Search

Climate change is expected to increase the frequency and intensity of rainfall extremes. Understanding future changes in rainfall is necessary for adaptation planning. Eastern Africa is vulnerable to rainfall extremes because of low adaptive capacity and high future population growth. Convection-permitting climate models have been found to better represent moderate (yearly) rainfall extremes than parameterized convection models, but there is limited analysis of rare extremes that occur less frequently than once per year. These events often have the largest socioeconomic impacts. We use extreme value theory and regional frequency analysis to quantify rare rainfall extremes over East Africa in a convection-permitting climate model (CP4A). We compare the results with its parameterized counterpart (P25), the Coordinated Regional Climate Downscaling Experiment for the African region (CORDEX-Africa) ensemble, and observations to understand how the convection parameterization impacts the results. We find that CP4A better matches observations than the parameterized models. With climate change, we find the parameterized convection models have unrealistically high changes in the shape parameter of the extreme value distribution, which controls the tail behavior (i.e., the most extreme events), leading to large increases in return levels of events with a return period of >20 years. This suggests that parameterized convection models may not be suitable for looking at relative changes in rare rainfall events with climate change and that convection-permitting models should be preferred for this type of work. With the more realistic CP4A, RCP8.5 end-of-century climate change leads to 1-in-100-yr events becoming 1-in-23-yr events, which will necessitate serious adaptation efforts to avoid devastating socioeconomic impacts. Significance Statement We use a new, high-resolution climate model to examine how rare extreme rainfall events in East Africa might change in the future with climate change and compare the results with those from standard-resolution climate models. We find that the standard-resolution models have unrealistically large increases in rainfall for events that occur less frequently than every 20 years. The high-resolution model is more realistic and is required to illustrate possible future changes in rare rainfall extremes. Extreme events will become more common with climate change, and in the more realistic model we show that a 1-in-100-yr event may become a 1-in-23-yr event by the end of the century if greenhouse gas emissions are not significantly reduced.

The Maritime Continent experiences some of the world’s most severe convective rainfall, with an intense diurnal cycle. Akey feature is offshore propagation of convection overnight, having peaked over land during the evening. Existing hypotheses suggest this propagation is due to the nocturnal land breeze and environmental wind causing low-level convergence; and/or gravity waves triggering convection as they propagate. We use a convection-permitting configuration of the Met Office Unified Model over Sumatra to test these hypotheses, verifying against observations from the Japanese Years of the Maritime Continent field campaign. In selected case studies there is an organized squall line propagating with the land breeze density current, possibly reinforced by convective cold pools, at ∼3 m s−1 to around 150–300 km offshore. Propagation at these speeds is also seen in a composite mean diurnal cycle. The density current is verified by observations, with offshore low-level wind and virtual potential temperature showing a rapid decrease consistent with a density current front, accompanied by rainfall. Gravity waves are identified in the model with a typical phase speed of 16 m s−1. They trigger isolated cells of convection, usually further offshore and with much weaker precipitation than the squall line. Occasionally, the isolated convection may deepen and the rainfall intensify, if the gravity wave interacts with a substantial pre-existing perturbation such as shallow cloud. The localized convection triggered by gravity waves does not generally propagate at the wave’s own speed, but this phenomenon may appear as propagation along a wave trajectory in a composite that averages over many days of the diurnal cycle.

The CORDEX Flagship Pilot Study ELVIC (climate Extremes in the Lake VICtoria basin) was recently established to investigate how extreme weather events will evolve in this region of the world and to provide improved information for the climate impact community. Here we assess the added value of the convection-permitting scale simulations on the representation of moist convective systems over and around Lake Victoria. With this aim, 10-year present-day model simulations were carried out with five regional climate models at both PARameterized (PAR) scales (12-25km) and Convection-Permitting (CP) scales (2.5-4.5km), with COSMO-CLM, RegCM, AROME, WRF and UKMO. Most substantial systematic improvements were found in metrics related to deep convection. For example, the timing of the daily maximum in precipitation is systematically delayed in CP compared to PAR models, thereby improving the agreement with observations. The large overestimation in the total number of rainy events is alleviated in the CP models. Systematic improvements were found in the diurnal cycle in Top-Of-Atmosphere (TOA) radiation and in some metrics for precipitation intensity. No unanimous improvement nor deterioration was found in the representation of the spatial distribution of total rainfall and the seasonal cycle when going to the CP scale. Furthermore, some substantial biases in TOA upward radiative fluxes remain. Generally our analysis indicates that the representation of the convective systems is strongly improved in CP compared to PAR models, giving confidence that the models are valuable tools for studying how extreme precipitation events may evolve in the future in the Lake Victoria basin and its surroundings.

The high frequency of intense convective storms means there is a great demand to improve predictions of high-impact weather across Africa. The low skill of numerical weather prediction over Africa, even for short lead times highlights the need to deliver nowcasting based on satellite data. The Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project is working to improve the nowcasting of African convective systems and so the ability to provide timely warnings.

