The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an “overtuning” to the “home-region”, which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the K？ppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intra-seasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different
References
[1]
WCRP CORDEX. Available online: http://wcrp.ipsl.jussieu.fr/SF_RCD_CORDEX.html (accessed on 20 October 2010).
[2]
Jacob, D.; Barring, L.; Christensen, O.B.; Christensen, J.H.; de Castro, M.; Deque, M.; Giorgi, F.; Hagemann, S.; Lenderink, G.; Rockel, B.; et al. An inter-comparison of regional climate models for Europe: Model performance in present-day climate. Clim. Change 2007, 81, 31–52, doi:10.1007/s10584-006-9213-4.
[3]
Simmons, A.; Uppala, S.; Dee, D.; Kobayashi, S. ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newslett. 2006, 110, 25–35.
[4]
Takle, E.S.; Roads, J.; Rockel, B.; Gutowski, W.J., Jr.; Arrit, R.W.; Meinke, I.; Jones, C.G.; Zadra, A. Transferability intercomparison: An opportunity for new insight on the global watercycle and energy budget. Bull. Am. Meteor. Soc. 2007, 88, 375–384, doi:10.1175/BAMS-88-3-375.
[5]
Jacob, D.; Podzun, R. Sensitivity studies with the regional climate model REMO. Meteorol. Atmos. Phys. 1997, 63, 119–129, doi:10.1007/BF01025368.
[6]
Trewatha, G.T. An Introduction to Climate, 3rd ed.; McGraw-Hill: New York, NY, USA, 1954.
[7]
Perkins, S.E.; Pitman, A.J.; Holbrook, N.J.; McAneney, J. Evaluation of the AR4 climate model’s simulated daily maximum temperature, minimum temperature, and precipitation over Australia using probability density functions. J. Climate 2007, 20, 4356–4376, doi:10.1175/JCLI4253.1.
[8]
Jacob, D. A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorol. Atmos. Phys. 2001, 77, 61–73, doi:10.1007/s007030170017.
[9]
Roeckner, E.; Arpe, K.; Bengtsson, L.; Christoph, M.; Claussen, M.; Dümenil, L.; Esch, M.; Giorgetta, M.; Schlese, U.; Schulzweida, U. The Atmospheric General Circulation Model Echam-4: Model Description and Simulation of the Present Day Climate; Report No. 218; Max-Planck-Institute for Meteorology: Hamburg, Germany, 1996.
[10]
Majewski, D. The Europa-Modell of the Deutscher Wetterdienst. In Proceedings of the ECMWF Seminar on Numerical Methods in Atmospheric Models, Reading, UK, 9–13 September 1991; pp. 147–191.
[11]
Tiedtke, M. A comprehensive mass flux scheme for cumulus parameterization in large scale models. Mon. Weather Rev. 1989, 117, 1779–1800, doi:10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.
[12]
Nordeng, T.E. Extended Versions of the Convective Parametrization Scheme at ECMWF and Their Impact on the Mean and Transient Activity of the Model in the Tropics; Technical Report No. 206; European Centre for Medium-Range Weather Forecasts: Reading, UK, 1994.
[13]
Pfeifer, S. Modeling Cold Cloud Processes with the Regional Climate Model Remo; Reports on Earth System Science 23; Max-Planck-Institute for Meteorology: Hamburg, Germany, 2006.
[14]
Morcrette, J.J.; Smith, L.; Fourquart, Y. Pressure and temperature dependance of the absorption in longwave radiation parameterizations. Beitr. Phys. Atmos. 1986, 59, 455–469.
[15]
Giorgetta, M.; Wild, M. The Water Vapour Continuum and Its Representation in Echam4; Report No. 162; Max-Planck-Institute for Meteorology: Hamburg, Germany, 1995.
[16]
Louis, J.F. A parametric model of vertical eddy fluxes in the atmosphere. Bound. Layer Meteorol. 1979, 17, 187–202, doi:10.1007/BF00117978.
[17]
Lohmann, U.; Roeckner, E. Design and performance of a new cloud microphysics scheme developed for the ECHAM4 general circulation model. Clim. Dyn. 1996, 12, 557–572, doi:10.1007/BF00207939.
