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Melt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterisations struggle to link open ocean properties to ice-shelf basal melt rates for the range of current sub-shelf cavity geometries around Antarctica. We present a novel parameterisation based on deep learning. With a simple feedforward neural network, or multilayer perceptron, acting on each grid cell separately, we emulate the behavior of circum-Antarctic cavity-resolving ocean simulations. We explore different neural network sizes and find that, in all cases containing at least one hidden layer, this kind of emulator produces reasonable basal melt rates for our training ensemble, closer to the reference simulation than traditional parameterisations. For testing, we use an independent ensemble of simulations that was produced with the same ocean model but with different model parameters, different cavity geometries and different forcing. In this challenging test, traditional and neural network parameterisations yield similar results on present conditions. In much warmer conditions than the training ensemble, both traditional parameterisations and neural networks struggle, but the neural networks tend to produce basal melt rates closer to the reference than a majority of traditional parameterisations. These neural networks are therefore suitable for century-scale Antarctic ice-sheet projections.

Save the glaciers! is an educational escape kit designed for teenagers (11-18 years old) to learn what glaciers are, how they move, how they react to climate change and what people can do to slow down their melt.We made two versions of the game: a physical version and an online version. In both versions players have to sequentially solve 4 enigmas to unlock the next enigma until they reach the end of the game. Each enigma is designed to make the player discover and understand the following processes: Melting of mountains glaciers contributes to sea level rise Anatomy and mass-balance of a mountain glacier: What is it made of? How does it form? How does it grow and shrink? Glacier sliding: glaciers are not static, they slowly slide under their own weight Mountain glaciers are losing mass all around the world due to climate change, which can be observed by looking at their retreating termini. The physical version is designed to be played by 5 people; the online version can be played alone. Three levels of difficulty have been designed for the physical game: secondary-school pupils, high-school pupils and university students. In total more than 200 people have played the game, across both the physical and online versions.The game has been developed as a collaboration between the H2020 project PROTECT and a team at the Institute of Environmental Geosciences.

One of the largest sources of uncertainty when projecting the Antarctic contribution to sea-level rise is the ocean-induced melt at the base of Antarctic ice shelves. This is because resolving the ocean circulation and the ice-ocean interactions occurring in the cavity below the ice shelves is computationally expensive.Instead, for large ensembles and long-term projections of the ice-sheet evolution, ice-sheet models currently rely on parameterisations to link the ocean temperature and salinity in front of ice shelves to the melt at their base. However, current physics-based parameterisations struggle to accurately simulate basal melt patterns.As an alternative approach, we explore the potential use of a deep feedforward neural network as a basal melt parameterisation. To do so, we train a neural network to emulate basal melt rates simulated by highly-resolved circum-Antarctic ocean simulations. We explore the influence of different input variables and show that the neural network struggles to generalise to ice-shelf geometries unseen during training, while it generalises better on timesteps unseen during training. We also test the parameterisation on separate coupled ocean-ice simulations to assess the neural network’s performance on independent data.

Climate change is all over the news. But what exactly do we mean by “climate” and “climate change?” The word “climate” describes the average state of the atmosphere. It is a result of the composition and interactions between the natural elements: the air, oceans, plants and animals, ice and snow, and rocks. There are several different climate zones around the world. The state of the global climate is described by the average global air temperature. This temperature depends on how much heat the Earth receives from the Sun and how much of this heat is sent back to space. If the amount of heat received through the sun’s rays or the composition of one or several of the natural elements changes, the amount of heat taken up by the Earth changes. All elements are connected and constantly interacting, and this has consequences for the whole planet: climate change.

Approximately 10% of the global mean sea level rise over 2005–2010 was attributed to the glaciers flowing into the Amundsen Sea. This was mostly driven by changes in intrusions of Circumpolar Deep Water and subsequent ice shelf melt. Yet, projecting future ice shelf melt remains challenging because of large biases of CMIP models near Antarctica and because resolving the ocean circulation below the relatively small ice shelves in this sector requires a relatively high model resolution. Previously, we built atmospheric projections of the Amundsen sector at 10km resolution constrained by the rcp85 CMIP5 multi-model mean (Donat-Magnin et al. 2021). Here we use this atmospheric forcing to drive an ensemble of three 1/12° NEMO projections of the Amundsen Sea circulation and ice shelf melting. We find that melt rates are typically increased by 50% to 100% at the end of the 21st century compared to present day. Approximately half of this increase is explained by remote ocean changes transmitted through the model boundaries, while increased iceberg discharge does not have a significant effect. We describe the mechanisms at play through the terms of the ocean heat budget equations. We then use these projections to re-discuss some of the ISMIP6 projections (Seroussi et al. 2020, Edwards et al. 2021).