Squall lines dominate rainfall in the West African Sahel, and evidence suggests they have increased in intensity over recent decades. Stronger wind shear may be a key driver of this trend and could continue to strengthen with climate change. However, global numerical models struggle to capture the role of shear for organised convection, making predictions of changing rainfall intensities in the Sahel uncertain. To investigate the impact of recent and possible future environmental changes, and to isolate thermodynamic effects from shear effects, idealised squall line simulations were initialised with a profile representative of the present day: this profile was then modified using trends from reanalyses and climate projections. Increased shear led to increased storm intensity and rainfall, but the effects of the thermodynamic changes dominated the effects from shear. Simulations initiated with future profiles produced shorter‐lived storms, likely due to increased convective inhibition and the absence of large‐scale convergence or synoptic variability in the idealised model. A theoretical model based on the relative inflow of convectively unstable air and moisture was found to predict bulk characteristics of the storms accurately, including mean rain rates and area‐averaged maximum vertical velocities, explaining the role of shear. However, the model is not a prognostic tool as rainfall is dependent on the storm speed, which remains a free parameter. The study shows the importance of shear to long‐term rainfall trends and highlights the need for climate models to include effects of shear to capture changes in extreme rainfall.

Current-climate precipitation and temperature extremes have been identified by decision makers in West Africa as among the more impactful weather events causing lasting socioeconomic damage. In this article, we use a plausible future-climate scenario (RCP8.5) for the end of the twenty-first century to explore the relative commonness of such extremes under global warming. The analysis presented considers what a typical day in the future climate will feel like relative to current extrema. Across much of West Africa, we see that the typical future-climate day has maximum and minimum temperatures greater than 99.5% of currently experienced values. This finding exists for most months but is particularly pronounced during the Boreal spring and summer. The typical future precipitation event has a daily rainfall rate greater than 95% of current storms. These findings exist in both a future scenario model run with and without parameterised convection, and for many of the Coupled Model Inter-comparison Project version 5 ensemble members. Additionally, agronomic monsoon onset is projected to occur later and have greater inter-annual variability in the future. Our findings suggest far more extreme conditions in future climate over West Africa. The projected changes in temperature and precipitation could have serious socioeconomic implications, stressing the need for effective mitigation given the potential lack of adaptation pathways available to decision makers.

The Hadley circulation and tropical rain belt are dominant features of African climate. Moist convection provides ascent within the rain belt, but must be parameterized in climate models, limiting predictions. Here, we use a pan-African convection-permitting model (CPM), alongside a parameterized convection model (PCM), to analyze how explicit convection affects the rain belt under climate change. Regarding changes in mean climate, both models project an increase in total column water (TCW), a widespread increase in rainfall, and slowdown of subtropical descent. Regional climate changes are similar for annual mean rainfall but regional changes of ascent typically strengthen less or weaken more in the CPM. Over a land-only meridional transect of the rain belt, the CPM mean rainfall increases less than in the PCM (5% vs 14%) but mean vertical velocity at 500 hPa weakens more (17% vs 10%). These changes mask more fundamental changes in underlying distributions. The decrease in 3-hourly rain frequency and shift from lighter to heavier rainfall are more pronounced in the CPM and accompanied by a shift from weak to strong updrafts with the enhancement of heavy rainfall largely due to these dynamic changes. The CPM has stronger coupling between intense rainfall and higher TCW. This yields a greater increase in rainfall contribution from events with greater TCW, with more rainfall for a given large-scale ascent, and so favors slowing of that ascent. These findings highlight connections between the convective-scale and larger-scale flows and emphasize that limitations of parameterized convection have major implications for planning adaptation to climate change.

Eastern Africa’s fast-growing population is vulnerable to changing rainfall and extremes. Using the first pan-African climate change simulations that explicitly model the rainfall-generating convection, we investigate both the climate change response of key mesoscale drivers of eastern African rainfall, such as sea and lake breezes, and the spatial heterogeneity of rainfall responses. The explicit model shows widespread increases at the end of the century in mean (~40%) and extreme (~50%) rain rates, whereas the sign of changes in rainfall frequency has large spatial heterogeneity (from −50% to over +90%). In comparison, an equivalent parameterized simulation has greater moisture convergence and total rainfall increase over the eastern Congo and less over eastern Africa. The parameterized model also does not capture 1) the large heterogeneity of changes in rain frequency; 2) the widespread and large increases in extreme rainfall, which result from increased rainfall per humidity change; and 3) the response of rainfall to the changing sea breeze, even though the sea-breeze change is captured. Consequently, previous rainfall projections are likely inadequate for informing many climate-sensitive decisions—for example, for infrastructure in coastal cities. We consider the physics revealed here and its implications to be relevant for many other vulnerable tropical regions, especially those with coastal convection.

Children (< 5 years) are highly vulnerable during hot weather, however the impacts of past and future warming on child mortality has never been estimated. Here, we use CMIP6 global climate models to quantify, for the first time, the heat-related child mortality that has already occurred (1995 – 2020) in Africa due to climate change, as well as estimate future burdens (2020 – 2050). By 2009, heat-related child mortality was already double what it would have been without climate change and outweighed any contributions from general improvements in development. Under a high emission scenario (SSP585) mortality will double by 2049 compared to 2005 - 2014. Mitigation will save lives; if 2050 temperature increases are kept to 1.5ºC, approx. 3900 – 6300 children could be saved annually in Africa compared to the SSP585 scenario. Our findings support the need for urgent mitigation and adaptation measures to save lives now and in the future.

Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37-75% for 2070-2100 (mean 55%, and 18-30% for 2030-2060). This is considerably higher than the +0-60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.

