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In this study, we use run-time bias-correction to correct for ARPEGE atmospheric model systematic errors on large-scale atmospheric circulation. The bias correction terms are built using the climatological mean of the adjustment terms on tendency errors in an ARPEGE simulation relaxed towards ERA-Interim reanalyses. The improvements with respect to the AMIP-style uncorrected control run for the general atmospheric circulation in the Southern Hemisphere are significant for mean state and daily variability. Comparisons for the Antarctic Ice Sheet with the polar-oriented regional atmospheric models MAR and RACMO2 and in-situ observations also suggest substantial bias reduction for near-surface temperature and precipitation in coastal areas. Applying the method to climate projections for the late 21st century (2071–2100) leads to large differences in the projected changes of the atmospheric circulation in the Southern high latitudes and of the Antarctic surface climate. The projected poleward shift and strengthening of the southern westerly winds are greatly reduced. These changes result in a significant 0.7 to 0.9 K additional warming and a 6 to 9 % additional increase in precipitation over the grounded ice sheet. The sensitivity of precipitation increase to temperature (+7.7 and +9 %.K−1) found is also higher than previous estimates. Highest additional warming rates are found over East Antarctica in summer. In winter, there is a dipole of weaker warming and weaker precipitation increase over West Antarctica, contrasted by a stronger warming and a concomitant stronger precipitation increase from Victoria to Adélie Land, associated with a weaker intensification of the Amundsen Sea Low.

In response to the high demand for more skillful climate forecasts at the seasonal timescale, innovative climate prediction systems are developed with improved physics and increased spatial resolution. Alongside the model development process, seasonal predictions need to be evaluated on past years to provide robust information on the forecast performance. This work presents the quality assessment of the Meteo-France coupled climate prediction system, taking advantage of an experiment performed with 90 ensemble members over a 37-year re-forecast period from 1979 to 2015. We focus on the boreal winter season initialised in November. Beyond typical skill measures we evaluate the model capability in reproducing ENSO and NAO teleconnections on precipitation and near surface temperature respectively. Such an assessment is carried out first through a composite analysis, and shows that the model succeeds in reproducing the main patterns for near surface temperature and precipitation. A covariance method leads to consistent results. Finally we find that the teleconnection representation of the model is not affected by shortening the verification period and reducing the ensemble size and therefore can be used to evaluate operational seasonal forecast systems.

This article investigates the climatic response of a series of indicators for characterizing annual snow conditions and corresponding meteorological drivers at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future changes were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5–EURO-CORDEX GCM–RCM pairs spanning historical (1950–2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006–2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20 % typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in average snow conditions, and sustained interannual variability. The impact of the RCPs becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Changes in local meteorological and snow conditions show significant correlation with global temperature changes. Global temperature levels 1.5 and 2 ∘C above preindustrial levels correspond to a 25 and 32 % reduction, respectively, of winter mean snow depth with respect to the reference period 1986–2005. Larger reduction rates are expected for global temperature levels exceeding 2 ∘C. The method can address other geographical areas and sectorial indicators, in the field of water resources, mountain tourism or natural hazards.

Early indication of an increased risk of extremely warm conditions could help alleviate some of the consequences of severe heat waves on human health. This study focuses on boreal spring heat wave events over West Africa and the Sahel and examines the long-range predictability and forecast quality of these events with two coupled forecasting systems designed at Météo-France, both based on the CNRM-CM coupled global climate model: the operational seasonal forecasting System 5 and the experimental contribution to the World Weather Research Programme/World Climate Research Programme (WWRP/WCRP) subseasonal-to-seasonal (S2S) project. Evaluation is based on past reforecasts spanning 22 years, from 1993 to 2014, compared to reference data from reanalyses. On the seasonal time scale, skill in reproducing interannual anomalies of heat wave duration is limited at a gridpoint level but is significant for regional averages. Subseasonal predictability of daily humidity-corrected apparent temperature drops sharply beyond the deterministic range. In addition to reforecast skill measures, the analysis of real-time forecasts for 2016, both in terms of anomalies with respect to the reforecast climatology and using a weather-type approach, provides additional insight on the systems’ performance in giving relevant information on the possible occurrence of such events.

An intense heat wave struck West Europe in early July 2015. The degree of anticipation of that event is assessed through the new CNRM near-real time subseasonal to seasonal forecast system. A warm anomaly over France was detected for the first week of July in all the successive forecasts issued in June, even up to one month ahead. On the other hand, the positive 500 hPa geopotential anomaly observed during that period was little anticipated. Despite the limited skill of the forecast system beyond twelve days, the relatively successful anticipation of that event pleads for a predictability study based on a multi-system assessment.

