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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. 

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. 

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future. 

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond. 

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm. 

This dataset contains upper ocean temperature and salinity profiles made during July – September, 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Central Arctic. The primary aim of these profiles was to capture the stratification of the upper ocean due to meltwater input throughout the summer melt season and the transition to fall freeze-up. The dataset includes data from two instruments: (i) YSI probe, and (ii) Sontek Castaway. The YSI probe was used to take point measurements of temperature and salinity, allowing for more fine-scale profiles in the upper couple of meters. The Sontek Castaway is a small conductivity, temperature, and depth (CTD) device that was used to make profiles over the upper 10s of meters, here typically in complement to the YSI observations, and are processed to 15 centimeters (cm) vertical resolution. Profiles were made in two primary locations: (i) near-surface of leads surrounding the sea ice floe, using both YSI and Castaway, and (ii) upper ocean directly beneath the sea ice, typically using YSI only. A small number of additional observations were made in coincident melt ponds and the upper ocean directly underneath. Details of collection and processing methods, including quality control for both instruments, can be found in data archive descriptions. 

This dataset contains broadband albedo measurements made on the sea ice surface from approximately 1-meter (m) elevation during April – September 2020 as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Central Arctic Ocean. Measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters (or 10 meters on leg 3), (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. Broadband albedo data was collected using a Kipp and Zonen albedometer. This dataset is collocated with the spectral albedo dataset (doi.org/10.18739/A2FT8DK8Z) and albedo photo dataset (doi.org/10.18739/A2B27PS3N). 

This dataset contains spectral albedo data recorded on the sea ice surface June-September, 2020, during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition expedition in the Central Arctic Ocean. Measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters, (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. Spectral albedo data was collected using an Analytical Spectral Devices (ASD) FieldSpec Pro spectroradio meter with a custom spectralon cosine collector. Incident and reflected values were recorded subsequently, with 10 scans averaged for each.Processing of the data includes calculating an albedo from the relative values of incident and reflected scans, and completing quality control to (i) correct for parabolic offset between sensors, (ii) add flag quantifying variability of incident light that may be used to filter scans, (iii) remove scans with physically unrealistic values or slopes, and (iv) remove and filter noisy parts of the spectrum. This dataset is collocated with the broadband albedo dataset (doi.org/10.18739/A2KK94D36) and albedo photo dataset (doi.org/10.18739/A2B27PS3N). 

This dataset contains the corresponding photos of the albedo data recorded on the sea ice surface June-September, 2020, during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition expedition in the Central Arctic Ocean. The corresponding measurements were made in three modes: (i) along ‘albedo lines’, between 60-200 meters (m) in length, with measurements every 5 meters (or 10 meters on leg 3), (ii) at specific ‘library sites,’ or (iii) ‘experiments’. Albedo lines were chosen with the aim of crossing representative surface conditions during the summer sea ice evolution, including snow-covered ridges, bare ice, and ponded ice. Included in the dataset are classification of the surface cover and depth for most measurements. This dataset is collocated with the spectral albedo dataset (doi.org/10.18739/A2FT8DK8Z) and broadband albedo dataset (doi.org/10.18739/A2KK94D36).