linked in logo | IS-ENES | Contact

Exploring climate model data

France: Providing high-resolution climate change scenarios and evaluating uncertainties in the France Seine and Somme river basins

France: Providing high-resolution climate change scenarios and evaluating uncertainties in the France Seine and Somme river basins

Goal: Providing climate change scenarios for evaluating impacts on France Seine and Somme river basins, as well as evaluating changes in hydrological extremes and uncertainties using several downscaling methodologies, climate and hydrological models. This Use Case is not implemented in the portal yet.

This use-case describes the course of actions needed to evaluate the impact of anthropogenic climate change on hydrologic extremes in the Seine and Somme river basins. Uncertainties will be evaluated using several downscaling methods, climate and hydrological models. This Use Case is a special case since it is part of a research project. Hence, most of the data needs are specified beforehand using available data.


Christian Pagé, CERFACS, France


Climate research groups (CERFACS , CNRM-GAME ), hydrological research groups (Sisyphe , Armines, CEMAGREF Lyon , BRGM Orléans , SOGREAH , Hydratec, INRA ), public stake-holders (MEEDM ).

Data needs

Atmospheric variables

High-resolution DAILY climate data is needed covering the France Seine and Somme river basins (1660 data points), for the following variables. This dataset is thereafter called DAILY_SET.
  • 2-meters Daily Maximum and Minimum Temperatures
  • Cumulated Total (liquid + solid) Daily Precipitation
  • Cumulated Daily Potential Evapotranspiration
  • Daily Mean Surface Incoming Shortwave Radiation
High-resolution HOURLY climate data is needed covering the France Seine and Somme river basins (re-aggregated over each basin individually, 29 basins for the Seine river and 1 basin for the Somme river), for the following variables. This dataset is thereafter called HOURLY_BASIN_SET.
  • 2-meters Temperature
  • Surface Incoming Shortwave Radiation
  • Surface Incoming Longwave Radiation
  • 2-meters Specific Humidity
  • 10-meters Wind Module
  • Liquid Precipitation
  • Solid Precipitation
High-resolution HOURLY climate data is needed covering the whole Metropolitan France (9892 data points) for the following variables. This dataset is thereafter called HOURLY_FRANCE_SET.
  • 2-meters Temperature
  • Surface Incoming Shortwave Radiation
  • Surface Incoming Longwave Radiation
  • 2-meters Specific Humidity
  • 10-meters Wind Module
  • Liquid Precipitation
  • Solid Precipitation
The period covered by the scenarios needed are variable. These periods will be needed, and they are selected because of scenario data availability:
  • 1950-2100
  • 1960-2000; 2046-2065; 2081-2100
  • 2070-2100
For the DAILY_SET and the HOURLY_BASIN_SET, the following climate scenarios are provided. For the HOURLY_FRANCE_SET, the climate scenarios provided are highlighted in gray. The downscaled methodologies are statistical (RT: weather typing; ANOM: anomaly) and dynamical (QQ: quantile-quantile).
Climate Model CO2 scenario Time period Data Centre Downscaling methodology
CCCMA CGCM3.1 T63 A1B 1960-2000; 2046-2065; 2081-2100 CCCMA RT
CNRM-CM3 A1B 1960-2000; 2046-2065; 2081-2100 CNRM Meteo-France RT
CSIRO-MK3.0 A1B 1960-2000; 2046-2065; 2081-2100 CSIRO RT
GFDL-CM2.0 A1B 1960-2000; 2046-2065; 2081-2100 GFDL RT
GFDL-CM2.1 A1B 1960-2000; 2046-2065; 2081-2100 GFDL RT
GISS-AOM A1B 1960-2000; 2046-2065; 2081-2100 GISS RT
GISS-ER A1B 1960-2000; 2046-2065; 2081-2100 GISS RT
IAP-FGOALS A1B 1960-2000; 2046-2065; 2081-2100 FGOALS RT
INGV ECHAM4 A1B 1960-2000; 2046-2065; 2081-2100 INGV RT
IPSL CM4 A1B 1960-2000; 2046-2065; 2081-2100 IPSL RT
MIROC 3.2 MEDRES A1B 1960-2000; 2046-2065; 2081-2100 MIROC RT
MIUB ECHO-G A1B 1960-2000; 2046-2065; 2081-2100 MIUB RT
MPI-ECHAM5 A1B 1960-2000; 2046-2065; 2081-2100 MPI RT
MRI CGCM 2.3.2a A1B 1960-2000; 2046-2065; 2081-2100 MRI RT
NCAR CCSM3 A1B 1960-2000; 2046-2065; 2081-2100 NCAR RT
ARPEGE V4 A1B 1950-2100 (ensemble 4 members) CNRM Meteo-France RT/QQ/ANOM
ARPEGE V4 A2 1950-2000; 2070-2100 CNRM Meteo-France RT/QQ/ANOM

