across Africa, Anopheles gambiae breeds in stagnant pools of water left by rainfall. The vector is also an indoor-resting mosquito so is responsive to vector control such as indoor residual spraying. Sociopolitical status and humanitarian crises can affect the accessibility of healthcare such as antimalarials.
	Dengue: Kenya: Although climate conditions were shown to be important predictors of the number, timing and duration of dengue outbreaks in a predictive study in Kenya, empirical evidence found no association between temperature and precipitation directly on dengue incidence. However, extremely wet conditions in Kenya between 2013-2019 were associated with elevated vector abundance. Although climate factors can determine the size of dengue outbreaks, other factors such as the introduction of new serotypes influences outbreak occurrence.	Caldwell et al. (2021) (dengue predictions, Kenya), Nosrat et al. (2021) (dengue vector abundance,		Dengue: The dengue virus is carried and spread by Aedes mosquitoes, primarily Aedes aegypti, and to a lesser extent Aedes albopictus, which is becoming increasingly important. Aedes aegypti rest and breed in and around dwellings, particular in urban areas with high population density
Asia	Observations Dengue: Dengue is the highest burden vector-borne disease in Asia, although malaria is important seasonally. Dengue is endemic and large outbreaks of dengue typically occur seasonally. Dengue transmission occurs synchronously across southeast Asia, with particularly high incidence between 1997-1998.	van Panhuis et al. (2015), Lai et al. (2018)
	Malaria: A large proportion of Asia is endemic for Plasmodium vivax malaria transmission.	Battle et al. (2019)
	Attribution Dengue: Across Asia, variation in temperature is an important climatic driver of seasonal and interannual variations in dengue incidence. Local weather anomalies, such as above average temperatures and rainfall, can trigger dengue outbreaks.	Servadio et al. (2018)	Moderate sensitivity of malaria incidence to climate variation, low confidence () Moderate sensitivity of dengue to climate variation, medium confidence (*)	Dengue: During rainfall events induced by El Niño and monsoon season, high rainfall increases the availability of Aedes mosquito breeding habitats, subsequently increasing Aedes mosquito abundance. Warm and humid conditions accelerate the development of Aedes mosquitoes and viruses such as dengue virus inside the mosquito. Urbanization and population mobility can increase dengue risk due to introduction of new serotypes and increased breeding habitats in and around homes in urban areas.
	South and Southeast Asia: A non-linear association between maximum average monthly temperature and the risk of an outbreak of mosquito-borne disease has been reported in South and Southeast Asia between 1980 and 2013, with a peak at temperatures of 33.5°C. Cambodia: In Siem Reap, Cambodia an increase of 1°C in maximum temperatures was associated with a 36.9% increase in dengue cases and there was a minimal effect of rainfall. Thailand: A study for Thailand (1982-2013) found that 8 % of the interannual variation of dengue relative risk can be explained by interannual variations in precipitation and temperature in the previous month, once seasonality and spatial variation have been accounted for.	Servadio et al. (2018) (mosquito-borne disease outbreaks, South and Southeast Asia, 1980-2013) Choi et al. (2016) (dengue, Cambodia 1998-2012), Lowe et al. (2016) (dengue interannual variation, Thailand)		Dengue: During rainfall events induced by El Niño and monsoon season, high rainfall increases the availability of Aedes mosquito breeding habitats, subsequently increasing Aedes mosquito abundance. Warm and humid conditions accelerate the development of Aedes mosquitoes and viruses such as dengue virus inside the mosquito. Urbanization and population mobility can increase dengue risk due to introduction of new serotypes and increased breeding habitats in and around homes in urban areas.
	China: Ecological niche models constructed to explain dengue outbreaks across mainland China between 1980-2016 showed that mean temperature of the coldest quarter contributed 62.6% to observed dengue outbreaks. In addition, the East Asian summer monsoon, bringing hot and rainy conditions is also important in determining dengue occurrence.	Liu et al. (2020) (China, dengue outbreaks), Sang et al. (2014) (dengue, temperature, China 2006-2012)		Dengue: During rainfall events induced by El Niño and monsoon season, high rainfall increases the availability of Aedes mosquito breeding habitats, subsequently increasing Aedes mosquito abundance. Warm and humid conditions accelerate the development of Aedes mosquitoes and viruses such as dengue virus inside the mosquito. Urbanization and population mobility can increase dengue risk due to introduction of new serotypes and increased breeding habitats in and around homes in urban areas.
