IJG  Vol.6 No.9 , September 2015
Spatial Distribution of Cordex Regional Climate Models Biases over West Africa
ABSTRACT
The objective of this work is to analyze the spatial distribution of biases of nine (9) regional climate models (RCMs) and their ensemble average used under the framework of COordinated Regional climate Downscaling EXperiment (CORDEX) project over West Africa during the summer period. We assessed the ability of RCMs to represent adequately West African summer rainfall by analyzing some statistical parameters such as the relative bias, the standard deviation, the root mean square error (RMSE) and the correlation coefficient between observation data (GPCP used as reference) and regional climate models outputs. We first analyzed the relative bias between GPCP climatology and the other available observed data (CRU, CMAP, UDEL, GPCC, TRMM and their ensemble mean). This analysis highlights the big uncertainty on the quality of these observed rainfall data over West Africa which may be largely due to the rarity of in situ measurement data over this region. The statistical analysis with respect to GPCP rainfall shows the presence of large relative bias values over most part of West Africa for engaged RCMs. However their ensemble mean outperforms individual RCMs by exhibiting the weakest relative change. The RMSE values are weak over West Africa except over and off the Guinea highlands for RCMs and the Era-interim reanalysis. The spatial distribution of the coefficient of correlation between the observation data and RCMs shows that all models (except HIRHAM) present positive values over the Northern Sahel and the Gulf of Guinea. The model of the DMI exhibits the weakest values of correlation coefficient. This study shows that RCMs simulate West African climate in a satisfactory way despite the fact that they exhibit systematic biases.

Cite this paper
Sarr, A. , Camara, M. and Diba, I. (2015) Spatial Distribution of Cordex Regional Climate Models Biases over West Africa. International Journal of Geosciences, 6, 1018-1031. doi: 10.4236/ijg.2015.69081.
References
[1]   James, R., Washington, R. and Rowell, D.P. (2014) African Climate Change Uncertainty in Perturbed Physics Ensembles: Implications of Global Warming to 4°C and Beyond. Journal of Climate, 27, 4677-4692. http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00612.1

[2]   Skinner, C.B. and Diffenbaugh, N.S. (2014) Projected Changes in African Easterly Wave Intensity and Track in Response to Greenhouse Forcing. Proceedings of the National Academy of Sciences of the United States of America, 111, 6882-6887.
http://www.pnas.org/content/early/2014/04/23/1319597111.abstract

[3]   Taylor, K.E., Stouffer, R.J. and Meehl, G.A. (2012) An Overview of CMIP5 and the Experiment Design. Bulletin of American Meteorology Society, 93, 485-498. http://dx.doi.org/10.1175/BAMS-D-11-00094.1

[4]   Rummukainen, M. (2010) State-of-the-Art with Regional Climate Models. Climate Change, 1, 96-82. http://dx.doi.org/10.1002/wcc.8

[5]   Giorgi, F and Mearns, L.O. (1999) Introduction to Special Section: Regional Climate Modeling Revisited. Journal of Geophysical Research, 104, 6335-6352. http://dx.doi.org/10.1029/98JD02072

[6]   Paeth, H., Capo-Chichi, A. and Endlicher, W. (2008) Climate Change and Food Security in Tropical West Africa—A Dynamic-Statistical Modelling Approach. Erdkunde, 62, 101-115.
http://dx.doi.org/10.3112/erdkunde.2008.02.01

[7]   Philippon, N., Martiny, N. and Camberlin, P. (2009) Forecasting the Vegetation Photosynthetic Activity over the Sahel: A Model Output Statistics Approach. International Journal of Climatology, 29, 1463-1477. http://dx.doi.org/10.1002/joc.1797

[8]   Hoerling, M, Hurrell, J., Eischeid, J. and Phillips, A. (2006) Detection and Attribution of Twentieth-Century Northern and Southern African Rainfall Change. Journal of Climate, 19, 3989-4008.
http://dx.doi.org/10.1175/JCLI3842.1

[9]   Afiesimama, A.E., Pal, J.S., Abiodun, B.J., Gutowski, W.J. and Adedoyin, A. (2006) Simulation of West African Monsoon Using the RegCM3. Part I: Model Validation and Interannual Variability. Theoretical and Applied Climatology, 86, 23-37. http://dx.doi.org/10.1007/s00704-005-0202-8

[10]   Hsieh, J.S, and Cook, K.H. (2007) A Study of the Energetic of African Easterly Waves Using Regional Climate Model. Journal of the Atmospheric Sciences, 64, 421-440. http://dx.doi.org/10.1175/JAS3851.1

[11]   Sylla, M.B., Dell’Aquila, A., Ruti, P.M. and Giorgi, F. (2010) Simulation of the Intraseasonal and the Interannual Variability of Rainfall over West Africa with RegCM3 during the Monsoon Period. International Journal of Climatology, 30, 1865-1883.

[12]   Van der Linden, P. and Mitchell, J.F.B. (2009) ENSEMBLES: Climate Change and Its Impact: Summary of Research and the Results from the ENSEMBLES Project. Met Office Hadley Centre, Exeter.

[13]   Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Buchner, M., Cerezo-Mota, R., et al. (2012) Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate Simulations. Journal of Climate, 25, 6057-6078. http://dx.doi.org/10.1175/JCLI-D-11-00375.1

[14]   Camara, M., Diedhiou, A., Sow, B.A., et al. (2013) Analyse de la pluie simulée par les modèles climatiques régionaux de CORDEX en Afrique de l’Ouest. Sécheresse, 24, 14-28.

