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With progressing global warming, snowfall in Antarctica is expected to increase, which could counteract or even temporarily overcompensate increased ice-sheet mass losses caused by increased ice discharge and melting. For sea-level projections it is therefore vital to understand the processes determining snowfall changes in Antarctica. Here we revisit the relationship between Antarctic temperature changes and precipitation changes, identifying and explaining regional differences and deviations from the theoretical approach based on the Clausius–Clapeyron relationship. Analysing the latest estimates from global (CMIP6, Coupled Model Intercomparison Project Phase 6) and regional (RACMO2.3) model projections, we find an average increase of 5.5 % in annual precipitation over Antarctica per degree of warming, with a minimum sensitivity of 2 % K−1 near Siple Coast and a maximum sensitivity of > 10 % K−1 at the East Antarctic plateau region. This large range can be explained by the prevailing climatic conditions, with local temperatures determining the Clausius–Clapeyron sensitivity that is counteracted in some regions by the prevalence of the coastal wind regime. We compare different approaches of deriving the sensitivity factor, which in some cases can lead to sensitivity changes of up to 7 percentage points for the same model. Importantly, local sensitivity factors are found to be strongly dependent on the warming level, suggesting that some ice-sheet models which base their precipitation estimates on parameterisations derived from these sensitivity factors might overestimate warming-induced snowfall changes, particularly in high-emission scenarios. This would have consequences for Antarctic sea-level projections for this century and beyond.

The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone, with differences in model structure and parametrizations being one of the main sources of uncertainty. One particularly challenging aspect in modelling is the representation of terrestrial processes in permafrost-affected regions, which are often governed by spatial heterogeneity far below the resolution of the models' land surface components. Here, we use the Max Planck Institute (MPI) Earth System Model to investigate how different plausible assumptions for the representation of permafrost hydrology modulate land–atmosphere interactions and how the resulting feedbacks affect not only the regional and global climate, but also our ability to predict whether the high latitudes will become wetter or drier in a warmer future. Focusing on two idealized setups that induce comparatively “wet” or “dry” conditions in regions that are presently affected by permafrost, we find that the parameter settings determine the direction of the 21st-century trend in the simulated soil water content and result in substantial differences in the land–atmosphere exchange of energy and moisture. The latter leads to differences in the simulated cloud cover during spring and summer and thus in the planetary energy uptake. The respective effects are so pronounced that uncertainties in the representation of the Arctic hydrological cycle can help to explain a large fraction of the inter-model spread in regional surface temperatures and precipitation. Furthermore, they affect a range of components of the Earth system as far to the south as the tropics. With both setups being similarly plausible, our findings highlight the need for more observational constraints on the permafrost hydrology to reduce the inter-model spread in Arctic climate projections.

We analyze the evolution of heat fluxes and the resulting surface energy balance at the Arctic sea-ice edge in CMIP6 model simulations. We build on the study of Notz and Stroeve (2016), in which they show the existence of a strong linear relationship between Arctic sea-ice area and cumulative anthropogenic CO2 emissions. In explaining this linear relationship, the authors claim that the surface energy balance at the sea-ice edge remains constant, following the conceptual idea of the sea-ice edge retreating northwards to compensate for the increasing longwave radiative input due to global warming by a decrease in shortwave radiation at higher latitudes. We examine the validity of this hypothesis by first identifying the sea-ice edge in the model data, and then scrutinize whether or not the surface energy balance at that location stays constant under future sea-ice retreat. Furthermore, we decompose the energy balance into its constituents to explore dynamical effects and oceanic influence.We find that the annual mean surface energy balance shows stronger spatial than temporal variations. Looking at individual months, we find that the surface energy balance is negative in winter and positive in summer along the ice edge. Towards the end of the 21st century, the surface energy balance enters a new regime, becoming less negative in winter, and more positive in summer. By showing a consistently negative relation between downwelling shortwave radiation and downwelling longwave radiation, we finally confirm the idea of the compensation of increasing longwave input by a decrease in shortwave incoming radiation due to a northward migration of the sea-ice edge.