CCR (Caisse Centrale de Reassurance) is a French reinsurance company playing a major role in the natural catastrophe coverage in France. Since 2003, CCR has been developing tools for the estimation of its exposure to climatic risks. These tools cover three main perils: flood, storm surge and drought. Models are used to estimate the insurance losses and are systematically used for all major climatic events. Both modelling calibration and validation are based on an important policy and claim database. It was created in 2003 and supplied every year with insurer’s data. In order to evaluate the financial exposure for insurance of extreme events, a stochastic approach has been developed since 2011, for flood, storm surge and drought. The simulation of the stochastic event set allows us to estimate the mean annual losses and losses associated with different return periods. The objective of this approach is to connect the impact models for all perils with a large set of climate simulations. ARPEGE-Climate (Meteo-France) is a model that is used to generate two sets of 200 years of hourly atmospheric time series: at current conditions and at year 2050 conditions according to RCP (Representative Concentration Pathways) 4.5. The main climate data used are: hourly rainfall, wind speed and atmospheric pressure and the Soil Wetness Index that is issued from a complementary surface model. The hazard and vulnerability models developed are based on the climatic data to compute continuous loss estimations. The method proposed will take into consideration development scenarios to evaluate the consequences of demographic growth and insured values evolution. The simulations show a global loss increase in 2050 which can be attributed to climatic factors such as extreme rainfall increase or sea level rise as well as, for a major part, the population and insured value growth in areas at risk.

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Nino-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Nino and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Nino conditions suggests a possible role for a stratospheric pathway.

We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter-annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20 of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter-basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real-time forecasts. © 2018 Crown copyright, Met Office Weather © 2018 Royal Meteorological Society This article is published with the permission of the Controller of HMSO and the Queen˘2019s Printer for Scotland

Future sea surface temperature and sea-ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcings for the downscaling of future climate experiments. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly method and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea-ice concentration (SIC) are presented. For SIC, we also propose a new analogue method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiments and some real-case applications using observations. We find that with respect to other previously existing methods, the analogue method is a substantial improvement for the bias correction of future SIC. Consistency between the constructed SST and SIC fields is an important constraint to consider, as is consistency between the prescribed sea-ice concentration and thickness; we show that the latter can be ensured by using a simple parameterisation of sea-ice thickness as a function of instantaneous and annual minimum SIC.

Soil moisture anomalies are expected to be a driver of summer predictability for the U.S. Great Plains since this region is prone to intense and year-to-year varying water and energy exchange between the land and the atmosphere. However, dynamical seasonal forecast systems struggle to deliver skillful summer temperature forecasts over that region, otherwise subject to a consistent warm-season dry bias in many climate models. This study proposes two techniques to mitigate the impact of this precipitation deficit on the modeled soil water content in a forecast system based on the CNRM-CM6-1 model. Both techniques lead to increased evapotranspiration during summer and reduced temperature and precipitation bias. However, only the technique based on a correction of the precipitation feeding the land surface throughout the forecast integration enables skillful summer prediction. Although this result cannot be generalized for other parts of the globe, it confirms the link between bias and skill over the U.S. Great Plains and pleads for continued efforts of the modeling community to tackle the summer bias affecting that region.

International audience; A better anticipation of high-impact heat and drought on human activity is the underlying motivation of many climate studies focused on the summer season. Although a large body of research has already highlighted the prominent impact of soil moisture anomalies on summer mid-latitudes climate variability and predictability, it still leaves room for a wide range of uncertainty and sometimes contradictions. The present work aims at revisiting soil moisture sensitivity studies by comparing an idealized ensemble model experiment in which soil moisture conditions are prescribed with a reference experiment in which soil moisture evolves freely. Two regional climate models centered over Europe contribute to these experiments and generate very similar results. Simulations with constrained soil moisture display significantly increased correlation between observed and simulated seasonal anomalies of maximum temperature, precipitation and surface solar radiation, as compared to the reference experiment. This widespread increase is not restricted to regions already known as hot-spots of land-atmosphere coupling such as southern Europe, where evapotranspiration is mainly driven by soil moisture. In spite of a limited change in the ensemble spread, the sensitivity experiments show a substantially modified magnitude of temperature and precipitation variability. A focus on two case studies reveal contrasting results for the 2003 and 2010 heat waves. These results stress the prominent role of soil moisture as a boundary condition of the climate system in Europe, including regions that have not been highlighted by previous sensitivity works.

