Evaluation over the reference period (1981–2010)
Figure 1 shows the observed (GPCC_FDD, 1983–2010, with a spatial resolution of 1°, Ziese et al. 2018) and modelled spatial distribution of June–July–August (JJA) daily precipitation characteristics. Similar maps for December–January–February (DJF) are shown in SI Fig. 1. Here we focus on ensemble mean results only, whereas the uncertainty (spread) of the different ensembles are analyzed later.
The spatial distributions of seasonal mean (hereafter SM) precipitation produced by all models ensembles are qualitatively similar to each other and to the observations, although, locally, differences are visible, with the regional models capturing the details associated with a more realistic representation of the topography (e.g. Ethiopian highlands).
Differences between global and regional models are more marked when other daily precipitation characteristics are examined, such as the mean precipitation on rainy days (SDII), the number of rainy days (RR1) and the maximum daily precipitation rate (RX1day). In fact, we note that over the monsoon precipitation band, CORDEX and CORE results are closer to the observed values than CMIP5 and CMIP6 models that, in general, overestimate the number of rainy days but underestimate SDII and RX1day. Other studies have highlighted the CMIP6 GCMs difficulty in simulating accurately the West African monsoon precipitation intensities (Klutse et al. 2021) and their tendency to underestimate SDII over east Africa (Ayugi et al. 2021b). Underestimation of the extreme precipitation rates by the global models (especially CMIP5) is also reported by Coppola et al. (2021).
On the other hand, several studies showed the ability of the RCMs to better simulate, compared to the driving GCMs, higher order statistics and extreme events (e.g., Giorgi et al. 2014; Gibba et al. 2019), although first order statistics (such as seasonal mean precipitation) are not always improved by the downscaling (e.g., Dosio et al. 2015, 2019; Akinsanola and Zhou 2019) as the geographical distribution of seasonal precipitation simulated by the RCMs is strongly affected by the boundary conditions.
Several inconsistencies are found in the observed precipitation between different observational products, especially over regions where station networks are sparse (Sylla et al. 2013; Maidment et al. 2015; Diaconescu et al. 2015; Herold et al. 2017; Hua et al. 2019; Masunaga et al. 2019; Bador et al. 2020), even for mean quantities (Fig. 2). When model simulations are compared to a large ensemble of observational products including gauge-based, satellite-based and reanalysis products (which have been evaluated in Dosio et al. in review), all ensembles generally reproduce the annual cycle of monthly averaged daily precipitation over many African subregions. Model ensemble means generally lie within the uncertainty range of observed datasets, although large differences exist among individual ensemble members. However, over the Sahel region (West Sahel, SAH_W and East Sahel, SAH_E) global models tend to underestimate the precipitation peak during June–September. Over western southern Africa (SAF_W) all models overestimate December-March precipitation. None of the model ensemble means are able to reproduce the double precipitation peak over the coast of the Gulf of Guinea (GN_C) and all ensemble means underestimate (overestimate) the April (October) precipitation peak over the Horn of Africa (HRN). Similar findings are reported by Almazroui et al. (2020) and Sian et al. (2021) for CMIP6, Yang et al. (2015), Agyekum et al. (2018), Zebaze et al. (2019), and Ongoma et al. (2019) for CMIP5 and e.g., Endris et al. (2016), Favre et al. (2016) and Tamoffo et al. (2019) for CORDEX.
Individual model results can vary greatly (e.g., James and Washington (2013) and Washington et al. (2013) for CMIP5; Klutse et al. (2021) for CMIP6, Teichmann et al. (2020) and Gnitou et al. (2021) for CORE, Dosio et al. (2019) for CORDEX): generally the spread amongst regional model results is smaller than that of the global models (apart, notably, for RX1day), although this may crucially depend on the different size of the model ensembles (in particular for the CORE experiment, which has used only three CMIP5 GCMs as boundary conditions).