We examine the seasonal and regional evolution of sea-ice coverage in the Arctic in response to changes in the forcing. Using satellite and reanalysis data in combination with CMIP6 model simulations, we build on previous studies that have found a strong linear relationship between the September sea-ice area of the northern hemisphere and global atmospheric air temperature (TAS) as well as anthropogenic CO2 emissions. Instead of focusing on the whole Arctic and September sea ice only, we perform sensitivity analyses on higher-resolved regional and seasonal scales, aiming to identify the atmospheric and oceanic drivers that govern the evolution of sea-ice coverage on these scales and to derive simple empirical relationships that describe the impact of these processes. We find clear linkages also on these higher-resolved scales, with different regions and different seasons showing diverse sensitivities of sea-ice area evolution with respect to TAS and anthropogenic CO2. Furthermore, we use a multivariate metric to quantify the "quality" of a single simulation matching the observations, thereby considering the different sensitivities of all seasons of the year. Building the combined covariance matrix of observations and simulations as a measure of the joint uncertainties, we can determine how "close" to the observations every single member of the simulations is. This allows us to separate models whose sensitivities are in overall good agreement with the observations from those that are apparently not capable of properly simulating the response of the sea ice to the forcing throughout all months. Based on our findings we can infer the dominant drivers that force Arctic sea-ice evolution on a regional and seasonal scale and also derive projections for the future evolution of Arctic sea ice for different climate scenarios based on simple empirical relationships that can directly be estimated from observational records.

We investigate the reasons for the intermodel spread of simulated Arctic September sea-ice sensitivity. Previous studies have found that Arctic September sea-ice area declines linearly with cumulative CO2 emissions both in observations and climate-model simulations. However, the models’ sensitivity differs substantially, with the models generally underestimating the sensitivity of sea-ice area to CO2 emissions. We here examine the reasons for the large intermodel spread in order to be also able to understand the general underestimation.We identify a chain of processes contributing to the overall sea-ice sensitivity and investigate the simulation of each sub-process separately in each CMIP6 model. The process chain considers the global-mean temperature response to CO2 increase, Arctic amplification, the increase in incoming longwave radiation, the total non-shortwave heat flux in the Arctic, and the resulting sea-ice loss. In addition, we separately examine the impact of the simulated incoming longwave radiation for the spread of sea-ice sensitivity. Doing so, we find that clouds play a minor role for the spread of simulated incoming longwave radiation but that temperature rise and water vapour content in the Arctic are relevant.Based on these analyses, we identify three processes whose different representation in climate models likely is the main cause for the intermodel spread of simulated sea-ice sensitivity, and which need to be improved to improve the modeled sensitivity of Arctic sea ice: firstly the global-mean temperature response to CO2 increase, secondly the Arctic amplification and thirdly local sea-ice processes. The first two factors highly impact the evolution of temperature in the Arctic which affects the incoming longwave radiation and thus the evolution of sea ice.

The net Arctic sea-ice area (SIA) can be estimated from the routine monitoring of sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example, of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty . We here derive this uncertainty by taking spatial and temporal error correlations of the underlying local sea ice concentration products into account. Doing so, we find that the observational uncertainties of both sea-ice area and sea-ice extent (SIE) in 2015 are about 300 000 km2 for daily and weekly estimates and 160 000 km2 for monthly estimates. This is about half of the spread in estimated sea-ice area from different passive microwave SIC products, showing that random SIC errors play at least as much a role in SIA uncertainties as inter-SIC-product biases. We further show that the trend of SIA in September, which is traditionally the month with least Arctic sea ice is 105 000 km2 a-1 ± 9 000 km2 a-1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.

The coldest, the windiest, the driest: the continent of Antarctica is a place of extremes. Located at the South Pole, Antarctica is covered by a vast ice sheet, millions of years old and in some areas more than 4,000 m thick. If all this ice were to melt, sea levels would rise by roughly 58 m. Despite its massive size, the Antarctic ice sheet is vulnerable, losing more and more ice as the climate is warming. Most of this ice loss happens along the coast, where the ice sheet slowly flows into the ocean and forms ice shelves, which melt from below because of the comparably warmer ocean water. While the ice loss is still relatively slow right now, several processes could accelerate it and eventually even make it partly unstoppable. Wide-spread ice loss can only be prevented on the long-term if we manage to limit global warming to well below 2°C.

