Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value
- First Online:
- 3.5k Downloads
In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate downscaling experiment. An ensemble of climate change projections has been created by downscaling the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.
KeywordsCOSMO-CLM regional climate model CORDEX-Africa CMIP5 GCMs Added value
Africa is one of the regions most vulnerable to weather and climate variability (IPCC 2007). Due to its low adaptive capacity, projected climate change may lead to severe impacts on many vital sectors such as agriculture, water management, and health. For these reasons, and the general lack of climate projections based on Regional Climate Downscaling tools, Africa was selected as the first target region for the World Climate Research Programme CORDEX (Coordinated Regional climate Downscaling Experiment) (Giorgi et al. 2009). CORDEX aims to foster international collaboration in order to generate an ensemble of high-resolution historical and future climate projections at regional scale, by downscaling different Global Climate Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Taylor et al. 2012).
It is very challenging for climate models to replicate the multitude of physical processes and the complexity of their feedbacks, which span multiple temporal and spatial scales, over such a large and heterogeneous continent. In fact, despite GCMs have demonstrated the ability to generally replicate the precipitation trend over the second half of the twentieth century, they may present significant deficiencies in simulating the African climate, especially complex systems like the West Africa Monsoon (WAM), which is driven by the interaction of atmosphere, ocean, and land-surface, initiated by differential heating of the ocean and land surface (e.g. Steiner et al. 2009), and also strongly related to mid-tropospheric circulation such as the African Easterly Jet (AEJ) (Cook 1999).
By using the information provided by GCMs as lateral boundary condition, limited area, high resolution climate models (Regional Climate Models-RCMs) are used to provide climate information at spatial scales much finer than the GCMs’ grid (usually of the order of hundred of km). By better representing the topographical details, coastlines, and land-surface heterogeneities, RCMs allows the reproduction of small-scale processes and details that are most useful for instance for impact assessment and adaptation policies (e.g. Wang et al. 2004; Paeth and Mannig 2012; Lee and Hong 2013).
Many studies in the past have investigated the ability of RCMs to reproduce the general features of the African climate, especially over Southern and West-Africa (e.g. Jenkins et al. 2005; Afiesimama et al. 2006; Abiodun et al. 2008), where they generally improved the climate simulations by GCMs but also shared similar biases (IPCC 2007). A comprehensive effort has been subsequently undertaken in data collecting and modeling activities focused primarily on the West Africa region, 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 RCMs driven by ’observed’ lateral boundary conditions (ERA-Interim), have been evaluated (Nikulin et al. 2012; Endris et al. 2013; Kalognomou et al. 2013; Kim et al. 2013; Gbobaniyi et al. 2013; Panitz et al. 2014) in order to asses the ’structural bias’ of the models (Laprise et al. 2013). It is shown that in general RCMs simulate the precipitation seasonal mean and annual cycle quite accurately, although individual models can exhibit significant biases in some subregions and seasons.
When RCMs are driven by GCMs, however, the downscaled climate may present even larger biases, as the ones inherited through the lateral boundary conditions are added to those introduced by the RCM by means, for instance, of model errors and parameterizations (e.g. Dosio and Paruolo 2011; Hong and Kanamitsu 2014). As downscaling is not able to improve the simulation skills of large-scale fields over those simulated by the GCMs (Castro et al. 2005; Rockel et al. 2008), it is essential to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs over the present climate, before applying them for climate projections. An increasing number of works (e.g. Kim et al. 2002; Diallo et al. 2012; Paeth and Mannig 2012; Diaconescu and Laprise 2013; Crétat et al. 2013; Laprise et al. 2013; Lee et al. 2014) have recently investigated the added value of downscaling GCMs, which is expected to be found in the fine scales and in the ability of RCM to simulate extreme events (Diaconescu and Laprise 2013).
Here we use the COSMO-CLM (CCLM) RCM over the CORDEX-Africa domain to downscale the simulations of four CMIP5 GCMs and we compare the results of CCLM to those of the driving GCMs over the present climate. It is important first to generally evaluate the ability of CCLM to reproduce the general characteristics of the African climate (e.g., seasonal distribution of temperature and precipitation, and WAM climatology) and, second, to investigate whether the downscaled simulations add value to the ones by the driving GCMs. Therefore, we focus not only on the main climate statistics, but we investigate also the ability of CCLM to reproduce precipitation variability and probability distribution functions (PDF), and, finally, indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.