The comparison of modelled and observed daily precipitation indices over several subregions are shown in Fig. 3. All model ensembles perform generally satisfactorily for most indices over all regions, although global models tend to underestimate SDII and RX1day (except for southern Africa) and overestimate the number of rainy days. Kim et al. (2020) noted that CMIP6 models simulate more intense precipitation than CMIP5 models over most regions of the world; this is in agreement with our findings particularly over central and southern Africa. Akinsanola et al. (2021) noted that CMIP6 models overestimate mean precipitation but underestimate SDII, CDD and extreme precipitation rates over the Horn of Africa in March–April–May (MAM) and September–October–November (SON). Similar results for East Africa are shown by Ayugi et al. (2021b) for both CMIP5 and CMIP6. Klutse et al. (2021) showed that most CMIP6 models overestimate the frequency of wet days over the coast of the Gulf of Guinea during the West African monsoon season.
On the other hand, regional models tend to overestimate maximum daily precipitation rates, especially over the western Sahel, East Africa and southern Africa. In addition, the RCMs interquartile range is particularly large over the Sahel and GN_C, particularly for CORDEX, although the CMIP6 full range can be similar or even larger than those of the RCMs over e.g. central Africa. As mentioned previously, the ability of RCMs to add value to the driving GCM in simulating precipitation characteristics (especially higher order statistics) has been investigated in several studies (e.g. Dosio et al. 2015; Pinto et al. 2016; Nikiema et al. 2017; Fotso-Nguemo et al. 2017; Gibba et al. 2019; Tamoffo et al. 2020; Gnitou et al. 2021). It must be noted that added value, i.e. non-negligible fine-scale information that is absent in the lower resolution simulations, stems from physical mechanisms resolved at higher resolution, for either present-day or future climate (Dosio et al. 2019).
In summary, the analysis of the results presented in Figs. 1, 2 and 3 provides evidence on the ability of global and regional models in simulating different precipitation characteristics over the reference period, and hence, their fitness for purpose for generating reliable future projections. Generally, both global and regional models reproduce, in mean, the observed indices within the observational uncertainties (with few exceptions), although large differences exist amongst individual simulations, especially for precipitation extremes. There is not clear evidence of an improvement in CMIP6 performances compared to CMIP5, with both ensembles generally overestimating the number of rainy days, and consequently, underestimating the daily precipitation intensity. Although the performances of regional models depend on the driving GCMs, especially for mean quantities, and their performances for other characteristics depend on the region and index, RCMs show a tendency to better simulate e.g. SDII but to overestimate RX1day. Finally, Dosio et al. (2019) showed that, although large biases exit in e.g. the simulated position, extension and intensity of the precipitation band simulated by the CORDEX RCMs, a wet (dry) bias on the present climate does not necessarily imply a tendency towards wetter (dryer) future precipitation characteristics, making any attempt to select a ‘best-performing’ model (or class of models), or even linking future projections to simulation skills over the present climate, very challenging see also e.g., Almazroui et al. (2021).
Projection of future precipitation characteristics
Figure 4 shows the DJF projected change in precipitation indices at the end of the century under the SSP5-RCP8.5 emission scenario. Results for the other seasons are shown in the SI Figs. 3–5. Here we focus on the main similarities and disagreements across the different ensembles, thus highlighting regions where a consistent message can be drawn and those where results are contrasting. Seasonal mean precipitation in DJF is projected to exhibit a robust increase according to all model ensembles over the Horn of Africa, and parts of Angola, Kenya and Tanzania, whereas a robust decrease is projected over part of the Atlas region and the northern coasts of Morocco and Algeria. On the other hand, large discrepancies exist between global and regional models on the projected change in mean precipitation over central Africa, where both CMIP5 and CMIP6 models project a robust increase in precipitation. It should be noted that for the GCMs the increase is robust even for SSP1-RCP26 (see SI Fig. 6). In contrast, CORDEX models show mainly little change or uncertain signal, while CORE simulations show a robust decrease. Drying is projected by all ensembles over most of the western South Africa, Namibia and Botswana, but the change is robust only in the CORE simulations. Also in MAM (SI Fig. 3) the robust increase in precipitation over central Africa simulated by the global models is not present in the regional models.
