Africa Rainfall Climatology Research Articles

Evolution and copula modelling of drought duration and severity over Africa using CORDEX‐CORE regional climate models

AbstractThis study assessed the dependence of drought severity and duration across four subregions (Southern, Western, Eastern and Central) in Africa using copula modelling. The analysis was carried out for the reference period 1991–2020 and the future period 2071–2099. Simulated daily precipitation at a horizontal resolution of 0.22° were obtained from three regional climate models (RCMs) participating in the Coordinated Output for Regional Evaluations within the Coordinated Regional Downscaling Experiment (CORDEX‐CORE). The RCMs were downscaled by three global climate models and validated using three high‐resolution gridded daily precipitation products obtained from The Climate Hazards Group InfraRed Precipitation with Stations data (CHIRPS), Climate Prediction Center Africa Rainfall Climatology Version 2.0 (CPC‐ARC2) and Tropical Applications of Meteorology using SATellite data and ground‐based observations (TAMSAT). Comparison of precipitation from the RCMs with the three gridded reference observations shows relatively good performance across the different regions with median value within the range of 3–5 mm·day−1 in Central Africa, 1.5–4.5 mm·day−1 in Western Africa, 1.8–2.8 mm·day−1 in Eastern Africa and 0.5–2.2 mm·day−1 in Southern Africa. On the other hand, the correlation values of drought duration–severity from CORDEX‐CORE models in the different regions of Africa exhibit predominantly strong values greater than 0.8 for both historical and projected climate. The analysis also considered two families of copulas: Archimedean (Frank, Clayton, Gumbel) and Elliptical (Gaussian, Student's t). The performance of the copula functions were estimated using the Akaike information criteria (AIC). Generally, there is a good agreement in the distribution of observed precipitation among the three observational data products across the subregion of Africa, with slight differences attributed to the different processing algorithms of the products. Across West Africa, mean precipitation values from CPC, CHIRPS and TAMSAT was 2.86, 3.16 and 3.07 mm·day−1, respectively. The CCLM‐NCC underestimated the mean values of precipitation reported in the observation data, while CCLM‐MPI and CCLM‐HAD overestimated the mean values of precipitation in the West Africa region.

Malaria smear positivity among Kenyan children peaks at intermediate temperatures as predicted by ecological models

BackgroundAmbient temperature is an important determinant of malaria transmission and suitability, affecting the life-cycle of the Plasmodium parasite and Anopheles vector. Early models predicted a thermal malaria transmission optimum of 31 °C, later revised to 25 °C using experimental data from mosquito and parasite biology. However, the link between ambient temperature and human malaria incidence remains poorly resolved.MethodsTo evaluate the relationship between ambient temperature and malaria risk, 5833 febrile children (<18 years-old) with an acute, non-localizing febrile illness were enrolled from four heterogenous outpatient clinic sites in Kenya (Chulaimbo, Kisumu, Msambweni and Ukunda). Thick and thin blood smears were evaluated for the presence of malaria parasites. Daily temperature estimates were obtained from land logger data, and rainfall from National Oceanic and Atmospheric Administration (NOAA)’s Africa Rainfall Climatology (ARC) data. Thirty-day mean temperature and 30-day cumulative rainfall were estimated and each lagged by 30 days, relative to the febrile visit. A generalized linear mixed model was used to assess relationships between malaria smear positivity and predictors including temperature, rainfall, age, sex, mosquito exposure and socioeconomic status.ResultsMalaria smear positivity varied between 42–83% across four clinic sites in western and coastal Kenya, with highest smear positivity in the rural, western site. The temperature ranges were cooler in the western sites and warmer in the coastal sites. In multivariate analysis controlling for socioeconomic status, age, sex, rainfall and bednet use, malaria smear positivity peaked near 25 °C at all four sites, as predicted a priori from an ecological model.ConclusionsThis study provides direct field evidence of a unimodal relationship between ambient temperature and human malaria incidence with a peak in malaria transmission occurring at lower temperatures than previously recognized clinically. This nonlinear relationship with an intermediate optimal temperature implies that future climate warming could expand malaria incidence in cooler, highland regions while decreasing incidence in already warm regions with average temperatures above 25 °C. These findings support efforts to further understand the nonlinear association between ambient temperature and vector-borne diseases to better allocate resources and respond to disease threats in a future, warmer world.