We examine the seasonal and regional evolution of sea-ice coverage in the Arctic. Using satellite and reanalysis data in comparison with model simulations of the 6th model intercomparison project (CMIP6), we build on studies (e.g. Niederdrenk and Notz, 2018; SIMIP Community, 2020) that have found a strong linear relationship between September sea-ice area of the northern hemisphere and global atmospheric air temperature (TAS) as well as anthropogenic CO2 emissions. Instead of focusing on the whole Arctic and September sea ice, we perform sensitivity analyses on higher-resolved regional and seasonal scales, aiming to identify the atmospheric and oceanic drivers that govern the evolution of sea-ice coverage on these scales and to derive simple empirical relationships that describe the impact of these processes. We find clear linkages also on these higher-resolved scales, with different regions and different seasons showing diverse sensitivities of sea-ice area evolution with respect to TAS and anthropogenic CO2. Furthermore, we make use of the Mahalanobis distance as a multivariate metric to quantify the "quality" of a single simulation matching the observations, thereby considering the different sensitivities of all months of the year. Building the combined covariance matrix of the observations and simulations as a measure of the joint uncertainties, we can determine how "close" to the observations each single member of the simulations is. This allows us to separate models whose sensitivities are in overall good agreement with the observations from those that are apparently not capable of properly simulating the response of the sea ice to the forcing throughout all months.Based on our findings we can infer the dominant drivers that force Arctic sea-ice evolution on a regional and seasonal scale and also derive projections for the future evolution of Arctic sea ice for different climate scenarios based on simple empirical relationships that can directly be estimated from observational records.References:Niederdrenk, A. L., & Notz, D. (2018). Arctic sea ice in a 1.5 °C warmer world. Geophysical Research Letters, 45, 1963–1971. https://doi.org/10.1002/2017GL076159SIMIP Community (2020). Arctic sea ice in CMIP6. Geophysical Research Letters, 47, e2019GL086749. https://doi.org/10.1029/2019GL086749

Snowfall is by far the most important positive contributor to the overall mass balance of the Antarctic Ice Sheet, potentially buffering temperature-induced dynamical ice loss in a warming climate. Previous studies have proposed that Antarctic snowfall will increase along the Clausius-Clapeyron relationship, describing the saturation water vapour pressure as a function of temperature (7% change for 1°C of warming). Due to cold temperatures and continentality in the interior, this general, first-order explanation may not hold true for snowfall changes across the ice sheet. In this study, we investigate how this first-order approximation can be modified to more reliably represent snowfall changes in a warming climate for simulations of the Antarctic Ice Sheet.To characterise the present-day precipitation pattern, we use reanalysis data and make use of state-of-the-art model data from the CMIP6 modelling project as well as regional model data. We analyse how the sensitivity of Antarctic precipitation to temperature changes is represented in models and how it potentially changes in the future. We use least-squares linear regression to determine the sensitivity factor, Antarctica’s x-factor, that is used in ice-sheet models to scale precipitation. With our statistical analyses, we show that sensitivities of column-integrated water vapour, precipitation, snowfall, net precipitation, and surface mass balance to temperature changes are fairly similar under present-day conditions; implying that the exponential relationship of saturation water vapour pressure to temperature could generally lead to additional mass gains of the Antarctic Ice Sheet with warming. However, we find that the relationship of Antarctic precipitation to temperatures across the ice sheet is not constant, but decreases with ongoing warming. Taking these changes into account could give a more reliable estimate of future precipitation changes than existing approaches. We demonstrate that a linear approximation of the exponential relationship between Antarctic precipitation and temperature becomes more and more imprecise in a warming climate, both for computing the sensitivity factor and to scale Antarctic precipitation in models.We propose a new way to extract the sensitivity factor of Antarctic precipitation to temperature which takes regional variations and the temperature dependence into account. The temperature dependence becomes more important the higher the warming becomes. Considering local warming rates, we show the necessity of introducing a temperature-dependent scaling factor in ice-sheet models, especially for high-end or long-term sea-level projections.

Surface temperatures in the Arctic are increasing more than twice as fast as the global average due to Arctic amplification. This warming gives rise to new types of extreme events that can have particularly large impacts. Here, we study the interplay of mean warming and changes in internal variability to better understand and constrain the intensity and frequency of temperature extremes in the Arctic, both regionally and seasonally.For this study, we analyze projected mean and extreme surface air temperatures in the Arctic for different levels of global warming based on output data from multiple single-model initial-condition large ensembles, with the Max Planck Institute Grand Ensemble (MPI-GE) at the core of the analysis. We use a time-slice approach to construct representative samples of the pre-industrial climate and the climate at different levels of global warming, including the Paris Agreement targets of 1.5 °C and 2 °C.Considering pan-Arctic temperatures, we find that the mean warming is strongest in winter (~3.5 times annual mean global warming) and lowest in summer (~1.05 times annual mean global warming), which leads to a weakening of the Arctic seasonal cycle with global warming. Moreover, the change in the return levels of extreme temperatures is particularly strong for cold extremes, rendering extremely cold temperatures seldom in a warming Arctic. The level of global warming is strongly impacting the frequency of extreme events. For example, warm extremes that occur every 100 years at 1.5 °C of global warming, occur more than once in 10 years at 2 °C of global warming, and cold extremes that occur every 10 years at 1.5 °C global warming, occur only about every 200 years at 2 °C of global warming (based on MPI-GE data). The response of Arctic mean temperatures to global warming results from a local temperature response that varies substantially for different regions and types of surfaces (land, ice sheet, open ocean, sea ice). We find the most drastic warming, accompanied by a strong reduction of variability, in winter temperatures over the northern Barents Sea linked to its ‘Atlantification’. Lastly, we also note a considerable difference in the Arctic temperature response for the same level of global warming in a transient versus a quasi-equilibrium climate state.The results of our study allow us to quantify expected changes in the Arctic temperature range with global warming and also to determine when and where, for example, climate mitigation measures are most likely to be visible.

