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Abstract. Ice flow models of the Antarctic ice sheet are commonly used to simulate its future evolution inresponse to different climate scenarios and assess the mass loss that would contribute tofuture sea level rise. However, there is currently no consensus on estimates of the future massbalance of the ice sheet, primarily because of differences in the representation of physicalprocesses, forcings employed and initial states of ice sheet models. This study presentsresults from ice flow model simulations from 13 international groups focusing on the evolutionof the Antarctic ice sheet during the period 2015–2100 as part of the Ice Sheet ModelIntercomparison for CMIP6 (ISMIP6). They are forced with outputs from a subset of models from theCoupled Model Intercomparison Project Phase 5 (CMIP5), representative of the spread in climatemodel results. Simulations of the Antarctic ice sheet contribution to sea level rise in responseto increased warming during this period varies between −7.8 and 30.0 cm of sea level equivalent(SLE) under Representative ConcentrationPathway (RCP) 8.5 scenario forcing. These numbers are relative to a control experiment withconstant climate conditions and should therefore be added to the mass loss contribution underclimate conditions similar to present-day conditions over the same period. The simulated evolution of theWest Antarctic ice sheet varies widely among models, with an overall mass loss, up to 18.0 cm SLE, in response to changes in oceanic conditions. East Antarctica mass change varies between −6.1 and8.3 cm SLE in the simulations, with a significant increase in surface mass balance outweighingthe increased ice discharge under most RCP 8.5 scenario forcings. The inclusion of ice shelfcollapse, here assumed to be caused by large amounts of liquid water ponding at the surface ofice shelves, yields an additional simulated mass loss of 28 mm compared to simulations without iceshelf collapse. The largest sources of uncertainty come from the climate forcing, the ocean-induced melt rates, thecalibration of these melt rates based on oceanic conditions taken outside of ice shelf cavitiesand the ice sheet dynamic response to these oceanic changes. Results under RCP 2.6 scenario basedon two CMIP5 climate models show an additional mass loss of 0 and 3 cm of SLE on average compared tosimulations done under present-day conditions for the two CMIP5 forcings used and displaylimited mass gain in East Antarctica. info:eu-repo/semantics/published

Abstract. Arctic sea ice kinematics and deformation play significant roles in heat and momentum exchange between the atmosphere and ocean, and at the same time they have profound impacts on biological processes and biogeochemical cycles. However, the mechanisms regulating their changes on seasonal scales and their spatial variability remain poorly understood. Using position data recorded by 32 buoys in the Pacific sector of the Arctic Ocean (PAO), we characterized the spatiotemporal variations in ice kinematics and deformation for autumn–winter 2018/19, during the transition from a melting sea ice regime to a nearly consolidated ice pack. In autumn, the response of the sea ice drift to wind and inertial forcing was stronger in the southern and western PAO compared to the northern and eastern PAO. These spatial heterogeneities gradually weakened from autumn to winter, in line with the seasonal increases in ice concentration and thickness. Correspondingly, ice deformation became much more localized as the sea ice mechanical strength increased, with the area proportion occupied by the strongest (15 %) ice deformation decreasing by about 50 % from autumn to winter. During the freezing season, ice deformation rate in the northern PAO was about 2.5 times higher than in the western PAO and probably related to the higher spatial heterogeneity of oceanic and atmospheric forcing in the north. North–south and east–west gradients in sea ice kinematics and deformation within the PAO, as observed especially during autumn in this study, are likely to become more pronounced in the future as a result of a longer melt season, especially in the western and southern parts.

Computer models are necessary for understanding and predicting marine ice sheet behaviour. However, there is uncertainty over implementation of physical processes at the ice base, both for grounded and floating glacial ice. Here we implement several sliding relations in a marine ice sheet flow-line model accounting for all stress components and demonstrate that model resolution requirements are strongly dependent on both the choice of basal sliding relation and the spatial distribution of ice shelf basal melting.Sliding relations that reduce the magnitude of the step change in basal drag from grounded ice to floating ice (where basal drag is set to zero) show reduced dependence on resolution compared to a commonly used relation, in which basal drag is purely a power law function of basal ice velocity. Sliding relations in which basal drag goes smoothly to zero as the grounding line is approached from inland (due to a physically motivated incorporation of effective pressure at the bed) provide further reduction in resolution dependence.A similar issue is found with the imposition of basal melt under the floating part of the ice shelf: melt parameterisations that reduce the abruptness of change in basal melting from grounded ice (where basal melt is set to zero) to floating ice provide improved convergence with resolution compared to parameterisations in which high melt occurs adjacent to the grounding line.Thus physical processes, such as sub-glacial outflow (which could cause high melt near the grounding line), impact on capability to simulate marine ice sheets. If there exists an abrupt change across the grounding line in either basal drag or basal melting, then high resolution will be required to solve the problem. However, the plausible combination of a physical dependency of basal drag on effective pressure, and the possibility of low ice shelf basal melt rates next to the grounding line, may mean that some marine ice sheet systems can be reliably simulated at a coarser resolution than currently thought necessary.