This work presents snapshot simulations of the late 20th and late 21st century Antarctic climate under the RCP8.5 scenario carried out with an empirically bias‐corrected global atmospheric general circulation model (AGCM), forced with bias‐corrected sea‐surface temperatures and sea ice and run with about 100‐km resolution over Antarctica. The bias correction substantially improves the simulated mean late 20th century climate. The simulated atmospheric circulation of the bias‐corrected model compares very favorably to the best available AMIP (Atmospheric Model Intercomparison Project)‐type climate models. The simulated interannual circulation variability is improved by the bias correction. Depending on the metric, a slight improvement or degradation is found in the simulated variability on synoptic timescales. The simulated climate change over the 21st century is broadly similar in the corrected and uncorrected versions of the atmospheric model, and atmospheric circulation patterns are not geographically “pinned” by the applied bias correction. These results suggest that the method presented here can be used for bias‐corrected climate projections. Finally, the authors discuss different possible choices in terms of the place of bias corrections and other intermediate steps in the modeling chain leading from global coupled climate simulations to impact assessment.

In this study, the atmospheric model ARPEGE is used with a stretched grid in order to reach a average horizontal resolution of 35 kilometers over Antarctica. Over the historical period (1981–2010), ARPEGE is forced by the historical sea surface conditions (SSC, i.e. sea surface temperature and sea-ice concentration) from MIROC and NorESM1-M CMIP5 historical runs and by observed SSC (AMIP-experiment). These three simulations are evaluated against ERA-Interim for atmospheric general circulation and against MAR regional climate model and in-situ observations for surface climate. As lower boundary conditions for simulations for the period 2071–2100, we use SSC from coupled climate model CMIP5 simulations of the same models following the RCP8.5 emission scenario. We use these output both directly and with an anomaly method based on quantile mapping. We assess the uncertainties linked to the choice of the coupled model and the impact of the method (direct output and anomalies). For the simulation using SSC from NorESM1-M, we do not find significant changes in climate change signals over Antarctica when using bias-corrected SSC. For the simulation using MIROC-ESM output, an additional increase of +185 Gt yr−1 in precipitation and of +0.8 K in winter temperatures for the grounded Antarctic ice-sheet was obtained when using bias-corrected SSC.

Sources of spring predictability of the hydrological system over France were studied on a seasonal time scale over the 1960–2005 period. Two random sampling experiments were set up in order to test the relative importance of the land surface initial state and the atmospheric forcing. The experiments were based on the SAFRAN-ISBA-MODCOU hydrometeorological suite which computed soil moisture and river flow forecasts over a 8-km grid and more than 880 river-gauging stations. Results showed that the predictability of hydrological variables primarily depended on the seasonal atmospheric forcing (mostly temperature and total precipitation) over most plains, whereas it mainly depended on snow cover over high mountains. However, the Seine catchment area was an exception as the skill mainly came from the initial state of its large and complex aquifers. Seasonal meteorological hindcasts with the Météo-France ARPEGE climate model were then used to force the ISBA-MODCOU hydrological model and obtain seasonal hydrological forecasts from 1960 to 2005 for the entire March-April-May period. Scores from this seasonal hydrological forecasting suite could thus be compared with the random atmospheric experiment. Soil moisture and river flow skill scores clearly showed the added value in seasonal meteorological forecasts in the north of France, contrary to the Mediterranean area where values worsened.

We introduce the method ADAMONT v1.0 to adjust and disaggregate daily climate projections from a regional climate model against an observational dataset at hourly time resolution. The method uses a refined quantile mapping approach for statistical adjustment and an analogous method for sub-daily disaggregation. The method produces ultimately adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. While the method is generic and can be employed on any appropriate observation time series, here we focus on the description and evaluation of the method in the French mountainous regions. The observational dataset used here is the SAFRAN meteorological reanalysis, which covers the entire French Alps split into 23 massifs, within which meteorological conditions are provided for several 300 m elevation bands. In order to evaluate the skills of the method itself, it is applied to the ALADIN-Climate v5 RCM using the ERA-Interim reanalysis as boundary conditions, for the time period from 1980 to 2010. Results of the ADAMONT method are compared to the SAFRAN reanalysis itself. Various evaluation criteria are used for temperature, precipitation, but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data from either the adjusted RCM data, or the SAFRAN reanalysis itself. The evaluation addresses in particular the time transferability of the method (using various learning/application time periods), the impact of the RCM grid point selection procedure for each massif/altitude band configuration, and the inter-variable consistency of the adjusted meteorological data generated by the method. Results show that the performance of the method is satisfactory, with similar or even better evaluation metrics than alternative methods. However, results for air temperature are generally better than for precipitation. Results in terms of snow depth are satisfactory, which can be viewed as indicating a reasonably good inter-variable consistency of the meteorological data produced by the method. In terms of temporal transferability (evaluated over time periods of 15 years only), results depend on the learning period. In terms of RCM grid point selection technique, the use of a complex RCM grid points selection technique, taking into account horizontal but also altitudinal proximity to SAFRAN massif centre points/altitude couples, generally degrades evaluation metrics for high altitudes, compared to a simpler grid point selection method based on horizontal distance.

