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WASCAL – Regional Climate Simulations for West Africa

Global climate change is clearly noticeable at regional scales as well, and is one of the main challenges in West Africa in the 21st century. Countries in this region need to develop effective adaptation and mitigation strategies to counteract the negative effects of land use changes and of climate change on the population and on the environment, as, for example, on the hydro-meteorological variability. WASCAL is a research-oriented project aimed at deriving solutions for these challenges. A team of climate modellers in Germany and West Africa is developing a regional climate modelling system (“dynamical downscaling” of global modelling results) specifically for Northwest Africa.

At DKRZ many climate variables such as temperature and precipitation, for example, are being computed in the project “WASCAL – Regional Climate Simulations for West Africa”, using spatially and temporally highly resolving regional climate models. A multi-model ensemble, consisting of three different global Earth System Models (ESMs) and three regional climate models (RCMs), shows the strengths and weaknesses of each model and makes a probability estimate of the occurrence of extreme climate events possible. The onset of the rainy season is such an event of great economic importance, which is governed, just like its duration, by the West African monsoon system.

The global models used for driving the regional models are MPI-ESM MR [1] of the Max Planck Institute for Meteorology in Germany, HadGEM2-ES of the UK Met Office Hadley Center [2], and GFDL-ESM2M of the Geophysical Fluid Dynamics Laboratory in the US [3]. Dynamical downscaling is carried out with the regional climate models WRFV3.5.1 (NCAR Weather Research & Forecasting Model, USA) [4], CCLM4 (Cosmo Model in Climate Mode, Germany) [5] and RegCM4.3 (ICTP Regional Climate Model, Italy) [6], with the latter having been employed only at ICTP, and not yet on the HPC platform at DKRZ. 

 

For the regional climate model simulations a much higher horizontal resolution (factor ≥ 10) is being used than in the global models , but for a limited region (Fig. 1). WRF, for example, was configured for the WASCAL window with a model resolution of 12 km, resulting in 500 x 330 x 40 grid points in West-East, North-South, and vertical direction [7, 8]. The WASCAL partner countries Bénin, Burkina Faso, Côte d'Ivoire, Gambia, Ghana, Mali, Niger, Nigeria, Sénégal and Togo are highlighted in green. 

Although the higher resolution allows a more detailed representation of the topography and of the distribution of relevant soil properties, this does not necessarily lead to better model results. The reasons are systematic errors produced by both the global as well as the regional models, and that physical parameterizations do not automatically stay valid at smaller scales. A popular approach is a correction of the bias produced by the global model. Two different algorithms for such bias correction were tested before the actual simulations were done, but were then not pursued any further due to their negative side effects. All correction methods applicable to the simulations at hand are leading to physical inconsistencies and also to a loss of information, i.e. relevant signals of climate change [9]. These tests were carried out for the region shown in figure 2. 

 

We therefore chose driving models which are representative for a “wet” (GFDL-ESM2M) and a “dry” (HadGem2-ES) extreme, as well as one for a CMIP5 mean condition (MPI-ESM MR) (figure 3). 

 

 

Furthermore control simulations are being carried out with ERA-Interim re-analysis forcing data in order to draw conclusions with respect to the realism of the regional models with “ideal” boundary values. At present (December 2014) control runs with WRF/ERA-Interim and CCLM/ERA-Interim are underway for the time period between 1979 and 2013. Simulations with WRF/Echam6 for the historical time period from 1979 until 2005 have already been completed and climate projections up to 2100 have been initiated (figure 4).

 

 

In addition WRF was optimized for the West African region by testing different combinations of the model physics and parameterizations included and the spatial resolution (figure 5) [13]. The above average rainy season of the year 1999 and the weak rainy season observed in 2002 were simulated for the West African monsoon region using an average resolution of 24 km. Model results are validated with data stemming from the joint observation networks in the WASCAL countries [14], for example by comparing them with the measurements of meteorological variables at climate stations, with precipitation values synthesized from satellite and rain gauge data on a 0.25 x 0.25 degree grid [15], and using data products of the GPCP (Global Precipitation Climatology Project, 1 x 1 degree grid) [16]. 

 

Model result data are to be made available for further investigations, e.g. for the estimation of consequences of climate change, or for the analysis of the effects of land-use changes on various environmental and societal sectors.

An example of a concrete application is the fully coupled atmospheric-hydrological model system which reproduces the hydro-meteorological fluxes in the Sissili watershed (figure 6). Only insufficient answers to the question how land cover changes influence the fluxes of matter at the surface-atmosphere interface may be found with conventional global and regional climate models, because they neglect land surface processes. In order to consider the effects of lateral hydrological processes like, for example, surface runoff, a new land surface model is coupled to WRF and called WRF-Hydro. With WRF-Hydro bi-directional atmosphere-hydrology simulations may be carried out for specific watersheds. The results of an atmospheric model driven by ERA-Interim re-analysis data were first dynamically downscaled with WRF to 10 km, then resolved even finer in the Sissili watershed (2 km grid distance), and were there coupled with the NCAR Distributed Hydrological Modeling System [17] [18] (500 m grid distance).

 

 

Technical simulation details:

Model periods: 1979-2013 for the evaluation runs with ERA-Interim forcing; 1975-2005 for the historical control experiments for three driving ESMs; 2006-2100 for ESM-driven RCM RCP4.5 projections. 

Performance of WRF: To simulate one model day and using a time step of Δt =72s, WRF required 12 minutes on blizzard, i.e. used 14,000 CPUh for one model year. Output data are stored every 3 hours on 24 pressure levels in NetCDF4 format (CF-1.6), corresponding to a storage volume of 1 GB per model day or 3.6 TB for 10 model years.


