Search

Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. [ABSTRACT FROM AUTHOR]

A large range of climate change impacts is expected during the twenty-first century in vulnerable regions such as West Africa, where local populations largely rely on livestock systems as their main food production and income source. As climate change threatens livestock systems in various ways, here we assess how regional livestock could be exposed to cumulated climate-related stressors in the future. Using the world's largest multi-model climate impacts simulations database ISIMIP, we find that a large part of West Africa will experience at least 5–6 cumulated multiple climate stressors before the 2030s, including amplified severe heat stress conditions and flood risks. Consequently, about 30% of the current total regional livestock could be exposed to these cumulated stressors, with sheep and goat as the most affected species. This paper brings new quantifications that may help policy makers to prioritize decisions that could prepare local populations to face multiple climate-related impacts. [ABSTRACT FROM AUTHOR]

Climate change directly threatens food security in West Africa through a negative impact on productivity of the main staple food crops. However, providing consistent future crop yield projections in the region remains challenging because of uncertainty in the response of the regional climate to the CO2 increase and in the response of the cultivated crop to this altered climate with more CO2 in the atmosphere. Here, we analyse a set of idealized climate simulations to investigate the effect of CO2 concentration increase on the West African monsoon and potential impacts on crop yields of maize. On the one hand, simulations with prescribed SST and quadrupled CO2 concentration are analysed to study the atmospheric response to direct radiative forcing induced by increasing CO2 concentration, not mediated by ocean heat capacity. On the other hand, simulations with prescribed SST augmented by 4 K are analysed to study the atmospheric response to the global ocean warming expected as a consequence of the increasing CO2 radiative forcing. We show that if CO2 concentration increase has a positive impact on crop yield due to the fertilization effect, it also has a direct effect on the monsoon which acts to increase (decrease) rainfall in the eastern (western) part of the Sahel and increase (decrease) crop yields consequently. Finally, we show that SST warming acts to reduce rainfall and increase local temperatures leading to strong reduction of crop yield. The reduction of crop yield is more important in the eastern part of the Sahel where the warming is more intense than in the western part of the Sahel. Overall, positive effects are weaker and more uncertain than the negative effects in the analysed simulations. Crop yield response to rainfall and temperature variations in the GLAM model. Pixel by pixel difference against the domain average for mean yield and total growing season rainfall (left) and mean temperature (right). Values are then averaged over the 30 years of the control experiment to give more than 400 values expressed in percentage. [ABSTRACT FROM AUTHOR]

ABSTRACT: The reference evapotranspiration (ET0) is an integrated climatic variable from which many crop models derive simulated crop yields. In most of these models, different equations are parameterized leaving the choice of the equation to the user. However, the impact of the choice of the ET0 equations on crop yield prediction has been little studied. The present study proposes a sensitivity analysis of the impact of the choice of the ET0 equation on simulated millet yields using SARRA‐H crop model over 12 Senegalese stations representative of the Sudano‐Sahelian climate conditions of West Africa. Priestley‐Taylor, a modified Priestley‐Taylor and Hargreaves equations lead to simulated yields up to 19% than those calculated using the Penman‐Monteith equation. Despite high biases in wind speed, among the tested methods, the Penman‐Monteith method remains the most robust to derive ET0 and yield over the major part of Senegal, Hargreaves equation being more appropriated under dry climates. The choice of ET0 formulation introduces uncertainties representing 8% of baseline yield regardless of precipitation changes; for wet conditions these uncertainties approach 30% of the overall climate change impact. The choice of ET0 equation is increasingly important, with local temperature changes out to 4 °C, while extreme changes above 6 °C depend less on the ET0 equation. [ABSTRACT FROM AUTHOR]

We present work using two long-term climate datasets to show that nitrogen fertiliser is an important aspect of yield projection for three major crops. The ability of linear models using climate variables as predictors to accurately project the yield of maize, rice and wheat over multi-decadal scales is improved with the addition of fertiliser as an input. Highly productive nations including Argentina, India, Poland and South Africa show significant improvement in yield simulations and show that fertiliser use should not be discounted when estimating yield variability. The use of nitrogen fertiliser in the generalised linear models improves yield forecast by 18% using the Princeton climate dataset and 23% using the WFDEI climate dataset. This work therefore supports the use of additional predictors than climate for improving the ability of statistical models to reconstruct yield variability. [ABSTRACT FROM AUTHOR]

In the coming decades, the already fragile agricultural system in West Africa will face further challenges in meeting food security, both from increasing population and from the impacts of climate change. Optimal prioritization of adaptation investments requires the assessment of various possible adaptation options and their uncertainties; successful adaptations of agriculture to climate change should not only help farmers deal with current climate risks, but also reduce negative (or enhance positive) impacts associated with climate change using robust climate projections. Here, we use two well-validated crop models (APSIM v7.5 and SARRA-H v3.2) and an ensemble of downscaled climate forcing from the CMIP5 models to assess five possible and realistic adaptation options for the production of the staple crop sorghum ( Sorghum bicolor Moench .): (i) late sowing, (ii) intensification of seeding density and fertilizer use, (iii) increasing cultivars’ thermal time requirement, (iv) water harvesting, and (v) increasing resilience to heat stress during the flowering period. We adopt a new assessment framework to account for both the impacts of proposed adaptation options in the historical climate and their ability to reduce the impacts of future climate change, and we also consider changes in both mean yield and inter-annual yield variability. We target the future period of 2031–2060 for the “business-as-usual” scenario (RCP8.5), and compare with the historical period of 1961–1990. Our results reveal that most proposed “adaptation options” are not more beneficial in the future than in the historical climate (−12% to +4% in mean yield), so that they do not really reduce the climate change impacts. Increased temperature resilience during the grain number formation period is the main adaptation that emerges (+4.5%). Intensification of fertilizer inputs can dramatically benefit yields in the historical/current climate (+50%), but does not reduce negative climate change impacts except in scenarios with substantial rainfall increases. Water harvesting contributes to a small benefit in the current climate (+1.5% to +4.0%) but has little additional benefit under climate change. Our analysis of uncertainties arising from crop model differences (conditioned on the used model versions) and various climate model projections provide insights on how to further constrain uncertainties for assessing future climate adaptation options. [ABSTRACT FROM AUTHOR]

Global climate change will increase the frequency of hot temperatures, impairing health and productivity for millions of working people and raising labor costs. In mainland China, high-temperature subsidies (HTSs) are allocated to employees for each working day in extremely hot environments, but the potential heat-related increase in labor cost has not been evaluated so far. Here, we estimate the potential HTS cost in current and future climates under different scenarios of socioeconomic development and radiative forcing (Representative Concentration Pathway), taking uncertainties from the climate model structure and bias correction into account. On average, the total HTS in China is estimated at 38.6 billion yuan/y (US $6.22 billion/y) over the 1979-2005 period, which is equivalent to 0.2% of the gross domestic product (GDP). Assuming that the HTS standards (per employee per hot day) remain unchanged throughout the 21st century, the total HTS may reach 250 billion yuan/y in the 2030s and 1,000 billion yuan/y in 2100. We further show that, without specific adaptation, the increased HTS cost is mainly determined by population growth until the 2030s and climate change after the mid-21st century because of increasingly frequent hot weather. Accounting for the likely possibility that HTS standards follow the wages, the share of GDP devoted to HTS could become as high as 3% at the end of 21st century. [ABSTRACT FROM AUTHOR]

