Search

The Arctic Ocean is an exceptional environment where hydrosphere, cryosphere, and atmosphere are closely interconnected. Changes in sea-ice extent and thickness affect ocean currents, as well as moisture and heat exchange with the atmosphere. Energy and water fluxes impact the formation and melting of sea ice and snow cover. Here, we present a comprehensive statistical analysis of the stable water isotopes of various hydrological components in the central Arctic obtained during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020, including the understudied Arctic winter. Our dataset comprises >2200 water, snow, and ice samples. Snow had the most depleted and variable isotopic composition, with d18O (–16.3%) increasing consistently from surface (–22.5%) to bottom (–9.7%) of the snowpack, suggesting that snow metamorphism and wind-induced transport may overprint the original precipitation isotope values. In the Arctic Ocean, isotopes also help to distinguish between different sea-ice types, and whether there is a meteoric contribution. The isotopic composition and salinity of surface seawater indicated relative contributions from different freshwater sources: lower d18O (approximately –3.0%) and salinities were observed near the eastern Siberian shelves and towards the center of the Transpolar Drift due to river discharge. Higher d18O (approximately –1.5%) and salinities were associated with an Atlantic source when the RV Polarstern crossed the Gakkel Ridge into the Nansen Basin. These changes were driven mainly by the shifts within the Transpolar Drift that carried the Polarstern across the Arctic Ocean. Our isotopic analysis highlights the importance of investigating isotope fractionation effects, for example, during sea-ice formation and melting. A systematic full-year sampling for water isotopes from different components strengthens our understanding of the Arctic water cycle and provides crucial insights int

Correction to: Scientific Data, published online 22 June 2023 The original version showed the wrong image for Figure 3, with the image for Figure 4 used for both. This has been corrected in the pdf and HTML versions of the article, with the correct version of Figure 3 replacing the duplicated figure. The dates in the figure captions were also incorrect and have been amended as follows: Figure 3 caption: “from 2019-10-25 - 2020-07-30” modified to “from 2019-10-25 - 2020-05-15” Figure 4 caption: “from 2020-02-25 - 2020-07-30” modified to “from 2020-06-13 - 2020-07-30”.

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.

Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly 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 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the drop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed.

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.

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 m 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 m and a preci

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.

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 pre

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

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.

Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic. The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples. This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges. First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and m