How well are daily intense rainfall events captured by current climate models over Africa?

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1 How well are daily intense rainfall events captured by current climate models over Africa? Julien Crétat*, Edward K. Vizy, and Kerry H. Cook Department of Geological Sciences Jackson School of Geosciences, The University of Texas at Austin 1 University Station C1100 Austin, TX Submitted to Climate Dynamics January 11, 2012 Revised April 25, 2013 * Corresponding author address: Julien Crétat jc@jsg.utexas.edu 1

2 31 Abstract The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events over Africa is evaluated by analyzing regional climate model (RCM) simulations at 90- and 30-km along with output from four atmospheric general circulation models (AGCMs) and coupled atmosphere-ocean general circulation models (AOGCMs) of the Climate Model Intercomparison Project 5. Daily intense rainfall events are extracted at grid point scale using a 95 th percentile threshold approach applied to all rainy days (i.e., daily rainfall 1 mm day -1 ) over the period for which two satellite-derived precipitation products are available. Both RCM simulations provide similar results. They accurately capture the spatial and temporal characteristics of intense events, while they tend to overestimate their number and underestimate their intensity. The skill of AGCMs and AOGCMs is generally similar over the African continent and similar to previous global climate model generations. The majority of the AGCMs and AOGCMs greatly overestimate the frequency of intense events, particularly in the tropics, generally fail at simulating the observed intensity, and systematically overestimate their spatial coverage. The RCM performs at least as well as the most accurate global climate model, demonstrating a clear added value to GCM simulation and the usefulness of regional modeling for investigating the physics leading to intense events and their change under global warming Keywords Africa CMIP5 AGCMs/AOGCMs daily intense rainfall regional climate model 2

3 55 1. Introduction Daily intense rainfall events have a profound impact on the environment and society, often leading to agricultural, economic, and human loss. Africa is among the most vulnerable regions in the world because of aggravating factors such as political instability, ill-equipped infrastructures and weak crop reserves. Considering that intense rainfall events are difficult for models to capture accurately due to their characteristically small space scales and high spatiotemporal variability, and that their frequency and intensity are expected to change under global warming (Intergovernmental Panel on Climate Change Assessment Report 4 [IPCC AR4], Meehl et al. 2007), simulating properly their distributions is of great importance. Recent climate model studies evaluating daily intense rainfall events over different regions of Africa are primarily focused on better understanding the leading physical mechanisms and processes involved (Knippertz and Martin 2005; Williams et al. 2008; Nicholls and Mohr 2010), and/or how intense events are projected to change under global warming (Shongwe et al. 2009, 2011; Sylla et al. 2012a; Vizy and Cook 2012). To our knowledge, the capability of state-of-the-art climate models to capture the main observed characteristics of daily intense rainfall events has not been quantified over Africa. A clear evaluation of their strengths and limitations to simulate them is necessary before assessing changes under global warming. This study aims to fill this gap by providing an evaluation of simulated intense rainfall events over the entire African continent at the daily timescale. The mean spatial and temporal characteristics of African daily intense rainfall events are investigated from grid point to regional scales. Various datasets are analyzed, including gridded satellite-derived observations and output from atmospheric general circulation models (AGCMs) 3

4 and coupled atmosphere-ocean general circulation models (AOGCMs) from the Climate Model Intercomparison Project Phase 5 (CMIP5, Taylor et al. 2011), and two regional climate model (RCM) simulations at 90- and 30-km horizontal resolution. Multiple sources of observations are utilized to take uncertainty into account, while different models are compared to evaluate their respective strengths and weaknesses and to assess the potential added value of using a higher resolution RCM. Background is reviewed in Section 2. Section 3 discusses the observations and the GCMs analyzed, the RCM experimental design, and the methodology used to detect daily intense rainfall events. Intense rainfall events are analyzed at grid point and regional scales in Sections 4 and 5, respectively. Conclusions are summarized in Section Background Except for localized regions that contain dense long-term in situ measurement networks (e.g., South Africa), daily rain gauges are scarce over Africa. As a result, rain gauge networks have not been used often to document daily intense rainfall events over the continent (e.g., New et al. 2003; Aguilar et al. 2009) and these measurements are insufficient for quantifying model biases at the continental scale. High-resolution satellite-derived products developed in recent years are widely used as an alternative for evaluating climate output over many regions, including Africa (e.g., Rocha et al. 2008; Shongwe et al. 2009, 2011; Williams et al. 2010; Sylla et al. 2010a; 2012a-b; Vizy and Cook 2012). Despite uncertainties related to merging observations and errors associated with satellite measurements, extraction algorithms, and 4

5 interpolation techniques (Sylla et al. 2012b), satellite observations currently provide the best information for evaluating our ability to simulate intense precipitation events over Africa. A number of studies have documented deficiencies of GCMs in accurately simulating the frequency and intensity of intense rainfall events (Dai 2006; Sun et al. 2006, 2007; Randall et al. 2007; Boyle and Klein 2010; Li et al. 2011a). Both AGCMs and AOGCMs overestimate the number of rainy days and underestimate the intensity of heavy precipitation over the globe. One possible cause of such biases is horizontal resolution (Wehner et al. 2010; Li et al. 2011b) but, even with 20-km resolution, the MRI JMA AGCM produces too many light rainfall days (5 15 mm day -1 ) and too few heavy rainfall days (> 15 mm day -1 ) over tropical and subtropical Africa (Kamiguchi et al. 2006). Another possible problem is the convective parameterizations, since these are designed primarily to reproduce measurements of climatological mean precipitation (Li et al. 2012). The ideal approach is to explicitly resolve convection which in most cases requires non-hydrostatic models with resolution finer than 4-km. For now the computational expense and amount of wall-clock time required to run such highresolution simulations remain an obstacle for century-long integrations at global scale or over large spatial domains. Overall, there has not been a significant increase in the horizontal resolution of the CMIP5 simulations compared to the CMIP3 simulations used for producing projections for IPCC AR4. Most models are still coarser than 1 longitude 1 latitude as CMIP5 model development activities were geared toward developing completely coupled Earth System Models. The coarse resolution of the CMIP5 GCMs is thought to preclude their reliability for providing intense rainfall statistics at regional scales (Jones et al. 2011). Studies are still needed to better evaluate 5

6 how well intense daily rainfall events are represented in these and other higher resolution models. To bridge the gap between large-scale climate information and impact assessment, various statistical (Wilks 2011) and dynamical (Laprise 2008) approaches have been developed. Among these approaches, RCMs complement global models since they are built on a similar physical basis but better resolve topography and allow for optimizing physical parameterizations for the region of interest. Providing higher resolution information, they are theoretically better adapted to produce improved higher-order statistics such as intense rainfall events. Williams et al. (2010) evaluate the ability of the UK Meteorological Office Hadley Centre s climate model in both regional (0.5 x 0.5 ) and global (2.5 x 3.75 ) mode to reproduce daily rainfall extremes over southern Africa. The spatio-temporal variability of daily rainfall is better simulated by the RCM which reduces the AGCM s dry biases during the rainy seasons. Both AGCM and RCM capture ~70% of the observed intense events, and their spatial distribution is close to observations. The number of events is, however, overestimated at subtropical latitudes and underestimated over equatorial and tropical latitudes. The RCM partially corrects the AGCM s biases in the southeastern part of southern Africa, but fails at correcting them further north. Ibrahim et al. (2012) compare rainfall simulated by five different 50-km resolution RCMs run over West Africa under different driving fields (reanalyses and GCMs) to daily rainfall from ten stations in Burkina Faso. The RCMs significantly overestimate the frequency of rainy events in the mm day -1 range and the magnitude of intense rainfall events. The distribution of rainfall intensity has the largest inter-model spread, and the deficiencies are intrinsic to the models since no improvement is found when RCM simulations are driven by reanalysis. 6