WASCAL Climate Modeller Partners

In Germany the WASCAL regional climate modeling team consists of scientists working at the Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU, Campus Alpin, Karlsruhe Institute of Technology, Garmisch-Partenkirchen), the Institute of Geography at the University of Augsburg, the German Aerospace Center (DLR, Oberpfaffenhofen) and at DKRZ in Hamburg. Team members in West Africa are scientists at the WASCAL Competence Center in Ouagadougou (Burkina Faso) and graduate students of the WASCAL Graduate Research Program „West African Climate System“ (Federal University of Technology Akure (FUTA) in Akure, Nigeria).

Contact (authors):

Ilse Hamann, DKRZ: [Email protection active, please enable JavaScript.]

Dominikus Heinzeller, Institut für Umweltforschung (IMK-IFU, KIT Campus Alpin in Garmisch-Partenkirchen): [Email protection active, please enable JavaScript.]

 

The project WASCAL is funded by the German Federal Ministry of Education and Research (BMBF), reference number 01LG1202H. The responsibility for the content of this publication lies with the authors.

 

References:

  1. Stevens, B., Giorgetta, M.A., and 15 co-authors (2013): Atmospheric component of the MPI-M Earth System Model: ECHAM6. Journal of Advances in Modeling Earth Systems 5, 146-172.

  2. Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., and 14 co-authors: Development and evaluation of an Earth-system model HadGEM2, Geosci. Model Dev. Discuss., 4, 997--1062, 2011

  3. Dunne, J.P., John, J.G., Adcroft, A.J., and 17 co-authors (2012): GFDL’s ESM2 Global Coupled Climate¬ Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics. J. Climate 25, 6646-6665, http://dx.doi.org/10.1175/JCLI-D-11-00560.1

  4. Skamarock, W.C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang, W., Powers, J.G. et al. (2008): A Description of the Advanced Research WRF Version 3, NCAR/TN–475+STR, NCAR TECHNICAL NOTE, June 2008, http://www2.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

  5. Rockel, B., Will, A., Hense, A. (2008 The Regional Climate Model COSMO-CLM (CCLM), Meteorologische Zeitschrift, 17/ 4, 347-348, 2008, http://dx.doi.org/10.1127/0941-2948/2008/0309

  6. Giorgi, F., Coppola, E., Solmon F, Mariotti, L. and others (2012): RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7-29, http://dx.doi.org/10.3354/cr01018

  7. Paeth, H., Hall, N. M., Gaertner, M. A., Alonso, M. D., Moumouni, S., Polcher, J., et al. (2011): Progress in regional downscaling of West African precipitation. Atmospheric Science Letters, 12, 75-82.

  8. Browne, N. A. K., Sylla, M. B. (2012): Regional Climate Model Sensitivity to Domain Size for the Simulation of the West African Summer Monsoon Rainfall, International Journal of Geophysics, vol. 2012, Article ID 625831, 17 pages, 2012. http://dx.doi.org/10.1155/2012/625831

  9. Heinzeller, D. Klein, C, Kunstmann, H. (2014): Towards a climate modeling system for West Africa: Sensitivity studies and input bias correction for WRF. Extended abstract for 15th WRF Workshop,UCAR/NCAR, June 2014.

  10. Jones, P. & Harris, J.: University of East Anglia Climatic Research Unit, CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 of High Resolution Gridded Data of Month-by-month Variation in Climate (Jan. 1901 -- Dec. 2012). NCAS British Atmospheric Data Centre, 2013

  11. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., Ziese, M.: GPCC Full Data Reanalysis Version 6.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data. http://dx.doi.org/10.5676/DWD_GPCC/FD_M_V6_050

  12. Fan, Y. & van den Dool, H.: A global monthly land surface air temperature analysis for 1948-present. J. Geophys. Res., 113, 2008. http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.GHCN_CAMS/.gridded/.dataset_documentation.html

  13. Klein, C., Bliefernicht, J., Heinzeller, D. Kunstmann, H. (2014): WRF model performance and sensitivity to model physics in a medium- and high-resolution downscaling experiment for West Africa, http://meetingorganizer.copernicus.org/EGU2014/EGU2014-6376.pdf

  14. Bliefernicht, J., Kunstmann, H., Hingerl, L., Rummler, T., Andresen, S., Mauder, M., Steinbrecher, R., Frieß, R., Gochis, D.,Gessner, U., Quensah, E., Awotuse, A., Neidl, F. Jahn, F., Barry, B. (2013): Field and simulation experiments for investigating regional land–atmosphere interactions in West Africa: experimental setup and first results. http://elib.dlr.de/88029/1/Bliefernicht_etal_2013.pdf

  15. TRMM Tropical Rainfall Measuring Mission, Data Products, http://trmm.gsfc.nasa.gov/data_dir/data.html

  16. Global Precipitation Climatology Project (GPCP), http://www.gewex.org/gpcpdata.htm

  17. Arnault, J., Hingerl, L., Andresen, S., Rummler, T., Kunstmann, H. (2014): Evaluation of a fully coupled atmospheric-hydrological modeling system for the Sissili watershed in the West African Sudanian Savannah, http://gewex.org/2014conf/posters_Monday.html#T23

  18. Gochis, D. J., Wei, Y., David, N. Y. (2010): The NCAR Distributed Hydrological Modeling System (NDHMS): User's guide and technical description.

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