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International audience; Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr−1 between 1992 and 1996 to 372 Gt yr−1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr−1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr−1 in 2017 to 444 Gt yr−1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr−1) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr−1). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr−1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

Heat storage within the Earth system is a fundamental metric for understanding climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage, combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.8 ± 2.0 × 10(21) J during the period 1960–2020, but the distribution of heat among the three components is not homogeneous. The sensible diffusion of heat through the ground accounts for ∼90 % of the continental heat storage, with inland water bodies and permafrost degradation (i.e. latent heat) accounting for ∼0.7 % and ∼9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the solid ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory., Earth System Dynamics, 14 (3), ISSN:2190-4987, ISSN:2190-4979

The Earth climate system is out of energy balance, and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere, and the atmosphere. According to the Sixth Assessment Report by Working Group I of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance (EEI) and allows for quantifying how much heat has accumulated in the Earth system, as well as where the heat is stored. Here we show that the Earth system has continued to accumulate heat, with 381±61 ZJ accumulated from 1971 to 2020. This is equivalent to a heating rate (i.e., the EEI) of 0.48±0.1 W m−2. The majority, about 89 %, of this heat is stored in the ocean, followed by about 6 % on land, 1 % in the atmosphere, and about 4 % available for melting the cryosphere. Over the most recent period (2006–2020), the EEI amounts to 0.76±0.2 W m−2. The Earth energy imbalance is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. Moreover, this indicator is highly complementary to other established ones like global mean surface temperature as it represents a robust measure of the rate of climate change and its future commitment. We call for an implementation of the Earth energy imbalance into the Paris Agreement's Global Stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al. (2020), is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations and we also call for urgently needed actions for enabling continuity, archiving, rescuing, and calibrating efforts to assure improved and long-term monitoring capacity of the global climate observing system. The data for the Earth heat inventory are publicly available, and more details are provided in Table 4.

Ice losses from the Greenland and Antarctic Ice Sheets have accelerated since the 1990s, accounting for a significant increase in global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume and in Earth’s gravity field. Between 1992 and 2020, the ice sheets contributed 21.0 ± 1.9 mm to global mean sea-level, with the rate of mass loss rising from 105 Gt yr-1 between 1992 and 1996 to 372 Gt yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169 ± 9 Gt yr-1 between 1992 and 2020 but there are large inter-annual variations in mass balance with mass loss ranging from 86 Gt yr-1 in 2017 to 444 Gt yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (-82 ± 9 Gt yr-1) and to a lesser extent from the Antarctic Peninsula (-13 ± 5 Gt yr-1). East Antarctica remains close to a state of balance (3 ± 15 Gt yr-1), but is the most uncertain component of Antarctica’s mass balance.

Heat storage within the Earth system is a fundamental metric to understand climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.9±0.4×1021 J during the period 1960–2020, but the distribution of heat among the three components is not homogeneous. The ground stores ~90 % of the continental heat storage, with inland water bodies and permafrost degradation accounting for ~0.7 % and ~9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory.

The Earth climate system is out of energy balance and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere and the atmosphere. According to the 6th Assessment Report of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance, and allows for quantifying how much heat has accumulated in the Earth system, and where the heat is stored. Here we show that 380 ± 62 ZJ of heat has accumulated in the Earth system from 1971 to 2020, at a rate of 0.48 ± 0.1 W m−2, with 89 ± 17 % of this heat stored in the ocean, 6 ± 0.1 % on land, 4 ± 1 % in the cryosphere and 1 ± 0.2 % in the atmosphere. Over the most recent decade (2006–2020), the Earth heat inventory shows increased warming at rate of 0.48 ± 0.3 W m−2/decade, and the Earth climate system is out of energy balance by 0.76 ± 0.2 Wm−2. The Earth heat inventory is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. We call for an implementation of the Earth heat inventory into the Paris agreement’s global stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al, 2020, is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations as coordinated by the Global Climate Observing System (GCOS). We also call for urgently needed actions for enabling continuity, archiving, rescuing and calibrating efforts to assure improved and long-term monitoring capacity of the relevant GCOS Essential Climate Variables (ECV) for the Earth heat inventory.

