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This paper combines population and climate data to estimate the volume of migration induced by the drought events that have hit Mali since the late 1980s. The results show that droughts have had the effect of decreasing net migration rates in the affected localities. This is true for both men and women, regardless of their age. The effect of drought episodes, however, is found to differ according to localities and households’ capacity to adapt to climatic constraints: it fades in localities characterized by more diversified crops and in areas that receive more rainfall on average. Climate shocks also had an impact on international mobility: over the 2004–2009 period, around 2300 additional departures per year can be attributed to the droughts that hit Mali during the 2000s. We forecast that, under different climate scenarios and population growth projections, mobility induced by drought events will substantially grow in the next decades.

Mosquitoes are climate-sensitive disease vectors. They need an aquatic environment for the development of their immature stages (egg-larva-nymph). The presence and maintenance of these egg-laying sites depends on rainfall. The development period of mosquitoes is reduced when temperature increases, up to a lethal threshold. Global warming will impact vector’s distribution and the diseases they transmit. The last deglaciation taught us that the melting of the ice sheet is highly non-linear and can include acceleration phases corresponding to sea level rise of more than 4 m per century. In addition, glacial instabilities such as iceberg break-ups (Heinrich events) had significant impacts on the North Atlantic Ocean circulation, causing major global climate changes. These melting processes and their feedbacks on climate are not considered in current climate models and their detailed impacts on health have not yet been studied.To simulate an accelerated partial melting of the Greenland ice sheet, a freshwater flux corresponding to a sea level rise of +1 and +3 m over a 50-year period is superimposed on the standard RCP8.5 radiative forcing scenario. These scenarios are then used as inputs for the IPSL-CM5A climate model to simulate global climate change for the 21st century. These simulations allow to explore the consequences of such melting on the distribution of two vector-borne diseases which affect the African continent: malaria and Rift Valley Fever (RVF). Malaria is a parasitic disease that causes more than 200 million cases and more than 600,000 deaths annually worldwide. RVF causes deaths and high abortion rates in herds and poses health risks to humans through contact with infected blood. Former studies have already characterised the evolution of the global distribution of malaria according to standard RCPs. Using the same malaria mathematical models, we study the impact of an accelerated Greenland melting on simulated malaria transmission risk in Africa. Future malaria transmission risk decreases over the Sahel and increases over East African highlands. The decrease over the Sahel is stronger in our simulations with respect to the standard RCP8.5 scenario, while the increase over east Africa is more moderate. Malaria risk strongly increases over southern Africa due to a southern shift of the rain belt which is induced by Greenland ice sheet melting.,. For RVF, the disease model correctly simulates historical epidemics over Somalia, Kenya, Mauritania, Zambia and Senegal. However, our results show the difficulty to validate continental scale models with available health data. It is essential to develop climate scenarios that consider climate tipping points. Assessing the impact of these tipping point scenarios and the associated uncertainties on critical sectors, such as public health, should be a future research priority.

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP6 experiment using the ERA5-Land reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.1°x 0.1°, comprises 5 climate models and includes two surface daily variables at monthly resolution: air temperature and precipitation. Two greenhouse gas emissions scenarios are available: one with mitigation policy (SSP126) and one without mitigation (SSP585). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modelling community standards and value checking for outlier detection.

The study of past climates has demonstrated the occurrence of Heinrich events during which major ice discharges occurred at the polar ice sheet, leading to significant additional sea level rise. Heinrich events strongly influenced the oceanic circulation and global climate. However, standard climate change scenarios (Representative Concentration Pathways or RCPs) do not consider such potential rapid ice sheet collapse; RCPs only consider the dynamic evolution of greenhouse gas emissions. We carried out water-hosing simulations using the Institute Pierre Simon Laplace global Climate Model (IPSL-CM5A) to simulate a rapid melting of the Greenland and Antarctic ice sheets, equivalent to +1 and +3 m additional sea level rise (SLR). Freshwater inputs were added to the standard RCP8.5 emission scenario over the 21st century. The contribution to the SLR from Greenland or from Antarctic ice sheets has differentiated impacts. The freshwater input in the Antarctic is diluted by the circumpolar current, and its global impact is moderate. Conversely, a rapid melting of the ice sheet in the North Atlantic slows down the Atlantic Meridional Overturning Circulation. This slowdown leads to changes in winds, inter-hemispheric temperature and pressure gradients, resulting in a southward shift of the tropical rain belt over the Atlantic and eastern Pacific region. The American and African monsoons are strongly affected and shift to the south. Changes in the North American monsoon occur later, while changes in the South American monsoon start earlier. The North African monsoon is drier during boreal summer, while the southern African monsoon intensifies during austral summer. Simulated changes were not significant for the Asian and Australian monsoons.

Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments., Release of freshwater into the oceans as a result of ice sheet melting could impact the distribution of climate-sensitive diseases. Here, the authors show that a rapid ice sheet melting in Greenland could cause an emergence of malaria in Southern Africa whilst transmission risks in West Africa may decline.

Anthropogenic greenhouse gas emissions have led to sustained global warming over the last decades1. This is already reshaping the distribution of biodiversity across the world and can lead to the occurrence of large-scale singular events, such as the melting of polar ice sheets2,3. The potential impacts of such a melting event on species persistence across taxonomic groups – in terms of magnitude and geographic extent – remain unexplored. Here we assess impacts on biodiversity of global warming and melting of Greenland’s ice sheet on the distribution of 21,146 species of vascular plants and tetrapods across twelve megadiverse countries. We show that high global warming would lead to widespread reductions in species’ geographic ranges (median range loss, 35–78%), which are magnified (median range loss, 95–99%) with the added contribution of Greenland’s melting and its potentially large impact on oceanic circulation and regional climate changes. Our models project a decline in the geographical extent of species hotspots across countries (median reduction, 48–95%) and a substantial alteration of species composition in the near future (mean temporal dissimilarity, 0.26–0.89). These results imply that, in addition to global warming, the influence of Greenland’s melting can lead to the collapse of biodiversity across the globe, providing an added domino in its cascading effects.

Rising temperatures can lead to the occurrence of a large-scale climatic event, such as the melting of Greenland ice sheet, weakening the AMOC and further increasing dissimilarities between current and future climate. The impacts of such an event are still poorly assessed. Here, we evaluate those impacts across megadiverse countries on 21,146 species of tetrapods and vascular plants using the pessimistic climate change scenario (RCP 8.5) and four different scenarios of Greenland's ice sheet melting. We show that RCP 8.5 emission scenario would lead to a widespread reduction in species' geographic ranges (28-48%), which is projected to be magnified (58-99%) with any added contribution from the melting of Greenland. Also, declines in the potential geographical extent of species hotspots (12-89%) and alterations of species composition (19-91%) will be intensified. These results imply that the influence of a strong and rapid Greenland ice sheet melting, resulting in a large AMOC weakening, can lead to a faster collapse of biodiversity across the globe.

Anthropogenic greenhouse gas emissions have led to sustained global warming over the last decades1. This is already reshaping the distribution of biodiversity across the world and can lead to the occurrence of large-scale singular events, such as the melting of polar ice sheets2,3. The potential impacts of such a melting event on species persistence across taxonomic groups – in terms of magnitude and geographic extent – remain unexplored. Here we assess impacts on biodiversity of global warming and melting of Greenland’s ice sheet on the distribution of 21,146 species of vascular plants and tetrapods across twelve megadiverse countries. We show that high global warming would lead to widespread reductions in species’ geographic ranges (median range loss, 35–78%), which are magnified (median range loss, 95–99%) with the added contribution of Greenland’s melting and its potentially large impact on oceanic circulation and regional climate changes. Our models project a decline in the geographical extent of species hotspots across countries (median reduction, 48–95%) and a substantial alteration of species composition in the near future (mean temporal dissimilarity, 0.26–0.89). These results imply that, in addition to global warming, the influence of Greenland’s melting can lead to the collapse of biodiversity across the globe, providing an added domino in its cascading effects.

