Climate models struggle to realistically represent the West African monsoon (WAM), which hinders reliable future projections and the development of adequate adaption measures. Low-level clouds over southern West Africa (5°–10°N, 8°W–8°E) during July–September are an integral part of the WAM through their effect on the surface energy balance and precipitation, but their representation in climate models has received little attention. Here 30 (20) years of output from 18 (8) models participating in phase 5 of the Coupled Model Intercomparison Project (Year of Tropical Convection) are used to identify cloud biases and their causes. Compared to ERA-Interim reanalyses, many models show large biases in low-level cloudiness of both signs and a tendency to too high elevation and too weak diurnal cycles. At the same time, these models tend to have too strong low-level jets, the impact of which is unclear because of concomitant effects on temperature and moisture advection as well as turbulent mixing. Part of the differences between the models and ERA-Interim appear to be related to the different subgrid cloud schemes used. While nighttime tendencies in temperature and humidity are broadly realistic in most models, daytime tendencies show large problems with the vertical transport of heat and moisture. Many models simulate too low near-surface relative humidities, leading to insufficient low cloud cover and abundant solar radiation, and thus a too large diurnal cycle in temperature and relative humidity. In the future, targeted model sensitivity experiments will be needed to test possible feedback mechanisms between low clouds, radiation, boundary layer dynamics, precipitation, and the WAM circulation.

The summertime West African Sahel has the worldwide highest degree of thunderstorm organisation into long-lived, several hundred-kilometre elongated, fast propagating systems that contribute 90% to the annual rainfall. All current global weather prediction and climate models represent thunderstorms using simplified parameterisation schemes which deteriorates the modelled distribution of rainfall from individual storms and the entire West African monsoon circulation. It is unclear how this misrepresentation of Sahelian convection affects forecasts globally. Our study is the first to demonstrate how a computationally feasible increase of model resolution over West Africa – allowing to avoid convection parameterisation – yields a better representation of organised convection in the Sahel and of moisture within the monsoon system, ultimately improving 5–8-day tropical and mid-latitude weather forecasts. We advocate an operational use of a modelling strategy similar to the one presented here for a cost-effective improvement of global weather prediction and potentially even (sub-)seasonal and climate simulations.

Two extreme, high-impact events of heavy rainfall and severe floods in West African urban areas (Ouagadougou on 1 September 2009 and Dakar on 26 August 2012) are investigated with respect to their atmospheric causes and statistical return periods. In terms of the synoptic–convective dynamics, the Ouagadougou case is truly extraordinary. A succession of two slow-moving African easterly waves (AEWs) caused record-breaking values of tropospheric moisture. The second AEW, one of the strongest in recent decades, provided the synoptic forcing for the nighttime genesis of mesoscale convective systems (MCSs). Ouagadougou was hit by two MCSs within 6 h, as the strong convergence and rotation in the AEW-related vortex allowed a swift moisture refueling. An AEW was also instrumental in the overnight development of MCSs in the Dakar case, but neither the AEW vortex nor the tropospheric moisture content was as exceptional as in the Ouagadougou case. Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation data show some promise in estimating centennial return values (RVs) using the “peak over threshold” approach with a generalized Pareto distribution fit, although indications for errors in estimating extreme rainfall over the arid Sahel are found. In contrast, the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) dataset seems less suitable for this purpose despite the longer record. Notably, the Ouagadougou event demonstrates that highly unusual dynamical developments can create extremes well outside of RV estimates from century-long rainfall observations. Future research will investigate whether such developments may become more frequent in a warmer climate.

