A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12.5 km was completed for the new emission scenarios RCP4.5 and RCP8.5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX. The results strengthen those obtained in ENSEMBLES, but need further investigations. The analysis of impact indices shows that for RCP8.5, there is a substantially larger change projected for temperature-based indices than for RCP4.5. The difference is less pronounced for precipitation-based indices. Two effects of the increased resolution can be regarded as an added value of regional climate simulations. Regional climate model simulations provide higher daily precipitation intensities, which are completely missing in the global climate model simulations, and they provide a significantly different climate change of daily precipitation intensities resulting in a smoother shift from weak to moderate and high intensities.

SURFEX is a new externalized land and ocean surface platform that describes the surface fluxes and the evolution of four types of surfaces: nature, town, inland water and ocean. It is mostly based on pre-existing, well-validated scientific models that are continuously improved. The motivation for the building of SURFEX is to use strictly identical scientific models in a high range of applications in order to mutualise the research and development efforts. SURFEX can be run in offline mode (0-D or 2-D runs) or in coupled mode (from mesoscale models to numerical weather prediction and climate models). An assimilation mode is included for numerical weather prediction and monitoring. In addition to momentum, heat and water fluxes, SURFEX is able to simulate fluxes of carbon dioxide, chemical species, continental aerosols, sea salt and snow particles. The main principles of the organisation of the surface are described first. Then, a survey is made of the scientific module (including the coupling strategy). Finally, the main applications of the code are summarised. The validation work undertaken shows that replacing the pre-existing surface models by SURFEX in these applications is usually associated with improved skill, as the numerous scientific developments contained in this community code are used to good advantage.

Detailed studies of snow cover processes require models that offer a fine description of the snow cover properties. The detailed snowpack model Crocus is such a scheme, and has been run operationally for avalanche forecasting over the French mountains for 20 yr. It is also used for climate or hydrological studies. To extend its potential applications, Crocus has been recently integrated within the framework of the externalized surface module SURFEX. SURFEX computes the exchanges of energy and mass between different types of surface and the atmosphere. It includes in particular the land surface scheme ISBA (Interactions between Soil, Biosphere, and Atmosphere). It allows Crocus to be run either in stand-alone mode, using a time series of forcing meteorological data or in fully coupled mode (explicit or fully implicit numerics) with atmospheric models ranging from meso-scale models to general circulation models. This approach also ensures a full coupling between the snow cover and the soil beneath. Several applications of this new simulation platform are presented. They range from a 1-D stand-alone simulation (Col de Porte, France) to fully-distributed simulations in complex terrain over a whole mountain range (Massif des Grandes Rousses, France), or in coupled mode such as a surface energy balance and boundary layer simulation over the East Antarctic Ice Sheet (Dome C).

The assessment of regional climate change requires the development of reference long‐term retrospective meteorological datasets. This article presents an 8‐km‐resolution atmospheric reanalysis over France performed with the the Safran‐gauge‐based analysis system for the period 1958–2008. Climatological features of the Safran 50‐year analysis—long‐term mean values, inter‐annual and seasonal variability—are first presented for all computed variables: rainfall, snowfall, mean air temperature, specific humidity, wind speed and solar and infrared radiation. The spatial patterns of precipitation, minimum and maximum temperature are compared with another spatialization method, and the temporal consistency of the reanalysis is assessed through various validation experiments with both dependent and independent data. These experiments demonstrate the overall robustness of the Safran reanalysis and the improvement of its quality with time, in connection with the sharp increase in the observation network density that occurred in the 1990s. They also show the differentiated sensitivity of variables to the number of available ground observations, with precipitation and air temperature being the more robust ones. The comparison of trends from the reanalysis with those from homogenized series finally shows that if spatial patterns are globally consistent with both approaches, care must be taken when using literal values from the reanalysis and corresponding statistical significance in climate change detection studies. The Safran 50‐year atmospheric reanalysis constitutes a long‐term forcing datasets for land surface schemes and thus enables the simulation of the past 50 years of water resources over France. Copyright © 2009 Royal Meteorological Society

Système d’analyse fournissant des renseignements atmosphériques à la neige (SAFRAN) is a mesoscale atmospheric analysis system for surface variables. It produces an analysis at the hourly time step using ground data observations. One of SAFRAN’s main features is that it is based on climatically homogeneous zones and is able to take vertical variations into account. Originally intended for mountainous areas, it was later extended to cover France. This paper focuses on the validation of the extended version. The principle of the analysis is described and its quality was tested for five parameters (air temperature, humidity, wind speed, rainfall, and incoming radiation), using Météo-France’s observation network and data of some well-instrumented stations. Moreover, SAFRAN’s rainfall was compared with another analysis, known as analyse utilisant le relief pour l’hydrométéorologie (Aurelhy). Last, two different versions of SAFRAN were compared for mountain conditions. Temperature and relative humidity were well reproduced, presenting no bias. Wind speed was also well reproduced; however, its bias was −0.3 m s–1. The interpolation from the 6-h time step of the analysis to the 1-h time step was one of the sources of error. The precipitation analysis was robust and not biased; its root-mean-square error was 2.4 mm day−1. This error was mainly due to the spatial heterogeneity of the precipitation within the geographical zones of analysis (1000 km2). The analysis of incoming solar radiation presented some biases, especially in coastal areas. The results of the comparison with some well-instrumented sites were encouraging. SAFRAN is being run operationally at Météo-France on a real-time basis for various applications.

Two downscaling methods designed for the study of the hydrological impact of climate change on the Seine basin in France are tested for present climate. First, a multivariate statistical downscaling (SD) methodology based on weather typing and conditional resampling is described. Then, a bias correction technique for dynamical downscaling based on quantile–quantile mapping is introduced. To evaluate the end‐to‐end SD methodology, the atmospheric forcing derived from the large‐scale circulation (LSC) of the ERA40 reanalysis by SD is used to force a hydrological model. Simulated discharges reproduce historical values reasonably well. Next, the dynamical and statistical approaches are compared using the Météo–France ARPEGE general circulation model in a variable resolution configuration (resolution around 60 km over France). The ARPEGE simulation is downscaled using the two methodologies, and hydrological simulations are performed. Regarding downscaled temperature and precipitation, the statistical approach is more efficient in reproducing the temporal and spatial autocorrelation properties. The simulated river discharges from the two approaches are nevertheless very similar: the two methods reproduce well the seasonal cycle and the daily distribution of streamflows. Finally, the results of the study are discussed from a practical impact study perspective. Copyright © 2007 Royal Meteorological Society