International audience; Most climate models do not explicitly model urban areas and at best describe them as rock covers. Nonetheless, the very high resolutions reached now by the regional climate models may justify and require a more realistic parameterization of surface exchanges between urban canopy and atmosphere. To quantify the potential impact of urbanization on the regional climate, and evaluate the benefits of a detailed urban canopy model compared with a simpler approach, a sensitivity study was carried out over France at a 12-km horizontal resolution with the ALADIN-Climate regional model for 1980-2009 time period. Different descriptions of land use and urban modeling were compared, corresponding to an explicit modeling of cities with the urban canopy model TEB, a conventional and simpler approach representing urban areas as rocks, and a vegetated experiment for which cities are replaced by natural covers. A general evaluation of ALADIN-Climate was first done, that showed an overestimation of the incoming solar radiation but satisfying results in terms of precipitation and near-surface temperatures. The sensitivity analysis then highlighted that urban areas had a significant impact on modeled near-surface temperature. A further analysis on a few large French cities indicated that over the 30 years of simulation they all induced a warming effect both at daytime and nighttime with values up to + 1.5 °C for the city of Paris. The urban model also led to a regional warming extending beyond the urban areas boundaries. Finally, the comparison to temperature observations available for Paris area highlighted that the detailed urban canopy model improved the modeling of the urban heat island compared with a simpler approach.

International audience; Med-CORDEX simulations over the period 1979–2011 are evaluated with regard to their capability to represent observed decadal variations over the Euro-Mediterranean region and improve upon previous generation simulations from the ENSEMBLES project in their various experimental set-ups. Such an evaluation is needed to inform the use of these simulations and also future model development. For temperature, both Med-CORDEX and ENSEMBLES simulations tend to provide comparable results: they generally capture the sign and timing of the anomalies but not the amplitude. In general, no clear stratification appears when considering different types of Med-CORDEX regional modeling systems. Rather, it is remarkable that certain periods are poorly represented by all systems with a general underestimation of the observed long-term temperature trend, mostly in the summer season, even with respect to the corresponding global drivers. For precipitation, the Med-CORDEX simulations are closer to observations than the other datasets, with some improvement with respect to ENSEMBLES dataset. In general, all the systems experience difficulties in representing anomalies during specific periods or for specific regions. These appear in part due to limitations in the reanalysis boundary forcing data. For instance, in the second part of 1980s, the spatial patterns of surface air temperature during DJF/MAM are generally poorly represented, as well as the regionally averaged MAM/JJA surface air temperature decadal anomalies. Overall, the evaluation suggests limited improvement in Med-CORDEX simulations compared to ENSEMBLES, and a lack of sensitivity to resolution or coupling configuration, with persisting problems in part likely related to the representation of surface processes that could also affect the viability of future projections (e.g. the estimation of temperature trends). A set of decadal variability evaluation metrics, as applied in this study, could be useful in the context of a broader evaluation framework.

Future sea–surface temperature and sea–ice concentration from coupled ocean–atmosphere general circulation models such as those from the CMIP5 experiment are often used as boundary forcing for the downscaling of future climate experiment. Yet, these models show some considerable biases when compared to the observations over present climate. In this paper, existing methods such as an absolute anomaly and a quantile–quantile method for sea surface temperature (SST) as well as a look-up table and a relative anomaly method for sea–ice concentration (SIC) are presented. For SIC, we also propose a new analog method. Each method is objectively evaluated with a perfect model test using CMIP5 model experiment and some real-case applications using observations. With respect to other previously existing methods for SIC, the analog method is a substantial improvement for the bias correction of future sea–ice concentrations.