The analysis of the projections of future climate change will be presented in a forthcoming work.
2 Model description, setup and observational data
In this study the three-dimensional non-hydrostatic regional climate model COSMO-CLM (CCLM) is used, in the same configuration as the ’evaluation runs’ (i.e., forced by the ERA-Interim reanalysis) described in Panitz et al. (2014). Here, therefore, only the main characteristics are only briefly described.
Numerical integration is performed on an Arakawa-C grid with a Runge-Kutta scheme, using the time splitting method by Wicker and Skamarock (2002), with a time step of 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. Also 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 simulations of four GCMs from the new CMIP5 global 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. The historical control 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.
Summary of available precipitation dataset used for the evaluation of the model’s results
Time res. (highest)
Spatial res. (deg.)
Legates and Willmott (1990)
Mitchell and Jones (2005)
Rudolf et al. (2010)
Adler et al. (2003)
Huffman et al. (2009)
Several evaluation sub-regions have been defined in the CORDEX protocol (see Fig. 1, and http://www.smhi.se/forskning/forskningsomraden/klimatforskning/1.11299), which have been used in previous single- and multi-model evaluation studies over CORDEX-Africa (e.g Nikulin et al. 2012; Laprise et al. 2013). Similarly, seasonal statistics have been calculated for boreal winter (January-February-March—JFM) and summer (July-August-September—JAS).
In this section we critically analyze the ability of CCLM, forced by different GCMs, to reproduce the principal characteristics of the African climatology. It is important to note that the skill of an RCM driven by a GGM must be viewed as the skill of the GCM-RCM combination, and as such, the difference between the forcing and the downscaled data can be used to identify added value. We first discuss the geographical and temporal distribution of mean sea level pressure (MSLP), temperature and precipitation. We also compare spatial and temporal statistics (Bias, RMSE, Pattern Correlation, and interannual variability) of seasonal temperature and precipitation. Subsequently, annual cycles of daily precipitation are investigated over selected regions, together with the WAM main characteristics. In order to compare the ability of GCMs and RCM to simulate the dominant components (temporal scales) of the annual evolution of the WAM, a singular spectral analysis (SSA) is performed for the daily precipitation time series over region WA_S (see Fig. 1) along the Gulf of Guinea. Standard deviation and PDFs of daily precipitation have been also calculated. Finally, some precipitation indices are also evaluated, such as the number of consecutive wet and dry days, and the frequency of heavy rain events.
3.1 Seasonal climatology
In JAS, both CNRM-CM5 and EC-Earth show too low values of MSLP over the majority of the land areas and oceans, whereas the Sahara Heat Low is too weak for HadGEM2-ES and MPI-ESM-LR, which may result in an underestimation of the land-sea pressure gradient and, in turn, the WAM (Brands et al. 2013). CCLM partly corrects some of the GCMs’ biases over land, especially over the Sahel and Central Africa, except for the EC-Earth downscaling, which does not show a significant improvement compared to the driving field.
In a recent work, Laprise et al. (2013) noted that MPI-ESM-LR (and CanESM2, the other GCM used in their study) tend to overestimate the Sea Surface Temperature (SST) over the Guinea Gulf and the West coast of sub-equatorial Africa. Brands et al. (2013) also noted the same overestimation of (2 m) sea temperature in all the analyzed GCMs (including MPI-ESM-LR, CNRM-CM5, and HadGEM2-ES). In addition, by analyzing the wind components at both 500 and 800 hPa they found that GCMs usually underestimate the monsoonal winds over the Sahel during the core of the WAM, producing also too strong Subtropical Jet and a too weak African Easterly Jet. These misrepresentations of the circulation are introduced as boundary conditions to CCLM, with repercussion on, for instance, the precipitation and WAM climatology, as it will be discussed.
Spatial statistics of seasonal temperature (°), averaged over continental Africa (land points only)
Table 3 shows the temporal standard deviation (calculated over the period 1989–2005) of JFM temperature, which is a measure of the interannual variability (Lee and Hong 2013; Kim et al. 2013). Both GCMs’ and RCM’s values are relatively close to the observed ones (UDEL), with CCLM slightly improving over HadGEM2-ES and MPI-ESM-LR.