This study estimates statistical models emulating maize yield responses to changes in temperature and precipitation simulated by global gridded crop models. We use the unique and newly released Inter-Sectoral Impact Model Intercomparison Project Fast Track ensemble of global gridded crop model simulations to build a panel of annual maize yields simulations from five crop models and corresponding monthly weather variables for over a century. This dataset is then used to estimate statistical relationship between yields and weather variables for each crop model. The statistical models are able to closely replicate both in- and out-of-sample maize yields projected by the crop models. This study therefore provides simple tools to predict gridded changes in maize yields due to climate change at the global level. By emulating crop yields for several models, the tools will be useful for climate change impact assessments and facilitate evaluation of crop model uncertainty. [ABSTRACT FROM AUTHOR]

Highlights: [•] We simulate crop yield using state-of-the-art satellite rainfall estimates. [•] Satellite data use can introduce large biases in crop yield predictions. [•] Biases differ strongly among the four crops considered in two sites. [•] Biases are not simple extrapolation of each satellite product total rainfall biases. [•] A realistic distribution of rainfall can be very important for crop yield prediction. [Copyright &y& Elsevier]

This article predicts an unexpected positive effect of climate change on cotton production in Cameroon. Global warming could threaten cotton production in Africa due to increasing temperature and CO, and rainfall uncertainties. This situation is worsened by the fact that most African farmers grow cotton as their cash crop and have few or no possible alternatives. Assessing the impact of climate change on cotton production is therefore critical. Here, we used CROPGRO, a process-based crop model that can simulate the main features of cotton growth and management. We applied this model to two regions in North Cameroon and a set of six regional climate projections combining general climate models and regional climate models from the ENSEMBLES project. The model was calibrated and validated with a data set of observations made in farmer fields from 2001 to 2005 and at an experimental station in 2010. Our results show unexpectedly that climate change from 2005 to 2050 in North Cameroon will have a positive effect on cotton yields with an increase of 1.3 kg ha year in yield, especially if conservation agriculture systems are adopted. The predicted increase of 0.05 °C year in temperature will shorten crop cycles by 0.1 day year with no negative effect on yields. Moreover, the fertilizing effect of CO enrichment will increase yields by approximately 30 kg ha. The rainfall pattern is likely to change, but the six regional models used to generate future weather patterns did not predict a decrease in rainfall. One model even forecast an increase in rainfall amounts. According to our findings, climate changes in North Cameroon can be anticipated by tailoring alternative cropping systems and adaptation techniques to cope with climate change. [ABSTRACT FROM AUTHOR]

Abstract: A multi-year (2004–2009) field survey of on-farm sowing practices in 10 villages located in south-west Niger close to Niamey, is analysed to investigate the relationships (i) between rainfall and the sowing date of pearl millet and the risk of sowing failure, (ii) between sowing and meteorological/agro-climatic onset dates, (iii) between sowing/onset dates, and simulated and observed yield/biomass at the end of the season. Even if some villages sow without any synchronous or anterior rainfall, most parcels (73% out of the 1551 available cases) are sown during and just after a 2-day wet spell receiving at least 10mm. In fact, there is a strong correlation (r =0.82–0.95 depending on onset definition) between the spatial averages of onset and of sowing dates. Most of the failed sowings (≈22% of total sowings) are related to dry spells lasting at least 7 days after an initial 2-day wet spell receiving less than 10mm. Simulations with the “Système d’Analyse Régionale des Risques Agronomiques – version Habillée” crop model show that the ideal sowing date, retrospectively computed as the one maximizing simulated yield, is on average about 6 days later than the observed one. Despite the large inter-village variance and the relatively weak inter-annual signal in onset dates and seasonal amounts, there is a tendency for weaker yields and especially weaker amounts of biomass for late onset. But crop simulations show that sowing very early, as for example during or just after the first wet spell when at least 90% of rainfall stations receive simultaneously at least 1mm in two consecutive days (i.e. meteorological onset), does not necessarily maximize simulated yield because of the high risk of long-lasting post-onset dry spells. The farmers’ strategy, that is sowing their field during or just after the first significant wet spell, is combined with the use of photoperiodic varieties to provide the best-suited response to the temporal and spatial variability of onset of the rainy season. [Copyright &y& Elsevier]

The article presents a study which assesses the effect of rainfall in cotton yields in West Africa through multi-scales and multi-sites analyses. The climate-yields relationships manifested by two completely different sites were determined using an experimental plot with a long-term historical yield-survey and yields of farmers from 28 administrative units of Cameroon. It also discusses the duration of rainy season and its effect to year-to-year and spatial distribution of cotton productivity.

The variability of the West African monsoon on the intraseasonal time scale is a major issue for agricultural strategy, as the occurrence of dry spells can strongly impact yields of rain-fed crops. This study investigates this intraseasonal variability of rainfall over West Africa and gives a first overview of its predictability at a medium lead time. A statistical method, the singular spectrum analysis, is applied to a ground-based rainfall index in West Africa to describe first temporal patterns of the main leading modes of intraseasonal variability. The results point out the existence of one oscillatory mode of 34 days, one of 20 days, and one of 14 days. The same methodology is applied to rainfall from two reanalysis datasets and to deep convection from satellite data in order to assess the accuracy of the representation of intraseasonal variability in these datasets. It is shown that although the day-to-day variability of rainfall is not well captured in these datasets, intraseasonal features and, in particular, the low-frequency mode are very well reproduced. The medium lead-time predictability (5–10 days) of the intraseasonal modes is investigated using both the dynamical forecast scheme of the ECMWF and a statistical method, the maximum entropy method. For the latter method, an operational application using unfiltered input data is also considered. The performance of these prediction schemes is compared using a simple reference technique in which forecasts are based entirely on persistence. It is found that statistical predictions are much more promising than the dynamical ones, though they encounter problems when applied operationally. In an operational application, the forecast skill for the 10–90-day intraseasonal band is low but the predictability of individual intraseasonal modes is higher. The stability of the forecast skill levels is influenced by the characteristics of the intraseasonal mode. When the characteristics (i.e., amplitude and period) of the considered intraseasonal mode are well defined, skillful forecasts can be obtained. However, when the characteristics change rapidly, the forecast fails. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study investigates the diurnal cycle of the West African monsoon and its seasonal modulation with particular focus on the monsoon onset period. A composite analysis around the monsoon onset date is applied to the 1979–2000 NCEP–DOE reanalysis and 40-yr ECMWF Re-Analysis (ERA-40) at 0000, 0600, 1200, and 1800 UTC. This study points out two independent modes describing the space–time variability of the diurnal cycle of low-level wind and temperature. While the first mode appears to belong to a gradual and seasonal pattern linked with the northward migration of the whole monsoon system, the second mode is characterized by more rapid time variations with a peak of both temperature and wind anomalies around the monsoon onset date. This latter mode is connected with the time pattern of a nocturnal jet reaching its highest values around the onset date. The diurnal cycle of dry and deep convection is also investigated through the same method. A distinct diurnal cycle of deep convection in the ITCZ is evidenced with a peak at 1200 UTC before the monsoon onset, and at 1800 UTC after the monsoon onset. Strong ascending motions associated with deep convection may generate a gravity wave that propagates northward and reaches the Saharan heat low region 12 h later. The diurnal cycle of the dry convection in the Saharan heat low is similar during the preonset and the postonset periods with a peak at night (0000 UTC) consistent with the nocturnal jet intensification. This convection is localized at 15° and 20°N before and after the monsoon onset, respectively. Both during the first rainy season in spring and the monsoon season in summer, the nocturnal jet brings moisture in the boundary layer north of the ITCZ favoring humidification and initiation of new convective cells, helping the northward progression of the ITCZ. At the end of the summer the southward return of the ITCZ is associated with the disappearance of the core of the monsoon jet. Despite a lot of similarities between the results obtained using NCEP–DOE and ERA-40 reanalyses, giving confidence in the significance of these results, some differences are identified, especially in the diurnal cycle of deep convection, which limit the interpretation of some of these results and highlight discrepancies in the reanalyses. [ABSTRACT FROM AUTHOR]

