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Melt ponds are a core component of the summer sea ice system in the Arctic, increasing the uptake of solar energy and impacting the ice-associated ecosystem. They were thus one of the key topics during the 1-year drift campaign Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) in the Transpolar Drift 2019/2020. Pond depth is a dominating factor in describing the surface meltwater volume; it is necessary to estimate budgets and used in model parameterization to simulate pond coverage evolution. However, observational data on pond depth are spatially and temporally strongly limited to a few in situ measurements. Pond bathymetry, which is pond depth spatially fully resolved, remains unexplored. Here, we present a newly developed method to derive pond bathymetry from aerial images. We determine it from a photogrammetric multi-view reconstruction of the summer ice surface topography. Based on images recorded on dedicated grid flights and facilitated assumptions, we were able to obtain pond depth with a mean deviation of 3.5 cm compared to manual in situ observations. The method is independent of pond color and sky conditions, which is an advantage over recently developed radiometric airborne retrieval methods. It can furthermore be implemented in any typical photogrammetry workflow. We present the retrieval algorithm, including requirements for the data recording and survey planning, and a correction method for refraction at the air–pond interface. In addition, we show how the retrieved surface topography model synergizes with the initial image data to retrieve the water level of individual ponds from the visually determined pond margins. We use the method to give a profound overview of the pond coverage on the MOSAiC floe, on which we found unexpected steady pond coverage and volume. We were able to derive individual pond properties of more than 1600 ponds on the floe, including their size, bathymetry, volume, surface elevation above sea level, and temporal evolution. We present a scaling factor for single in situ depth measurements, discuss the representativeness of in situ pond measurements and the importance of such high-resolution data for new satellite retrievals, and show indications for non-rigid pond bottoms. The study points out the great potential to derive geometric properties of the summer sea ice surface emerging from the increasingly available visual image data recorded from uncrewed aerial vehicles (UAVs) or aircraft, allowing for an integrated understanding and improved formulation of the thermodynamic and hydrological pond system in models. [ABSTRACT FROM AUTHOR]

This paper presents a methodological tool for dynamic reconstruction of the state of the ocean, based, as an example, on observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) experiment. The data used in this study were collected in the Amundsen Basin between October 2019 and January 2020. Analysing observational data to assess tracer field and upper-ocean dynamics is highly challenging when measurement platforms drift with the ice pack due to continuous drift speed and direction changes. We have equipped the new version of the coastal branch of the global Finite-volumE sea ice–Ocean Model (FESOM-C) with a nudging method. Model nudging was carried out assuming a quasi-steady state. Overall, the model can reproduce the lateral and vertical structure of the temperature, salinity, and density fields, which allows for projecting dynamically consistent features of these fields onto a regular grid. We identify two separate depth ranges of enhanced eddy kinetic energy located around two maxima in buoyancy frequency: the depth of the upper halocline and the depth of the warm (modified) Atlantic Water. Simulations reveal a notable decrease in surface layer salinity and density in the Amundsen Basin towards the north but no significant gradient from east to west. However, we find a mixed-layer deepening from east to west, with a 0.084 m km -1 gradient at 0.6 m km -1 standard deviation, compared to a weak deepening from south to north. The model resolves several stationary eddies in the warm Atlantic Water and provides insights into the associated dynamics. The model output can be used to further analyse the thermohaline structure and related dynamics associated with mesoscale and submesoscale processes in the central Arctic, such as estimates of heat fluxes or mass transport. The developed nudging method can be utilized to incorporate observational data from a diverse set of instruments and for further analysis of data from the MOSAiC expedition. [ABSTRACT FROM AUTHOR]