Interannual variability (standard deviation) of seasonal temperature (°), averaged over continental Africa (land points only) calculated over the period 1989–2005
From this analysis it is evident that it is very difficult to discern a systematic and homogeneous improvement of performances of the downscaled simulations over the GCMs’ ones. Mariotti et al. (2011) suggest that surface temperature (especially over such a large domain as Africa) is more influenced by the RCM’s internal processes rather than the forcing through lateral boundary conditions. Our results are somehow in agreement with these findings, especially over areas, such as the Sahel, where CCLM’s bias seems to be independent of the driving GCM. Over other areas, CCLM’s own structural bias (i.e. the one of the evaluation run driven by ERA-Interim) is added to that of the driving GCM: this can result in an improvement but also in a deterioration of the temperature bias.
Spatial statistics of seasonal precipitation (mm/day), averaged over continental Africa (land points only), in JFM (upper rows) and JAS (bottom rows)
Interannual variability (standard deviation) of seasonal precipitation (mm/day), averaged over continental Africa in JFM and JAS (land points only)
The inability of RCMs to systematically and homogeneously improve GCMs’ seasonal precipitation was also noted by Mariotti et al. (2011) and Laprise et al. (2013), confirming that RCM processes (soil parameterization, convection schemes, etc.) play a larger role than the lateral boundary conditions on the precipitation distribution.
In addition, it is essential to remember, when evaluating the performance of a model, that uncertainty in the observations can be very large, especially over Africa, and the choice of the reference dataset may lead to very different results. For instance from Table 4 we note that in JFM, taking GPCP as a reference CCLM bias is always larger than that of the driving GCM; however, if TRMM is used as a reference, CCLM bias is closer to the observed one for 3 GCMs out of 4 (−0.13 mm/day for CCLM-CNRM-CM5, −0.20 mm/day for CCLM-MPI-ESM-LR, −0.06 mm/day for CCLM-EC-Earth, compared to −0.16 mm/day for TRMM). Similarly, CCLM bias is more similar to the reference one for 2 out of 4 GCMs when compared to either UDEL of CRU. In JAS, CCLM performs similarly or better than 3 GCMs out of 4 using GPCP and UDEL, and 2 GCMs out of 4 using either TRMM or CRU. This reinforces the conclusion that when evaluating a model the use of a single observational dataset may be inconclusive, or even misleading, especially in regions such as Africa where observations are very sparse (in space and time) and therefore, not always reliable. As climate change projections are increasingly relying on large ensembles of multi-model simulations, in order to spawn the entire inter-model variability, so it should be for the observational dataset when evaluating the models performances, in order to fully address the uncertainty of observations.
3.2 Annual cycles
This ability of CCLM of reproducing the bimodal distribution over the WA_S region is further investigated by performing a Singular Spectral Analysis for the precipitation daily rate.
3.3 Singular spectral analysis
For the GPCP dataset, used here as reference (results with TRMM are similar and therefore not shown), the singular spectrum becomes flat for M > 6 (black crosses in the second row); as a consequence, the precipitation time series can be significantly reconstructed by using only the first six EOFs. The result, shown in the first row of Fig. 8, is indeed very close to the original time series (Fig. 7). The three significant components (k = 1, 3, 5), (third row) correspond to a 12, 6 and 4-month oscillation, respectively.
For the GCMs (red lines and crosses), the singular spectrum becomes flat for M > 4 (M > 2 for CNRM-CM5) and the first eigenvalue is predominant. The intensity of the first (annual) RC is therefore generally overestimated, whereas both the semiannual and 4-month oscillations are much weaker than for GPCP (compare, for each column, the fourth and third row): as a consequence, the reconstructed signal shows only a unimodal structure, with no sign of the peaks in June and October.
The annual RC of CCLM is generally close to that of the driving GCM (compare the last with the fourth row); this is expected as large-scale features are directly related to the driving boundary conditions. However, the higher frequency components are closer to the observed ones, especially the 4-month one. As a result, the reconstructed signal (first row) is closer to GPCP than the GCM ones, especially for CCLM-HadGEM2-ES where both the intensity and the phase are reproduced satisfactorily. In cases of CCLM-EC-Earth and CCLM-MPI-ESM-LR the strong overestimation of the precipitation intensity seems to be related to the overestimation of the annual component, similar to that of the driving GCM, as shown by the corresponding values of the first components of the singular spectrum.