Abstract: Agriculture in the Sudano-Sahelian zone is heavily dependent on the seasonal characteristics of rainfall. This study seeks to characterise components of regional climatic variability and their impact on simulated, attainable, plot level yields of millet. First we describe at a regional scale two main events in the seasonal pattern of the monsoon over West Africa by using a daily rainfall dataset over the 1968–1990 period, that is, (i) the onset of the summer monsoon characterised by an abrupt northward shift of the ITCZ from 5°N to 10°N around 24 June, and (ii) large and coherent intra-seasonal rainfall fluctuations at two different time scales, around 15 and 40 days. Second, we investigate the impact of these regional phenomena on local crop yields using SARRAH, a crop model simulating attainable yield, i.e. water and climate limited but not nutrient limited yield, by means of sensitivity analyses. The response of attainable yield to sowing date is studied for 19 years of the 1968–1990 period for a 90-day millet crop at Niamey. The results indicate that information on regional climate dynamics might help improve crop production locally. It is shown that the regional onset of the monsoon is very close to the ideal sowing date, derived from simulations, at Niamey and that simulated yields are much higher for these dates than for those identified with the traditional rule based on local rainfall. Taking into account the regional onset of monsoon thus seems to improve the relationship between water available and water used by the plant, and thus seems to potentially increase crop water use. Where attainable, simulated yields using the monsoon onset criterion are low, they are generally caused by intra-seasonal dry spells that have differential impact depending on phenological stage of the crop. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Intraseasonal variability in the West African monsoon is documented by using daily gridded datasets of rainfall and convection, and reanalyzed atmospheric fields, over the period 1968–90. Rainfall and convection over West Africa are significantly modulated at two intraseasonal timescales, 10–25 and 25–60 day, leading to variations of more than 30% of the seasonal signal. A composite analysis based on the dates of the maximum (minimum) of a regional rainfall index in wet (dry) sequences shows that these sequences last, on average, 9 days and belong to a main quasiperiodic signal of about 15 days. A secondary periodicity of 38 days is present but leads to a weaker modulation. During a wet (dry) sequence, convection in the ITCZ is enhanced (weakened) and its northern boundary moves to the north (south), while the speed of the African easterly jet decreases (increases), the speed of the tropical easterly jet increases (decreases), and the monsoon flow becomes stronger (weaker), all these features being similar to the ones associated with interannual variability characterizing wet and dry years. This modulation of convection at intraseasonal timescales is not limited to West Africa but corresponds to a westward-propagating signal from eastern Africa to the western tropical Atlantic. An enhanced monsoon phase is associated with stronger cyclonic activity in the low levels over the Sahel associated with stronger moisture advection over West Africa. Five days before the full development of the wet phase, a stronger cyclonic circulation at 20°E induces enhanced southerly winds along 25°E where convection enhances, while another westward-propagating cyclonic circulation is located at 0°. This atmospheric pattern is linked to the enhancement of the subsiding branch of the northern Hadley cell at 35°N, northerly advection of drier air over West Africa, and to increased dry convection in the heat low at 20°N. It propagates westward, leading to a zonally extended area of enhanced monsoon winds over West Africa consistent with the occurrence of the wet phase. [ABSTRACT FROM AUTHOR]

The arrival of the summer monsoon over West Africa has been documented by using daily gridded rainfall data and NCEP–NCAR reanalyses during the period 1968–90, and OLR data over the period 1979–90. Two steps have been characterized through a composite approach: the preonset and the onset of the summer monsoon. The preonset stage corresponds to the arrival in the intertropical front (ITF) at 15°N, that is, the confluence line between moist southwesterly monsoon winds and dry northeasterly Harmattan, bringing sufficient moisture for isolated convective systems to develop in the Sudano–Sahelian zone while the intertropical convergence zone (ITCZ) is centered at 5°N. The mean date for the preonset occurrence is 14 May and its standard deviation is 9.5 days during the period 1968–90. This leads to a first clear increase of the positive rainfall slope corresponding to the beginning of the rainy season over this Sudano–Sahelian area. The onset stage of the summer monsoon over West Africa is linked to an abrupt latitudinal shift of the ITCZ from a quasi-stationary location at 5°N in May–June to another quasi-stationary location at 10°N in July–August. The mean date for the onset occurrence is 24 June and its standard deviation is 8 days during the period 1968–90. This leads to a second increase of the positive rainfall slope over the Sudano–Sahelian zone signing the northernmost location of the ITCZ and the beginning of the monsoon season. This abrupt shift occurs mostly between 10°W and 5°E, where a meridional land–sea contrast exists, and it is characterized by a temporary rainfall and convection decrease over West Africa. Preonset dates, onset dates, and summer rainfall amount over the Sahel are uncorrelated during the period 1968–90. The atmospheric dynamics associated with the abrupt ITCZ shift has been investigated. Between the preonset and the onset stages, the heat low dynamics associated with the ITF controls the circulation in the low and midlevels. Its meridional circulation intensity is the highest at the beginning of the monsoon onset. This can lead to 1) increased convective inhibition in the ITCZ through intrusion of dry and subsiding air from the north, and 2) increased potential instability through a greater inland moisture advection and a higher monsoon depth induced by a stronger cyclonic circulation in the low levels, through higher vertical wind shear due to westerly monsoon wind and midlevel African easterly jet (AEJ) increases, through enhancement of the instability character of the AEJ, and through increased shortwave radiation received at the surface. During the monsoon onset, once the rainfall minimum occurred due to the convective inhibition, the accumulated potential instability breaks the convective inhibition, the inertial instability of the monsoon circulation is released, and the associated regional-scale circulation increases, leading to the abrupt shift of the ITCZ. Then the ITCZ moves north up to 10°N, where thermodynamical conditions are favorable. It is suggested by the authors that the abrupt shift of the ITCZ, initiated by the amplification of the heat low dynamics, could be due to an interaction with the northern orography of the Atlas–Ahaggar Mountains. Subsidence over and north of this orography, due to both the northern branches of the heat low and of the northern Hadley-type cell, contributes to enhance the high geopotentials north of these mountains and the associated northeasterly winds. This leads to the development of a leeward trough that reinforces the heat low dynamics, maintaining an active convective ITCZ through enhanced moist air advection from the ocean, increasing the northern Hadley circulation, which reinforces the high geopotentials and the interaction with the orography through a positive feedback. The fact that an abrupt shift of the ITCZ is only observed on the western part of West Africa may result from the enhancement of moisture advection, which comes from the west and has a stronger impact west of the Greenwich meridian. The northwest–southeast orientation of the Atlas–Ahaggar crest can induce the interaction with the heat low, first in the east where the mountains are nearer to the ITF than in the west, and second in the west. Another consequence of the possible orography-induced interaction with the atmospheric circulation is that the induced leeward trough, increasing the cyclonic vorticity in the heat low, may stimulate moisture convergence in the oceanic ITCZ near the western coast of West Africa. [ABSTRACT FROM AUTHOR]