	India: Using correlation analyses over 1994-2015, a study showed that an increase in the intensity of El Niño events during November-February increases the probability of a malaria outbreak in the following year, in northern and eastern states of India. These findings are supported by a more recent study showing a high correlation between ENSO and dengue cases, with more cases during the monsoon season following large El Niño events across most states.	Dhiman and Sarkar (2017) (India, malaria and El Nino), Pramanik et al. (2020) (India, ENSO)		Dengue: During rainfall events induced by El Niño and monsoon season, high rainfall increases the availability of Aedes mosquito breeding habitats, subsequently increasing Aedes mosquito abundance. Warm and humid conditions accelerate the development of Aedes mosquitoes and viruses such as dengue virus inside the mosquito. Urbanization and population mobility can increase dengue risk due to introduction of new serotypes and increased breeding habitats in and around homes in urban areas.
	Malaria: Republic of Korea: Generalised linear models and distributed lag nonlinear models for the period 2001-2009 showed a positive association between climate variation and P. vivax malaria in temperate Republic of Korea, with every 1°C increase in temperature increasing incidence by 17.7%, and a 10 mm increase in rainfall increasing incidence by 19.1%, when accounting for the time taken for mosquito development and parasite incubation.	Kim et al. (2012) (P. vivax malaria interannual variation, Republic of Korea)		Malaria: Warmer temperatures can increase malaria transmission by accelerating development and breeding of Anopheles mosquitoes, as well as shortening the Plasmodium parasite incubation period inside the mosquito vector, meaning mosquitoes become infectious quicker. Rainfall creates stagnant pools of water, which act as important mosquito breeding habitats for important vectors Anopheles dirus and An. minimus in the Greater Mekong subregion. An. stephensi is a major vector in urban areas of south Asia.
Australasia	Observations In Australia, Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three of the most common and clinically important vector-borne diseases with notable seasonal patterns. RRV and BFV contribute the largest annual disease burden and are endemic to Australia. Dengue exhibits periodic epidemic activity currently limited to the northeast corner of Australia.	Bannister-Tyrrell et al. (2013), Stratton et al. (2017)
	Attribution RRV: Regression models fitted to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters captured an average of 50-65% variability of the data. Climate variables play a dominant role in explaining the inter-annual variability of these vector-borne diseases.	Stratton et al. (2017) (regression models 1991-2015)	Moderate sensitivity of vector-borne disease (Ross River virus, Barmah Forest virus, and dengue) to climate variations, low confidence (*)
	Dengue: Between 1993-2005, lower values in the Southern Oscillation Index, coinciding with warmer conditions were associated with an increase in areas of Queensland, Australia reporting dengue cases.	Hu et al. (2010b) (Queensland 1993-2005)		In Cairns, Australia, most dengue transmission occurs in the warmer, wetter months (Oct-Mar, which is explained by the reduction in the extrinsic incubation period of the dengue virus at higher temperatures and the increased abundance of Ae. aegypti during the wet season.
Central and South America	Observations Dengue: New arboviruses have emerged, including chikungunya and Zika and their burden is greater in the Americas than anywhere else in the world. There was a large Zika epidemic in 2016 Zika: Zika emerged and spread in Brazil in 2015, causing a large epidemic in 2016 with 205,578 cases reported.	PAHO and WHO (2021) Lowe et al. (2018b), Puntasecca et al. (2021) Lowe et al. (2018a)
	Malaria: Incidence of malaria in areas that were on track for elimination, have increased in recent years between 2015-2017, including in Ecuador, Venezuela, Colombia, Dominican Republic, Panama.	WHO (2020)
	Attribution Dengue: Brazil: An analysis of monthly dengue cases reported in Brazil between 2001 and 2016 showed that the spatial and temporal pattern of ‘dengue waves’ is partly controlled by weather conditions, in particular precipitation. While human mobility patterns are in general the dominant predictors, precipitation was more important than human mobility for the seasonality of dengue at the mesoregion and finer spatial levels. An empirical analysis of monthly dengue cases data for the 558 microregions of Brazil (2001-2019) showed that the relative risk of dengue increased on average by 1.56 one month after extremely wet conditions compared to normal conditions and 1.43 four months after drought conditions.	Churakov et al. (2019) (2001-2016), Lowe et al. (2021) (2001-2019)	Moderate sensitivity of dengue incidence to climate variations, high confidence (*) moderate sensitivity of malaria incidence to climate variations, medium confidence ()	Dengue: Droughts can augment transmission as they drive water storage near households bringing Aedes mosquito breeding sites near humans via household water storage, while extremely wet conditions provide additional mosquito breeding sites.