In the beginning of this century, impacts studies due to climate change were carried out directly with the outputs of the general circulation models of the Atmosphere and the Ocean (AOGCM). However, these models had very low resolutions in the order of several degrees and the climate of some areas, such as monsoon regions, was poorly reproduced. These two disadvantages make it difficult to study the evolution of extremes. Recently, more impact studies are using outputs from multiple AOGCM models that are downscaled and unbiased. The ISIMIP consortium (https://www.isimip.org/) participates in the dissemination of this practice by proposing several AOGCM models with a resolution of 0.5° X 0.5°.In our study, a high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed (Noël et al. accepted). This dataset is obtained by using the quantile – quantile method Cumulative Distribution Function transform (CDFt) (Vrac et al. 2012, 2016,, developed over 10 years to bias correct or downscale climate model output, and ERA5 land data as a reference . TWe propose in this communication to present the climate variability by the end of the century in terms of extreme climate indicators such as heat waves or heavy rainfall at the local/grid point level (e.g. city level). Particular attention will be paid to the magnitude of the changes as well as the associated uncertainty. ReferencesVrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., & Somot, S. (2012). Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment.Nat. Hazards Earth Syst. Sci., 12, 2769–2784.Vrac, M., Noël, T., & Vautard, R. (2016). Bias correction of precipitation through Singularity Stochastic Removal: Because occurrences matter. Journal of Geophysical Research: Atmospheres, 121(10), 5237-5258.Noël, T., Loukos, H., Defrance, D., Vrac, M., & Levavasseur, G. (2020). High-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with ERA5 reanalyses for climate change impact assessments. Data in Brief (accepted, https://doi.org/10.31223/X53W3F)

International audience; Rainfed crop production predominates in West Africa. Rice is an important staple food, especially in Senegal. The scope for increase in rice production under irrigated conditions is uncertain. Rainfed rice is therefore a key component for regional food security impelling agronomists to assess climate change impact on rainfed rice yield and to design rainfed rice ideotypes suited to future climate scenarios. The DSSAT CSM-CERES-Rice model was thus calibrated and evaluated on 19 agronomic experiments conducted in 2012, 2013, and 2014, in 6 locations, with 21 cultivars and two fertilization levels (20 and 80 kg N ha−1). Simulations were then carried out with the crop model forced with the downscaled projections of seven climate models, with and without considering the impact of an increase in atmospheric [CO2], using an ensemble of global circulation models and two Representative Concentration Pathways (RCP2.6 and RCP8.5). Simulated rice yield was divided by two over the century with RCP8.5 and stagnated with RCP2.6. Elevated [CO2] significantly increased yields, but this effect could not offset the yield decline due to elevated temperatures. Cultivars with longer vegetative phases and greater temperature tolerance were better adapted to climate change than current cultivars. Using these new cultivars with the recommended fertilization rate (80 kg N ha−1) could offset the yield decline due to climate change. For the first time, we bring together a study based on a process-based crop model handling crop response to elevated [CO2], a large set of field experiments and up-to-date climate projections (i) to provide useful insights into plausible impacts of climate change on rainfed rice in Senegal and (ii) to identify cultivar characteristics relevant for adaptation to future possible climates. Our findings will help set priorities for breeding resilient cultivar in the region.

International audience; A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Impacts on ecosystems and biodiversity are a prominent area of research in climate change. However, little is known about the effects of abrupt climate change and climate catastrophes on them. The probability of occurrence of such events is largely unknown but the associated risks could be large enough to influence global climate policy. Amphibians are indicators of ecosystems’ health and particularly sensitive to novel climate conditions. Using state-of-the-art climate model simulations, we present a global assessment of the effects of unabated global warming and a collapse of the Atlantic meridional overturning circulation (AMOC) on the distribution of 2509 amphibian species across six biogeographical realms and extinction risk categories. Global warming impacts are severe and strongly enhanced by additional and substantial AMOC weakening, showing tipping point behavior for many amphibian species. Further declines in climatically suitable areas are projected across multiple clades, and biogeographical regions. Species loss in regional assemblages is extensive across regions, with Neotropical, Nearctic and Palearctic regions being most affected. Results underline the need to expand existing knowledge about the consequences of climate catastrophes on human and natural systems to properly assess the risks of unabated warming and the benefits of active mitigation strategies., Julián Velasco et al. use climate model simulations to show how the collapse of the Atlantic meridional overturning circulation and unabated global warming under the RCP 8.5 scenario affect the global distribution of 2509 amphibian species. These results show severe and synergistic impacts of global warming, with particularly strong effects shown in the Neotropical, Nearctic and Palearctic regions.

