Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models
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In the framework of the coordinated regional climate downscaling experiment (CORDEX), an ensemble of climate change projections for Africa has been created by downscaling the simulations of four global climate models (GCMs) by means of the consortium for small-scale modeling (COSMO) regional climate model (RCM) (COSMO-CLM, hereafter, CCLM). Differences between the projected temperature and precipitation simulated by CCLM and the driving GCMs are analyzed and discussed. The projected increase of seasonal temperature is found to be relatively similar between GCMs and RCM, although large differences (more than 1 °C) exist locally. Differences are also found for extreme-event related quantities, such as the spread of the upper end of the maximum temperature probability distribution function and, in turn, the duration of heat waves. Larger uncertainties are found in the future precipitation changes; this is partly a consequence of the inter-model (GCMs) variability over some areas (e.g. Sahel). However, over other regions (e.g. Central Africa) the rainfall trends simulated by CCLM and the GCMs show opposite signs, with CCLM showing a significant reduction in precipitation at the end of the century. This uncertain and sometimes contrasting behaviour is further investigated by analyzing the different models’ response to the land–atmosphere interaction and feedback. Given the large uncertainty associated with inter-model variability across GCMs and the reduced spread in the results when a single RCM is used for downscaling, we strongly emphasize the importance of exploiting fully the CORDEX-Africa multi-GCM/multi-RCM ensemble in order to assess the robustness of the climate change signal and, possibly, to identify and quantify the many sources of uncertainty that still remain.
KeywordsCOSMO-CLM Regional climate model CORDEX-Africa CMIP5 GCMs Land–atmosphere interaction
As one of the most vulnerable regions to weather and climate variability (IPCC 2007), Africa was selected as the first target region for the World Climate Research Programme CORDEX (coordinated regional climate downscaling experiment) (Giorgi et al. 2009), which aims to foster international collaboration to generate an ensemble of high-resolution historical and future climate projections at regional scale, by downscaling the global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al. 2012).
In order to properly simulate the climate of such a large and heterogeneous continent, models need to replicate correctly the many physical processes and their complex feedback over multiple temporal and spatial scales. By better representing the topographical details, coastlines, and land-surface heterogeneities, regional climate models (RCMs) allow the reproduction of small-scale processes that are unresolved by the low-resolution GCMs. In the past, RCMs have been proved to be able to reproduce the general features of the African climate over specific sub-regions, in particular South Africa (e.g. Sylla et al. 2012; Diallo et al. 2014) and, especially, West Africa (e.g. Jenkins et al. 2005; Afiesimama et al. 2006; Abiodun et al. 2008), where comprehensive efforts were undertaken in both data collecting and modeling activities, including the West African monsoon modelling and evaluation (WAMME) initiative (Druyan et al. 2010; Xue et al. 2010), the African multidisciplinary monsoon analysis (AMMA) (Redelsperger et al. 2006; Ruti et al. 2011), and the ensembles-based prediction of climate changes and their impacts (ENSEMBLES) (Paeth et al. 2011). More recently, in the framework of the CORDEX initiative, several different RCMs were employed over the whole African continent driven by ‘perfect’ lateral boundary conditions (ERA-Interim) (Nikulin et al. 2012; Endris et al. 2013; Kalognomou et al. 2013; Kim et al. 2013; Krähenmann et al. 2013; Gbobaniyi et al. 2014; Panitz et al. 2014) in order to asses the ‘structural bias’ of the models (Laprise et al. 2013): although RCMs simulate the precipitation seasonal mean and annual cycle quite accurately, large differences and biases exist amongst the models in some regions and seasons.