	Venezuela: Periodic cycles of dengue in northern Venezuela (1991-2016) were shown to correspond to local and ENSO-related climatic variation at seasonal and inter-annual timescales. During El Niño events when conditions were warmer and drier, peaks in dengue were more prevalent. However, other factors that contribute to inter-annual patterns of dengue were not explicitly accounted for, such as introduction of dengue virus serotypes and population immunity.	Vincenti-Gonzalez et al. (2018)		Dengue: Droughts can augment transmission as they drive water storage near households bringing Aedes mosquito breeding sites near humans via household water storage, while extremely wet conditions provide additional mosquito breeding sites.
	Ecuador: An empirical analysis of the interannual variability in dengue fever in southern coastal Ecuador (1995-2010) showed that morbidity rates were higher during El Niño events, which are associated with warm and wet conditions, with 28% more cases for each degree of warming of Pacific sea surface temperatures. A subsequent modelling study using incidence data from 2002-2014 showed that a 1°C increase in mean temperature would result in a 40% increase in dengue incidence, although results were highly influenced by the choice of climate data product used in the model. Overall, the climatic variables explained 5% of the interannual variation in the standardised morbidity ratio for dengue. The modelling approach was also used to show that the particularly early dengue peak in 2016 may be explained by El Nino induced flooding.	(Stewart-Ibarra and Lowe, 2013) (Ecuador, 1995-2010) Lowe et al. (2017) (Ecuador, El Nino), Fletcher et al. (2021) (Ecuador, 2002-2014)		Dengue: Droughts can augment transmission as they drive water storage near households bringing Aedes mosquito breeding sites near humans via household water storage, while extremely wet conditions provide additional mosquito breeding sites.
	Zika: The rapid spread of Zika in Brazil in 2016 was partly attributed to the major 2015-2016 El Niño event.	Muñoz et al. (2017)		Dengue: Droughts can augment transmission as they drive water storage near households bringing Aedes mosquito breeding sites near humans via household water storage, while extremely wet conditions provide additional mosquito breeding sites.
	Malaria: Ecuador: In southern coastal Ecuador (1990-2018), for every 1°C increase in minimum temperature, cases of P. falciparum malaria were found to increase by 136% and P. vivax malaria to a lesser extent, by 77% . Additionally, local temperature variations were found to account for almost all the seasonal variation in P. falciparum malaria but only a minimal proportion of P. vivax variation, which was much less sensitive to local climate variation	Fletcher et al. (2020) (malaria, Ecuador 1990-2018)		Malaria: Warmer temperatures increase transmission of malaria, by speeding up the development of Anopheles mosquitoes, and replication of the parasite inside the mosquito. This relationship is stronger for P. falciparum, which is more sensitive to climate than P. vivax because of the characteristic relapses in vivax infections.
Europe	Observations Dengue and other arboviruses: The Asian tiger mosquito (Aedes albopictus) that transmits dengue, chikungunya, Zika viruses, is present in many southern European countries including Italy, eastern Spain, southern France and western Balkans. During the summer, the majority of continental Europe has suitable climate conditions to sustain seasonal dengue epidemics. A major outbreak of dengue, resulting in more than 2000 cases, occurred in Madeira in 2012. More recently, the south of France experienced an autochthonous outbreak of dengue and in summer 2017, Italy experienced a number of outbreaks of chikungunya with more than 200 cases reported.	Semenza and Suk (2018) Liu-Helmersson et al. (2016) (albopictus distribution), Wilder-Smith et al. (2014) (Madeira outbreak), Succo et al. (2016) (France, dengue 2015 outbreak), Rezza (2018) (Italy 2017 chikungunya outbreak)
	West Nile Fever (WNF): West Nile virus generally causes sporadic outbreaks in Europe, but larger outbreaks can occur. Data from 2002 to 2013 show an increase in the number of districts reporting West Nile Fever cases from 2010. In summer 2010 the number of WNF cases in previously uninfected areas in Europe and its neighboring countries was the highest number ever reported. During the 2018 transmission season, which started unusually early, more infections were reported than the total from the previous seven years.	Sambri et al. (2013) (Europe outbreaks), European Center for Disease Prevention and Control (ECDC) (2010 record of cases), Haussig et al. (2018) (WNF outbreak in summer 2018), ECDC (2018)(2018 season)
