High-resolution regional climate modeling for the Volta region of West Africa

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jd007951, 2007 High-resolution regional climate modeling for the Volta region of West Africa Gerlinde Jung 1,2 and Harald Kunstmann 1 Received 23 August 2006; revised 8 May 2007; accepted 19 July 2007; published 13 December [1] The Volta region is a climate-sensitive semiarid to subhumid region in West Africa. To investigate the impact of expected global climate change on regional water availability, regional climate modeling was performed. Two time slices ( and ) of the ECHAM4 scenario IS92a were dynamically downscaled with MM5 to a spatial resolution of 9 km. The quality of MM5 simulations in reproducing regional climate was assessed using reanalysis data for initial and boundary conditions. Although an underestimation of coastal rainfall was detected, sufficient accuracy in the Volta Basin could be achieved. The regional climate simulations show an annual mean temperature increase of C in the Volta region. This temperature change significantly exceeds interannual variability. A mean annual change in precipitation from 20% to +50% ( 150 to +200 mm) is simulated, with a spatial mean increase of 5% (45 mm). In the rainy season, rainfall predominantly increases, whereas a strong decrease is found for April, which is connected to a delay in the onset of the rainy season. In addition, interannual variability in the Volta region increases in the early stage of the rainy season. The climate change signals in infiltration excess and evapotranspiration show a nonlinear response to precipitation change. Aridity, expressed by the de Martonne aridity index, does not change significantly. The change signal in precipitation predominantly lies within the range of interannual variability. In contrast, the decrease in April exceeds interannual variability in the Sahel region. Citation: Jung, G., and H. Kunstmann (2007), High-resolution regional climate modeling for the Volta region of West Africa, J. Geophys. Res., 112,, doi: /2006jd Introduction [2] The Volta region is situated in West Africa. The livelihood of the population is mainly dependent on rainfed agriculture, especially in the north, the Sahel. Energy production and hence development potential in Ghana (and to a minor extent also in the neighboring countries) are linked to hydroelectric power production at the Akosombo Dam in the south, which dams the three major rivers of the region (the Black Volta, the White Volta, and the Oti) to form one of the world s largest artificial lakes, Lake Volta. The dependence on water availability for agriculture, as well as industry, prompted the central research subject covered by this paper, namely, the estimation of the impact of a globally changing climate on regional climate, with a focus on rainfall and rainfall variability as well as on the combined impact of temperature and precipitation changes on the aridity of the region. [3] The climate of the Volta region is semiarid to subhumid. Mean annual precipitation ranges from less than 300 mm in the north up to more than 1500 mm in the south. 1 Institute of Meteorology and Climate Research, Forschungszentrum Karlsruhe, Garmisch-Partenkirchen, Germany. 2 Now at CNR-Institute for Atmospheric Pollution, Rende, Italy. Copyright 2007 by the American Geophysical Union /07/2006JD However, not only is a north-south gradient evident, a quite strong west-east gradient can also be found [Jung, 2006]. In the southwestern corner of Ghana, annual precipitation exceeds 2100 mm, whereas it is less than 800 mm in southeastern Ghana. The main features of the west-east gradient are the Togo gap and Dahomey gap [Vollmert et al., 2003], which are dry zones along the coast of Ghana/ Togo and further inland in Benin that are caused by coactions of certain small-scale features (e.g., topography, coastal upwelling of cold water, and land-sea effects). [4] Flow dynamics is generally characterized by the intraannual movement of the Inter-Tropical Discontinuity (ITD), triggered by the position of the sun, as well as the strength and position of the African Easterly Jet (AEJ) (found at hpa) and the Tropical Easterly Jet (TEJ) (at 200 hpa). Instabilities of the AEJ are responsible for the formation of African Easterly Waves (AEW) [Redelsperger et al., 2002; Burpee, 1972]. These waves in turn trigger the formation of squall lines and mesoscale convective complexes [Fink and Reiner, 2003]. [5] One of the most striking characteristics of the rainfall regime in the Volta region and in West Africa in general is its high interannual as well as interdecadal variability. A common notion is that African rainfall variability is related to an anomalous excursion of the ITD [Kraus, 1977]. According to Nicholson [2000], this can only be observed in some of the wet years, whereas no systematic anomalous 1of17