Achieving food security goals in West Africa will depend on the capacity of the agricultural sector to feed the rapidly growing population and to moderate the adverse impacts of climate change. Indeed, a number of studies anticipate a reduction of the crop yield of the main staple food crops in the region in the coming decades due to global warming. Here, we found that crop production might have already been affected by climate change, with significant yield losses estimated in the historical past. We used a large ensemble of historical climate simulations derived from an atmospheric general circulation model and two process-based crop models, SARRA-H and CYGMA, to evaluate the effects of historical climate change on crop production in West Africa. We generated two ensembles of 100 historical simulations of yields of sorghum and millet corresponding to two climate conditions for each crop model. One ensemble is based on a realistic simulation of the actual climate, while the other is based on a climate simulation that does not account for human influences on climate systems (that is, the non-warming counterfactual climate condition). We found that the last simulated decade, 2000–2009, is approximately 1 °C warmer in West Africa in the ensemble accounting for human influences on climate, with more frequent heat and rainfall extremes. These altered climate conditions have led to regional average yield reductions of 10–20% for millet and 5–15% for sorghum in the two crop models. We found that the average annual production losses across West Africa in 2000–2009 associated with historical climate change, relative to a non-warming counterfactual condition (that is, pre-industrial climate), accounted for 2.33–4.02 billion USD for millet and 0.73–2.17 billion USD for sorghum. The estimates of production losses presented here can be a basis for the loss and damage associated with climate change to date and useful in estimating the costs of the adaptation of crop production systems in the region. [ABSTRACT FROM AUTHOR]

The need for climate services in West Africa can be strongly argued for by the strong climate impacts that the region will be confronted with in the future, which add to its already existing specific biophysical and social vulnerability to climate variations. In order to be of best use, the development of climate services in West Africa should pay dedicated attention to the particular profiles, needs and skills of their future users in this region.Both the ISIpedia and CLIMAP projects, funded by the European Joint Programming Initiative "Connecting Climate Knowledge for Europe", respectively the French Ministry for an Ecological and Solidary Transition, have recently tried to achieve this through defined stakeholder engagement processes in West Africa, with the final objective of developing a portal delivering climate and climate-impact information. Within each project, a user survey targeted at the potential future users of these portals was conducted independently between the end of 2017 and April 2018. The ISIpedia survey collected 131 usable answers worldwide, including 23 from West Africa which mainly come from government members or scientists working on climate-related science in this region. 58 persons responded to the CLIMAP survey in total, and these are currently working for companies, universities, government agencies or development banks in Senegal.About 30% of the respondents to the CLIMAP survey declared using climate projections at least once a month, while the integrality of the West African respondents to the ISIpedia survey use climate-impact information at this frequency. The collected responses are therefore useful to reveal some key insights on how to tailor climate services to the needs of West African users. They point at barriers to making use of available climate or climate-impact information that are shared globally, such as large uncertainties or a spatial scale mismatch between delivered information and the respondents' requirements. However, they also consistently emphasise issues that are more acute in the West African region, such as the lack of skills to understand or use the available information, infrastructure problems (e.g., poor Internet access), or the lacking information in another language than English. Both the ISIpedia and CLIMAP projects have already and will further make use of these survey results in order to design and develop content for portals delivering climate-related information in West Africa (eventually targeting a global coverage in the case of ISIpedia), as well as accompanying capacity-building activities. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, temperatures are generally conducive to malaria transmission, and rainfall provides mosquitoes with optimal breeding conditions. The objective of this study is to assess the impact of future climate change on malaria transmission in West Africa using community-based vector-borne disease models, TRIeste (VECTRI). This VECTRI model, based on bias-corrected data from the Phase 6 Coupled Model Intercomparison Project (CMIP6), was used to simulate malaria parameters, such as the entomological inoculation rate (EIR). Due to the lack of data on confirmed malaria cases throughout West Africa, we first validated the forced VECTRI model with CMIP6 data in Senegal. This comparative study between observed malaria data from the National Malaria Control Program in Senegal (Programme National de Lutte contre le Paludisme, PNLP, PNLP) and malaria simulation data with the VECTRI (EIR) model has shown the ability of the biological model to simulate malaria transmission in Senegal. We then used the VECTRI model to reproduce the historical characteristics of malaria in West Africa and quantify the projected changes with two Shared Socio-economic Pathways (SSPs). The method adopted consists of first studying the climate in West Africa for a historical period (1950–2014), then evaluating the performance of VECTRI to simulate malaria over the same period (1950–2014), and finally studying the impact of projected climate change on malaria in a future period (2015–2100) according to the ssp245 ssp585 scenario. The results showed that low-latitude (southern) regions with abundant rainfall are the areas most affected by malaria transmission. Two transmission peaks are observed in June and October, with a period of high transmission extending from May to November. In contrast to regions with high latitudes in the north, semi-arid zones experience a relatively brief transmission period that occurs between August, September, and October, with the peak observed in September. Regarding projections based on the ssp585 scenario, the results indicate that, in general, malaria prevalence will gradually decrease in West Africa in the distant future. But the period of high transmission will tend to expand in the future. In addition, the shift of malaria prevalence from already affected areas to more suitable areas due to climate change is observed. Similar results were also observed with the ssp245 scenario regarding the projection of malaria prevalence. In contrast, the ssp245 scenario predicts an increase in malaria prevalence in the distant future, while the ssp585 scenario predicts a decrease. These findings are valuable for decision makers in developing public health initiatives in West Africa to mitigate the impact of this disease in the region in the context of climate change. [ABSTRACT FROM AUTHOR]

The ability of a region to feed itself in the upcoming decades is an important issue. TheWest 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 1.5K above preindustrial 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the Sudano–Sahelian zone, which includes Northern Cameroon, the inter-annual variability of the rainy season is high and irrigation scarce. As a consequence, bad rainy seasons have a detrimental impact on crop yield. In this paper, we assess the risk mitigation capacity of weather index-based insurance for cotton farmers. We compare the ability of various indices, mainly based on daily rainfall, to increase the expected utility of a representative risk-averse farmer. We first give a tractable definition of basis risk and use it to show that weather index-based insurance is associated with a large basis risk, whatever the index considered. It has thus limited potential for income smoothing, a conclusion which is robust to the utility function. Second, in accordance with the existing agronomical literature we find that the length of the cotton growing cycle, in days, is the best performing index considered. Third, we show that using observed cotton sowing dates to define the length of the growing cycle significantly decreases the basis risk, compared to using simulated sowing dates. Finally we find that the gain of the weather-index based insurance is lower than that of hedging against cotton price fluctuations provided by the national cotton company. This casts doubt on the strategy of supporting weather–index insurances in cash crop sectors selling at international market prices without recommending any price stabilisation scheme. [Copyright &y& Elsevier]