	Attribution Dengue: There is limited evidence that weather extremes, such as summer heatwaves in Europe are linked to dengue outbreaks, most outbreaks in Europe have been shown to be highly associated with travel patterns, with 70% of variation in imported cases 2010-2015 in Europe between explained by connectivity.	Campbell et al. (2015) (dengue, Europe) Caminade et al. (2012) Massad et al. (2018) (dengue, connectivity), Salami et al. (2020) (dengue, connectivity 2010-2015)	Low sensitivity of dengue incidence to climate variations, low confidence () Moderate sensitivity of West Nile Fever incidences to climate variations, medium confidence (*)	Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
	West Nile Fever (WNF): A logistic regression analysis of WNF outbreaks reported in Europe between 2002 and 2013 showed elevated West Nile risk with higher temperature anomalies and the detection of water bodies. However, other important non-climatic factors include bird migratory routes, wetlands, and previous outbreak occurrences. A study investigating the 2010 summer West Nile Fever outbreak in Europe showed significant positive correlations between cases and elevated temperatures. A less formal analysis of the timing and spatial pattern of the outbreak in summer 2018 also indicates a positive association of WNF cases with temperature and precipitation patterns. Temperatures over the summer of 2018 were higher than the 1981-2010 average in West Nile-affected areas and some countries such as Italy and the Adriatics experienced above average rainfall. Warmer temperatures allowed for establishment of the Culex pipiens mosquito vector, which was found to be positive for West Nile virus at 35% of surveillance stations in Serbia. In northern Italy, 2010-2015, West Nile Fever outbreaks were preceded by hot summer temperatures. The effect of rainfall on West Nile Fever varies geographically and results are less consistent. In northern Italy, heavy rainfall was linked to increases in West Nile Fever incidence. In contrast, in Romania between 2011-2013 drought conditions were associated with increases in the Culex vector abundance and subsequent West Nile virus infections.	Tran et al. (2014) (high temperatures and satellite-based detection of free water bodies predictors for the occurrence of WNF on district level, 2002-2013), Paz et al. (2013) (linkage between the 2010 heat and the WNF eruptions), Haussig et al. (2018) (summer 2018 outbreak), Moirano et al. (2018) (Italy, 2010-2015), (Cotar et al., 2016) (Romania, 2011-2013)		West Nile Fever: Temperature speeds up the vectorial capacity of Culex mosquitoes as well as viral replication of West Nile. Abundance of Culex mosquitoes is sensitive to rainfall, with large amounts flushing out habitats and drought conditions bringing hosts and mosquito vectors into close contact via water storage practices.
North America	Observations West Nile Fever: Large epidemic transmission of West Nile Fever occurred in the US, with unprecedented spread along the eastern coast in 2002-2003. A high number of cases (over 2,000) of West Nile were reported between 2013-2018, with peaks in the summer season. The economic cost of West Nile Fever is substantial (US$56 million estimated per year) and treatment is limited.	Ronca et al. (2021)
	Dengue: Dengue is primarily limited to the southern US, where climate is suitable for seasonal summer transmission, particularly in Texas, Hawaii and Florida. Around 100 imported cases are reported each year.	Bouri et al. (2012)
	Lyme disease: Lyme disease in the USA exhibits a seasonal pattern, with peaks in the summer months (June-July). Large outbreaks occurred in the summers of 2009 and 2017.	Nelson et al. (2015) Rochlin et al. (2019a) CDC (2021)
	Attribution West Nile Fever: Positive associations between West Nile fever seasonality, infection risk and warmer temperatures have been found in Connecticut (2000-2005) and in California (2003-2009). Specifically, a study across 17 states in the USA 2001-2005 found that an increase in weekly temperature of 5°C was associated with up to 50% higher reported infections of West Nile Fever. Although Lyme closely follows variations in temperature, it is also influenced by human behaviour such as outdoor activity. Drought conditions have been shown to enhance WNF outbreaks in southern Florida.	Soverow et al. (2009) (West Nile, US 2001-2005), Liu et al. (2009) (Connecticut, 2000-2005), Hartley et al. (2012) (California 2003-2009), Shaman et al. (2005) (drought, Florida 2001-2003)	moderate sensitivity of West Nile Fever to temperature, medium confidence (*) low sensitivity of dengue outbreaks to rainfall and temperature, low confidence () moderate sensitivity of Lyme disease to temperature and rainfall, high confidence (**) For Figure 16.2 we summarize: sensitivities range from low (dengue), low confidence (), to moderate (Lyme), high confidence (***)	West Nile Fever: Temperature speeds up the vectorial capacity of Culex mosquitoes as well as viral replication of West Nile. Abundance of Culex mosquitoes is sensitive to rainfall, with large amounts flushing out habitats and drought conditions bringing hosts and mosquito vectors into close contact via water storage practices.
