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Abstract Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km²drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Usingin-situmeasurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements. 

Abstract. The melt of snow and sea ice during the Arctic summer is a significant source of relatively fresh meltwater in the central Arctic. The fate of this freshwater – whether in surface melt ponds, or thin layers underneath the ice and in leads – impacts atmosphere-ice-ocean interactions and their subsequent coupled evolution. Here, we combine analyses of datasets from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition (June–July, 2020) to understand the key drivers of the sea ice freshwater budget in the Central Arctic and the fate of this water over time. Freshwater budget analyses suggest that a relatively high fraction (58 %) is derived from surface melt. Additionally, the contribution from stored precipitation (snowmelt) significantly outweighs by five times the input from in situ summer precipitation (rain). The magnitude and rate of local meltwater production are remarkably similar to that observed on the prior Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. A relatively small fraction (10 %) of freshwater from melt remains in ponds, which is higher on more deformed second-year ice compared to first-year ice later in the summer. Most meltwater drains via lateral and vertical drainage channels, with vertical drainage enabling storage of freshwater internally in the ice by freshening of brine channels. In the upper ocean, freshwater can accumulate in transient meltwater layers on the order of 10 cm to 1 m thick in leads and under the ice. The presence of such layers substantially impacts the coupled system by reducing bottom melt and allowing false bottom growth, reducing heat, nutrient and gas exchange, and influencing ecosystem productivity. Regardless, the majority fraction of freshwater from melt is inferred to be ultimately incorporated into upper ocean (75 %) or stored internally in the ice (14 %). Comparison of key source and sink terms with estimates from the CESM2 climate model suggest that simulated freshwater storage in melt ponds is dramatically underestimated. This suggests pond drainage terms should be investigated as a likely explanation. 

The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) produced a wealth of observational data along the drift of the R/V Polarstern in the Arctic Ocean from October 2019 to September 2020. These data can further process-level understanding and improvements in models. However, the observational records contain temporal gaps and are provided in different formats. One goal of the MOSAiC Single Column Model Working Group (MSCMWG: https://mosaic-expedition.org/science/cross-cutting_groups/) is to provide consistently-formatted, gap-filled, merged datasets representing the conditions at the MOSAiC Central Observatory (the intensively studied region within a few km of R/V Polarstern) that are suitable for driving models on this spatial domain (e.g., single column models, large eddy simulations, etc). The MSCMWG is an open group, please contact the dataset creators if you would like to contribute to future versions of these merged datasets (including new variables). This dataset contains version 1 of these merged datasets, and comprises the variables necessary to force a single column ice model (e.g., Icepack: https://zenodo.org/doi/10.5281/zenodo.1213462). The atmospheric variables are primarily derived from Met City (~66 percent (%) of record, https://doi.org/10.18739/A2PV6B83F), with temporal gaps filled by bias and advection corrected data from Atmospheric Surface Flux Stations ( https://doi.org/10.18739/A2XD0R00S, https://doi.org/10.18739/A25X25F0P, https://doi.org/10.18739/A2FF3M18K). Some residual gaps in shortwave radiation were filled with ARM ship-board radiometer data. Three different options for snowfall precipitation rate (prsn) are provided, based on in-situ observations that precipitation greatly exceeded accumulation on level ice, and accumulation rates varied on different ice types. MOSAiC_kazr_snow_MDF_20191005_20201001.nc uses 'snowfall_rate1' derived from the vertically-pointing, ka-band radar on the vessel (https://doi.org/10.5439/1853942). MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc and MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc use snow accumulation measurements from manual mass balance sites (https://doi.org/10.18739/A2NK36626) to derived a pseudo-precipitation. MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc is based on the First Year Ice (fyi) sites. MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc is based on the Second Year Ice (syi) sites. The other atmospheric variables for these files are identical. Oceanic variables are in MOSAiC_ocn_MDF_20191006_20200919.nc and are derived from https://doi.org/10.18739/A21J9790B. The data are netCDF files formatted according to the Merged Data File format (https://doi.org/10.5194/egusphere-2023-2413, https://gitlab.com/mdf-makers/mdf-toolkit). The code 'recipes' that were used to produce these data are available at: https://doi.org/10.5281/zenodo.10819497. If you use these datasets, please also cite the appropriate publications: Meteorological variables (excluding precipitation): Cox et al., 2023 (https://doi.org/10.1038/s41597-023-02415-5) Oceanographic variables: Schulz et al., 2023 (https://doi.org/10.31223/X5TT2W) KAZR-derived precipitation: Matrosov et al., 2022 (https://doi.org/10.1525/elementa.2021.00101) Accumulation-derived pseudo-precipitation: Raphael et al., in review. The following are known issues that will be addressed in future dataset releases: 1. Residual gaps occupy approximately 20% of the data record (see addendum) 2. Some transitions to shiprad downwelling shortwave are unreasonable abrupt 3. MDF format does not currently include a field for point-by-point data source Addendum: For atmospheric variables, below indicates the percentage sourced from each dataset (and the amount missing a.k.a NaN) Air Temperature metcity 0.661943 NaN 0.193333 asfs30 0.134910 asfs40 0.008607 asfs50 0.001207 Specific Humidity metcity 0.658890 NaN 0.196298 asfs40 0.008695 Wind Velocity metcity 0.666334 NaN 0.255003 asfs30 0.068828 asfs40 0.008630 asfs50 0.001205 Downwelling Longwave metcity 0.549417 asfs30 0.241502 NaN 0.209081 Downwelling Shortwave metcity 0.674166 NaN 0.158814 asfs30 0.140794 shipradS1 0.026226 Note that the 21 day gap from the end of Central Observatory 2 to the start of Central Observatory 3 occupies 5.8% of the record. 

