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Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) transport large amounts of moisture from the mid- to high-latitudes and they are a primary driver of the most extreme snowfall events, along with surface melting, in Antarctica. In this study, we characterize the climatology and surface impacts of ARs on West Antarctica, focusing on the Amundsen Sea Embayment and Marie Byrd Land. First, we develop a climatology of ARs in this region, using an Antarctic-specific AR detection tool combined with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) atmospheric reanalyses. We find that while ARs are infrequent (occurring 3 % of the time), they cause intense precipitation in short periods of time and account for 11 % of the annual surface accumulation. They are driven by the coupling of a blocking high over the Antarctic Peninsula with a low-pressure system known as the Amundsen Sea Low. Next, we use observations from automatic weather stations on Thwaites Eastern Ice Shelf with the firn model SNOWPACK and interferometric reflectometry (IR) to examine a case study of three ARs that made landfall in rapid succession from 2 to 8 February 2020, known as an AR family event. While accumulation dominates the surface impacts of the event on Thwaites Eastern Ice Shelf (> 100 kgm-2 or millimeters water equivalent), we find small amounts of surface melt as well (< 5 kgm-2). The results presented here enable us to quantify the past impacts of ARs on West Antarctica's surface mass balance (SMB) and characterize their interannual variability and trends, enabling a better assessment of future AR-driven changes in the SMB. [ABSTRACT FROM AUTHOR]

We use the BISICLES adaptive mesh ice sheet model to carry out one, two, and three century simulations of the fast-flowing ice streams of the West Antarctic Ice Sheet, deploying sub-kilometer resolution around the grounding line since coarser resolution results in substantial underestimation of the response. Each of the simulations begins with a geometry and velocity close to present-day observations, and evolves according to variation in meteoric ice accumulation rates and oceanic ice shelf melt rates. Future changes in accumulation and melt rates range from no change, through anomalies computed by atmosphere and ocean models driven by the E1 and A1B emissions scenarios, to spatially uniform melt rate anomalies that remove most of the ice shelves over a few centuries. We find that variation in the resulting ice dynamics is dominated by the choice of initial conditions and ice shelf melt rate and mesh resolution, although ice accumulation affects the net change in volume above flotation to a similar degree. Given sufficient melt rates, we compute grounding line retreat over hundreds of kilometers in every major ice stream, but the ocean models do not predict such melt rates outside of the Amundsen Sea Embayment until after 2100. Within the Amundsen Sea Embayment the largest single source of variability is the onset of sustained retreat in Thwaites Glacier, which can triple the rate of eustatic sea level rise. [ABSTRACT FROM AUTHOR]

Using the original setup described in Gallée et al. (2013), the MAR regional climate model including a coupled snowpack/aeolian snow transport parameterization, was run at a fine spatial (5 km horizontal and 2m vertical) resolution over 1 summer month in coastal Adélie Land. Different types of feedback were taken into account in MAR including drag partitioning caused by surface roughness elements. Model outputs are compared with observations made at two coastal locations, D17 and D47, situated respectively 10 and 100 km inland. Wind speed was correctly simulated with positive values of the Nash test (0.60 for D17 and 0.37 for D47) but wind velocities above 10 m s-1 were underestimated at both D17 and D47; at D47, the model consistently underestimated wind velocity by 2 m s-1. Aeolian snow transport events were correctly reproduced with the right timing and a good temporal resolution at both locations except when the maximum particle height was less than 1 m. The threshold friction velocity, evaluated only at D17 for a 7-day period without snowfall, was overestimated. The simulated aeolian snow mass fluxes between 0 and 2m at D47 displayed the same variations but were underestimated compared to the second-generation FlowCapt™ values, as was the simulated relative humidity at 2m above the surface. As a result, MAR underestimated the total aeolian horizontal snow transport for the first 2 m above the ground by a factor of 10 compared to estimations by the second-generation FlowCapt™. The simulation was significantly improved at D47 if a 1-order decrease in the magnitude of z0 was accounted for, but agreement with observations was reduced at D17. Our results suggest that z0 may vary regionally depending on snowpack properties, which are involved in different types of feedback between aeolian transport of snow and z0. [ABSTRACT FROM AUTHOR]

The historical simulations of sea ice during 1979 to 2005 by the Coupled Model Intercomparison Project Phase 5 (CMIP5) are compared with satellite observations, Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output data and Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS) output data in this study. Forty-nine models, almost all of the CMIP5 climate models and earth system models with historical simulation, are used. For the Antarctic, multi-model ensemble mean (MME) results can give good climatology of sea ice extent (SIE), but the linear trend is incorrect. The linear trend of satellite-observed Antarctic SIE is 1.29 (±0.57)×105 km2 decade–1; only about 1/7 CMIP5 models show increasing trends, and the linear trend of CMIP5 MME is negative with the value of –3.36 (±0.15)×105 km2 decade–1. For the Arctic, both climatology and linear trend are better reproduced. Sea ice volume (SIV) is also evaluated in this study, and this is a first attempt to evaluate the SIV in all CMIP5 models. Compared with the GIOMAS and PIOMAS data, the SIV values in both the Antarctic and the Arctic are too small, especially for the Antarctic in spring and winter. The GIOMAS Antarctic SIV in September is 19.1×103 km3, while the corresponding Antarctic SIV of CMIP5 MME is 13.0×103 km3 (almost 32% less). The Arctic SIV of CMIP5 in April is 27.1×103 km3, which is also less than that from PIOMAS SIV (29.5×103 km3). This means that the sea ice thickness simulated in CMIP5 is too thin, although the SIE is fairly well simulated. [ABSTRACT FROM AUTHOR]

The article presents a study on a firn densification model that simulates Antarctic firn density profiles as well as its temporal evolution and surface height. It mentions the model's utilization of an ameliorated firn densification expression that is adjusted to fit depth-density measurements. It also notes its steady-state and time-dependent model versions which respectively simulate strong variability in firn layer thickness and operate in Antarctic places with various climate conditions.