Ice shelves at the outskirts of the Antarctic ice sheet are thinning due to warm ocean water intruding into their cavities. Thinning reduces the ice shelves' buttressing potential, which means that the restraining force that they exert on the ice flowing across the grounding line is lower and more ice is discharged into the ocean. Taking into account ocean-induced melt, or basal melt, is therefore crucial for accurate sea-level projections. Still, its current representation in ice-sheet models is the main source of uncertainty associated with the Antarctic contribution to global sea-level rise in climate projections.An increasing amount of high-resolution ocean models are now able to resolve the circulation in the cavities below the ice shelves. However, running such models on multi-centennial scales or in a large ensemble is computationally expensive, especially when coupled with ice-sheet models. Instead, several parameterisations of varying complexity have been developed in past decades to describe the link between hydrographic properties in front of the ice shelf and basal melt rates. Previous studies have shown that the performance of these parameterisations depends on the ice shelf and that individual adjustments and corrections are needed for each ice shelf when applying them on the circum-Antarctic scale.In this study, we assess the potential of a range of existing basal melt parameterisations to emulate basal melt rates simulated by a cavity-resolving ocean model on the circum-Antarctic scale, without regional adjustments. To do so, we re-tune the parameters of the different parameterisations using an ensemble of simulations from the ocean model NEMO as our reference. We find that the quadratic dependence of melt to thermal forcing and the plume parameterisation yield the best compromise, in terms of integrated shelf melt rates and spatial melt rate patterns. Parameterisations based on the box model, however, yield basal melt rates further from the reference. Additionally to the newly tuned parameters, we also provide uncertainty estimates for the tuned parameters, for applications in large ensembles.

Ocean-induced ice-shelf melt is one of the largest uncertainty factors in the Antarctic contribution to future sea-level rise. Several parameterisations exist, linking oceanic properties in front of the ice shelf to melt at the base of the ice shelf, to force ice-sheet models. Here, we assess the potential of a range of these existing basal melt parameterisations to emulate basal melt rates simulated by a cavity-resolving ocean model on the circum-Antarctic scale. To do so, we perform two cross-validations, over time and over ice shelves respectively, and re-tune the parameterisations in a perfect-model approach, to compare the melt rates produced by the newly tuned parameterisations to the melt rates simulated by the ocean model. We find that the quadratic dependence of melt to thermal forcing without dependency on the individual ice-shelf slope and the plume parameterisation yield the best compromise, in terms of integrated shelf melt and spatial patterns. The box parameterisation, which separates the sub-shelf circulation into boxes, the PICOP parameterisation, which combines the box and plume parameterisation, and quadratic parameterisations with dependency on the ice slope yield basal melt rates further from the model reference. The linear parameterisation cannot be recommended as the resulting integrated ice-shelf melt is comparably furthest from the reference. When using offshore hydrographic input fields in comparison to properties on the continental shelf, all parameterisations perform worse; however, the box and the slope-dependent quadratic parameterisations yield the comparably best results. In addition to the new tuning, we provide uncertainty estimates for the tuned parameters.

Our planet’s climate has been warming much faster over the past 100 years than it has over the 10,000 years before that. In this article, we will explore how we know that the climate has changed so quickly in the last century, what carbon dioxide (CO2) has to do with climate change, and why humans are responsible for the recent increase of CO2 in the atmosphere. Understanding the problem is the best way to find a solution!

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.

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.

In an era where communicating your science goes hand in hand with doing your science, many scientists devote time to develop tools and learn new skills and strategies for Science Communication. The European Geosciences Union (EGU) has put in place one of those tools: the Divisions’ Blog. Most of the current EGU Divisions has an active blog run mainly by one or more volunteer early-career scientists. Regularly, both editors, and regular and guest authors write about research in their field, talk about relevant topics discussed within the scientific community, and highlight interesting facts for scientists and the general public. The goal is to provide a platform for enhancing communication among geoscientists in ways that go beyond the means of peer-reviewed publication or scientific conferences. At the same time, we aim at engaging with the general public, by writing in a technically sound, but more accessible form. Each Division’s blog has its character, like the teams behind it, making the blogs a diversified and exciting digital environment.Here we show the main numbers, statistics, and feedback from each Division Blog, thus providing a measure of the efforts put in and the impact made so far by the broad Geoscience community. We discuss best practices, blog styles and topics which do work well or not, based on readership statistics. We also show the channels chosen for advertising the blogs, such as social media, and the impact of the choices made. Finally, we show that even though EGU has its base in Europe, we reach an audience beyond Europe thanks to active members based outside Europe and to topics addressing particular geographical areas.We conclude that, within the increasingly essential role played by Science Communication in every research field, the EGU Divisions’ Blogs are successful at sharing research related to their fields with the broad geoscientific and non-scientific community. This success mainly relies on the time, effort, motivation, and creativity of editors and guest authors.

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.

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.

© 2014. American Geophysical Union. All Rights Reserved. Global statistics of snowfall are currently only available from the CloudSat satellite. But CloudSat cannot provide observations of clouds and precipitation within the so-called blind zone, which is caused by ground-clutter contamination of the CloudSat radar and covers the last 1200 m above land/ice surface. In this study, the impact of the blind zone of CloudSat on derived snowfall statistics in polar regions is investigated by analyzing three 12 month data sets recorded by ground-based Micro Rain Radar (MRR) at the Belgian Princess Elisabeth station in East Antarctica and at Ny-Ålesund and Longyearbyen in Svalbard, Norway. MRR radar reflectivity profiles are investigated in respect to vertical variability in the frequency distribution, changes in the number of observed snow events, and impacts on total precipitation. Results show that the blind zone leads to reflectivity being underestimated by up to 1 dB, the number of events being altered by ±5% and the precipitation amount being underestimated by 9 to 11 percentage points. Besides investigating a blind zone of 1200 m, the impacts of a reduced blind zone of 600 m are also analyzed. This analysis will help in assessing future missions with a smaller blind zone. The reduced blind zone leads to improved representation of mean reflectivity but does not improve the bias in event numbers and precipitation amount. ispartof: Journal of Geophysical Research: Atmospheres vol:119 issue:24 pages:13604-13620 status: published