Similarly, in JJA (SI Fig. 4) consistent robust drying is projected over part of the western Sahel (e.g. Senegal) and part of the Guinean region (e.g. Guinea Highland). However, while global models show wetter conditions over the eastern Sahel and the Ethiopian Highlands, CORDEX and CORE results show uncertain change and robust drying, respectively (see also Dosio et al. 2019). Consistent wetting is projected over the Horn of Africa and drying over most of Southern Africa and Madagascar for SON, with a robust increase in the length of dry spells (SI Fig. 5). Similar results have been found for CMIP6 (Almazroui et al. 2020; Ukkola et al. 2020), CORDEX (e.g. Bichet et al. 2020; Gibba et al. 2019; Dosio et al. 2019) and CORE (Teichman et al. 2020). Other studies find discrepancies between RCMs and the driving GCMs (e.g., Saeed et al. 2013; Teichmann et al. 2013; Diallo et al. 2016; Dosio and Panitz 2016; Pinto et al. 2018).
Although results for seasonal mean precipitation may be contrasting for specific regions and seasons across the different model ensembles, the change in other indices shows better agreement. For instance, for DJF all ensembles project an increase in SDII over central Africa, and an increase (decrease) in RR1 over Tanzania and the Atlas region (Mozambique). Similarly, for JJA a consistent robust decrease in RR1 is visible over the Guinean coast and western Sahel, northern coast of Algeria and Morocco and parts of South Africa (see e.g., Pinto et al. 2016 for CORDEX). Also, Ukkola et al. (2020) noted an increase in meteorological drought duration over southern Africa, Guinea and the northern African coasts in the CMIP5 and CMIP6 ensembles. Ayugi et al. (2021a) found an increase in CDD, along with an intensification of extreme precipitation over East Africa, while Moon and Ha (2020) noted a thermodynamically driven increase in precipitation rates over the monsoon regions.
It should be noted that the classification of results according to the robustness of the signal is crucially dependent on many factors, including the threshold used, the ensemble size and the modelled internal variability over the reference period, which can vary greatly amongst ensembles (see SI Fig. 2). In fact, it is evident for instance that the spatial distribution of the changes in RX1day and CDD for DJF (and, to a lesser extent, RR1 and other indices in other seasons) look remarkably similar across the ensembles, although the magnitude of the change can vary substantially.
The results of the different ensemble can also be compared by calculating the fraction of land for which the projected change in an index is robust, uncertain or non-significant, as shown in Figs. 5, 6 and 7 and SI Figs. 7, 8. This is useful to investigate the similarities and discrepancies of the information derived from different ensembles. For instance, over the Atlas region in DJF, all ensembles suggest that under SSP5-RCP8.5 the majority of the land is projected to face a robust reduction in mean precipitation (Fig. 5) and number of rainy days (Fig. 7), accompanied by longer dry spells (SI Fig. 8). Reduction in maximum precipitation intensity (SI Fig. 7) is less consistent, whereas SDII is projected to not change significantly over most of the region even under the high emission scenario (Fig. 6). Crucially, all indices show a non-significant change over most of the region under SSP1-RCP2.6, which emphasizes the benefit of implementing effective mitigation policies. Results are also consistent across all the ensembles and indices in SON for HRN, which is projected to face more frequent and intense rains, and in DJF for EAF, where, despite an increase in mean precipitation, the number of rainy days is projected to decrease over a substantial fraction of land, with a consequent increase in the length of dry spells.
For southern Africa, despite CORE projecting a decrease in mean precipitation over a vast fraction of land, in contrast to the other ensembles, all ensembles agree on an intensification of rainfall for 15–30% of land in DJF, with CORE projecting a robust reduction in rainy days and an increase in CDD over more than 80% of land, under SSP5-RCP8.5.
