Bias-Corrected CMIP5 Projections for Climate Change and Assessments of Impact on Malaria in Senegal under the VECTRI Model.

On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP's community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.

Heat waves and health risks in the northern part of Senegal: analysing the distribution of temperature-related diseases and associated risk factors.

The Sahelian zone of Senegal experienced heat waves in the previous decades, such as 2013, 2016 and 2018 that were characterised by temperatures exceeding 45°C for up to 3 successive days. The health impacts of these heat waves are not yet analysed in Senegal although their negative effects have been shown in many countries. This study analyses the health impacts of observed extreme temperatures in the Sahelian zone of the country, focusing on morbidity and mortality by combining data from station observation, climate model projections, and household survey to investigate heat wave detection, occurrence of climate-sensitive diseases and risk factors for exposure. To do this, a set of climatic (temperatures) and health (morbidity, mortality) data were collected for the months of April, May and June from 2009 to 2019. These data have been completed with 1246 households' surveys on risk factor exposure. Statistical methods were used to carry out univariate and bivariate analyses while cartographic techniques allowed mapping of the main climatic and health indicators. The results show an increase in temperatures compared to seasonal normal for the 1971-2000 reference period with threshold exceedances of the 90th percentiles (42°C) for the maxima and (27°C) the minima and higher temperatures during the months of May and June. From health perspective, it was noted an increase in cases of consultation in health facilities as well as a rise in declared morbidity by households especially in the departments of Kanel (17.7%), Ranérou (16.1 %), Matam (13.7%) and Bakel (13.7%). The heat waves of May 2013 were also associated with cases of death with a reported mortality (observed by medical staff) of 12.4% unequally distributed according to the departments with a higher number of deaths in Matam (25, 2%) and in Bakel (23.5%) than in Podor (8.4%) and Kanel (0.8%). The morbidity and mortality distribution according to gender shows that women (57%) were more affected than men (43%). These health risks have been associated with a number of factors including age, access to drinkable water, type of fuel, type of housing and construction materials, existence of fan and an air conditioner, and health history.The heat wave recurrence has led to a frequency in certain diseases sensitive to rising temperatures, which is increasingly a public health issue in the Sahelian zone of Senegal.

Robust assessment of the time of emergence of precipitation change in West Africa.

The time of emergence (TOE) of climate change is defined as the time when a new climate state emerges from a prior one. TOE assessment is particularly relevant in West Africa, a region highly threatened by climate change and urgently needing trustworthy climate predictions. In this paper, the TOE of precipitation change in West Africa is assessed for the first time, by analyzing 6 precipitation metrics (cumulated precipitation, number of wet and very wet days, onset and length of the rainy season) computed from the output of 29 state-of-the-art climate models. In West Sahel, climate conditions characterized by reduced occurrence of wet days are likely to emerge before 2036, leading to the possible emergence of a dryer climate in 2028-2052. In East Sahel, a wetter precipitation regime characterized by increased occurrence of very wet days is likely to emerge before 2054. Results do not provide a clear indication about a possible climate shift in the onset and length of the rainy season. Although uncertainty in climate model future projections still limits the robust determination of TOE locally, this study provides reliable time constraints to the expected climate shift in West Africa at the sub-regional scale, supporting adaptation measures to the future change in the precipitation regime.

Author Correction: Tropical explosive volcanic eruptions can trigger El Niño by cooling tropical Africa.

The original version of this Article omitted a reference to previous work in 'Mann, M.E., Cane, M.A., Zebiak, S.E., Clement, A., Volcanic and Solar Forcing of the Tropical Pacific Over the Past 1000 Years, J. Climate 18, 447-456 (2005)'. This has been added as reference 62 at the end of the fourth sentence of the fourth paragraph of the Introduction: 'Early studies using simple coupled ocean-atmosphere models26 proposed that following volcano-induced surface cooling, upwelling in the eastern equatorial Pacific acting on a reduced vertical temperature contrast between the ocean surface and interior leads to anomalous warming in this region, thereby favouring El Niño development the following year12, 27, 62.' This has been corrected in the PDF and HTML versions of the Article.

Publisher Correction: Tropical explosive volcanic eruptions can trigger El Niño by cooling tropical Africa.

A correction to this article has been published and is linked from the HTML version of this article.

Tropical explosive volcanic eruptions can trigger El Niño by cooling tropical Africa.