This article introduces climate variations of annual-scale indicators for seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future variations were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5/EURO-CORDEX GCM/RCM pairs spanning historical (1950–2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006–2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20 % typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in mean interannual snow conditions, and maintained interannual variability. The impact of the RCP becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Variations of local meteorological and snow conditions show significant correlation with global temperature variations. Global temperature levels on the order of 1.5 °C above pre-industrial levels correspond to a 25 % reduction of winter mean snow depth (reference 1986–2005). Even larger reduction is expected for global temperature levels exceeding 2 °C. The method can address other sectorial indicators, in the field of hydropower, mountain tourism or natural hazards.

Sea ice plays a crucial role in the Earth's energy and water budget and has a substantial impact on local and remote atmospheric and oceanic circulations. Predictions of Arctic sea-ice conditions a few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea-ice predictability on these time-scales. Predictability mainly originates from persistence or advection of sea-ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state-of-the-art models, current sea-ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea-ice forecasting are presented, along with suggestions for future research priorities.

We investigated high-resolution simulations of regional climate models (RCMs) driven by ERA-40 reanalyses over areas of selected European countries (Austria, Czech Republic, Hungary, Slovakia and Romania) for the period 1961-1990. RCMs were run at a spatial resolution of 10 km in the framework of the CECILIA project, and their outputs were compared with the EOBS dataset of gridded observations and RCM simulations at coarser 25 km resolution from the ENSEMBLES project to identify a possible gain from the CECILIA experiments over ENSEMBLES. Cold biases of air temperature and wet biases of precipitation dominate in the CECILIA simulations. Spatial variability and distribution of the air temperature field are well captured. The precipitation field, relative to observations, often shows inadequately small spatial variability and lowered correlations but is nevertheless comparable to the ENSEMBLES model. Inter-annual variability (IAV) of air temperature is captured differently among seasons but mostly improved in CECILIA compared with ENSEMBLES. Precipitation IAV shows a similar or worse score. The detected weaknesses found within the validation of the CECILIA RCMs are attributed to the resolution dependence of the set of physical parameterizations in the models and the choice of integration domain. The gain obtained by using a high resolution over a small domain (as in CECILIA) relative to a lower resolution (25 km) over a larger domain (as in ENSEMBLES) is clear for air temperature but limited for precipitation.

Unfortunately, the article “Extreme rainfall in Mediterranean France during the fall: added value of the CNRM‑AROME Convection‑Permitting Regional Climate Model”, written by Quentin Fumière was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 24 July 2019 without open access with incorrect copyright holder as “© Springer-Verlag GmbH Germany, part of Springer Nature 2019”.

Snowfall changes in mountain areas in response to anthropogenic forcing could have widespread hydrological, ecological and economic impacts. In this paper, the robustness of snowfall changes over the French Alps projected during the 21st century and the associated uncertainties are studied. In particular, the role of temperature changes on snowfall changes is investigated. Those issues are tackled through the analysis of the results of a very large ensemble of high-resolution regional climate projections, obtained either through dynamical or statistical downscaling. We find that, at the beginning and at the end of the cold season extending from November to March (included), temperature change is an important source of spread in snowfall changes. However, no link is found between temperature and snowfall changes in January and February. At the beginning and at the end of the cold season, the rate of change in snowfall per Kelvin does not depend much on the bias correction step, the period or the greenhouse gas scenario but mostly on the downscaling method and the climate models, the latter uncertainty source being dominant.

Les regions de montagne sont tres vulnerables aux changements climatiques et sont susceptibles d'etre parmi les zones les plus impactees par le rechauffement global. Or les projections climatiques pour la fin du 21e siecle sont elaborees avec les modeles de circulation generale du climat, qui ne presentent pas une resolution horizontale suffisante pour evaluer precisement les impacts du rechauffement sur ces regions. Des techniques de desagregation sont alors utilisees pour realiser une descente d'echelle spatiale a une dizaine kilometres de resolution. Il existe deux categories de methodes de desagregation : les methodes dynamiques, qui necessitent un calculateur puissant permettant la realisation de simulations climatiques regionales a haute resolution, et les methodes statistiques, qui necessitent peu de ressources numeriques mais une periode d'observations importante et de bonne qualite. Dans cette etude, les simulations climatiques du modele atmospherique global ARPEGE de Meteo-France sont desagregees sur la France avec methode dynamique en utilisant le modele regional ALADIN-Climat, et une methode statistique en utilisant le logiciel DSClim developpe au CERFACS.Les methodes de desagregations utilisees sont presentees, puis les resultats sont evalues sur les montagnes francaises pour le climat present. Les deux methodes donnent des resultats similaires pour les precipitations solides moyennes. En revanche les evenements extremes de precipitation totale (secheresse, episodes de precipitation intense) sont sous-estimes par la methode statistique. Ensuite, les resultats des deux methodes de desagregation sont compares pour deux projections du climat futur, selon le scenario d'emission des gaz a effet de serre A1B du GIEC. On constate que les deux methodes s'accordent bien sur la diminution du nombre de jours de gel, ainsi que sur une importante diminution des quantites de precipitations solides et une augmentation moyenne du pourcentage de jours secs superieure a 10 %. Les resultats obtenus sur la Corse sont plus contrastes mais ils sont discutables car le domaine spatial reduit ne permet sans doute pas un echantillonnage statistique tres pertinent.