Twenty-seven models participated in the Earth System Model–Snow Model Intercomparison Project (ESM-SnowMIP), the most data-rich MIP dedicated to snow modeling. Our findings do not support the hypothesis advanced by previous snow MIPs: evaluating models against more variables and providing evaluation datasets extended temporally and spatially does not facilitate identification of key new processes requiring improvement to model snow mass and energy budgets, even at point scales. In fact, the same modeling issues identified by previous snow MIPs arose: albedo is a major source of uncertainty, surface exchange parameterizations are problematic, and individual model performance is inconsistent. This lack of progress is attributed partly to the large number of human errors that led to anomalous model behavior and to numerous resubmissions. It is unclear how widespread such errors are in our field and others; dedicated time and resources will be needed to tackle this issue to prevent highly sophisticated models and their research outputs from being vulnerable because of avoidable human mistakes. The design of and the data available to successive snow MIPs were also questioned. Evaluation of models against bulk snow properties was found to be sufficient for some but inappropriate for more complex snow models whose skills at simulating internal snow properties remained untested. Discussions between the authors of this paper on the purpose of MIPs revealed varied, and sometimes contradictory, motivations behind their participation. These findings started a collaborative effort to adapt future snow MIPs to respond to the diverse needs of the community.

Sur une grande partie de l'Antarctique, le bilan de masse (c'est-à-dire de neige) de surface est dominé par quelques événements de précipitations extrêmes. Ces événements dépendent d'intrusions de masses d'air très humide associées à des phénomènes dénommés rivières atmosphériques en provenance de l'océan Austral. Ces rivières atmosphériques influencent fortement le climat ; pourtant, les caractéristiques, les mécanismes et les impacts associés restent mal connus en Antarctique. Nous résumons ici l'état des connaissances sur la mise en place de ces événements extrêmes et leurs impacts à la fois sur l'accumulation de neige, le réchauffement et la fonte en surface de la calotte. Over much of Antarctica, the surface mass balance (i.e. the resultant of snow fluxes at the surface of the ice sheet) is dominated by a few extreme precipitation events. It has recently been shown that these events are linked to intrusions of highly humid air masses related with atmospheric rivers traversing the Southern Ocean. These atmospheric rivers strongly influence the climate, yet their meteorological characterization and associated impacts remain poorly understood in Antarctica. We summarize here the latest research regarding the development of these extreme events and their impacts on snow accumulation, warming, and surface melt on the Antarctic ice sheet.

25 pages; International audience; Here, we define weather regimes in the East Antarctica—Southern Ocean sector based on daily anomalies of 700 hPa geopotential height derived from ERA5 reanalysis during 1979–2018. Most regimes and their preferred transitions depict synoptic-scale disturbances propagating eastwards off the Antarctic coastline. While regime sequences are generally short, their interannual variability is strongly driven by the polarity of the Southern Annular Mode (SAM). Regime occurrences are then intersected with atmospheric rivers (ARs) detected over the same region and period. ARs are equiprobable throughout the year, but clearly concentrate during regimes associated with a strong atmospheric ridges/blockings on the eastern part of the domain, which act to channel meridional advection of heat and moisture from the lower latitudes towards Antarctica. Both regimes and ARs significantly shape climate variability in Antarctica. Regimes favorable to AR occurrences are associated with anomalously warm and humid conditions in coastal Antarctica and, to a lesser extent, the hinterland parts of the Antarctic plateau. These anomalies are strongly enhanced during AR events, with warmer anomalies and dramatically amplified snowfall amounts. Large-scale conditions favoring AR development are finally explored. They show weak dependency to the SAM, but particularly strong atmospheric ridges/blockings over the Southern Ocean appear as the most favorable pattern, in which ARs can be embedded, and to which they contribute.