2 southward displacement of the ITD is evident in dry years. A number of influences on the variability as well as on the persistence of rainfall regimes have been identified from the results of several investigations. These include the influence of SST variations in neighboring oceans on the variability of rainfall [Lamb, 1978; Druyan, 1991; Vizy and Cook, 2001, 2002; Bader, 2005] and the influence of biogeophysical feedback mechanisms, for example by land-use change, on the persistence of a particular rainfall regime [Charney, 1975; Lare and Nicholson, 1994]. [6] Decreasing rainfall amounts and increasing temperatures have been observed in West Africa over the past decades and analyzed in a variety of studies [LeBarbé and Lebel, 1997; Servant et al., 1998; Amani, 2001; Nicholson, 1993, 2001; Hulme et al., 2001; LeBarbé etal., 2002]. A striking decrease in annual rainfall in the Sahel region was observed after 1968, with a decrease of around 20 to 40% from to by Nicholson [2001]. According to Nicholson [1993], the 1980s were the driest period of the 20th century in West Africa. A weak increase in rainfall occurred in the 1990s, but never reached values comparable to the 1960s. Hulme et al. [2001] found a decrease in precipitation exceeding 25% within the last century over some western and eastern parts of the Sahel. For most of equatorial Africa, however, they found an increase in precipitation of up to 10% during the 20th century. Similar results were obtained by Nicholson [2001], who compared the 30-a mean of with the mean. Nicholson [2005] demonstrated a recovery of rainfall in the Sahel, from 1997 to 2003, which was most pronounced in the western Sahel and generally weakest in August. In contrast to this, in the northernmost study region, the Saharan margin (18 20 N) drought conditions remained. [7] Temperature observations show a warming of the African continent in the last 100 a by about 0.5 C. The rates of warming as well as the periods of most rapid warming ( and from 1970 onward) were similar to observed global trends [Intergovernmental Panel on Climate Change, 2001]. [8] Concentrating on the Volta region, a linear trend analysis was performed by Neumann et al. [2007]. Within this analysis, the linear trend of temperature, precipitation, and discharge time series of Ghana and Burkina Faso were derived. These trends were tested for their levels of significance and trend stabilities. Regarding the temperature time series, the trend analysis predominantly showed positive, significant and stable trends. It was concluded that there has been a clear trend of an increase in temperature over the last decades. For the precipitation time series, both negative and positive trends could be derived, but only a few were significant and stable. As almost all significant trends were negative, a weak trend toward a decrease in precipitation was determined for the Volta region. The strongest decrease signal was found in the early rainy season, for stations along the Guinea coast. [9] Many factors influencing future climate and climate variability, such as feedback mechanisms between land surface and atmosphere, are neither properly understood nor do they follow a linear relationship. Consequently, a recently observed trend cannot simply be extrapolated to the future. For a projection of global future climate, global climate simulations are performed. Because of their coarse resolution, however, these global climate simulations lack regional-scale forcing and feedbacks of the climate system. Maynard et al. [2002] performed global climate simulations with the ARPEGE climate model in order to investigate the mechanisms underlying West African monsoon variability in the model. They confirmed that a better representation of topography and orography and hence a downscaling may be needed for a detailed analysis of the West African monsoon. [10] Regional downscaling is applied using either statistical or dynamical downscaling methods. The first attempts at a dynamical downscaling to obtain long-term regional climate simulations were performed by Dickinson et al. [1989], Giorgi [1990], and Giorgi and Mearns [1999]. Undoubtedly, a more physical representation of the effects of, e.g., orographic precipitation is achieved by dynamical downscaling [e.g., Machenhauer et al., 1996]. In regions where orographic contrasts are not striking, however, uncertainties due to physics parameterizations and their sensitivity to grid spacing might outweigh any benefit of a higher resolution [Duffy et al., 2003; Nobre et al., 2001]. In contrast to this, statistical downscaling requires a high observational data density to provide comprehensive statistical downscaling functions. The scarcity of data is a problem frequently faced by scientists in developing countries. For this reason, dynamical downscaling is often preferred. Moreover, dynamical downscaling offers a higher degree of transferability between different climate states, due to their physical realism [Hay et al., 2002]. Statistical downscaling implicitly assumes that the statistical relationships developed for the present-day climate also hold under different forcing conditions of possible future climates. A dynamical downscaling approach was chosen by Paeth et al. [2005], who tested the performance of the hydrostatic regional climate model REMO for West Africa at a horizontal resolution of 0.5. The results with respect to the description of West African climate dynamics were reasonable, but were not applied to future climate scenarios. The hydrostatic regional climate model MAR [Gallée et al., 2004] proved to be suitable for representing intraseasonal rainfall variability, as well as the abrupt northward shift of precipitation zones that is observed at the end of June [Ramel et al., 2006]. MAR was also applied by Messager et al. [2004] in a study that investigated the influence of regional sea surface temperature on the rainfall regime in West Africa. [11] Nevertheless, few regional downscaling studies for climate change projections have been performed so far for an African environment. A statistical downscaling approach was utilized by Penlap et al. [2003] for Cameroon. They analyzed the Little Rainy Season of Cameroon for the global climate change scenario IS92a. They investigated the periods and for CO 2 forcing and the years for CO 2 +SO 4 forcing. The results did not reveal any significant change signal in precipitation with respect to the uncertainty inherent in the global circulation model (GCM) and downscaling. 2. Regional Climate Modeling for West Africa by High-Resolution Dynamic Downscaling [12] The mesoscale meteorological model MM5 [Grell et al., 1995] was used as a regional climate model (RCM) to 2of17