The Leeds Africa Climate Hackathon aimed to generate user-relevant narratives of possible future climate in East and West Africa relevant to hydroelectric power generation and agriculture respectively. Here we discuss how the virtual hackathon was organised, present the results, and examine the lessons learned from running such a hackathon. We found East African hydroelectric power generation will need to store more water during heavier rain events and cope with longer drought periods in future. Agriculture in Ghana will face a much greater possibility of severe droughts by mid-century especially if 1.5 degC global warming targets are not met.

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics, and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The Global Challenges Research Fund (GCRF) African Science for Weather Information and Forecasting Techniques (SWIFT) project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement, and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps. Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training, modeling and operational prediction, and communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather forecasting “Testbeds”—the first of their kind in Africa—which have been used to bring new evaluation tools, research insights, user perspectives, and communications pathways into a semioperational forecasting environment.

The West African monsoon (WAM) is the dominant feature of West African climate providing the majority of annual rainfall. Projections of future rainfall over the West African Sahel are deeply uncertain, with a key reason likely to be moist convection, which is typically parameterized in global climate models. Here, we use a pan-African convection-permitting simulation (CP4), alongside a parameterized convection simulation (P25), to determine the key processes that underpin the effect of explicit convection on the climate change of the central West African Sahel (12°–17°N, 8°W–2°E). In current climate, CP4 affects WAM processes on multiple scales compared to P25. There are differences in the diurnal cycles of rainfall, moisture convergence, and atmospheric humidity. There are upscale impacts: the WAM penetrates farther north, there is greater humidity over the northern Sahel and the Saharan heat low regions, the subtropical subsidence rate over the Sahara is weaker, and ascent within the tropical rain belt is deeper. Under climate change, the WAM shifts northward and Hadley circulation weakens in P25 and CP4. The differences between P25 and CP4 persist, however, underpinned by process differences at the diurnal scale and large scale. Mean rainfall increases 17.1% in CP4 compared to 6.7% in P25 and there is greater weakening in tropical ascent and subtropical subsidence in CP4. These findings show the limitations of parameterized convection and demonstrate the value that explicit convection simulations can provide to climate modelers and climate policy decision makers.

The canonical view of the Maritime Continent (MC) diurnal cycle is deep convection occurring over land during the afternoon and evening, tending to propagate offshore overnight. However, there is considerable day-to-day variability in the convection, and the mechanism of the offshore propagation is not well understood. We test the hypothesis that large-scale drivers such as ENSO, the MJO and equatorial waves, through their modification of the local circulation, can modify the direction or strength of the propagation, or prevent the deep convection from triggering in the first place. Taking a local-to-large scale approach we use in situ observations, satellite data and reanalyses for five MC coastal regions, and show that the occurrence of the diurnal convection and its offshore propagation is closely tied to coastal wind regimes we define using the k-means cluster algorithm. Strong prevailing onshore winds are associated with a suppressed diurnal cycle of precipitation; while prevailing offshore winds are associated with an active diurnal cycle, offshore propagation of convection and a greater risk of extreme rainfall. ENSO, the MJO, equatorial Rossby waves and westward mixed Rossby-gravity waves have varying levels of control over which coastal wind regime occurs, and therefore on precipitation, depending on the MC coastline in question. The large-scale drivers associated with dry and wet regimes are summarised for each location as a reference for forecasters.

International audience; Due to associated hydrological risks, there is an urgent need to provide plausible quantified changes in future extreme rainfall rates. Convection-permitting (CP) climate simulations represent a major advance in capturing extreme rainfall and its sensitivities to atmospheric changes under global warming. However, they are computationally costly, limiting uncertainty evaluation in ensembles and covered time periods. This is in contrast to the Climate Model Intercomparison Project (CMIP) 5 and 6 ensembles, which cannot capture relevant convective processes, but provide a range of plausible projections for atmospheric drivers of rainfall change. Here, we quantify the sensitivity of extreme rainfall within West African storms to changes in atmospheric rainfall drivers, using both observations and a CP projection representing a decade under the Representative Concentration Pathway 8.5 around 2100. We illustrate how these physical relationships can then be used to reconstruct better-informed extreme rainfall changes from CMIP, including for time periods not covered by the CP model. We find reconstructed hourly extreme rainfall over the Sahel increases across all CMIP models, with a plausible range of 37%–75% for 2070–2100 (mean 55%, and 18%–30% for 2030–2060). This is considerably higher than the +0–60% (mean +30%) we obtain from a traditional extreme rainfall metric based on raw daily CMIP rainfall, suggesting such analyses can underestimate extreme rainfall intensification. We conclude that process-based rainfall scaling is a useful approach for creating time-evolving rainfall projections in line with CP model behaviour, reconstructing important information for medium-term decision making. This approach also better enables the communication of uncertainties in extreme rainfall projections that reflect our current state of knowledge on its response to global warming, away from the limitations of coarse-scale climate models alone.

