Emergence of climate change signal in CMIP6 extreme indices

Abstract

Climate and weather extremes are becoming more frequent due to the influence of anthropogenic climate change. Knowing when and where we can expect these changes to occur is essential for both climate change mitigation and developing adaptation measures. We investigate the time of emergence-meaning the earliest time at which the climate change signal can be detected from the noise of natural variability-for 27 annual and 2 seasonal climate extreme indices related to surface temperature and precipitation. An ensemble of 21 CMIP6 global climate models (including several with a large number of initializations) is combined with a model weighting scheme that accounts for both model performance and independence to provide robust ensemble statistics of the emergence of climate extremes and to explore model uncertainty. Results indicate that spatial and temporal emergence patterns differ between types of temperature indices, for instance we find that percentile-based indices emerge earlier than their absolute counterparts. Indices related to daily maxima tend to emerge later than those related to daily minima. For many temperature indices, emergence occurs during the historical period (before 2015) and first in tropical regions with the exception of annual minimum indices, which show a more spatially uniform pattern. Precipitation indices tend to emerge later (mostly after 2030), only in some parts of the globe and/or primarily under high emissions scenarios. Some indices show spatial variations in the sign of change, although both positive and negative changes can lead to emergence. The main regions of emergence are the northern high latitudes and central Africa, however there is substantial disagreement between models about whether or not emergence occurs elsewhere. The results of this study provide a consistent basis for understanding and comparing the emergence of different types of extremes, and they highlight opportunities for further research into the underlying drivers, as well as impact- or region-specific risk assessments.