3 Figure 1. Simulation domain setup: D1, 81 km; D2, 27 km; and D3, 9 km horizontal resolution. dynamically downscale the output of the IS92a scenario run of the coupled Atmosphere-Ocean Global Circulation Model (AOGCM) ECHAM4/OPYC [Roeckner et al., 1996]. The horizontal resolution of the global model was T42 (2.81 ) with 19 vertical layers. The IS92a scenario is a so-called business as usual scenario, with an annual increase in atmospheric CO 2 of 1% starting in The RCM simulations were performed in the nonhydrostatic mode due to its fine horizontal resolution, as the hydrostatic assumption does not hold below a resolution of 10 km [Kalnay, 2003] Domain Setting [13] A one-way nesting approach was chosen (Figure 1) for the three nesting domains with resolutions of 81 km (D1), 27 km (D2), and 9 km (D3), respectively. Vertically, 25 layers were calculated up to the model top at 30 hpa to account for the high altitude of the tropical tropopause. [14] In the tropics, where lateral boundary forcing is generally weak and when using large RCM domains, it is more likely that RCM simulations develop a circulation that differs from that of the GCM. Whether or not this is an advantage for the credibility of the RCM simulations has been widely discussed. Some researchers claim that the circulation of the RCM should not differ significantly from the GCM circulation [e.g., Jones et al., 1995], whereas others assume that large domains should be used in regional climate simulations to allow the atmospheric circulation to be modified by the RCM on spatial scales that are not well represented by the GCM [e.g., Wang et al., 2004]. Nevertheless, the limitations concerning lateral boundary conditions have to be addressed in order to identify the correct domain choice for reliable regional climate simulations. [15] For this study, the first modeling domain was chosen to be large enough to allow distinct circulations to develop. Additionally, it is advisable to avoid placing the RCM lateral boundaries over regions with known GCM biases [Wang et al., 2004] or erroneous moisture transport. Therefore special care was taken in the two smaller domains: The lateral boundaries over the ocean, where errors in moisture transport might occur and boundary effects could be observed, were positioned far away from the area of interest, as has been recommended, for example, by Warner et al. [1997]. Lateral and lower boundaries of the model domains were updated every 12 h in D1 and every 6 h in D2 and D Modeling Procedure [16] As MM5 was originally developed as a meteorological model, one simulation year consists of 365 (366 for leap years) d. The GCM simulation of ECHAM4, however, covers 12 equidistant months of 30 d, resulting in an equal length of 360 d for all years. A time period with all 5 (6) d missing in 1 month of the year can lead to errors in the energy balance. To minimize this effect, these 5 (6) d were removed but in a way such that they were uniformly distributed over the year. Therefore the simulation had to be performed on a monthly basis. To avoid a monthly reinitialization, which would lead to a lack of soil-atmosphere feedback representation, the most important variables were simulated continuously. Technically, this means that these variables were transferred from the last output time step of the simulation of 1 month to the input fields of the following month, which (except in case of those days left out of the simulations) belonged to an identical date and time. The variables that were passed for all domain runs are soil moisture and soil temperature in all four soil layers. This is essential for long-term runs, as it allows feedbacks to develop between the atmosphere and soil. [17] Additionally, the temperature, mixing ratio, cloud water, rainwater, cloud ice, and graupel were transferred for D1. For the two smaller domains, a short spin-up time for the atmospheric variables and a stronger dependence on the mother domain dynamics can be assumed, which made it unnecessary to pass the atmospheric variables. [18] Wang et al. [2004] mentioned that initial soil moisture conditions can have a significant effect on predictability, due to its long-lasting influence. Soil moisture and soil temperature were initialized with National Centers for Environmental Prediction (NCEP) reanalysis data for January 1992, at the beginning of the 10-a simulation period. This month represents an average January of the 1990s. It is a month during the dry season, where no large differences in soil moisture and temperature, neither interannually, nor spatially are observed. Therefore it is considered suitable for initialization of soil moisture and soil temperature for that region and time of the year Choice of Parameterization Schemes [19] To find an optimal MM5 parameterization scheme for the Volta region, precipitation data measured at 28 stations from 15 July 1998 to 14 August 1998 were used for comparison with a model simulation for D3. This model run used NCEP reanalysis data. The chosen episode extends from the first maximum of the bimodal rainy season to the intermediate minimum of the Little Dry Season in August. An entire set of 16 simulations was performed to determine the optimal model configuration. In combination with the Oregon State University Land Surface Model (OSU-LSM) soil-vegetation-atmosphere-transfer (SVAT) model [Chen and Dudhia, 2001], the MRF-PBL scheme 3of17

4 Table 1. RMSE of Precipitation for Different Parameterizations of Microphysics, Cumulus Scheme, Radiation Scheme, and Nesting Configuration a Radiation: Dudhia Cloud Radiation Scheme Grell and Reisner Graupel Anthes-Kuo Grell Kain-Fritsch Betts-Miller Parameter Value Dudhia cloud radiation Warm rain CCM2 radiation scheme Simple ice RRTM long-wave scheme Mixed phase one-way versus two-way nesting Goddard microphysics Grell and Reisner graupel and Dudhia cloud radiation Reisner graupel two-way Schulz microphysics one-way 57.4 a RMSE is in mm. The radiation scheme is named after Grell et al. [1995]. [Hong and Pan, 1996] was the only choice for the parameterization of the planetary boundary layer. This SVAT model is essential for long-term integrations with MM5, as it accounts for feedback mechanisms between soil, vegetation and atmosphere. The simulation of feedback mechanisms between soil moisture and precipitation using MM5/OSU-LSM and the importance of these feedbacks especially for West Africa and the Volta region was demonstrated by Kunstmann and Jung [2003]. [20] The results for the different configurations of gridscale and subgrid-scale precipitation as well as radiation schemes are given in Table 1. As a criterion of the quantitative performance of the model, the root-meansquare error (RMSE) is calculated (equation (1)), in this case for the mean of the 4 nearest grid points and the respective station location. Convective (i.e., cumulus) parameterization according to Grell and Kuo [1991], microphysics according to Reisner et al. [1998], and the radiation scheme according to Dudhia [1993] showed the smallest RMSE and are therefore assumed to be the optimal model configuration for simulating rainfall in West Africa and the Volta region. There was no major difference between the one-way and the two-way nesting approach. Consequently, the one-way nesting approach was chosen for practical reasons [Kunstmann and Jung, 2003]. 3. Model Validation 3.1. Validation With Dynamically Downscaled Reanalyses [21] A first quality check of dynamic downscaling is demonstrated in Figure 2, where dynamically downscaled NCEP (National Center for Environmental Prediction) reanalyses are compared for April 1992 (which is the sensitive time of the beginning of the rainy season) at 70 precipitation stations, running from south (coast) to north (Sahel). [22] For a major validation, two 12-month simulations were performed and validated against observed station values of precipitation and temperature. These simulations were run with the above mentioned configuration, using NCEP reanalysis data as input for the year 1997 and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data for These years chosen for validation were the extremely wet year of 1968 and the comparably dry year 1997, representative for the climate of the 1990s. Thus MM5 was evaluated for two generally different climate conditions. [23] To quantify the modeling error, both the bias and the RMSE (root-mean-square error) defined as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ux N RMSE ¼ t ðx m x o Þ N 2 i¼1 were calculated for the modeled monthly mean value (in the case of temperature) and for the monthly sum (in the case of precipitation) at the nearest grid point (for precipitation additionally for the mean over the four nearest grid points) (x m ), and compared to the respective station value (x o ). N denotes the number of x m and x o values. [24] In case of precipitation, a normalized RMSE RMSE norm ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P N i¼1 ðx m x o Þ N 2 was defined to account for the strong latitudinal and interannual dependence of absolute rainfall amounts in West Africa. Figure 2. Precipitation observed at several stations and simulated precipitation of the respective nearest grid points [cm], April 1992, ordered from south (left) to north (right), D3. x o ð1þ ð2þ 4of17