When RCMs are driven by GCMs, biases inherited through the lateral boundary conditions are added to those of the RCM (e.g. Hong and Kanamitsu 2014); as a result, downscaling is not always able to improve the simulation skills of large-scale GCMs although added value in downscaling GCMs is found especially in the fine scales and in the ability of RCM to simulate extreme events (e.g. Kim et al. 2002; Diallo et al. 2012; Paeth and Mannig 2012; Diaconescu and Laprise 2013; Crétat et al. 2013; Haensler et al. 2013; Laprise et al. 2013; Lee and Hong 2013; Buontempo et al. 2014; Lee et al. 2014; Giorgi et al. 2014; Dosio et al. 2015)
In this work we present the results of the application of the COSMO-CLM RCM (CCLM) in the production of climate change projections for the CORDEX-Africa domain. This work builds on two previous studies: Panitz et al. (2014) investigated the structural bias of CCLM driven by ERA-Interim (evaluation run), whereas Dosio et al. (2015) analyzed the added value of downscaling low-resolution GCMs over the present climate (historical runs). Here we complete the analysis of CCLM climate runs for CORDEX-Africa not only by analyzing the climate change projections for mean variables and extreme-events related quantities, but also by comparing CCLM results to those of the driving GCMs. In fact, several discrepancies between the results of RCMs and GCMs have been found in recent studies (e.g. Mariotti et al. 2011, 2014; Laprise et al. 2013; Teichmann et al. 2013; Bouagila and Sushama 2013; Saeed et al. 2013; Coppola et al. 2014; Buontempo et al. 2014). In some of these works, RCM runs are forced by only one GCM, whether other studies are lacking a detailed analysis of the causes of the differences between RCM and GCMs’ results. Although Mariotti et al. (2014) suggested that GCMs and RCMs discrepancies may arise from the difference in the representation of the large-scale circulation (e.g. African Easterly Waves), other studies showed that the different climate sensitivity between GCMs and RCMs is related to local processes, rather than being the effect of the boundary conditions; in particular, local processes linked to land–atmosphere interaction and parameterization play a relevant role in the simulation of e.g. the precipitation trend under global warming. This aspect will be therefore analyzed in detail.
2 Model description and simulations setup
In this study, we use the three-dimensional non-hydrostatic regional climate model COSMO-CLM (CCLM) in the same configuration described in Panitz et al. (2014) and Dosio et al. (2015). Briefly, numerical integration is performed on an Arakawa-C grid with a Runge–Kutta scheme, with a time splitting method by Wicker and Skamarock (2002). The time step is 240 s. A vertical hybrid coordinate system with 35 levels is used, with the upper most layer at 30 km above sea level. The main physical parameterizations include: the radiative transfer scheme by Ritter and Geleyn (1992); the Tiedtke parameterization of convection (Tiedtke 1989) being modified by D. Mironow (German Weather Service); a turbulence scheme (Raschendorfer 2001; Mironov and Raschendorfer 2001) based on prognostic turbulent kinetic energy closure at level 2.5 according to Mellor and Yamada (1982); a one-moment cloud microphysics scheme, a reduced version of the parameterization of Seifert and Beheng (2001); a multi layer soil model (Schrodin and Heise 2001, 2002; Heise et al. 2003); subgrid scale orography processes (Schulz 2008; Lott and Miller 1997). After a series of sensitivity runs, the lower height of the damping layer was increased from its standard value, 11 km, to the approximate height of the tropical tropopause, 18 km, in order to avoid unphysical and unrealistic results. The soil albedo was replaced by a new dataset, derived from MODIS (Moderate Resolution Imaging Spectroradiometer) (Lawrence and Chase 2007), which gives more realistic results over the deserts. A thorough description of the dynamics, numerics and physical parametrizations can be found in the model documentation (e.g. Doms 2011).
An ensemble of climate change projections has been created by downscaling the results of four GCMs from the CMIP5 climate projections, namely: the Max Plank Institute MPI-ESM-LR , the Hadley Center HadGEM2-ES , the National Centre for Meteorological Research CNRM-CM5, and EC-Earth, i.e., the Earth System Model of the EC-Earth Consortium (http://ecearth.knmi.nl/). The historical runs, forced by observed natural and anthropogenic atmospheric composition, cover the period from 1950 until 2005, whereas the projections (2006–2100) are forced by two Representative Concentration Pathways (RCP) (Moss et al. 2010; Vuuren et al. 2011), namely, RCP4.5 and RCP8.5.
