Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal |
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| Authors: | Matthias Jakob Themeßl Andreas Gobiet Georg Heinrich |
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| Institution: | (1) Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics, and Meteorology, University of Graz, Graz, Austria |
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| Abstract: | Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization
in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile
mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors,
its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal
(CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias
of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order
of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation
of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement
of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends
in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly
derived indices of extremes, such as threshold indices. |
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