Climate change over High Mountain Asia (HMA, including the Tibetan Plateau) is investigated over the period 1979–2014 and in future projections following the four Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. The skill of 26 Coupled Model Intercomparison Project phase 6 (CMIP6) models is estimated for near-surface air temperature, snow cover extent and total precipitation, and 10 of them are used to describe their projections until 2100. Similarly to previous CMIP models, this new generation of general circulation models (GCMs) shows a mean cold bias over this area reaching −1.9 [−8.2 to 2.9] ∘C (90 % confidence interval) in comparison with the Climate Research Unit (CRU) observational dataset, associated with a snow cover mean overestimation of 12 % [−13 % to 43 %], corresponding to a relative bias of 52 % [−53 % to 183 %] in comparison with the NOAA Climate Data Record (CDR) satellite dataset. The temperature and snow cover model biases are more pronounced in winter. Simulated precipitation rates are overestimated by 1.5 [0.3 to 2.9] mm d−1, corresponding to a relative bias of 143 % [31 % to 281 %], but this might be an apparent bias caused by the undercatch of solid precipitation in the APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) observational reference. For most models, the cold surface bias is associated with an overestimation of snow cover extent, but this relationship does not hold for all models, suggesting that the processes of the origin of the biases can differ from one model to another. A significant correlation between snow cover bias and surface elevation is found, and to a lesser extent between temperature bias and surface elevation, highlighting the model weaknesses at high elevation. The models with the best performance for temperature are not necessarily the most skillful for the other variables, and there is no clear relationship between model resolution and model skill. This highlights the need for a better understanding of the physical processes driving the climate in this complex topographic area, as well as for further parameterization developments adapted to such areas. A dependency of the simulated past trends on the model biases is found for some variables and seasons; however, some highly biased models fall within the range of observed trends, suggesting that model bias is not a robust criterion to discard models in trend analysis. The HMA median warming simulated over 2081–2100 with respect to 1995–2014 ranges from 1.9 [1.2 to 2.7] ∘C for SSP1-2.6 to 6.5 [4.9 to 9.0] ∘C for SSP5-8.5. This general warming is associated with a relative median snow cover extent decrease from −9.4 % [−16.4 % to −5.0 %] to −32.2 % [−49.1 % to −25.0 %] and a relative median precipitation increase from 8.5 % [4.8 % to 18.2 %] to 24.9 % [14.4 % to 48.1 %] by the end of the century in these respective scenarios. The warming is 11 % higher over HMA than over the other Northern Hemisphere continental surfaces, excluding the Arctic area. Seasonal temperature, snow cover and precipitation changes over HMA show a linear relationship with the global surface air temperature (GSAT), except for summer snow cover which shows a slower decrease at strong levels of GSAT.

The site level component of the Earth System Model – Snow Model Intercomparison Project has 28 participating model variants. We summarize model performance at the Boreal Ecosystem Research and Monitoring Sites (BERMS) Old Aspen (OAS), Old Black Spruce (OBS) and Old Jack Pine (OJP) forests in Saskatchewan.Many CMIP5 models have been previously shown to overestimate the winter albedo in the boreal forest due to errors in plant functional type (PFT) and leaf area index (LAI). In this project provided values for PFT and LAI were not implemented in a few models, but many models show a positive albedo bias in excess of 0.1 and some show a much larger positive bias. A larger positive albedo bias at OAS by some models suggests that snow masking by leafless trees requires attention. Average albedo bias from these off-line simulations, which lack atmospheric feedbacks, is not strongly related to bias in snowpack properties or the treatment or lack thereof of intercepted snow.About half the models simulated snow water equivalent (SWE) with a RMSE smaller than the standard deviation of the observations. Snow depth was simulated slightly worse and only three models met this standard with respect to snowpack density. SWE was underestimated by just over half the models but the density of these sheltered snowpacks was overestimated by most models, resulting in snowpack depth being underestimated by an average 0.1 m. Models with multiple simplified surface parameterizations tend to show the greatest underestimation of SWE and depth and overestimation of density.Biases in above-canopy radiative, snow surface and bulk snowpack temperatures are not consistent with respect to size and sign; many models show a combination of positive and negative biases. Radiative and snowpack surface temperatures are associated with trends in turbulent heat fluxes. Models with multiple simplified surface parameterizations (e.g. large or fixed density or thermal conductivity values, a composite snowpack, no organic soil) show more negative soil temperature biases and appear to be associated with a colder snowpack, but unfortunately, bulk snowpack temperature was not reported for many such models. Negative SWE and depth biases are associated with colder winter soil temperatures and shorter snow seasons. Most models simulate snow thermal conductivity with one of many relationships with density. Soil temperature bias is highly sensitive to the choice of snow thermal conductivity parameterization.Models with many snow layers tend to show smaller errors in snowpack properties and are less likely to show cold biases in the snowpack and soil compared with composite or single layer models. However, as found in previous SnowMIPs, some single-layer models occupy the same bias range as multi-layer models. Models employing a multi-layer snowpack tend not to employ multiple “simplified parameterizations” as described above whereas the models with a single snow layer employ surface parameterizations with a range of sophistication.

