Abstract
Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology using European heat waves and illustrates the spatial and temporal properties of simulations.
Highlights
The summer heat waves in western Europe in 2003 or in Russia in 2010 were record breaking events but outliers of the temperature distribution by exceeding several standard deviations
One can ask whether such events were unprecedented because the observational records are too short or because climate change created new conditions of emergence
Since we focus on extremely hot temperature spells, this proof-of-concept study is limited to summer, and we consider the period between 1 June to 31 August
Summary
The summer heat waves in western Europe in 2003 or in Russia in 2010 were record breaking events but outliers of the temperature distribution by exceeding several standard deviations. In order to solve this conundrum, it is necessary to be able to simulate the most intense event over a given region that is compatible with present-day conditions and compare it with observed records.
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