The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in “weather forecast mode.” The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses. The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.

The impact of a +2 °C global warming on temperature and precipitation over tropical Africa is examined, based on an ensemble of 12 regional climate model scenario simulations. These 12 scenarios are re-phased so that they all correspond to the same global warming of 2 °C with respect to pre-industrial conditions. The continental temperature increase is above the global average. If heat waves are defined with the same temperature threshold in the reference climate and in the scenario, their frequency increases by a factor of 10. When the temperature threshold is adapted to future conditions, there is still a slight increase in frequency. The average precipitation does not show a significant response, due to model-to-model spread. However two compensating phenomena occur, which are robust among the models: (a) the number of rain days decreases whereas the precipitation intensity increases, and (b) the rain season occurs later during the year with less precipitation in early summer and more precipitation in late summer. Simulated daily temperature and precipitation data are combined in two impact models, one for the hydrology of the Nile and Niger basins, one for the food security of the different countries. They show that the main feature of the climate change is not a continuous trend signal, but an alternation of dry and wet decadal to multidecadal episodes.

Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

Quantifying how C fluxes will change in the future is a complex task for models because of the coupling between climate, hydrology, and biogeochemical reactions. Here we investigate how pedogenesis of the Peoria loess, which has been weathering for the last 13 kyr, will respond over the next 100 yr of climate change. Using a cascade of numerical models for climate (ARPEGE), vegetation (CARAIB) and weathering (WITCH), we explore the effect of an increase in CO2 of 315 ppmv (1950) to 700 ppmv (2100 projection). The increasing CO2 results in an increase in temperature along the entire transect. In contrast, drainage increases slightly for a focus pedon in the south but decreases strongly in the north. These two variables largely determine the behavior of weathering. In addition, although CO2 production rate increases in the soils in response to global warming, the rate of diffusion back to the atmosphere also increases, maintaining a roughly constant or even decreasing CO2 concentration in the soil gas phase. Our simulations predict that temperature increasing in the next 100 yr causes the weathering rates of the silicates to increase into the future. In contrast, the weathering rate of dolomite – which consumes most of the CO2 – decreases in both end members (south and north) of the transect due to its retrograde solubility. We thus infer slower rates of advance of the dolomite reaction front into the subsurface, and faster rates of advance of the silicate reaction front. However, additional simulations for 9 pedons located along the north–south transect show that the dolomite weathering advance rate will increase in the central part of the Mississippi Valley, owing to a maximum in the response of vertical drainage to the ongoing climate change. The carbonate reaction front can be likened to a terrestrial lysocline because it represents a depth interval over which carbonate dissolution rates increase drastically. However, in contrast to the lower pH and shallower lysocline expected in the oceans with increasing atmospheric CO2, we predict a deeper lysocline in future soils. Furthermore, in the central Mississippi Valley, soil lysocline deepening accelerates but in the south and north the deepening rate slows. This result illustrates the complex behavior of carbonate weathering facing short term global climate change. Predicting the global response of terrestrial weathering to increased atmospheric CO2 and temperature in the future will mostly depend upon our ability to make precise assessments of which areas of the globe increase or decrease in precipitation and soil drainage.

