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Projecting future local precipitation and its extremes for Sweden

Journal article
Authors Deliang Chen
Christine Achberger
Tinghai Ou
Ulrika Postgård
Alexander Walther
Yaoming Liao
Published in Geografiska Annaler
Volume 97
Issue 1
Pages 25-39
ISSN 0435-3676
Publication year 2015
Published at Department of Earth Sciences
Pages 25-39
Language en
Links dx.doi.org/10.1111/geoa.12084
Keywords weather generator;statistical downscaling;daily precipitation;climate change scenarios;Sweden
Subject categories Earth and Related Environmental Sciences

Abstract

A procedure to obtain future local precipitation characteristics focused on extreme conditions has been developed based on a weather generator. The method involves six major steps: (1) the weather generator was calibrated using observed daily precipitation at 220 Swedish stations during 1961–2004; (2) present and future daily precipitation characteristics for the Swedish stations from two global climate models, namely ECHAM5 and HadCM3, were used to calculate weather generator parameters for the present and future climates at global climate model spatial scales; (3) the ratio of the weather generator parameters for the present climate simulated by the global climate models to those calculated for each station falling into the global climate model grid box were computed for all the stations; (4) these ratios were also assumed to be valid in the future climate, that way the future parameters for each station for the global climate model projected future climate could be calculated; (5) using the estimated future parameters of the weather generator, the future daily precipitation at each station could be simulated by the weather generator; (6) the simulated daily precipitation was used to compute eight indices describing mean and extreme precipitation climates. The future mean and extreme precipitation characteristics at the stations under the Second Report on Emission Scenarios A2 scenario were obtained and presented. An overall increasing trend for frequency and intensity of the indices are identified for the majority of the stations studied. The developed downscaling methodology is relatively simple but useful in deriving local precipitation changes, including changes in the precipitation extremes.

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