The Arctic Ocean's snow cover is crucial in moderating interactions between sea-ice and the atmosphere, yet fully grasping its isotopic composition and the processes shaping it presents substantial challenges. This study employs a unique dataset from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition to explore the complex interactions between deposition processes and post-depositional changes affecting snow on Arctic sea ice. By examining 911 individual snow isotope measurements collected over a full year, we identify a clear layering within the snowpack: the top layer, with lower δ18O values and higher d-excess values, indicates fresh meteoric snowfall, while the bottom layer, affected by the sea ice beneath, shows higher δ18O values and lower d- d-excess values. By integrating these discrete snow samples with continuous vapour isotope data, our research provides insight into interactions between snow and the atmosphere, as well as the processes that alter isotopic signatures within Arctic snow. We observe a significant difference in δ18O values between snow and vapor during autumn, mainly due to delays in sampling after precipitation events, with d-excess ranges suggesting the impact of Atlantic moisture. Winter months exhibit sharp differences in δ18O and d-excess values, indicating kinetic fractionation amid extreme cold as the RV Polarstern traverses from the Siberian to the Atlantic sector of the Arctic Ocean. Conversely, summer months display a convergence in isotopic signatures, reflecting conditions favouring equilibrium fractionation, highlighted by increased air temperatures and humidity levels. While δ18O in vapour readily responds to changes in air temperature and humidity, surface snow δ18O is influenced more by subsequent processes such as sublimation and wind-driven redistribution. Sublimation, intensified by the snow's prolonged surface residence and facilitated by the porosity of snow, plays a key role in isotopic enrichment. Wind-driven snow redistribution, occurring 67 % of the winter, led to a homogenised and depleted surface snow δ18O signal across the sea ice by spreading lower δ18O meteoric snow. This effect was especially pronounced in ridge snow profiles, where the top layers showed a uniform δ18O signal, in stark contrast to flat ice samples. Furthermore, distinct isotopic patterns were detected along the MOSAiC expedition route from a region close to Samoylov Island to Fram Straight near Ny-Ålesund. Snow samples close to Samoylov Island exhibited notable seasonal δ18O variations, which were indicative of a continental climate. In contrast, samples from Ny-Ålesund displayed more consistent fluctuations, influenced by steady Atlantic moisture. [ABSTRACT FROM AUTHOR]

Leads and fractures in sea ice play a crucial role in the heat and gas exchange between the ocean and atmosphere, impacting atmospheric, ecological, and oceanic processes. We estimated lead fractions from high-resolution divergence obtained from satellite synthetic aperture radar (SAR) data and evaluated them against existing lead products. We derived two new lead fraction products from divergence with a spatial resolution of 700 m calculated from daily Sentinel-1 images. For the first lead product, we advected and accumulated the lead fractions of individual time instances. With those accumulated divergence-derived lead fractions, we comprehensively described the presence of up to 10 d old leads and analyzed their deformation history. For the second lead product, we used only divergence pixels that were identified as part of linear kinematic features (LKFs). Both new lead products accurately captured the formation of new leads with widths of up to a few hundred meters. We presented a Lagrangian time series of the divergence-based lead fractions along the drift of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic Ocean during winter 2019–2020. Lead activity was high in fall and spring, consistent with wind forcing and ice pack consolidation. At larger scales of 50–150 km around the MOSAiC expedition, lead activity on all scales was similar, but differences emerged at smaller scales (10 km). We compared our lead products with six others from satellite and airborne sources, including classified SAR, thermal infrared, microwave radiometer, and altimeter data. We found that the mean lead fractions varied by 1 order of magnitude across different lead products due to different physical lead and sea ice properties observed by the sensors and methodological factors such as spatial resolution. Thus, the choice of lead product should align with the specific application. [ABSTRACT FROM AUTHOR]

This study evaluates methods to derive the surface mixing layer (SML) height of the Arctic atmospheric boundary layer (ABL) using in situ measurements inside the Arctic ABL during winter and the transition period to spring. An instrumental payload carried by a tethered balloon was used for the measurements between December 2019 and May 2020 during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Vertically highly resolved (centimeter scale) in situ profile measurements of mean and turbulent parameters were obtained, reaching from the sea ice to several hundred meters above ground. Two typical conditions of the Arctic ABL over sea ice were identified: cloudless situations with a shallow surface-based inversion and cloudy conditions with an elevated inversion. Both conditions are associated with significantly different SML heights whose determination as accurately as possible is of great importance for many applications. We used the measured turbulence profile data to define a reference of the SML height. With this reference, a more precise critical bulk Richardson number of 0.12 was derived, which allows an extension of the SML height determination to regular radiosoundings. Furthermore, we have tested the applicability of the Monin–Obukhov similarity theory to derive SML heights based on measured turbulent surface fluxes. The application of the different approaches and their advantages and disadvantages are discussed. [ABSTRACT FROM AUTHOR]

