Publication Details

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Title (Primary) Robust ensemble selection by multivariate evaluation of extreme precipitation and temperature characteristics
Author Thober, S.; Samaniego, L.;
Journal Journal of Geophysical Research-Atmospheres
Year 2014
Department CHS;
Volume 119
Issue 2
Language englisch;
POF III (all) T34;
Keywords RCM; Ensemble; Rejection rate; Extremes; Significance test; Stepwise selection
UFZ wide themes RU5;
Abstract Extreme hydro-meteorological events often cause severe socio-economic damage. For water resources assessments and policy recommendations, future extreme hydro-meteorological events must be correctly estimated. For this purpose, projections from Regional Climate Models (RCMs) are increasingly used to provide estimates of meteorological variables such as temperature and precipitation. The main objective of this study is to investigate whether a full ensemble or a subset of RCMs reproduces the spatio-temporal variability of observed extremes better than single models. The implications for policy recommendations and impact assessments are then discussed. In particular, the key conditions under which a subset of RCMs could be used for impact assessments are examined. Temperature and precipitation fields of 13 ENSEMBLES RCMs are compared against observations from Germany between 1961 and 2000. Eleven indices characterizing extreme meteorological events were selected for this comparison. The ability of the individual RCMs is estimated based on an overall score and a rejection rate. The former quantifies the biases of these indices. The latter estimates the mean statistical significance quantified by the Wilcoxon rank-sum test. The performance of all possible combinations of RCMs is investigated. Computationally feasible algorithms for finding the best-performing subensemble are also presented and evaluated. One of the proposed algorithms is able to find subensembles with the lowest rejection rate, which are useful for either policy recommendations or impact assessments. These subsets of RCMs showed smaller and less significant bias than single RCMs or the full ensemble over several regions.
ID 14329
Persistent UFZ Identifier
Thober, S., Samaniego, L. (2014):
Robust ensemble selection by multivariate evaluation of extreme precipitation and temperature characteristics
J. Geophys. Res.-Atmos. 119 (2), 594 - 613