Typical course of events

The stakeholders launch a France National Research Program for the Management and Impacts of Climate Change (GICC).
  1. A research project proposal is written by several research groups. This specific case describes a project to evaluate the impact of anthropogenic climate change on hydrologic extremes in the Seine and Somme river basins (REXHYSS). The project proposal specifies the objectives, the problematic, the data needs, the methodologies, the tools, the uncertainties.
  2. Research projects are selected by the stakeholders.
  3. The research project team organises a kick-off meeting to begin the project.
  4. Communications between research groups is done through emails, phone calls, meetings.
  5. The hydrologic models research groups ask for downscaled climate change scenarios atmospheric data to the climate models groups. There are several hydrological models involved to evaluate uncertainties related to these models.
  6. Each climate group produces downscaled climate scenario data using different methodologies (dynamical and statistical) for the selected scenarios, to quantity uncertainties related to the climate models as well as to the downscaling methodologies. The statistical methodologies used are anomalies and weather typing, while the dynamical downscaling uses the ARPEGE regional model associated with a quantile-quantile correction. For the dynamical downscaling methodology, the choices of climate scenarios is more limited because it is model-specific (in this case, only the ARPEGE regional model could be downscaled).
  7. Because there are too many available climate scenarios for the statistical downscaling methodology, most of the hydrological research groups ask for a reduced set.
  8. The climate research group, responsible for the weather typing statistical downscaling methodology, select a subset of the climate scenarios using a partially objective methodology (involving spatial maps and summary graphs) which tries to keep as much dispersion as possible compared to the full scenarios set and hence, keeping most of the uncertainties of the full set.
  9. The climate research group extracts data of the downscaled scenario data over the requested data points covering the domain of interest and calculate complementary parameters (in this case, potential evapotranspiration and total precipitation for the DAILY set), and reformats this data into ASCII data files for easy processing by the hydrologic research teams. One of the team used some binary files. To perform the process of ASCII output, a tool is used which can read NetCDF CF-1.0 files, select data covering a given region or individual points, and output into easy ASCII files following the specifications of the hydrological research teams.
  10. The climate research group prepares documentation describing the data, the data access method.
  11. The climate research group copy data files to ftp server for data retrieval access by the hydrological research teams.
  12. The hydrological research teams retrieve the downscaled ASCII data files from the climate research groups ftp servers.
  13. The hydrological research teams perform hydrological simulations with the climate scenarios subset as input to their hydrological numerical models. Uncertainty assessment is evaluated using the different climate scenarios in the dataset.
  14. The project team analyse data and write a report for stakeholders, about climate change vulnerabilities and impacts. They evaluate the uncertainties of different types using three downscaling methods, two SRES scenarios, several climate and hydrological numerical models. The project team communicates through the means of e-mails, phone and meetings.
  15. The report is presented in a series of seminars to the other project teams and the stakeholders.
  16. Stakeholders receive the report and take decisions using report conclusions and summary.

