Article the Simulation of European Climate (1961–2000) with Regcm3 at the Time of Access Or May Contain More Experiments

Geosci. Model Dev., 7, 1297–1333, 2014 www.geosci-model-dev.net/7/1297/2014/ doi:10.5194/gmd-7-1297-2014 © Author(s) 2014. CC Attribution 3.0 License. Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble S. Kotlarski1, K. Keuler2, O. B. Christensen3, A. Colette4, M. Déqué5, A. Gobiet6, K. Goergen7,8, D. Jacob9,10, D. Lüthi1, E. van Meijgaard11, G. Nikulin12, C. Schär1, C. Teichmann9,10, R. Vautard13, K. Warrach-Sagi14, and V. Wulfmeyer14 1Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland 2Chair of Environmental Meteorology, Brandenburg University of Technology (BTU), Cottbus-Senftenberg, Germany 3Danish Meteorological Institute, Copenhagen, Denmark 4Institut National de l’Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France 5Météo-France/CNRM, CNRS/GAME, Toulouse, France 6Wegener Center for Climate and Global Change, University of Graz, Graz, Austria 7Centre de Recherche Public – Gabriel Lippmann, Belvaux, Luxembourg 8Meteorological Institute, University of Bonn, Bonn, Germany 9Climate Service Center 2.0, Hamburg, Germany 10Max Planck Institute for Meteorology, Hamburg, Germany 11KNMI Royal Netherlands Meteorological Institute, De Bilt, the Netherlands 12Swedish Meteorological and Hydrological Institute, Norrköping, Sweden 13Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA/CNRS/UVSQ, Gif-sur-Yvette, France 14Institute of Physics and Meteorology, University of Hohenheim, Stuttgart, Germany Correspondence to: S. Kotlarski ([email protected]) Received: 2 December 2013 – Published in Geosci. Model Dev. Discuss.: 14 January 2014 Revised: 9 May 2014 – Accepted: 13 May 2014 – Published: 10 July 2014 Abstract. EURO-CORDEX is an international climate The analysis confirms the ability of RCMs to capture the downscaling initiative that aims to provide high-resolution basic features of the European climate, including its vari- climate scenarios for Europe. Here an evaluation of the ERA- ability in space and time. But it also identifies nonnegligible Interim-driven EURO-CORDEX regional climate model deficiencies of the simulations for selected metrics, regions (RCM) ensemble is presented. The study documents the per- and seasons. Seasonally and regionally averaged tempera- formance of the individual models in representing the basic ture biases are mostly smaller than 1.5 ◦C, while precipita- spatiotemporal patterns of the European climate for the pe- tion biases are typically located in the ±40 % range. Some riod 1989–2008. Model evaluation focuses on near-surface bias characteristics, such as a predominant cold and wet bias air temperature and precipitation, and uses the E-OBS data in most seasons and over most parts of Europe and a warm set as observational reference. The ensemble consists of 17 and dry summer bias over southern and southeastern Europe simulations carried out by seven different models at grid reflect common model biases. For seasonal mean quantities resolutions of 12 km (nine experiments) and 50 km (eight averaged over large European subdomains, no clear bene- experiments). Several performance metrics computed from fit of an increased spatial resolution (12 vs. 50 km) can be monthly and seasonal mean values are used to assess model identified. The bias ranges of the EURO-CORDEX ensem- performance over eight subdomains of the European conti- ble mostly correspond to those of the ENSEMBLES simula- nent. Results are compared to those for the ERA40-driven tions, but some improvements in model performance can be ENSEMBLES simulations. identified (e.g., a less pronounced southern European warm summer bias). The temperature bias spread across different Published by Copernicus Publications on behalf of the European Geosciences Union. 1298 S. Kotlarski et al.: Regional climate modeling on European scales configurations of one individual model can be of a simi- today be considered as a well-established standard technique lar magnitude as the spread across different models, demon- for the generation of regional climate change scenarios. Re- strating a strong influence of the specific choices in physical cent climate scenario products tailored for use in climate parameterizations and experimental setup on model perfor- impact assessment, such as (1) the CH2011 Swiss climate mance. Based on a number of simply reproducible metrics, change scenarios (CH2011, 2011), (2) the German climate the present study quantifies the currently achievable accuracy impacts and adaptation initiative (Jacob et al., 2008), (3) the of RCMs used for regional climate simulations over Europe German “consortium runs” (Hollweg et al., 2008), (4) the and provides a quality standard for future model develop- Styrian STMK12 (Klimaszenarien für die