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  1. 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.

     
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    Free, publicly-accessible full text available June 22, 2024
  2. Low-salinity meltwater from Arctic sea ice and its snow cover accumulates and creates under-ice meltwater layers below sea ice. These meltwater layers can result in the formation of new ice layers, or false bottoms, at the interface of this low-salinity meltwater and colder seawater. As part of the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC), we used a combination of sea ice coring, temperature profiles from thermistor strings and underwater multibeam sonar surveys with a remotely operated vehicle (ROV) to study the areal coverage and temporal evolution of under-ice meltwater layers and false bottoms during the summer melt season from mid-June until late July. ROV surveys indicated that the areal coverage of false bottoms for a part of the MOSAiC Central Observatory (350 by 200 m2) was 21%. Presence of false bottoms reduced bottom ice melt by 7–8% due to the local decrease in the ocean heat flux, which can be described by a thermodynamic model. Under-ice meltwater layer thickness was larger below first-year ice and thinner below thicker second-year ice. We also found that thick ice and ridge keels confined the areas in which under-ice meltwater accumulated, preventing its mixing with underlying seawater. While a thermodynamic model could reproduce false bottom growth and melt, it could not describe the observed bottom melt rates of the ice above false bottoms. We also show that the evolution of under-ice meltwater-layer salinity below first-year ice is linked to brine flushing from the above sea ice and accumulating in the meltwater layer above the false bottom. The results of this study aid in estimating the contribution of under-ice meltwater layers and false bottoms to the mass balance and salt budget for Arctic summer sea ice.

     
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  3. The increased fraction of first year ice (FYI) at the expense of old ice (second-year ice (SYI) and multi-year ice (MYI)) likely affects the permeability of the Arctic ice cover. This in turn influences the pathways of gases circulating therein and the exchange at interfaces with the atmosphere and ocean. We present sea ice temperature and salinity time series from different ice types relevant to temporal development of sea ice permeability and brine drainage efficiency from freeze-up in October to the onset of spring warming in May. Our study is based on a dataset collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 and 2020. These physical properties were used to derive sea ice permeability and Rayleigh numbers. The main sites included FYI and SYI. The latter was composed of an upper layer of residual ice that had desalinated but survived the previous summer melt and became SYI. Below this ice a layer of new first-year ice formed. As the layer of new first-year ice has no direct contact with the atmosphere, we call it insulated first-year ice (IFYI). The residual/SYI-layer also contained refrozen melt ponds in some areas. During the freezing season, the residual/SYI-layer was consistently impermeable, acting as barrier for gas exchange between the atmosphere and ocean. While both FYI and SYI temperatures responded similarly to atmospheric warming events, SYI was more resilient to brine volume fraction changes because of its low salinity (<2). Furthermore, later bottom ice growth during spring warming was observed for SYI in comparison to FYI. The projected increase in the fraction of more permeable FYI in autumn and spring in the coming decades may favor gas exchange at the atmosphere-ice interface when sea ice acts as a source relative to the atmosphere. While the areal extent of old ice is decreasing, so is its thickness at the onset of freeze-up. Our study sets the foundation for studies on gas dynamics within the ice column and the gas exchange at both ice interfaces, i.e. with the atmosphere and the ocean.

