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The development of renewable electricity in Africa could be massive in coming decades, as a response to the rapid rising electricity demand while complying with the Paris Agreements. This study shows that in the high-resolution climate experiments of CORDEX-AFRICA, the annual mean solar potential is expected to decrease on average by 4% over most of the continent by the end of the century, reaching up to 6% over the Horn of Africa, as a direct result of decrease in solar radiation and increase in air surface temperature. These projections are associated with large uncertainties, in particular over the Sahel and the elevated terrains of eastern Africa. While the expected decrease may affect the sizing of the numerous solar projects planned in Africa for the next decades, this study suggests that it does not endanger their viability. At last, this study indicates that the design of such projects also needs to account for the non-negligible uncertainties associated with the resource.

At the end of February 2018 the Mediterranean area of Montpellier in France was struck by a significant snowfall that turned into an intense rain event caused by an exceptional atmospheric situation. This rain-on-snow event produced pronounced damage to many buildings of different types. In this study, we report a detailed back analysis of the roof collapse of a large building, namely Irstea Cévennes. Attention is paid to the dynamics of the climatic event, on the one hand, and the mechanical response of the metal roof structure to different snow and rain loads, on the other hand. The former aspect relies on multiple sources of information that provide reliable estimates of snow heights in the area before the rain came into play and substantially modified the load on the roof. The latter aspect relies on detailed finite-element simulations of the mechanical behavior of the roof structure in order to assess the pressure due to snow and rain loading, which could theoretically lead to failure. By combining the two approaches, it is possible to reconstruct the most probable scenario for the roof failure before its full collapse. As an example of building behavior and vulnerability to an atypical rain-on-snow event in the Mediterranean area of France, this detailed case study provides useful key points to be considered in the future for a better mitigation of such events in non-mountainous areas.

Snow avalanches are a prevalent threat in mountain territories. Large-scale mapping of avalanche-prone terrain is a prerequisite for land-use planning where historical information about past events is insufficient. To this aim, the most common approach is the identification of potential release areas (PRAs) followed by numerical avalanche simulations. Existing methods for identifying PRAs rely on terrain analysis. Despite their efficiency, they suffer from (i) a lack of systematic evaluation on the basis of adapted metrics and past observations over large areas and (ii) a limited ability to distinguish PRAs corresponding to individual avalanche paths. The latter may preclude performing numerical simulations corresponding to individual avalanche events, questioning the realism of resulting hazard assessments. In this paper, a method that accurately identifies individual snow avalanche PRAs based on terrain parameters and watershed delineation is developed, and confusion matrices and different scores are proposed to evaluate it. Comparison to an extensive cadastre of past avalanche limits from different massifs of the French Alps used as ground truth leads to true positive rates (recall) between 80 % and 87 % in PRA numbers and between 92.4 % and 94 % in PRA areas, which shows the applicability of the method to the French Alps context. A parametric study is performed, highlighting the overall robustness of the approach and the most important steps/choices to maximize PRA detection, among which the important role of watershed delineation to identify the right number of individual PRAs is highlighted. These results may contribute to better understanding avalanche hazard in the French Alps. Wider outcomes include an in-depth investigation of the issue of evaluating automated PRA detection methods and a large data set that could be used for additional developments, and to benchmark existing and/or new PRA detection methods.

Climate change projections indicate that extreme snowfall is expected to increase in cold areas, i.e., at high latitudes and/or high elevation, and to decrease in warmer areas, i.e., at mid-latitudes and low elevation. However, the magnitude of these contrasting patterns of change and their precise relations to elevation at the scale of a given mountain range remain poorly known. This study analyzes annual maxima of daily snowfall based on the SAFRAN reanalysis spanning the time period 1959–2019 and provided within 23 massifs in the French Alps every 300 m of elevation. We estimate temporal trends in 100-year return levels with non-stationary extreme value models that depend on both elevation and time. Specifically, for each massif and four elevation ranges (below 1000, 1000–2000, 2000–3000, and above 3000 m), temporal trends are estimated with the best extreme value models selected on the basis of the Akaike information criterion. Our results show that a majority of trends are decreasing below 2000 m and increasing above 2000 m. Quantitatively, we find an increase in 100-year return levels between 1959 and 2019 equal to +23 % (+32kgm-2) on average at 3500 m and a decrease of −10 % (-7kgm-2) on average at 500 m. However, for the four elevation ranges, we find both decreasing and increasing trends depending on location. In particular, we observe a spatially contrasting pattern, exemplified at 2500 m: 100-year return levels have decreased in the north of the French Alps while they have increased in the south, which may result from interactions between the overall warming trend and circulation patterns. This study has implications for natural hazard management in mountain regions.