Performance of satellite-based and GPCC 7.0 rainfall products in an extremely data-scarce country in the Nile Basin

The rain gage networks in the African countries are notorious for their poor density and low frequency of observations. Rainfall products based on satellite estimates and/or ground observations have proven to be a viable alternative in the recent evolving campaigns to overcome this deficiency. The newly-borne country within the Nile Basin, South Sudan, has extremely few operating rain gage stations. Herein, evaluation of six long-term (1983 onward) rainfall products, i.e. the Global Precipitation Climatology Centre full data reanalysis version 7.0 (GPCC 7.0) and five Satellite-Rainfall Products (SRPs), is undertaken. Data from the only currently operating long-term stations (five) with reasonably up-to-date records are used to conduct point-to-pixel evaluation for the six products (from 1983 to 2010). The results of error and linear fit metrics rank GPCC 7.0 as the best performing product on monthly, maximum monthly, and annual scales, followed by Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS v2.0). As regards the variability of annual rainfall, GPCC 7.0 outperforms the products whereas the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) and CHIRPS v2.0 have the second-best performance. GPCC 7.0 and the Multi-Source Weighted-Ensemble Precipitation version 2.0 (MSWEP 2.0) show better agreement of variability of monthly rainfall with that of the station rainfall. The Africa Rainfall Climatology version 2.0 (ARC2) performs the best in capturing the variability in the maximum monthly rainfall followed by GPCC 7.0. In relation to capturing the median rainfall, a complex performance is evident across the stations with the following remarks: relatively good performance from MSWEP 2.0 on the annual scale followed by PERSIANN-CDR; GPCC 7.0 (PERSIANN-CDR) is mostly (relatively) operational for the median maximum monthly rainfall; GPCC 7.0 (CHIRSPS v2.0) is mostly (partly) suitable as an estimator for the monthly median. All the present SRPs unequivocally under-estimate the monthly peaks. Enhancing the rainfall estimation and observation network is key to improving the understanding and modeling of the hydrological processes and phenomena occurring in the basin in general and in the floodplains of the country in particular.

Evaluation of multiple climate data sources for managing environmental resources in East Africa

Abstract. Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.

East Africa Rainfall Trends and Variability 1983–2015 Using Three Long-Term Satellite Products

Daily time series from the Climate Prediction Center (CPC) Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and Tropical Applications of Meteorology using SATellite (TAMSAT) African Rainfall Climatology And Time series version 2 (TARCAT) high-resolution long-term satellite rainfall products are exploited to study the spatial and temporal variability of East Africa (EA, 5S–20N, 28–52E) rainfall between 1983 and 2015. Time series of selected rainfall indices from the joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices are computed at yearly and seasonal scales. Rainfall climatology and spatial patterns of variability are extracted via the analysis of the total rainfall amount (PRCPTOT), the simple daily intensity (SDII), the number of precipitating days (R1), the number of consecutive dry and wet days (CDD and CWD), and the number of very heavy precipitating days (R20). Our results show that the spatial patterns of such trends depend on the selected rainfall product, as much as on the geographic areas characterized by statistically significant trends for a specific rainfall index. Nevertheless, indications of rainfall trends were extracted especially at the seasonal scale. Increasing trends were identified for the October–November–December PRCPTOT, R1, and SDII indices over eastern EA, with the exception of Kenya. In March–April–May, rainfall is decreasing over a large part of EA, as demonstrated by negative trends of PRCPTOT, R1, CWD, and R20, even if a complete convergence of all satellite products is not achieved.

Evaluation of satellite rainfall estimates over the Lake Tana basin at the source region of the Blue Nile River

Satellite rainfall estimates (SREs) have become alternative sources of rainfall data for several applications. However, the accuracy of the SREs is likely to vary from region to region and must be evaluated on a local basis. This study evaluated the accuracy of three SREs for the Lake Tana basin in northwestern Ethiopia. This basin is characterized by complex topography comprising both lowlands and highlands. The three SREs were the Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), the Climate Hazard Infrared Precipitation with Stations (CHIRPS), and the Africa Rainfall Climatology (ARC). The SREs were compared with gauge measurements in lowland and highland regions during the period 1995–2010 on a point-to-pixel basis at daily, dekadal (10 days), monthly, and seasonal periodicities. The results show that the three SREs underestimated rainy events, but TAMSAT captured rainfall occurrence relatively well in both regions. ARC better estimated light rain rates (1–5 mm d−1) than did TAMSAT and CHIRPS; however, all the SREs markedly underestimated moderate and heavier rain rates (≥10 mm d−1). TAMSAT and CHIRPS estimated the amount of rainfall reasonably well (high efficiency, low random errors, and bias <10%) at daily, dekadal, and monthly time scales, whereas ARC did not perform satisfactorily (high random errors, low efficiency, and bias >20%) at any time scale. On a seasonal scale, CHIRPS estimated the secondary rainy season (March–May) rainfall better than did ARC and TAMSAT, whereas TAMSAT outperformed both CHIRPS and ARC during the primary rainy season (June–September). Overall, the rainfall detection capabilities and rainfall amount estimates of the SREs were better over the lowlands, and the cumulative rainfall estimates tended to improve with increasing integration time (i.e., from daily to seasonal totals).