Summarizing, SSA shows that the precipitation time series simulated by CCLM in WA_S can be separated in three main components; the first one, the annual oscillation, is directly inherited from the driving GCM and affects the intensity of the resulting signal; the second component (semiannual) is somehow reproduced by the GCMs but generally underestimated, whereas CCLM results are closer to GPCP; the third component (4-month) is not present in any of the GCMs results and only CCLM is able to realistically reproduce this high frequency oscillation, and, as a consequence, the bimodal distribution of the observed precipitation.
3.4 WAM climatology
CCLM results are heterogeneous, as the influence of the driving GCM is added to the RCM’s own deficiencies. For instance, the first peak is usually largely overestimated, as a result of CCLM overestimating precipitation over the sea in the Guinea region (compare Fig. 6). However, some improvement compared to the GCMs is visible, such as the latitudinal extension of the monsoon rainbelt for CCLM-HadGEM2-ES, and the intensity and position of the summer peak over the Sahel for CCLM-CNRM-CM5. For the other GCMs, it is hard to discern a significant improvement of the WAM climatology. As stated by Laprise et al. (2013), if driven by incorrect boundary conditions, there is a limit to what RCMs are able to correct.
3.5 Variability and probability distribution function of daily precipitation
From the analysis so far we found that CCLM is able to reproduce the general African climatology (with results comparable to GCMs and other RCMs) but determining whether the downscaled simulations are consistently improving over the large-scale driving ones is not straightforward. As stated by e.g., Rockel et al. (2008) downscaling is not able to improve the simulation skills of large-scale fields over those simulated by the GCMss, and, according to Di Luca et al. (2012)), in order to add value, regional climate statistics have to contain fine scale variability that is absent on a coarser grid.
3.6 Impact-relevant precipitation indices
Impact models, such as hydrological and crop models, are affected not only by the mean spatial and temporal precipitation characteristics, but also by extreme events and higher order statistics. Therefore, here we evaluate the ability of CCLM to reproduce three impact-relevant indices, namely the number of consecutive wet (i.e., daily precipitation >1 mm) and dry days, and the number of intense precipitation events (i.e., number of rainy days when precipitation exceeds the 95th percentile). As extreme events are characterized by high spatial and temporal variability, especially at small/local scales, it is very challenging for climate models to correctly reproduce them (Crétat et al. 2013; Lee and Hong 2013).
4 Summary and concluding remarks
We presented the results of the application of the COSMO-CLM regional climate model over the CORDEX-Africa domain. This study builds on the previous work by Panitz et al. (2014) where the CCLM model driven by ERA-Interim was thoroughly evaluated, and it lays the basis for an upcoming study where the climate change simulations will be analyzed.
Here it was important first to generally evaluate the ability of CCLM to reproduce the general characteristics of the African climate (e.g., seasonal distribution of temperature and precipitation, and WAM climatology) and, second, to investigate whether the downscaled simulations add value to those of the driving GCMs. In addition, whereas previous works usually showed RCM results either forced by only one GCM or evaluated against only one observational dataset, our study involving 4 driving GCMs and several observational datasets clearly represents a step forward in a comprehensive and thorough analysis of the performances of the RCM.
It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCM): for instance, GCMs show a marked cold bias, especially in JFM, which CCLM is only partially able to correct, especially in areas such as Central and South Africa where the evaluation runs showed a slight warm bias. In the region along the Guinea Gulf and over the Sahel, regions where the temperature in the ERA-interim driven simulation was already colder than the observed one, the cold bias inherited by the GCMs in JAS is generally worsened.
Concerning precipitation, the influence of the lateral boundary condition is evident especially in JFM as most of the GCMs misplace the position of the monsoonal rain belt. As expected, the geographical distribution of seasonal precipitation as simulated by CCLM follows closely the one inherited by the GCMs. Precipitation intensity over land is therefore not always better reproduced by the RCM, which shows a general dry bias, consistent to the ’structural’ bias of the evaluation run driven by ERA-Interim. However, some improvement is evident, e.g. over South Africa in JFM, where the GCMs’ wet bias is corrected. In the WAM area it is difficult to discern a homogeneous and consistent improvement of the RCM simulations over the driving GCMs. However, by performing a SSA over the regions along the Gulf of Guinea it was shown that CCLM is able to better represent the sub-annual principal components of the precipitation time series, in turn reproducing satisfactorily the bimodal distribution of the annual cycle, whereas GCMs are not able to simulate this feature and they show a unimodal distribution.