7 Vizy and Cook (2012) document changes in rainfall extremes under greenhouse gas forcing in Africa north of 10 S using an RCM at 90-km resolution. As part of their study, late 20 th century simulations are evaluated against gridded observations to evaluate the model s ability to simulate intense rainfall events. Their results indicate that the spatial distribution of intense events and their average intensity are more realistically simulated than their magnitude. The RCM captures the observed spatial distribution of intense rainfall events despite overestimation of the number of events and underestimation of their mean intensity. Two approaches are used to extract extreme wet days (the 95 th percentile and 1.5 x interquartile range of the local wet daily rainfall distribution) and both lead to similar spatial distributions The studies discussed above suggest that RCMs hold some promise for improving our ability to simulate intense rainfall events at the daily timescale. Here, global and regional climate model output is evaluated and compared to observations to diagnose their strengths and limitations in simulating daily intense rainfall events over Africa and assess what benefits are gained by utilizing regional model predictions Data and methodology 3.1 Satellite-derived rainfall estimates Two satellite-derived products combining infrared (IR) imagery, passive microwave (PM) estimations of instantaneous rain rates, and gauge analysis are used in this study. PM and IR combining methods assure a strong physical basis for rainfall estimates, and provide more 7

8 accurate high spatio-temporal rainfall estimates than the IR and PM only methods (e.g., Ebert et al. 1996; Adler et al. 2001). The 3B42-V7 Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007) rainfall estimates use a rich constellation of satellite-borne precipitation sensors to provide 3-hourly estimates at a spatial resolution of 0.25 for the 1998-present period. Its previous versions have demonstrated their skill to reasonably capture rainfall intensity at different timescales over West Africa (Nicholson et al. 2003) and East Africa (Dinku et al. 2007) and, more recently, intense rain-producing systems such as mesoscale convective complexes over South America (Durkee et al. 2009) and southern Africa (Blamey and Reason 2013), and tropical cyclones (Jiang and Zipser 2010). Daily rainfall aggregated from 3-hourly values is taken as a reference in this study since the fine horizontal resolution is useful for documenting the characteristics of daily intense rainfall events. The V1.1 Global Precipitation Climatology Project product (GPCP; Huffman et al. 2009) is used to supply a range of uncertainty for observations. This product provides daily rainfall estimates on a 1 x 1 regular grid since October GCM simulations Our long-term objective being to evaluate how daily intense rainfall events are likely to change in the future, AOGCM simulations are examined here in addition to AGCM simulations. To that end, we use the AMIP and historical experiments designed according to the World Climate Research Programme s CMIP5 framework and run with more than twenty state-of-theart GCMs. The AMIP experiment uses AGCMs integrated over the period, while the CMIP5 experiment uses AOGCMs simulating the period. 8

9 Four GCMs are selected based upon the availability of output at the daily timescale when this study began. AGCM and AOGCM output from each model is analyzed but we do not directly compare the two versions of a model because there may be many differences between the two versions. The GCMs analyzed are the NCAR Community Climate System Model 4 (CCSM4), the National Centre for Meteorological Research Climate Model 5 (CNRM-CM5), the Model for Interdisciplinary Research on Climate 5 (MIROC5), and the Meteorological Research Institute CGCM3 (MRI-CGCM3). These four models are used to document inter-model uncertainties resulting from a range of parameters, including the horizontal resolution (Table 1) and the protocol (i.e., AMIP versus CMIP) RCM simulations A 20-yr long nested RCM simulation is run using the NCAR/NOAA Weather Research and Forecasting (WRF; Skamarock et al. 2008) model V A one-way nesting methodology is applied with a 90-km horizontal resolution outer domain that encompasses all of Africa and most of the adjacent Atlantic and Indian Oceans, and a 30-km nested domain covering Africa. Figure 1 shows the placement of the domains and the topography as resolved at 30-km over Africa. 90- km resolution is selected for use because previous studies (Patricola and Cook 2010, 2011; Cook and Vizy 2012; Vizy and Cook 2012; Vizy et al. 2013) demonstrate that the model can realistically simulate the African climate at this resolution and it is close to the resolution of most of the available CMIP5 simulations to support a more direct comparison. The lateral boundaries are placed far away from Africa to minimize the effects of their constraints in the analysis region. Lateral boundary conditions for the 30-km domain are derived from the 90-km domain, but the 30-km simulation results are not fed back to the coarser domain so the influence of 9

10 spatial resolution on the projections can be directly evaluated. Both domains have 32 vertical levels with the top of the atmosphere set at 20 hpa. A model time step of 180 s is used for the outer domain, and 60 s for the inner domain. Physical parameterizations are the same for both domains, and include the Yonsei University planetary boundary layer (Hong et al. 2006), Monin-Obukhov surface layer, new Kain-Fritsch cumulus convection (Kain 2004), Purdue Lin microphysics (Chen and Sun 2002), RRTM longwave radiation (Mlawer et al. 1997), Dudhia shortwave radiation (Dudhia 1989), and the unified Noah land surface model (Chen and Dudhia 2001). These parameterization selections are chosen because they produce a realistic simulation of the African climate (Cook and Vizy 2012; Vizy and Cook 2012; Vizy et al. 2013). Surface characteristics are interpolated from the United States Geological Survey (USGS) database, which describes a 24 category land-use index based on climatological averages, and 17 category United Nations Food and Agriculture Organization soil data, both available at 10 arc minutes. The 90-km outer domain lateral boundaries are constrained by the NCEP2 reanalyses (Kanamitsu et al. 2002), furnished every 6 h at 18 pressure levels and projected onto the model sigma coordinate grid. Sea surface temperatures (SSTs) are prescribed and updated every 6 h from the NCEP2 reanalysis. The run is initialized on 00Z 15 March Data are archived every 3 h from 00Z 1 January 1989 to 00Z 1 January 2009, i.e., after a 292-day spin-up. The 3 h output is averaged to formulate daily means which are analyzed Extraction of daily intense rainfall events 10

11 There are a variety of ways intense rainfall events can be defined (Easterling et al. 2000). The definition used here is based solely on rainfall amounts, with the advantage of being applicable to both present and future periods. For each dataset and each grid point, a daily rainfall amount is considered as intense if it exceeds the 95 th percentile threshold computed for all rainy days, where a rainy day is defined as when the daily accumulated precipitation reaches 1 mm day -1. Successfully applied at both global (Frich et al. 2002; Tebaldi et al. 2006) and regional (Kendon et al. 2008; Vizy and Cook 2012) scales, this approach is used to determine the threshold value at each data point based upon the probability distribution of the individual datasets. It allows both spatial and inter-dataset comparisons because the same part of the probability distribution is sampled (Klein Tank et al. 2009), which would not be the case with a fixed threshold value. To avoid statistical artifacts resulting from non-uniform lengths of time series, analyses are conducted over the 11 common years for the TRMM and GPCP records, namely, 1998 to Similarly, only the 11 years constrained by boundary conditions are analyzed from the RCM and AGCM output, while the 11 last years of the historical experiment simulations are used for each of the four AOGCMs (Table 1). Note that it is important to compare the RCM and AGCM output and observations over common years since observed SSTs are prescribed for these simulations. This is not crucial for the AOGCM output since SSTs are predicted for these simulations. Furthermore, it is worth keeping in mind that model inter-comparisons are not fully satisfactory since the RCM is forced by both atmospheric lateral boundary and SST observations, unlike AGCMs and even more AOGCMs, and that each model has its own physical parameterizations. 11

