Regional impacts poorly constrained by climate sensitivity

TL;DR

This study shows that relying solely on climate sensitivity metrics like EffCS to select models can misrepresent regional climate impacts, as no consistent relationship exists between EffCS and regional climate change indicators.

Contribution

The paper demonstrates that model selection based only on EffCS is unjustified, highlighting the importance of considering a broader range of models for regional impact assessments.

Findings

No universal link between EffCS and regional climate impacts.

Internal variability affects projected impacts as much as EffCS.

Excluding hot models based on EffCS may underestimate climate risks.

Abstract

Climate risk assessments must account for a wide range of possible futures, so scientists often use simulations made by numerous global climate models to explore potential changes in regional climates and their impacts. Some of the latest-generation models have high effective climate sensitivities or EffCS. It has been argued these so-called hot models are unrealistic and should therefore be excluded from analyses of climate change impacts. Whether this would improve regional impact assessments, or make them worse, is unclear. Here we show there is no universal relationship between EffCS and projected changes in a number of important climatic drivers of regional impacts. Analysing heavy rainfall events, meteorological drought, and fire weather in different regions, we find little or no significant correlation with EffCS for most regions and climatic drivers. Even when a correlation is found, internal variability and processes unrelated to EffCS have similar effects on projected changes in the climatic drivers as EffCS. Model selection based solely on EffCS appears to be unjustified and may neglect realistic impacts, leading to an underestimation of climate risks.