Eurasian spring snow cover is widely considered as an important predictor of Asian summer monsoon rainfall, but its possible role in the formation of the north–south dipole structure of rainfall anomalies (NSDR)—a major mode of the eastern China summer rainfall variability—remains elusive. Here, we show that, there is a close connection between the western Eurasian spring snow cover (WESS) and NSDR during our research period 1967–2018, with less WESS tends to be accompanied by a wetter south-drier north pattern over eastern China, and vice versa. However, this relationship was not significant before the late 1990s, but has since become significant. Further analyses demonstrate that the shift in the WESS–NSDR relationship could be attributed to the modulation of summer North Atlantic Oscillation (SNAO). After the late 1990s, the WESS-related anomalous atmospheric circulations during summer are largely reinforced by the constructive superposition of those with same signs induced by SNAO, which in turn would intensify the impact of WESS and hence lead to a strong WESS–NSDR connection. In contrast, the influences of WESS are counteracted by those with opposite signs associated with SNAO before the late 1990s and thereby result in a weak snow–rainfall relationship. Our findings, along with the decline in Eurasian spring snow cover, provide a potential explanation for the recent ‘South Flood–North Drought’ pattern observed over eastern China.

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 the current context of climate change in the poles, one of the objectives of the APRES3 (Antarctic Precipitation Remote Sensing from Surface and Space) project is to characterize the vertical structure of precipitation in order to better simulate it. Nowadays, the precipitation simulated by models in Antarctica is very widespread and overestimated the data. Sensitivity studies have been conducted using two models and compared to the observations obtained at the Dumont d'Urville coast station, obtained by a Micro Rain Radar (MRR). The MAR meso-scale model specifically developed for the polar regions and the LMDz/IPSL general circulation model, with zoomed configuration over Dumont d'Urville, have been considered for this study. These models being different in resolution and physical configuration, performing an inter-comparison required numerical, dynamic and physical adjustments in LMDz. A sensitivity study was conducted on the physical and numerical parameters of the LMDz model and on the resolution of the MAR with the aim of estimating their contribution to the precipitation simulation. Sensitivity tests with MAR revealed that this model is well adjusted for precipitation modeling in polar climates, this confirming that this model is a reference in polar climate modeling. Regarding LMDz, sensitivity experiments revealed that modifications in the sedimentation and sublimation parameters do not significantly impact precipitation rate. However, dissipation of the LMDz model, which is a numerical process that dissipates spatially excessive energy and keeps the model stable, impacts precipitation indirectly but very strongly. A suitable adjustment of the dissipation reduces significantly precipitation over Antarctic peripheral area, thus providing a simulated profile in better agreement with the MRR observations.

CMIP5, CMIP6 and ERA5 antarctic precipitations are evaluated against CloudSat data. At continental and regional scales, ERA5 and CMIP models median are biased high, with insignificant improvement from CMIP5 to CMIP6 despite near-surface temperature improvement. However, less models yield outlying overestimation in CMIP6. AMIP configurations perform better than historical ones and, surprisingly, relative errors in areas of complex topography are higher (up to 50 %) in the 5 higher resolution models. The seasonal cycle is well reproduced by the median of the CMIP models but not by ERA5. There is limited progress from CMIP5 to CMIP6 and still room for improvement.

Human-induced climate change impacts the hydrological cycle and thus the availability of water resources. However, previous assessments of observed warming-induced changes in dryness have not excluded natural climate variability and show conflicting results due to uncertainties in our understanding of the response of evapotranspiration. Here we employ data-driven and land-surface models to produce observation-based global reconstructions of water availability from 1902 to 2014, a period during which our planet experienced a global warming of approximately 1 °C. Our analysis reveals a spatial pattern of changes in average water availability during the driest month of the year over the past three decades compared with the first half of the twentieth century, with some regions experiencing increased and some decreased water availability. The global pattern is consistent with climate model estimates that account for anthropogenic effects, and it is not expected from natural climate variability, supporting human-induced climate change as the cause. There is regional evidence of drier dry seasons predominantly in extratropical latitudes and including Europe, western North America, northern Asia, southern South America, Australia and eastern Africa. We also find that the intensification of the dry season is generally a consequence of increasing evapotranspiration rather than decreasing precipitation. Regional changes in dry-season water availability over recent decades can be attributed to human-induced climate change, according to analyses of global reconstructions.