We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice surface fraction (ISF) – SIC minus the per-grid-cell melt pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the National Oceanographic and Atmospheric Administration (NOAA) National Snow and Ice Data Center (NSIDC) SIC climate data record (CDR). Group III consists of Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) and National Aeronautics and Space Administration (NASA) Team (NT) algorithm products, and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find widespread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20 %–25 % for groups I and III and up to 30 %–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from group I products agrees with MODIS within 2 %–5 % accuracy during the entire melt period from May through September. Group II and IV products overestimate MODIS Arctic average SIC by 5 %–10 %. Out of group III, ASI is similar to group I products while NT SIC underestimates MODIS Arctic average SIC by 5 %–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically overestimated by all products; NT provides the smallest overestimations (up to 25 %) and group II and IV products the largest overestimations (up to 45 %). The spatial distribution of the observed overestimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice surface properties other than melt ponds, i.e. wet snow and coarse-grained snow/refrozen surface, on brightness temperatures and their ratios used as input to the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for (i) the observed differences between PMW SIC and MODIS ISF and for (ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the unknown number of melt pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and recommend conducting further research in this field.

Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.

To investigate the inherent predictability of sea ice and its representation in climate models, we compare the seasonal-to-interannual memory of Arctic sea ice as given by lagged correlations of sea-ice area anomalies in large model ensembles (Max Planck Institute Grand Ensemble and Coupled Model Intercomparison Project phase 6) and multiple observational products. We find that state-of-the-art climate models significantly overestimate the memory of pan-Arctic sea-ice area from the summer months into the following year. This cannot be explained by internal variability. We further show that the observed summer memory can be disentangled regionally into a reemergence of positive correlations in the perennial ice zone and negative correlations in the seasonal ice zone; the latter giving rise to the discrepancy between observations and model simulations. These findings could explain some of the predictability gap between potential and operational forecast skill of Arctic sea-ice area identified in previous studies.

We investigate the impact of three different parameterizations of ice–ocean heat exchange on modeled sea ice thickness, sea ice concentration, and water masses. These three parameterizations are (1) an ice bath assumption with the ocean temperature fixed at the freezing temperature; (2) a two-equation turbulent heat flux parameterization with ice–ocean heat exchange depending linearly on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is kept at the freezing point of the seawater; and (3) a three-equation turbulent heat flux approach in which the ice–ocean heat flux depends on the temperature difference between the underlying ocean and the ice–ocean interface, whose temperature is calculated based on the local salinity set by the ice ablation rate. Based on model simulations with the stand-alone sea ice model CICE, the ice–ocean model MPIOM, and the climate model COSMOS, we find that compared to the most complex parameterization (3), the approaches (1) and (2) result in thinner Arctic sea ice, cooler water beneath high-concentration ice and warmer water towards the ice edge, and a lower salinity in the Arctic Ocean mixed layer. In particular, parameterization (1) results in the smallest sea ice thickness among the three parameterizations, as in this parameterization all potential heat in the underlying ocean is used for the melting of the sea ice above. For the same reason, the upper ocean layer of the central Arctic is cooler when using parameterization (1) compared to (2) and (3). Finally, in the fully coupled climate model COSMOS, parameterizations (1) and (2) result in a fairly similar oceanic or atmospheric circulation. In contrast, the most realistic parameterization (3) leads to an enhanced Atlantic meridional overturning circulation (AMOC), a more positive North Atlantic Oscillation (NAO) mode and a weakened Aleutian Low.

