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Comparison of WRF Model Physics Parameterizations over the MENA-CORDEX Domain

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http://www.scirp.org/journal/PaperInformation.aspx?PaperID=52884#.VKtPhcnQrzE

We investigated the performance of 12 different physics configurations of the climate version of the Weather, Research and Forecasting (WRF) Model over the Middle East and North Africa (MENA) domain. Possible combinations among two Planetary Boundary Layer (PBL), three Cumulus (CUM) and two Microphysics (MIC) schemes were tested. The 2-year simulations (December 1988-November 1990) have been compared with gridded observational data and station measurements for several variables, including total precipitation and maximum and minimum 2-meter air temperature. An objective ranking method of the 12 different simulations and the selection procedure of the best performing configuration for the MENA domain are based on several statistical metrics and carried out for relevant sub-domains and individual stations. The setup for cloud microphysics is found to have the strongest impact on temperature biases while precipitation is most sensitive to the cumulus parameterization scheme and mainly in the tropics.
Cite this paper
Zittis, G. , Hadjinicolaou, P. and Lelieveld, J. (2014) Comparison of WRF Model Physics Parameterizations over the MENA-CORDEX Domain. American Journal of Climate Change, 3, 490-511. doi: 10.4236/ajcc.2014.35042. 
 

[1] Van Oldenborgh, G.J., Collins, M., Arblaster, J., Christensen, J.H., Marotzke, J., Power, S.B., Rummukainen, M.T. and Zhou, T. (2013) Annex I: Atlas of Global and Regional Climate Projections. In: Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P.M., Eds., Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, and New York.
[2] Lelieveld, J., Hadjinicolaou, P., Kostopoulou, E., Chenoweth, J., Maayar, M., Giannakopoulos, C., Hannides, C., Lange, M.A., Tanarhte, M., Tyrlis, E. and Xoplaki, E. (2012) Climate Change and Impacts in the Eastern Mediterranean and the Middle East. Climatic Change, 114, 667-687.
http://dx.doi.org/10.1007/s10584-012-0418-4
[3] Chenoweth, J., Hadjinicolaou, P., Bruggeman, A., Lelieveld, J., Levin, Z., Lange, M.A., Xoplaki, E. and Hadjikakou, M. (2011) Impact of Climate Change on the Water Resources of the Eastern Mediterranean and Middle East Region: Modeled 21st Century Changes and Implications. Water Resources Research, 47, Article ID: W06506+.
[4] Myers, N., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. and Kent, J. (2000) Biodiversity Hotspots for Conservation Priorities. Nature, 403, 853-858.
http://dx.doi.org/10.1038/35002501
[5] Fischlin, A., Midgley, G.F., Price, J.T., Leemans, R., Gopal, B., Turley, C., Rounsevell, M.D.A., Dube, O.P., Tarazona, J. and Velichko, A.A. (2007) Ecosystems, Their Properties, Goods and Services. In: Parry, M., Canziani, O.F., Palutikof, J.P., van der Linden, P. and Hanson, C.E., Eds., Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge.
[6] Leung, L., Ruby, L., Mearns, O., Giorgi, F. and Wilby, R.L. (2003) Regional Climate Research. Bulletin of the American Meteorological Society, 84, 89-95.
http://dx.doi.org/10.1175/BAMS-84-1-89
[7] Deque, M., Jones, R.G., Wild, M., Giorgi, F., Christensen, J.H., Hassell, D.C., Vidale, P.L., Rockel, B., Jacob, D., Kjellstrom, E., de Castro, M., Kucharski, F. and van den Hurk, B. (2005) Global High Resolution Versus Limited Area Model Climate Change Projections over Europe: Quantifying Confidence Level from PRUDENCE Results. Climate Dynamics, 25, 653-670.
http://dx.doi.org/10.1007/s00382-005-0052-1
[8] Skamarock, W.C., et al. (2008) A Description of the Advanced Research WRF Version 3. NCAR Technical Notes, NCAR/TN-4751STR.
[9] Salathe, E.P., Leung, L.R., Qian, Y. and Zhang, Y. (2010) Regional Climate Model Projections for the State of Washington. Climatic Change, 102, 51-75.
http://dx.doi.org/10.1007/s10584-010-9849-y
[10] Chotamonsak, C., Salathe, E.P., Kreasuwan, J., Chantara, S. and Siriwitayakorn, K. (2011) Projected Climate Change over Southeast Asia Simulated Using a WRF Regional Climate Model. Atmospheric Science Letters, 12, 213-219.
http://dx.doi.org/10.1002/asl.313
[11] Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., Christensen, O.B., Deque, M., Fernandez, J., Hansler, A., van Meijgaard, E., Samuelsson, P., Sylla, M.B. and Sushama, L. (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
[12] Soares, P., Cardoso, R., Miranda, P., Medeiros, J., Belo-Pereira, M. and Espirito-Santo, F. (2012) WRF High Resolution Dynamical Downscaling of ERA-Interim for Portugal. Climate Dynamics, 39, 2497-2522.
http://dx.doi.org/10.1007/s00382-012-1315-2
[13] Warrach-Sagi, K., Schwitalla, T., Wulfmeyer, V. and Bauer, H.S. (2013) Evaluation of a Climate Simulation in Europe Based on the WRF-NOAH Model System: Precipitation in Germany. Climate Dynamics, 41, 755-774.
http://dx.doi.org/10.1007/s00382-013-1727-7
[14] Warrach-Sagi, K., Schwitalla, T., Bauer, H.S. and Wulfmeyer, V. (2013) A Regional Climate Model Simulation for EURO-CORDEX with the WRF Model. In: Resch, M., et al. Eds., Sustained Simulation Performance 2013, Springer International Publishing, Berlin.