Senegal is particularly vulnerable to precipitation variability. To investigate the influence of large-scale circulation on local-scale precipitation, a full spatial-statistical description of precipitation occurrence and amount for Senegal is developed. These regression-type models have been built on the basis of daily records at 137 locations and were developed in two stages: (i) a baseline model describing the expected daily occurrence probability and precipitation amount as spatial fields from monsoon onset to offset, and (ii) the inclusion of weather types defined from the NCEP-NCAR reanalysis 850-hPa winds and 925-hPa relative humidity establishing the link to the synoptic-scale atmospheric circulation. During peak phase, the resulting types appear in two main cycles that can be linked to passing African easterly waves. The models allow the investigation of the spatial response of precipitation occurrence and amount to a discrete set of preferred states of the atmospheric circulation. As such, they can be used for drought risk mapping and the downscaling of climate change projections. Necessary choices, such as filtering and scaling of the atmospheric data (as well as the number of weather types to be used), have been made on the basis of the precipitation models' performance instead of relying on external criteria. It could be demonstrated that the inclusion of the synoptic-scale weather types lead to skill on the local and daily scale. On the interannual scale, the models for precipitation occurrence and amount capture 26% and 38% of the interannual spatially averaged variability, corresponding to Pearson correlation coefficients of rO = 0.52 and ri = 0.65, respectively. [ABSTRACT FROM AUTHOR]

Compared with the southwest monsoon, the mechanisms and history of northeast monsoon (NEM) variability over South Asia are poorly known. The NEM nevertheless contributes >/50% of rainfall to semi-arid southeast India, and has underpinned the success, over the last 2000 years, of a widespread indigenous water resource management system based on rainwater harvesting and storage in man-made lakes. Based on a power spectrum analysis of 60- to 100-yr instrumental rainfall records, we show that NEM rainfall is both chaotic in the high-frequency time domain and spatially unpredictable, but that the sustainability of the agricultural water-harvesting infrastructure is tied to excess rain from NEM cyclones. Cyclonic activity has declined sharply since 1977. Based on twentieth-century analogues, we propose a qualitative model in which storminess decreases when NEM wind intensities increase and Eurasian winter temperatures are anomalously low. Extrapolating to the Holocene time frame, model expectations for the palaeorainfall record correlate with the independently established chronologies of Indian runoff-irrigation development and the ‘Mediaeval Warm Period’—‘Little Ice Age’ oscillations, and further suggest that NEM and SWM rainfall anomalies have covaried with the same sign from seasonal to millennial timescales. The fortunes of the reservoir system are therefore tangibly climate-driven. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To contribute to building sustainable and effective climate change adaptation solutions avoiding usability gap, it is largely recommended to engage in the process of co-production, integrating expertise and knowledge from various academic and non-academic actors. We want to learn if and how co-production, believed to effectively link knowledge and decision-making, and thus suggested as the best practice in building decision-support frameworks, is really applied in the frameworks that are being implemented on the ground. A literature review allowed us to identify integrated decision-support frameworks for climate-change adaptation in agriculture developed and used over the period of the last 10 years and involving non-academic stakeholders. To analyse them, we chose as an assessment tool the four co-production principles proposed by Norström and colleagues: context-based, pluralistic, goal-oriented and interactive. The useful entry points for incorporating co-production in the design of decision-support that we found in the reviewed frameworks include among the others adequate participants selection strategy, building on existing interaction spaces, developing a theory of change with the participants, and involving participants in the design of different elements of the method. The architectures of the analyzed frameworks contained more elements that responded to pluralistic and interactive principles than to context-based and goal oriented principles, we have also identified gaps in the design, such as taking into account the personal characteristics of researchers that could strengthen a framework's implementation and its impact, or attempts at bridging different levels of decision making, to cover the triad of science, policy and practice. A detailed look at the decision-frameworks that are actually being applied allows for a critical reflection whether and how we as researchers use what we preach as an effective way of responding to sustainability challenges in agriculture. Co-production principles turn out to be a useful tool for analysis and we suggest they can be used as a check-list when designing decision-support frameworks for climate-change adaptation. This papers offers useful examples of how to shift the research-led processes of decision-support towards more co-production with non-academic actors, to increase chances of bridging the gaps between science, policy and practice. [Display omitted] • Knowledge co-production is largely suggested as the best practice to bridge the gaps between science, policy and practice. • Evidence is lacking for the actual use of co-production in decision-support for climate-change adaptation in agriculture. • We identify useful entry points for applying co-production principles from already used decision-support frameworks. • Researchers are rarely selected to build decision-support frameworks based on their ability to support co-production. • When designing decision-support frameworks co-production principles can be used as a guiding tool. [ABSTRACT FROM AUTHOR]

Sustainable intensification of cereal cropping systems can contribute to reducing food insecurity while mitigating biodiversity losses thanks to land sparing. Yet, intensifying crop production with more nutrient inputs often increases sensitivity to climate hazards and yields inter-annual variability. How different options for sustainable intensification interact and how they could help achieve greater and less variable yields is not well known for key cereal cropping systems of sub-Saharan Africa. Here, we aim to assess the impact on productivity and its interannual variation of three contrasted strategies of crop intensification, namely i) the integration of cowpea intercropped with sorghum or as sole crop, ii) improved varieties with high harvest index and greater nitrogen use efficiency, and iii) the use of more mineral fertilizer. We did this assessment using a calibrated soil-crop model for a case study of sorghum-based cropping systems of semi-arid southern Mali, using a historical weather series from 1980 to 2010. We found that the incremental combinations of two or three strategies helped increase productivity in terms of energy and protein. However, greater productivity went hand in hand with greater inter-annual variability. Intercropping sorghum with cowpea helped to reduce the inter-annual variability of productivity, but only when mineral fertilizer was not added. With mineral fertilizer input, sorghum contributed the largest share of the intercrop productivity, and sorghum yield was more variable from a year to another than cowpea yield. The overall productivity of intercropping sorghum with cowpea was always greater than having the two sole crops on separate plots. This was possible thanks to the facilitation of light, water and N acquisition by the two crops in the intercropping, regardless of the specificities of the growing season with regard to water and nitrogen stress. However, the variability of productivity was smaller for the cultivation of the two sole crops on separate plots, regardless of fertilizer input. The often-claimed benefit of intercropping on reducing the inter-annual variability of production does not hold when the three strategies are combined. Our results suggest that the integration of legumes does not help cushion farmers against the risk related to the intensification of cropping systems with more nutrients and improved seeds. Careful gross margin analysis that accounts for price variability and uncertainty would be required to refine this risk assessment for specific farm contexts. We discuss the implication of these findings in light of the current research agenda for the sustainable intensification of cropping systems in sub-Saharan Africa. • We explored the impact of climate variability on legume diversification using a crop model. • The case study is land-constrained and semi-arid southern Mali. • Improved variety and mineral fertilizer contribute to increase variability of productivity. • Intercropping help decrease variability of productivity, but only without fertilizer input. • Integrating a sole legume stabilizes productivity, but with smaller gains than intercropping. [ABSTRACT FROM AUTHOR]