Tailored climate change information is essential to understand future climate risks and identify relevant adaptation strategies. However, distilling and effectively communicating decision-relevant information from climate science remains challenging. In this paper, we develop and apply an iterative stakeholder engagement approach and a Site Specific Synthesis of Projected Range (SPR), to co-produce future climate information for Africa’s largest tea producing nations - Kenya and Malawi - for the mid-and late-21st century. SPR provides a novel analysis approach, which combines long-term station observations with projections from 29 global climate models and the first convection-permitting high-resolution climate projection for Africa (CP4A). This addresses the mismatch between spatial scales of projections, large-scale modelling uncertainties and stakeholder need for site-specific information. Iterative stakeholder engagement and communication helped to build trust, allowed use of new observation data and improved visualisations of climate information for stakeholders. SPR demonstrates site-specificity in changes in all metrics, showing risks of large changes in tea crop sensitive metrics. All nine locations analysed show substantial (up to four times) increases in heatwave days and large decreases in cold nights by 2050s compared to the current climate. While tea producers are already witnessing changing climatic conditions, potential future changes will greatly affect the resilience of tea production, thereby affecting the sustainability and quality of tea production in the region. Site specific climate information iteratively co-produced with stakeholders helps them to identify location-specific adaptation strategies and investment priorities, potentially safeguarding supply-chains and millions of livelihoods.

Land–atmosphere interactions have an important influence on Amazon precipitation (P), but evaluation of these processes in climate models has so far been limited. We analysed relationships between Amazon P and evapotranspiration (ET) in the 5th Coupled Model Intercomparison Project models to evaluate controls on surface moisture fluxes and assess the credibility of regional P projections. We found that only 13 out of 38 models captured an energy limitation on Amazon ET, in agreement with observations, while 20 models instead showed Amazon ET is limited by water availability. Models that misrepresented controls on ET over the historical period projected both large increases and decreases in Amazon P by 2100, likely amplified by unrealistic land–atmosphere interactions. In contrast, large future changes in annual and seasonal-scale Amazon P were suppressed in models that simulated realistic controls on ET, due to modulating land–atmosphere interactions. By discounting projections from models that simulated unrealistic ET controls, our analysis halved uncertainty in basin-wide future P change. The ensemble mean of plausible models showed a robust drying signal over the eastern Amazon and in the dry season, and P increases in the west. Finally, we showed that factors controlling Amazon ET evolve over time in realistic models, reducing climate stability and leaving the region vulnerable to further change.

The representation of convection remains one of the most important sources of bias in global models, and evaluation methods are needed that show that models provide the correct mean state and variability, both for the correct reasons. Here we develop a novel approach for evaluating rainfall variability due to convectively coupled Kelvin waves (CCKWs) in this region. A phase cycle was defined for the CCKW cycle in OLR and used to composite rainfall anomalies. We characterize the observed (TRMM) rainfall response to CCKWs over tropical Africa in April and evaluate the performance of regional climate model (RCM) simulations: a parameterized convection simulation (P25) and the first pan-Africa convection-permitting simulation (CP4). TRMM mean rainfall is enhanced and suppressed by CCKW activity, and the occurrence of extreme rainfall and dry days is coupled with CCKW activity. Focusing on regional differences, we show for the first time that there is a dipole between West Africa and the Gulf of Guinea involving onshore/offshore shifts in rainfall, and the transition to enhanced rainfall over west equatorial Africa occurs one phase before the transition over east equatorial Africa. The global model used to drive the RCMs simulated CCKWs with mean amplitudes of 75%–82% of observations. The RCMs simulated coherent responses to the CCKWs and captured the large-scale spatial patterns and phase relationships in rainfall although the simulated rainfall response is weaker than observations and there are regional biases that are bigger away from the equator. P25 produced a closer match to TRMM mean rainfall anomalies than CP4 although the response in dry days was more closely simulated by CP4.

An observed decline in the Eastern African Long Rains from the 1980s to late 2000s appears contrary to the projected increase under future climate change. This “Eastern African climate paradox” confounds use of climate projections for adaptation planning across Eastern Africa. Here we show the decline corresponds to a later onset and earlier cessation of the long rains, with a similar seasonal maximum in area-averaged daily rainfall. Previous studies have explored the role of remote teleconnections, but those mechanisms do not sufficiently explain the decline or the newly identified change in seasonality. Using a large ensemble of observations, reanalyses and atmospheric simulations, we propose a regional mechanism that explains both the observed decline and the recent partial recovery. A decrease in surface pressure over Arabia and warmer north Arabian Sea is associated with enhanced southerlies and an earlier cessation of the long rains. This is supported by a similar signal in surface pressure in many atmosphere-only models giving lower May rainfall and an earlier cessation. Anomalously warm seas south of Eastern Africa delay the northward movement of the tropical rain-band, giving a later onset. These results are key in understanding the paradox. It is now a priority to establish the balance of mechanisms that have led to these trends, which are partially captured in atmosphere-only simulations. Springtime drying trends in East Africa can be explained by shifts in rainfall seasonality and not reductions in daily rainfall. The East African “Long Rains” — which fall during March, April and May — deliver the majority of precipitation to the region. Since the mid-1980s, observations reveal a reduction in rainfall which some climate models fail to capture. Using a suite of observations, reanalyses, and atmospheric simulations, Caroline Wainwright from the University of Reading, UK, and colleagues investigate the mechanisms behind this change. The reduction in Long Rain precipitation is characterised by a later onset and earlier cessation — and thereby a shortening — of the rainy season, attributed to a more rapid movement of the rain band associated with warmer SSTs off East Africa. Understanding these mechanisms will benefit climate projections and adaptation measures in an already-vulnerable region.