Over the past decades, large variations of precipitation were observed in Africa, which often led to dramatic consequences for local society and economy. To avoid such disasters in the future, it is crucial to better anticipate the expected changes, especially in the current context of climate change and population growth. To this date, however, projections of precipitation over Africa are still associated with very large uncertainties. To better understand how this uncertainty can be reduced, this study uses an advanced Bayesian analysis of variance (ANOVA) method to characterize, for the first time in the regional climate projections of CORDEX-AFRICA, the different sources of uncertainty associated with the projections of precipitation over Africa.By 2090, the ensemble mean precipitation is projected to increase over the Horn of Africa from September to May and over the eastern Sahel and Guinea Coast from June to November. It is projected to decrease over the northern coast and southern Africa all year long, over western Sahel from March to August, and over the Sahel and Guinea Coast from March to May. Most of these projections however are not robust, i.e., the magnitude of change is smaller than the associated uncertainty. Over time, the relative contribution of internal variability (excluding interannual variability) to total uncertainty is moderate and quickly falls below 10%. By 2090, it is found that over the Horn of Africa, northern coast, southern Africa, and Sahel, most of the uncertainty results from a large dispersion across the driving Global Climate Models (in particular MIROC, CSIRO, CCCma, and IPSL), whereas over the tropics and parts of eastern Africa, most of the uncertainty results from a large dispersion across Regional Climate Models (in particular CLMcom).

The increasing frequency and intensity of precipitation extremes poses a serious challenge for societies that must adapt to a changing climate. Communicating these changes in terms of their magnitude at a given lead time (e.g., 2100) or at a given level of global warming (e.g., +2 °C) can give the misleading impression that climate change is a distant issue; yet, adaptation measures to cope with future hydro-climatic conditions may be designed and implemented today. Contextualizing the potential future consequences of precipitation intensification in a current temporal frame of reference may help perceive climate change as an ongoing phenomenon, in turn encouraging adaptation planning. Using an ensemble of climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in a non-stationary extreme value framework, we quantify the time it takes for the frequency of extreme 1-day and 7-day precipitation accumulations –as estimated in the current climate– to double; the frequency double time (FDT) is estimated for a range of event rarities over land regions. Vast parts of the Northern Hemisphere high-latitudes are found to have FDT  <  80 years. Substantial parts of some densely populated mid-latitude regions have FDT in the next 5–6 decades for some rare events. The fastest frequency doubling, in the coming two decades, is found in the highly vulnerable tropical regions of Western and South Eastern Africa, with strong implications for hydrological risk management there. In addition, the rarest events are found to have smaller FDT compared to more ‘common’ extremes; infrastructures designed to withstand the strongest events are thus more exposed to premature obsolescence.

IPCC reports and climate change impact studies generally exploit ensembles of climate projections based on different socio-economic pathways and climate models, which provide the temporal evolution of plausible future climates. However, The Paris Agreement and many national and international commitments consider adaptation and mitigation plans targeting future global warming levels. Model uncertainty and scenario uncertainty typically affect both the crossing-time of future warming levels and the climate features at a given global warming level. In this study, we assess the uncertainties in a multi-model multi-member CMIP6 ensemble (MME) of seasonal and regional temperature and precipitation projections. In particular, we show that the uncertainties of regional temperature projections are considerably reduced if considered at a specific global warming level, with a limited effect of the emission scenarios and a reduced influence of GCM sensitivity. We also describe in detail the large uncertainties related to the different behavior of the GCMs in some regions.

The ability to understand and predict sediment transport in torrent catchments is a key element for the protection and prevention against the associated hazards. In this study, we collected data describing sediment supply at 100 torrential catchments in the Northern French Alps. These catchments have long records of past events and sediment supply due to debris deposition basin management enabling estimation of sediment supply frequency. The mean annual, the 10-year return period and the reference volume (i.e. the 100-year return level or the largest observed volume) of sediment supply were derived for studied torrents. We examined the relationships between sediment supply volumes and several explanatory variables using multivariate statistical analyses. Several predictive models were developed in order to estimate the sediment supply in torrents that are not equipped with sedimentation structures.

Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

Analytical multiplicative random cascades (MRCs) are widely used for the temporal disaggregation of coarse-resolution precipitation time series. This class of models applies scaling models to represent the dependence of the cascade generator on the temporal scale and the precipitation intensity. Although determinant, the dependence on the external precipitation pattern is usually disregarded in the analytical scaling models. Our work presents a unified MRC modelling framework that allows the cascade generator to depend in a continuous way on the temporal scale, precipitation intensity and a so-called precipitation asymmetry index. Different MRC configurations are compared for 81 locations in Switzerland with contrasted climates. The added value of the dependence of the MRC on the temporal scale appears to be unclear, unlike what was suggested in previous works. Introducing the precipitation asymmetry dependence into the model leads to a drastic improvement in model performance for all statistics related to precipitation temporal persistence (wet–dry transition probabilities, lag-n autocorrelation coefficients, lengths of dry–wet spells). Accounting for precipitation asymmetry seems to solve this important limitation of previous MRCs. The model configuration that only accounts for the dependence on precipitation intensity and asymmetry is highly parsimonious, with only five parameters, and provides adequate performances for all locations, seasons and temporal resolutions. The spatial coherency of the parameter estimates indicates a real potential for regionalisation and for further application to any location in Switzerland.