Incorporating circulation statistics in bias correction of GCM ensembles: hydrological application for the Rhine basin View Full Text


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Article Info

DATE

2015-04-07

AUTHORS

Christiana Photiadou, Bart van den Hurk, Aarnout van Delden, Albrecht Weerts

ABSTRACT

An adapted statistical bias correction method is introduced to incorporate circulation-dependence of the model precipitation bias, and its influence on estimated discharges for the Rhine basin is analyzed for a historical period. The bias correction method is tailored to time scales relevant to flooding events in the basin. Large-scale circulation patterns (CPs) are obtained through Maximum Covariance Analysis using reanalysis sea level pressure and high-resolution precipitation observations. A bias correction using these CPs is applied to winter and summer separately, acknowledging the seasonal variability of the circulation regimes in North Europe and their correlation with regional precipitation rates over the Rhine basin. Two different climate model ensemble outputs are explored: ESSENCE and CMIP5. The results of the CP-method are then compared to observations and uncorrected model outputs. Results from a simple bias correction based on a delta factor (NoCP-method) are also used for comparison. For both summer and winter, the CP-method offers a statistically significant improvement of precipitation statistics for subsets of data dominated by particular circulation regimes, demonstrating the circulation-dependence of the precipitation bias. Uncorrected, CP and NoCP corrected model outputs were used as forcing to a hydrological model to simulate river discharges. The CP-method leads to a larger improvement in simulated discharge in the Alpine area in winter than in summer due to a stronger dependence of Rhine precipitation on atmospheric circulation in winter. However, the NoCP-method, in comparison to the CP-method, improves the discharge estimations over the entire Rhine basin. More... »

PAGES

187-203

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-015-2578-1

DOI

http://dx.doi.org/10.1007/s00382-015-2578-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027690989


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