	Dengue: Outbreaks typically occur as a result of increased travel to endemic areas and subsequent importation of cases, with an estimated 100 imported cases every year. Local transmission in the southern US could be sensitive to weather fluctuations, particularly in Texas, Hawaii and Florida, where climate is suitable for seasonal summer transmission. In an urban area of Georgia in 2015, anomalous increases in daily maximum temperatures led to increases in Aedes albopictus mosquito emergence.	Murdock et al. (2017) (albopictus, Georgia)		Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance.
	Lyme: In New York State (1991-2006), Lyme disease cases were higher when minimum temperatures were higher, with one additional spring day increasing summer cases by 6-8% on average. In contrast, high temperatures have been shown to lead to decreased incidence of Lyme disease in the northeast US (2002-2006). An earlier onset of the Lyme disease season was associated with reduced rainfall across 12 endemic US states 1992-2007. Although Lyme closely follows variations in temperature, it is also influenced by human behaviour such as outdoor activity.	Lin et al. (2019) (Lyme, New York State), Moore et al. (2014) (rainfall, 12 US states), Tran and Waller (2013) (NE US, high temperature, 2002-2006)		Lyme: Warmer temperatures shorten the the lifecycle and increase abundance of Ixodes ticks that carry Lyme disease. Increased temperatures also expand the distribution and range of rodent and deer hosts, as well as their activity, increasing human exposure to Lyme disease. High temperatures can leave tick larval nymphs susceptible to desiccation, which can lead to subsequent decreases in Lyme disease.
Small Islands	Observations In recent years, the Caribbean region has experienced an unprecedented crisis of co-occurring epidemics of dengue, chikungunya, and Zika viruses. Between 2013 and 2019, 186,050 cases of dengue, 911,842 cases of chikungunya, and 143,127 cases of Zika were reported. The Pacific often experiences epidemic outbreaks of dengue with an explosive outbreak e.g. occurring in the Republic of the Marshall Islands in 2011 infecting 3% of the population. The Cook Islands are currently experiencing a dengue outbreak of rising concern, with an estimated 300 cases reported as of mid-2021. In New Caledonia, cases of dengue are detected every year, causing recurrent outbreaks, along with co-circulation of the Zika virus.	PAHO/WHO (2019) (arboviruses in the Caribbean), Sharp et al. (2014) (dengue, Republic of the Marshall Islands, 2011-2012), Uwishema et al. (2021), WHO (2021) (dengue, Cook Island, 2021), Inizan et al. (2019) (dengue, New Caledonia)
Small Islands	Attribution Dengue: Barbados: Nonlinear and lagged functions of minimum temperature and the standardised precipitation index (SPI-6) explained 14% more of the variation in dengue cases than a baseline model including only monthly and yearly random effects. The greatest increase in dengue risk was found 3-5 months following drought conditions and 0-2 monthly following extremely wet conditions. Puerto Rico: monthly dengue transmission rates between 2000 and 2011 were 3.4 times higher (95% CI: 1.9-6.1) for each 1°C increase in SST and 2.2 higher (95% CI: 1.3-3.5) for each 1°C increase in minimum air surface temperature. Fiji: Following one of the most powerful storms recorded in the South Pacific, Cyclone Winston, 2016 twice the average number of cases were observed. A rapid risk assessment conducted by Fiji’s Ministry of Health and Medical Services and WHO identified several factors that increased the risk of disease transmission and outbreaks including large displaced populations, overcrowded emergency shelters, limited access to clean water, disruption of the sanitation infrastructure, and increased exposure to mosquitos and other disease vectors. New Caledonia: During epidemic dengue years between 1971-2010 in New Caledonia case distribution was highly seasonal and associated with temperatures in the previous 1-2 months, and coincided with maximum levels of rainfall and humidity. Interannual case variation was also associated with temperature and rainfall, but not with large-scale climate forcings such as ENSO.	Lowe et al. (2018b) (Barbados), Méndez-Lázaro et al. (2014) (Puerto Rico), Sheel et al. (2019) (Fiji), Descloux et al. (2012) (New Caledonia)	moderate sensitivity of dengue to temperature and precipitation variation, medium confidence (**)	Dengue: Warmer temperatures between 27-29°C speed up Aedes mosquito development and viral replication and incubation inside the mosquito. Rainfall is crucial for the water-dependent stages of mosquito development and increases Aedes abundance. Compromised sanitation and water access as a result of flooding and extreme weather events can create new mosquito breeding sites and increase human exposure to Aedes mosquitoes.
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Observed impacts of climate change

References