Reliable and accurate weather forecasts, particularly those of rainfall and its extremes, have the potential to improve living conditions in densely populated southern West Africa (SWA). The limited availability of observations has long impeded a rigorous evaluation of current state-of-the-art forecast models.The field campaign of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project in June-July 2016 has created an unprecedentedly dense set of measurements from surface stations and radiosondes.Here we present results from a comprehensive evaluation of both numerical model forecasts and satellite products using these data on a regional and local level. Results reveal a substantial observational uncertainty showing considerable underestimations in satellite estimates of rainfall and low-cloud cover with little correlation at the local scale. Models have a dry bias of 0.1-1.9 mm ⋅ day −1 in rainfall and too low column relative humidity. They tend to underestimate low clouds, leading to excess surface solar radiation of 43 W ⋅ m −2 . Remarkably, most models show some skill in representing regional modulations of rainfall related to synoptic-scale disturbances, while local variations in rainfall and cloudiness are hardly captured. Slightly better results are found with respect to temperature and for the post-onset rather than for the pre-onset period. Delicate local features such as the Maritime Inflow phenomenon are also rather poorly represented, leading to too cool, dry and cloudy conditions at the coast. Differences between forecast days 1 and 2 are relatively small and hardly systematic, suggesting a relatively quick error saturation. Using explicit convection leads to more realistic spatial variability in rainfall, but otherwise no marked improvement. Future work should aim at improving the subtle balance between the diurnal This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Abstract. As part of the Modular Earth Submodel System (MESSy), the Multi-Model-Driver (MMD v1.0) was developed to couple online the regional Consortium for Smallscale Modeling (COSMO) model into a driving model, which can be either the regional COSMO model or the global European Centre Hamburg general circulation model (ECHAM) (see Part 2 of the model documentation). The coupled system is called MECO(n), i.e., MESSy-fied ECHAM and COSMO models nested n times. In this article, which is part of the model documentation of the MECO(n) system, the second generation of MMD is introduced. MMD comprises the message-passing infrastructure required for the parallel execution (multiple programme multiple data, MPMD) of different models and the communication of the individual model instances, i.e. between the driving and the driven models. Initially, the MMD library was developed for a one-way coupling between the global chemistry-climate ECHAM/MESSy atmospheric chemistry (EMAC) model and an arbitrary number of (optionally cascaded) instances of the regional chemistry-climate model COSMO/MESSy. Thus, MMD (v1.0) provided only functions for unidirectional data transfer, i.e. from the larger-scale to the smallerscale models.Soon, extended applications requiring data transfer from the small-scale model back to the larger-scale model became of interest. For instance, the original fields of the larger-scale model can directly be compared to the upscaled small-scale fields to analyse the improvements gained through the smallscale calculations, after the results are upscaled. Moreover, the fields originating from the two different models might be fed into the same diagnostic tool, e.g. the online calculation of the radiative forcing calculated consistently with the same radiation scheme. Last but not least, enabling the two-way data transfer between two models is the first important step on the way to a fully dynamical and chemical two-way coupling of the various model instances.In MMD (v1.0), interpolation between the base model grids is performed via the COSMO preprocessing tool INT2LM, which was implemented into the MMD submodel for online interpolation, specifically for mapping onto the rotated COSMO grid. A more flexible algorithm is required for the backward mapping. Thus, MMD (v2.0) uses the new MESSy submodel GRID for the generalised definition of arbitrary grids and for the transformation of data between them.In this article, we explain the basics of the MMD expansion and the newly developed generic MESSy submodel GRID (v1.0) and show some examples of the abovementioned applications.

Abstract. Southern West Africa has one of the fastest-growing populations worldwide. This has led to a higher water demand and lower air quality. Over the last 3 decades, most of the region has experienced decreasing rainfall during the little dry season (LDS; mid-July to end of August) and more recently also during the second rainy season (SRS; September–October), while trends during the first rainy season (FRS; mid-May to mid-July) are insignificant. Here we analyse spatio-temporal variations in precipitation, aerosol, radiation, cloud, and visibility observations from surface stations and from space to find indications for a potential contribution of anthropogenic air pollution to these rainfall trends. The proposed mechanism is that the dimming of incoming solar radiation by aerosol extinction contributes to reducing vertical instability and thus convective precipitation. To separate a potential aerosol influence from large-scale climatic drivers, a multilinear-regression model based on sea-surface temperature (SST) indices is used. During both LDS and SRS, weakly statistically significant but accelerating negative rainfall trends unrelated to known climatic factors are found. These are accompanied by a strong increase in pollution over the upstream tropical Atlantic caused by fire aerosol from Central Africa, particularly during the LDS. Over southern West Africa, no long-term aerosol records are available, inhibiting a direct quantification of the local man-made effect. However, significant decreases in horizontal visibility and incoming surface solar radiation are strong indicators for an increasing aerosol burden, in line with the hypothesized pollution impact on rainfall. The radiation trend is further enhanced by an increase in low-level cloudiness. The large spatial extent of potentially aerosol-related trends during the LDS is consistent with the stronger monsoon flow and less wet deposition during this season. Negligible aerosol impacts during the FRS are likely due to the high degree of convective organization, which makes rainfall less sensitive to surface radiation. The overall coherent picture and the accelerating trends – some of which are concealed by SST effects – should alarm policymakers in West Africa to prevent a further increase in air pollution as this could endanger water supply and food and energy production for a large and growing population.

Abstract. Southern West Africa has one of the fastest growing populations worldwide. This has led to a higher water demand and lower air quality. Over the last three decades, most of the region has experienced decreasing rainfall during the little dry season (LDS, mid-July to end of August) and more recently also during the second rainy season (SRS, September–October), while trends during the first rainy season (FRS, mid-May to mid-July) are insignificant. Here we use spatio-temporal variations of precipitation, radiation, cloud and visibility observations from surface stations and from space to investigate whether the increased pollution has contributed to suppressing rainfall by dimming incoming solar radiation. To isolate a potential aerosol influence, a multi-linear regression model based on sea-surface temperature (SST) indices is used. During both LDS and SRS weakly statistically significant but accelerating negative rainfall trends unrelated to known climatic factors are found. These are accompanied by a strong increase of pollution over the upstream tropical Atlantic caused by fire aerosol from Central Africa, particularly during the LDS. Over southern West Africa, where no long-term aerosol records are available, significant decreases in horizontal visibility and incoming surface solar radiation are consistent with the hypothesized pollution impact. The latter trend is further enhanced by an increase in low-level cloudiness. The larger spread of potentially aerosol-related effects during the LDS is consistent with the stronger monsoon flow and less wet deposition. Negligible aerosol impacts during the FRS are likely due to the high degree of convective organization, which makes rainfall generation less sensitive to surface radiation. The overall coherent picture and the accelerating trends – some of which concealed by SST effects – should alarm policymakers in West Africa to prevent a further increase in air pollution, as this could endanger water supply, and food and energy production for a large and growing population.

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