12 Various statistic techniques (Student s t-test, standard deviation, root-mean square errors [RMSE], Bravais-Pearson correlations [r]) are used to evaluate the model accuracy and perform comparisons Mean spatial characteristics at the grid point scale 4.1 Mean annual frequency of daily rainfall intense events Figure 2 shows the mean annual number of intense rainfall events based on the 95 th percentile threshold approach for the observations and the models. Similar spatial distributions are found for TRMM (Fig.2a) and GPCP (Fig.2b). The number of observed events is greatest over the Congo Basin (~10 events per year), the Ethiopian Highlands (~10 events per year), and over the Guinean Highlands extending westward over the eastern tropical Atlantic Ocean (~5 10 events per year). The number of events decreases rapidly poleward of 10 N and 15 S. The RCM simulations at 90- (Fig. 2c) and 30-km (Fig. 2d) resolution are similar to each other, demonstrating a weak impact of horizontal resolution at these scales (i.e., between 90- and 30-km) on the spatial distribution of the number of rainy days. While not perfect, they both closely resemble the observed distributions, and they are more similar to the GPCP distribution (Fig. 2b) than the TRMM distribution (Fig. 2a). The greatest RCM deficiency concerns the larger spatial coverage of the observed maxima over the Congo Basin. The four AGCMs (Figs. 2e-h) also capture the main observed spatial characteristics. CCSM4, CNRM-CM5 and MIROC5, however, greatly overestimate the number of intense events almost everywhere, particularly between 10 N and 15 S inland and over the Indian Ocean. On the other hand, MRI-CGCM3 significantly outperforms the other three AGCMs, providing agreement with observations similar to the RCM simulations. 12

13 Overall, the spatial distribution of intense events simulated by the four AOGCMs (Figs. 2il) is similar to that in the AGCM simulations, particularly CCSM4 and MIROC5. The greatest differences are found over the Guinean Gulf in the two remaining models. The CNRM-CM5 AOGCM simulates too many intense events there so that the observed ocean coast zonal gradient is reversed, while the MRI-CGCM3 AOGCM simulates too few events along the equator, resulting in an exaggerated ocean coast zonal gradient. Figure 3 shows differences in the number of intense events from TRMM for the GPCP observations, the 90- and 30-km RCM simulations, and the four GCMs in both AMIP and CMIP modes. Differences between the two observation products (Fig. 3a) are small over the Sahara desert and south of 15 S, and over the Atlantic Ocean from equatorial to subtropical latitudes. Except in these regions, most of the differences are statistically significant. Intense events are 20 60% more frequent in TRMM around Madagascar, with ~1.5 6 events more than GPCP per year. The reverse is generally found elsewhere. Over the continent, the weakest significant differences ( events per year) are found over the Sahel and the Horn of Africa. Since these two regions experience semi-arid conditions with a low occurrence of intense events, these differences are not negligible, reaching % over the Sahel and exceeding 200% over the Horn of Africa. The greatest significant differences (1.5 4 events per year) are located over the Democratic Republic of Congo (DRC) and along the Guinean Coast where GPCP provides 30 40% more events, and up to 60% locally. Since the extraction of daily intense events is based on a percentile approach, the number of rainy days varies. Differences between the observational datasets found in Fig. 3a are directly related to differences in the number of rainy days, with the coarser horizontal resolution product (GPCP) systematically producing more rainy days than the finer resolution product (TRMM). 13

14 The ability of the 90- and 30-km RCM simulations to capture the magnitude of intense event occurrence is regionally dependent (Figs. 3b-c). Both RCM simulations underestimate them by 2 8 days over northern Ethiopia, around the Victoria Lake, and over west Angola and northwest Namibia. On the other hand, the number of intense events is overestimated by 2 4 days over central and southern Sudan (twice the observed magnitude) and southeastern South Africa (up to 125% the observed magnitude), as well as over the Atlantic and Indian Oceans. Elsewhere, the RCM simulations better match observed magnitudes, providing slightly more events than GPCP over the DRC, the Horn of Africa, and along the Guinean Coast (2 6 events per year more than TRMM), and falling between the observational range over the Sahel. As for the spatial distribution, the magnitude of the number of intense events is only slightly impacted by the horizontal resolution, with the 90-km RCM simulation biases reduced over the Horn of Africa in the 30-km RCM simulation. Three out of four AGCM simulations (Figs. 3d-g) fail at simulating reasonable numbers of intense events. CCSM4, CNRM-CM5 and MIROC5 overestimate them almost everywhere, up to 6 events per year over the continent and 12 events per year over the Atlantic and Indian Oceans. They simulate two to six times more intense events than TRMM over the Sahel and east South Africa, and at least eight times more over the Horn of Africa. MIROC5 even simulates times more events than TRMM over the tropical Atlantic Ocean. The limited skill of these three models results from a too frequent triggering of convection independent of their horizontal resolution (Table 1), as has already been pointed out in previous GCM generations (Sun et al. 2006, 2007; Randall et al. 2007; Boyle and Klein 2010; Li et al. 2011a). The MRI-CGCM3 AGCM (Fig. 3g) outperforms the other GCMs, simulating magnitudes comparable to those predicted by the RCM over most of the DRC and western Namibia, and along the Guinean Coast 14

15 and a northeast southwest band linking Ethiopia to Zambia. This model generally better fits observations over the oceans than the RCM does, but produces larger biases over the southern part of southern Africa. The biases are even more pronounced for the AOGCM simulations over the oceans (Figs. 3h-k) with an increasing occurrence of intense events, particularly over the Atlantic Ocean, between the equator and 15 S. Over the continent, they provide similar magnitudes. This suggests that ocean-atmosphere coupling does not significantly affect the triggering of convection over the African continent Average rainfall intensity Figure 4 shows the average intensity of daily intense events in the observations and simulations. The TRMM (Fig. 4a) and GPCP (Fig. 4b) products agree in locating the lowest rainfall intensities over the Sahara desert and the subtropical Atlantic Ocean, and the largest rainfall intensities in the tropics and over the Indian Ocean. The magnitude of the rainfall intensity, however, strongly differs between them. The rainfall intensity produced by TRMM during intense events is highly variable in space. Three main regions emerge. The first receives more than 80 mm day -1 per event and includes the western and eastern parts of the Sahel, off the Sierra Leone coast, the eastern part of the Gulf of Guinea, the Horn of Africa, and the Mozambique Channel. The second region receives mm day -1 per event and occurs in western and central Africa, the Congo Basin, and on the east coast from southern Somalia to northern Mozambique. The third region encompasses a northeast-to-southwest band linking Ethiopia to Namibia and most of southern Africa, and receives mm day -1 per event. 15