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parameterizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow dataset from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have evaluated for the first time the seasonal evolution of the snow's and denser snow-ice interface layers' thermal conductivity above different ice ages (refrozen leads, first-year ice, and second-year ice) and topographic features (ridges). Our dataset has a density range of snow and ice between 50 and 900 kgm-3 , and corresponding anisotropy measurements, meaning we can test the current parameterizations of thermal conductivity for this density range. Combining different measurement parameterizations and assessing the robustness against spatial heterogeneity, we found the average thermal conductivity of snow (<550 kgm-3) on sea ice remains approximately constant (0.26 ± 0.05 WK-1m-1) over time irrespective of underlying ice type, with substantial spatial and vertical variability. Due to this consistency, we can state that the thermal resistance is mainly influenced by snow height, resulting in a 2.7 times higher average thermal resistance on ridges (1.42 m2KW-1) compared to first-year level ice (0.51 m2KW-1). Our findings explain how the scatter of thermal conductivity values directly results from structural properties. Now, the only step is to find a quick method to measure snow anisotropy in the field. Suggestions to do this are listed in the discussion. [ABSTRACT FROM AUTHOR]

To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as a heat and humidity feeding mechanism which can modify Arctic cloud properties. Cloud microphysical properties in the central Arctic are analysed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. The Cloudnet classification algorithm is used to characterize the clouds based on remote sensing observations and the atmospheric thermodynamic state from the observatory on board the research vessel (RV) Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapour transport (WVT) being conveyed towards RV Polarstern has been utilized as a mechanism to associate upwind sea ice conditions with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer. Only a conical sub-sector of sea ice concentration (SIC) and the lead fraction (LF) centred on the RV Polarstern location and extending up to 50 km in radius and with an azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT and selected by LF regimes. Liquid water path of low-level clouds is found to increase as a function of LF, while the ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to generally have a lower cloud base and larger thickness than decoupled clouds. Thermodynamically, for coupled cases the cloud-top temperature is warmer and accompanied by a temperature inversion at the cloud top, whereas the decoupled cases are found to be closely compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface–atmosphere processes. With this formulation, long-term observations can be analysed to enforce the statistical significance of the asymmetries. Furthermore, our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system. [ABSTRACT FROM AUTHOR]

Despite its relevance for the Arctic climate and ecosystem, modeling sea-ice deformation, i.e., the opening, shearing, and ridging of sea ice, along linear kinematic features (LKFs) remains challenging, as the mechanical properties of sea ice are not yet fully understood. The intersection angles between LKFs provide valuable information on the internal mechanical properties, as they are linked to them. Currently, the LKFs emerging from sea-ice rheological models do not reproduce the observed LKF intersection angles, pointing to a gap in the model physics. We aim to obtain an intersection angle distribution (IAD) from observational data to serve as a reference for high-resolution sea-ice models and to infer the mechanical properties of the sea-ice cover. We use the sea-ice vorticity to discriminate between acute and obtuse LKF intersection angles within two sea-ice deformation datasets: the RADARSAT Geophysical Processor System (RGPS) and a new dataset from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift experiment. Acute angles dominate the IAD, with single peaks at 48∘±2 and 45∘±7. The IAD agrees well between both datasets, despite the difference in scale, time period, and geographical location. The divergence and shear rates of the LKFs also have the same distribution. The dilatancy angle (the ratio of shear and divergence) is not correlated with the intersection angle. Using the IAD, we infer two important mechanical properties of the sea ice: we found an internal angle of friction in sea ice of μI=0.66±0.02 and μI=0.75±0.05. The shape of the yield curve or the plastic potential derived from the observed IAD resembles a teardrop or a Mohr–Coulomb shape. With these new insights, sea-ice rheologies used in models can be adapted or redesigned to improve the representation of sea-ice deformation. [ABSTRACT FROM AUTHOR]

