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This work introduces the S2M (SAFRAN–SURFEX/ISBA–Crocus–MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2021. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2021) and the best possible set of available in situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends) and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in situ snow depth observations. Further, we describe the technical handling of this open-access dataset, available at https://doi.org/10.25326/37#v2020.2. The S2M data are provided by Météo-France – CNRS, CNRM, Centre d'Études de la Neige, through AERIS (Vernay et al., 2022).

The Antarctic Climate is characterized by strong interactions between the Southern Ocean, its sea ice cover, and the overlying atmosphere taking place over a wide range of spatio-temporal scales. This coupling constrains our ability to isolate the role of specific components of the climate system on the dynamics of the Antarctic Climate, especially with stand-alone approaches neglecting the feedbacks at play. Based on coupled model simulations, we explore how the ocean can drive the interactions with the cryosphere and atmosphere at two distinct spatio-temporal scales. First, the role of ocean mesoscale eddies is investigated. We describe the imprint of mesoscale eddies on the sea ice and atmosphere in a high-resolution simulation of the Adélie Land sector (East Antarctica) performed with a regional coupled ocean--sea ice--atmosphere model (NEMO-MAR). Specific attention is given to the role of the sea ice in the modulation of the air-sea interactions at mesoscale and to the influence of eddy-driven fluxes on the ocean and sea ice. We show that mesoscale eddies affect near-surface winds and air temperature both in ice-free and ice-covered conditions due to their imprint on the sea ice cover. In addition, eddies promote northward sea ice transport and decrease momentum transfer by surface stress to the ocean. In a second section, we move to larger spatial and temporal scales and delve into the influence of the ocean on the seasonal to interannual variability of the sea ice, atmosphere, and ice shelves basal melt at the scale of the Southern Ocean. This work is based on early results from a new coupled ocean–sea ice--atmosphere--ice sheet configuration with explicit under-ice shelf cavities called PARASO. We focus on subsurface heat content variability and its influence on the interactions between the ocean, the sea ice, the atmosphere, and the Antarctic Ice Sheet.

How well is the Antarctic climate over the last decades represented in climate models and how predictable is its future evolution? These questions delve into the specificities of the Antarctic climate, a system characterized by large natural fluctuations and complex interactions between the ice sheet, ocean, sea ice and atmosphere. The PARAMOUR project aims at improving our understanding of key processes which control the variability and predictability of the Antarctic climate at the decadal timescale. In this context, we introduce PARASO, a novel fully-coupled regional ocean - sea-ice - ice-sheet - atmosphere climate model over an Antarctic circumpolar domain covering the full Southern Ocean. The state-of-the-art models used are f.ETISh1.7 (ice sheet), NEMO3.6 (ocean), LIM3.6 (sea ice), COSMO5.0 (atmosphere) and CLM4.5 (land), which are run at a horizontal resolution close to 1/4°. One key feature of this tool resides in a novel two-way coupling interface for representing the ocean - ice-sheet interactions, through explicitly resolved ice-shelf cavities. We also consider the impact of atmospheric processes on the Antarctic ice sheet through surface mass exchanges between COSMO-CLM and f.ETISh. Our developments include a new surface tiling approach to combine open-ocean and sea-ice covered cells within COSMO. Using a 30 year-long run, we investigate the model performance and the interannual-to-decadal variability of the simulated Antarctic climate. The focus is on the interactions between the atmosphere, ocean and ice components at the regional scale and the links with larger spatial scales. Specific attention is paid to the mass balance of ice sheets and ice shelves, which influences both the ice sheet dynamics and the changes in the ocean and atmosphere. The system and its performance will be documented in this presentation together with some aspects of decadal variability from a 30-year integration forced with reanalyses (ERA5 and ORAS5). Early results of a 3-member retrospective forecast driven by EC-Earth will also be presented.