16 The spatial variability of rainfall intensity is weaker in the GPCP data, with values rarely exceeding 50 mm day -1 over the continent. This product however captures maxima over the western and eastern Sahel, off the coast of Sierra Leone, along the Guinean Coast, and over the Mozambique Channel. The 90- and 30-km RCM simulations (Figs. 4c-d) fall between the two observations. Simulated average rainfall intensities are closer to TRMM off the Sierra Leone coast, over West Africa, and along the Mozambique Channel. Over and west of the Mozambique Channel, however, the RCM simulates rainfall intensities of at least 60 mm day -1 more than TRMM. This clear wet bias, slightly larger and wider spatially in the 30-km RCM simulation and already pointed out by Crétat et al. (2012; see their Fig. 3), may be related to the proximity of the warm Agulhas Current which is found to be associated with enhanced latent heat fluxes that increase the low-level moisture in the atmospheric water cycle over this region when using the Yonsei University PBL parameterization (Crétat 2011). Simulated average rainfall intensities are closer to the GPCP data over the Sahel, the Horn of Africa, and the Congo Basin. The RCM tends to simulate higher rainfall intensity almost everywhere when set at a finer horizontal resolution. Except around the Mozambique Channel, the 30-km RCM simulation better matches TRMM, particularly over West Africa. The four AGCMs (Figs. 4e-h) capture the maxima located off the Sierra Leone coast. Their ability to simulate the spatial distribution and magnitude of rainfall intensity are modeldependent elsewhere. CCSM4 (Fig. 4e) produces rainfall intensities that are too homogenous over both land and ocean, with values systematically lower than in GPCP and rarely exceeding 30 mm day -1. MRI-CGCM3 (Fig. 4h) is similar to CCSM4 over the continent, and somewhat close to the observations over the Indian Ocean. Rainfall intensities simulated by CNRM-CM5 16

17 and MIROC5 (Figs. 4f-g) are fairly accurate over West and Central Africa, with values close to those simulated by the RCM. Set with a coarser horizontal resolution, MIROC5 is the only AGCM that simulates a maximum over the Mozambique Channel and it outperforms the RCM simulations over the Congo Basin. Overall, the spatial distribution of rainfall intensities simulated by the AOGCMs (Figs. 4i-l) does not differ significantly from the AGCM simulations. Differences mainly concern the magnitude of rainfall intensity, and are again model dependent. Rainfall intensities tend to be larger over the Indian Ocean in CCSM4 (Fig. 4i) and MRI-CGCM3 (Fig. 4l), lower over Central Africa in CNRM-CM5 (Fig. 4j), and larger over West Africa in MIROC5 (Fig. 4k) Statistical summary Taylor diagrams (Taylor 2001) shown in Figs. 5a and b synthesize the results obtained for the spatial variability of the mean annual frequency of intense events over the entire analysis region shown in Fig. 2, including both land and water points and for African land points alone, respectively. Figs. 5c-d are Taylor diagrams for the average intensity. Such diagrams quantify inter-observation and inter-model spread as well as the models skill by confronting GPCP and climate model output to TRMM in terms of spatial co-variability (r; blue dash-dot lines), spatial dispersion (standard deviation; black dotted lines) and spatial bias (RMSE; green dashed lines). Including and excluding the oceanic regions allows a consideration of the impact of oceanatmosphere coupling. Results are summarized below. For the two observational datasets, results are not sensitive to the choice of the region. The spatial characteristics of the number of intense events (Figs. 5a-b) are very similar between the two datasets, with r and a spatial dispersion that is 15 20% larger in GPCP. In contrast, 17

18 large discrepancies are found when the average intensity is considered (Figs. 5c-d), with r values ranging between and +0.7 and a spatial dispersion that is 40 50% weaker in the GPCP data than in the TRMM data. These differences suggest that the spatial distribution of the number of intense events mainly depends on large-scale mechanisms, while rainfall intensity is also controlled by regional to local processes. This is consistent with the large underestimation of rainfall intensity in the GPCP data (Fig. 4b) which has large RMSE values (Figs. 5c-d: mm day -1 per event). Note, however, that the relatively low RMSE found in the number of intense events (~0.8 events per year) masks large regional differences, especially over the Sahel and the Horn of Africa where the GPCP data produce % more intense events. The good inter-observation agreement found for the number of intense events could be related to the use of similar large-scale monthly precipitation, from gauge networks of the Global Precipitation Climatology Centre version 4 product, used to scale TRMM and GPCP data (Huffman et al. 2009; Huffman and Bolvin 2013). On the other hand, the large TRMM GPCP differences in intensity could be related to horizontal resolution differences and to the fact that both products adjust rainfall intensities differently, the calibration being directly based on radar estimations for TRMM, while on indirect relationships between cloud properties and rain rate for GPCP. Similarly to the GPCP data, the spatial characteristics of the number of intense events simulated by all climate models is in better agreement with the TRMM data than is their average intensity. For the number of intense events (Figs. 5a-b), the co-variability between the climate models and the TRMM data is slightly higher over Africa (+0.85 r +0.95) than over the entire analysis region (+0.7 r < +0.9). This indicates that the frequency of simulated intense events varies weakly with the region and is mostly controlled by large-scale mechanisms, similar 18

19 to observations. The co-variability is weaker for the average intensity, and it appears to be regionally sensitive (Figs. 5c-d) since r varies from +0.4 to (excluding the MRI-CGCM3 AOGCM outlier which simulates unrealistically high rainfall intensity over the cold tropical Atlantic Ocean) over the entire analysis region, but it varies from +0.2 to +0.4 over Africa. The two remaining metrics (RMSE and standard deviation) are more highly model dependent than regionally dependent. The two RCM simulations, and both the MRI-CGCM3 AGCM and AOGCM output, match the characteristics of the number of intense events in the GPCP data, and are 40 50% lower than the RMSE values produced by other simulations (and 40 50% lower in standard deviation). All the climate models struggle to simulate the average rainfall intensities in the TRMM observations, and each has its own strengths and weaknesses. The GCMs simulate, for example, weaker RMSE values than the RCM simulations (16 20 mm day -1 against mm day -1 ) which is consistent with the wet biases produced by both RCM simulations over subtropical latitudes (Figs. 4c-d). In contrast, the RCM and MIROC5 simulations more accurately capture the spatial dispersion depicted in the TRMM data than other models. Both RCM simulations provide very similar results, and this is also true of the AGCM and AOGCM results. The mean spatial characteristics of intense events thus appear to be marginally modified by the horizontal resolution within the range represented here and the inclusion of air/sea interactions, suggesting that they are primarily controlled by continent-based physics, such as locally-controlled convection, land/atmosphere interactions, and PBL dynamics The analysis demonstrates that the mean spatial characteristics of daily intense events (i.e., their number and average intensity) are better captured by the RCM, the latter performing as well 19

20 as the most accurate global models (MRI-CGCM3 for the number of intense events; CNRM- CM5 and MIROC5 for their average intensity). The following section focuses on regional space scales to evaluate the models ability to capture rainfall amounts during intense events and the temporal variability of these events Distribution and mean temporal variability at the regional scale The ability of state-of-the-art climate models to capture the distribution and temporal variability of daily intense rainfall events is explored focusing on six African regions similar to those used by Sylla et al. (2010b) and Vizy and Cook (2012). These regions, shown in Fig. 1, are listed in Table 2 along with the number of grid points for each dataset. Three metrics are computed using the native grid of each dataset. The first quantifies the range of rainfall intensity associated with intense events. All intense events occurring during the 11-yr period (Table 3) are extracted at each grid point within a region using that grid point s 95 th percentile threshold and are then combined to produce frequency rainfall intensity histograms. Two other metrics document the mean annual cycle of daily intense rainfall events, focusing on their spatial coverage and their average intensity. The spatial coverage of daily intense rainfall events is computed for each day of each year of the 11-yr period as the ratio between the total number of grid points for which daily rainfall amounts exceed the 95 th percentile threshold and the total number of grid points within a region (Table 2). Similarly, for each day of each year of the 11-yr period, we compute the spatial average of rainfall intensity of all grid points for which daily rainfall amounts exceed the 95 th percentile threshold. The mean annual cycle of these two metrics is then formed by averaging the eleven years. 20