Thermodynamic and dynamic sea ice thickness processes are affected by differing mechanisms in a changing climate. Independent observational datasets of each are essential for model validation and accurate projections of future sea ice conditions. Here, we present a monthly, Arctic-basin-wide, and 25 km resolution Eulerian estimation of thermodynamic and dynamic effects on wintertime sea ice thickness from 2010–2021. Estimates of thermodynamic growth rate are determined by coupling passive microwave-retrieved snow–ice interface temperatures to a simple sea ice thermodynamic model, total growth is calculated from a weekly Alfred Wegener Institute (AWI) European Space Agency (ESA) CryoSat-2 and Soil Moisture and Ocean Salinity (SMOS) combination product (CS2SMOS), and dynamic effects are calculated as their difference. The dynamic effects are further separated into advection and residual effects using a sea ice motion dataset. Our results show new detail in these fields and, when summed to a basin-wide or regional scale, are in line with previous studies. Across the Arctic, dynamic effects are negative and about one-fourth the magnitude of thermodynamic growth. Thermodynamic growth varies from less than 0.1 m per month in the central Arctic to greater than 0.3 m per month in the seasonal ice zones. High positive dynamic effects of greater than 0.1 m per month, twice that of thermodynamic growth or more in some areas, are found north of the Canadian Arctic Archipelago, where the Transpolar Drift and Beaufort Gyre deposit ice. Strong negative dynamic effects of less than -0.2 m per month are found where the Transpolar Drift originates, nearly equal to and opposite the thermodynamic effects in these regions. Monthly results compare well with a recent study of the dynamic and thermodynamic effects on sea ice thickness along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift track during the winter of 2019–2020. Couplets of deformation and advection effects with opposite signs are common across the Arctic, with positive advection effects and negative deformation effects found in the Beaufort Sea and negative advection effects and positive deformation effects found in most other regions. The seasonal cycle shows residual deformation effects and overall dynamic effects increasing as the winter season progresses. [ABSTRACT FROM AUTHOR]

The Arctic Ocean is a region important for global and regional climate. Although generally quiescent compared to mid-latitudes, the upper Arctic ocean hosts mesoscale and smaller scale processes. These processes can have a profound impact on vertical ocean fluxes, stratification, and feedback with the sea ice and atmosphere. Sparse and non-synoptic in-situ observations of the polar oceans are limited by the distribution of manual observing platforms and autonomous instrumentation. Analyzing observational data to assess tracer field gradients and upper ocean dynamics becomes highly challenging when measurement platforms drift with the ice pack due to continuous changes in drift speed direction. This work presents a dynamical reconstruction of the ocean state, based on observations of the Multidisciplinary Observatory for the Study of Arctic Climate (MOSAiC) experiment. Overall, the model can reproduce the lateral and vertical structure of the temperature, salinity, and density fields, which allows for projecting dynamically consistent features of these fields onto a regular grid. We identify two separate depth ranges of enhanced eddy kinetic energy, which are located around two maxima in buoyancy frequency: the depth of the upper halocline and the depth of the warm (modified) Atlantic Water. Simulations reveal a notable decrease in surface layer salinity and density towards the north, accompanied by high variability in the mixed layer depth in the south-north direction. And no significant horizontal gradients in salinity and density fields but an increase in mixed layer depth from west to east 0.084 m/km gradient with 0.6 m/km standard deviation, indicating opposite characteristics compared to the south-north direction. The model resolves several stationary eddies in the warm Atlantic Water and provides insights into the associated dynamics. The obtained three-dimensional fields of temperature and salinity can be used for further analysis of the thermohaline structure and related dynamics associated with submesoscale processes in the Central Arctic. Dynamic characteristics and eddy fields can be used for further analysis and comparison with state-of-the-art climate and Earth System Models. The developed nudging method can be used to utilize future observational data obtained from a diverse set of instruments. [ABSTRACT FROM AUTHOR]

