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In early April 2021 several days of harsh frost affected central Europe. This led to very severe damage in grapevine and fruit trees in France, in regions where young leaves had already unfolded due to unusually warm temperatures in the preceding month (March 2021). We analysed with observations and 172 climate model simulations how human-induced climate change affected this event over central France, where many vineyards are located. We found that, without human-caused climate change, such temperatures in April or later in spring would have been even lower by 1.2 ∘C (0.75 to 1.7 ∘C). However, climate change also caused an earlier occurrence of bud burst that we characterized in this study by a growing degree day index value. This shift leaves young leaves exposed to more winter-like conditions with lower minimum temperatures and longer nights, an effect that overcompensates the warming effect. Extreme cold temperatures occurring after the start of the growing season such as those of April 2021 are now 2 ∘C colder (0.5 to 3.3 ∘C) than in preindustrial conditions, according to observations. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change with each of the five climate model ensembles used here simulating a cooling of growing-period annual temperature minima of 0.41 ∘C (0.22 to 0.60 ∘C) since preindustrial conditions. The 2021 growing-period frost event has become 50 % more likely (10 %–110 %). Models accurately simulate the observed warming in extreme lowest spring temperatures but underestimate the observed trends in growing-period frost intensities, a fact that yet remains to be explained. Model ensembles all simulate a further intensification of yearly minimum temperatures occurring in the growing period for future decades and a significant probability increase for such events of about 30 % (20 %–40 %) in a climate with global warming of 2 ∘C.

In the autumn, the French Mediterranean area is frequently exposed to heavy precipitation events whose daily accumulation can exceed 300 mm. One of the key processes contributing to these precipitation amounts is deep convection, which can be explicitly resolved by state-of-the-art convection-permitting models to reproduce heavy rainfall events that are comparable to observations. This approach has been tested and performed at climate scale in several studies in recent decades for different areas. In this research, we investigate the added value of using an ensemble of three climate simulations at convection-permitting resolution (approx. 3 km) to replicate extreme precipitation events at both daily and shorter timescales over the south of France. These three convection-permitting simulations are performed with the Weather Research and Forecasting (WRF) Model. They are forced by three EURO-CORDEX simulations, which are also run with WRF at the resolution of 0.11∘ (approx. 12 km). We found that a convection-permitting approach provides a more realistic representation of extreme daily and 3-hourly rainfall in comparison with EURO-CORDEX simulations. Their similarity to observations allows use for climate change studies and its impacts.

The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe.

SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature, and Penman–Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method, which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus has paved the way for improving knowledge on specific past meteorological events or for improving knowledge on climate variability, since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use and can be downloaded as NetCDF files from https://doi.org/10.5281/zenodo.1299760 for precipitation, https://doi.org/10.5281/zenodo.1299712 for temperature, and https://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

Currently, the analysis of heat waves and the representation of such events in a comprehensible and accessible way is a crucial challenge for climate services, in particular for delivering scientific support to policy makers. In order to fulfil this need, a new method for analysing the heat waves in France has been defined. Heat wave detection is based on the high quantiles of daily temperature distributions, and can be applied on any series of temperature. The heat waves are characterised by their duration, maximal temperature and global intensity. Their characteristics are calculated for historical and future climate based on the EURO-CORDEX regional multi-model ensemble, under two different Representative Concentration Pathway scenarios: RCP4.5 and RCP8.5. The historical simulations are evaluated against the SAFRAN reanalysis data. The EURO-CORDEX ensemble simulates heat waves which characteristics are consistent with the events detected from the SAFRAN thermal indicator between 1971 and 2005. Models are able to simulate waves as intense as the 2003 outstanding event. Under future climate conditions, whatever the considered scenario, the heat waves become more frequent and have higher mean duration and intensity. Moreover, heat waves could occur during a larger part of summer. The 2003 event corresponds to a typical event at the end of the century, and its duration and intensity are much lower than the strongest waves that could occur over the last 30 years of the 21st century. However, the intensity of the evolution during the end of the century will strongly depend on climate policies.