This poster aims at presenting the main features of a climate web portal in Senegal called CLIMAP and the main stages of its development. In order to provide guidelines to design this portal, we performed a survey analysis of users' needs for such a tool, focusing on the agricultural sector in Senegal. The survey is structured in 6 different parts : (i) User description, (ii) Climate services use, (iii) Knowledge about climate projections, (iv) Climate projections use, (v) Needs and interest for a climate data web portal. We completed these results with specific semi-structured interviews and a participatory workshop and we then created a mirror group of users to test the website. Results show that there is a strong interest for climate projections, especially for sectoral data. However, in order to be used efficiently, such a portal development has to pay attention to several specific issues like language, internet connection, capacity building and training, providing the relevant variables at the relevant resolution. We also underline that choosing the relevant organization hosting the website seems to be a key point for an effective use. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 agroecosystem. 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. [ABSTRACT FROM AUTHOR]

Background: Europe has warmed more than the global average (land and ocean) since pre-industrial times, and is also projected to continue to warm faster than the global average in the twenty-first century. According to the climate models ensemble projections for various climate scenarios, annual mean temperature of Europe for 2071-2100 is predicted to be 1-5.5 °C higher than that for 1971-2000. Climate change and elevated CO concentration are anticipated to affect grassland management and livestock production in Europe. However, there has been little work done to quantify the European-wide response of grassland to future climate change. Here we applied ORCHIDEE-GM v2.2, a grid-based model for managed grassland, over European grassland to estimate the impacts of future global change. Results: Increases in grassland productivity are simulated in response to future global change, which are mainly attributed to the simulated fertilization effect of rising CO. The results show significant phenology shifts, in particular an earlier winter-spring onset of grass growth over Europe. A longer growing season is projected over southern and southeastern Europe. In other regions, summer drought causes an earlier end to the growing season, overall reducing growing season length. Future global change allows an increase of management intensity with higher than current potential annual grass forage yield, grazing capacity and livestock density, and a shift in seasonal grazing capacity. We found a continual grassland soil carbon sink in Mediterranean, Alpine, North eastern, South eastern and Eastern regions under specific warming level (SWL) of 1.5 and 2 °C relative to pre-industrial climate. However, this carbon sink is found to saturate, and gradually turn to a carbon source at warming level reaching 3.5 °C. Conclusions: This study provides a European-wide assessment of the future changes in productivity and phenology of grassland, and their consequences for the management intensity and the carbon balance. The simulated productivity increase in response to future global change enables an intensification of grassland management over Europe. However, the simulated increase in the interannual variability of grassland productivity over some regions may reduce the farmers' ability to take advantage of the increased long-term mean productivity in the face of more frequent, and more severe drops of productivity in the future. [ABSTRACT FROM AUTHOR]

Several lines of evidence point to European managed grassland ecosystems being a sink of carbon. In this study, we apply ORCHIDEE-GM a process-based carbon cycle model that describes specific management practices of pastures and the dynamics of carbon cycling in response to changes in climatic and biogeochemical drivers. The model is used to simulate changes in the carbon balance [i.e., net biome production (NBP)] of European grasslands over 1991-2010 on a 25 km × 25 km grid. The modeled average trend in NBP is 1.8-2.0 g C m-2 yr-2 during the past two decades. Attribution of this trend suggests management intensity as the dominant driver explaining NBP trends in the model (36-43% of the trend due to all drivers). A major change in grassland management intensity has occurred across Europe resulting from reduced livestock numbers. This change has 'inadvertently' enhanced soil C sequestration and reduced N2O and CH4 emissions by 1.2-1.5 Gt CO2-equivalent, offsetting more than 7% of greenhouse gas emissions in the whole European agricultural sector during the period 1991-2010. Land-cover change, climate change and rising CO2 also make positive and moderate contributions to the NBP trend (between 24% and 31% of the trend due to all drivers). Changes in nitrogen addition (including fertilization and atmospheric deposition) are found to have only marginal net effect on NBP trends. However, this may not reflect reality because our model has only a very simple parameterization of nitrogen effects on photosynthesis. The sum of NBP trends from each driver is larger than the trend obtained when all drivers are varied together, leaving a residual - nonattributed - term (22-26% of the trend due to all drivers) indicating negative interactions between drivers. [ABSTRACT FROM AUTHOR]

Intercropping is a key entry point for sustainable intensification of cropping systems in sub-Saharan Africa where variable rainfall conditions prevail. Crop simulation models can complement field experiments to assess the agronomic and environmental performances of intercropping systems under diverse climatic conditions, including hypothetical future climate. So far, crop models that can handle intercropping, such as STICS, have not often been extensively evaluated for tropical conditions and for species grown by farmers in sub-Saharan Africa. The objective of this study was to evaluate the performance of the calibrated STICS model to simulate sorghum-cowpea intercropping systems in rainfed conditions in West Africa. We used data from field experiments conducted at the N'Tarla Agronomic Station in Mali in 2017 and 2018. Two varieties of sorghum (local and improved) with contrasting photoperiod sensitivities were grown as sole crop and intercropped with cowpea, with additive design. Two sowing dates and two levels of mineral fertilization were also investigated. Model simulations were evaluated with observed data for phenology, leaf area index (LAI), aboveground biomass, grain yield and in-season soil moisture. Large variations in aboveground biomass of sorghum and cowpea was observed in the experiment (i.e. 3.5 – 9.6 t/ha for sorghum and 0.4–2.5 t/ha for cowpea), owing to the treatments (i.e. sole vs intercrop, early vs late sowing, no fertilizer input vs fertilizer input). Such variations were satisfactorily reproduced by the model, with EF of 0.81 in calibration and 0.58 in evaluation (with relative rRMSE of 23 % and 43 %) across crops. Sorghum AGB simulations were more accurate (rRMSE of 21 % and EF of 0.54) than cowpea AGB simulations (rRMSE of 25 % and EF of −0.09). The two main observed features of the intercropping system were well reproduced by the model. Firstly, cowpea and sorghum aboveground biomass decreased with intercropping compared with sole cropping, and the decrease in cowpea biomass was greater than the decrease in sorghum biomass. Secondly, despite a reduction in sorghum and cowpea yield, Land Equivalent Ratio of the intercropping for aboveground biomass was always above one. With regard to grain yield, observed LER was above one only in the non-fertilized treatment. The model failed at reproducing this behavior, probably because of insufficiently accurate calibration of the process leading to grain yield formation: rRMSE for grain yield was 49 % in calibration and 41 % in evaluation. Based on these findings, we discuss avenues to improve model calibration and use the model to explore options for sustainable intensification in land constrained sub-Saharan Africa. • Accurate STICS simulations of intercropped sorghum and cowpea aboveground biomass. • Inaccurate STICS simulations of intercropped sorghum and cowpea grain yield. • The model can be used to start exploring inter-annual performance of intercropping. • Experiments looking at N dynamics in intercropping needed to refine calibration. [ABSTRACT FROM AUTHOR]