5 Figure 3. Longitudinal mean bias of monthly precipitation (observed-modeled) [mm], 1997, black rhombi indicate latitudinal station locations, D Precipitation [25] For precipitation validation, 108 observation stations were available for the year 1968, whereas data from only 59 stations could be used for In 1997, the biases in the north are quite small, but large underestimations of the absolute rainfall amounts, especially in the early rainy season (June), can be found along the coast (Figure 3). In 1968, a negative bias is found almost over the entire area and the entire annual cycle (Figure 4). Although the absolute bias is large in 1968, exceeding 200 mm in June and August near the coast, the relative bias is smaller than in 1997, because annual rainfall amounts in the southernmost regions reached as much as 3000 mm in The relative bias can reach values of 300% at a single station in April, due to a simulated monthly simulation of 50 mm, because rainfall amounts in the north, in April, are low. In contrast, in the rainy season, even the extreme absolute biases along the coast never mean more than a relative bias of 70%. The RMSE norm and the RMSE values for precipitation, for the nearest grid point and, to account for the spatially highly heterogeneous nature of precipitation, also for the mean over the four nearest grid points, are summarized in Table 2. A relatively high RMSE of precipitation of over 80% is evident for both years. [26] The validation results are strongly limited by the general low predictability of precipitation, especially in the case of convective precipitation. Pielke [1984] already stated that there is a major problem in point-to-point validation of rainfall, as it can lead to poor verification results even if the magnitude of the simulated pattern is almost exact. Hence the underestimation of rainfall amounts obtained from the RMSE norm and the absolute and relative error values is to some extent at least due to the high probability of a spatial and/or temporal displacement in rainfall patterns, except for the coastal region. Furthermore, uncertainties in point-to-point validation are obvious when comparing the simulated grid point value representing the mean rainfall over a grid cell with a real point value, the observation. Figure 5 shows the modeled annual precipitation distribution for the year 1968 and all available station values, with the color in the circles indicating the annual precipitation amount at the respective station. The underestimation of coastal rainfall is obvious. In the northern part of the basin, annual rainfall amounts lie within the observed range, even though the exact location is not always identified. [27] A similar pattern of spatial and temporal bias was found for both years. This indicates a systematic model shortcoming. The strong deficit along the coast during the rainy season needs to be mentioned. The reasons are not yet fully understood and might be due to an erroneous representation of the SST and the incorrect or missing mesoscale structures which would result, as oceanic information always is derived from the coarsely resolved GCM output (even in the highest resolution domain). On the other hand, an erratic boundary layer representation due to the still insufficient resolution of the PBL or an insufficient boundary layer parameterization scheme might explain the incorrect representation of coastal rainfall. The coastal rainfall deficit was found to persist in all three model domains. This underestimation is not a model-specific problem of MM5, but seems to be a general weakness of regional-scale models in West Africa, as similar validation results were obtained by Ramel et al. [2006] and Paeth and Hense [2006] for the regional climate models MAR and REMO, respectively. Reasons are thought to be found in topography, as well as in a possible poor performance of the subgrid-scale precipitation parameterization scheme. Afiesimama et al. [2006] Figure 4. Longitudinal mean bias of monthly precipitation (observed-modeled) [mm] for 1968, black rhombi indicate latitudinal station locations, D3. Table 2. RMSE for Precipitation (1968 and 1997), Relative and Absolute Values for the Nearest Grid Point and the Mean Over the Four Nearest Grid Points to the Respective Observation Station GP 4GP 1GP 4GP RMSE, mm RMSE, L/L of17

