Non-stationary time series attribution for heatwaves over Europe
Pascal Meurer, Sebastian Buschow, Svenja Szemkus, Petra Friederichs

TL;DR
This paper introduces a novel non-stationary Markov process approach using bivariate extreme value theory to attribute heatwaves over Europe to anthropogenic climate change, accounting for temporal dependence and clustering of extremes.
Contribution
It develops a new method for attributing entire time series of heatwaves to human influence, considering temporal dependence and spatial extremes, which improves attribution accuracy.
Findings
Strong evidence for anthropogenic influence on European heatwaves since the 2000s.
Attribution of heatwaves to human activity is possible even when removing mean warming effects.
No significant change in temporal dependence or extreme value distribution shape beyond temperature increase.
Abstract
The increasing occurrence of extreme weather events since the beginning of the 21st century has led to the development of new methods to attribute extreme events to anthropogenic climate change. How the extreme event is defined has a major influence on the attribution result. A frequently disregarded or evaded aspect concerns the temporal dependence and the clustering of extremes. This study presents an approach for attributing complete time series during extreme events to anthropogenic forcing. The approach is based on a non-stationary Markov process using bivariate extreme value theory to model the temporal dependence of the time series. We calculate the likelihood ratio of an observational time series from ERA5 given the distributions as estimated from CMIP6 simulations with historical natural-only and natural and anthropogenic forcing scenarios. The spatial fields are condensed by…
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Taxonomy
TopicsClimate variability and models · Climate Change and Health Impacts · Meteorological Phenomena and Simulations