Seasonal statistics are calculated for boreal winter, defined as January–February–March (JFM), and summer (July–August–September—JAS).
3.1 Temperature climatology
In this study we define the reference period (present climate) as 1981–2010, by combining the model results of the historical simulations (1981–2005) with the first five years of the projection runs (2006–2010) under RCP4.5 (results using the first five years of the RCP8.5 runs are very similar).
In JAS, in the reference period, CCLM is warmer than the GCMs over South Africa and the area above 20°N, where the downscaled results are closer to the observed temperatures than the GCMs (Dosio et al. 2015). On the other hand, CCLM is colder than the GCMs over the area between the Equator and 20°N, where the downscaling is not able to significantly add value to the low resolution simulations. At the end of the Century, both GCMs and CCLM project a strong warming, up to more than 6 °C over North Africa and the Arabian peninsula. CCLM climate signal is stronger over Central Africa and the Sahel (up to 1 and 2 °C warmer, respectively, for RCP8.5), and slightly colder over southern Africa and the Arabian peninsula.
Differences in the temperature climate signal over Africa between the driving GCM and the downscaled runs have been recently observed in several other studies (Mariotti et al. 2011; Laprise et al. 2013; Teichmann et al. 2013; Coppola et al. 2014; Buontempo et al. 2014), although the number of GCMs used was smaller (one or two, although Buontempo et al. (2014) used a large perturbed physic ensemble of the same GCM). Mariotti et al. (2011) claimed that this different climate sensitivity is related to local processes linked to land processes and parameterization. This aspect will be studied more in detail in Sect. 3.3.
Seasonal mean temperature for the reference period (1981–2010), end of the century (2071–2100 under RCP8.5) and the corresponding climate change signal (RCP8.5—reference), averaged over the evaluation regions (land points only)
Cl. Ch. signal
Cl. Ch. signal
3.2 Precipitation climatology
Figures 10 and 11 show maps of mean daily precipitation as simulated by the GCMs and CCLM runs. A thorough comparison of GCMs and CCLM results over the present climate has been already conducted by Dosio et al. (2015). Briefly, the geographical distribution of seasonal precipitation simulated by CCLM follows closely the one inherited by the GCMs (e.g the monsoon rainbelt); however, whereas GCMs tend to somehow overestimate the precipitation intensity, CCLM shows a general dry bias. Some improvement by the downscaling is evident especially over South Africa in JFM, where the GCMs wet bias is corrected. In addition, over the regions along the Gulf of Guinea it was shown that CCLM is able to better represent the bimodal distribution of the annual cycle, whereas GCMs are not able to simulate this feature and they show a unimodal distribution.