6 Figure 5. Simulated annual precipitation (shaded) and station observations (circles), 1968 [mm], D3. applied the regional climate model RegCM3, which is based on MM5 to West Africa. Driving RegCM3 with NCEP reanalysis data they observed an overestimation of rainfall for the Guinea coast region and a shift of the monsoonal rainfall zones to the south. As potential reasons they mention the large spatial resolution (90km), weak driving input fields and the convective parameterization scheme used. In the present study, apart from the coastal rainfall deficit, a sufficient accuracy of rainfall simulation was obtained for the mostly inland Volta region Temperature [28] For the temperature validation, only 20 observation stations were available, the majority being situated in the Burkinabè part of the basin. Figure 6 shows the longitudinally averaged mean monthly bias in temperature versus latitude. For the dry season, temperature is underestimated for almost all latitudes. Along the coast, this underestimation persists over the entire year. Temperature is dependent on soil moisture and the subsequent partitioning between latent and sensible heat fluxes. A bias in precipitation may, due to its influence on soil moisture, lead to a bias in nearsurface air temperature. In fact, an overestimation of temperature is observed during the rainy season in the northern part of the region, where a negative bias in precipitation was found GCM Output Analysis [29] The first question of interest concerning model reliability is whether recent climate can be simulated sufficiently accurately by the GCM. Therefore a comparison of the ECHAM4 simulation to present-day climate conditions is particularly important, as the 10-year time span of the regional climate simulations is comparatively short for the representation of the long-term mean present climate conditions. For the GCM comparison to present-day climate, temperature and precipitation output fields of ECHAM4 (resolution: 2.81 ) are compared to gridded observational data of the Climate Research Unit (CRU) (resolution: 0.5, [30] As demonstrated by Jung [2006] temperature is represented sufficiently well in the ECHAM4 simulations for the region of West Africa, compared to CRU gridded observational data. Only a slight overestimation of temperature could be observed for the Saharan region during the rainy season and during the dry season in southern West Africa. [31] Figures 7 and 8 show ECHAM4 precipitation output fields, compared to CRU data for the time period of Precipitation amounts, simulated with ECHAM4 are Figure 6. Longitudinal mean bias of mean monthly temperature (observed-modeled) [ C], 1997, black rhombi indicate latitudinal station locations, D3. 6of17

7 Figure 7. (IS92a). Monthly mean precipitation [mm/d]. (left) CRU gridded observational data, (right) ECHAM4 at first view comparable to the CRU data, but maximum values are in general too low. In addition, precipitation zones, as well as the region of maximum rainfall are shifted slightly to the north. The largest difference occurs in the northern Sahel and the Sahara, where precipitation zones widen and spread some degrees further north than observed. Vizy and Cook [2002] stated that GCMs in general tend to produce wetter conditions than observed over the Sahara during the rainy season, which also holds true for the ECHAM4 simulation. This was found to be connected with a wet bias over the Saharan boundary layer [Vizy and Cook, 2001], the reasons for which are not yet understood Validation of the Simulated High-Resolution Recent Climate [32] Precipitation in both the GCM and the RCM simulations is strongly dependent on the subgrid-scale parameterization scheme used. Therefore the RCM simulation results were also compared to the observed long-term mean climate variables. Because of the regions strong decadal variability, a comparison of the 10-year time slice with a time slice of the same length of observed climate is not feasible. Therefore, since averaging of the observations over a longer time span removes part of the low-frequency variability within this time series, the means of the simulation were compared with the long-term mean observational values, to obtain a more reliable validation of the regional climate simulation output with respect to reference (present-day) climate conditions Precipitation [33] For precipitation, the 30 year of observational data available for Ghana and Burkina Faso were spatially interpolated to the model grid of D3 and compared to the MM5 climate run for [34] The interpolation data set consisted of 95 stations with a minimum length of 30 year of data each. Spatial interpolation was performed with a combination of inverse distance weighting (IDW) and multiple linear regression (MLR), the IDW share being 60%. For MLR, latitude, longitude, height, slope, curvature, and aspect were considered. [35] The spatial representation of annual rainfall deviations (modeled-observed) revealed an underestimation of rainfall along the coast, as was obtained from the MM5 reanalysis runs. Rainfall in the Sahel, however, was slightly overestimated. This is in agreement with the wet bias of ECHAM4 that was found for the Sahel region. Nevertheless, the spatially and monthly averaged values of precipitation obtained from the long-term observations and the MM5 run in Figure 9 indicate sufficient accuracy of the representation of the annual cycle of precipitation and the spatial means of precipitation sums. 7of17

8 Figure 8. (IS92a). Monthly mean precipitation [mm/d]. (left) CRU gridded observational data, (right) ECHAM ITD Position [36] A very important characteristic that influences the climate in West Africa is the annual movement of the ITD. Liu and Moncrieff [2004] demonstrated that the position of the ITD is dependent on the convective parameterization scheme used. Therefore it may be expected that the position of the ITD is modified by the RCM simulation as well. To examine this possibility, RCM simulations and long-term means were compared with respect to the ITD position. As a measure of the position of the ITD in the MM5 simulations, the meridional wind direction was chosen, as it changes its algebraic sign at the ITD. [37] Figures show simulated and observed ITD positions for January, June, and September, both to gain an insight into the mean displacement of the ITD for the ECHAM4-MM5 simulated years , and also to establish the long-term mean position for the respective month [Leroux, 2001]. The southernmost position of the ITD in the year (December/January) is reproduced well. The movement of the ITD to the north appears to happen too fast, however, showing a larger deviation of the simulated from the climate mean position from April to June. In the simulations, the ITD has already reached its northernmost position by June, whereas in the long-term mean it is reached 1 month later, in July. The ITD stays more or less at this northern position for 2 months (3 months) for the mean climate (RCM simulations). The northernmost position of the RCM simulation again is in very good agreement with the observations in July and August. At the end of the rainy season, the modeled position of the ITD again deviates from the long-term mean, as the displacement of the ITD to the south in September and October occurs too quickly in the model. Still, a good agreement with observations is found for the latitudinal position of the ITD from December to February. As the onset and the secession of the rainy season are strongly triggered by the position of the ITD, this possibly has an influence both on the start and the length of the rainy season. Figure 9. Modeled precipitation ( ) and observed long-term monthly means [mm], D3. 8of17