Food security is a crucial issue in the Sahel and could be endangered by climate change and demographic pressure during the 21st century. Indeed, people in rural areas mainly practice rainfed agriculture for subsistence during the monsoon rainfall season. Higher temperatures and changes in rainfall induced by global warming are threatening food production systems in this region while the population of this region is expected to increase approximately threefold until 2050 according to several demographic scenarios. However climate change is very uncertain, with monsoon rainfall that might either increase or decrease, according to different climate models and greenhouse gases emissions scenarios. Our study quantifies the impact of climate change on food security by combining climate, crops yield and demographic evolution. Using 16 global climate models, we investigate the evolution of solar radiation, temperature and precipitation under two future climatic scenarios. We simulate yield for the main crops in West-Africa: maize, sorghum and millet. Finally, we estimate in terms of production in the future and is scaled by population changes in order to assess the number of available cereal production per capita in five countries in West Africa. We found that, although uncertain, the evolution of the African monsoon is different between the countries with a rainfall increase in the Eastern Sahel for Niger and Nigeria and a decrease in the Western Sahel for Senegal under the Representative Concentration Pathway 8.5 scenario. With regard to the abundance of food for the inhabitants, all the scenarios in each country show that in 2050, local agricultural production will be below than 50 kilograms per capita, which corresponds to the basic threshold for feeding all the inhabitants of West Africa. The prospects for improving or adapting current agronomic techniques and/or expanding the cultivated areas do not allow to reach this threshold even by taking into account the [CO2] effect, which has a positive effect in drought situations. This would place additional pressures on resources in the region, with potentially strong impacts on crops import and regional migration.

International audience; Highlights:• Unprecedented report on 23 precipitation datasets reliability across West Africa.• Large discrepancies are observed between the precipitation datasets reliability.• The datasets reliability differ in space and according to the considered time step.• MSWEP is the most accurate dataset to represent the daily precipitation dynamic.• CHIRPS is the most accurate dataset to represent the monthly precipitation dynamic.Abstract: This study aims reporting on 23 gridded precipitation datasets (P-datasets) reliability across West Africa through direct comparisons with rain gauges measurement at the daily and monthly time scales over a 4 years period (2000-2003). All P-datasets reliability vary in space and time. The most efficient P-dataset in term of Kling–Gupta Efficiency (KGE) changes at the local scale and the P-dataset performance is sensitive to seasonal effects. Satellite-based P-datasets performed better during the wet than the dry season whereas the opposite is observed for reanalysis P-datasets. The best overall performance was obtained for MSWEP v.2.2 and CHIRPS v.2 for daily and monthly time-step, respectively. Part of the differences in P-dataset performance at daily and monthly time step comes from the time step used to proceed the gauges adjustment (i.e day or month) and from a mismatch between gauge and satellite reporting times. In comparison to the others P-datasets, TMPA-Adj v.7 reliability is stable and reach the second highest KGE value at both daily and monthly time step. Reanalysis P-datasets (WFDEI, MERRA-2, JRA-55, ERA-Interim) present among the lowest statistical scores at the daily time step, which drastically increased at the monthly time step for WFDEI and MERRA-2. The non-adjusted P-datasets were the less efficient, but, their near-real time availability should be helpful for risk forecast studies (i.e. GSMaP-RT v.6). The results of this study give important elements to select the most adapted P-dataset for specific application across West Africa.

Climate change studies in recent decades have been based on Global Climate Models (GCMs), and the changes in the distribution of climatic regions over time extracted from these models can be represented using the Koppen climatic classification system, which predicts the global distribution of biomes based on monthly precipitation and average temperatures. In this study, the Koppen classification is used to evaluate the impacts of the melting of the Greenland and Antarctic Ice Sheets on GCM simulation results, on regional and global scales. To assess the impacts of accelerated ice sheet melting, an approach is utilized which is based on numerical simulations from the IPSL-CM5A-LR GCM; here, freshwater is introduced near the ice sheets and is superimposed on the RCP8.5 scenario. The changes in the distribution of the Koppen climatic regions under various scenarios (a historical run from observations, RCP8.5, and various examples of polar ice sheet melting) and comparisons between them reveal that major changes will occur on the global scale during the period 2041–2060. The analysis of group shifts within the Koppen classification system reveals that when freshwater from Greenland or Antarctica is introduced into the ocean, the inter-tropical belt undergoes greater change than it does under the RCP8.5 scenario. A focus on sub-group shifts within the Koppen classification system shows that changes in precipitation have major impacts on the climate in the Southern Hemisphere. Further, the changes are more drastic if the freshwater originates from Greenland than from Antarctica or from both locations. However, changes in temperature strongly impact the climate in the Northern Hemisphere and are significantly affected by the melting of the Greenland Ice Sheet. This study highlights the importance of considering ice sheet melting in the modeling of future global climate.