Seasonal mean precipitation for the reference period (1981–2010), end of the Century (2071–2100 under RCP8.5) and the corresponding climate change signal (RCP8.5—reference), averaged over the evaluation regions (land points only)
Cl. Ch. signal
Cl. Ch. signal
−0.14 (−0.11, −0.20)
−0.03 (0.03, −0.07)
−0.11 (−0.05, −0.15)
−0.16 (−0.08, −0.22)
−0.03 (0.02, −0.10)
0.00 (0.53, −0.34)
−0.03 (0.02, −0.03)
−0.46 (0.99, −1.15)
−0.11 (0.01, −0.29)
−0.11 (−0.08, −0.14)
−0.27 (1.50, −1.33)
−0.09 (0.01, −0.36)
0.27 (0.61, −0.21)
−0.24 (−0.16, −0.31)
−0.85 (−0.47, −1.17)
0.27 (0.44, 0.02)
−0.11 (−0.06, −0.19)
−0.74 (−0.39, −1.17)
−0.21 (−0.06, −0.31)
−0.03 (0.11, −0.17)
0.04 (0.28, −0.32)
−0.17 (−0.04, −0.17)
−1.35 (−0.63, −1.74)
−0.05 (−0.02, −0.11)
0.00 (0.08, −0.06)
−0.04 (−0.03, −0.06)
−0.21 (0.57, −0.84)
−0.09 (−0.04, −0.11)
−0.08 (−0.06, −0.11)
−0.07 (0.35, −0.42)
−0.24 (−0.19, −0.36)
−0.23 (−0.03, −0.36)
−0.21 (−0.19, −0.23)
−0.06 (0.45, −0.70)
−0.16 (−0.02, −0.18)
−0.35 (0.03, −0.73)
−0.13 (0.0, −0.10)
Significant discrepancies between GCMs and RCM precipitation signals were found also by Laprise et al. (2013), Teichmann et al. (2013), Bouagila and Sushama (2013), Saeed et al. (2013), Coppola et al. (2014), Buontempo et al. (2014), and Mariotti et al. (2014) especially over Central Africa, the Ethiopian plateau, and the coast of the Guinea gulf. Although differences in precipitation trends were related to several processes, such as large-scale circulation (African easterly Waves), local topographic detail, and response to sea surface temperature, an important role was found to be played by the different description of the land–atmosphere interaction and their feedback. This aspect will be therefore analyzed more in detail in Sect. 3.3.
3.3 Land–atmosphere interaction and inter-model variability
Soil moisture is a key variable of the climate system being both a water and energy storage, and impacting the partitioning of the incoming energy in latent and sensible heat fluxes (Seneviratne et al. 2010). For instance, a negative soil moisture anomaly can lead to an increase in surface temperature through a negative anomaly of evapotranspiration (latent heat flux). Koster et al. (2006) and Seneviratne et al. (2006) identified regions of (boreal summer) strong soil moisture/temperature coupling; our results are shown in Fig. 14. In JFM, both GCMs and CCLM results show positive correlation over sub-equatorial central Africa and negative correlation over South Africa. Regions with negative correlation are generally characterized by a soil moisture-limited evapotranspiration regime, whereas positive correlation indicates regions with energy limitation (i.e evaporative fraction is independent of the soil moisture content). At the end of the century, the geographical distribution of the correlation does not vary under either RCP scenario, although a strengthening of the negative correlation is visible in correspondence to areas of anomalous temperature increase, such as South Africa (see Fig. 2). In JAS, GCMs show negative correlation over the Sahel, the Atlas region, East and sub-equatorial Africa, whereas positive correlation is found along the coasts of Guinea, over Cameroon and the Central African Republic, and the Ethiopian Highlands. CCLM is generally in agreement with the driving GCMs, except for the western area of the gulf of Guinea, where a slightly negative correlation is found. At the end of the century, the areas of positive correlation are reduced and those of negative correlation are strengthened. Similarly to JFM, we note the correspondence between areas of strong negative correlation and anomalous temperature increase (Fig. 3).
The many processes contributing to the soil moisture/precipitation coupling are very complex, and somehow still uncertain, as models do not always agree on the sign of the feedback between evapotranspiration and precipitation (Seneviratne et al. 2010). In order to analyze the relationship between soil moisture and precipitation anomalies, and the different models’ response, we focus on three regions where: (a) CCLM and GCM show a consistent and unambiguous precipitation trend (SA_E in JAS, see Table 2); (b) CCLM and the GCMs show a markedly different sign of the precipitation climate change signal (CA_SH in JFM); (c) both CCLM and GCMs show large uncertainties in the sign of the precipitation signal (WA_N in JAS).