21 Rainfall distribution Figures 6a-f present frequency intensity histograms of all daily intense rainfall events from the TRMM and GPCP observations over the Sahel, West Africa, Central Africa, Horn of Africa, Congo Basin and tropical southern Africa regions, respectively. In each region, the observed distributions are skewed to the right in both datasets, meaning that the probability of occurrence of daily intense rainfall events has some inverse proportionality to intensity. Both observations also demonstrate similar frequencies of medium range daily intense rainfall events, with ~20% of events receiving mm day -1 over the Sahel (Fig. 6a), West Africa (Fig. 6b) and the Horn of Africa (Fig. 6d), and ~25 30% of events receiving mm day -1 over Central Africa (Fig. 6c) and tropical southern Africa (Fig. 6f). The shape of the observed distributions, however, differs, with a wider spread in the TRMM data than in the GPCP data. When considering the average of the six regions combined, 75% of daily intense rainfall events in the TRMM observations occur in the mm day -1 range, with the remaining 25% of events greater than 70 mm day -1. In contrast, 90% of the intense events from GPCP fall in the mm day -1 range, while only a few events exceed this range. Figures 7a-f show the RCM results over the 6 regions. The 30-km RCM simulation generally simulates a greater number of the highest intensity events, toward the right tail of the distribution, than the 90-km resolution simulation. The RCM 90- and 30-km resolution distributions are, however, fairly similar to one another and fall within the range of the observations. Most daily intense rainfall events from the RCM range between mm day -1 which is in better agreement with the GPCP data than with the TRMM data, while the frequency 21

22 of the most intense events is more in line with the TRMM observations. Regionally, the RCM is least accurate over the Sahel (Fig. 7a: 55% of events in the mm day -1 range against 20% in GPCP and less than 3% in TRMM), and most accurate over tropical southern Africa (Fig. 7f). Figs. 8a-f show results from the CCSM4 AGCM and AOGCM over the 6 regions. Figs. 8gl, m-r, and s-x are structured the same as Fig. 8a-f but for the CNRM-CM5, MIROC5 and MRI- CGCM3 GCMs, respectively. The AGCM and AOGCM histograms are similar to one another for most regions. The exceptions are CCSM4 and CNRM-CM5 over the Sahel (Figs. 8a and g, respectively) and MRI-CGCM3 over West Africa (Fig. 8t), which all simulate 15 20% more events in the mm day -1 range when coupled atmosphere/ocean interactions are included. MIROC5 provides distributions as accurate as the RCM for most of the regions. Over tropical southern Africa (Fig. 8r) MIROC5 s distribution closely matches the TRMM observations. The other GCMs generally overestimate the frequency of the less intense events (i.e., at least three quarters of events in the mm day -1 range on average), particularly CNRM-CM5 over the Horn of Africa (Fig. 8j) and Congo Basin (Fig. 8k) regions where more than 60% of events fall in the mm day -1 range compared to less than 1% in both observations, and MRI-CGCM3 over the Sahel (Fig. 8s) and Central Africa (Fig. 8u) where all events fall in the 5 30 mm day -1 range Mean annual cycle of spatial coverage Figures 9a-f show the mean annual cycle of the spatial coverage of intense rainfall events from the TRMM and GPCP observations over the Sahel, West Africa, Central Africa, Horn of Africa, Congo Basin and tropical southern Africa regions, respectively. 22

23 The observed spatial coverage of intense events is always small, exceeding 5% only over West Africa (Fig. 9b) and Central Africa (Fig. 9c). Spatial coverage is 30 40% larger in the GPCP data compared to the TRMM data, particularly during the summer monsoon season over the Sahel (Fig. 9a) and West Africa (Fig. 9b). The TRMM and GPCP time-series are highly correlated and both time-series vary in-phase with their respective mean annual rainfall cycle (not shown), indicating that the probability of occurrence and spatial coverage of daily intense rainfall events are linked with rainfall regimes. The Sahel (Fig. 9a) and tropical southern Africa (Fig. 9f) regions experience a single-peak regime, with daily intense rainfall events occurring exclusively during summer (June October and October May, respectively) and maximum spatial coverage peaking during the height of the rainy season (July September and December February, respectively). The West Africa (Fig. 9b) and Central Africa (Fig. 9c) regions exhibit a longer duration of the rainy season and larger day-to-day variability compared to the Sahel and tropical southern Africa regions. Daily intense rainfall events occur mainly between April and October, with an early peak in June followed by the full establishment of the July September monsoon. A double-peak regime is found over the Horn of Africa and Congo Basin regions. Over the Horn of Africa (Fig. 9d), daily intense rainfall events occur during the March May long rains and the October November short rains, with maximum spatial coverage during the long rains. Over the Congo Basin (Fig. 9e), the first peak occurs in March April and the second in October November, consistent with the seasonal meridional position of the inter-tropical convergence zone. Figures 10a-f show the mean spatial coverage of daily intense rainfall events simulated by the RCM. Both RCM simulations realistically capture the magnitude of the observed spatial 23

24 coverage and its temporal variability over the Sahel (Fig. 10a) and tropical southern Africa (Fig. 10f). Over West Africa (Fig. 10b) and Central Africa (Fig. 10c), the simulated spatial coverage during the boreal summer is overestimated by up to 130% and 80%, respectively. Additionally, the demise in spatial coverage over Central Africa during September is more abrupt than observed. Over the Horn of Africa (Fig. 10d) the spatial coverage of intense events is overestimated by % during the long-rains. The Congo Basin (Fig. 10e) yields the largest differences from the observations ( % during the two observed wet seasons) and the weakest co-variability with observations (the first and second peaks in rainfall occur few weeks too late and early, respectively). The four GCMs (Figs. 11a-f to s-x) struggle to capture the observed magnitude and temporal variability of the spatial coverage of daily intense rainfall events. Generally speaking, the MRI-CGCM3 (Figs. 11s-x) is the most realistic of the 4 GCMs evaluated in most regions, and for some of the analyzed regions the results are as realistic as the RCM. Over the Sahel (Fig. 11s), the MRI-CGCM3 simulates intense rainfall events during the boreal spring that are not observed. Over the Horn of Africa (Fig. 11v) its AOGCM version underestimates the spatial coverage of daily intense rainfall events associated with the long-rains while the short-rain events are overestimated. CCSM4, CNRM-CM5 and MIROC5 overestimate the magnitude of the observed spatial coverage by ~2 3 times during summer over the Sahel, West Africa and Central Africa, and by up to ~4 5 times over the Horn of Africa and the Congo Basin. More importantly, these GCMs often fail at simulating the annual cycle of daily intense rainfall events. An unrealistic doublepeaked regime is simulated over West Africa by the CCSM4 (Fig. 11b), CNRM-CM5 (Fig. 11h) and MIROC5 (Fig. 11n) AGCMs, and over Central Africa by CCSM4 (Fig. 11c) and MIROC5 24