Fully coupled climate models have long shown a wide range of Antarctic sea ice states and evolution over the satellite era. Here, we present a high-level evaluation of Antarctic sea ice in 40 models from the most recent phase of the Coupled Model Intercomparison Project (CMIP6). Many models capture key characteristics of the mean seasonal cycle of sea ice area (SIA), but some simulate implausible historical mean states compared to satellite observations, leading to large intermodel spread. Summer SIA is consistently biased low across the ensemble. Compared to the previous model generation (CMIP5), the intermodel spread in winter and summer SIA has reduced, and the regional distribution of sea ice concentration has improved. Over 1979-2018, many models simulate strong negative trends in SIA concurrently with stronger-than-observed trends in global mean surface temperature (GMST). By the end of the 21st century, models project clear differences in sea ice between forcing scenarios.

The diversity in sea-ice concentration observational estimates retrieved from brightness temperatures measured from space is a challenge for our understanding of past and future sea-ice evolution as it inhibits reliable climate model evaluation and initialisation. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model to be compared to observations from space at this frequency. We use simple temperature and salinity profiles inside the snow and ice column based on the output of the Earth System Model to compute these brightness temperatures. In this study, we evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrisation in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialisation, an exciting perspective for further developing this and similar methods.

The strong decline of Arctic sea ice in recent years has raised growing interest in seasonal-to-interannual predictions of Arctic sea ice. Previous studies have revealed a large predictability gap between potential and operational forecast skill of Arctic sea ice, which could indicate a strong potential for improvement of operational sea ice predictions or hint at a systematic overestimation of sea ice memory in current climate models.Here, we assess and compare memory of Arctic sea ice in terms of lagged correlations of sea ice area anomalies on seasonal to interannual time scales in a large model ensemble (MPI Grand Ensemble) as well as several reanalysis and observational products. While the different datasets show good agreement for short-term memory on time scales of a few months on which persistence is the dominant source of memory, we find substantial differences between model and observational memory behaviour on longer time scales. In particular, we find that memory from the summer sea ice minimum into the following year is significantly overestimated in the model, as lagged correlation values in all observational datasets are outside the range of model variability. Reanalysis data show correlation values that lie in between observational and model mean values, underpinning the hybrid nature of reanalyses combining observations and model behaviour. Extending the analysis of sea ice memory to a regional scale provides further information on the spatial origin of specific memory features in the different datasets and helps in understanding differences between model and real-world behaviour on a physical process level.

Arctic food webs are short and relatively species poor, rendering them vulnerable to changes or perturbations at any individual trophic level. High-latitude warming represents one major source of potential perturbation to Arctic marine and terrestrial food webs, which may experience cascading effects derived from changes in primary production through so-called “bottom-up” effects. We synthesize current knowledge on i) the changing Arctic marine icescape, ii) the drivers of biological changes for Arctic marine primary production, iii) the different pulses of Arctic marine primary production, iv) patterns of marine trophic and phenological changes, and iv) some mechanisms through which sea-ice dynamics ostensibly influence terrestrial primary productivity. We deliver a set of predictions for key productivity indicators, propose a semi-quantitative model of the expected future changes in primary production in the ice-covered Arctic Ocean, and close with an overview of the challenges ahead for reaching a holistic and comprehensive understanding of the ecosystem dynamical consequences and associated impacts on human life of warming-related sea-ice decline.

The observational uncertainty in sea-ice-concentration estimates from remotely-sensed passive-microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model. We evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

We explore the feasibility of an observation operator producing passive microwave brightness temperatures for sea ice at a frequency of 6.9 GHz. We investigate the influence of simplifying assumptions for the representation of sea-ice vertical properties on the simulation of microwave brightness temperatures. We do so in a one-dimensional setup, using a complex 1D thermodynamic sea-ice model and a 1D microwave emission model. We find that realistic brightness temperatures can be simulated in winter from a simplified linear temperature profile and a self-similar salinity profile in the ice. These realistic brightness temperatures can be obtained based on profiles interpolated to as few as five layers. Most of the uncertainty resulting from the simplifications is introduced by the simplification of the salinity profiles. In summer, the simplified salinity profile leads to too high liquid water fractions at the surface. To overcome this limitation, we suggest using a constant brightness temperature for the ice during summer and to treat melt ponds as water surfaces. Finally, in our setup, we cannot assess the effect of snow properties during melting. As periods of melting snow with intermediate moisture content typically last for less than a month, our approach allows one to estimate reasonable brightness temperatures at 6.9 GHz from climate model output for about 11 months throughout the year.