[18]
Hagemann, S. An Improved Land Surface Parameter Dataset for Global and Regional Climate Models; Report No. 336; Max-Planck-Institute for Meteorology: Hamburg, Germany, 2002.
[19]
Rechid, D.; Raddatz, T.J.; Jacob, D. Parameterization of snow-free land surface albedo as a function of vegetation phenology based on MODIS data and applied in climate modelling. Theor. Appl. Climatol. 2009, 95, 245–255, doi:10.1007/s00704-008-0003-y.
[20]
Davies, H.C. A lateral boundary formulation for multi-level prediction models. Quart. J. R. Meteor. Soc. 1976, 102, 405–418.
[21]
CRU Datasets-CRU TS Time-Series; British Atmospheric Data Centre: Didcot, UK, 2008. Available online: http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_1256223773328276 (accessed on 20 October 2010).
[22]
Brohan, P.; Kennedy, J.J.; Harris, I.; Tett, S.F.B.; Jones, P.D. Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. J. Geophys. Res. 2006, 111.
[23]
Legates, D.R.; Willmott, C.J. Mean seasonal and spatial variability in gauge-corrected, global precipitation. Int. J. Climatol. 1990, 10, 111–127, doi:10.1002/joc.3370100202.
[24]
Lohmann, U.; Sausen, R.; Bengtsson, L.; Cubasch, U.; Perlwitz, J.; Roeckner, E. The K？ppen climate classification as a diagnostic tool for general circulation models. Clim. Res. 1993, 3, 177–193, doi:10.3354/cr003177.
[25]
de Castro, M.; Gallardo, C.; Jylha, K.; Tuomenvirta, H. The use of a climate-type classification for assessing climate change effects in Europe from an ensemble of nine regional climate models. Clim. Change 2007, 81, 329–341, doi:10.1007/s10584-006-9224-1.
[26]
Tapiador, F.J.; Sánchez, E.; Romera, R. Exploiting an ensemble of regional climate models to provide robust estimates of projected changes in monthly temperature and precipitation probability distribution functions. Tellus A 2009, 61, 57–71, doi:10.1111/j.1600-0870.2008.00374.x.
[27]
Kleidon, A.; Heimann, M. Assessing the role of deep rooted vegetation in the climate system with model simulations: Mechanism, comparison to observations and implications for Amazonian deforestation. Clim. Dyn. 2000, 16, 183–199, doi:10.1007/s003820050012.
[28]
Haensler, A.; Hagemann, S.; Jacob, D. Dynamical downscaling of ERA40 reanalysis data over southern Africa: Added value in the representation of seasonal rainfall characteristics. Int. J. Climatol. 2011, 31, 2338–2349, doi:10.1002/joc.2242.
[29]
Richter, I.; Mechoso, C.R. Orographic influences on subtropical stratocumulus. J. Atmos. Sci. 2006, 63, 2585–2601, doi:10.1175/JAS3756.1.
[30]
Yu, J.-Y.; Mechoso, C.R. Links between annual variations of peruvian stratocumulus clouds and of SST in the Eastern Equatorial Pacific. J. Clim. 1999, 12, 3305–3318.
[31]
Yang, D.; Kane, D.; Zhang, Z.; Legates, D.; Goodison, B. Bias corrections of long-term (1973-2004) daily precipitation data over the northern regions. Geophys. Res. Lett. 2005, 32.
[32]
Dai, A.; Qian, T.; Trenberth, K.E.; Milliman, J.D. Changes in continental freshwater discharge from 1948 to 2004. J. Clim. 2009, 22, 2773–2791, doi:10.1175/2008JCLI2592.1.
[33]
Dai, A.; Trenberth, K.E. Estimates of freshwater discharge from continents: Latitudinal and seasonal variations. J. Hydrometeor. 2002, 3, 660–687, doi:10.1175/1525-7541(2002)003<0660:EOFDFC>2.0.CO;2.
[34]
Hagemann, S.; Duemenil, L. A parameterization of the lateral waterflow for the global scale. Clim. Dyn. 1998, 14, 17–31, doi:10.1007/s003820050205.
Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World map of the K？ppen-Geiger climate classification updated. Meteorol. Z. 2006, 15, 259–263, doi:10.1127/0941-2948/2006/0130.
