Comunicación presentada en: EGU General Assembly 2016 celebrada del 17 al 22 de abril de 2016 en Viena, Austria

Bartholy, Judit

Brands, Swen

Cardoso, Rita M.

Casado Calle, María Jesús

Hertig, Elke

Huth, Radan

Kotlarski, Sven

Maraun, Douglas

Pongracz, R.

Soares, Pedro M. M.

English

Pongracz, Rita

Agencia Estatal de Meteorología

Geophysical Research AbstractsVol. 18, EGU2016-12824, 2016EGU General Assembly 2016© Author(s) 2016. CC Attribution 3.0 License.Results from the VALUE perfect predictor experiment: process-basedevaluationDouglas Maraun (1), Pedro Soares (2), Elke Hertig (3), Swen Brands (4), Radan Huth (5), Rita Cardoso (2), SvenKotlarski (6), Maria Casado (7), Rita Pongracz (8), and Judit Bartholy (8)(1) University of Graz, Wegener Center for Climate and Global Change, Graz, Austria (douglas.maraun@uni-graz.at), (2)University of Lisbon, Instituto Dom Luiz, Lisbon, Portugal, (3) University of Augsburg, Department of Geography, Augsburg,Germany, (4) MeteoGalicia, Santiago de Compostela, Spain, (5) Charles University Prague, Deptartment of PhysicalGeography and Geoecology, (6) MeteoSwiss, Zurich, Switzerland, (7) Spanish Meteorological Agency, Madrid, Spain, (8)Eotvos Lorand University, Department of MeteorologyUntil recently, the evaluation of downscaled climate model simulations has typically been limited to surfaceclimatologies, including long term means, spatial variability and extremes. But these aspects are often, at leastpartly, tuned in regional climate models to match observed climate. The tuning issue is of course particularlyrelevant for bias corrected regional climate models. In general, a good performance of a model for these aspectsin present climate does therefore not imply a good performance in simulating climate change. It is now widelyaccepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climatemodels simulate relevant underlying processes. In other words, it is important to assess whether downscaling doesthe right for the right reason.Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictorexperiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008,reference observations are given by a network of 85 meteorological stations covering all European climates.More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, onlyvariables provided by both types of approaches could be considered. This limited the analysis to conditioninglocal surface variables on variables from driving processes that are simulated by ERA-Interim.We considered the following types of processes: at the continental scale, we evaluated the performance ofdownscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations.At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, theUnited Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as theMistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surfacevariables to underlying processes and ultimately to improve climate models.

Results from the VALUE perfect predictor experiment: process-based evaluation

https://repositorio.aemet.es/bitstream/20.500.11765/11740/1/EGU2016-12824.pdf

Results from the VALUE perfect predictor experiment: process-based evaluation

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Agencia Estatal de Meteorología