Articles | Volume 19, issue 10
Hydrol. Earth Syst. Sci., 19, 4055–4066, 2015
https://doi.org/10.5194/hess-19-4055-2015
Hydrol. Earth Syst. Sci., 19, 4055–4066, 2015
https://doi.org/10.5194/hess-19-4055-2015

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. Gobiet et al.

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Cited articles

Bellprat, O., Kotlarski, S., Lüthi, D., and Schär, C.: Physical constraints for temperature biases in climate models, Geophys. Res. Lett., 40, 4042–4047, https://doi.org/10.1002/grl.50737, 2013.
Boberg, F. and Christensen, J. H.: Overestimation of Mediterranean summer temperature projections due to model deficiencies, Nature Climate Change, 2, 433–436, https://doi.org/10.1038/nclimate1454, 2012.
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008.
Déqué, M.: Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: model results and statistical correction according to observed values, Global Planet. Change, 57, 16–26, https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007.
Dobler, A. and Ahrens, B.: Precipitation by a regional climate model and bias correction in Europe and South Asia, Meteorol. Z., 17, 499–509, https://doi.org/10.1127/0941-2948/2008/0306, 2008.
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Short summary
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.