The ability of a region to feed itself in the upcoming decades is an important issue. The West African population is expected to increase significantly in the next 30 years. The responses of crops to short-term climate change is critical to the population and the decision makers tasked with food security. This leads to three questions: how will crop yields change in the near future? What influence will climate change have on crop failures? Which adaptation methods should be employed to ameliorate undesirable changes? An ensemble of near-term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic period (1986–2005) and a near-term future when global temperatures are 1.5 K above pre-industrial levels to assess the change in yield, yield variability and crop failure rate. Four crop models were used to simulate maize, millet and sorghum in West Africa in the historic and future climates. Across the majority of West Africa the maize, millet and sorghum yields are shown to fall. In the regions where yields increase, the variability also increases. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond 1 standard deviation during the historic period. The increasing variability increases the frequency of crop failures across West Africa. The return time of crop failures falls from 8.8, 9.7 and 10.1 years to 5.2, 6.3 and 5.8 years for maize, millet and sorghum respectively. The adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model as an idealized sensitivity test. The generalized doption of a cultivar resistant to high-temperature stress during flowering is shown to be more beneficial than using rainwater harvesting.

That is a preprint of an article suubmitted at Applied Greography (20tf of March 19). Climate change studies in the last decades have been based on Global Climate Models (GCM), and the distribution of climatic regions over time extracted from these models can be represented using the Köppen climatic classification. The Köppen approach predicts the distribution of biomes worldwide on the basis of monthly precipitation and average temperature. This study aims to use the Köppen classification to evaluate the impact of the Greenland and Antarctic ice sheets melting on GCM simulation results at the regional and global scale. To assess the impact of accelerated ice-sheet melting, our approach is based on numerical simulations from the IPSL-CM5A-LR GCM with the introduction of freshwater near the ice sheets superimposed on the RCP8.5 scenario, leading to a global warming of +5°C. Static mapping of distribution changes in Köppen climatic regions under various scenarios (historical run from observations, RCP 8.5, and various cases of polar sheets melting) and comparisons between them reveal that major changes occur at the global scale for the period 2041-2060. A first level of analysis revealed that, when the input of freshwater originates from Greenland or Antarctica, the inter-tropical belt undergoes greater change than under the RCP8.5 scenario. A second level of analysis showed that changes in precipitation have major impacts on the southern hemisphere, with more drastic changes if the freshwater came from the Greenland ice sheet than if it came from Antarctica or from the combination of both. Changes in temperature, however, strongly impact the northern hemisphere, and are significantly affected by the melting of the Greenland ice sheet. This study highlights the importance of considering ice sheets melting in the modelling of future global climate.

This article describes extreme indices maps (Data Cube, raster X Time) for different scenarios with a more important contribution to the sea level rise from Greenland and/or Antarctica during the 21st century under the Representative Concentration Pathway (RCP) 8.5 emission scenario. The indices are produced annually and globally with a resolution of 0.5°X0.5° from 1951 to 2099. The data were generated by simulating daily maximum and minimum temperature and precipitation from the IPSL-CM5A-LR model from Coupled Model Intercomparison Project Phase 5 (CMIP5). These climatic data are unbiased and downscaled to the 0.5°x0.5 scale with the the Cumulative Distribution Function transform (CDFt) and EWEMBI dataset compiled to support the bias correction of climate input data for ISIMIP. Finally, each extreme indice is compute on the unbiased data on each grid cell on all continents. These data are available at http://dx.doi.org/10.17632/fbsdj87gjg.1 .