We introduce a method – called ADAMONT (ADAptation of RCM outputs to MOuNTain regions) v1.0 – to downscale and adjust daily climate projections from a regional climate model against a regional reanalysis of hourly meteorological conditions using quantile mapping. The method produces adjusted hourly time series of temperature, precipitation, wind speed, humidity, and short- and longwave radiation, which can in turn be used to force any energy balance land surface model. The ADAMONT method is evaluated through its application to the ALADIN-Climate v5 RCM forced by the ERA-Interim reanalysis, compared to the SAFRAN reanalysis, used as the pseudo-observation database covering the entire French Alps split into 23 massifs within which meteorological conditions are provided for several elevation bands separated by 300 m altitude. Different evaluation criteria are analysed for temperature, precipitation, but also snow depth, which is computed by the SURFEX/ISBA-Crocus model using the meteorological driving data generated using this method. The impact of the learning period and of the method used to select neighbouring RCM grid points for each SAFRAN massif/altitude configuration is tested. The performance of the method is satisfying, with similar or even better evaluation metrics than previous literature findings. Results for temperature are generally better than for precipitation. Snow depth yields good results, which can be viewed as indicating a reasonably good inter-variable consistency of the meteorological data produced by the method. The temporal transferability of the method is assessed through the comparison of results obtained using different learning periods, and shows that the method is sensitive to the period considered due to the empirical treatment of values beyond the 99.5 th quantile. The use of a complex RCM grid points selection technique taking into account horizontal but also altitudinal proximity to SAFRAN massif centroids/altitude couples generally degrades evaluation metrics for high altitudes, compared to a simpler 2-dimensional proximity selection technique.

In this study, an ensemble of four multi-year climate simulations is performed with the regional climate model ALADIN to evaluate its ability to simulate the climate over North America in the CORDEX framework. The simulations differ in their driving fields (ERA-40 or ERA-Interim) and the nudging technique (with or without large-scale nudging). The validation of the simulated 2-m temperature and precipitation with observationally-based gridded data sets shows that ALADIN performs similarly to other regional climate models that are commonly used over North America. Large-scale nudging improves the temporal correlation of the atmospheric circulation between ALADIN and its driving field, and also reduces the warm and dry summer biases in central North America. The differences between the simulations driven with different reanalyses are small and are likely related to the regional climate model’s induced internal variability. In general, the impact of different driving fields on ALADIN is smaller than that of large-scale nudging. The analysis of the multi-year simulations over the prairie and the east taiga indicates that the ALADIN 2-m temperature and precipitation interannual variability is similar or larger than that observed. Finally, a comparison of the simulations with observations for the summer 1993 shows that ALADIN underestimates the flood in central North America mainly due to its systematic dry bias in this region. Overall, the results indicate that ALADIN can produce a valuable contribution to CORDEX over North America.

Predicting how European air quality could evolve over the next decades in the context of changing climate requires the use of climate models to produce results that can be averaged in a climatologically and statistically sound manner. This is a very different approach from the one that is generally used for air quality hindcasts for the present period; analysed meteorological fields are used to represent specifically each date and hour. Differences arise both from the fact that a climate model run results in a pure model output, with no influence from observations (which are useful to correct for a range of errors), and that in a "climate" set-up, simulations on a given day, month or even season cannot be related to any specific period of time (but can just be interpreted in a climatological sense). Hence, although an air quality model can be thoroughly validated in a "realistic" set-up using analysed meteorological fields, the question remains of how far its outputs can be interpreted in a "climate" set-up. For this purpose, we focus on Europe and on the current decade using three 5-yr simulations performed with the multiscale chemistry-transport model MOCAGE and use meteorological forcings either from operational meteorological analyses or from climate simulations. We investigate how statistical skill indicators compare in the different simulations, discriminating also the effects of meteorology on atmospheric fields (winds, temperature, humidity, pressure, etc.) and on the dependent emissions and deposition processes (volatile organic compound emissions, deposition velocities, etc.). Our results show in particular how differing boundary layer heights and deposition velocities affect horizontal and vertical distributions of species. When the model is driven by operational analyses, the simulation accurately reproduces the observed values of O3, NOx, SO2 and, with some bias that can be explained by the set-up, PM10. We study how the simulations driven by climate forcings differ, both due to the realism of the forcings (lack of data assimilated and lower resolution) and due to the lack of representation of the actual chronology of events. We conclude that the indicators such as mean bias, mean normalized bias, RMSE and deviation standards can be used to interpret the results with some confidence as well as the health-related indicators such as the number of days of exceedance of regulatory thresholds. These metrics are thus considered to be suitable for the interpretation of simulations of the future evolution of European air quality.

International audience; The knowledge of offshore and coastal wave climate evolution towards the end of the twenty-first century is particularly important for human activities in a region such as the Bay of Biscay and the French Atlantic coast. Using dynamical downscaling, a high spatial resolution dataset of wave conditions in the Bay of Biscay is built for three future greenhouse gases emission scenarios. Projected wave heights, periods and directions are analysed at regional scale and more thoroughly at two buoys positions, offshore and along the coast. A general decrease of wave heights is identified (up to -20 cm during summer within the Bay of Biscay), as well as a clockwise shift of summer waves and winter swell coming from direction. The relation between those changes and wind changes is investigated and highlights a complex association of processes at several spatial scales. For instance, the intensification and the north-eastward shift of strong wind core in the North Atlantic Ocean explain the clockwise shift of winter swell directions. During summer, the decrease of the westerly winds in the Bay of Biscay explains the clockwise shift and the wave height decrease of wind sea and intermediate waves. Finally, the analysis reveals that the offshore changes in the wave height and the wave period as well as the clockwise shift in the wave direction continue toward the coast. This wave height decrease result is consistent with other regional projections and would impact the coastal dynamics by reducing the longshore sediment flux.