Results for West Africa, central Africa and the Ethiopian Highlands are less consistent. In general all ensembles agree on a robust increase in SDII over parts of these regions, in particular central Africa (both CAF_N in SON and CAF_S in DJF), where SDII (and Rx1day) is projected to increase over a large fraction of land (more than 90% for the global models, Fig. 6 and SI Fig. 7). Conversely, a reduction of the number of rainy days is projected over the majority of western Sahel (between 50 and 95% of land) by all ensembles (Fig. 7). However, other indices over these regions show contrasting results between global and regional models. In particular, while global models project a robust increase in SM over most of SAH_E and part of ETH in JJA, with up to 95% of land for CMIP6, regional models project a robust decrease. For the eastern Sahel in JJA, this projected change is due to an increase (in global models) and reduction (in RCMs) in the number of rainy days, as precipitation mean (SDII) and maximum intensity are projected to mostly increase. Over the western Sahel in JJA, mean precipitation is projected to mainly decrease, apart from CMIP6 that shows an increase over nearly 40% of the land area. However, all ensembles show a robust reduction in the number of rainy days over an area ranging from nearly 50% (CMIP6) to nearly 100% (CORE) with consequent increase of the length of dry spells.
Over parts of West and Central Africa, some ensembles show an uncertain change. As mentioned, this means that models do not agree on the sign of change, but this change is nevertheless significant for more than 2/3 of the models. Hence, for instance, over the west Sahel in JJA, all ensembles show that a large fraction of land (up to nearly 90% for CMIP6) will face a significant change in mean precipitation, although over some of this land the direction of change is uncertain.
Likewise, for some indices and regions the change is robust over a substantial fraction of land also for moderate or even low emission scenarios (especially for CMIP6). This may have important consequences for the planning of adaptation measures independently of the effectiveness of the mitigation policies.
The change in precipitation indices averaged over the subregions is shown in Figs. 8, 9 and 10 and SI Figs. 9, 10. The results for seasonal mean precipitation (Fig. 8) show that the intermodel spread is always very large, with many cases where models’ results show opposite signs in the direction of the projected change. A notable exception is the Horn of Africa in SON, where at least 75% of models in all ensembles show a positive change, which is also robust in at least 30% of land. Consistency across model ensembles in other regions is scarce; for instance, over SH_E in JJA CMIP5, CORDEX and CORE project a decrease (in terms of the ensemble means) which is robust over more than 30% of land, but CMIP6 projects an increase. Over GN_C and CAF_N in SON, and EAF in DJF CMIP5, CMIP6 and CORDEX project an increase in precipitation, but CORE a decrease. Over SAH_E and ETH in JJA and CAF_S in DJF global models project an increase in precipitation, but the RCMs a decrease.
Results for other indices are much more consistent: for instance, SDII (Fig. 9) shows a general increase in all regions (except for SAH_W from CORE) and scenarios (although the increase is robust over more than 30% of land mostly under SSP5-RCP8.5). RR1 shows a consistent increase over HRN in SON and a decrease over SAH_W in JJA and CAF_S, EAF, SAF_E and SAF_W in DJF. Also RX1day (SI Fig. 9) shows remarkable agreement amongst all ensembles, with a general tendency toward an increase over all regions, although model spread can be particularly large.
Figures 8, 9 and 10 can also be helpful to investigate the impact of the choice of the GCMs used to drive CORDEX and CORE simulations, and to answer the question of whether the RCMs results are an adequate sample of the full CMIP5 uncertainty range.
First we note that the subset of CMIP5 models used in the CORDEX runs reflects the entire CMIP5 range for SM in GN_C, CAF_N, CAF_S and ETH. However, over other regions (and for other indices) the represented range is much smaller than for the spanning CMIP5, especially over the HRN, SAH_W and southern Africa. As a consequence, the range of CORDEX projections is usually smaller than that of the full CMIP5 ensemble (apart notably for GN_C). However, for other indices the situation is different. For instance, for SDII the CORDEX range is usually comparable or even larger than for the full CMIP5 ensemble, whereas for RR1 the range is usually smaller and more similar to that of the subset of CMIP5 models used for downscaling. These results suggest that some precipitation characteristics such as the number of rainy days are critically dependent on the driving GCMs (especially in regions that are most affected by the position of the monsoon band inherited through the boundary conditions, or teleconnection patterns, see e.g. Endris et al. 2013), whereas the precipitation intensity is more dependent on the RCM parameterizations (such as convection scheme etc.). This is also generally in line with the findings of Bichet et al. (2020) who states that most of the uncertainty in CORDEX results over the Horn of Africa, coasts of North Africa, and southern Africa derives from that of the driving GCMs, whereas over the tropics and parts of the eastern Africa, most of the uncertainty results from a large dispersion across RCMs. However, Pinto et al. (2018) found that part of the disagreement in precipitation projections between GCMs and RCMs over southern Africa is due to the inconsistencies in the physical parameterizations of precipitation processes rather than inconsistencies in regional‐scale circulation patterns.