The climate of the polar regions is characterized by large fluctuations and has experienced dramatic changes over the past decades. In particular, the patterns of changes in sea ice and ice sheet mass are complex in the Southern Hemisphere. The Antarctic Ice Sheet has also lost mass in the past decades, especially in Western Antarctica, with a spectacular thinning and weakening of ice shelves, i.e., the floating extensions of the grounded ice sheet. Despite recent advances in observing and modelling the Antarctic climate, the mechanisms behind this long-term mass loss remain poorly understood because of the limited amount of observations and the large biases of climate models in polar regions, in concert with the large internal variability prevailing in the Antarctic. Among all the processes involved in the mass variability, changes in the general atmospheric circulation of the Southern Hemisphere may have played a substantial role. One of the most important atmospheric modes of climate variability in the Southern Ocean is the Southern Annular Mode (SAM), which represents the position and the strength of the westerly winds. During years with a positive SAM index, lower sea level pressure at high latitudes and higher sea level pressure at low latitudes occur, resulting in a stronger pressure gradient and intensified Westerlies. However, the current knowledge of the impact of these fluctuations of the Westerlies on the Antarctic cryosphere is still limited. Over the past few years, some efforts investigated the impact of the SAM on the Antarctic sea ice and the surface mass balance of the ice sheet from an atmosphere-only perspective. Recently, a few oceanic studies have focused on the local impact of SAM-related fluctuations on the ice-shelf basal melt in specific regions of Antarctica, particularly Western Antarctica. However, to our knowledge, there is no such study at the scale of the whole Antarctic continent. In this study, we performed idealized experiments with a pan-Antarctic regional ice-shelf cavity-resolving ocean - sea-ice model for different phases of the SAM. We show that positive (negative) phases lead to increased (decreased) upwelling and subsurface ocean temperature and salinity close to ice shelves. A one-standard-deviation increase of the SAM leads to a net basal mass loss of 40 Gt yr-1, with strong regional contrasts: increased melt in the Western Pacific and Amundsen-Bellingshausen sectors and the opposite response in the Ross sector. Taking these as a baseline sensitivity, we estimate last millennium and end-of-21st-century ice-shelf basal melt changes due to SAM of -60.7 Gt yr-1 and 1.8 to 26.8 Gt yr-1 (depending on the emission scenario considered), respectively, compared to the present.

We introduce PARASO, a novel five-component fully-coupled regional climate model over an Antarctic circumpolar domain covering the full Southern Ocean. The state-of-the-art models used are f.ETISh1.7 (ice sheet), NEMO3.6 (ocean), LIM3.6 (sea ice), COSMO5.0 (atmosphere) and CLM4.5 (land), which are here run at an horizontal resolution close to 1/4°. One key-feature of this tool resides in a novel two-way coupling interface for representing ocean – ice-sheet interactions, through explicitly resolved ice-shelf cavities. The impact of atmospheric processes on the Antarctic ice sheet is also conveyed through computed COSMO-CLM – f.ETISh surface mass exchanges. In this technical paper, we briefly introduce each model's configuration and document the developments that were carried out in order to establish PARASO. The new offline-based NEMO – f.ETISh coupling interface is thoroughly described. Our developments also include a new surface tiling approach to combine open-ocean and sea-ice covered cells within COSMO, which was required to make this model relevant in the context of coupled simulations in polar regions. We present results from a 2000–2001 coupled two-year experiment. PARASO is numerically stable and fully operational. The 2-year simulation conducted without fine tuning of the model reproduced the main expected features, although remaining systematic biases provide perspectives for further adjustment and development.