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day−1, with summer melt rates (June to July) as large as 5 cm day−1. Air temperatures changed as much as 2°C hour−1 but were dampened to <0.3°C hour−1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m−2, with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m−1 °C−1, higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget. 

Precise measurements of Arctic sea ice mass balance are necessary to understand the rapidly changing sea ice cover and its representation in climate models. During the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we made repeat point measurements of snow and ice thickness on primarily level first- and second-year ice (FYI, SYI) using ablation stakes and ice thickness gauges. This technique enabled us to distinguish surface and bottom (basal) melt and characterize the importance of oceanic versus atmospheric forcing. We also evaluated the time series of ice growth and melt in the context of other MOSAiC observations and historical mass balance observations from the Surface Heat Budget of the Arctic (SHEBA) campaign and the North Pole Environmental Observatory (NPEO). Despite similar freezing degree days, average ice growth at MOSAiC was greater on FYI (1.67 m) and SYI (1.23 m) than at SHEBA (1.45 m, 0.53 m), due in part to initially thinner ice and snow conditions on MOSAiC. Our estimates of effective snow thermal conductivity, which agree with SHEBA results and other MOSAiC observations, are unlikely to explain the difference. On MOSAiC, FYI grew more and faster than SYI, demonstrating a feedback loop that acts to increase ice production after multi-year ice loss. Surface melt on MOSAiC (mean of 0.50 m) was greater than at NPEO (0.18 m), with considerable spatial variability that correlated with surface albedo variability. Basal melt was relatively small (mean of 0.12 m), and higher than NPEO observations (0.07 m). Finally, we present observations showing that false bottoms reduced basal melt rates in some FYI cases, in agreement with other observations at MOSAiC. These detailed mass balance observations will allow further investigation into connections between the carefully observed surface energy budget, ocean heat fluxes, sea ice, and ecosystem at MOSAiC and during other campaigns. 

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development. 

Abstract Snow plays an essential role in the Arctic as the interface between the sea ice and the atmosphere. Optical properties, thermal conductivity and mass distribution are critical to understanding the complex Arctic sea ice system’s energy balance and mass distribution. By conducting measurements from October 2019 to September 2020 on the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have produced a dataset capturing the year-long evolution of the physical properties of the snow and surface scattering layer, a highly porous surface layer on Arctic sea ice that evolves due to preferential melt at the ice grain boundaries. The dataset includes measurements of snow during MOSAiC. Measurements included profiles of depth, density, temperature, snow water equivalent, penetration resistance, stable water isotope, salinity and microcomputer tomography samples. Most snowpit sites were visited and measured weekly to capture the temporal evolution of the physical properties of snow. The compiled dataset includes 576 snowpits and describes snow conditions during the MOSAiC expedition. 

Abstract. 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. 

This dataset contains measurements of sea ice thickness along drill lines. These measurements were taken in the Central Arctic during Leg 4 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, July 14-29, 2020. Thickness measurements include observations of rafted ice, false bottoms, and sea ice freeboard. Measurements were made through holes in the ice made with a 2-inch sea ice drill, with thickness and features observations made using a combination of thickness tape and a snow stick adapted for the purpose. The primary aim of these observations was to capture the presence and distribution of false bottom features under the ice during the melt season. 

During the Arctic melt season, relatively fresh meltwater layers can accumulate under sea ice as a result of snow and ice melt, far from terrestrial freshwater inputs. Such under-ice meltwater layers, sometimes referred to as under-ice melt ponds, have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from saltier water below, and by driving formation of ‘false bottoms’ below the sea ice. Such layers form at the interface of the fresher under-ice layer and the colder, saltier seawater below. During the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition in the Central Arctic, we observed the presence of under-ice meltwater layers and false bottoms throughout July 2020 at primarily first-year ice locations. Here, we examine the distribution, prevalence, and drivers of under-ice ponds and the resulting false bottoms during this period. The average thickness of observed false bottoms and freshwater equivalent of under-ice meltwater layers was 0.08 m, with false bottom ice comprised of 74–87% FYI melt and 13–26% snow melt. Additionally, we explore these results using a 1D model to understand the role of dynamic influences on decoupling the ice from the seawater below. The model comparison suggests that the ice-ocean friction velocity was likely exceptionally low, with implications for air-ice-ocean momentum transfer. Overall, the prevalence of false bottoms was similar to or higher than noted during other observational campaigns, indicating that these features may in fact be common in the Arctic during the melt season. These results have implications for the broader ice-ocean system, as under-ice meltwater layers and false bottoms provide a source of ice growth during the melt season, potentially reduce fluxes between the ice and the ocean, isolate sea ice primary producers from pelagic nutrient sources, and may alter light transmission to the ocean below.