It must be noted that only one RCM (SMHI-RCA4) downscaled all ten of the GCMs used in CORDEX-Africa, whereas many other RCMs downscaled fewer GCMs, and in some cases, only one, which may impact the range of CORDEX future projections. In addition, Dosio et al. (2019) noted that where CORDEX results are uncertain, especially over Central Africa and parts of West Africa, subsampling the model ensemble (e.g. according to the RCM or the driving GCM) does not necessarily reduce the uncertainty or infer a more robust result.
For CORE, the situation is more complicated because not only is the subsample of CMIP5 downscaled very limited (3 GCMs compared to 10 for CORDEX) but also the number of RCMs used is small (3 RCMS compared to 7 in CORDEX). Despite this, for some indices and regions, the range of CORE results is comparable (and sometimes larger) to that of CORDEX, especially over GN_C and SAH_W.
This aspect is further investigated by analyzing the CORE performances in simulating present and future characteristics of the West African monsoon (Figs. 11 and 12). First we note that both CORE and CORDEX ensemble means provide a satisfactory representation of the present climate precipitation over the region, with superior results for SDII and RR1 compared to the global models (SI Fig. 11, see also Gnitou et al. 2021). However, the projected precipitation characteristics are strikingly different between CORE and all the other ensembles (Fig. 11). The global models and CORDEX show a decrease in mean precipitation between May and June over the coast of the Gulf of Guinea and an increase between July and November, especially over the Sahel (although the spatial pattern and intensity differ between CORDEX and the GCMs). The CORE simulations, on the other hand, show a marked drying over the entire monsoon precipitation band throughout the year, accompanied by a decrease in SDII between May and July that is absent in the other ensembles. The reduction of the number of rainy days, although projected by all ensembles, is also much stronger in the CORE simulations. To investigate this further, the individual CORE results are shown in Fig. 12 (for SM only). It is clear that the ensemble mean results are strongly influenced by the CLMcom-KIT-CLM5.0 simulations, all of which show marked drying throughout the year. However, the March-July drying of all nine CORE runs is stronger than the average of all the other ensembles (compare Figs. 11 and 12). Crucially, the CORE results presented by Teichmann et al. (2020) and Coppola et al. (2021) do not include the CLMcom-KIT-CLM5.0 simulations. Ashfaq et al. (2020) analyzed the ICTP-RegCM CORE runs and found a late arrival of the monsoon onset in response to warming, with the strongest delay in the start of the rainy season over the Sahel. They relate this delay to a suppression of the mostly convective pre-monsoon precipitation, linked to an increased boundary layer height over land and limited moisture supply as winds predominantly blow from the dry land regions. Dosio et al. (2020) investigated the different future precipitation over West Africa in CORDEX runs by separating the ‘dry’ from the ‘wet’ runs. They found that dry and wet models show similar patterns of the dynamic and thermodynamic terms of the moisture budget, although magnitudes are larger in the dry models. The largest discrepancies are found in the strength of the land–atmosphere coupling, with dry models showing a marked decrease in soil moisture and evapotranspiration. Also Diallo et al. (2016) highlighted the importance of the balance between evaporation and precipitation in projections over West Africa by the RCMs and their driving GCMs. By analyzing RCM runs at different resolutions over Africa, Wu et al (2020) found that the ability of RCMs to simulate precipitation (compared to their driving reanalysis) in many cases are simply related to model formulation (especially convection scheme) rather than resolution, which, however, controls the amplitude of the bias.