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CL3.2

Climate impacts at different levels of warming. Dealing with uncertainty in multi-model intercomparisons
Convener: Carl-Friedrich Schleussner  | Co-Conveners: Erich Fischer , Christopher Reyer , Dai Yamazaki 
Orals
 / Fri, 17 Apr, 08:30–10:00  / Room Y8
Posters
 / Attendance Fri, 17 Apr, 10:30–12:00  / Yellow Posters
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Our understanding of climate change impacts on human and natural systems has improved substantially over the last years as reflected in the Fifth Assessment Report of the IPCC. Still, much work remains to be done to create a comprehensive assessment of impact projections for different levels of global mean temperature increases (explored through a range of greenhouse gas emission pathways). In light of the negotiations under the UNFCCC, differentiation between impacts at stabilization targets such as 1.5°C, 2°C or 2.5°C global mean surface air temperature increase relative to preindustrial temperature levels are highly relevant. However, projections of climate impact models for different levels of warming also suffer from a number of uncertainties, which may render a differentiation between impacts at different levels of warming difficult.

This session highlights research aimed at bridging this gap and welcomes contributions from climate and climate impact modelers that focus on the question of climate impacts at different temperature levels and explore limitations and opportunities with regard to such a differentiation.

Large uncertainties in impact projections due to natural variability and inter-ensemble spread or parameter uncertainty may obscure differences between impacts at different temperature levels. Contributions addressing this issue (e.g. by advanced statistical analysis) and help to attribute uncertainty to its individual components as well as submissions focusing on statistical techniques like pattern scaling are more than welcomed.

In particular, contributions based on multi-model intercomparison projects such as the Coupled Model Intercomparison Project (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) Fast-Track database and the Agricultural Model Improvement and Intercomparison Project (AgMIP) are encouraged.