9 Figure 10. Mean position of the Inter Tropical Discontinuity, modeled (shaded) versus observed [Leroux, 2001] (black line), January, D Influence of Dynamic Downscaling on Model Results Comparison of RCM to GCM Output [38] In addition to the evaluation of the GCM output with respect to observations for a longer (30 a) time slice, the GCM simulated a 10-year period ( ) which was compared to the respective MM5 simulation (D1). Because regional climate simulations are driven by the GCM signal at the lower and lateral boundaries, it is expected that the results of atmospheric variables are in the range of the GCM simulations, but show smaller-scale patterns when more highly resolved land use, topography and land-sea mask effects are included. For the amount of precipitation this is not necessarily true however, as precipitation is highly dependent on the convective parameterization scheme used. Nevertheless the coarse distribution of precipitation should be similar, as it is, to a major part, determined by large-scale dynamics and moisture transport. [39] Precipitation, as illustrated in Figure 13 is less in the MM5 simulations for D1, than in the ECHAM simulations. For observed rainfall (compare CRU data, Figures 7 and 8) a maximum is found over the mountain range along the border between Cameroon and Nigeria. This rainfall maximum is not noticeably represented within the GCM simulation, but it is in the regional simulations of D1. Nevertheless the precipitation change signal in both simulations is similar (Figure 14). A precipitation increase was simulated over most of continental West Africa. In the western part of West Africa a decrease is found in both simulations. The largest difference is seen in the southern coastal region of West Africa, where in the MM5 simulation an increase in precipitation is found while in the ECHAM simulation, there is a decrease. [40] Temperature showed a pronounced positive deviation in the MM5 simulation with respect to the ECHAM4 run, as described by Jung [2006]. The region that showed the strongest deviation was the Sahara. This is most likely due to a lack of soil moisture as a consequence of the negative bias in precipitation, as described above. For the Nigerian region a positive deviation is found on the northwestern side of the mountains along the border with Cameroon, and an underestimation of temperature can be found on the other side. This is linked to differences in rainfall amounts on either side of the mountains, and is a good example of the finer structure that is introduced to the spatial distribution of meteorological variables through Figure 11. Mean position of the Inter-Tropical Discontinuity, modeled (shaded) versus observed [Leroux, 2001] (black line), June, D1. Figure 12. Mean position of the Inter-Tropical Discontinuity, modeled (shaded) versus observed [Leroux, 2001] (black line), September, D1. 9of17

10 Figure 13. Mean annual precipitation ( ) [mm]. (left) MM5 (D1), (right) ECHAM4. more highly resolved topographical information. Temperature change in Figure 15 shows a very good agreement between GCM and RCM simulations. [41] Summarizing, it can be concluded that topographic influences are clearly better resolved, for both temperature and precipitation in the RCM. Consequently an improvement of the spatial representation could be achieved Impact of RCM Model Resolution [42] A comparison of the different MM5 model domains (compare Figures 16 and 17, D2 and D3) reveals the improvement in rainfall amounts with an increase in model resolution. Hence high-resolution downscaling is required when regional-scale conclusions are to be drawn. First, a general increase in rainfall amounts with increasing resolution can be observed, which is most likely due to the insufficient representation of subgrid-scale precipitation by the low-resolution simulation. This especially is a problem in West Africa, where a large amount of rainfall is of convective origin and occurs below a scale of 20 km. Another improvement with resolution is the better representation of the orographic enhancement of precipitation, for example in the mountainous region of central Benin, due to the more highly resolved topography. 4. Results of the Simulated High-Resolution Future Regional Climate Temperature [43] Mean annual temperature shows a clear increase for the Volta region (Figure 18). This increase reaches from 1 C in the maritime south to 1.5 C in the continental north. An increase in temperature can be detected not only for the annual mean, but also over the entire annual cycle (Figure 19) with a maximum in April (2.1 C) and a minimum in September (0.7 C). Figure 14. Mean annual precipitation change ( versus ) [%]. (left) MM5 (D1), (right) ECHAM4. 10 of 17

11 Figure 15. Mean annual temperature change ( versus ) [ C]. (left) MM5 (D1), (right) ECHAM Precipitation [44] The climate change signal is less obvious for precipitation than it is for temperature. The mean annual precipitation change of the Volta region reveals a heterogeneous pattern, from 20 to +50% depending on the region studied (Figure 20). The highest precipitation decrease can be found in a southeast to northwest band that stretches from Nigeria to Ghana and into southwestern Burkina Faso. The highest percentage increase is found along the coast from Benin to Nigeria. This is also the strongest signal in terms of absolute values, due to its location in the rainfall-intense regions of the tropical south. Consequently, the highest precipitation increases are encountered in the regions bordering the Volta Basin, but not within the basin itself. Absolute precipitation change, spatially averaged over the land area of model domain D3, is quite small, 44.7 mm, which is 5.1% of the spatially averaged annual precipitation. Although the change in annual mean precipitation is small, some changes in the intraseasonal distribution can be found for the spatial average. Figure 21 illustrates the mean annual cycles of both time slices and the respective relative changes. A precipitation deficit is found in April. This was not only found for the mean over the domain, but all over the model domain [Jung, 2006]. In percentage values, this mean precipitation change signal in April over the entire model domain is the strongest signal in the annual cycle. Nevertheless, due to the small amount of rain occurring in April this does not mean a lack of a large amount of precipitation. For July, precipitation change exhibits a strong decrease north of the coast. Finally, the month with the strongest increase in rainfall in absolute values is September. The dominant change during the rainy season (with the excep- Figure 16. Simulated mean annual precipitation [mm] ( ), D2. Figure 17. Simulated mean annual precipitation [mm] ( ), D3. 11 of 17

