Introduction to Dynamical Downscaling of ARPEGE-Climat Output Using WRF Over West Africa

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1 Introduction to Dynamical Downscaling of ARPEGE-Climat Output Using WRF Over West Africa N. Vigaud(1), P. Roucou(1), S. Sijikumar(1), B. Fontaine(1) and S. Tyteca(2) (1): Centre de Recherches de Climatologie (CRC), UMR/CNRS 5210-Université de Bourgogne, Dijon (France) (2): Centre National de Recherches Météorologiques (CNRM), Toulouse (France) ABSTRACT The Weather Regional Forecast (WRF) model is used to downscale low-resolution data over West Africa. First, the performance of the regional model is estimated through contemporary period experiments ( ) forced by ARPEGE-CLIMAT GCM output (ARPEGE) and ERA-40 re-analyses. Key features of the West African monsoon circulation are reasonably well represented. WRF atmospheric dynamics and summer rainfall compare better to observations than ARPEGE forcing data. WRF simulated moisture transport over West Africa is also consistent in both structure and variability with re-analyses, emphasizing the substantial role played by the West African Monsoon (WAM) and African Easterly Jet (AEJ) flows. The statistical significance of potential climate changes for the A2 scenario between is enhanced in the downscaling from ARPEGE by the regional experiments, with substantial rainfall increases over the Guinea Gulf and eastern Sahel. Future scenario WRF simulations are characterized by higher temperatures over the eastern Tropical Atlantic and increased moisture advection towards eastern Guinea Gulf regions through a strengthened WAM flow supporting surface moisture convergence over West Africa. Warmer conditions over both the Mediterranean region and northeastern Sahel could also participate in enhancing moisture transport within the AEJ, intensified northwards due to the strengthening of the thermal gradient between the Sahara and Guinean regions. Mid-tropospheric moisture divergence tends to be favored within the AEJ region contributing to deep moist convection over the Sahel where late summer rainfall regimes are sustained. WRF proved to be a valuable and efficient tool to help downscaling GCM projections over West Africa, and thus assessing issues such as water resources vulnerability locally. 1.INTRODUCTION In most rural regions of Africa, agriculture is of the rain-food subsistence type, making the local economy very vulnerable to climatic variations. Thus, short and medium term projections are of crucial importance in regards to adaptation strategies for local societies. Up-to-date, Global Coupled Models (GCMs) are the only tool to simulate future climatic scenario. Over West Africa, most GCMs do not reproduce correctly key features of the local atmospheric circulation. This, in turn, leads to uncertainties in terms of simulated changes of future rainfall (Douville et al., 2006; Joly et al., 2007; Caminade et al., 2009) as illustrated in Figure 1. Moreover, due to their spatial resolution, GCM outputs cannot be used directly for hydrological/agricultural applications, and dynamical downscaling is a technique which can be put at contribution. Figure 1: Rainfall evolution (5-20 N;20-30 W) between 2030's (A2 scenario) and 1980's (20CM3 scenario) This study aims at documenting dynamical downscaling of GCM outputs over West Africa, illustrated here through the use of the Weather Regional Forecast (WRF) model to disaggregate A2 scenario projection from ARPEGE-CLIMAT (ARPEGE). First, the performance of the model is investigated for contemporary experiments forced by ARPEGE and

