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How well do regional climate models simulate the spatial dependence of precipitation? An application of pair‐copula constructions
Author(s) -
Hobæk Haff Ingrid,
Frigessi Arnoldo,
Maraun Douglas
Publication year - 2015
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1002/2014jd022748
Subject(s) - copula (linguistics) , orography , tail dependence , climate model , precipitation , spatial dependence , climatology , environmental science , univariate , spatial ecology , residual , quantile , terrain , climate change , geology , multivariate statistics , meteorology , geography , mathematics , statistics , econometrics , oceanography , ecology , cartography , algorithm , biology
We investigate how well a suite of regional climate models (RCMs) from the ENSEMBLES project represents the residual spatial dependence of daily precipitation. The study area we consider is a 200 km × 200 km region in south central Norway, with RCMs driven by ERA‐40 boundary conditions at a horizontal resolution of approximately 25 km × 25 km. We model the residual spatial dependence with pair‐copula constructions, which allows us to assess both the overall and tail dependence in precipitation, including uncertainty estimates. The selected RCMs reproduce the overall dependence rather well, though the discrepancies compared to observations are substantial. All models overestimate the overall dependence in the west‐east direction. They also overestimate the upper tail dependence in the north‐south direction during winter, and in the west‐east direction during summer, whereas they tend to underestimate this dependence in the north‐south direction in summer. Moreover, many of the climate models do not simulate the small‐scale dependence patterns caused by the pronounced orography well. However, the misrepresented residual spatial dependence does not seem to affect estimates of high quantiles of extreme precipitation aggregated over a few grid boxes. The underestimation of the area‐aggregated extreme precipitation is due mainly to the well‐known underestimation of the univariate margins for individual grid boxes, suggesting that the correction of RCM biases in precipitation might be feasible.