An ensemble of regional climate simulations is analyzed to evaluate the ability of 10 regional climate models (RCMs) and their ensemble average to simulate precipitation over Africa. All RCMs use a similar domain and spatial resolution of ~50 km and are driven by the ECMWF Interim Re-Analysis (ERA-Interim) (1989-2008). They constitute the first set of simulations in the Coordinated Regional Downscaling Experiment in Africa (CORDEX-Africa) project. Simulated precipitation is evaluated at a range of time scales, including seasonal means, and annual and diurnal cycles, against a number of detailed observational datasets. All RCMs simulate the seasonal mean and annual cycle quite accurately, although individual models can exhibit significant biases in some subregions and seasons. The multimodel average generally outperforms any individual simulation, showing biases of similar magnitude to differences across a number of observational datasets. Moreover, many of the RCMs significantly improve the precipitation climate compared to that from their boundary condition dataset, that is, ERA-Interim. A common problem in the majority of the RCMs is that precipitation is triggered too early during the diurnal cycle, although a small subset of models does have a reasonable representation of the phase of the diurnal cycle. The systematic bias in the diurnal cycle is not improved when the ensemble mean is considered. Based on this performance analysis, it is assessed that the present set of RCMs can be used to provide useful information on climate projections over Africa. The authors thank the European Centre for Medium-Range Weather Forecasts (ECMWF), the Goddard Space Flight Center (GSFC), Climate Prediction Center (CPC), the British Atmospheric Data Centre (BADC), the University of Delaware, and the Global Precipitation Climatology Centre (GPCC) for providing observational data. Katja Winger (UQAM) and Neil MacKellar (DMI) are greatly acknowledged for preparing the model data. We would also like to thank George Huffman (GSFC) for providing detail information about the TRMM and GPCP datasets. Matthias Büchner is a representative of the CLM-Community CORDEX Africa group, consisting of Hans-Jürgen Panitz (KIT), Matthias Büchner (PIK), Alessandro Dosio (EC-JRC), Burkhardt Rockel (Helmholtz-Zentrum Geesthacht), Bodo Ahrens (University Frankfurt), and Andreas Will (BTU Cottbus). J. F. acknowledges financial support from the Spanish Ministry of Science and Innovation (CORWES Project CGL2010-22158-C02). We also thank three anonymous reviewers for their helpful comments.

Mediterranean regions are regularly affected by high-precipitating events (HPEs) that often lead to devastating flash floods. The evolution of the occurrence and severity of such extreme events in the frame of climate change remains an open question. To address this issue, we have designed a statistico-dynamical downscaling method to apply to climate-model outputs. This statistico-dynamical downscaling is based on a two-step method: first, representative situations are selected among an ensemble of propitious synoptic environments to HPEs for the present (1961–2000) and future (2070–2099) climates and second, for these situations simulations are carried out with a fine-scale non-hydrostatic model. Large-scale circulation patterns (LSCs) propitious to high-impact weather events have been identified by a statistical method. This has been applied to the coupled atmosphere–ocean regional climate model ARPEGE Climate/OPAMED simulation considering an enhanced greenhouse climate following the IPCC-SRES-A2 scenario. Then dynamical downscaling is performed by simulating the 21 selected cases with the French MESO-NH numerical model at 2.5 km horizontal resolution. Initial and boundary conditions are provided by the climate-model scenarios. The high-resolution simulations for the 21 cases give accumulated rainfall exceeding 100 mm, often 300 mm and even 500 mm. Results highlight that the statistico-downscaling method is able to select LSCs leading to HPEs and to reproduce precipitating systems typical of Mediterranean events. This study also shows that it is possible to infer some conclusions about the evolution of the geographical location, occurrence and intensity of these intense events. The number of days with LSCs propitious to heavy precipitation significantly increases in the future climate. In particular, the simulated rainfall for the selected 21 cases shows an increase in the spatial variability of such events in the future climate associated with a slight increase of rainfall maxima. Copyright © 2011 Royal Meteorological Society