25 (Fig. 11o) for both the AGCM and AOGCM versions of each model. Over the Horn of Africa, the CCSM4 AOGCM (Fig. 11d) and the CNRM-CM5 AGCM (Fig. 11j) do not clearly distinguish the long and short rains seasons, producing daily intense rainfall events all year long, while other GCMs overestimate the bi-modal peak. Interestingly, this region experiences the weakest co-variability between the two versions of each GCM (r ~+0.26 and even weaker in MRI-CGCM3), and the AGCMs do not always outperform the AOGCMs in regards to spatial coverage. Taylor diagrams shown in Figs. 12a-f synthesize these results over the Sahel, West Africa, Central Africa, Horn of Africa, Congo Basin and tropical southern Africa regions, respectively. They allow a more direct comparison among the datasets by applying the same statistics as Fig. 5 to the time-series shown in Figs , and are primarily used to discuss RCM GCM differences. The two observational products are systematically in good agreement, with r ranking from ~+0.9 over the Congo Basin (Fig. 12e) to more than over the Sahel (Fig. 12a) and tropical southern Africa (Fig. 12f). Owning to slightly larger spatial coherence of daily intense rainfall events (Fig. 9), the GPCP observations display slightly larger standard deviation, and this weakens the RMSE values between the two products. The Congo Basin (Fig. 12e) is the only region where the RCM does not outperform the GCMs. In this region, the RCM and GCMs roughly capture the observed temporal variability of the spatial coverage of intense events (+0.5 r +0.6) and overestimate their magnitude during both rainy seasons (Figs. 10e and 11e, k, q and w, respectively) so that standard deviations are larger than for the observations and large RMSE values occur. 25

26 The RCM performs as well as the most accurate GCM over the Horn of Africa (Fig. 12d: MRI-CGCM3), and largely outperforms the four GCMs elsewhere. The RCM better captures the day-to-day variability over the Sahel (Fig. 12a), Central Africa (Fig. 12c), the Horn of Africa (Fig. 12d) and, to a lesser extent, West Africa (Fig. 12b), indicating the RCM s ability to better capture the temporal distribution of daily intense rainfall events in these regions. The RCM also better captures the magnitude of the spatial coverage of daily intense rainfall events over the Sahel, Horn of Africa and tropical southern Africa regions, with a standard deviation sometimes closer to TRMM than GPCP (i.e., over the Sahel and tropical southern Africa) and weak RMSE values Mean annual cycle of average intensity Figures 13a-f show the mean annual cycle of the average rainfall amounts associated with daily intense rainfall events from the TRMM and GPCP observations over the Sahel, West Africa, Central Africa, Horn of Africa, Congo Basin and tropical southern Africa regions, respectively. TRMM depicts relatively flat distributions in the average daily rainfall intensity associated with intense rainfall events over West Africa (Fig. 13b), Central Africa (Fig. 13c), the Congo Basin (Fig. 13e) and tropical southern Africa (Fig. 13f). The magnitude for West Africa is approximately 60 mm day -1, and about 50 mm day -1 for the other three regions. Over the Sahel (Fig. 13a) and Horn of Africa (Fig. 13d) a clear seasonality emerges, with larger rainfall amounts during their respective rainy seasons. The GPCP observations do not really capture the seasonal cycle for these two regions, instead producing systematically flat distributions with a magnitude around 30 mm day

27 Despite dry biases, the 90- and 30-km RCM simulations (Figs. 14a-f) tend to more closely resemble the TRMM data than the GPCP data. The RCM captures the seasonal cycle over the Sahel (Fig. 13a) and, to a lesser extent, over the Horn of Africa (Fig. 13d), and simulates approximately the appropriate magnitude of rainfall amounts over Central Africa (Fig. 13c). However, the RCM simulates a more distinct seasonal cycle over West Africa (Fig. 13b) compared to the TRMM data, underestimating rainfall amounts over the Congo Basin by 605 approximately 20 mm day -1 (Fig. 13e) and overestimating rainfall intensities over tropical southern Africa during December February (Fig. 13f). The four AGCMs and AOGCMs (Figs. 15a-f to s-x) produce similar results except over the Sahel in CCSM4 (Fig. 15a) and CNRM-CM5 (Fig. 15g), and over West Africa in MRI-CGCM3 (Fig. 15t) with larger rainfall amounts in the AGCMs, consistent with Fig. 8. MIROC5 indicates similar strengths and weaknesses to the RCM as discussed earlier, except over the Horn of Africa (Fig. 15p) where it has a larger dry bias, and over tropical southern Africa (Fig. 15r) where it more realistically simulates the intensity of intense events during December February. The other GCMs fail to realistically simulate the seasonal cycle of rainfall intensity associated with daily intense rainfall events over the Sahel, particularly CCSM4 (Fig. 15a) and MRI-CGCM3 (Fig. 15s) whose dry biases exceed 40 mm day -1. CNRM-CM5 and MRI-CGCM3 simulate an unrealistic seasonal cycle over West Africa (Figs. 15h and t, respectively). Over other regions, these three GCMs largely underestimate rainfall amounts, consistent with Fig. 8. Taylor diagrams cannot be applied with robustness to the time-series shown in Figs because of the high variability of this parameter. Furthermore, over regions experiencing flat distributions of average intensity of daily intense rainfall events (e.g., West Africa and Congo 27

28 Basin), weak disagreements between two time-series induce poor (even negative) correlations, which do not reflect the results discussed above Conclusion and Discussion The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events is assessed over Africa. Two RCM simulations are run at 90- and 30-km resolution and analyzed along with output from four different CMIP5 AGCMs and AOGCMs. Daily intense rainfall events from the two RCM simulations and the CMIP5 AMIP experiment are extracted using a 95 th percentile threshold approach computed on all rainy days of the period for which two satellite-derived rainfall estimates are available (TRMM and GPCP). The last 11 years of the CMIP5 historical experiment (i.e., ) is used for AOGCM output, which is not an issue since SSTs are predicted for these simulations. The 95 th percentile threshold approach allows both spatial and inter-dataset comparisons and has the advantage of being easily applicable to future periods. At the grid point scale, the spatial distribution and magnitude of the mean annual number of daily intense rainfall events are analyzed, as well as their average intensity. At the regional scale, intensity frequency histograms and the mean annual cycle of the spatial coverage and average intensity of daily intense rainfall events are examined in six African regions. Confidence is evaluated based on comparisons to the fine resolution TRMM product using different statistical metrics (Student s t-test and Taylor diagrams) and on comparisons to the inter-observation spread. 28