The objective of this paper is to present a new data set of bias-corrected CMIP5 global climate models (GCMs) daily data over Africa. This dataset was obtained in using the Cumulative Distribution Function Transform (CDF-t) method, a method that has been applied on several regions and contexts but never on Africa. Here CDF-t is used over the period 1950–2099 combining historical runs and climate change scenarios on 6 variables, precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface down-welling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. Evaluation of the results is carried out over West Africa on a list of priority users-based metrics that was discussed and selected with stakeholders and on simulated yield using a crop model simulating maize growth. Bias-corrected GCMs data are compared with another available dataset of bias-corrected GCMs, and the impact of three different reference datasets on bias-corrections is also examined in details. CDF-t is very effective in removing the biases and in reducing the high inter-GCMs scattering. Differences with other bias-corrected GCMs data are mainly due to the differences between the reference datasets. This is particular true for surface down-welling shortwave radiation, which has impacts in terms of simulated maize yields. Projections of future yields over West Africa have quite different levels, depending on bias-correction method used, but they all show a similar relative decreasing trend over the 21st century.

The ability of a country or region to feed itself in the upcoming decades is a question of importance. The population in West Africa is expected to increase significantly in the next 30 years. The responses of food crops to short term climate change is therefore critical to the population at large and the decision makers tasked with providing food for their people. An ensemble of near term climate projections are used to simulate maize, millet and sorghum in West Africa in the recent historic and near term future. The mean yields are not expected to alter significantly, while there is an increase in inter annual variability. This increase in variability increases the likelihood of crop failures, which are defined as yield negative anomalies beyond one standard deviation during a period of 20 years. The increasing variability increases the frequency and intensity of crop failures across West Africa. The mean return frequency between mild maize crop failures from process based crop models increases from once every 6.8 years to once every 4.5 years. The mean return time frequency for severe crop failures (beyond 1.5 standard deviations) also almost doubles from once every 16.5 years to once every 8.5 years. Two adaptation responses to climate change, the adoption of heat-resistant cultivars and the use of captured rainwater have been investigated using one crop model in an idealised sensitivity test. The generalised adoption of a cultivar resistant to high temperature stress during flowering is shown to be more beneficial than using rainwater harvesting by both increasing yields and the return frequency of crop failures.

International audience; The acceleration of ice sheet melting has been observed over the last few decades. Recent observations and modeling studies have suggested that the ice sheet contribution to future sea level rise could have been underestimated in the latest Intergovernmental Panel on Climate Change report. The ensuing freshwater discharge coming from ice sheets could have significant impacts on global climate, and especially on the vulnerable tropical areas. During the last glacial/deglacial period, megadrought episodes were observed in the Sahel region at the time of massive iceberg surges, leading to large freshwater discharges. In the future, such episodes have the potential to induce a drastic destabilization of the Sahelian agro-ecosystem. Using a climate modeling approach, we investigate this issue by superimposing on the Representative Concentration Pathways 8.5 (RCP8.5) baseline experiment a Greenland flash melting scenario corresponding to an additional sea level rise ranging from 0.5 m to 3 m. Our model response to freshwater discharge coming from Greenland melting reveals a significant decrease of the West African monsoon rainfall, leading to changes in agricultural practices. Combined with a strong population increase, described by different demography projections, important human migration flows could be potentially induced. We estimate that, without any adaptation measures, tens to hundreds million people could be forced to leave the Sahel by the end of this century. On top of this quantification, the sea level rise impact over coastal areas has to be superimposed, implying that the Sahel population could be strongly at threat in case of rapid Greenland melting.

International audience; Different CMIP exercises show that the simulations of the future/current temperature and precipitation are complex with a high uncertainty degree. For example, the African monsoon system is not correctly simulated and most of the CMIP5 models underestimate the precipitation. Therefore, Global Climate Models (GCMs) show significant systematic biases that require bias correction before it can be used in impacts studies. Several methods of bias corrections have been developed for several years and are increasingly using more complex statistical methods. The aims of this work is to show the interest of the CDFt (Cumulative Distribution Function transfom (Michelan-geli et al.,2009)) method to reduce the data bias from 29 CMIP5 GCMs over Africa and to assess the impact of bias corrected data on crop yields prediction by the end of the 21st century. In this work, we apply the CDFt to daily data covering the period from 1950 to 2099 (Historical and RCP8.5) and we correct the climate variables (temperature, precipitation, solar radiation, wind) by the use of the new daily database from the EU project WATer and global CHange (WATCH) available from 1979 to 2013 as reference data. The performance of the method is assessed in several cases. First, data are corrected based on different calibrations periods and are compared, on one hand, with observations to estimate the sensitivity of the method to the calibration period and, on other hand, with another bias-correction method used in the ISIMIP project. We find that, whatever the calibration period used, CDFt corrects well the mean state of variables and preserves their trend, as well as daily rainfall occurrence and intensity distributions. However, some differences appear when compared to the outputs obtained with the method used in ISIMIP and show that the quality of the correction is strongly related to the reference data. Secondly, we validate the bias correction method with the agronomic simulations (SARRA-H model (Kouressy et al., 2008)) by comparison with FAO crops yields estimations over West Africa. Impact simulations show that crop model is sensitive to input data. They show also decreasing in crop yields by the end of this century. Michelangeli, P. A., Vrac, M., & Loukos, H. (2009). Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36(11). Kouressy M, Dingkuhn M, Vaksmann M and Heinemann A B 2008: Adaptation to diverse semi-arid environments of sorghum genotypes having different plant type and sensitivity to photoperiod. Agric. Forest Meteorol., http://dx.