This study analyzes turbulent properties in, and the thermodynamic structure of the Arctic atmospheric boundary layer (ABL) during winter and the transition to spring. These processes influence the evolution and longevity of clouds, and impact the surface radiative energy budget in the Arctic. For the measurements we have used an instrumental payload carried by a helium filled tethered balloon. This system was deployed between December 2019 and May 2020 during the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Vertically highly resolved in situ measurements of profiles of turbulent parameters were obtained reaching from the sea ice up to several hundred meters height. The two typical states of the Arctic ABL were identified: cloudless situations with a shallow and stable ABL, and cloudy conditions maintaining a mixed ABL. We have used profile data to estimate the height of the surface mixing layer. For this purpose, a bulk Richardson number criterion approach was introduced. By deriving a critical bulk Richardson number for wintertime in high latitudes, we have extended the analysis to radiosonde data. Furthermore, we have tested the applicability of the Monin-Obukhov similarity theory to derive surface mixing layer heights based on measured surface fluxes. [ABSTRACT FROM AUTHOR]

To investigate the influence of sea ice openings like leads on wintertime Arctic clouds, the air mass transport is exploited as humidity feeding mechanism which modifies cloud properties like total water content, cloud phase partitioning, cloud altitude, and thickness. Cloud microphysical properties in the Central Arctic are analyzed as a function of sea ice conditions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020. A state-of-the-art cloud classification algorithm is used to characterize the clouds based on observations by vertical pointing lidar, radar, microwave radiometer, and atmospheric thermodynamic state from the observatory on board the research vessel Polarstern. To link the sea ice conditions around the observational site with the cloud observations, the water vapor transport (WVT) being conveyed towards the Polarstern has been exploited as a mechanism to associate sea ice conditions upwind with the measured cloud properties. This novel methodology is used to classify the observed clouds as coupled or decoupled to the WVT based on the location of the maximum vertical gradient of WVT height relative to the cloud-driven mixing layer extending above and below the cloud top and base, respectively. Only a conical sub-sector of sea ice concentration (SIC) and lead fraction (LF) centered at the Polarstern location and extending up to 50 km radius and azimuth angle governed by the time-dependent wind direction measured at the maximum WVT is related to the observed clouds. We found significant asymmetries for cases when the clouds are coupled or decoupled to the WVT, and when cases are selected by LF regimes. Liquid water path of low level clouds is found to increase as a function of LF while ice water path does so only for deep precipitating systems. Clouds coupled to WVT are found to be low level clouds and are thicker than decoupled clouds. Thermodynamically, we found that for coupled cases the cloud top temperature is warmer and accompanied by a temperature inversion at cloud top, whereas the decoupled cases are found to closely be compliant with the moist adiabatic temperature lapse rate. The ice water fraction within the cloud layer has been found to present a noticeable asymmetry when comparing coupled versus decoupled cases. This novel approach of coupling sea ice to cloud properties via the WVT mechanism unfolds a new tool to study Arctic surface-atmosphere processes. With this formulation long-term observations can be analyzed to enforce the statistical significance of the asymmetries. Our results serve as an opportunity to better understand the dynamic linkage between clouds and sea ice and to evaluate its representation in numerical climate models for the Arctic system. [ABSTRACT FROM AUTHOR]