The inability of RCMs to significantly improve (seasonal) mean climatology is somehow expected, as, in order to add value, regional climate statistics have to contain fine scale variability that would be absent on a coarser grid (Feser et al. 2011).
This may lead to the question of how much a RCM can be trusted in the representation of the extreme climate if the mean feature of the climate are not better (and in some case even worse) than a GCM? We believe that the fact that CCLM may not add significant value to the representation of the general climatology over Africa depends on several factors, including the biases inherited by the driving GCM (e.g., the misrepresentation of the monsoon rainbelt in JFM), structural biases of the RCM (e.g., the dry bias over land, which may be related to soil parameterization), and, last but not least, the choice of the observational dataset (e.g. CCLM scores better when compared to TRMM rather than GPCP). However, by analyzing the Standard Deviation and probability distribution function of daily precipitation, we have shown that CCLM’s results are clearly closer to observations (especially high-resolution ones, such as TRMM) than the GCMs’ ones, and both tails of the PDF are better reproduced by CCLM over all the evaluation areas. Furthermore, it was shown that CCLM is able to better simulate some precipitation indices such as the number of consecutive wet and dry days, and the frequency of heavy rain event: these are indeed the areas where added value is expected to be found, and, therefore, supposedly most reliable when looking at climate change projections. Although some issue remain open to further research such as, for instance, the parameterization of convective precipitation, which may play a relevant role especially in tropical regions, we demonstrated that RCMs are useful tools for the generation of climate change projections, especially for those characteristics that may be more relevant to impact and adaptation communities.
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 U.S. 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).
- Adler R, Huffman G, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Elkin E (2003) The version 2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
- Davies H (1983) Limitations of some common lateral boundary schemes used in regional NWP models. Mon Weather Rev 111:1002–1012. doi:10.1175/1520-0493(1983)111<1002:LOSCLB>2.0.CO;2
- Doms G (2011) A description of the nonhydrostatic regional COSMO model part 1: dynamics and numerics. DWD, Offenbach, Germany, http://www.cosmo-model.org/content/model/documentation/core/default.htm
- Endris HS, Omondi P, Jain S, Lennard C, Hewitson B, Chang’a L, Awange JL, Dosio A, Ketiem P, Nikulin G, Panitz HJ, Büchner M, Stordal F, Tazalika L (2013) Assessment of the performance of CORDEX regional climate models in simulating east African rainfall. J Clim 26(21):8453–8475. doi:10.1175/JCLI-D-12-00708.1 CrossRefGoogle Scholar
- Gbobaniyi E, Sarr A, Sylla MB, Diallo I, Lennard C, Dosio A, Dhiédiou A, Kamga A, Klutse NAB, Hewitson B, Nikulin G, Lamptey B (2014) Climatology, annual cycle and interannual variability of precipitation and temperature in CORDEX simulations over West Africa. Int J Climatol 34:2241–2257. doi:10.1002/joc.3834 CrossRefGoogle Scholar
- Giorgi F, Jones C, Asrar G (2009) Addressing climate information needs at the regional level: the CORDEX framework. World Meteorol Organ (WMO) Bull 58(July):175–183Google Scholar
- Hassani H (2007) Singular spectrum analysis: methodology and comparison. J Data Sci 5:239–257Google Scholar
- Heise E, Lange M, Ritter B, Schrodin R (2003) Improvement and validation of the multilayer soil model. COSMO Newsl 3:198–203Google Scholar
- IPCC (2007) Climate change 2007: the physical science basis: contribution of Working Group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USAGoogle Scholar
- Kim J, Waliser DE, Mattmann CA, Goodale CE, Hart AF, Zimdars PA, Crichton DJ, Jones C, Nikulin G, Hewitson B, Jack C, Lennard C, Favre A (2013) Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors. Clim Dynam. doi:10.1007/s00382-013-1751-7