29 It is worth noting that the frequency of intense rainfall events (spatial distribution and annual cycle of its spatial coverage) is generally consistent between the two observations. Their associated intensity, however, largely differs, with the GPCP data displaying a systematically lower spatial and temporal variability than the TRMM data, and lower average rainfall amounts associated with daily intense rainfall events. Using the 3B42-V6 TRMM data, Nikulin et al. (2012) found opposite results at the seasonal timescale, with GPCP 50% wetter over large areas, indicating that the sign and magnitude of the inter-product differences depend on the temporal scale and the range of intensity considered (i.e., mean sub-sampling for just wet days versus averaging over all days). The authors attribute such differences to the adjustment of large-scale satellite estimates to different rain gauge products, which is no more valid in the 3B42-V7 TRMM data which uses the GPCC monitoring product version 4 (Huffman and Bolvin 2013), like the GPCP. This similarity may explain the good inter-observation agreement found for the frequency of intense rainfall events which results from large-scale mechanisms, but it does not explain the large local differences shown in Figs. 4a-b. For the latter, probable reasons include the difference of horizontal resolution and the methodology used to calibrate rainfall intensities. Despite uncertainties in both products, intensity provided by the TRMM data is in better agreement with quantities associated with rain-producing systems occurring over Africa such as tropical extratropical troughs or mesoscale convective complexes (e.g., Knippertz and Martin 2005; Blamey and Reason 2009, 2013; Hart et al. 2010). This justifies using TRMM data as the primary reference for comparison in this study. Overall, the RCM and GCM simulations tend to overestimate the frequency of intense events and to underestimate their intensity. This indicates that state-of-the-art climate models still cannot realistically simulate daily intense rainfall events with high accuracy, and suggests 29

30 that there are physical parameterization deficiencies for the range of horizontal resolution represented here. One region that exemplifies these model inadequacies is the Congo Basin, where all climate models analyzed overestimate the frequency and spatial coverage of intense rainfall events. The fact that both RCM and, to a greater extent, GCM simulations overestimate the frequency of intense daily rainfall events suggests that erroneous or, at least, overly simplified assumptions are used in the convective schemes of most climate models. Analyzing 10 RCM simulations run at 50-km horizontal over Africa for the Coordinated Regional Downscaling Experiment (CORDEX-Africa) project, this deficiency has been recently pointed out by Nikulin et al. (2012), who found that the diurnal cycle of rainfall is poorly simulated due to convection occurring too early in the afternoon. The analysis indicates that the GCM s skill is model dependent, relatively insensitive to the inclusion of air/sea interactions over continental Africa, and much more regionally dependent than the RCM. Further work is needed to better understand why the skill of the RCM and GCMs vary so much in different regions of Africa. The RCM almost always performs at least as well as the best GCM (i.e., MRI-CGCM3 for the frequency of daily intense rainfall events and CNRM- CM5 and MIROC5 for their intensity). The greatest RCM added value comes from the following: the frequency of daily intense rainfall events, which is largely overestimated by three out of four GCMs with too frequent rainy days (e.g., Mearns et al. 1995; Chen et al. 1996; Dai et al. 1999; Dai 2006; Sun et al. 2006; Boyle and Klein 2010; Li et al. 2011a); the magnitude and temporal distribution of the mean annual cycle of the spatial coverage of daily intense rainfall events, which is close to the TRMM and GPCP observations while large deficiencies are found in the GCMs. While the more realistic temporal distribution 30

31 simulated by the RCM may be associated with the realistic SST and atmospheric lateral boundary condition forcing to some extent, the more accurate magnitude results from intrinsic RCM improvements (e.g., horizontal resolution and physic optimization for the region of interest). The RCM is not perfect by any means, as it struggles to capture the observed rainfall intensity accurately, providing average intensity and frequency intensity histograms close to CNRM-CM5 and MIROC5 and often falling between the two observations analyzed. Contrary to, for example, Rauscher et al. (2010), there is no large difference between the 90- and 30-km RCM simulations. One possible cause is that resolutions finer than 30-km are needed to represent small scale features. The state of available observations is not yet quite there (TRMM resolves 25-km), making it difficult to further evaluate high horizontal resolution simulations at this time. Two other possible causes are the weaker spatial spin-up (i.e., the characteristic distance that the large-scale fluxes need to cover before developing small-scale features; Leduc and Laprise 2009) in the 30-km domain compared to the larger 90-km domain, and a weak impact of horizontal resolution on the low-level fluxes known to influence convection in the mass flux Kain-Fritsch scheme (Stensrud 2007). The fact that a coarser resolution GCM (MIROC5) more realistically simulates rainfall intensity for daily intense rainfall events than a finer resolution GCM (CCSM4) supports the idea that the physics is of first-order importance when convection is parameterized. This suggests that high-resolution GCMs do not necessarily better capture the magnitude of rainfall during intense events compared to coarse-resolution GCMs, and that advances can result from better understanding and improving the physics of GCMs without increasing the horizontal resolution. This may not hold 31

32 for convection-permitting climate models, which seem to better represent the duration and spatial extent of intense rainfall events (e.g., Kendon et al. 2012). This study demonstrates a clear interest of utilizing an RCM to simulate daily intense rainfall events over Africa. While not perfect, the RCM captures the observed spatial and temporal characteristics, suggesting realistic physical mechanisms and processes. Additional work is needed to verify to what extent the physics of daily intense rainfall events are captured by the RCM over Africa and to investigate possible changes during the 21 st century associated with global warming Acknowledgments Support from the U.S. Department of Energy Office of Science (award DE-FG02-10ER65092) is gratefully acknowledged. WRF was provided by the University Corporation for Atmospheric Research ( Simulations are performed on the high performance computing platform at the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. We also gratefully acknowledge the GCM modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the World Climate Research Program's Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP5 multi-model dataset. Support of this dataset is provided by the Office of Science, U. S. Department of Energy. We also thank two anonymous reviewers for their helpful comments

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39 Rocha A, Melo-Goncalves P, Marques C, Ferreira J, Castanheira JM (2008) High-frequency precipitation changes in southeastern Africa due to anthropogenic forcing. Int J Climatol 28: Shongwe, ME, van Oldenborgh GJ, van den Hurk BJJM, de Boer B, Coelho CAS, van Aalst MK (2009) Projected changes in mean and extreme precipitation in Africa under Global Warming. Part I: Southern Africa. J Clim 22: Shongwe ME, van Oldenborgh GJ, van den Hurk BJJM, van Aalst MK (2011) Projected changes in mean and extreme precipitation in Africa under Global Warming. Part II: East Africa. J Clim 24: Skamarock W, Klemp JB, Dudhia J, Gill D, Barker D, Duda M, Huang X, Wang W, Powers J (2008) A description of the advanced research WRF version 3. NCAR Technical Note, NCAR/TN\u ? STR, 123 pp. Stensrud DJ (2007) Parameterization schemes: keys to understanding numerical weather prediction models. Cambridge University Press, Cambridge Sun Y, Solomon S, Dai A, Portmann RW (2006) How often does it rain? J Clim 19: Sun Y, Solomon S, Dai A, Portmann RW (2007) How often will it rain? J Clim 20: Sylla MB, Gaye AT, Jenkins GS, Pal JS, Giorgi F (2010a) Consistency of projected drought over the Sahel with changes in the monsoon circulation and extremes in a regional climate model projections. J Geophys Res Atmo 115: D16108, doi: /2009jd Sylla MB, Coppola E, Mariotti L, Giorgi F, Ruti PM, Dell Aquila A, Bi X (2010b) Multiyear simulation of the African climate using a regional climate model (RegCM3) with the high resolution ERA-interim reanalysis. Clim Dyn 35:

