A Multivariate Conditional Probability Ratio Framework for the Detection and Attribution of Compound Climate Extremes

Felicia Chiang, Peter Greve, Omid Mazdiyasni, Yoshihide Wada, Amir AghaKouchak

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Most attribution studies tend to focus on the impact of anthropogenic forcing on individual variables. However, studies have already established that many climate variables are interrelated, and therefore, multidimensional changes can occur in response to climate change. Here, we propose a multivariate method which uses copula theory to account for underlying climate conditions while attributing the impact of anthropogenic forcing on a given climate variable. This method can be applied to any relevant pair of climate variables; here we apply the methodology to study high temperature exceedances given specified precipitation conditions (e.g., hot droughts). With this method, we introduce a new conditional probability ratio indicator, which communicates the impact of anthropogenic forcing on the likelihood of conditional exceedances. Since changes in temperatures under droughts have already accelerated faster than average climate conditions in many regions, quantifying anthropogenic impacts on conditional climate behavior is important to better understand climate change.
Original languageEnglish (US)
JournalGeophysical Research Letters
Volume48
Issue number15
DOIs
StatePublished - Aug 16 2021
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-18

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