The Paluclim project applied the tele-epidemiology approach, linking climate, environment and public health (CNES, 2008), to rural malaria in Nouna (Burkina Faso). It was to analyze the climate impact on vectorial risks, and its consequences on entomological risks forecast. The objectives were to: 1) produce entomological risks maps in the Nouna region, 2) produce dynamic maps on larvae sites and their productivity, 3) study the climate impact on malaria risks, and 4) evaluate the feasibility of strategic larviciding approach.

The FP7 project EUPORIAS was a great opportunity for the climate community to co-design with stakeholders some original and innovative climate services at seasonal time scales. In this framework, Météo-France proposed a prototype that aimed to provide to water resource managers some tailored information to better anticipate the coming season. It is based on a forecasting system, built on a refined hydrological suite, forced by a coupled seasonal forecast model. It particularly delivers probabilistic river flow prediction on river basins all over the French territory. This paper presents the work we have done with "EPTB Seine Grands Lacs" (EPTB SGL), an institutional stakeholder in charge of the management of 4 great reservoirs on the upper Seine Basin. First, we present the co-design phase, which means the translation of classical climate outputs into several indices, relevant to influence the stakeholder's decision making process (DMP). And second, we detail the evaluation of the impact of the forecast on the DMP. This evaluation is based on an experiment realised in collaboration with the stakeholder. Concretely EPTB SGL has replayed some past decisions, in three different contexts: without any forecast, with a forecast A and with a forecast B. One of forecast A and B really contained seasonal forecast, the other only contained random forecasts taken from past climate. This placebo experiment, realised in a blind test, allowed us to calculate promising skill scores of the DMP based on seasonal forecast in comparison to a classical approach based on climatology, and to EPTG SGL current practice.

Due to the evolution of the observation network, hourly 2 m temperature analysis performed by reanalysis systems shows temporal inhomogeneities. The observation network gap is less present for extreme daily temperature observations. In order to reduce inhomogeneities and enable a climatological use of temperature analysis, information from extreme temperatures could be useful. In this study, the diurnal temperature cycle has been reconstructed for stations which only record extreme temperatures. These new "pseudo" hourly temperature observations are then provided to the analysis system. Two methods have been used to deduce hourly temperatures from extremes and compared to real observations. The results have shown that using those new pseudo-observations as an input for two different reanalysis systems enables reducing the bias in temperature analysis.

Low-flow simulation and forecasting remains a difficult issue for hydrological modellers, and intercomparisons can be extremely instructive for assessing existing low-flow prediction models and for developing more efficient operational tools. This research presents the results of a collaborative experiment conducted to compare low-flow simulation and forecasting models on 21 unregulated catchments in France. Five hydrological models (four lumped storage-type models – Gardenia, GR6J, Mordor and Presages – and one distributed physically oriented model – SIM) were applied within a common evaluation framework and assessed using a common set of criteria. Two simple benchmarks describing the average streamflow variability were used to set minimum levels of acceptability for model performance in simulation and forecasting modes. Results showed that, in simulation as well as in forecasting modes, all hydrological models performed almost systematically better than the benchmarks. Although no single model outperformed all the others for all catchments and criteria, a few models appeared to be more satisfactory than the others on average. In simulation mode, all attempts to relate model efficiency to catchment or streamflow characteristics remained inconclusive. In forecasting mode, we defined maximum useful forecasting lead times beyond which the model does not bring useful information compared to the benchmark. This maximum useful lead time logically varies between catchments, but also depends on the model used. Simple multi-model approaches that combine the outputs of the five hydrological models were tested to improve simulation and forecasting efficiency. We found that the multi-model approach was more robust and could provide better performance than individual models on average.

