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Warm air intrusions over Arctic sea ice can rapidly change the snow and ice surfaceconditions and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC.Here we focus on two warm moist air intrusions that produced surface glazing duringthe Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC)expedition that reached the research vessel Polarstern in mid-April 2020. After theevents, we observe increased SIC deviations between different satellite products,including climate data records, and especially an underestimation of SIC for algorithmsbased on polarization difference.To examine the causes of this underestimation, we use the extensive MOSAiC snowand ice measurements to computationally model the brightness temperatures of thesurface on a local scale. We further investigate the brightness temperatures observedby ground-based radiometers at frequencies 6.9 GHz, 19 GHz and 89 GHz.We show that the drop in the retrieved sea ice concentration of some satellite productscan be attributed to large-scale surface glazing, i.e., the formation of a thin ice crust atthe top of the snowpack, caused by the warming events.Another mechanism affecting satellite products which are mainly based on gradientratios of brightness temperatures, is the interplay of the changed temperature gradientin the snow and snow metamorphism.From the two analyzed climate data record products, one is less affected by thewarming events.The low frequency channels at 6.9 GHz were less sensitive to these snow surfacechanges, which could be exploited in future retrievals of sea ice concentration.

We assess the influence of snow on sea ice in experiments using the Community Earth System Model version 2 for a preindustrial and a 2xCO2 climate state. In the preindustrial climate, we find that increasing simulated snow accumulation on sea ice results in thicker sea ice and a cooler climate in both hemispheres. The sea ice mass budget response differs fundamentally between the two hemispheres. In the Arctic, increasing snow results in a decrease in both congelation sea ice growth and surface sea ice melt due to the snow's impact on conductive heat transfer and albedo, respectively. These factors dominate in regions of perennial ice but have a smaller influence in seasonal ice areas. Overall, the mass budget changes lead to a reduced amplitude in the annual cycle of ice thickness. In the Antarctic, with increasing snow, ice growth increases due to snow–ice formation and is balanced by larger basal ice melt, which primarily occurs in regions of seasonal ice. In a warmer 2xCO2 climate, the Arctic sea ice sensitivity to snow depth is small and reduced relative to that of the preindustrial climate. In contrast, in the Antarctic, the sensitivity to snow on sea ice in the 2xCO2 climate is qualitatively similar to the sensitivity in the preindustrial climate. These results underscore the importance of accurately representing snow accumulation on sea ice in coupled Earth system models due to its impact on a number of competing processes and feedbacks that affect the melt and growth of sea ice.

Wind transport alters the snow topography and microstructure on sea ice through snow redistribution controlled by deposition and erosion. The impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution on Arctic sea ice from Ka- and Ku-band radar signatures. Measurements were obtained during two wind events in November 2019 during the MOSAiC expedition. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coincident with surface height changes measured from a terrestrial laser scanner are observed. At both frequencies, snow redistribution events increased the dominance of the air/snow interface at nadir as the dominant radar scattering surface, due to wind densifying the snow surface and uppermost layers. The radar waveform data also detect the presence of previous air/snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air/snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite radar altimeters. The relative scattering contribution of the air/snow interface decreases, and the snow/sea ice interface increases with increasing incidence angles. Relative to pre-wind conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Binned backscatter within 5° azimuth bins reveals substantial backscatter variability in the radar footprint at all incidence angles and polarizations. The sensitivity of the co-polarized phase difference is linked to changes in snow settling and temperature-gradient induced grain metamorphism, demonstrating the potential of the radar to discriminate between newly deposited and older snow on sea ice. Our results reveal the importance of wind, through its geophysical impact on Ka- and Ku-band radar signatures of snow on sea ice and has implications for reliable interpretation of airborne and satellite radar measurements of snow-covered sea ice.

Microstructures, including crystallographic fabric, within the margin of streaming ice can exert strong control on flow dynamics. To characterize a natural setting, we retrieved three cores, two of which reached bed, from the flank of Jarvis Glacier, eastern Alaska Range, Alaska. The core sites lie ~1 km downstream of the source, with abundant water present in the extracted cores and at the base of the glacier. All cores exhibit dipping layers, a combination of debris bands and bubble-free domains. Grain sizes coarsen on average approaching the lateral margin. Crystallographic orientations are more clustered and withc-axes closer to horizontal nearer the lateral margin. The measured fabric is sufficiently weak to induce little mechanical anisotropy, but the data suggest that despite the challenging conditions of warm ice, abundant water and a short flow distance, many aspects of the microstructure, including measurable crystallographic fabric, evolved in systematic ways.

Vertical heat conduction through young ice is a major source of wintertime sea ice growth in the Arctic. However, field observations indicate that young ice preferentially accumulates wind-blown snow, resulting in greater snow thickness on young ice than would be expected from precipitation alone, and hence greater snow thickness on young ice than climate models represent. As snow has a low thermal conductivity, this additional snow thickness due to redistribution will reduce the actual heat conduction. We present new observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate Expedition which show that young ice rapidly accumulates a snow thickness of 2.5–8 cm, when wind-blown snow is available from the nearby mature ice. By applying a simple redistribution scheme and heat flux model to simulated conditions from the Community Earth System Model 2.0, we suggest that neglecting this snow redistribution onto young ice could result in the potential overestimation of conductive heat flux—and hence ice growth rates—by 3–8% on average in the Arctic in the winter in the absence of climate feedbacks. The impacts of snow redistribution are highest in the springtime and in coastal regions.