Climate forecasts need to be postprocessed to obtain user-relevant climate information, to develop and implement strategies of adaptation to climate variability and to trigger decisions. Several postprocessing methods are gathered into CSTools (short for Climate Service Tools) for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. Besides an overview of the methods and documentation available in CSTools, a practical example is demonstrated. The objective of this practical example is to postprocess a seasonal forecast with a set of CSTools functions in order to obtain the required data to produce forecasts of mountain snow resources. Quantile mapping bias-correction and RainFARM stochastic downscaling methods are applied to raw seasonal forecast daily precipitation data to derive 1 km resolution fields. Bias-adjusted and downscaled precipitation data are then employed to drive a snow model, SNOWPACK, and generate snow depth seasonal forecasts at selected high-elevation sites in North-Western Italian Alps. The computational resources required by CSTools to process the forecasts will be discussed. This assessment is relevant given the memory requirements for the use case: while seasonal forecast data occupies ~10MB (8 x 8 grid cells, 215 forecast time steps for 30 different initializations with 25 members each), the data post-processed reaches ~1TB (the RainFARM downscaling requires a refinement factor 100 for the SNOWPACK model increasing the spatial resolution to 800 x 800 grid cells and creating 10 stochastic realizations for each ensemble member). In addition to one strategy using conventional loops, startR is introduced as an efficient alternative. startR is an R package that allows implementing the MapReduce paradigm, i.e. chunking the data and processing them either locally or remotely on high-performance computing systems, leveraging multi-node and multi-core parallelism where possible.

Debris-covered glaciers constitute a substantial part of the worldwide cryosphere (Scherler et al. 2018). However, their long-term response to multi-millennial climate variability has rarely been studied, in particular in the Southern Hemisphere. The presence of both debris-covered and debris-free glaciers on Kerguelen Archipelago (49°S, 69°E) offers therefore an excellent opportunity to investigate and compare long-term evolution of these two types of glaciers. To do so, we used the cosmogenic 36Cl surface dating method on moraine boulders that allows to establish temporal constraints of glacier oscillation. We provide here the first Late Glacial and Holocene glacier chronology of a still active debris-covered glacier from the archipelago: the Gentil Glacier. Results show that the Gentil Glacier advanced once at ~14.3 ka, i.e. during the Late Glacial (19.0 – 11.6 ka), and re-advanced during the Late Holocene at ~2.6 ka (Charton et al., 2020). Both debris-covered and debris-free glaciers experienced a broadly synchronous advance during the Late Glacial, that may be assigned to the Antarctic Cold Reversal event (14.5 – 12.9 ka) (Jomelli et al., 2017; 2018). This suggests that both types (debris-covered and debris-free) of glaciers at Kerguelen were sensitive to large amplitude temperature fluctuations recorded in Antarctic ice cores (WAIS divide Project Members, 2013), associated with increased precipitations (Van der Putten, 2015). However, during the Late Holocene, the advance at about ~2.6 ka was not observed on other glaciers and seems to be a specific response of the debris-covered Gentil Glacier, either related to distinct ice dynamics or an individual response to precipitation changes. Charton et al., 2020 : Ant. Sci. 1-13Jomelli et al., 2017 : Quat. Sci. Rev. 162, 128-144Jomelli et al., 2018 : Quat. Sci. Rev. 183, 110-123Scherler et al., 2018 : GRL. 45, 11,798-11,805Van der Putten et al., 2015 : Quat. Sci. Rev. 122, 142-157WAIS Divide Project Members, 2013: Nature. 500, 440-444

