%0 Journal Article %1 Druyan2010WAMME %A Druyan, LeonardM %A Feng, Jinming %A Cook, KerryH %A Xue, Yongkang %A Fulakeza, Matthew %A Hagos, SamsonM %A Konaré, Abdourahamane %A Moufouma-Okia, Wilfran %A Rowell, DavidP %A Vizy, EdwardK %A Ibrah, SeidouSanda %B Climate Dynamics %D 2010 %I Springer-Verlag %J Climate Dynamics %K lakevictoria %N 1 %P 175--192 %R 10.1007/s00382-009-0676-7 %T The WAMME regional model intercomparison study %U http://dx.doi.org/10.1007/s00382-009-0676-7 %V 35 %X Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.

@article{Druyan2010WAMME, abstract = {Results from five regional climate models ({RCMs}) participating in the West African Monsoon Modeling and Evaluation ({WAMME}) initiative are analyzed. The {RCMs} were driven by boundary conditions from National Center for Environmental Prediction reanalysis {II} data sets and observed sea-surface temperatures ({SST}) over four {May–October} seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the {RCMs}, except that lateral boundary conditions were derived from a continuous global climate model ({GCM}) simulation forced with observed {SST} data. {RCM} and {GCM} simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of {RCM} skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. {RCM} spatial patterns of {June–September} mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of {10°N} the correlations average only about 0.44. The mean spatial correlation coefficient between {RCM} and observed surface air temperature over West Africa is 0.88. {RCMs} show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two {RCMs} overestimate moisture convergence over West Africa. The 0.5° computing grid enables three {RCMs} to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two {RCMs} for which this was assessed. The benefits of dynamic downscaling the {GCM} seasonal climate prediction are analyzed and discussed.}, added-at = {2019-08-14T18:35:35.000+0200}, author = {Druyan, LeonardM and Feng, Jinming and Cook, KerryH and Xue, Yongkang and Fulakeza, Matthew and Hagos, SamsonM and Konar\'{e}, Abdourahamane and Moufouma-Okia, Wilfran and Rowell, DavidP and Vizy, EdwardK and Ibrah, SeidouSanda}, biburl = {https://www.bibsonomy.org/bibtex/272d3bc11f9b99ca1bb697291e0d9c860/karinawilliams}, booktitle = {Climate Dynamics}, citeulike-article-id = {5956176}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s00382-009-0676-7}, citeulike-linkout-1 = {http://www.springerlink.com/content/07p5v040164161v3}, citeulike-linkout-2 = {http://link.springer.com/article/10.1007/s00382-009-0676-7}, doi = {10.1007/s00382-009-0676-7}, interhash = {76727b9fdf4c38e0a8c3660ba5ff971c}, intrahash = {72d3bc11f9b99ca1bb697291e0d9c860}, journal = {Climate Dynamics}, keywords = {lakevictoria}, number = 1, pages = {175--192}, posted-at = {2014-04-03 15:04:42}, priority = {2}, publisher = {Springer-Verlag}, timestamp = {2019-08-14T18:35:35.000+0200}, title = {The {WAMME} regional model intercomparison study}, url = {http://dx.doi.org/10.1007/s00382-009-0676-7}, volume = 35, year = 2010 }