The need for the development of adaptation strategies for climate change in Africa is becoming critical. For example, infrastructure with a long lifespan now needs to be designed or adapted to account for a future climate that will be different to the past or present. There is a growing necessity for the climate information used in decision making to change from traditional science-driven metrics to decision-driven metrics. This is particularly relevant in East Africa, where limited adaptation and socio-economic capacity makes this region acutely vulnerable to climate change. Here we employ an interdisciplinary consultation process to define and analyse a number of such decision-oriented metrics. These metrics take a holistic approach, addressing the key East African sectors of agriculture, water supply, fisheries, flood management, urban infrastructure and urban health. A multi-faceted analysis of multi-model climate projections then provides a repository of user-focused information on climate change and its uncertainties, for all metrics, seasons, and two future time horizons. The spatial character and large inter-model uncertainty of changes in temperature and rainfall metrics are described, as well as those of other relevant metrics such as evaporation and solar radiation. Inter-model relationships amongst metrics are also explored, with two clear clusters forming around rainfall and temperature metrics. This latter analysis determines the extent to which model weights could, or could not, be applied across multiple climate metrics. Further work must now focus on maximising the utility of model projections, and developing tailored risk-based communication strategies.

Seasonal forecasts of rainfall are considered the priority timescale by many users in the tropics. In East Africa, the primary operational seasonal forecast for the region is produced by the Greater Horn of Africa Climate Outlook Forum (GHACOF), and issued ahead of each rainfall season. This study evaluates and compares the GHACOF consensus forecasts with dynamical model forecasts from the UK Met Office GloSea5 seasonal prediction system for the two rainy seasons. GloSea demonstrates positive skill (r = 0.69) for the short rains at 1 month lead. In contrast, skill is low for the long rains due to lack of predictability of driving factors. For both seasons GHACOF forecasts show generally lower levels of skill than GloSea. Several systematic errors within the GHACOF forecasts are identified; the largest being the tendency to over-estimate the likelihood of near normal rainfall, with over 70% (80%) of forecasts giving this category the highest probability in the short (long) rains. In a more detailed evaluation of GloSea, a large wet bias, increasing with forecast lead time, is identified in the short rains. This bias is attributed to a developing cold SST bias in the eastern Indian Ocean, driving an easterly wind bias across the equatorial Indian Ocean. These biases affect the mean state moisture availability, and could act to reduce the ability of the dynamical model in predicting interannual variability, which may also be relevant to predictions from coupled models on longer timescales.

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.

Haboob occurrence strongly impacts the annual variability of airborne desert dust in North Africa. In fact, more dust is raised from erodible surfaces in the early summer (monsoon) season when deep convective storms are common but soil moisture and vegetation cover are low. On 27 June 2018, a large dust storm is initiated over North Africa associated with an intensive westward dust transport. Far away from emission sources, dust is transported over the Atlantic for the long distance. Dust plume is emitted by a strong surface wind and further becomes a type of haboob when it merges with the southwestward deep convective system in central Mali at 0200 UTC (27 June). We use satellite observations to describe and estimate the dust mass concentration during the event. Approximately 93% of emitted dust is removed the dry deposition from the atmosphere between sources (10°N–25°N; 1°W–8°E) and the African coast (6°N–21°N; 16°W–10°W). The convective cold pool has induced large economic and healthy damages, and death of animals in the northeastern side of Senegal. ERA5 reanalysis has shown that the convective mesoscale impacts strongly the climatological location of the Saharan heat low (SHL).

Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly and high uncertainty in remote-sensing ET retrievals, the spatial and temporal patterns in Amazon ET remain poorly understood. In this study, we estimated ET over the Amazon and 10 sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff, and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from Phase 5 and Phase 6 of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6 respectively), and in situ flux tower measurements to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon Basin, a strong seasonal cycle in basin-mean ET primarily controlled by net incoming radiation, and no trend in ET over the past 2 decades. This approach has a degree of uncertainty, due to errors in each of the terms of the water budget; therefore, we conducted an error analysis to identify the range of likely values. Satellite datasets, reanalysis, and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon as well as a need to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

A coherent synoptic sequence, mostly over North Africa, is identified whereby an upper-level midlatitude trough (in November–March) excites several days of quasi-stationary near-surface warming across the Sahara, leading to rainfall events over northern Congo (NC), and perturbed weather more widely. Ahead of NC rainfall events, composite sequences first identify troughs for several days near Iberia, followed by relatively quick transfer to the Central Mediterranean (CMed). Iberia and CMed daily trough-strength indices reveal that both lead to warming and NC rainfall. Iberia trough linkages develop through West Africa and take longer to reach NC, while CMed linkages reach NC faster (2–3 days), with impact extent focused mostly south and east of CMed. Building up to the rainfall events, initial warming over the central Sahara migrates southeastward close to NC, ultimately with typical magnitude of about 1–2°C at 10–15°N. Such anomalies are statistically predictive for NC daily rainfall and associated nearby atmospheric features: anomalous low-level southerly wind and increased moisture; anomalous low-level westerly wind and vertical easterly shear to 600 hPa; increased mid-level moisture (600 hPa), which along with low-level moisture, connects northward into midlatitudes. A secondary route identified by which Iberia troughs can impact NC rainfall is through direct atmospheric teleconnection with precipitation to the west of NC, and subsequent migration of that convection eastward into NC. The eastern side of NC generally shows a small lag on western parts, and links more strongly to CMed troughs. Taken together, the lagged synoptic expression of Iberia and CMed troughs is widespread over several days, including much of North Africa (to equatorial latitudes), southwestern Asia, eastern Africa and the western Indian Ocean. Overall, these results can contribute to situational awareness for weather forecasters across the zones influenced by the troughs, while also providing a framework for climate timescale analyses.