The climate of polar regions is characterized by large fluctuations and has experienced dramatic changes over the past decades. In the high latitudes of the Southern Hemisphere, the patterns of changes in sea ice and ice sheet mass, in particular, are more complex than for the Northern Hemisphere. Some regions have warmed less than the global average with some sea-ice advance, in particular in the Ross Sea, while other regions such as the Bellingshausen Sea have warmed significantly and displayed sea-ice loss. The Antarctic Ice Sheet has also lost mass in the past decades, with a spectacular thinning and weakening of ice shelves, i.e., the floating extensions of the grounded ice sheet. Despite recent advances in observing and modelling the Antarctic climate, the mechanisms at the origin of those trends are very uncertain because of the limited amount of observations and the large biases of climate models in polar regions, in concert with the large internal variability prevailing in the Antarctic. One of the most important atmospheric modes of climate variability in the Southern Ocean is the Southern Annular Mode (SAM), which represents the position and the strength of the westerly winds. During years with a positive SAM index, lower sea level pressure at high latitudes and higher sea level pressure at low latitudes occur, resulting in a stronger pressure gradient and intensified Westerlies. However, the current knowledge of the impact of these fluctuations of the Westerlies on the Southern Ocean and Antarctic cryosphere is still limited. Some efforts have been devoted over the past few years to the impact of the SAM on the Antarctic sea ice and the surface mass balance of the ice sheet from an atmospheric-specific perspective. Recently, a few studies have focused on the local impact on ice-shelf basal melt in specific regions of Antarctica. However, to our knowledge, there is no such study of the impact of the SAM on ice-shelf basal melt at the pan-Antarctic scale. In this communication, we will address this issue by using simulations performed with the regional ocean and sea-ice model NEMO-LIM3.6 at a spatial resolution of 0.25° forced by the ERA5 reanalysis over the period 1979-2018 CE. The impact of both the annular and the non-annular components of the SAM on ice-shelf basal melt will be assessed through regressions and correlations between the seasonal or annual averages of the SAM index and the ice-shelf basal melt.

From at least 1979 up until 2016, the surface of the Southern Ocean cooled down, leading to a small Antarctic sea ice extent increase, which is in stark contrast with the Arctic Ocean. The attribution of the origin of these robust observations is still very uncertain. Among other phenomena, the direct, two-way interactions between the Southern Ocean and the Antarctic ice sheet, through basal melting of its numerous and large ice-shelf cavities, have been suggested as a potentially important contributor of this cooling. In order to address this question, we perform multidecadal coupled ice sheet – ocean numerical simulations relying on f.ETISh-v1.7 and NEMO3.6-LIM3 for simulating the Antarctic ice sheet and Southern Ocean (including sea ice), respectively. This presentation is twofold. First, we present the technical aspects of the coupling infrastructure (e.g. workflow and exchanged information in between models). Second, we investigate the ice sheet – ocean feedbacks on the Southern Ocean, their interactions, and the roles of the related physical mechanisms on the ocean surface cooling.

The availability of climate data has never been larger, as evidenced by the development of the Copernicus Climate Change Service. However, availability of climate data does not automatically translate into usability and sophisticated post-processing is often required to turn these climate data into user-relevant climate information allowing them to develop and implement strategies of adaptation to climate variability and to trigger decisions. Developed under the umbrella of the ERA4CS Medscope project by multiple European partners, here we present an R package currently in development, which aims to provide tools to exploit dynamical seasonal forecasts such as to provide information relevant to public and private stakeholders at the seasonal timescale. This toolbox, called CSTools (short for Climate Service Tools), contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to presenting some of the tools that are contained in the package, we also present a short overview of the development strategy adopted for this toolbox. The latter relies on a version controlling system established such as to allow scientists and developers to work within a common framework using a platform where they can exchange with other developers, test the various functionalities and discuss issues arising from the work, amongst other things. Furthermore, we will also present some vignettes, which are one of the mechanisms that allows users to understand and visualize the capabilities of CSTools. For instance, CSTools contains a step by step vignette showing how to use and visualize the output of MultivarRMSE, which gives an indication of the forecast performance (RMSE) for multiple variables simultaneously. While the extensive community of R users offers the opportunity of merging climate forecaster experts with final users, CSTools can also be used by other communities, such as Python users through the interface rpy. Finally, the publication of this package on CRAN (the Comprehensive R Archive Network) makes it easily accessible to interested users and ensures its proper functioning on different operational systems.