Steiermark) sce- ments. narios in the eastern Alps (Gobiet et al., 2012), (5) the French high-resolution climate scenarios (Lemond et al., 2011; Vau- tard et al., 2013a), or (6) the climate change scenarios for 1 Introduction the Netherlands (van den Hurk, 2007) are in large part based on the analysis of RCM ensembles. Concerning the inter- Assessing the impacts of expected 21st century climate play between dynamical and statistical downscaling, recent change and developing response strategies requires local- to climate impact applications suggest that a combination of regional-scale information on the nature of these changes, the two approaches is optimal (e.g., Bosshard et al., 2013; including a sound assessment of inherent projection uncer- Paeth, 2011). Apart from their role in climate scenario devel- tainties. Driven by a suite of IPCC (Intergovernmental Panel opment, RCMs also became important tools to advance the on Climate Change) assessment reports and accompanied by understanding of regional-scale climate processes and asso- increasing public awareness of ongoing climate change, the ciated feedbacks (e.g., Fischer and Schär, 2009; Hohenegger past decades have seen a rapid development in the corre- et al., 2009; Langhans et al., 2013; Seneviratne et al., 2006). sponding methods for climate scenario generation. Part of An integral part of regional model development is the eval- this evolution has been the development and the refinement uation and quantification of model performance by compar- of climate-downscaling techniques, which aim at translating ison against observation-based reference data. For this pur- coarse-resolution information as obtained from global cli- pose, the standard procedure is to carry out evaluation exper- mate models (GCMs) into regional- and local-scale condi- iments for the recent decades in a perfect boundary setting, tions (e.g., Hewitson and Crane, 1996; Wilby and Fowler, i.e., applying reanalysis products as lateral boundary forc- 2011). While statistical downscaling methods attempt to ing for the regional model. Although atmospheric reanalyses, bridge the scale gap by applying empirically derived trans- themselves, are based on imperfect models and considerable fer functions between the coarse resolution climate model differences can exist between different reanalysis products output and local weather conditions (e.g., Benestad et al., with corresponding impacts on downscaling results (Brands 2008; Fowler et al., 2007; Maraun et al., 2010; Themeßl et et al., 2012) this technique allows isolating model biases in- al., 2012; Widmann et al., 2003), dynamical downscaling troduced by the nesting procedure and/or the RCM formula- employs high-resolution regional climate models (RCMs) tion from biases introduced by a potentially erroneous large- nested into global model output (e.g., Giorgi, 2006; Laprise, scale forcing. Model evaluation in a perfect boundary context 2008; McGregor, 1997; Wang et al., 2004). This technique is an important component of RCM development. It high- allows for a considerably higher spatial resolution over the lights areas of model deficiencies, though without necessar- domain of interest and, hence, for a more realistic repre- ily uncovering the physical reasons for the found biases. It sentation of important surface heterogeneities (such as to- is furthermore the basis for model calibration efforts (e.g., pography, coast lines, and land surface characteristics) and Bellprat et al., 2012b) and can be used for weighting indi- of mesoscale atmospheric processes. Dynamical downscal- vidual RCMs in multimodel ensembles (Christensen et al., ing has originally been developed for the purpose of nu- 2010, and further studies in that Climate Research special merical weather prediction and was first applied in a cli- issue) or for excluding models with identifiable severe short- mate context in the late 1980s and early 1990s (Dickinson comings. A proper and physically consistent representation et al., 1989; Giorgi, 1990). Since then, considerable efforts of the present-day climate by RCMs is generally considered were put into further methodological and technical develop- as a prerequisite for their ability to capture the response of ments, and ever increasing computational resources facili- regional climates to enhanced greenhouse gas conditions. As tated simulations of multidecadal length. Large collaborative such, model evaluation results are an important piece of in- research projects such as MERCURE (e.g., Hagemann et al., formation provided to end users of regional climate projec- 2004), PRUDENCE (Christensen et al., 2007), NARCCAP tions. (Mearns et al., 2009), and ENSEMBLES (van der Linden A large number of previous studies have been concerned

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