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Dynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications

TitleDynamical and statistical downscaling of seasonal temperature forecasts in Europe: Added value for user applications
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2018
AuthorsManzanas, R., Gutiérrez J.M., Fernández J., van Meijgaard E., Calmanti Sandro, Magariño M.E., Cofiño A.S., and Herrera S.
JournalClimate Services
Volume9
Pagination44-56
ISSN24058807
Abstract

This work describes the results of a comprehensive intercomparison experiment of dynamical and statistical downscaling methods performed in the framework of the SPECS (http://www.specs-fp7.eu) and EUPORIAS (http://www.euporias.eu) projects for seasonal forecasting over Europe, a region which exhibits low-to-moderate seasonal forecast skill. We considered a 15-member hindcast provided by the EC-EARTH global model (similar to ECMWF System 4, but using bias corrected SST) for the period 1991–2012. In particular, we focus on summer mean temperature and evaluate the added value of downscaling for representation of the local climatology (mean values and extremes), improvement of model skill and performance in particular heatwave episodes. Whereas the suitability of dynamical downscaling for reducing the orographic biases of the global model depends on the region and model considered, statistical downscaling can systematically reduce errors in different order moments, from the mean to the extremes (as represented by the 95th percentile here). However, both dynamical and statistical methods lead to similar skill patterns with about the same overall performance as the global model, which shows higher values in south-eastern Europe. Therefore, no relevant added value is found in terms of model skill improvement. Finally, when focusing on the heatwaves of 2003, 2006, 2010 and 2012, the limitations of the global model to detect these hot episodes are inherited by all dynamical and statistical downscaling methods so no added value is neither found in this aspect. This work provides, to our knowledge, the largest and most comprehensive intercomparison of statistical and dynamical downscaling for seasonal forecasting over Europe. © 2017 The Authors

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85020800951&doi=10.1016%2fj.cliser.2017.06.004&partnerID=40&md5=4542248e0d458007df96637e0072873b
DOI10.1016/j.cliser.2017.06.004
Citation KeyManzanas201844