Decadal climate predictions and forced climate projections both provide potentially useful information to users for the next ten years. They only differ in the former being initialised with observations, while the latter is not. Bringing together initialised decadal climate predictions and non-initialised climate projections in order to provide seamless climate information for users over the next decades is a new challenging area of research. This can be achieved by comparing the forecast quality of global initialised and non-initialised simulations in their common prediction time horizons (up to 10 years ahead), and quantify in how far initialisation improves the forecast quality. Forecast quality has been usually explored through skill assessment. However, the impact of initialisation on the reliability, which quantifies the agreement between the predicted probabilities and observed relative frequencies of a given event, of decadal predictions has not yet been investigated sufficiently. Hence, users of probabilistic predictions are particularly sensitive to the potential lack of reliability which would imply that the probabilities are not trustworthy and this can have negative consequences for decision-making. In this communication, initialised decadal hindcasts (or retrospective forecasts) from 12 forecasting systems of the Coupled Model Intercomparison Project Phase 5 are compared to the corresponding non-initialised historical simulations in terms of reliability over their common period 1961-2005. We show that reliability varies greatly depending on the region or model ensemble analysed and on the correction applied. In particular, the North Atlantic and Europe stand out as regions where there is some added-value of initialised decadal hindcasts over non-initialised historical simulations in terms of reliability, mainly because of smaller biases and/or a better representation of the trend. Furthermore, we show that post-processed data display more reliable results, indicating that bias correction and calibration are fundamental to obtain reliable climate information.

Climate change is increasingly regarded as a threat for winter tourism due to the combined effect of decreasing natural snow amounts and decreasing suitable periods for snowmaking. The present work investigated the snow reliability of 175 ski resorts in France (Alps and Pyrenees), Spain and Andorra under past and future conditions using state-of-the-art snowpack modelling and climate projections. The natural snow reliability (i.e. without snowmaking) elevation showed a significant spatial variability in the reference period (1986–2005) and to be highly impacted by the on-going climate change. The technical reliability (i.e. including snowmaking) is projected to rise by 200 m to 300 m in the Alps and by 400 m to 600 m in the Pyrenees in the near future (2030–2050) compared to the reference period for all climate scenarios. While 99 % of ski lift infrastructures are reliable in the reference period thanks to snowmaking, a significant fraction (14 % to 25 %) may be considered "at risk" in the near future. Beyond the mid century, climate projections highly depend on the scenario with steady conditions compared to the near future (RCP 2.6) or continuous decrease of snow reliability (RCP 8.5). According to the "business as usual" scenario (RCP 8.5), there would no longer be any snow reliable ski resorts based on natural snow conditions in French Alps and Pyrenees (France, Spain and Andorra) at the end of the century (2080–2100). Only 24 resorts are projected to remain technically reliable, all being located in the Alps.

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.

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.

Polar ice sheets mass balance is a timely topic intensively studied in the context of global change and sea-level rise. However, obtaining mass balance estimates in Antarctica in particular, remains difficult due to various logistical problems. In the framework of the TASTE-IDEA program, labeled as an International Polar Year project, continuous Ground Penetrating Radar (GPR) measurements were carried out during a traverse realised in Adelie Land (East Antarctica) during the 2008–2009 austral summer between the Italo-French Dome C (DC) polar plateau site and French Dumont D'Urville (DdU) coastal station. The aim of this study was to process and interpret GPR data in terms of snow accumulation, to analyse its spatial and temporal variability along the DC-DdU traverse and compare it with historical data and modeling. The emphasis has been put on the last 300 yr, from the pre-industrial to recent time period. Beta-radioactivity counting and gamma spectrometry were studied in cores at LGGE laboratory, providing a depth-age calibration for radar measurements. Over the 600 km of usable GPR data, depth and snow accumulation were determined with the help of three distinct layers visible on the radargrams (≈1730, 1799 and 1941 AD). Preliminary results reveal a gradual accumulation increase towards the coast and the occurrence of previously undocumented undulating structures between 300 and 600 km from DC. Results agree fairly well with data from previous studies and modeling. Concluding on temporal variations is difficult because of the margin of error introduced by density estimation. This study should have various applications such as for model validation.