We present projections of West-Antarctic surface mass balance (SMB) and surface melting to 2080–2100, under the RCP8.5 scenario and based on a regional model at 10 km resolution. Our projections are built by adding a CMIP5 (5th Coupled Model Intercomparison Project) multi-model-mean seasonal climate-change anomaly to the present-day model boundary conditions. Using an anomaly has the advantage to reduce CMIP5 model biases, and a perfect-model test reveals that our approach captures most characteristics of future changes, despite a 16–17 % underestimation of projected SMB and melt rates. SMB over the grounded ice sheet in the sector between Getz and Abbot increases from 336 Gt yr−1 in 1989–2009 to 455 Gt yr−1 in 2080–2100, which would reduce the global sea level changing rate by 0.33 mm yr−1. Snowfall indeed increases by 7.4 to 8.9 % per °C of near-surface warming, due to increasing saturation water vapour pressure in warmer conditions, reduced sea-ice concentrations, and more marine air intrusion. Ice-shelf surface melt rates increase by an order of magnitude along the 21st century, mostly due to higher downward radiation from increased humidity, and to reduced albedo in the presence of melting. Eastern ice shelves (Abbot, Cosgrove and Pine Island) experience significant runoff in the future, while western ice shelves (Thwaites, Crosson, Dotson and Getz) remain without runoff. This is explained by the evolution of the melt-to-snowfall ratio: below a threshold of 0.60 to 0.85, firn air is not entirely depleted by melt water, while entire depletion and runoff occur for higher ratios. This suggests that western ice shelves might remain unaffected by hydrofracturing for more than a century under RCP8.5, while eastern ice shelves have a high potential for hydrofracturing before the end of this century.

Two decades into the 21st century there is growing evidence for global impacts of Antarctic and Southern Ocean climate change. Reliable estimates of how the Antarctic climate system would behave under a range of scenarios of future external climate forcing are thus a high priority. Output from new model simulations coordinated as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides an opportunity for a comprehensive analysis of the latest generation of state‐of‐the‐art climate models following a wider range of experiment types and scenarios than previous CMIP phases. Here the main broad‐scale 21st century Antarctic projections provided by the CMIP6 models are shown across four forcing scenarios: SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5. End‐of‐century Antarctic surface‐air temperature change across these scenarios (relative to 1995–2014) is 1.3, 2.5, 3.7 and 4.8°C. The corresponding proportional precipitation rate changes are 8, 16, 24 and 31%. In addition to these end‐of‐century changes, an assessment of scenario dependence of pathways of absolute and global‐relative 21st century projections is conducted. Potential differences in regional response are of particular relevance to coastal Antarctica, where, for example, ecosystems and ice shelves are highly sensitive to the timing of crossing of key thresholds in both atmospheric and oceanic conditions. Overall, it is found that the projected changes over coastal Antarctica do not scale linearly with global forcing. We identify two factors that appear to contribute: (a) a stronger global‐relative Southern Ocean warming in stabilisation (SSP2‐4.5) and aggressive mitigation (SSP1‐2.6) scenarios as the Southern Ocean continues to warm and (b) projected recovery of Southern Hemisphere stratospheric ozone and its effect on the mid‐latitude westerlies. The major implication is that over coastal Antarctica, the surface warming by 2100 is stronger relative to the global mean surface warming for the low forcing compared to high forcing future scenarios.

