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| Titel |
The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal |
| VerfasserIn |
A. Gobiet, M. Suklitsch, G. Heinrich |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 19, no. 10 ; Nr. 19, no. 10 (2015-10-06), S.4055-4066 |
| Datensatznummer |
250120819
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| Publikation (Nr.) |
 copernicus.org/hess-19-4055-2015.pdf |
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| Zusammenfassung |
| 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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