A Seasonal Rainfall Performance Probability Tool for Famine Early Warning Systems

AbstractThis paper reports on the development of a new statistical tool that generates probabilistic outlooks of seasonal precipitation anomaly categories over Africa. Called the seasonal performance probability (SPP), it quantitatively evaluates the probability of precipitation to finish at predefined percent-of-normal anomaly categories corresponding to below-average (&lt;80% of normal), average (80%–120% of normal), and above-average (&gt;120% of normal) conditions. This is accomplished by applying methods for kernel density estimation (KDE), which compute smoothed, continuous density functions on the basis of more than 30 years of historical precipitation data from the Africa Rainfall Climatology, version 2, dataset (ARC2) for the remaining duration of a monsoon season. Discussion of various parameterizations of KDE and testing to determine optimality of density estimates (and thus performance of SPP for operational monitoring) are presented. Verification results from 2006 to 2015 show that SPP reliably provides probabilistic outcomes of seasonal rainfall anomaly categories by after the early to midstages of rain seasons for the major monsoon regions in East Africa, West Africa, and southern Africa. SPP has proven to be a useful tool by enhancing operational climate monitoring at CPC for its prognostic capability for famine early warning scenarios over Africa. These insights are anticipated to translate into better decision-making in food security, planning, and response objectives for the U.S. Agency for International Development/Famine Early Warning Systems Network (USAID/FEWS NET).

Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

The West Africa rainfall regime constitutes a considerable challenge for Regional Climate Models (RCMs) due to the complexity of dynamical and physical processes that characterise the West African Monsoon. In this paper, daily precipitation statistics are evaluated from the contributions to the AFRICA-CORDEX experiment from two ERA-Interim driven Canadian RCMs: CanRCM4, developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) and CRCM5, developed at the University of Quebec at Montreal. These modelled precipitation statistics are evaluated against three gridded observed datasets—the Global Precipitation Climatology Project (GPCP), the Tropical Rainfall Measuring Mission (TRMM), and the Africa Rainfall Climatology (ARC2)—and four reanalysis products (ECMWF ERA-Interim, NCEP/DOE Reanalysis II, NASA MERRA and NOAA-CIRES Twentieth Century Reanalysis). The two RCMs share the same dynamics from the Environment Canada GEM forecast model, but have two different physics’ packages: CanRCM4 obtains its physics from CCCma’s global atmospheric model (CanAM4), while CRCM5 shares a number of its physics modules with the limited-area version of GEM forecast model. The evaluation is focused on various daily precipitation statistics (maximum number of consecutive wet days, number of moderate and very heavy precipitation events, precipitation frequency distribution) and on the monsoon onset and retreat over the Sahel region. We find that the CRCM5 has a good representation of daily precipitation statistics over the southern Sahel, with spatial distributions close to GPCP dataset. Some differences are observed in the northern part of the Sahel, where the model is characterised by a dry bias. CanRCM4 and the ERA-Interim and MERRA reanalysis products overestimate the number of wet days over Sahel with a shift in the frequency distribution toward smaller daily precipitation amounts than in observations. Both RCMs and reanalyses have difficulties in reproducing the local onset date over the Sahel region. Nevertheless, the large-scale features of the monsoon precipitation evolution over West Africa are well reproduced by the RCMs, whereas the northern limit of the rainy bands is less accurately reproduced. Both RCMs exhibit an overall good representation of the local retreat index over the Sahel region.

Thirty-two Years of Sahelian Zone Growing Season Non-Stationary NDVI3g Patterns and Trends

We update the Global Inventory Modeling and Mapping Studies (GIMMS) analysis of Sahelian vegetation dynamics and trends using the normalized difference vegetation index (NDVI; version 3g) 1981 to 2012 data set. We compare the annual NDIV3g and July to October growing season averages with the three rainfall data sets: the Africa Rainfall Climatology from 1983 to 2012, the Variability Analyses of Surface Climate Observations Version-1.1 from 1951 to 2000, and the Nicholson ground-station precipitation rainfall data from 1981 to 1994. We use the Nicholson ground-station annual precipitation data to determine the reliability of the two continental precipitation data sets for specific locations and specific times, extrapolate these confirmed relationships over the Sahelian Zone from 1983 to 2012 with the Africa Rainfall Climatology, and then place these zonal findings within the 1951 to 2000 record of the Variability Analyses of Surface Climate Observations Version-1.1 precipitation data set. We confirm the extreme nature of the 1984–1985 Sahelian drought, a signature event that marked the minima during the 1980s desiccation period followed within ten years by near-maxima rainfall event in 1994 and positive departures is NDVI, marking beginning of predominantly wetter conditions that have persisted to 2012. We also show the NDVI3g data capture “effective” rainfall, the rainfall that is utilized by plants to grow, as compared to rainfall that evaporates or is runoff. Using our effective rainfall concept, we estimate average effective rainfall for the entire Sahelian Zone for the 1984 extreme drought was 223 mm/yr as compared to 406 mm/yr in during the 1994 wet period. We conclude that NDVI3g data can used as a proxy for analyzing and interpreting decadal-scale land surface variability and trends over semi arid-lands.