The Boreal Summer Intraseasonal Oscillation (BSISO) is a major mode of intraseasonal variability in the Indian summer monsoon. The characteristic pattern includes northward/north-eastward propagating anomalies of convection and circulation over the Indian longitudes, and concurrent eastward propagating anomalies that move through the tropics from the equatorial Indian ocean. In the Indian monsoon region, the BSISO interacts with other processes to affect the rainfall variability on a range of spatial and temporal scales. Convection-permitting simulations are known to improve the representation of some of these smaller-scale processes, but until recently, it has not been feasible to use convection-permitting simulations to model the entire BSISO because of the temporal and spatial scales on which it occurs. Here we assess how well a global multi-year convection-permitting simulation with a coarse grid-spacing of ~10km at the equator models the BSISO. Using Empirical Orthogonal Function (EOF) analysis, we show that overall, the convection-permitting simulation does not give a substantially better representation of the BSISO, when compared with a simulation which parametrises convection. In the observations, the first two EOF eigenvectors and their Principal Component (PC) time series describe the BSISO. The characteristic northwest-to-southeast slope of the observed EOF 1 and 2 patterns is not captured in the parametrised simulation but is better captured in the convection-permitting simulation. However, the convection-permitting simulation does not capture the observed relationship between the PC1 and PC2 time series that describe the strength and phase of the BSISO. The observed pattern is of a fairly constant phase difference between the PC1 and PC2 time series, but in the convection-permitting simulation, there are periods of both negative and positive phase differences. Our results demonstrate that the BSISO is very sensitive to the representation of convection and future higher resolution runs will provide useful routes for understanding scale interactions in the BSISO.

Mean temperatures and their extremes have increased over Africa since the latter half of the 20th century and this trend is projected to continue, with very frequent, intense and often deadly heatwaves likely to occur very regularly over much of Africa by 2100. It is crucial that we understand the scale of the future increases in extremes and the driving mechanisms. We diagnose daily maximum wet bulb temperature heatwaves, which allows for both the impact of temperature and humidity, both critical for human health and survivability. During wet bulb heatwaves, humidity and cloud cover increase, which limits the surface shortwave radiation flux but increases longwave warming. It is found from observations and ERA5 reanalysis that approximately 30% of wet bulb heatwaves over Africa are associated with daily rainfall accumulations of more than 1 mm/day on the first day of the heatwave. The first ever pan-African convection-permitting climate model simulations of present-day and RCP8.5 future climate are utilised to illustrate the projected future change in heatwaves, their drivers and their sensitivity to the representation of convection. Compared to ERA5, the convection-permitting model better represents the frequency and magnitude of present-day wet bulb heatwaves than a version of the model with more traditional parameterised convection. The future change in heatwave frequency, duration and magnitude is also larger in the convective-scale simulation, suggesting CMIP-style models may underestimate the future change in wet bulb heat extremes over Africa. The main reason for the larger future change appears to be the ability of the model to produce larger anomalies relative to its climatology in precipitation, cloud and the surface energy balance.

East Africa has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which may increase soil erosion. Soil erosion estimates require information on rainfall erosivity, which is calculated using sub-daily storm characteristics that are known to be biased in traditional parameterized convection climate models. Convection-permitting climate models, which are run at higher resolution to negate the need for convection parameterisation, generally better represent rainfall intensity and frequency. We use a novel convection-permitting pan-Africa regional climate model (CP4A) to estimate rainfall erosivity in Tanzania and Malawi, and compare it to its parameterized counterpart (P25), to determine if there is a benefit to using convection permitting climate models to look at rainfall erosivity. We use 8-year historical and end-of-century RCP8.5 simulations to examine the impact of climate change on rainfall erosivity. We then apply the Revised Universal Soil Loss Equation (RUSLE), using the rainfall erosivity estimates from CP4A and P25, to calculate soil erosion in Tanzania and Malawi. The distribution of rainfall intensity and duration was closer to the TRMM rainfall observations in the convection permitting model than in the parameterized model before and after bias-correction. We found that rainfall erosivity was lower in the parameterized convection model than in CP4A due to differences in storm characteristics, even after bias-correction. These results suggest that parameterized convection regional and global climate models might under-estimate rainfall erosivity, and the associated soil erosion. We found high values of present day erosion associated with mountainous regions in Tanzania and Malawi in CP4A. Under climate change, areas at high risk of soil erosion expanded due to increases in rainfall intensity in CP4A. The levels of soil erosion were high enough to negatively impact on agricultural yields. Soil management was less effective in the future at reducing soil erosion risk than in the present day, and more extensive soil management may be required in the future to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.