The greenhouse gas ( GHG) balance of European grasslands ( EU-28 plus Norway and Switzerland), including CO2, CH4 and N2O, is estimated using the new process-based biogeochemical model ORCHIDEE- GM over the period 1961-2010. The model includes the following: (1) a mechanistic representation of the spatial distribution of management practice; (2) management intensity, going from intensively to extensively managed; (3) gridded simulation of the carbon balance at ecosystem and farm scale; and (4) gridded simulation of N2O and CH4 emissions by fertilized grassland soils and livestock. The external drivers of the model are changing animal numbers, nitrogen fertilization and deposition, land-use change, and variable CO2 and climate. The carbon balance of European grassland ( NBP) is estimated to be a net sink of 15 ± 7 g C m−2 year−1 during 1961-2010, equivalent to a 50-year continental cumulative soil carbon sequestration of 1.0 ± 0.4 Pg C. At the farm scale, which includes both ecosystem CO2 fluxes and CO2 emissions from the digestion of harvested forage, the net C balance is roughly halved, down to a small sink, or nearly neutral flux of 8 g C m−2 year−1. Adding CH4 and N2O emissions to net ecosystem exchange to define the ecosystem-scale GHG balance, we found that grasslands remain a net GHG sink of 19 ± 10 g C- CO2 equiv. m−2 year−1, because the CO2 sink offsets N2O and grazing animal CH4 emissions. However, when considering the farm scale, the GHG balance ( NGB) becomes a net GHG source of −50 g C- CO2 equiv. m−2 year−1. ORCHIDEE- GM simulated an increase in European grassland NBP during the last five decades. This enhanced NBP reflects the combination of a positive trend of net primary production due to CO2, climate and nitrogen fertilization and the diminishing requirement for grass forage due to the Europe-wide reduction in livestock numbers. [ABSTRACT FROM AUTHOR]

The aim of this work is to define over the period 1979-2002 the main synoptic weather regimes relevant for understanding the daily variability of rainfall during the summer monsoon season over Senegal. 'Interannual' synoptic weather regimes are defined by removing the influence of the mean 1979-2002 seasonal cycle. This is different from Part I where the seasonal evolution of each year was removed, then removing also the contribution of interannual variability. As in Part I, the self-organizing maps approach, a clustering methodology based on non-linear artificial neural network, is combined with a hierarchical ascendant classification to compute these regimes. Nine weather regimes are identified using the mean sea level pressure and 850 hPa wind field as variables. The composite circulation patterns of all these nine weather regimes are very consistent with the associated anomaly patterns of precipitable water, mid-troposphere vertical velocity and rainfall. They are also consistent with the distribution of rainfall extremes. These regimes have been then gathered into different groups. A first group of four regimes is included in an inner circuit and is characterized by a modulation of the semi-permanent trough located along the western coast of West Africa and an opposite modulation on the east. This circuit is important because it associates the two wettest and highly persistent weather regimes over Senegal with the driest and the most persistent one. One derivation of this circuit is highlighted, including the two driest regimes and the most persistent one, what can provide important dry sequences occurrence. An exit of this circuit is characterised by a filling of the Saharan heat low. An entry into the main circuit includes a southward location of the Saharan heat low followed by its deepening. The last weather regime is isolated from the other ones and it has no significant impact on Senegal. It is present in June and September, and missing in July and August, meaning that this is a weather regime more specific of the intermediate seasons than the summer. It is included in a large-scale pattern covering the northern latitudes of Europe. The correspondence between these 'interannual' synoptic weather regimes and the 'pure' synoptic regimes defined in Part I has been established. By selecting a high statistical significance level for these correspondences, each of five out of nine 'interannual' weather regimes has a close correspondence with one 'pure' synoptic weather regime, one out of them have links with two 'pure' regimes, and the last three regimes have no significant correspondence in terms of 'pure' regimes. However when considering more moderate links, two out of these three regimes show a connection with a 'pure' regime, and the last one remains isolated. The ensemble of the weather regimes occurrences can explain a significant part of interannual variability of summer rainfall amount over Senegal, especially linked to the driest and the wettest weather regimes occurrences. It is also shown that Senegal rainfall state is very sensitive to a small displacement or deformation of the weather regime patterns. [ABSTRACT FROM AUTHOR]

The aim of this work is to define over the period 1979-2002 the main synoptic weather regimes relevant for understanding the daily variability of rainfall during the summer monsoon season over Senegal. 'Pure' synoptic weather regimes are defined by removing the influence of seasonal and interannual time scales, in order to highlight the day by day variability of the atmospheric circulation. The Self-Organizing Maps (SOM) approach, a clustering methodology based on non-linear artificial neural network, is combined with a Hierarchical Ascendant Classification to compute these regimes. Nine weather regimes are identified using the mean sea level pressure and 850 hPa wind field as variables, and gathered into three classes. Two of these weather regimes represent the classical 3-5-day African easterly waves with a mean wavelength of about 3,000 km. Three others are characterized by a modulation of the semi-permanent trough located along the western coast of West Africa and might be interpreted in terms of the 6-9-day easterly waves. The last four weather regimes are characterized by a more or less strong north-south dipole of circulation. They can be interpreted as a northward/southward displacement of the Saharan Heat Low for two of them, and a filling/deepening of this depression for the other two. The circulation patterns of all these nine weather regimes are very consistent with the associated anomaly patterns of precipitable water, mid-troposphere vertical velocity, outgoing longwave radiation, and finally rainfall. Rainfall distribution is also highlighted over the southwestern area of Senegal. [ABSTRACT FROM AUTHOR]

The comparison of circulation patterns (CPs) obtained from reanalysis data to those from general circulation model (GCM) simulations is a frequent task for model validation, downscaling of GCM simulations, or other climate change--related studies. Here, the authors suggest a set of measures to quantify the differences between CPs. A combination of clustering using Gaussian mixture models with a set of related difference measures allows for taking cluster size and shape information into account and thus provides more information than the Euclidean distances between cluster centroids. The characteristics of the various distance measures are illustrated with a simple simulated example. Subsequently, a five-component Gaussian mixture to define circulation patterns for the North Atlantic region from reanalysis data and GCM simulations is used. CPs are obtained independently for the NCEP--NCAR reanalysis and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), as well as for twentieth-century simulations from 14 GCMs of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) database. After discussing the difference of CPs based on spherical and nonspherical clusters for the reanalysis datasets, the authors give a detailed evaluation of the cluster configuration for two GCMs relative to NCEP--NCAR. Finally, as an illustration, the capability of reproducing the NCEP--NCAR probability density function (pdf) defining the Greenland anticyclone CP is evaluated for all 14 GCMs, considering that the size and shape of the underlying pdfs complement the commonly used Euclidean distance of CPs'' mean values. [ABSTRACT FROM AUTHOR]