Time evolution of several temperature and precipitation related variables are shown for SA_E in JAS in Fig. 15, where values are reported for each single GCM and corresponding CCLM downscaled simulation. We first note that generally CCLM mean temperature is very similar to that of the driving GCM, apart for CNRM-CM5, which is colder than CCLM and the reference observational dataset (CRU) over the present climate (1989–2005). CCLM shows however a reduced temperature range (i.e the difference between maximum and minimum temperature, Tx and Tn, respectively) compared to the GCMs, especially due to overestimation of minimum temperature. Krähenmann et al. (2013) thoroughly investigated the ability of CCLM (driven by ’perfect’ boundary condition, i.e. ERA-Interim) to simulate the temperature range over Africa; over the tropics, result show a moderate warm bias in Tn but a strong warm bias in Tx, whereas the diurnal temperature range was mainly underestimated over the Sahara, due to uncertainty in the cloud cover parameterization (Kothe and Ahrens 2010) and soil thermal conductivity. Here, however, both cloud cover and shortwave radiation (rsds) are simulated similarly by CCLM and the driving GCM. Larger differences exist between CCLM and GCMs’ surface fluxes, namely latent heat flux (hfls) and sensible heat flux (hfss), with the RCM always underestimating hfls. In JAS, SA_E is a generally dry area (with present climate precipitation less than 1 mm/day see Table 2): in these conditions, evapotranspiration (and hfls) is extremely sensitive to soil moisture (soil moisture limited evapotranspiration regime) but its value and variations are too small to impact climate variability (Seneviratne et al. 2010). In addition, over South Africa the correlation between evapotranspiration and temperature is negative (Fig. 14), and hfls decreases as temperature increase under the RCP8.5 scenario, for all GCMs and CCLM runs.
Present climate precipitation is usually overestimated by the GCMs, whereas CCLM results are closer to the observed present values. As a consequence, higher-order precipitation statistics such as CDD and R95ptot (i.e., the ratio of the precipitation sum at wet days with precipitation greater than the reference, 1981–2010, 95th percentile) are also simulated better by CCLM. It is worth noting that, as pointed out by Orlowsky and Seneviratne (2011), regions that are hot-spots for dryness as defined above (i.e. with a consistent decrease in soil moisture and increase in CDD) often display decreased evapotranspiration (hfls), i.e, enhanced soil moisture limitation. This is the case for all simulations (GCMs and CCLM), which eventually show a very similar trend in the precipitation statistics and mean climate change signal (see Table 2).
On the contrary, over CA_SH in JFM CCLM and the GCMs project an opposite trend in precipitation at the end of the century (Fig. 12; Table 2), with all the RCM runs showing a decrease and all GCMs a small increase in rainfall. From Fig. 16 we note that despite differing by around 1 °C, mean temperatures simulated by CCLM and the driving GCMs are usually reasonably similar to the observation, exception being the EC-Earth simulation, noticeably too cold [see also Panitz et al. (2014)]. Larger differences between RCM and GCMs results exist for Tx and Tn, especially for CNRM-CM5 and EC-Earth. Differences in temperatures are related to a different value of the cloud cover, especially for CNRM-CM5 and HadGEM2-EA, whereas solar radiation is usually underestimated by CCLM compared to the GCMs. Also, sensible and latent heat fluxes show different behaviours: in particular hfss as simulated by CCLM tends to increase significantly at the end of the century (especially in the CNRM-CM5 and MPI-ESM-LR runs), a feature that is not shown by the GCMs. As shown in Fig. 14, CA_SH in JFM is the region where the correlation between temperature and evapotranspiration is mainly positive, i.e., not limited by soil moisture (atmosphere limited regime) and hfls increase with temperature, especially for the GCMs. For CCLM, hfls keeps more constant, but at the expenses of soil moisture, which decreases sensibly at the end of the century (Seneviratne et al. 2013).