The East African precipitation seasonal cycle is of significant societal importance, and yet the current generation of coupled global climate models fails to correctly capture this seasonality. The use of convective parameterization schemes is a known source of precipitation bias in such models. Recently, a high-resolution regional model was used to produce the first pan-African climate change simulation that explicitly models convection. Here, this is compared with a corresponding parameterized-convection simulation to explore the effect of the parameterization on representation of East Africa precipitation seasonality. Both models capture current seasonality, although an overestimate in September–October in the parameterized simulation leads to an early bias in the onset of the boreal autumn short rains, associated with higher convective instability and near-surface moist static energy. This bias is removed in the explicit model. Under future climate change both models show the short rains getting later and wetter. For the boreal spring long rains, the explicit convection simulation shows the onset advancing but the parameterized simulation shows little change. Over Uganda and western Kenya both simulations show rainfall increases in the January–February dry season and large increases in boreal summer and autumn rainfall, particularly in the explicit convection model, changing the shape of the seasonal cycle, with potential for pronounced socioeconomic impacts. Interannual variability is similar in both models. Results imply that parameterization of convection may be a source of uncertainty for projections of changes in seasonal timing from global models and that potentially impactful changes in seasonality should be highlighted to users.

The 2019 October-December rains over East Africa were one of the wettest seasons on record, with many locations receiving more than double the climatological rainfall, leading to floods and landslides across the region. Above average rainfall continued into January 2020. The persistently high rainfall also contributed to the locust plagues that affected much of East Africa in January 2020. Wet conditions in East Africa are typically associated with El Nino and/or positive Indian Ocean Dipole events. In October-December 2019 a warm anomaly was present in the western Indian Ocean while a cool anomaly was present in the eastern Indian Ocean (a positive Indian Ocean Dipole); conditions known to give above average rainfall over East Africa. The warm anomaly in the western Indian Ocean persisted into January 2020. Seasonal and monthly forecasts correctly predicted above average rainfall during the October-December season. January rainfall is found to be correlated with sea surface temperatures over the western Indian Ocean. Climate model projections suggest that strong positive Indian Ocean Dipole events and wet October-December seasons may become more frequent under future climate change, with associated increased risks of floods.

Constructing climate information to inform climate change risk-related decision-making is challenging and requires a rigorous interrogation and understanding of multiple lines of evidence and an assessment of the values, limits and uncertainties involved. Critically, there is no definitive approach agreed on by all climate scientists. Rather, a range of approaches and assumptions are used, with implications for robustness, reliability and uncertainty. Often these choices and assumptions are informed by the values and objectives of climate science rather than the decision context. We propose an approach, information distillation, that makes explicit and open for deliberation many of the implicit decisions and value judgements that occur throughout the process of constructing information. We argue that this approach must engage substantively with the decision context and open up choices and assumptions in a transparent manner to deliberation across climate scientists and context experts. This should ensure relevance and usability, and build understanding and trust to form an important basis for effective uptake of information. Two case studies are described demonstrating the effectiveness of these approaches and illustrating several important principles for transparent information distillation.

Global climate models struggle to simulate both the convection and cloud ice fundamental to lightning formation. We use the first convection‐permitting, future climate simulations for the lightning hot spot of Africa, at the same time utilizing an ice‐based lightning parametrization. Both the model and observations show that lightning over Africa's drier areas, as well as the moist Congo, have more lightning per rainfall than other regions. Contrary to results in the literature, the future projection shows little increase in total lightning (~107 flashes (or 2%) per degree warming). This is a consequence of increased stability reducing the number of lightning days, largely offsetting the increased graupel and updraft velocity driving an increase in lightning per lightning day. The next step is to establish if these results are robust across other models and, if combined with parametrized‐convection models, whether ensemble‐based information on the possible responses of lightning to climate change can be investigated. Plain Language Summary Lightning depends on ascending air in thunderstorms and the collision of cloud ice particles, which charge the thundercloud. Many climate models have too coarse a resolution to reliably capture these processes. We focus on Africa, which has some of the most frequent lightning in the world. We use a model that is much higher resolution than usual, and this allows us to explicitly simulate the deep convection associated with thunderstorms as well as provide more detailed representation of the distribution of cloud ice particles. Our results show that in drier regions, as well as the much wetter Congo, there is relatively more lightning per kilogram of surface rainfall than there is in other parts of the continent. Lightning does increase across the continent under climate change, but by a relatively small amount. This is despite the number of days with lightning decreasing as the lower atmosphere becomes more stable. On days with lightning, there are more lightning flashes because there is an increase in cloud ice and intensity of convection. This study gives much more detailed information about African lightning than previous work. However, it is a single simulation. Future research should look at these results across other climate models.

Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly, plus the high uncertainty and discrepancies across remote-sensing retrievals of ET, spatial and temporal patterns in this key component of the Amazon hydrological cycle remain poorly understood. In this study, we estimated ET over the Amazon and ten sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6), and in-situ flux-tower measurements, to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon basin, a strong seasonal cycle in basin-mean ET controlled by net incoming radiation, and no trend in ET over the past two decades. Satellite datasets, reanalysis and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon, and to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

Satellite nowcasting potentially provides a vital opportunity to mitigate against the risks of severe weather in tropical Africa, where population growth and climate change are exposing an ever growing number of people to weather hazards. Numerical weather prediction demonstrates limited skill for much of Africa and weather radars are rare. However, geostationary satellites provide excellent spatial and temporal coverage of the often long‐lasting convective storms that deliver heavy rain, lightning and strong winds, presenting a valuable opportunity for satellite nowcasting. Here, we evaluate the skill of satellite nowcasting products for tropical Africa: these products are routinely generated, but to our best knowledge never routinely used in tropical Africa before the Global Challenges Research Fund African SWIFT (Science for Weather Information and Forecasting Techniques) project. Focusing in particular on convective rainfall rate (CRR) and rapidly developing thunderstorm convection warning (RDT‐CW) products, we demonstrate that both are useful nowcasting tools. The CRR product produces very different rainfall climatologies for day and night in tropical Africa. This is associated with greater skill of the product during daytime, particularly for heavier rain rates. The RDT‐CW product is able to identify around 60% of heavy (>5 mm·hr−1) rainfall events with the fraction detected increasing with increasing rainfall rate. For both products, extrapolation forwards in time (up to 90 and 60 min, respectively) maintains useful skill in tropical Africa, motivating work to develop longer lead‐time nowcasts. We conclude that widespread uptake of satellite nowcasting could provide new skilful weather predictions on short time‐scales in much of tropical Africa.