- Lee JW, Hong SY (2013) Potential for added value to downscaled climate extremes over Korea by increased resolution of a regional climate model. Theor Appl Climatol. doi:10.1007/s00704-013-1034-6
- Mariotti L, Coppola E, Sylla MB, Giorgi F, Piani C (2011) Regional climate model simulation of projected 21st century climate change over an all-Africa domain: comparison analysis of nested and driving model results. J Geophys Res 116(d15): doi:10.1029/2010JD015068
- Mironov D, Raschendorfer M (2001) Evaluation of empirical parameters of the new LM surface-layer parameterization scheme: results from numerical experiments including soil moisture analysis. COSMO technical report 1, DWD, Offenbach, GermanyGoogle Scholar
- Nikulin G, Jones C, Giorgi F, Asrar G, Büchner M, Cerezo-Mota R, Christensen OBs, Déqué M, Fernandez J, Hänsler A, van Meijgaard E, Samuelsson P, Sylla MB, Sushama L (2012) Precipitation climatology in an ensemble of CORDEX-Africa regional climate simulations. J Clim 25(18):6057–6078. doi:10.1175/JCLI-D-11-00375.1 CrossRefGoogle Scholar
- Paeth H, Hall NM, Gaertner MA, Alonso MD, Moumouni S, Polcher J, Ruti PM, Fink AH, Gosset M, Lebel T, Gaye AT, Rowell DP, Moufouma-Okia W, Jacob D, Rockel B, Giorgi F, Rummukainen M (2011) Progress in regional downscaling of west African precipitation. Atmos Sci Lett 12(1):75–82. doi:10.1002/asl.306 CrossRefGoogle Scholar
- Panitz HJ, Dosio A, Büchner M, Lüthi D, Keuler K (2014) COSMO-CLM (CCLM) climate simulations over CORDEX-Africa domain: analysis of the ERA-Interim driven simulations at 0.44 and 0.22 resolution. Clim Dynam. doi:10.1007/s00382-013-1834-5
- Rangarajan G (1994) Singular spectral analysis of homogeneous Indian monsoon (HIM) rainfall. Proc Indian Acad Sci Earth 4:439–448Google Scholar
- Raschendorfer M (2001) The new turbulence parameterization of LM. COSMO Newsl 1:90–98Google Scholar
- Rudolf B, Becker A, Schneider U, Meyer-christoffer A, Ziese M (2010) The new GPCC full data reanalysis version 5 providing high-quality gridded monthly precipitation data for the global land-surface is public available since December 2010. GPCC Status Report (December):1–7Google Scholar
- Ruti PM, Williams JE, Hourdin F, Guichard F, Boone A, Van Velthoven P, Favot F, Musat I, Rummukainen M, Domínguez M, Gaertner MA, Lafore JP, Losada T, Polcher J, Giorgi F, Xue Y, Bouarar I, Law K, Josse B, Barret B, Yang X, Mari C, Traore AK (2011) The West African climate system: a review of the AMMA model inter-comparison initiatives. Atmos Sci Lett 12(1):116–122. doi:10.1002/asl.305 CrossRefGoogle Scholar
- Schrodin R, Heise E (2001) The multi-layer version of the DWD soil model TERRA-LM. COSMO technical report 2, DWD, Offenbach, GermanyGoogle Scholar
- Schrodin R, Heise E (2002) A new multi-layer soil-model. COSMO Newsl 2:139–151Google Scholar
- Schulz JP (2008) Introducing sub-grid scale orographic effects in the COSMO model. COSMO Newsl 9:29–36Google Scholar
- Sylla MB, Giorgi F, Coppola E, Mariotti L (2012) Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation. Int J Climatol 33(7):n/a-n/a. doi:10.1002/joc.3551
- Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque JF, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109(1–2):5–31. doi:10.1007/s10584-011-0148-z CrossRefGoogle Scholar
- Wicker LJ, Skamarock WC (2002) Time-splitting methods for elastic models using forward time schemes. Mon Weather Rev 130(8):2088–2097. doi:10.1175/1520-0493(2002)130<2088:TSM>2.0.CO;2
- Xue Y, Sales F, Lau WKM, Boone A, Feng J, Dirmeyer P, Guo Z, Kim KM, Kitoh A, Kumar V, Poccard-Leclercq I, Mahowald N, Moufouma-Okia W, Pegion P, Rowell DP, Schemm J, Schubert SD, Sealy A, Thiaw WM, Vintzileos A, Williams SF, Wu MLC (2010) Intercomparison and analyses of the climatology of the West African Monsoon in the West African monsoon modeling and evaluation project (WAMME) first model intercomparison experiment. Clim Dynam 35(1):3–27. doi:10.1007/s00382-010-0778-2 CrossRefGoogle Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.