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IPCC definitions

Climate attribution

(from IPCC AR6 WGII Chapter 16, Section 16.2.1)

For climate attribution the main challenge is the separation of externally human forced changes in the climate-related systems from their internal variability while for impact attribution it often is the separation of the effects of other external forcings (i.e., direct human influences or natural disturbances) from the impacts of the changes in the climate-related systems.

Impact attribution

(from IPCC AR6 WGII Chapter 16, Section 16.2.1)

The section adopts the general definition of detection as ‘demonstration that a considered system has changed without providing reasons for the change’ and attribution as identifying the causes of the observed long-term change in an impact indicator or of the change in the temporal or spatial extent, the intensity or frequency of a specific event.

Based on these general definitions and following the approach applied in WGII AR5 Chapter 18 (Cramer et al. 2014), we define an observed impact as the difference between the observed state of a natural, human, or managed system and a counterfactual baseline that characterizes the system’s state in the absence of changes in the climate-related systems defined here as climate system including the ocean and the cryosphere as physical or chemical systems.

The difference between the observed and the counterfactual baseline state is considered the change in the natural, human, or managed system that is attributed to the changes in the climate-related systems (impact attribution). The counterfactual baseline may be stationary or may change over time, for example due to direct human influences such as changes in land use patterns, agricultural or water management affecting exposure and vulnerability to climate related hazards.

Identification of weather sensitivity

(from IPCC AR6 WGII Chapter 16, Section 16.2.1)

‘Identification of weather sensitivity’ refers to the attribution of the response of a system to fluctuations in weather and short-term changes in the climate-related systems including individual extreme weather events (e.g., a heatwave or storm surge). Typical questions addressed include: ‘How much of the observed variability of crop yields is due to variations in weather conditions compared to contributions from management changes?’ (e.g., Ray et al. 2015; Müller et al. 2017) and ‘Can weather fluctuations explain part of the observed variability in annual national economic growth rates?’ (e.g., Burke, Hsiang, and Miguel 2015). Identification of weather sensitivity may also address the effects of individual climate extremes, for example asking, ‘Was the observed outbreak of cholera triggered by an associated flood event?’ (e.g., Rinaldo et al. 2012; Moore et al. 2017). It is important to note that sensitivity could be described in diverse ways and that for example the fraction of the observed variability in a system explained by weather variability differs from the strength of the systems’ response to a specific change in a weather variable. Nevertheless, all these different measures are integrated in the ‘identification of weather sensitivity’ assessment where ‘sensitivity’ should not be considered a quantitative one-dimensional mathematical measure.

References

Burke, Marshall, Solomon M. Hsiang, and Edward Miguel. 2015. “Global Non-Linear Effect of Temperature on Economic Production.” Journal Article. Nature 527 (7577): 235–39. https://doi.org/10.1038/nature15725.
Cramer, W., G. W. Yohe, M. Auffhammer, C. Huggel, U. Molau, M. A. F. da Silva Dias, A. Solow, D. A. Stone, and L. Tibig. 2014. “Detection and Attribution of Observed Impacts.” Book Section. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part a: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir, M. Chatterjee, et al., 979–1037. Cambridge, United Kingdom; New York, NY, USA: Cambridge University Press. https://doi.org/10.1017/cbo9781107415379.023.
Moore, Sean M, Andrew S Azman, Benjamin F Zaitchik, Eric D Mintz, Joan Brunkard, Dominique Legros, Alexandra Hill, Heather McKay, Francisco J Luquero, and David Olson. 2017. “El Niño and the Shifting Geography of Cholera in Africa.” Journal Article. Proceedings of the National Academy of Sciences 114 (17): 4436–41. https://doi.org/10.1073/pnas.1617218114.
Müller, Christoph, Joshua Elliott, James Chryssanthacopoulos, Almut Arneth, Juraj Balkovic, Philippe Ciais, Delphine Deryng, et al. 2017. “Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.” Journal Article. Geoscientific Model Development 10 (4): 1403–22. https://doi.org/10.5194/gmd-10-1403-2017.
Ray, Deepak K., James S. Gerber, Graham K. MacDonald, and Paul C. West. 2015. “Climate Variation Explains a Third of Global Crop Yield Variability.” Journal Article. Nature Communications 6 (1): 5989. https://doi.org/10.1038/ncomms6989.
Rinaldo, A., E. Bertuzzo, L. Mari, L. Righetto, M. Blokesch, M. Gatto, R. Casagrandi, M. Murray, S. M. Vesenbeckh, and I. Rodriguez-Iturbe. 2012. “Reassessment of the 2010-2011 Haiti Cholera Outbreak and Rainfall-Driven Multiseason Projections.” Journal Article. Proceedings of the National Academy of Sciences 109 (17): 6602–7. https://doi.org/10.1073/pnas.1203333109.