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Can local climate variability be explained by weather patterns?

  • To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though,To understand past flood changes in the Rhine catchment and in particular the role of anthropogenic climate change in extreme flows, an attribution study relying on a proper GCM (general circulation model) downscaling is needed. A downscaling based on conditioning a stochastic weather generator on weather patterns is a promising approach. This approach assumes a strong link between weather patterns and local climate, and sufficient GCM skill in reproducing weather pattern climatology. These presuppositions are unprecedentedly evaluated here using 111 years of daily climate data from 490 stations in the Rhine basin and comprehensively testing the number of classification parameters and GCM weather pattern characteristics. A classification based on a combination of mean sea level pressure, temperature, and humidity from the ERA20C reanalysis of atmospheric fields over central Europe with 40 weather types was found to be the most appropriate for stratifying six local climate variables. The corresponding skill is quite diverse though, ranging from good for radiation to poor for precipitation. Especially for the latter it was apparent that pressure fields alone cannot sufficiently stratify local variability. To test the skill of the latest generation of GCMs from the CMIP5 ensemble in reproducing the frequency, seasonality, and persistence of the derived weather patterns, output from 15 GCMs is evaluated. Most GCMs are able to capture these characteristics well, but some models showed consistent deviations in all three evaluation criteria and should be excluded from further attribution analysis.show moreshow less

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Metadaten
Author:Aline MurawskiORCiDGND, Gerd BürgerORCiDGND, Sergiy VorogushynORCiDGND, Bruno MerzORCiDGND
URN:urn:nbn:de:kobv:517-opus4-410155
DOI:https://doi.org/10.25932/publishup-41015
ISSN:1866-8372
Parent Title (English):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
Subtitle (English):a multi-station evaluation for the Rhine basin
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (525)
Document Type:Postprint
Language:English
Date of first Publication:2019/01/17
Year of Completion:2016
Publishing Institution:Universität Potsdam
Release Date:2019/01/17
Tag:Europe; athmospheric circulation patterns; classification; precipitation; river Rhine; scenarios; stochastic rainfall model; temperature; trends; within-type variability
Issue:525
Pagenumber:24
Source:Hydrology and Earth System Sciences 20 (2016), pp. 4283-4306 DOI 10.5194/hess-20-4283-2016
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Peer Review:Referiert
Publication Way:Open Access
Grantor:Copernicus
Licence (German):License LogoCreative Commons - Namensnennung, 4.0 International