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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 five 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.

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice–water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice).We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

In September 2019, the research icebreaker Polarstern started the largest multidisciplinary Arctic expedition to date, the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) drift experiment. Being moored to an ice floe for a whole year, thus including the winter season, the declared goal of the expedition is to better understand and quantify relevant processes within the atmosphere–ice–ocean system that impact the sea ice mass and energy budget, ultimately leading to much improved climate models. Satellite observations, atmospheric reanalysis data, and readings from a nearby meteorological station indicate that the interplay of high ice export in late winter and exceptionally high air temperatures resulted in the longest ice-free summer period since reliable instrumental records began. We show, using a Lagrangian tracking tool and a thermodynamic sea ice model, that the MOSAiC floe carrying the Central Observatory (CO) formed in a polynya event north of the New Siberian Islands at the beginning of December 2018. The results further indicate that sea ice in the vicinity of the CO (<40 km distance) was younger and 36 % thinner than the surrounding ice with potential consequences for ice dynamics and momentum and heat transfer between ocean and atmosphere. Sea ice surveys carried out on various reference floes in autumn 2019 verify this gradient in ice thickness, and sediments discovered in ice cores (so-called dirty sea ice) around the CO confirm contact with shallow waters in an early phase of growth, consistent with the tracking analysis. Since less and less ice from the Siberian shelves survives its first summer (Krumpen et al., 2019), the MOSAiC experiment provides the unique opportunity to study the role of sea ice as a transport medium for gases, macronutrients, iron, organic matter, sediments and pollutants from shelf areas to the central Arctic Ocean and beyond. Compared to data for the past 26 years, the sea ice encountered at the end of September 2019 can already be classified as exceptionally thin, and further predicted changes towards a seasonally ice-free ocean will likely cut off the long-range transport of ice-rafted materials by the Transpolar Drift in the future. A reduced long-range transport of sea ice would have strong implications for the redistribution of biogeochemical matter in the central Arctic Ocean, with consequences for the balance of climate-relevant trace gases, primary production and biodiversity in the Arctic Ocean.

The ice-covered period of large Arctic rivers is shortening. To what extent will this affect biogeochemical processing of nutrients? Here we reveal, with silicon isotopes (δ 30 Si), a key winter pathway for nutrients under river ice. During colder winter phases in the Lena River catchment, conditions are met for frazil ice accumulation, which creates microzones. These are conducive to a lengthened reaction time for biogeochemical processes under ice. The heavier δ 30 Si values (3.5 ± 0.5 ‰) in river water reflect that 39 ± 11% of the Lena River discharge went through these microzones. Freezing-driven amorphous silica precipitation concomitant to increased ammonium concentration and changes in dissolved organic carbon aromaticity in Lena River water support microbially mediated processing of nutrients in the microzones. Upon warming, suppressing loci for winter intra-river nitrogen processing is likely to modify the balance between N 2 O production and consumption, a greenhouse gas with a large global warming potential. Frazil ice accumulation during winter creates micro-zones in the Lena River catchment that prolong reaction time for biogeochemical processing of nutrients under ice, according to a high resolution silicon isotope study.

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 δ18O (–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 δ18O (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 δ18O (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 into the interaction between atmosphere, sea ice, and ocean and their spatio-temporal variations during MOSAiC.

Emerging evidence indicates that groundwater flow significantly impacts the distribution and characteristics of subsea permafrost, as well as the geomorphology of the subarctic seafloor.

Along much of the Arctic coast, shoreline retreat and sea level rise combine to inundate permafrost. Once inundated by seawater, permafrost usually begins to degrade. Tuktoyaktuk Island (Beaufort Sea, Northwest Territories, Canada) is an important natural barrier protecting the harbor of Tuktoyaktuk but will likely be breached within the next 2 decades. The state of subsea permafrost and its depth distribution around the island are, however, still largely unknown. We collected marine electrical resistivity tomography (ERT) surveys (vertical electrical soundings) north and south of Tuktoyaktuk Island using a floating cable with 13 electrodes in a quasi-symmetric Wenner–Schlumberger array configuration. We filtered the data with a new approach to eliminate potentially incorrect measurements due to a curved cable and inverted the profiles with a variety of parameterizations to estimate the position of the ice-bearing permafrost table (IBPT) below the seafloor. Our results indicate that north of Tuktoyaktuk Island, where coastal erosion is considerably faster, IBPT depths range from 5 m below sea level (120 m from the shoreline) to around 20 m b.s.l. (up to 800 m from the shoreline). South of the island, the IBPT dips more steeply and lies at 10 m b.s.l. a few meters from the shore, and 200 m from the shore, it is more than 30 m b.s.l. We discuss how marine ERT can be improved by accurately recording electrode positions, although choices made during data inversion are likely a greater source of uncertainty in the determination of the IBPT position. At Tuktoyaktuk Island, IBPT depths below the seafloor increase with distance from the shoreline; comparing the northern and southern sides of the island, the inclination is inversely proportional to coastline retreat rates. On the island's north side, the historical coastal retreat rate suggests a mean permafrost degradation rate of 5.3 ± 4.0 cm yr−1.

Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.

The thermokarst lakes of permafrost regions play a major role in the global carbon cycle. These lakes are sources of methane to the atmosphere although the methane flux is restricted by an ice cover for most of the year. How methane concentrations and fluxes in these waters are affected by the presence of an ice cover is poorly understood. To relate water body morphology, ice formation and methane to each other, we studied the ice of three different water bodies in locations typical of the transition of permafrost from land to ocean in a continuous permafrost coastal region in Siberia. In total, 11 ice cores were analyzed as records of the freezing process and methane composition during the winter season. The three water bodies differed in terms of connectivity to the sea, which affected fall freezing. The first was a bay underlain by submarine permafrost (Tiksi Bay, BY), the second a shallow thermokarst lagoon cut off from the sea in winter (Polar Fox Lagoon, LG) and the third a land-locked freshwater thermokarst lake (Goltsovoye Lake, LK). Ice on all water bodies was mostly methane-supersaturated with respect to atmospheric equilibrium concentration, except for three cores from the isolated lake. In the isolated thermokarst lake, ebullition from actively thawing basin slopes resulted in the localized integration of methane into winter ice. Stable δ13CCH4 isotope signatures indicated that methane in the lagoon ice was oxidized to concentrations close to or below the calculated atmospheric equilibrium concentration. Increasing salinity during winter freezing led to a micro-environment on the lower ice surface where methane oxidation occurred and the lagoon ice functioned as a methane sink. In contrast, the ice of the coastal marine environment was slightly supersaturated with methane, consistent with the brackish water below. Our interdisciplinary process study shows how water body morphology affects ice formation which mitigates methane fluxes to the atmosphere.

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.