Present climate precipitation statistics are usually in agreement with the observed values, apart for MPI-ESM-LR which overestimated sensibly the seasonal mean precipitation. However, whereas the GCMs tend to project a slight increase at the end of the century, CCLM tends to become drier with associated increase of the duration of the dry spells (CDD), which is, however, usually better simulated by CCLM in the present climate. An interesting case is MPI-ESM-LR, which overestimates precipitation over the reference period, and also projects a further increase at the end of the century. The contextual increase in the intensity of extreme rainfall (R95ptot), leads to an increase of the runoff. The differences in the hydrological cycle between MPI-ESM-LR and the downscaling RCM REMO were found to be the cause of the opposite precipitation signals over central Africa in recent works by Saeed et al. (2013) and Haensler et al. (2013).
Lastly, we analyze the case of WA_N (Sahel) in JAS, where GCMs and CCLM display a large uncertainty in the projected future precipitation trends (Fig. 12; Table 2), as a results of the different, and sometimes opposite models’ response. First, from Fig. 17 we note that present day mean temperature is simulated very differently amongst GCMs: both MPI-ESM-LR and HadGEM2-ES largely overestimate mean temperature (up to more than 2 °C) whereas CCLM is very close to the observed value. On the contrary, both EC-Earth and its downscaled simulation underestimates it. Finally, CNRM-CM5 reproduces observed temperature satisfactorily, whereas CCLM underestimates it. Large discrepancies exist between GCMs and CCLM on the value of maximum temperature, where, except for EC-Earth, Tx simulated by CCLM is very close to the mean temperature simulated by the GCMs. This may be a result of the different simulation of cloud cover and solar radiation, which are respectively over- and underestimated by CCLM compared to the driving GCM. Striking is the cloud cover value for HadGEM2-ES, which is notable lower than all the other GCMs.
When analyzing the surface fluxes and precipitation statistics, results are even more differentiated: both CNRM-CM5 and EC-Earth show an increase of hfls with temperature, and a relatively constant hfss, accompanied by a very small change in the future precipitation statistics. This may be due to the general overestimation of present climate precipitation, especially for CNRM-CM5, and consequent underestimation of CDD. As in the case of CA_SH in JFM discussed earlier, these GCMs respond as if in atmosphere limited evaoptranspiration regime. On the other hand, MPI-ESM-LR and HadGEM2-ES show a marked decrease of hfls and an increase in hfss, with resulting decrease in mean precipitation (for MPI-ESM-LR starting from the second half of the century) and marked increase in the number of CDD. These GCMs act like in a soil moisture limited evapotranspiration regime, similarly to SA_E in JAS.
CCLM responds usually as in a moisture limited evapotranspiration regime, with decreasing precipitation and hfls, and increase in hfss, with the exception of the CNRM-CM5 driven simulation, which shows an increase in the projected precipitation signal, partially due to the already overestimated rainfall in the present climate.
4 Summary and concluding remarks
In this work we presented the results of the application of the COSMO-CLM Regional Climate Model in the production of climate change projections for the CORDEX-Africa domain. We not only analyzed the climate change projections for mean variables and extreme-events related quantities, but also compared CCLM results to those of the driving GCMs, as discrepancies between the results of RCMs and GCMs have been found in several recent studies (e.g. Mariotti et al. 2011; Laprise et al. 2013; Teichmann et al. 2013; Bouagila and Sushama 2013; Saeed et al. 2013; Coppola et al. 2014; Buontempo et al. 2014).
It is found that the temperature increase projected by CCLM is overall relatively similar (with differences usually smaller than 0.25 °C) to the GCMs’ one, with CCLM usually showing a less intense warming. However, large differences (more than 1 °C) exist locally, especially over South Africa in JFM, and central Africa and the Sahel in JAS, where CCLM climate change signal is warmer than that of the driving GCMs. In addition to the different climate sensitivity between CCLM’s and GCMs’ ensemble mean, uncertainty in the projected warming is also related to the large inter- and intra-model variability, with CCLM’s uncertainty usually smaller than the GCMs’ one. As pointed out by e.g. Buontempo et al. (2014), this is somehow expected when a single RCM is used to downscale a range of different GCMs. Differences between CCLM and the driving GCMs are also found for extreme-event related quantities, such as the spread of the upper end of the maximum temperature probability distribution function and the duration of heat waves, especially over central Africa, where CCLM projects longer heat waves.