Continuum sea ice models have been applied close to the presumed limits of their validity for many years, yet they remain compatible with current observations. The resolution requirements for sea ice models varies considerably depending on the application (e.g., large ensembles, paleoclimate simulations, short-range forecasting), and therefore continuum models will likely remain useful for many years to come. Meanwhile, it is highly desirable to explore the potential of DEMs. These models are expected to be more physically faithful at the highest resolutions envisioned for sea ice in ESMs, provided they incorporate all the required processes. DEMs may also prove more efficient for some new computer architectures. Such perspectives highlight the need for the sea ice modeling community to have a clear and consistent vision of the future evolution of HPC systems. Sea ice models are used for many different purposes and therefore benefit from modularity, which allows the activation or exclusion of parameterizations and code features. Thus, users can adjust model complexity to fit their specific application. Considering limited human resources among core sea ice modeling groups, engagement of the wider community has proven a very efficient way to advance large-scale sea ice models. However, there is still scope for further integration of the wider community in model development activities. An important feature of the Laugarvatn sea ice modeling workshop was the open minded atmosphere in which very different views were exchanged. The workshop successfully brought together model developers and users of sea ice models for Earth system modeling, operational forecasting and (re)analyses. International sea ice modeling workshops such as this foster collaboration and community engagement in the field of sea ice modeling. A recommendation from this workshop is that the exercise should be repeated every 2-3 years to maintain community engagement, exchange cutting-edge ideas, and reinforce collaborative momentum. © 2020 American Meteorological Society.

The flux of energy through the climate system determines the living conditions of our planet. In this contribution, I outline the main processes regulating this flux of energy, how these processes have changed throughout Earth history, and how today they are changing by human activities, in particular by activities related to energy production. The changes in the climate state of our planet, which have been ongoing ever since the formation of the Earth some 5 billion years ago, have shaped the world we live in today. Yet, today’s climate change is special in two overarching ways. First, it is the first time that a major climate change is globally affecting a civilisation that is perfectly adapted to thousands of years of stable climate conditions. Second, today’s climate change is occurring at a rate much faster than preceding natural climate changes. In combination, these two factors make today’s climate change a unique challenge to humankind, with direct consequences of future energy production as outlined in the other contributions to this volume.

We examine the seasonal cycle of Arctic sea ice in scenarios with limited future global warming. To do so, we analyse two sets of observational records that cover the observational uncertainty of Arctic sea-ice loss per degree of global warming. The observations are combined with 100 simulations of historical and future climate evolution from the MPI-ESM Grand Ensemble. Based on the high-sensitivity observations, we find that Arctic September sea ice is lost with low probability (P≈10%) for global warming of +1.5 ° C above pre-industrial levels and with very high probability (P>99%) for global warming of +2 ° C above pre-industrial levels. For the low-sensitivity observations, September sea ice is extremely unlikely to disappear for +1.5 ° C warming (P<

This study is on high-frequency temporal variability (HFV) and meso-scale spatial variability (MSV) of winter sea-ice drift in the Southern Ocean simulated with a global high-resolution (0.1°) sea ice-ocean model. Hourly model output is used to distinguish MSV characteristics via patterns of mean kinetic energy (MKE) and turbulent kinetic energy (TKE) of ice drift, surface currents, and wind stress, and HFV characteristics via time series of raw variables and correlations. We find that (1) along the ice edge, the MSV of ice drift coincides with that of surface currents, in particular such due to ocean eddies; (2) along the coast, the MKE of ice drift is substantially larger than its TKE and coincides with the MKE of wind stress; (3) in the interior of the ice pack, the TKE of ice drift is larger than its MKE, mostly following the TKE pattern of wind stress; (4) the HFV of ice drift is dominated by weather events, and, in the absence of tidal currents, locally and to a much smaller degree by inertial oscillations; (5) along the ice edge, the curl of the ice drift is highly correlated with that of surface currents, mostly reflecting the impact of ocean eddies. Where ocean eddies occur and the ice is relatively thin, ice velocity is characterized by enhanced relative vorticity, largely matching that of surface currents. Along the ice edge, ocean eddies produce distinct ice filaments, the realism of which is largely confirmed by high-resolution satellite passive-microwave data.

We investigate changes in the Arctic Ocean energy budget simulated by 26 general circulation models from the Coupled Model Intercomparison Project Phase 5 framework. Our goal is to understand whether the Arctic Ocean warming between 1961 and 2099 is primarily driven by changes in the net atmospheric surface flux or by changes in the meridional oceanic heat flux. We find that the simulated Arctic Ocean warming is driven by positive anomalies in the net atmospheric surface flux in 11 models, by positive anomalies in the meridional oceanic heat flux in 11 models, and by positive anomalies in both energy fluxes in four models. The different behaviors are mainly characterized by the different changes in meridional oceanic heat flux that lead to different changes in the turbulent heat loss to the atmosphere. The multimodel ensemble mean is hence not representative of a consensus across the models in Arctic climate projections.