Extreme rainfall is expected to increase under climate change, carrying potential socioeconomic risks. However, the magnitude of increase is uncertain. Over recent decades, extreme storms over the West African Sahel have increased in frequency, with increased vertical wind shear shown to be a cause. Drier midlevels, stronger cold pools, and increased storm organization have also been observed. Global models do not capture the potential effects of lower- to midtropospheric wind shear or cold pools on storm organization since they parameterize convection. Here we use the first convection-permitting simulations of African climate change to understand how changes in thermodynamics and storm dynamics affect future extreme Sahelian rainfall. The model, which simulates warming associated with representative concentration pathway 8.5 (RCP8.5) until the end of the twenty-first century, projects a 28% increase of the extreme rain rate of MCSs. The Sahel moisture change on average follows Clausius–Clapeyron scaling, but has regional heterogeneity. Rain rates scale with the product of time-of-storm total column water (TCW) and in-storm vertical velocity. Additionally, prestorm wind shear and convective available potential energy both modulate in-storm vertical velocity. Although wind shear affects cloud-top temperatures within our model, it has no direct correlation with precipitation rates. In our model, projected future increase in TCW is the primary explanation for increased rain rates. Finally, although colder cold pools are modeled in the future climate, we see no significant change in near-surface winds, highlighting avenues for future research on convection-permitting modeling of storm dynamics.

Extreme weather events, like heavy precipitation, heat waves, droughts and wind storms have a detrimental impact on East African societies. The Lake Victoria Basin (LVB) is especially vulnerable, since nightly storms on the lake catch fishermen by surprise. As the frequency and intensity of climate extremes is projected to further increase substantially with climate change, so do the risks, with potentially major consequences for livelihoods and policy in the LVB.The ultimate aim of the ELVIC CORDEX-FPS is to investigate how extreme weather events evolve in the future in the LVB and to provide improved probabilistic information to the impact community. ELVIC (Climate Extremes in the Lake Victoria Basin) brings together different research groups that perform simulations with multiple high-resolution regional climate models operating at the convection-permitting scale (CPS) (https://ees.kuleuven.be/elvic).As a first step towards this overall goal, the added value of the CPS on the representation of deep convective systems in Equatorial Africa was assessed. For this purpose, 10-year present-day model simulations were carried out with five regional climate models both at the CPS and at the scale where convection was parameterized, namely COSMO-CLM, RegCM, HCLIM-AROME, WRF and the Met Office Unified Model. From a comparison of model output with different observational products, no robust improvement was found for seasonal average meteorological variables. Moreover, the change in the seasonal precipitation when going to CPS differs between the models. A robust improved performance was found for deep convection, reflected in an improved representation of the daily precipitation cycle. Preliminary results also point towards an improvement in the representation of extreme precipitation. This suggests that regional climate model simulations at the convection-permitting scale are indeed relevant to assess the climate sensitivity of extreme precipitation in the Lake Victoria Basin.

Sub-Saharan Africa is one of the most food insecure regions in the world and is highly vulnerable to climate change. We use a comprehensive set of bias-corrected global (CMIP5) and regional (CORDEX-Africa) models and a new convection-permitting pan-Africa simulation (and its parameterized counterpart) to examine changes in rainfall and temperature and the impact on agricultural suitability of maize, cassava and soy in sub-Saharan Africa by 2100 (RCP8.5). This is the first time a convection-permitting projection has been used to examine agricultural suitability in Africa. Increasing temperatures and declining rainfall led to large parts of sub-Saharan Africa becoming unsuitable for multiple staple crops, which may necessitate a transition to more heat and drought resistant crops to ensure food and nutrition security. Soy was resilient to temperature increases, however maize and cassava were not, leading to declines in crop suitability. Inclusion of sensitivity to extreme temperatures led to larger declines in maize suitability than when this was excluded. The variation in rainfall projections within the multi-model ensemble was examined in detail for Tanzania, Malawi, Zambia and South Africa. In each country the range of projections included wetting and drying, but the majority of models projected rainfall declines, except in Tanzania, leading to declines in crop suitability. Overall, the CORDEX and CMIP5 models gave similar results for agricultural suitability. Explicit-convection led to more temperature extremes, but had little systematic impact on temperature and rainfall, and the resulting suitability analysis. Global model uncertainty, rather than convection parameterizations, still makes up the largest part of the uncertainty in future climate. Explicit-convection may have more impact if suitability included a more comprehensive treatment of extremes. This work highlights the key uncertainty from global climate projections for crop suitability projections, and the need for improved information on sensitivities of African crops to extremes, in order to give better predictions and make better use of the new generation of explicit-convection models.