Climate extremes in the context of natural climate variability and climate change - A step towards the prediction of climate extremes

• Antragstellerin: Professorin Dr. Jana Sillmann
• Fachliche Zuordnung: Physik und Chemie der Atmosphäre
• Förderung: Förderung von 2010 bis 2013
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 183955105

Climate extremes are an important aspect of the climate's variability and climate change. The investigation of their nature and the improvement of their prediction are of major concern for our society to minimize the substantial human and financial losses induced by these extremes. A considerable influence on the occurrence of climate extremes is exerted by patterns of climate variability. These patterns also serve as physical basis for long time scale predictability of the climate system. Global climate models (GCMs) are appropriate tools to simulate these large-scale patterns of climate variability, and can also provide long data series for the analysis of rare climate extremes. However, GCMs may not always realistically represent climate extremes. The aim of this project is thus to assess the ability of GCMs to simulate climate extremes and their relationship to patterns of climate variability. In this regard, techniques to incorporate covariates in the statistical modelling of extreme events will be applied to observational data and multi-model output. Finding appropriate covariates, which represent patterns of climate variability and modulate climate extremes, is likely to be important for improving the estimation of return values of high impact extreme events. Furthermore, indices for moderate climate extremes will be calculated from near-term GCM simulations (decadal hindcast and prediction experiments). Various skill measures will then be utilized to assess the ability of GCMs to simulate moderate extreme events under present climate and to estimate model biases, which can then be removed from future predictions. The results of this project will have important implications for dynamical and statistical downscaling of GCM output and for the prediction of climate extremes.

• DFG-Verfahren: Forschungsstipendien
• Internationaler Bezug: Kanada
• Gastgeber: John Scinocca, Ph.D.