The heat exchange between floating ice and the underlying ocean is determined by the interplay of diffusive fluxes directly at the ice–ocean interface and turbulent fluxes away from it. In this study, we examine this interplay through direct numerical simulations of free convection. Our results show that an estimation of the interface flux ratio based on direct measurements of the turbulent fluxes can be difficult because the flux ratio varies with depth. As an alternative, we present a consistent evaluation of the flux ratio based on the total heat and salt fluxes across the boundary layer. This approach allows us to reconcile previous estimates of the ice–ocean interface conditions. We find that the ratio of heat and salt fluxes directly at the interface is 83–100 rather than 33 as determined by previous turbulence measurements in the outer layer. This can cause errors in the estimated ice-ablation rate from field measurements of up to 40% if they are based on the three-equation formulation.

International audience; the accumulation of magma within the Monti Sabatini Volcanic District (MSVD), italy, coupled with the extensional tectonics of the region, pose both volcanic and tectonic hazards to the city of Rome, located 20 km to the southeast. We combine 40 Ar/ 39 Ar geochronology of volcanic deposits and a geomorphologic/stratigraphic/paleomagnetic study of fluvial terraces to determine the recurrence interval and the time elapsed since the last eruption of the MSVD. Moreover, we provide a date for the youngest known eruption of the MSVD and assess the timing of the most recent volcanic phase. Results of this study show: (i) The most recent eruptive phase occurred between 100 ka and 70 ka; (ii) the anomalously high elevation of the MIS 5 terrace indicates that it was concurrent with 50 m of uplift in the volcanic area; (iii) the time since the last eruption (70 ka) exceeds the average recurrence interval (39 ky) in the last 300 ky, as well as the longest previous dormancy (50 ky) in that time span. (iv) the current duration of dormancy is similar to the timespan separating the major explosive phase that occurred 590-450 ka. The magnitude and patterns of deformation of a volcanic field provide constraints on the magmatic processes operating beneath a volcano. A recent geomorphological study 1 reconstructed a series of paleo-surfaces along a 40-km-long stretch of the Tiber River Valley north of Rome, between Magliano Sabina and Monterotondo, located at the eastern margin of the Monti Sabatini Volcanic District (MSVD) (Fig. 1a). These paleo-surfaces are interpreted as fluvial terraces formed through the interplay between regional uplift and glacio-eustasy (e.g. 2) during a regressive phase that formed the hydrographic network of the Paleo-Tiber River since the end of the Santernian (1.78-1.5 Ma; lower Calabrian) 3. The reconstructed rates of uplift during the last 1.8 Ma recognized two major pulses: 0.86 through 0.5 Ma, and 0.25 Ma through the present time 1. The coincidence of the uplift pulses with the ages of the main volcanic phases 1 are interpreted as mainly driven by uprising magma bodies from a metasomatized mantle source of the Roman Magmatic Province (e.g. 4-6). This "magmatic" uplift overlaps a smaller isostatic component on the Tyrrhenian Sea Margin of central Italy. In the present study, we further refine the chronostratigraphy of the paleo-surfaces in the area previously investigated, providing five new 40 Ar/ 39 Ar age determinations on volcanic products intercalated within the

Lavergne, Thomas ... et al.-- 30 pages, 12 figures, 2 tables.-- Data availability. The three SIC CDRs presented in this study are publicly available and can be accessed by following the DOIs https://doi.org/10.15770/EUM_SAF_OSI_0008 (Toudal Pedersen et al., 2017a), https://doi.org/10.5285/f17f146a31b14dfd960cde0874236ee5 (Toudal Pedersen et al., 2017b), and https://doi.org/10.5285/5f75fcb0c58740d99b07953797bc041e (Toudal Pedersen et al., 2017c); see also Table 1, We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37GHz and either 6 or 19GHz), in their horizontal resolution (25 or 50km), and in the time period they cover. We introduce the underlying algorithms and provide an evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover, This study and the development of the three new SIC CDRs were funded by EUMETSAT (through the 2nd Continuous Developments and Operation Phase of OSI SAF) and ESA (through the Climate Change Initiative SeaIce_cci project)