Stratospheric aerosols from large tropical explosive volcanic eruptions backscatter shortwave radiation and reduce the global mean surface temperature. Observations suggest that they also favour an El Niño within 2 years following the eruption. Modelling studies have, however, so far reached no consensus on either the sign or physical mechanism of El Niño response to volcanism. Here we show that an El Niño tends to peak during the year following large eruptions in simulations of the Fifth Coupled Model Intercomparison Project (CMIP5). Targeted climate model simulations further emphasize that Pinatubo-like eruptions tend to shorten La Niñas, lengthen El Niños and induce anomalous warming when occurring during neutral states. Volcanically induced cooling in tropical Africa weakens the West African monsoon, and the resulting atmospheric Kelvin wave drives equatorial westerly wind anomalies over the western Pacific. This wind anomaly is further amplified by air-sea interactions in the Pacific, favouring an El Niño-like response.El Niño tends to follow 2 years after volcanic eruptions, but the physical mechanism behind this phenomenon is unclear. Here the authors use model simulations to show that a Pinatubo-like eruption cools tropical Africa and drives westerly wind anomalies in the Pacific favouring an El Niño response.

Frequency of extreme Sahelian storms tripled since 1982 in satellite observations.

The hydrological cycle is expected to intensify under global warming, with studies reporting more frequent extreme rain events in many regions of the world, and predicting increases in future flood frequency. Such early, predominantly mid-latitude observations are essential because of shortcomings within climate models in their depiction of convective rainfall. A globally important group of intense storms-mesoscale convective systems (MCSs)-poses a particular challenge, because they organize dynamically on spatial scales that cannot be resolved by conventional climate models. Here, we use 35 years of satellite observations from the West African Sahel to reveal a persistent increase in the frequency of the most intense MCSs. Sahelian storms are some of the most powerful on the planet, and rain gauges in this region have recorded a rise in 'extreme' daily rainfall totals. We find that intense MCS frequency is only weakly related to the multidecadal recovery of Sahel annual rainfall, but is highly correlated with global land temperatures. Analysis of trends across Africa reveals that MCS intensification is limited to a narrow band south of the Sahara desert. During this period, wet-season Sahelian temperatures have not risen, ruling out the possibility that rainfall has intensified in response to locally warmer conditions. On the other hand, the meridional temperature gradient spanning the Sahel has increased in recent decades, consistent with anthropogenic forcing driving enhanced Saharan warming. We argue that Saharan warming intensifies convection within Sahelian MCSs through increased wind shear and changes to the Saharan air layer. The meridional gradient is projected to strengthen throughout the twenty-first century, suggesting that the Sahel will experience particularly marked increases in extreme rain. The remarkably rapid intensification of Sahelian MCSs since the 1980s sheds new light on the response of organized tropical convection to global warming, and challenges conventional projections made by general circulation models.

Relationships between climate and year-to-year variability in meningitis outbreaks: a case study in Burkina Faso and Niger.

BACKGROUND: Every year, West Africa is afflicted with Meningococcal Meningitis (MCM) disease outbreaks. Although the seasonal and spatial patterns of disease cases have been shown to be linked to climate, the mechanisms responsible for these patterns are still not well identified. RESULTS: A statistical analysis of annual incidence of MCM and climatic variables has been performed to highlight the relationships between climate and MCM for two highly afflicted countries: Niger and Burkina Faso. We found that disease resurgence in Niger and in Burkina Faso is likely to be partly controlled by the winter climate through enhanced Harmattan winds. Statistical models based only on climate indexes work well in Niger showing that 25% of the disease variance from year-to-year in this country can be explained by the winter climate but fail to represent accurately the disease dynamics in Burkina Faso. CONCLUSION: This study is an exploratory attempt to predict meningitis incidence by using only climate information. Although it points out significant statistical results it also stresses the difficulty of relating climate to interannual variability in meningitis outbreaks.

Climate drives the meningitis epidemics onset in west Africa.

BACKGROUND: Every year West African countries within the Sahelo-Sudanian band are afflicted with major meningococcal meningitis (MCM) disease outbreaks, which affect up to 200,000 people, mainly young children, in one of the world's poorest regions. The timing of the epidemic year, which starts in February and ends in late May, and the spatial distribution of disease cases throughout the "Meningitis Belt" strongly indicate a close linkage between the life cycle of the causative agent of MCM and climate variability. However, mechanisms responsible for the observed patterns are still not clearly identified. METHODS AND FINDINGS: By comparing the information on cases and deaths of MCM from World Health Organization weekly reports with atmospheric datasets, we quantified the relationship between the seasonal occurrence of MCM in Mali, a West African country, and large-scale atmospheric circulation. Regional atmospheric indexes based on surface wind speed show a clear link between population dynamics of the disease and climate: the onset of epidemics and the winter maximum defined by the atmospheric index share the same mean week (sixth week of the year; standard deviation, 2 wk) and are highly correlated. CONCLUSIONS: This study is the first that provides a clear, quantitative demonstration of the connections that exist between MCM epidemics and regional climate variability in Africa. Moreover, this statistically robust explanation of the MCM dynamics enables the development of an Early Warning Index for meningitis epidemic onset in West Africa. The development of such an index will undoubtedly help nationwide and international public health institutions and policy makers to better control MCM disease within the so-called westward-eastward pan-African Meningitis Belt.

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact.

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.