12 Figure 18. Change in mean annual temperature [ C] ( versus ), D3. tion of July) is toward an increase in rainfall in the range of 10 to 20% Onset of the Rainy Season [45] The fact that a strong relative change in rainfall can be observed at the very beginning of the rainy season in April gives rise to the question whether this signal is simply due to a reduction in the amount of precipitation or whether it suggests a delay in the onset of the rainy season. [46] The definition of the onset date applied here closely follows Stern et al. [1981] with some variations from Dodd and Jolliffe [2001] for Burkina Faso. According to this definition, the following three criteria determine the onset of the rainy season: (1) a period of 6 consecutive days with at least 25 mm of rainfall, (2) the start day and at least two other days in the period are wet (at least 0.1 mm of rainfall recorded), and (3) no dry period of 10 or more consecutive days occurs in the following 40 d. This definition is based on agricultural requirements and not on the dynamics of the monsoon onset [e.g., Sultan and Janicot, 2003]. Figure 20. Change in annual precipitation [%] ( versus ), D3. [47] Table 3 demonstrates that the mean onset dates for the Sahel and the Guinea coast region are both delayed in the future simulation by 9.6 and 3.5 d, respectively. The lower value along the coast is basically due to an earlier start of the rainy season in the vicinity of the coastline, despite a later start in the rest of the region south of 10 N Shift of the ITD [48] It was verified by the large domain model output that the decrease in precipitation in April as well as the increase in September can be explained to some extent by a displacement of the ITD. Figure 22 demonstrates the changes in the mean monthly ITD position. In April, a negative displacement of almost 1.5, indicating a more southerly position in the future scenario, can be observed. In September, the mean ITD position lies further north in the future simulation than in the present reference state simulation. For further analysis of large-scale dynamical features explaining the climate change signals of the simulations, see Jung [2006]. Figure 19. Spatially mean monthly temperature [ C] ( and ) and temperature change [ C], D3. Figure 21. Spatially averaged mean monthly precipitation [mm] and change in precipitation [%] ( versus ), D3. 12 of 17

13 Table 3. Dates of the Onset of the Rainy Season and Mean Changes Sahel Guinea Coast Onset [DOY] Onset [DOY] Change, days Interannual Rainfall Variability [49] Another variable that is central to the analysis is the interannual variability [%] of precipitation. It is described using the coefficient of variation, defined as v c ¼ s P 100 with the standard deviation s and mean precipitation P. [50] In the future simulation with respect to the reference state time slice, the monthly values of the coefficient of variation indicate a strong increase in interannual variability for the months of April-June (Figure 23), i.e., in the early stage of the rainy season Evapotranspiration and Infiltration Excess [51] As MM5 includes a sophisticated SVAT model, the climate change signal for evapotranspiration and infiltration excess can be examined. Both variables are expected to react to changes in rainfall and temperature. [52] The spatial distribution of annual evapotranspiration change (Figure 24) indicates a high level of conformity with precipitation change. [53] A pronounced area of decreased evapotranspiration can be found, which follows the decrease in rainfall, but is spatially more extended. This is due to higher potential evapotranspiration due to an increase in temperature, which affects the change in regions with only minor increases in precipitation especially in the north, where temperature increase is highest. [54] The monthly change signal (Figure 25) shows differences between rainfall and evapotranspiration. A strong decrease in evapotranspiration can be observed in May due to the lower soil moisture following the rainfall deficit of April, although precipitation hardly shows any change. During the rainy season evapotranspiration increases due to ð3þ Figure 23. Variation coefficient of precipitation [%] ( and ). the potential evapotranspiration increase (resulting from higher temperatures) and sufficient availability of moisture, due to increasing precipitation (except for July). Generally, the percentage as well as the absolute change in evapotranspiration is smaller than that in precipitation. [55] By analogy to the evapotranspiration results, infiltration excess shows a spatially distributed signal of change that is strongly determined by the signal in rainfall (Figure 26). Figure 27 indicates that the percentage changes in infiltration excess in most months have the same sign as the precipitation changes, while the relative signal is stronger in magnitude. Only in November, in the early dry season, does infiltration excess increase despite a decrease in rainfall. This spatially averaged signal is introduced through a small region along the coast, where the soil is still saturated after the surplus rainfall in October. Hence, even if less precipitation is observed in November, a larger amount of water can infiltrate the soil Aridity [56] Interrelation of the changes in the two climatic variables of temperature and precipitation is a central issue Figure 22. Shift of the Inner Tropical Discontinuity [ ] ( versus ). Figure 24. Change in evapotranspiration [%] ( versus ), D3. 13 of 17