Finding a homogeneous consensus between GCMs and the RCM for future projections of precipitation is more problematic, though, as large uncertainties are found in the modelled precipitation changes; this is partly a consequence of the large inter-model (GCMs) variability over some areas (e.g. Sahel). However, over other areas (e.g. Central Africa) the GCMs and CCLM show a consistent but opposite sign in the rainfall trend, with CCLM showing a significant reduction in precipitation at the end of the century. Drought related quantities such as the change in the number of consecutive dry days are relatively similar between GCMs and CCLM. However, striking differences exist in the sign of the soil moisture anomaly, with CCLM showing a constant drying of the soil over large part of central Africa, in contrast with the GCMs. As pointed out by Seneviratne et al. (2010), regions showing a consistent and opposite change in the values of CDD and soil moisture can be regarded as hot-spots for dryness.
The importance of the different models’ response to the land–atmosphere interaction and feedback is further investigated. Over mainly dry regions, such as SA_E in JAS, evapotranspiration is extremely sensitive to soil moisture, but its value is too small to influence climate variability. The correlation between evapotranspiration and temperature is negative, with hfls decreasing as a function of temperature. Despite differences between the values of sensible and latent heat fluxes (also amongst GCMs), CCLM and GCMs show a general similar trend in the projected precipitation statistics (although the values over the present climate are better simulated by the RCM when compared to observations).
CA_SH in JFM is a region where the correlation between temperature and evapotranspiration is positive (atmosphere limited regime). In these conditions, hfls increases with temperature, especially for the GCMs. For CCLM, hfls remains more constant, but at the expenses of soil moisture, which decreases sensibly at the end of the century. As a result of the different hydrological cycle, CCLM and the CGMs show an opposite sign in the precipitation trend.
Finally, over WA_N in JAS both CCLM and GCMs show very large uncertainties in the projected precipitation trend: both CNRM-CM5 and EC-Earth show a decrease of hfls with time, and a very small change in the future precipitation statistics, which may be partly due to the overestimation of present climate precipitation and underestimation of CDD. On the other hand, MPI-ESM-LR and HadGEM2-ES show a marked decrease of hfls and an increase in hfss, with resulting decrease in mean precipitation and marked increase in the number of CDD. CCLM responds usually as in a moisture limited evapotranspiration regime, with decreasing precipitation and hfls, and increase in hfss, with the exception of the CNRM-CM5 driven simulation, which shows an increase in the projected precipitation signal.
As the African climate is strongly influenced by small scale processes, one expects that dynamically downscaling will indeed ‘add value’ to the projections of large-scale GCMs, due to the ability of RCMs to reproduce local features and heterogeneities and, in turn, better simulate higher order statistics and extreme events. However, for some areas the RCM shows behaviors in the precipitation trend that are not simply different from those of the driving GCMs, but even opposite in sign. This feature, not limited to CCLM, has been shown for other RCMs, and it is related to the different parameterization of e.g. the hydrological cycle and, in general, the different response to the soil moisture/precipitation feedbacks.
In addition, given: (a) the large uncertainty also associated with inter-model variability across GCMs, and, (b) the reduced spread in the results when a single RCM is used for downscaling, we strongly emphasize the importance of exploiting fully the CORDEX-Africa multi-GCM/multi-RCM ensemble in order to assess the robustness of the climate change signal and, possibly, to identify and quantify the many sources of uncertainty that still remain.
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Computational resources were made available by the German Climate Computing Centre (DKRZ) through support from the German Federal Ministry of Education and Research (BMBF).
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