References for the Use Case

  • Boé, J., L. Terray, F. Habets, and E. Martin, 2006: A simple statistical-dynamical downscaling scheme based on weather types and conditional resampling. J. Geophys. Res., 111 :D21106, 2006.
  • Déqué M. (2007). Frequency of precipitation and temperature extremes over France in an anthropogenic scenario: Model results and statistical correction according to observed values, Global and Planetary Change, 57,16-26, doi:10.1016/j.gloplacha.2006.11.030
  • Ducharne A., Baubion C., Beaudoin N., Benoit M., Billen G., Brisson N., Garnier J., Kieken H., Lebonvallet S.,Ledoux E., Mary B., Mignolet C., Poux X., Sauboua E., Schott C., Théry S. and Viennot P. (2007). Long term prospective of the Seine river system: Confronting climatic and direct anthropogenic changes. Science of the Total Environment, 375, 292-311, doi:10.1016/j.scitotenv.2006.12.011
  • Gascoin S, Ducharne A, Ribstein P, Carli M, Habets F. Adaptation of a catchment-based land surface model tothe hydrogeological setting of the Somme River basin (France), J. Hydrol., 368(1-4), 105-116, doi:10.1016/j.jhydrol.2009.01.039.
  • Gibelin, A.L. and Déqué, M., 2003 : Anthropogenic climate change over the Mediterranean region simulated by a global variable resolution model. Climate Dynamics, 20 : 327-339.
  • Habets F., A. Boone, J.L Champeaux, P. Etchevers, L. Franchistéguy, E.Leblois, E. Ledoux, P. Le Moigne, E. Martin, S. Morel, J. Noilhan, P.Quintana Segui F. Rousset-Regimbeau, P. Viennot (2008). The SAFRAN-ISBA-MODCOU hydrometeorological model applied over France, Journal of Geophysical Research (D: Atmospheres), 113, D06113(2008)18.
  • Korkmaz S, Ledoux E, Önder H (2009). Application of the coupled model to the Somme river basin, Journal of Hydrology, 366, 21-34. Ledoux E, Gomez E, Monget JM, Viavattene C, Viennot P, Ducharne A, Benoit M, Mignolet C, Schott C, Mary B (2007). Agriculture and Groundwater Nitrate Contamination in the Seine Basin. The STICS-MODCOU modelling chain. Science of the Total Environment, 375, 33-47, doi:10.1016/j.scitotenv.2006.12.002
  • Pagé, C., L. Terray et J. Boé, 2009: dsclim: A software package to downscale climate scenarios at regional scale using a weather-typing based statistical methodology. Technical Report TR/CMGC/09/21, CERFACS, Toulouse, France.
  • Pagé, C., J. Boé, et L. Terray, 2008 : Projections climatiques à échelle fine sur la France pour le 21ème siècle : les scénarii SCRATCH08. Technical Report TR/CMGC/08/64, CERFACS, Toulouse, France.
  • Perrin C., Michel C., Andréassian V. (2003). Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279(1-4): 275-289.
  • Quintana-Segui, P., Le Moigne, P., Durand, Y., Martin, E., Habets, F., Baillon, M., Canellas, C., Franchisteguy, L. and Morel, S., 2008: Analysis of Near-Surface Atmospheric Variables: Validation of the SAFRAN Analysis over France. J. Appl. Meteor. Climatol. 47, 92–107.
  • Thiéry D. (1990). MARTHE. Modélisation d’Aquifères par maillage Rectangulaire en régime Transitoire pour le calcul Hydrodynamique des Écoulements. Version 4.3. Rapport BRGM 4S/EAU n° R32210.
  • Thiéry D., Moutzopoulos, C. (1995). Un modèle hydrologique spatialisé pour la simulation de très grands bassins : le modèle EROS formé de grappes de modèles globaux élémentaires. VIIIèmes journées hydrologiques de l'ORSTOM "Régionalisation en hydrologie, application au développement". In Le Barbé et E. Servat (Ed.) ORSTOM Editions, pp. 285-295.
  • Thiéry D. (2003). Logiciel GARDÉNIA version 6.0. Guide d’utilisation. Rapport BRGM n° RP 52832. Thiéry D. (2004). Simulation d’une grappe de basins versants du Doubs et de la Loue avec le modèle ÉROS - Prise en compte d’exportations et d’importations. Note technique NT EAU 2004/23. udunits-1:
  • Russell, K. R., E. J. Hartnett, and J. Caron, 2006: NetCDF-4: Software Implementing an Enhanced Data Model for the Geosciences. 22nd International Conference on Interactive Information Processing Systems for Meteorology, Oceanography, and Hydrology. Convention CF-1.0 NetCDF:

Software used

  1. CERFACS dsclim downscaling software and library (Pagé et al., 2009)
  2. NetCDF-4 library (Russel et al., 2006)
  3. udunits-1 library
  4. CERFACS extract_nc data-extraction software
  5. Météo-France-CNRM Quantile-Quantile software (Déqué 2007)
  6. Météo-France-CNRM ARPEGE climate numerical model (Gibelin et Déqué, 2003)
  7. Hydrological models
Hydrological model Model Type Basin References
CLSM Hydro-meteorological Seine Somme Ducharne et al., 2007, Gascoin et al., 2009
EROS Hydrological with tanks Seine Thiéry et Moutzopoulos, 1995
GARDENIA Hydrological with tanks Somme Thiéry, 2003
GR4J Hydrological with tanks Seine Somme Perrin et al., 2003
MARTHE Hydro-geological Seine Thiéry, 1990
MODCOU Hydro-geological Seine Ledoux et al., 2007, Korkmaz et al., 2009
SIM Hydro-meteorological Seine Habets et al., 2008

File format(s)

  1. The provided files containing the data (downscaled scenarios) are provided in ASCII format. The HOURLY_FRANCE_SET was provided directly in the native SIM binary file format.
  2. The native format for CERFACS statistical downscaling data is in the NetCDF format using the CF-1.0 convention.

Sources and cascade of Uncertainty

Uncertainties exist from many sources: unknown future emissions of greenhouse gases and aerosols, the conversion of emissions to atmospheric concentrations and to radiative forcing of the climate, modelling the response of the climate system to forcing, and methods for regionalising Global Circulation Models (GCM), such as the statistical downscaling used here. This should be well documented and provided to the user of the data. Users must be trained to be aware of this and to know how to deal with it. It is unavoidable to use data from several scenarios as input given the current uncertainties. Read more on uncertainties



The ENES3 project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824084.