[15]   Gbobaniyi, E., Sarr, A., Sylla, M.B., et al. (2013) Climatology, Annual Cycle and Interannual Variability of Precipitation and Temperature in CORDEX Simulations over West Africa. International Journal of Climatology, 34, 2241-2257. http://dx.doi.org/10.1002/joc.3834

[16]   Kim, J., Waliser, D.E., Mattmann, C.A., Goodale, C.E., Hart, A.F., Zimdars, P.A., et al. (2014) Evaluation of the CORDEX-Africa Multi-RCM Hindcast: Systematic Model Errors. Climate Dynamics, 42, 1189-1202. http://dx.doi.org/10.1007/s00382-013-1751-7

[17]   Déqué, M. (2010) Regional Climate Simulation with a Mosaic of RCMs. Meteorologische Zeitschrift, 19, 259-266. http://dx.doi.org/10.1127/0941-2948/2010/0455

[18]   Christensen, O.B., Drews, M., Christensen, J.H., Dethloff, K., Ketelsen, K., Hebestadt, I. and Rinke, A. (2008) The Hirham Regional Climate Model Version 5. Technical Report 06-17, DMI.
http://www.dmi.dk/dmi/en/print/tr06-17.pdf

[19]   Pal, J.S., Giorgi, F., Bi, X., Elguindi, N., Solmon, F., Gao, X., Rauscher, S.A., Francisco, R., Zakey, A., Winter, J., Ashfaq, M., Syed, F.S., Bell, J.L., Diffenbaugh, N.S., Karmacharya, J., Konare, A., Martinez, D., da Rocha, R.P., Sloan, L.C. and Steiner, A.L. (2007) Regional Climatemodeling for the Developing World—The ICTP RegCM3 and RegCNET. Bulletin of the American Meteorological Society, 88, 1395-1409. http://dx.doi.org/10.1175/BAMS-88-9-1395

[20]   Jacob, D., Barring, L., Christensen, O.B., Christensen, J.H., Hagemann, S., Hirschi, M., Kjellstr?m, E., Lenderink, G., Rockel, B., Schar, C., Seneviratne, S.I., Somot, S., van Ulden, A. and van den Hurk, B. (2007) An Inter-Comparison of Regional Climate Models for Europe: Design of the Experiments and Model Performance. Climate Change, 81, 31-52. http://dx.doi.org/10.1007/s10584-006-9213-4

[21]   Meijgaard, E., van Ulft, L.H., van den Berg, W.J., Bosveld, F., van den Hurk, B., Lenderink, G. and Siebesma, A.P. (2008) The KNMI Regional Atmospheric Climate Model RACMO, Version 2.1. KNMI Technical Report 302, 43.

[22]   Jones, R., Noguer, M., Hassel, D., Hudson, D., Wilson, S., Jenkins, G. and Mitchell, J. (2004) Generating High Resolution Regional Climate Change Using PRECIS. Met Office Hadley Centre, Exeter, 40.

[23]   Samuelsson, P., Jones, C.G., Willén, U., Ullerstig, A., Gollvik, S., Hansson, U., Jansson, C., Kjellstr?m, E., Nikulin, G. and Wyser, K. (2011) The Rossby Centre Regional Climate Model RCA3: Model Description and Performance. Tellus A, 63, 4-23. http://dx.doi.org/10.1111/j.1600-0870.2010.00478.x

[24]   Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Wang, W. and Powers, J.G. (2008) A Description of the Advanced Research WRF Version 3. NCAR Tech 15 Note NCAR/TN– 475+STR, 113.

[25]   Zadra, A., Caya, D., C?té, J., Dugas, B., Jones, C., Laprise, R., Winger, K. and Caron, L. (2008) The Next Canadian Regional Climate Model. Physics in Canada, 64, 75-83.

[26]   Uppala, S.M., Dee, D., Kobayashi, S., et al. (2008) Towards a Climate Data Assimilation System: Status Update of ERA-Interim. ECMWF Newsletter, 115, 12-18.

[27]   Adler, R.F., Huffman, G.J., Charney, J.G., Chang, A., et al. (2003) The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). Journal of Hydrometeorology, 4, 1147-1167. http://dx.doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2

[28]   Huffman, G.J., Adler, R.F., Bolvin, D.T., et al. (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. Journal of Hydrometeorology, 8, 38-55. http://dx.doi.org/10.1175/JHM560.1

[29]   Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A. and Ziese, M. (2010) The New “GPCC Full Data Reanalysis Version 5” Providing High-Quality Gridded Monthly Precipitation Data for the Global Land-Surface Is Public Available since December 2010. GPCC Status Report, December 2010, 7 p.

[30]   Mitchell, T.D. and Jones, P.D. (2005) An Improved Method of Constructing a Database of Monthly Climate Observations and Associated High-Resolution Grids. International Journal of Climatology, 25, 693-712. http://dx.doi.org/10.1002/joc.1181

[31]   Legates, D.R. and Willmott, C.J. (1990) Mean Seasonal and Spatial Variability in Gauge-Corrected, Global Precipitation. International Journal of Climatology, 10, 111-127.
http://dx.doi.org/10.1002/joc.3370100202

[32]   Jobard, I., Chopin, F., Berges, J.C. and Roca, R. (2011) An Intercomparison of 10-Day Satellite Precipitation Products during West African Monsoon. International Journal of Remote Sensing, 32, 2353-2376. http://dx.doi.org/10.1080/01431161003698286

 
 
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