[15] García-Díez, M., Fernández, J., Fita, L. and Yagüe, C. (2013) Seasonal Dependence of WRF Model Biases and Sensitivity to PBL Schemes over Europe. Quarterly Journal of the Royal Meteorological Society, 139, 501-514.
http://dx.doi.org/10.1002/qj.1976
[16] Dudhia, J. (2014) A History of Mesoscale Model Development. Asia-Pacific Journal of Atmospheric Sciences, 50, 121-131.
http://dx.doi.org/10.1007/s13143-014-0031-8
[17] Evans, J., Ekstrom, M. and Ji, F. (2012) Evaluating the Performance of a WRF Physics Ensemble over South-East Australia. Climate Dynamics, 39, 1241-1258.
http://dx.doi.org/10.1007/s00382-011-1244-5
[18] Bukovsky, M. and Karoly, D. (2009) Precipitation Simulations Using WRF as a Nested Regional Climate Model. Journal of Applied Meteorology and Climatology, 48, 2152-2159.
http://dx.doi.org/10.1175/2009JAMC2186.1
[19] Ruiz, J., Saulo, J.C. and Nogues-Paegle, J. (2010) WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables. Monthly Weather Review, 138, 3342-3355.
http://dx.doi.org/10.1175/2010MWR3358.1
[20] Flaounas, E., Bastin, S. and Janicot, S. (2011) Regional Climate Modelling of the 2006 West African Monsoon: Sensitivity to Convection and Planetary Boundary Layer Parameterisation Using WRF. Climate Dynamics, 36, 1083-1105.
http://dx.doi.org/10.1007/s00382-010-0785-3
[21] Cretat, J., Pohl, B., Richard, Y. and Drobinski, P. (2012) Uncertainties in Simulating Regional Climate of Southern Africa: Sensitivity to Physical Parameterizations Using WRF. Climate Dynamics, 38, 613-634.
http://dx.doi.org/10.1007/s00382-011-1055-8
[22] Fita, L., Fernández, J. and Gracia-Diez, M. (2010) CLWRF: WRF Modifications for Regional Climate Simulation under Future Scenarios. 11th WRF User’s Workshop, NCAR, Boulder.
[23] Simmons, A., Uppala, S., Dee, D. and Kobayashi, S. (2006) ERA-Interim: New ECMWF Reanalysis Products from 1989 Onwards. ECMWF Newsletter, 110, 26-35.
[24] Hong, S.Y., Noh, Y. and Dudhia, J. (2006) A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Monthly Weather Review, 134, 2318-2341.
http://dx.doi.org/10.1175/MWR3199.1
[25] Janjic, Z.I. (1994) The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes. Monthly Weather Review, 122, 927-945.
http://dx.doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2
[26] Kain, J.S. and Fritsch, J.M. (1993) Convective Parameterization for Meso-Scale Models: The Kain-Fritsch Scheme. In: Emanuel, K.A. and Raymond, D.J., Eds., The Representation of Cumulus Convection in Numerical Models, American Meteorological Society, Boston, 1-246.
[27] Kain, J.S. (2004) The Kain-Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology, 43, 170-181.
http://dx.doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2
[28] Betts, A.K. and Miller, M.J. (1986) A New Convective Adjustment Scheme. Part II: Single Column Tests Using GATE Wave, BOMEX, and Arctic Air-Mass Data Sets. Quarterly Journal of the Royal Meteorological Society, 121, 693-709.
http://dx.doi.org/10.1002/qj.49711247308
[29] Janjic, Z.I. (2000) Comments on “Development and Evaluation of a Convection Scheme for Use in Climate Models”. Journal of the Atmospheric Science, 57, 3686.
http://dx.doi.org/10.1175/1520-0469(2000)057<3686:CODAEO>2.0.CO;2
[30] Hong, S.Y. and Lim, J.O.J. (2006) The WRF Single-Moment Microphysics Scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129-151.
[31] Tao, W.K., Simpson, J. and McCumber, M. (1989) An Ice-Water Saturation Adjustment. Monthly Weather Review, 117, 231-235.
http://dx.doi.org/10.1175/1520-0493(1989)117<0231:AIWSA>2.0.CO;2
[32] Collins, W.D., Rasch, P.J., Boville, B.A., Hack, J.J., McCaa, J.R., Williamson, D.L., Kiehl, J.T. and Briegleb, B. (2004) Description of the NCAR Community Atmosphere Model (CAM3.0). Technical Note TN-464+STR, National Center of Atmospheric Research, Boulder.