14 Figure 25. Spatially averaged monthly mean evapotranspiration [mm], ( and ) and change [%], D3. for the hydrological cycle as well as for the livelihood of the population of the Volta region. This is especially true, when rainfall changes are not particularly large, but a strong increase in temperature might influence the hydrological cycle via an increase in evapotranspiration. As a measure of this impact, the aridity index, according to de Martonne [1920] was selected, which is defined as dmi ¼ P T þ 10 where P is annual precipitation and T is the sum of monthly mean temperature of those months with a monthly mean temperature greater than 0 C, divided by 12. In the present study for the Volta region, this turns out to be the annual mean temperature. According to this definition, lower values indicate a higher aridity. [57] In line with de Martonne [1920], a dmi below 20 indicates the necessity for irrigation in agriculture. If the dmi lies in the range of 20 to 30, irrigation is often ð4þ Figure 27. Spatially averaged monthly mean infiltration excess [mm], ( and ) and change in infiltration excess [%], D3. performed, but not indispensable. Just below a dmi of 10, dry farming is not possible in general. The spatially averaged values found for present and future climate conditions are summarized in Table 4. [58] Although temperature change is noticeable, the dmi averaged over the two defined regions (Sahel and Guinea Coast) shows only a weak indication of change. Even though there is an increase in temperature, the aridity of the region is only. 5. Discussion 5.1. Climate Change or Interannual Variability? [59] A question arising from this study is whether the change signal detected indicates climate change or whether it is to be seen as an effect of local climate variability. The significance of a change signal with respect to the regions simulated climate variability must be analyzed to assess the reliability of a detected signal of change. For this purpose, the signal-to-noise ratio defined as SN ¼ jx fut X pres j s ð5þ was studied. Here, X fut and X pres are the mean monthly or annual value of a given variable for the future and present time slice respectively. s is the standard deviation of this variable for the present time slice. When considering the Sahel and the Guinea coast region separately, the only months with high levels of reliability (SN > 1) are April in the Sahel region, north of 10 N, and June in the coastal region, south of 10 N (Figure 28). [60] The delay in the onset of the rainy season is associated with a signal-to-noise ratio of 1.8 for the Sahelian region. For the Guinean area, the signal-to-noise ratio is low Figure 26. Change in infiltration excess [%] ( versus ), D3. Table 4. Changes in the de Martonne Aridity Index Sahel Guinea Coast of 17

15 optimal information, as interdecadal variability; in particular, sudden changes that can have a significant impact are not captured. Giorgi [2005] proposed to add measures of interdecadal variability to the ensemble averaged climate change signal. Nevertheless within the frame of this study, due to the high spatial resolution, such CPU time-consuming ensemble runs were not possible to perform. Figure 28. Signal-to-noise ratio of precipitation for the Sahel (north of 10 N) and Guinea coast region (south of 10 N). (0.4). This is a result of the high standard deviation for the coastal region in the present-day climate scenario and of the fact that the onset of the rain tends to start earlier in a very small region along the coast in the future time slice experiment, whereas rainfall start lags behind in most of the remaining area south of 10 N Interdecadal Variability [61] Apart from interannual variability, also a high interdecadal rainfall variability can be observed in West Africa. Therefore it is additionally important to investigate whether the simulated change lies within interdecadal variability. As this cannot be done for the downscaled climate simulations due to the time period of 10 years, an analysis is performed with the time series of ECHAM4 that was used to drive the RCM simulations. [62] Figure 29 shows a pronounced interdecadal rainfall variability for the ECHAM4 run expressed through the 10-year moving mean of the time series. Furthermore, the chosen time series are found in a period of relatively high rainfall values ( ) and relatively low rainfall ( ) with respect to the linear trend curve. Because of the conservative choice of time slices for the regional climate simulations a positive change signal of these two time series, as observed in the RCM simulations, is most likely not within interdecadal variability Modeling Uncertainties [63] A limiting factor to climate change assessment in this study is the fact that only one emission scenario was considered, and only one GCM simulation was downscaled with only one selected RCM. Rainfall especially, is highly dependent on the parameterization schemes used in a model and therefore may vary a lot from one model to another, leading to significantly different results in the climate change signal. Therefore this modeling experiment has to be considered as one of a large number of possible scenarios of future climate. To overcome, or at least evaluate the high degree of uncertainty in climate simulations, ensemble predictions are favored not only on the global, but also on the regional scale. Nevertheless, as Giorgi [2005] states, ensemble averaging alone might not be sufficient to provide 6. Summary and Conclusions [64] The dynamical downscaling approach allows the representation of changes in the dynamical regime, provided that the processes responsible can be simulated properly by MM5. Despite the uncertainty associated with the modeling cascade in general, there is the question of judgment of the simulated change signal simulated within the bounds of the chosen experiment. When taking this modeling approach as one possible state of the future climate, it is important to determine how significant the simulated climate change signal is with respect to the variability of climate in our experiment. [65] While simulated temperature change shows a clear signal of increase in the future simulations and leaves no doubt about a climate change signal in the experiment, rainfall does not. Changes in precipitation are small on average, and only 2 months, April for the Sahel region and June for the Guinea coast region show a distinct signal-tonoise ratio. Hence these signals can be traced back to an interdecadal, lower-frequency variability or climate change. An analysis of the GCM simulation output showed that interdecadal variability is unlikely to have caused or enhanced the positive precipitation change signal, due to the choice of the two RCM time slices. For the other months, the precipitation change signal simulated by MM5/ ECHAM4 lies within the interannual variability and consequently, it has to be seen as such. [66] Additionally, a delay in the onset of the rainy season as well as an increasing interannual precipitation variability in the early stage of the rainy season were detected in the simulation of the future climate. The signal of the delayed onset of the rainy season exhibited a larger signal-to-noise ratio for the Sahel than for the Guinea coast. This result is in agreement with present trend studies [Neumann et al., 2007] that show a decrease in rainfall in the early rainy season. Figure 29. ECHAM4, spatially averaged mean annual precipitation [mm] for West Africa (D1). 15 of 17