This study is the last in a series of papers addressing the dynamics of the West African summer monsoon at intraseasonal time scales between 10 and 90 days. The signals of convectively coupled equatorial Rossby (ER) waves within the summer African monsoon have been investigated after filtering NOAA outgoing longwave radiation (OLR) data within a box delineated by the dispersion curves of the theoretical ER waves. Two families of waves have been detected in the 10–100-day periodicity band by performing a singular spectrum analysis on a regional index of ER-filtered OLR. For each family the first EOF mode has been retained to focus on the main convective variability signal. Within the periodicity band of 30–100 days, an ER wave pattern with an approximate wavelength of 13 500 km has been depicted. This ER wave links the MJO mode in the Indian monsoon sector with the main mode of convective variability over West and central Africa. This confirms the investigations carried out in previous studies. Within the 10–30-day periodicity band, a separate ER wave pattern has been highlighted in the African monsoon system with an approximate wavelength of 7500 km, a phase speed of 6 m s−1, and a period of 15 days. The combined OLR and atmospheric circulation pattern looks like a combination of ER wave solutions with meridional wavenumbers of 1 and 2. Its vertical baroclinic profile suggests that this wave is forced by the deep convective heating. Its initiation in terms of OLR modulation is detected north of Lake Victoria, extending northward and then propagating westward along the Sahel latitudes. The Sahel mode identified in previous studies corresponds to the second main mode of convective variability within the 10–30-day periodicity band, and this has also been examined. Its pattern and evolution look like the first-mode ER wave pattern and they are temporally correlated with a coefficient of +0.6. About one-third of the Sahel mode events are concomitant with an ER wave occurrence. The main difference between these two signals consists of a stronger OLR and circulation modulation of the Sahel mode over East and central Africa. Thus, the Sahel mode occurrence and its westward propagation could be explained in part by atmospheric dynamics associated with the ER waves and in part by land surface interactions, as shown in other studies. [ABSTRACT FROM AUTHOR]

This paper is part of a series of studies addressing the dynamics of the West African summer monsoon at intraseasonal time scales between 10 and 90 days. The dominant mode of 25–90-day convective variability in the African monsoon was investigated, starting from previous results involving the excitation of dry equatorial Kelvin and Rossby waves by a negative diabatic heat source located over the warm pool. This evolution is consistent with a significant contribution by a convectively coupled equatorial Rossby wave and the MJO. On the other hand, convectively coupled Kelvin waves as well as the dry Kelvin wave signal have a very weak impact. However, there is more to the global control of the African summer monsoon than convectively coupled wave dynamics. The active/break cycle of the Indian monsoon, controlled by a northward-moving dipole of diabatic heating in the Indian sector, can also influence the African monsoon through atmospheric teleconnections. Simulations performed with a dry primitive equation model show that this influence may be transferred through the northern Indian heat source, which excites a Rossby cyclonic circulation propagating westward over North Africa that is cut off by the northward arrival of the equatorial Indian heat source and the associated intrusion of an anticyclonic ridge. Low-level westerly winds and moisture advection within the ITCZ consequently increase over Africa. The mean time lag between an active phase over India and over Africa is about 15–20 days. [ABSTRACT FROM AUTHOR]

Summary: Objectives: Meningococcal meningitis (MM) is still a huge threat in the African meningitis belt. To fight against epidemics, a strengthened health information system, based upon weekly collected data, was set up in Mali. We aimed to study the spatio-temporal dynamics of MM in this country between 1992 and 2003. Methods: We were first interested in the impact of population size on the disease persistence. We then used cross-correlation analysis to study the spread of the disease on three different spatial scales, i.e., inter-region (global) and inter-district and intra-district (local) levels. Results: We found no persistence of MM at district level in Mali during the whole of the study period. However, we found persistence on a nationwide scale after the 1997 big epidemics, as opposed to the 1992–1996 time periods. In terms of spread, two main regions seem to lead MM dynamics in Mali, even if on a local scale the ‘cities–villages’ diffusion pattern was not systematically observed. Conclusions: This study improves knowledge on the spread and persistence of MM in Mali in recent years. It constitutes a first spatial study describing persistence and spread of MM in an African meningitis belt country. The next step should be the integration of vaccination and genetic variability data to clarify the route of spread of the disease in the human population. [Copyright &y& Elsevier]

Background: Meningococcal meningitis (MM) represents an important public health problem especially in the "meningitis belt" in Africa. Although seasonality of epidemics is well known with outbreaks usually starting in the dry season, pluri-annual cycles are still less understood and even studied. In this context, we aimed at study MM cases time series across 9 sahelo-sudanian countries to detect pluri-annual periodicity and determine or not synchrony between dynamics. This global and comparative approach allows a better understanding of MM evolution in time and space in the long-term. Results: We used the most adapted mathematical tool to time series analyses, the wavelet method. We showed that, despite a strong consensus on the existence of a global pluri-annual cycle of MM epidemics, it is not the case. Indeed, even if a clear cycle is detected in all countries, these cycles are not as permanent and regular as generally admitted since many years. Moreover, no global synchrony was detected although many countries seemed correlated. Conclusion: These results of the first large-scale study of MM dynamics highlight the strong interest and the necessity of a global survey of MM in order to be able to predict and prevent large epidemics by adapted vaccination strategy. International cooperation in Public Health and crossdisciplines studies are highly recommended to hope controlling this infectious disease. [ABSTRACT FROM AUTHOR]

• Systematically examines climatic forcing data in agricultural model performance • Explores uncertainty across up to 91 climate data / crop model combinations • Isolates key climatic features driving interannual yield variation in each region • Quantifies performance of climatic forcing datasets for top countries and crop species • More extensive bias correction improves climatic forcing datasets for crop models We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region. [ABSTRACT FROM AUTHOR]

Flood recession farming practiced in flood-prone areas and on the banks of rivers and lakes in arid or semi-arid environments essentially depends on the soil water stock after the flood has receded. During these last few decades, this coveted agriculture is increasingly challenged by severe water constraints, due to increased hydrological hazards and development projects aimed at controlling floods. These challenges are difficult to anticipate, and are the subject of a great deal of uncertainty regarding the sustainability of development projects in the concerned areas. In this study, recent hydraulic data of the Senegal River were analyzed to understand the constraints related to the river management in flood-prone areas. Satellite imagery analysis techniques were used to estimate flooded areas and establish relationships with the river regime. Agricultural practices implemented by farmers were also analyzed to evaluate the resilience of this cropping system to the risk of water stress. The results confirmed many constraints of different importance related to the objectives assigned to the management of dams under multiple water use context. It clearly came out that the water resource management rules relegate flood-recession agriculture to the lowest priorities. In addition, there are safety issues related to unexpected effects of flooding on the water structures and in the nearby inhabited localities of flood-prone areas. Knowing some characteristics of the flooding and of the river's levels and their relationships can be useful within the framework of an organized climate service that would help farmers and communities to better anticipate constraints. [ABSTRACT FROM AUTHOR]

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 Köppen climatic classification system, which predicts the global distribution of biomes based on monthly precipitation and average temperatures. In this study, the Köppen 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 Köppen 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 Köppen 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. • Assess impacts of ice sheets melting on GCM simulations using Koppen classification. • Freshwater from Greenland/Antarctica influences changes already induced by RCP8.5 • Precipitation changes have major impacts on the climate in the Southern Hemisphere. • Temperature changes strongly impact the climate in the Northern Hemisphere. • Importance of ice sheet meltings in climate projections causing model uncertainty. [ABSTRACT FROM AUTHOR]