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Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

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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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Acknowledgements

The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged especially the CCLM simulations provided by the ETH Zurich. We furthermore acknowledge the E-OBS dataset from the ENSEMBLES project and the data providers in the ECA&D project (http://eca.knmi.nl) as well as the EU FP6 project CLAVIER which partly funded this study.

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Authors and Affiliations

  1. Wegener Center for Climate and Global Change and Institute for Geophysics, Astrophysics, and Meteorology, University of Graz, Graz, Austria

    Matthias Jakob Themeßl, Andreas Gobiet & Georg Heinrich

Authors
  1. Matthias Jakob Themeßl
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  2. Andreas Gobiet
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  3. Georg Heinrich
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Correspondence to Matthias Jakob Themeßl.

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Fig. S1

Annual bias characteristics of pint (top row), pn10 (middle row), and px1d (bottom row). Left column: uncorrected model; right column: corrected model. (GIF 111 kb)

High resolution image file (EPS 8722 kb)

Fig. S2

The same as in Fig. S1 but for tasx (top row), txn25 (middle row) and tnn20 (bottom row). (GIF 77 kb)

High resolution image file (EPS 8441 kb)

Fig. S3

Seasonal pdfs of precipitation amount (first row) and of mean temperature (third row) for the period 1971–2000 (dashed light grey) and 2021–2050 (black). The lower part of each panel displays the differences between scenario and control period at different percentiles. The second and fourth rows show the seasonal precipitation and temperature correction functions. Correction terms are derived from differences at different percentiles between observed and modeled ecdfs. The regional mean quantities corresponding to these percentiles are indicated on the respective x-axes. Results are shown for sub-region AL. (GIF 54 kb)

High resolution image file (EPS 281 kb)

Fig. S4

Annual mean maps of the uncorrected monthly CCS (left column), the difference between the uncorrected and the corrected CCS (middle column), and the respective annual cycles of the CCS for three sub-regions. Top row: pint; middle row: pn10; bottom row: px1d. In the lower part of the annual cycle plots change of significance is indicated with “o” (unchanged significance), “-” (loss of significance after correction), and “+” (significance established after correction). (GIF 96 kb)

High resolution image file (EPS 8471 kb)

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Themeßl, M.J., Gobiet, A. & Heinrich, G. Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change 112, 449–468 (2012). https://doi.org/10.1007/s10584-011-0224-4

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