Environmental Research Letters (Jan 2024)
Attribution of extremes to greenhouse gas-induced changes in regional climate variability, distinct from changes in mean climate
Abstract
Changes in regional climate variability serve as the initial indicators of positive climate feedback mechanisms, which are expected to intensify as greenhouse gas (GHG) emissions unfold. Therefore, it is crucial to examine the extent to which GHG-induced changes in regional climate variability, in isolation from changes in mean climate, contribute to the frequency of extreme weather events. In this study, I build upon the concept of the fraction of attributable risk (FAR) by introducing the fraction of preventable risk (FPR), allowing for the assessment of GHG forcing’s contribution to risk reduction in scenarios of decreasing risk extremes. Results derived from four global climate models indicate that while the predominant factor amplifying the frequency of hot extremes is the mean warming, with a 18-fold increase in probability and an attributable risk fraction of 0.96 to GHG forcing, changes in regional climate variability have already modified the probability of extremes. In South Asia, for instance, the 12-fold increase in hot extremes resulting from mean warming is compounded by an additional ∼3 times, solely attributed to the increased temperature variability. Conversely, during winter in the Arctic, the 10-fold increase in the frequency of hot extremes due to mean warming is offset by a ∼2.5-fold decrease resulting from diminished variability, with a preventable risk fraction of −0.55 to GHG forcing. Concerning heavy-precipitation events, in certain regions, GHG-induced changes in precipitation variability carry greater significance than changes in the mean. For example, in the West African summer monsoon region GHG forcing is amplifying the risk of extreme monsoon precipitation by ∼4 times. This amplified risk of potential flooding arises from increases in both mean precipitation and variability. Separating attribution metrics into mean and variability components offers valuable insights into region-specific mechanisms affecting extreme event frequency.
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