Abstract. Many glaciers in the Antarctic Peninsula are now rapidly losing mass. Understanding of the dynamics of these fast-flowing glaciers, and their potential future behaviour, can be improved through ice sheet modelling studies. Inverse methods are commonly used in ice sheet models to infer the spatial distribution of a basal friction coefficient, which has a large effect on the basal velocity and ice deformation. Here we use the full-Stokes Elmer/Ice model to simulate the Wordie Ice Shelf–Fleming Glacier system in the southern Antarctic Peninsula. With an inverse method, we infer the pattern of the basal friction coefficient from surface velocities observed in 2008. We propose a multi-cycle spin-up scheme to reduce the influence of the assumed initial englacial temperature field on the final inversion. This is particularly important for glaciers like the Fleming Glacier, which have areas of strongly temperature-dependent deformational flow in the fast-flowing regions. Sensitivity tests using various bed elevation datasets, ice front positions and boundary conditions demonstrate the importance of high-accuracy ice thickness/bed geometry data and precise location of the ice front boundary.

The Wordie Ice Shelf–Fleming Glacier system in the southern Antarctic Peninsula has experienced a long-term retreat and disintegration of its ice shelf in the past 50 years. Increases in the glacier velocity and dynamic thinning have been observed over the past two decades, especially after 2008 when only a small ice shelf remained at the Fleming Glacier front. It is important to know whether the substantial further speed-up and greater surface draw-down of the glacier since 2008 is a direct response to ocean forcing, or driven by feedbacks within the grounded marine-based glacier system, or both. Recent observational studies have suggested the 2008–2015 velocity change was due to the ungrounding of the Fleming Glacier front. To explore the mechanisms underlying the recent changes, we use a full-Stokes ice sheet model to simulate the basal shear stress distribution of the Fleming system in 2008 and 2015. This study is part of the first high resolution modelling campaign of this system. Comparison of inversions for basal shear stresses for 2008 and 2015 suggests the migration of the grounding line ∼9 km upstream by 2015 from the 2008 ice front/grounding line positions, which virtually coincided with the 1996 grounding line position. This migration is consistent with the change in floating area deduced from the calculated height above buoyancy in 2015. The retrograde submarine bed underneath the lowest part of the Fleming Glacier may have promoted retreat of the grounding line. Grounding line retreat may also be enhanced by a feedback mechanism upstream of the grounding line by which increased basal lubrication due to increasing frictional heating enhances sliding and thinning. Improved knowledge of bed topography near the grounding line and further transient simulations with oceanic forcing are required to accurately predict the future movement of the Fleming Glacier system grounding line and better understand its ice dynamics and future contribution to sea level.

Pond color, which creates the visual appearance of melt ponds on Arctic sea ice in summer, is quantitatively investigated using a two-stream radiative transfer model for ponded sea ice. The upwelling irradiance from the pond surface is determined and then its spectrum is transformed into RGB (red, green, blue) color space using a colorimetric method. The dependence of pond color on various factors such as water and ice properties and incident solar radiation is investigated. The results reveal that increasing underlying ice thickness Hi enhances both the green and blue intensities of pond color, whereas the red intensity is mostly sensitive to Hi for thin ice (Hi < 1.5 m) and to pond depth Hp for thick ice (Hi > 1.5 m), similar to the behavior of melt-pond albedo. The distribution of the incident solar spectrum F0 with wavelength affects the pond color rather than its intensity. The pond color changes from dark blue to brighter blue with increasing scattering in ice, and the influence of absorption in ice on pond color is limited. The pond color reproduced by the model agrees with field observations for Arctic sea ice in summer, which supports the validity of this study. More importantly, the pond color has been confirmed to contain information about meltwater and underlying ice, and therefore it can be used as an index to retrieve Hi and Hp. Retrievals of Hi for thin ice (Hi < 1 m) agree better with field measurements than retrievals for thick ice, but those of Hp are not good. The analysis of pond color is a new potential method to obtain thin ice thickness in summer, although more validation data and improvements to the radiative transfer model will be needed in future.

Abstract. For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.