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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 10 | Copyright
Hydrol. Earth Syst. Sci., 19, 4055-4066, 2015
https://doi.org/10.5194/hess-19-4055-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 06 Oct 2015

Research article | 06 Oct 2015

The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal

A. Gobiet1,2, M. Suklitsch2, and G. Heinrich1,a A. Gobiet et al.
  • 1Wegener Center for Climate and Global Change (WegCenter), University of Graz, Graz, Austria
  • 2Central Institute for Meteorology and Geodynamics (ZAMG), Department of forecasting models, Vienna, Austria
  • anow at: Department of Geography and Regional Science, University of Graz, Graz, Austria

Abstract. This study discusses the effect of empirical-statistical bias correction methods like quantile mapping (QM) on the temperature change signals of climate simulations. We show that QM regionally alters the mean temperature climate change signal (CCS) derived from the ENSEMBLES multi-model data set by up to 15 %. Such modification is currently strongly discussed and is often regarded as deficiency of bias correction methods. However, an analytical analysis reveals that this modification corresponds to the effect of intensity-dependent model errors on the CCS. Such errors cause, if uncorrected, biases in the CCS. QM removes these intensity-dependent errors and can therefore potentially lead to an improved CCS. A similar analysis as for the multi-model mean CCS has been conducted for the variance of CCSs in the multi-model ensemble. It shows that this indicator for model uncertainty is artificially inflated by intensity-dependent model errors. Therefore, QM also has the potential to serve as an empirical constraint on model uncertainty in climate projections. However, any improvement of simulated CCSs by empirical-statistical bias correction methods can only be realized if the model error characteristics are sufficiently time-invariant.

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The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the simulated climate change signals (CCS) is currently strongly discussed and is often regarded as deficiency of bias correction methods. We demonstrate that, quite the contrary, QM can lead to an improved CCS and also has the potential to serve as an empirical constraint on model uncertainty in climate projections.
The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the...
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