Drought events develop in both space and time and they are therefore best described through summary joint spatio-temporal characteristics, such as mean duration, mean affected area and total magnitude. This paper addresses the issue of future projections of such characteristics of drought events over France through three main research questions: (1) Are downscaled climate projections able to simulate spatio-temporal characteristics of meteorological and agricultural droughts in France over a present-day period? (2) How such characteristics will evolve over the 21st century? (3) How to use standardized drought indices to represent theoretical adaptation scenarios? These questions are addressed using the Isba land surface model, downscaled climate projections from the ARPEGE General Circulation Model under three emissions scenarios, as well as results from a previously performed 50-yr multilevel and multiscale drought reanalysis over France. Spatio-temporal characteristics of meteorological and agricultural drought events are computed using the Standardized Precipitation Index and the Standardized Soil Wetness Index, respectively, and for time scales of 3 and 12 months. Results first show that the distributions of joint spatio-temporal characteristics of observed events are well simulated by the downscaled hydroclimate projections over a present-day period. All spatio-temporal characteristics of drought events are then found to dramatically increase over the 21st century, with stronger changes for agricultural droughts. Two theoretical adaptation scenarios are eventually built based on hypotheses of adaptation to evolving climate and hydrological normals, either retrospective or prospective. The perceived spatio-temporal characteristics of drought events derived from these theoretical adaptation scenarios show much reduced changes, but they call for more realistic scenarios at both the catchment and national scale in order to accurately assess the combined effect of local-scale adaptation and global-scale mitigation.

Physically-based droughts can be defined as a water deficit in at least one component of the land surface hydrological cycle. The reliance of different activity domains (water supply, irrigation, hydropower, etc.) on specific components of this cycle requires drought monitoring to be based on indices related to meteorological, agricultural, and hydrological droughts. This paper describes a high-resolution retrospective analysis of such droughts in France over the last fifty years, based on the Safran-Isba-Modcou (SIM) hydrometeorological suite. The high-resolution 1958–2008 Safran atmospheric reanalysis was used to force the Isba land surface scheme and the hydrogeological model Modcou. Meteorological droughts are characterized with the Standardized Precipitation Index (SPI) at time scales varying from 1 to 24 months. Similar standardizing methods were applied to soil moisture and streamflow for identifying multiscale agricultural droughts – through the Standardized Soil Wetness Index (SSWI) – and multiscale hydrological droughts, through the Standardized Flow Index (SFI). Based on a common threshold level for all indices, drought event statistics over the 50-yr period – number of events, duration, severity and magnitude – have been derived locally in order to highlight regional differences at multiple time scales and at multiple levels of the hydrological cycle (precipitation, soil moisture, streamflow). Results show a substantial variety of temporal drought patterns over the country that are highly dependent on both the variable and time scale considered. Independent spatio-temporal drought events have then been identified and described by combining local characteristics with the evolution of area under drought. Summary statistics have finally been used to compare past severe drought events, from multi-year precipitation deficits (1989–1990) to short hot and dry periods (2003). Results show that the ranking of drought events depends highly on both the time scale and the variable considered. This multilevel and multiscale drought climatology will serve as a basis for assessing the impacts of climate change on droughts in France.

Little is known about the fluctuations of the Pyrenean glaciers. In this study, we reconstructed the evolution of Ossoue Glacier (42°46' N, 0.45 km2), which is located in the central Pyrenees, from the Little Ice Age (LIA) onwards. To do so, length, area, thickness, and mass changes in the glacier were generated from historical data sets, topographical surveys, glaciological measurements (2001–2013), a ground penetrating radar (GPR) survey (2006), and stereoscopic satellite images (2013). The glacier has receded considerably since the end of the LIA, losing 40 % of its length and 60 % of its area. Three periods of marked ice depletion were identified: 1850–1890, 1928–1950, and 1983–2013, as well as two short periods of stabilization: 1890–1894, 1905–1913, and a longer period of slight growth: 1950–1983; these agree with other Pyrenean glacier reconstructions (Maladeta, Coronas, Taillon glaciers). Pyrenean and Alpine glaciers exhibit similar multidecadal variations during the 20th century, with a stable period detected at the end of the 1970s and periods of ice depletion during the 1940s and since the 1980s. Ossoue Glacier fluctuations generally concur with climatic data (air temperature, precipitation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation). Geodetic mass balance over 1983–2013 was −1.04 ± 0.06 w.e.a−1 (−31.3 ± 1.9 m w.e.), whereas glaciological mass balance was −1.45 ± 0.85 m w.e. a−1 (−17.3 ± 2.9 m w.e.) over 2001–2013, resulting in a doubling of the ablation rate in the last decade. In 2013 the maximum ice thickness was 59 ± 10.3 m. Assuming that the current ablation rate remains constant, Ossoue Glacier will disappear midway through the 21st century.