     
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  4. First-year sea-ice thickness, draft, salinity, temperature, and density were measured during near-weekly surveys at the main first-year ice coring site (MCS-FYI) during the MOSAiC expedition (legs 1 to 4). The ice cores were extracted either with a 9-cm (Mark II) or 7.25-cm (Mark III) internal diameter ice corers (Kovacs Enterprise, US). This data set includes data from 23 coring site visits and were performed from 28 October 2019 to 29 July 2020 at coring locations within 130 m to each other in the MOSAiC Central Observatory. During each coring event, ice temperature was measured in situ from a separate temperature core, using Testo 720 thermometers in drill holes with a length of half-core-diameter at 5-cm vertical resolution. Ice bulk practical salinity was measured from melted core sections at 5-cm resolution using a YSI 30 conductivity meter. Ice density was measured using the hydrostatic weighing method (Pustogvar and Kulyakhtin, 2016) from a density core in the freezer laboratory onboard Polarstern at the temperature of –15°C. Relative volumes of brine and gas were estimated from ice salinity, temperature and density using Cox and Weeks (1983) for cold ice and Leppäranta and Manninen (1988) for ice warmer than –2°C.The data contains the event label (1), time (2), and global coordinates (3,4) of each coring measurement and sample IDs (13, 15). Each salinity core has its manually measured ice thickness (5), ice draft (6), core length (7), and mean snow height (22). Each core section has the total length of its top (8) and bottom (9) measured in situ, as well estimated depth of section top (10), bottom (11), and middle (12). The depth estimates assume that the total length of all core sections is equal to the measured ice thickness. Each core section has the value of its practical salinity (14), isotopic values (16, 17, 18) (Meyer et al., 2000), as well as sea ice temperature (19) and ice density (20) interpolated to the depth of salinity measurements. The global coordinates of coring sites were measured directly. When it was not possible, coordinates of the nearby temperature buoy 2019T66 were used. Ice mass balance buoy 2019T66 installation is described in doi:10.1594/PANGAEA.938134. Brine volume (21) fraction estimates are presented only for fraction values from 0 to 30%. Each core section also has comments (23) describing if the sample is from a false bottom, from rafted ice or has any other special characteristics.Macronutrients from the salinity core, and more isotope data will be published in a subsequent version of this data set. 
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  5. Abstract

    The amount of snow on Arctic sea ice impacts the ice mass budget. Wind redistribution of snow into open water in leads is hypothesized to cause significant wintertime snow loss. However, there are no direct measurements of snow loss into Arctic leads. We measured the snow lost in four leads in the Central Arctic in winter 2020. We find, contrary to expectations, that under typical winter conditions, minimal snow was lost into leads. However, during a cyclone that delivered warm air temperatures, high winds, and snowfall, 35.0 ± 1.1 cm snow water equivalent (SWE) was lost into a lead (per unit lead area). This corresponded to a removal of 0.7–1.1 cm SWE from the entire surface—∼6%–10% of this site's annual snow precipitation. Warm air temperatures, which increase the length of time that wintertime leads remain unfrozen, may be an underappreciated factor in snow loss into leads.

     
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  6. 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). 
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  7. 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). 
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  8. 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). 
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  9. We present sea ice temperature and salinity data from first-year ice (FYI) and second-year ice (SYI) relevant to the temporal development of sea ice permeability and brine drainage efficiency from the early growth phase in October 2019 to the onset of spring warming in May 2020. Our dataset was collected in the central Arctic Ocean during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition in 2019 to 2020. MOSAiC was an international transpolar drift expedition in which the German icebreaker RV Polarstern anchored into an ice floe to gain new insights into Arctic climate over a full annual cycle. In October 2019, RV Polarstern moored to an ice floe in the Siberian sector of the Arctic at 85 degrees north and 137 degrees east to begin the drift towards the North Pole and the Fram Strait via the Transpolar Drift Stream. The data presented here were collected during the first three legs of the expedition, so all the coring activities took place on the same floe. The end dates of legs 1, 2, and 3 were 13 December, 24 February, and 4 June, respectively. The dataset contributed to a baseline study entitled, Deciphering the properties of different Arctic ice types during the growth phase of the MOSAiC floes: Implications for future studies. The study highlights downward directed gas pathways in FYI and SYI by inferring sea ice permeability and potential brine release from several time series of temperature and salinity measurements. The physical properties presented in this paper lay the foundation for subsequent analyses on actual gas contents measured in the ice cores, as well as air-ice and ice-ocean gas fluxes. Sea ice cores were collected with a Kovacs Mark II 9 cm diameter corer. To measure ice temperatures, about 4.5 cm deep holes were drilled into the core (intervals varied by site and leg) . The temperatures were measured by a digital thermometer within minutes after the cores were retrieved. The ice cores were placed into pre-labelled plastic sleeves sealed at the bottom end. The ice cores were transported to RV Polarstern and stored in a -20 degrees Celsius freezer. Each of the cores was sub-sampled, melted at room temperature, and processed for salinity within one or two days. The practical salinity was estimated by measuring the electrical conductivity and temperature of the melted samples using a WTW Cond 3151 salinometer equipped with a Tetra-Con 325 four-electrode conductivity cell. The practical salinity represents the the salinity estimated from the electrical conductivity of the solution. The dataset also contains derived variables, including sea ice density, brine volume fraction, and the Rayleigh number. 
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  10. Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice. 
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