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

We present Surface Mass Balance (SMB) and surface melt rates projections in West Antarctica for the end of the 21st century using the MAR regional atmosphere and firn model (Gallée 1994; Agosta et al. 2019) forced by a CMIP5-rcp85 multi-model-mean seasonal anomaly added to the ERA-Interim 6-hourly reanalysis. First of all, we assess the validity of our projection method, following a perfect-model approach, with MAR constrained by the ACCESS-1.3 present-day and future climates. Changes in large-scale variables are well captured by our anomaly-based projection method, and errors on surface melting and SMB projections are typically 10%. Based on the CMIP5-rcp85 multi-model mean, SMB over the grounded ice sheet in the Amundsen sector is projected to increase by 35% over the 21st century. This corresponds to a SMB sensitivity to near-surface warming of 8.3%.°C-1. Increased humidity, resulting from both higher water vapour saturation in warmer conditions and decreased sea-ice concentrations, are shown to favour increased SMB in the future scenario. Ice-shelf surface melt rates at the end of the 21st century are projected to become 6 to 15 times larger than presently, depending on the ice shelf under consideration. This is due to enhanced downward longwave radiative fluxes related to increased humidity, and to an albedo feedback leading to more absorption of shortwave radiation. Interestingly, only three ice shelves produce runoff (Abbot, Cosgrove and Pine Island) in the future climate. For the other ice shelves (Thwaites, Crosson, Dotson, Getz), the future melt-to-snowfall ratio remains too low to produce firn air depletion and subsequent runoff.

Human-induced climate change poses potential impacts on the availability of water resources. Previous assessments of warming-induced changes in dryness, however, are influenced by short observational records and show conflicting results due to uncertainties in the response of evapotranspiration. In this study we use novel observation-based water availability reconstructions from data-driven and land surface models from 1902 to 2014; a period during which the Earth has warmed approximately 1°C relative to pre-industrial conditions. These reconstructions reveal consistent changes in average water availability of the driest month of the year during the last 30 years compared to the first half of the 20th century. We conduct a simple attribution approach based on a spatial correlation analysis between the reconstructions and different climate model simulations. Results indicate that the spatial pattern of changes is extremely likely influenced by human-induced greenhouse gas emissions as it is consistent with climate model estimates that include historical radiative forcing, whereas the pattern is not expected from natural climate variability given by climate simulations with greenhouse gas levels set to pre-industrial conditions. Changes in water availability are characterized by drier dry seasons predominantly in extratropical latitudes and including Europe, Western North America, Northern Asia, Southern South America, Australia, and Eastern Africa. Finally, we find that the intensification of the dry season is generally a consequence of increasing evapotranspiration rather than decreasing precipitation.

Conventional wisdom holds that confidence in future projections of snow cover extent and snow mass requires an understanding of the expected changes in future snow characteristics as a function of modelled snow processes. We will highlight contrasting results which suggest differing importance in the role of sub-grid scale processes on simulations of seasonal snow.The first study is an evaluation of simulated snow cover extent projections from models participating in the 6th phase of the World Climate Research Programme Coupled Model Inter-comparison Project (CMIP-6). We demonstrate a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all future climate scenarios. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8% relative to the 1995-2014 level per °C of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast response components of the cryosphere such as sea ice and near surface permafrost.The second study makes use of an ensemble of land surface models, downscaled to 5 km resolution across North America over the 2009-2017 period. In this case, uncertainty in total North American snow mass is dominated by differences among land surface model configurations. While the largest absolute spread in snow mass is found in mountainous regions, heavily vegetated boreal regions have the largest fractional spread compared to climatological values. In particular, differences in rain-snow partitioning and sublimation rates control the largest portions of the total uncertainty. These results suggest that projections of future snow mass depend specifically on how such processes are modelled and parameterized.

Permafrost is an important component of the Arctic system and its future fate is likely to control changes in northern high latitude hydrology and biogeochemistry. Here we evaluate the permafrost dynamics in the global models participating in the Coupled Model Intercomparison Project (present generation – CMIP6; previous generation – CMIP5) along with the the sensitivity of permafrost to climate change. Whilst the northern high latitude air temperatures are relatively well simulated by the climate models, they do introduce a bias into any subsequent model estimate of permafrost. Therefore evaluation metrics are defined in relation to the air temperature. This paper shows the climate, snow and permafrost physics of the CMIP6 multi-model ensemble is very similar to that of the CMIP5 multi-model ensemble. The main difference is that a small number of models have demonstrably better snow insulation in CMIP6 than in CMIP5 which improves their representation of the permafrost extent. The simulation of maximum summer thaw depth does not improve between CMIP5 and CMIP6. We suggest that models should include a better resolved and deeper soil profile as a first step towards addressing this. We use the annual mean thawed volume of the top 2 m of the soil defined from the model soil profiles for the permafrost region to quantify changes in permafrost dynamics. The CMIP6 models suggest this is projected to increase by 20–30 %/°C of global mean temperature increase. Under climate change and in equilibrium this may result in an additional 80–120 Gt C/°C of permafrost carbon becoming vulnerable to decomposition.