40 Sylla MB, Gaye AT, Jenkins GS (2012a) On the fine-scale topography regulating changes in atmospheric hydrological cycle and extreme rainfall over West Africa in a regional climate model projections. Int J Geophys, doi: /2012/ Sylla MB, Giorgi F, Coppola E, Mariotti L (2012b) Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation. Int J Climatol, doi: /joc.3551 Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA (2006) Going to extremes. An intercomparison of model-simulated historical and future changes in extreme events. Clim Change 79: Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106: Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc, doi: /bams-d Vizy EK, Cook KH (2012) Mid-twenty-first-century changes in extreme events over Northern and Tropical Africa. J Clim 25: Vizy EK, Cook KH, Crétat J, Neupane N (2013) Projections of a wetter Sahel in the 21 st century from global and regional models. J Clim, doi: /jcli-d Wehner MF, Smith RL, Bala G, Duffy P (2010) The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Clim Dyn 34: Wilks DS (2011) Statistical methods in the atmospheric sciences. Academic Press, 3rd edn. ISBN

41 Williams CJR, Kniveton DR, Layberry R (2008) Influence of South Atlantic sea surface temperatures on rainfall variability and extremes over Southern Africa. J Clim 21: Williams CJR, Kniveton DR, Layberry R (2010) Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa. Theor Appl Climatol 99:

42 918 Table Captions Table 1: Table 2: Table 3: Horizontal resolution and period used for each dataset. Number of grid points in each sub-region. Total number of daily intense events in each sub-region. This number corresponds to the sum of all daily intense events occurring in all grid points within a region during the 11-yr period

43 928 Figure Captions Figure 1: Domains used for the 90- and 30-km regional simulations. Solid box denotes position of the nested 30-km domain. Topography is shown at 30-km in the nested domain and at 90-km elsewhere. Dashed boxes denote the six regions used in Section Figure 2: Mean annual frequency of daily intense rainfall events (number of events per year) for (a) TRMM, (b) GPCP, (c) RCM90, (d) RCM30, and the four (e-h) AGCMs and (i-l) AOGCMs Figure 3: Mean annual frequency of daily intense rainfall difference from TRMM for (a) GPCP, (b) RCM90, (c) RCM30, and the four (d-g) AGCMs and (h-k) AOGCMs. White dots correspond to significant differences at 95% confidence level according to a student t-test. For visibility purpose, the interval between each red dot is set to 20 grid points Figure 4: As in Fig. 2 for the average intensity of daily intense rainfall events (mm day -1 ) Figure 5: (a) Taylor diagrams of the spatial variability of the mean annual frequency of daily intense rainfall events computed over the entire analysis region shown in Fig. 2. (b) Same as (a) over Africa solely. (c-d) Same as (a-b) for the average intensity of daily intense rainfall events. The radial coordinate (black dotted lines) gives the magnitude of total standard deviation. The angular coordinate (blue dash-dot lines) gives the correlation with 43

44 TRMM, selected as the reference dataset. The distance between TRMM and each dataset is proportional to the root-mean-square error (green dashed lines) Figure 6: Frequency intensity histograms of all daily intense events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. See text for details and Fig. 1 for the placement of each region Figure 7: Figure 8: Figure 9: As in Fig. 6 for the 90- and 30-km RCM simulations. As in Fig. 6 for the four AGCMs and AOGCMs. Mean annual cycle of the spatial coverage of daily intense rainfall events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions Figure 10: Figure 11: Figure 12: As in Fig. 9 for the 90- and 30-km RCM simulations. As in Fig. 9 for the four AGCMs and AOGCMs. Taylor diagrams of the temporal variability of the mean annual cycle of the spatial 971 coverage of daily intense rainfall events over the (a) Sahel, (b) West Africa, (c) Central 44

45 Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. See Fig. 5 for the legend Figure 13: Mean annual cycle of the average rainfall amounts associated with daily intense rainfall events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions Figure 14: Figure 15: As in Fig. 13 for the 90- and 30-km RCM simulations. As in Fig. 13 for the four AGCMs and AOGCMs. 45

46 OBSERVATIONS RCM AGCMs / AOGCMs Horizontal resolution TRMM 0.25 x 0.25 GPCP 1 x 1 RCM90 90-km RCM30 30-km CCSM x 0.95 CNRM-CM5 MIROC5 1.4 x 1.4 MRI-CGCM x Table 1: Horizontal resolution and period used for each dataset. Period CMIP AMIP

47 Sahel West Central Horn of Congo Tropical AF AF AF Basin SA TRMM GPCP RCM RCM CCSM CNRM-CM5 MIROC MRI-CGCM Table 2: Number of grid points in each sub-region. 47

48 Sahel West Central Horn of Congo Tropical AF AF AF Basin SA TRMM GPCP RCM RCM CCSM4 AGCM CNRM- CM5 MIROC5 MRI- CGCM3 AOGCM AGCM AOGCM AGCM AOGCM AGCM AOGCM Table 3: Total number of daily intense events in each sub-region. This number corresponds to the sum of all daily intense events occurring in all grid points within a region during the 11-yr period. 48

49 Figure 1: Domains used for the 90- and 30-km regional simulations. Solid box denotes position of the nested 30-km domain. Topography is shown at 30-km in the nested domain and at 90-km elsewhere. Dashed boxes denote the six regions used in Section 5. 49

50 Figure 2: Mean annual frequency of daily intense rainfall events (number of events per year) for (a) TRMM, (b) GPCP, (c) RCM90, (d) RCM30, and the four (e-h) AGCMs and (i-l) AOGCMs. 50

51 Figure 3: Mean annual frequency of daily intense rainfall difference from TRMM for (a) GPCP, (b) RCM90, (c) RCM30, and the four (d-g) AGCMs and (h-k) AOGCMs. White dots correspond to significant differences at 95% confidence level according to a student t- test. For visibility purpose, the interval between each red dot is set to 20 grid points. 51

52 Figure 4: As in Fig. 2 for the average intensity of daily intense rainfall events (mm day -1 ). 52

53 Figure 5: (a) Taylor diagrams of the spatial variability of the mean annual frequency of daily intense rainfall events computed over the entire analysis region shown in Fig. 2. (b) Same as (a) over Africa solely. (c-d) Same as (a-b) for the average intensity of daily intense rainfall events. The radial coordinate (black dotted lines) gives the magnitude of total standard deviation. The angular coordinate (blue dash-dot lines) gives the correlation with TRMM, selected as the reference dataset. The distance between TRMM and each dataset is proportional to the rootmean-square error (green dashed lines). 53

54 Figure 6: Frequency intensity histograms of all daily intense events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. See text for details and Fig. 1 for the placement of each region. 54

55 Figure 7: As in Fig. 6 for the 90- and 30-km RCM simulations. 55

56 Figure 8: As in Fig. 6 for the four AGCMs and AOGCMs. 56

57 Figure 9: Mean annual cycle of the spatial coverage of daily intense rainfall events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. 57

58 Figure 10: As in Fig. 9 for the 90- and 30-km RCM simulations. 58

59 Figure 11: As in Fig. 9 for the four AGCMs and AOGCMs. 59

60 Figure 12: Taylor diagrams of the temporal variability of the mean annual cycle of the spatial coverage of daily intense rainfall events over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. See Fig. 5 for the legend. 60

61 Figure 13: Mean annual cycle of the average rainfall amounts associated with daily intense rainfall events from TRMM and GPCP over the (a) Sahel, (b) West Africa, (c) Central Africa, (d) Horn of Africa, (e) Congo Basin and (f) tropical southern Africa regions. 61

62 Figure 14: As in Fig. 13 for the 90- and 30-km RCM simulations. 62

63 Figure 15: As in Fig. 13 for the four AGCMs and AOGCMs. 63

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