The Supplement related to this article is available online at doi:10.5194/esd-7-517-2016-supplement; International audience; The modifications of atmospheric circulation induced by anthropogenic effects are difficult to capture because wind fields feature a complex spectrum where the signal of large-scale coherent structures (planetary, baroclinic waves and other long-term oscillations) is mixed up with turbulence. Our purpose is to study the effects of climate changes on these two components separately by applying a wavelet analysis to the 700 hPa wind fields obtained in climate simulations for different forcing scenarios. We study the coherent component of the signal via a correlation analysis to detect the persistence of large-scale or long-lasting structures, whereas we use the theory of autoregressive moving-average stochastic processes to measure the spectral complexity of the turbulent component. Under strong anthropogenic forcing, we detect a significant climate change signal. The analysis suggests that coherent structures will play a dominant role in future climate, whereas turbulent spectra will approach a classical Kolmogorov behaviour.

In Europe, flash floods affect mainly the Mediterranean and mountainous regions, even if other regions also occasionally suffer from them. The catchments involved are usually small and ungauged, with short time of concentration. Forecasting this type of event remains difficult using hydrological models, and assessing the models is even more problematic. Typically, assessment is limited to gauged catchments that have relatively different geomorphological characteristics. The aim of this article is to present a method for assessing the models on real ungauged catchments through the use of damage reports and a multi-threshold approach, with assessment criteria that are based on a contingency table of the Critical Success Index type. The main conclusion, as demonstrated by Irstea's "Adaptation d'Information géographique pour l'Alerte en crue" for "Geographic information adaptation for flood warning" (AIGA) flood forecasting system and by the new version of AIGA for high-altitude catchments, is that while assessing hydrological models on gauged catchments is necessary, it is never sufficient and must be supplemented by assessments on ungauged catchments. This underlines the utility of building flood damage databases that are as exhaustive as possible. Such databases can be a valuable addition to more standard, often limited sources of data, especially for mountainous regions.

International audience; This article presents a comparison between real-time discharges calculated by a flash-flood warning system and post-event flood peak estimates. The studied event occurred on 15 and 16 June 2010 at the Argens catchment located in the south of France. Real-time flood warnings were provided by the AIGA (Adaptation d'Information Géographique pour l'Alerte en Crue) warning system, which is based on a simple distributed hydrological model run at a 1-km2 resolution using radar rainfall information. The timing of the warnings (updated every 15 min) was compared to the observed flood impacts. Furthermore, “consolidated” flood peaks estimated by an intensive post-event survey were used to evaluate the AIGA-estimated peak discharges. The results indicated that the AIGA warnings clearly identified the most affected areas. However, the effective lead-time of the event detection was short, especially for fast-response catchments, because the current method does not take into account any rainfall forecast. The flood peak analysis showed a relatively good correspondence between AIGA- and field-estimated peak values, although some differences were due to the rainfall underestimation by the radar and rainfall-runoff model limitations.; Cet article présente une comparaison des débits calculés en temps réel par un système d'avertissement de crues éclair avec des estimations post-événement des pics de crue. L'événement étudié s'est déroulé les 15 et 16 juin 2010 sur le bassin de l'Argens, situé dans le Sud de la France. Les débits en temps réel ont été calculés par le système d'avertissement AIGA (Adaptation d'Information Géographique pour l'Alerte en Crue), qui associe un modèle hydrologique distribué simple et une lame d'eau radar à la résolution de 1 km2. La chronologie des avertissements émis a été comparée aux dégâts observés. De plus, des estimations de pic de crue réalisées au cours de relevés de terrain après l'événement ont permis d'évaluer la pertinence des débits estimés par AIGA. Les résultats ont montré qu'AIGA a clairement indiqué les zones les plus affectées par la crue. Cependant, l'anticipation des avertissements a été courte, en particulier sur les bassins rapides, du fait que le système n'intègre pas de prévision de pluie. L'analyse des pics de crue a montré une relative bonne correspondance entre les débits calculés par AIGA et ceux estimés sur le terrain, avec cependant des différences. Celles-ci sont liées à une sous-estimation de la lame d'eau radar sur certains bassins et aux limites du modèle hydrologique.