This paper presents an analysis of observed and simulated historical snow cover extent and snow mass, along with future snow cover projections from models participating in the World Climate Research Programme Coupled Model Intercomparison Project Phase 6 (CMIP6). Where appropriate, the CMIP6 output is compared to CMIP5 results in order to assess progress (or absence thereof) between successive model generations. An ensemble of six observation-based products is used to produce a new time series of historical Northern Hemisphere snow extent anomalies and trends; a subset of four of these products is used for snow mass. Trends in snow extent over 1981–2018 are negative in all months and exceed -50×103 km2 yr−1 during November, December, March, and May. Snow mass trends are approximately −5 Gt yr−1 or more for all months from December to May. Overall, the CMIP6 multi-model ensemble better represents the snow extent climatology over the 1981–2014 period for all months, correcting a low bias in CMIP5. Simulated snow extent and snow mass trends over the 1981–2014 period are stronger in CMIP6 than in CMIP5, although large inter-model spread remains in the simulated trends for both variables. There is a single linear relationship between projected spring snow extent and global surface air temperature (GSAT) changes, which is valid across all CMIP6 Shared Socioeconomic Pathways. This finding suggests that Northern Hemisphere spring snow extent will decrease by about 8 % relative to the 1995–2014 level per degree Celsius of GSAT increase. The sensitivity of snow to temperature forcing largely explains the absence of any climate change pathway dependency, similar to other fast-response components of the cryosphere such as sea ice and near-surface permafrost extent.

International audience

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

We review recent literature on atmospheric, surface ocean and sea-ice observations and modeling results in the Antarctic sector and relate the observed climatic trends with the potential changes in the surface mass balance (SMB) of the ice sheet since 1900. Estimates of regional scale SMB distribution and trends remain subject to large uncertainties. Approaches combining and comparing multiple satellite and model-based assessments of ice sheet mass balance aim at reducing these knowledge gaps. During the last decades, significant changes in atmospheric circulation occurred around Antarctica, due to the exceptional positive trend in the Southern Annular Mode and to the climate variability observed in the tropical Pacific at the end of the twentieth century. Even though climate over the East Antarctic Ice-Sheet remained quite stable, a warming and precipitation increase was observed over the West Antarctic Ice-Sheet and over the West Antarctic Peninsula (AP) during the twentieth century. However, the high regional climate variability overwhelms climate changes associated to human drivers of global temperature changes, as reflected by a slight recent decadal cooling trend over the AP. Climate models still fail to accurately reproduce the multi-decadal SMB trends at a regional scale, and progress has to be achieved in reproducing atmospheric circulation changes related to complex ocean/ice/atmosphere interactions. Complex processes are also still insufficiently considered, such as (1) specific polar atmospheric processes (clouds, drifting snow, and stable boundary layer physics), (2) surface firn physics involved in the surface drag variations, or in firn air depletion and albedo feedbacks. Finally, progress in reducing the uncertainties relative to projections of the future SMB of Antarctica will largely depend on climate model capability to correctly consider teleconnections with low and mid-latitudes, and on the ability to correct them for biases, taking into account the coupling between ocean, ice, and atmosphere in high southern latitudes.

©2017. American Geophysical Union. All Rights Reserved. Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Eurasian spring snow cover may not be a faithful predictor of the Indian summer monsoon rainfall in a changing climate., Substantial progress has been made in understanding how Eurasian snow cover variabilities affect the Indian summer monsoon, but the snow-monsoon relationship in a warming atmosphere remains controversial. Using long-term observational snow and rainfall data (1967–2015), we identified that the widely recognized inverse relationship of central Eurasian spring snow cover with the Indian summer monsoon rainfall has disappeared since 1990. The apparent loss of this negative correlation is mainly due to the central Eurasian spring snow cover no longer regulating the summer mid-tropospheric temperature over the Iranian Plateau and surroundings, and hence the land-ocean thermal contrast after 1990. A reduced lagged snow-hydrological effect, resulting from a warming-induced decline in spring snow cover, constitutes the possible mechanism for the breakdown of the snow-air temperature connection after 1990. Our results suggest that, in a changing climate, Eurasian spring snow cover may not be a faithful predictor of the Indian summer monsoon rainfall.

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.