Challenges in developing low-likelihood high-warming storylines for climate extremes

Abstract:

Recent IPCC reports focused their assessment primarily on the likely range of change in extremes. However, it has recently been argued that such a focus on the likely range ignores changes with high levels of warming that are less likely to occur but nevertheless associated with the highest risks. This is particularly the case for climate extremes where impacts often non-linearly depend on changes in hazards and where uncertainties are typically large both due to model response uncertainty and internal variability. Low-likelihood high-warming storylines have been proposed as a powerful tool to assess and communicate the risk associated with such future climates.

Here, we compare different approaches for creating low-likelihood high-warming storylines for extremes based on CMIP6 models, and discuss their strength and limitations for temperature extremes, heavy rainfall and droughts. We demonstrate that all approaches yield storylines in which changes in hot extremes, extreme rainfall and droughts strongly exceed the multi-model mean over large parts of the globe. This suggests that a focus on the likely range may indeed substantially underestimate the risk associated with changes in extremes.

We further demonstrate that the choice of the storyline approach needs to be informed by the purpose of the assessment. We show that regional storylines are very powerful for regional or national assessments over a well-defined region and for a certain type of extreme. Pattern-scaling based storyline approaches are simple and easy to communicate, however, may lead to implausible global patterns and destroy physical consistency across different variables and thereby different types of extremes. Finally, a nearest-neighbour model selection is rather complex but has the advantage of generating storylines of globally coherent pattern of changes in extremes. Such an approach allows to assess physically consistent and spatially coherent global low-likelihood high-warming storylines of regional extremes that is suited for global impact assessment across different sectors.