2 ERA-40 re-analyses through analyses of simulated summer rainfall (JAS), atmospheric circulation and moisture transport. Thereafter, we examine A2 scenario projections over the West African region, from both ARPEGE and regional simulations forced by the GCM output, in terms of impacts on summer rainfall and associated atmospheric dynamics. 2.DATA & METHOD The Weather Regional Forecast model (WRF) version developed at NCAR, with the following physics, Yosei University (YSU) Planetary Boundary Layer Scheme, NOAAH Land Surface Model, Monin-Obukhov Surface Layer Scheme, Kain-Fritsch Cumulus Parametrization Scheme, RRTM/Dudhia Long/Shortwave Radiation Schemes. Regional atmospheric experiments were designed over a geographical domain between 10 S-45 N and 38 W-58 E. Two types of large-scale forcing were used, CNRM ARPEGE-CLIMAT GCM output and ERA-40 re-analyses (for the contemporary period), at a 2.8 x2.8 and 2.5 x2.5 spatial resolution respectively. Boundary conditions were imposed every 12hrs, while WRF simulated fields were output every 12hrs at a 50km x 50km horizontal resolution (with 28 vertical levels) after a 12 months spin-up period. For validation purposes, GPCP and CMAP rainfall estimates at a 2.5 x2.5 spatial resolution were used. 3.CONTEMPORARY PERIOD EXPERIMENTS 3.1.Summer Rainfall Regarding summer rainfall, more consistent spatial patterns and seasonality were observed for simulated rainfall (Fig.2&3) than the forcings when compared to observations. Noteworthingly, lesser rain producing events are better represented in the simulations (not shown). Figure 2: Mean JAS rainfall for WRF simulations forced by ARPEGE and ERA-40, together with their respective forcings and estimates from CMAP and GPCP from 1981 to Figure 3: Hovmoller of pentad rainfall from WRF simulations forced by ARPEGE and ERA-40, together with their respective forcings and estimates from CMAP and GPCP over the period.

3 3.2.Atmospheric dynamics Key features of the WAM system are well reproduced regardless of the forcings (Fig.4 and 5). At surface, a stronger simulated WAM compared to reanalyses. The flow from the Mediterranean regions is reasonably reproduced whereas, in the case of ARPEGE runs, it is absent from the initial forcing, At mid-tropospheric levels, zonal/meridional simulated AEJ appears to be weaker/stronger than observed in the re-analyses. For ARPEGE simulations, this feature is biased in the forcing, nevertheless it is reasonably reproduced in the experiment, Interestingly, a better coherence is found between atmospheric dynamics linked to the AEJ and observed summer rainfall regimes (not shown). Figure 5: Mean seasonal cycle of WAM (left), AEJ (center) and TEJ (right) from WRF simulations forced by ARPEGE and ERA40 together with those from their respective forcings over the period. Figure 4: Mean JAS 850hPa and 600hPa temperatures (color shaded), geopotentials (contours), and winds (vectors) for WRF simulations (left) forced by ARPEGE and ERA-40 (middle), and their differences (right) for the period. 4.PROJECTION SCENARIO EXPERIMENT 4.1.Impacts on summer rainfall Most substantial variations are located east of the prime meridian along the Sahelian band and over Guinea Gulf regions (Fig.6), with a weakened/strengthened early/late summer rainy season (Fig.7). Figure 6: Mean JAS rainfall for WRF simulations (left) forced by ARPEGE (right) over the (top) and (middle) periods together with their differences (bottom). Shaded areas correspond to scores significant at 90% and 95% level of Student t-test (light and dark grey respectively). Figure 7: Hovmoller of pentad rainfall from WRF simulations (right) forced by ARPEGE (left) over the period.

4 4.2.Atmospheric circulation dynamics Despite ARPEGE biases regarding the WAM system, WRF simulations allowed to highlight substantial alterations of the local atmospheric circulation. No much change is found in the WAM intensity at surface but a weakening of the TEJ can be noticed in the upper troposphere (Fig.9). A strengthening and shift northwards of the meridional surface temperature gradient between the Sahara and Guinean regions is to be noticed (Fig.8). Given the AEJ dependence on the position/intensity of the thermal gradient (Cook, 1998), this could favor the latitudinal shift of the jet found in Fig.9, which is associated to main variations in JAS rainfall (not shown), Figure 9: Mean JAS zonal winds averaged between 10 E and 10 W for WRF simulations (top) forced by ARPEGE (bottom) over the (middle) and (left) periods, together with their differences (right). Westerly/easterly winds are indicated in red/blue respectively. Figure 8: Mean JAS skin temperatures for WRF simulations (left) forced by ARPEGE (right) over the (top) and (middle) periods together with their differences. Shaded areas correspond to scores significant at 95% level of Student t-test. 4.3.Alterations within moisture transport Simulated zonal moisture fluxes (Fig.10) are consistent with changes in circulation patterns within the WAM system. A stronger WAM flow which could be linked to more evaporation over the Guinea Gulf favoring moisture convergence and advection at surface, in particular over eastern Guinean regions, is thus leading to more rainfall locally. Enhanced moisture transport is noticeable to the north of the AEJ climatological position with increased advection from both Mediterranean regions/central tropics through more evaporation. This could favor column moisture convergence (Rowell et al., 1992), and a shift of maximum convection to the north, most pronounced over east Sahel where rainfall are enhanced. Figure 10: Mean JAS zonal moisture fluxes averaged between 10 E and 10 W for WRF simulations (top) forced by ARPEGE (bottom) over the (middle) and (left) periods together with their respective differences (right). Westerly/easterly fluxes are indicated in red/blue respectively.