[33] Tewari, M.F., Chen, W., Wang, J., Dudhia, M.A., LeMone, K., Mitchell, M.E., Gayno, G., Wegiel, J. and Cuenca, R.H. (2004) Implementation and Verification of the Unified NOAH Land Surface Model in the WRF Model. 20th Conference on Weather Analysis and Forecasting. 16th Conference on Numerical Weather Prediction, Seattle, 11-15.
[34] National Center for Atmospheric Research, NCAR (2014) ARW Version 3 Modeling System’s User’s Guide.
http://www.mmm.ucar.edu/wrf/users/docs/user_guide_V3/ARWUsersGuideV3.pdf
[35] Mooney, P.A., Mulligan, F.J. and Fealy, R. (2012) Evaluation of the Sensitivity of the Weather Research and Forecasting Model to Parameterization Schemes for Regional Climates of Europe over the Period 1990-95. Journal of Climate, 26, 1002-1017.
http://dx.doi.org/10.1175/JCLI-D-11-00676.1
[36] Mercader, J., Codina, B., Sairouni, A. and Cunillera, J. (2010) Results of the Meteorological Model WRF-ARW over Catalonia, Using Different Parameterizations of Convection and Cloud Microphysics. Tethys, 7, 75-86.
http://dx.doi.org/10.3369/tethys.2010.7.07
[37] Argüeso, D., Hidalgo-Munoz, J.M., Gámiz-Fortis, S.R., Esteban-Parra, M.J., Dudhia, J. and Castro-Díez, Y. (2011) Evaluation of WRF Parameterizations for Climate Studies over Southern Spain Using a Multistep Regionalization. Journal of Climate, 24, 5633-5651.
http://dx.doi.org/10.1175/JCLI-D-11-00073.1
[38] Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2013) Updated High-Resolution Grids of Monthly Climatic Observations. International Journal of Climatology, 34, 623-642.
http://dx.doi.org/10.1002/joc.3711
[39] Gómez-Navarro, J.J., Montávez, J.P., Jerez, S., Jimenez-Guerrero, P. and Zorita, E. (2012) What Is the Role of the Observational Dataset in the Evaluation and Scoring of Climate Models? Geophysical Research Letters, 39, Article ID: L24701.
http://dx.doi.org/10.1029/2012GL054206
[40] Tanarhte, M., Hadjinicolaou, P. and Lelieveld, J. (2012) Intercomparison of Temperature and Precipitation Data Sets Based on Observations in the Mediterranean and the Middle East. Journal of Geophysical Research, 117, Article ID: D12102.
http://dx.doi.org/10.1029/2011JD017293
[41] Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., Schroder, M. and Stender, P. (2013) Global Gridded Precipitation over Land: A Description of the New GPCC First Guess Daily Product. Earth System Science Data Discussions, 6, 435-464.
http://dx.doi.org/10.5194/essdd-6-435-2013
[42] Klein Tank, A.M.G., et al. (2002) Daily Dataset of 20th-Century Surface Air Temperature and Precipitation Series for the European Climate Assessment. International Journal of Climatology, 22, 1441-1453.
http://www.ecad.eu http://dx.doi.org/10.1002/joc.773
[43] Willmott, C.J. (1981) On the Validation of Models. Physical Geography, 2, 184-194.
[44] Willmott, C.J. and Matsuura, K. (2005) Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research, 30, 79-82.
http://dx.doi.org/10.3354/cr030079
[45] Legates, D.R. and McCabe, G.J. (1999) Evaluating the Use of “Goodness-of-Fit” Measures in Hydrologic and Hydroclimatic Model Validation. Water Resources Research, 35, 233-241.
http://dx.doi.org/10.1029/1998WR900018
[46] Weiss, M., Hurk, B., Haarsma, R. and Hazeleger, W. (2012) Impact of Vegetation Variability on Potential Predictability and Skill of EC-Earth Simulations. Climate Dynamics, 39, 2733-2746.
http://dx.doi.org/10.1007/s00382-012-1572-0
[47] Lin, Y.L., Farley, R.D. and Orville, H.D. (1983) Bulk Parameterization of the Snow Field in a Cloud Model. Journal of Climate and Applied Meteorology, 22, 1065-1092.
http://dx.doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2
[48] Wu, L. and Petty, G.W. (2010) Intercomparison of Bulk Microphysics Schemes in Model Simulations of Polar Lows. Monthly Weather Review, 138, 2211-2228.
http://dx.doi.org/10.1175/2010MWR3122.1
[49] Jerez, S., Montavez, J., Gomez-Navarro, J., Lorente-Plazas, R., Garcia-Valero, J. and Jimenez-Guerrero, P. (2013) A Multi-Physics Ensemble of Regional Climate Change Projections over the Iberian Peninsula. Climate Dynamics, 41, 1749-1768.
http://dx.doi.org/10.1007/s00382-012-1551-5                eww150106lx
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