The anthropogenically forced decline of Arctic sea ice is superimposed on strong internal variability. Possible drivers for this variability include fluctuations in surface albedo, clouds and water vapour, surface winds and poleward atmospheric and oceanic energy transport, but their relative contributions have not been quantified. By isolating the impact of the individual drivers in an Earth system model, we here demonstrate that internal variability of sea ice is primarily caused directly by atmospheric temperature fluctuations. The other drivers together explain only 25% of sea-ice variability. The dominating impact of atmospheric temperature fluctuations on sea ice is consistent across observations, reanalyses and simulations from global climate models. Such atmospheric temperature fluctuations occur due to variations in moist-static energy transport or local ocean heat release to the atmosphere. The fact that atmospheric temperature fluctuations are the key driver for sea-ice variability limits prospects of interannual predictions of sea ice, and suggests that observed record lows in Arctic sea-ice area are a direct response to an unusually warm atmosphere. Atmospheric temperature fluctuations are the main influence on Arctic sea-ice variability, whereas other factors explain only 25% of variability, according to an analysis of Earth system model simulations.

Purpose of Review: The observed substantial loss of Arctic sea ice has raised prospects of a seasonally ice-free Arctic Ocean within the foreseeable future. In this review, we summarize our current understanding of the most likely trajectory of the Arctic sea-ice cover towards this state. Recent Findings: The future trajectory of the Arctic sea-ice cover can be described through a deterministic component arising primarily from future greenhouse gas emissions, and a chaotic component arising from internal variability. The deterministic component is expected to cause a largely ice-free Arctic Ocean during summer for less than 2 ∘C global warming relative to pre-industrial levels. To keep chances below 5 % that the Arctic Ocean will largely be ice free in a given year, total future CO2 emissions must remain below 500 Gt. Summary: The Arctic Ocean will become ice free during summer before mid-century unless greenhouse gas emissions are rapidly reduced. © 2018, The Author(s).

Why we are losing sea ice Arctic sea ice is disappearing rapidly, leading to predictions of an ice-free summer in the near future. Simulations of the timing of summer sea-ice loss differ substantially, making it difficult to evaluate the pace of the loss. Notz and Stroeve observed a linear relationship between the monthly-mean September sea-ice area and cumulative CO 2 emissions. This allowed them to predict Arctic summer sea ice directly from the observational record. Interestingly, most models underestimate this loss. Science , this issue p. 747

We discuss the current understanding of past and future sea-ice evolution as inferred from combining model simulations and observations. In such combined analysis, the models allow us to enhance our understanding behind the observed evolution of sea ice, while the observations allow us to assess how realistically the models represent the processes that govern sea-ice evolution in the real world. Combined, observations and models thus provide robust insights into the functioning of sea ice in the Earth's climate system, and can inform policy decisions related to the future evolution of the ice cover. We find that models and observations agree well on the sensitivity of Arctic sea ice to global warming and on the main drivers for the observed retreat. In contrast, a robust reduction of the uncertainty range of future sea-ice evolution remains difficult, in particular since the observational record is often too short to robustly examine the impact of internal variability on model biases. Process-based model evaluation and model evaluation based on seasonal-prediction systems provide promising ways to overcome these limitations.

We estimate the uncertainty of satellite-retrieved Arctic sea-ice thickness, sea-ice volume, and their trends stemming from the lack of reliable snow-thickness observations. To do so, we simulate a Cryosat2-type ice-thickness retrieval in an ocean-model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea-ice thickness using different snow climatologies and compare the resulting sea-ice thickness retrievals to each other and to the real sea-ice thickness of the reanalysis-forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice-volume trends derived from ice-thickness retrievals using any snow-depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume. ©2018. American Geophysical Union. All Rights Reserved.

We introduce the OSI-450, the SICCI-25km and the SICCI-50km climate data records of gridded global sea-ice concentration. These three records are derived from passive microwave satellite data and offer three distinct advantages compared to existing records: First, all three records provide quantitative information on uncertainty and possibly applied filtering at every grid point and every time step. Second, they are based on dynamic tie points, which capture the time evolution of surface characteristics of the ice cover and accommodate potential calibration differences between satellite missions. Third, they are produced in the context of sustained services offering committed extension, documentation, traceability, and user support. The three records differ in the underlying satellite data (SMMR & SSM/I & SSMIS or AMSR-E & AMSR2), in the imaging frequency channels (37 GHz and either 6 GHz or 19 GHz), in their horizontal resolution (25 km or 50 km) and in the time period they cover. We introduce the underlying algorithms and provide an initial evaluation. We find that all three records compare well with independent estimates of sea-ice concentration both in regions with very high sea-ice concentration and in regions with very low sea-ice concentration. We hence trust that these records will prove helpful for a better understanding of the evolution of the Earth's sea-ice cover.