5 5.DISCUSSION & CONCLUSIONS In this study we made an attempt to document dynamical downscaling of GCM large-scale data using WRF to disaggregate spatially (at a 50km x 50km horizontal resolution) ARPEGE A2 scenario projection over West Africa. Regarding contemporary runs ( ) forced by ERA-40 re-analyses and ARPEGE data, the main results are the following: (i) simulated summer precipitations are characterized by a better temporal and spatial variability than their respective forcings when compared to observations, (ii) the added value brought by regional modeling provides evidence of more consistent geographical extent and seasonality within rainfall patterns, but also in terms of distribution, with a better representation of lesser intense events, which are of crucial contribution locally. These results confirm that RCMs help improving spatial and temporal variability from a given boundary state, as emphasized in previous studies (Castro et al., 2005, Rockel et al., 2008, Lo et al., 2003). WRF has also been used to disaggregate spatially A2 scenario from ARPEGE. The main findings are as follows: significant changes in simulated rainfall are enhanced in the regional experiments with more coherence in terms of geographical extent, seasonality and differences when compared to summer rainfall climatology. Most substantial variations in precipitated volumes are located, for continental areas, eastwards to the prime meridian, particularly along the Sahelian band (from north Benin and Burkina-Faso eastwards to the south of Lac Chad), but also within eastern regions of the Tropical Atlantic, over the Guinea Gulf. The main limitation of this study lies in the fact that only one set of GCM output has been used in the present work. Given the uncertainties associated to GCM projections over the region, ensembles of GCM simulations would give more insights on the panel of potential variations regarding A2 scenario projections. Moreover, the results presented here are subject to the short period chosen for this study. Extending the simulations in time might help in analyzing a more robust signal. However, the WRF model proved to be an efficient and valuable tool to help downscaling GCM scenario projections at short and medium range horizons, and thus assessing issues such as water resources vulnerability over West Africa. 6.ACKNOWLEDGEMENTS We would like to thank the financial support of the RESSAC ANR Project. ECMWF ERA-40 re-analyses used in this study have been obtained from the ECMWF data server, CMAP and GPCP rainfall estimates from the referring institutions. 7.REFERENCES Caminade, C., and L. Terray (2009), Twentieth century Sahel rainfall variability as simulated by the ARPEGE AGCM, and future changes, Clim. Dyn., doi: /s , Castro,C., R. Pielke and G. Leoncini (2005), Dynamical downscaling: assessment of value retained and added using Regional Atmospheric Modelling System (RAMS), J. Geophys. Res., 110, doi: /2004jdo04721, Cook, K. (1998), Generation of the African Easterly Jet and its role in determining West African precipitation, J. of Clim., 12, , Douville, H., D. Salas-Méla and S. Tyteca (2006), On the tropical origin of uncertainties in the global land precipitation response to global warming, Clim. Dyn., 26, , Joly, M., A. Voldoire, H. Douville, P. Terray and J.F. Royer (2007), African monsoon teleconnections with tropical SSTs: validation and evolution of a set of IPCC4 simulations, Clim. Dyn., 29, 1-20, Lo, J.F., Z. Yang and R. Pielke (2008), Assessment of three dynamical climate downscaling methods using the Weather Regional Forecast (WRF) model, Geophys. Res. Let., 113, doi: /2007jd009216, Rockel, B., C. Castro, R. Pielke, H. von Storch and G. Leoncini (2008), Dynamical downscaling: assessment of model system dependant retained and added variability for two different regional climate models, J. Geophys. Res., doi: /2007jd Rowell, D., C. Folland, K. Maskell, J. Owen and M. Ward (1992), Modelling the influence of global sea surface temperatures on the variability and predictability of seasonal Sahelian rainfall, Geophys. Res. Lett., 19,