The Mediterranean region is subject to flash flood events that lead to many damages and sometimes to fatalities. Because not every little river's tributaries can be monitored, flash floods occur most often on small ungauged catchments with limited available data. In this context, the calibration of the hydrological model used within a flood warning system is a tricky task. The aim of this study is to evaluate the potential benefit of 'proxy data' in areas where no classical continuous flow measurements are available. Administrative services for example collect and archive large amounts of regional and local information over long periods based on observed damages, some of which can be used as proxy indicators for flood occurrences (e.g. infrastructure maintenance, insurance claims,...). To determine if such data could be helpful, a benchmark test is carried out, comparing three cases. In the first case the entire streamflow data series is used. In the second case, data from case 1 is degraded: only the date and value of the flood peak are retained. In the last case, only the date of the flood peak is used. This last case aims to imitate the case, where only proxy data collected by administrative services are available. The first case corresponds to the classical gauged basin case, which is used as reference. These 3 cases are carried out using a simple conceptual hydrological model (from the GR model family) and data coming from around 150 catchments located in the South East of France and covering a 10-year period (1997-2007). The performance of simulated runoff is evaluated using contingency statistics (CSI). A split-sample test is used in order to assess the robustness of the different calibrations. Results indicate that performances decrease from case 1 to case 3, case 2 being intermediate. Since the methodology developed for case 3 can be applied at ungauged locations, it seems that obtained alerts can be very helpful when no classical hydrological data is available to calibrate the hydrological model used in the flood warning system. This work has been made in the framework of the ongoing RHYTMME project. This project aims at developing a warning system in the south of French Alps, using radar rainfall data (http://rhytmme.cemagref.fr/synopsis).

International audience; During the 20th century, Sahelian drought episodes like those between 1972 and 1982 showed the vulnerability of the Sahelian agro-ecosystem provoking significant intraregional southward human migrations, to or near the coast. According to the latest IPCC report, the Sahel could become increasingly impacted by climate change during the 21st century because of a lagged and shorter rainfall season having the potential to induce a drastic destabilization of the Sahelian agro-ecosystem and to heavily impact the population. Such effects could be further amplified by a net increase of the Sahelian population. Drastic climate changes over tropical areas also occurred in the past: weakening of the West African Monsoon and megadrought Sahelian episodes have been reported with a close correspondence between the large rainfall decrease and the massive freshwater discharges following ice-sheet melting or iceberg surges. During the last decades a continuous acceleration of ice-sheet mass loss has been observed and post IPCC-AR5 studies suggest the ice-sheet contribution to future sea-level rise could be revised upward due partly to the lack of an accurate representation of ice-ocean interactions. The release of freshwater discharge in response to ice-sheet instability could have large consequences on the most vulnerable regions, such as the tropical areas. To investigate the impacts of large ice-sheet instability during the 21st century, we first explore the climatic signature of Greenland or Antarctic ice-sheet collapse scenarios corresponding to 0.5 to 1.5 meter of sea-level rise, superimposed to the RCP8.5 scenario. We show that a freshwater discharge coming from Greenland melting induces a significant decrease of summer monsoon rainfall, that may lead to changes in agricultural practices. Combined with increasing demography, this has the potential to induce important human migration flows. Without adaptation measures, we estimate that tens to hundreds million people could be forced to leave the Sahel by the end of the 21st century, not accounting for the direct migratory impact of sea-level rise over coastal areas.