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.

We developed a tilt sensor for studying ice deformation and installed our tilt sensor systems in two boreholes drilled close to the shear margin of Jarvis Glacier, Alaska to obtain kinematic measurements of streaming ice. We used the collected tilt data to calculate borehole deformation by tracking the orientation of the sensors over time. The sensors' tilts generally trended down-glacier, with an element of cross-glacier flow in the borehole closer to the shear margin. We also evaluated our results against flow dynamic parameters derived from Glen's exponential flow law and explored the parameter space of the stress exponentnand enhancement factorE. Comparison with values from ice deformation experiments shows that the ice on Jarvis is characterized by highernvalues than that is expected in regions of low stress, particularly at the shear margin (~3.4). The highernvalues could be attributed to the observed high total strains coupled with potential dynamic recrystallization, causing anisotropic development and consequently sped up ice flow. Jarvis'nvalues place the creep regime of the ice between basal slip and dislocation creep. TuningEtowards a theoretical upper limit of 10 for anisotropic ice with single-maximum fabric reduces thenvalues by 0.2.

The spatial heterogeneity of the snow cover on Arctic sea ice impacts the coupled Ice-Ocean-Atmosphere system. This spatial heterogeneity manifests in both the spatial distribution of snow thickness and the material properties of that snow (e.g. density, specific surface area [SSA], thermal conductivity, salinity, etc). This presents a challenge for observing the aggregate snow cover properties. Most material properties can only be measured in-situ and it is logistically difficult to measure material properties at a large number of sites. Here, we address this challenge by integrating repeat Terrestrial Laser Scan (TLS) data and in-situ observations of snow properties on an area several hundred meters across. We used TLS to map the topography of this area at cm-scale vertical resolution on approximately a biweekly basis throughout the winter during MOSAiC. By comparing successive scans, we map the spatial extent of snow layers as they build up the snow cover. Concurrently, we made in-situ penetration resistance force measurements using a SnowMicroPen (SMP) to quantify the snow properties at sites within the measurement area. These weekly point measurements, with 3mm vertical resolution, provide details of the grain type, snow density and SSA stratigraphy. Combining the TLS and SMP observations enables us to extrapolate the layer-wise properties of the snow cover throughout the measurement area. We examine how consistent snow properties are within layers and use this information to quantify aggregate snow cover properties for the entire region. For example, by integrating SMP-derived density with TLS-derived layers we estimate aggregate change in snow mass for this region for selected periods of the winter.

Four seasonal ice mass balance buoys were deployed as part of the MOSAiC distributed network. These instruments measured vertical profiles of snow and ice temperature, as well as snow depth and ice thickness every six hours. Ice growth, surface melt, and bottom melt, as well as temporally averaged estimates of ocean heat fluxes, were calculated from these measurements. The buoys were installed in October 2019, with durations ranging from February 2020 to July 2020. Three of the buoys were destroyed in ridging events in February, March, and June 2020. The fourth buoy lasted until floe breakup in July 2020. The sites were separated by tens of kilometers, but had very similar air temperatures. While air temperatures were similar, snow – ice interface temperatures at different buoys varied by as much as 15 C due to differences in snow depth and ice thickness. Initial ice thicknesses ranged from 0.30 to 1.36 meters. During the growth season snow depths typically were around 0.1 to 0.2 meters, except for one case where the buoy was in a snow drift and the snow depth exceeded 0.5 meter. Peak growth rates of about 0.8 cm per day occurred in January. In mid-January there was a rapid increase in ice thickness associated with an aggregation of platelet ice. This aggregation only lasted for two weeks. In mid-April, air temperatures increased to nearly 0 C, almost ending the growth season.

Sea ice plays a critical role in the Arctic climate system, regulating much of the energy transfer between the ocean and the atmosphere. Repeat measurements of ice mass balance at discrete points allow us to determine the direct response of sea ice mass to environmental conditions. We installed a network of mass balance measurement sites across the MOSAiC Central Observatories, distributed over a diverse range of ice types and features. The sites were composed of gridded arrays of 9-17 hotwire thickness gauges, each paired with a surface ablation stake. Seven sites were installed on first year ice, and seven on second or multi year ice, with a total of 120+ individual measurement stations. The sites were operational over different periods throughout the year; several were destroyed or became inaccessible during ridging events. Initial ice thicknesses ranged from 0.13-3.50 m. We made measurements of ice and snow interfaces and thicknesses with 1 cm precision at each station, at intervals of 2-3 weeks during the growth season and as few as 1-2 days during the melt season. From these measurements, we infer ice growth, ice bottom melt, ice surface melt, snow deposition, snow erosion, and snow melt. The time series spans October 2019–September 2020, with a five-week measurement gap beginning mid-May 2020. We present an overview of the measurements and preliminary analysis, partitioning results by ice type and comparing mass balance to concurrent atmosphere and ocean measurements. We identify trends in the seasonal evolution of different ice types, and give particular attention to notable events in the time series. As true point-measurements, the data are especially relevant in improving one-dimensional thermodynamic sea ice models. The results also provide validation for satellite and electromagnetic induction ice-thickness measurements made during MOSAiC, which offer higher areal coverage but lower measurement- and spatial-precision.