Information about sea ice surface topography and related deformation is crucial for studies of sea ice mass balance, sea ice modeling, and ship navigation through the ice pack. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), part of the National Aeronautics and Space Administration (NASA) Earth Observing System, has been on orbit for over 4 years, sensing the sea ice surface topography with six laser beams capable of capturing individual features such as pressure ridges. To assess the capabilities and uncertainties of ICESat-2 products, coincident high-resolution measurements of sea ice surface topography are required. During the yearlong Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the Arctic Ocean, we successfully carried out a coincident underflight of ICESat-2 with a helicopter-based airborne laser scanner (ALS), achieving an overlap of more than 100 km. Despite the comparably short data set, the high-resolution centimeter-scale measurements of the ALS can be used to evaluate the performance of ICESat-2 products. Our goal is to investigate how the sea ice surface roughness and topography are represented in different ICESat-2 products as well as how sensitive ICESat-2 products are to leads and small cracks in the ice cover. Here, we compare the ALS measurements with ICESat-2's primary sea ice height product, ATL07, and the high-fidelity surface elevation product developed by the University of Maryland (UMD). By applying a ridge-detection algorithm, we find that 16 % (4 %) of the number of obstacles in the ALS data set are found using the strong (weak) center beam in ATL07. Significantly higher detection rates of 42 % (30 %) are achieved when using the UMD product. While only one lead is indicated in ATL07 for the underflight, the ALS reveals many small, narrow, and only partly open cracks that appear to be overlooked by ATL07. [ABSTRACT FROM AUTHOR]

We provide sea ice classification maps of a sub-weekly time series of single (horizontal–horizontal, HH) polarization X-band TerraSAR-X scanning synthetic aperture radar (TSX SC) images from November 2019 to March 2020, covering the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This classified time series benefits from the wide spatial coverage and relatively high spatial resolution of TSX SC data and is a useful basic dataset for future MOSAiC studies on physical sea ice processes and ocean and climate modeling. Sea ice is classified into leads, young ice with different backscatter intensities, and first-year ice (FYI) or multiyear ice (MYI) with different degrees of deformation. We establish the per-class incidence angle (IA) dependencies of TSX SC intensities and gray-level co-occurrence matrix (GLCM) textures and use a classifier that corrects for the class-specific decreasing backscatter with increasing IAs, with both HH intensities and textures as input features. Optimal parameters for texture calculation are derived to achieve good class separation while maintaining maximum spatial detail and minimizing textural collinearity. Class probabilities yielded by the classifier are adjusted by Markov random field contextual smoothing to produce classification results. The texture-based classification process yields an average overall accuracy of 83.70 % and good correspondence to geometric ice surface roughness derived from in situ ice thickness measurements (correspondence consistently close to or higher than 80 %). A positive logarithmic relationship is found between geometric ice surface roughness and TSX SC HH backscatter intensity, similar to previous C- and L-band studies. Areal fractions of classes representing ice openings (leads and young ice) show prominent increases in middle to late November 2019 and March 2020, corresponding well to ice-opening time series derived from in situ data in this study and those derived from satellite synthetic aperture radar (SAR) and optical data in other MOSAiC studies. [ABSTRACT FROM AUTHOR]

The Arctic environment is rapidly changing due to accelerated warming in the region. The warming trend is driving a decline in sea ice extent, which thereby enhances feedback loops in the surface energy budget in the Arctic. Arctic aerosols play an important role in the radiative balance and hence the climate response in the region, yet direct observations of aerosols over the Arctic Ocean are limited. In this study, we investigate the annual cycle in the aerosol particle number size distribution (PNSD), particle number concentration (PNC), and black carbon (BC) mass concentration in the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. This is the first continuous, year-long data set of aerosol PNSD ever collected over the sea ice in the central Arctic Ocean. We use a k -means cluster analysis, FLEXPART simulations, and inverse modeling to evaluate seasonal patterns and the influence of different source regions on the Arctic aerosol population. Furthermore, we compare the aerosol observations to land-based sites across the Arctic, using both long-term measurements and observations during the year of the MOSAiC expedition (2019–2020), to investigate interannual variability and to give context to the aerosol characteristics from within the central Arctic. Our analysis identifies that, overall, the central Arctic exhibits typical seasonal patterns of aerosols, including anthropogenic influence from Arctic haze in winter and secondary aerosol processes in summer. The seasonal pattern corresponds to the global radiation, surface air temperature, and timing of sea ice melting/freezing, which drive changes in transport patterns and secondary aerosol processes. In winter, the Norilsk region in Russia/Siberia was the dominant source of Arctic haze signals in the PNSD and BC observations, which contributed to higher accumulation-mode PNC and BC mass concentrations in the central Arctic than at land-based observatories. We also show that the wintertime Arctic Oscillation (AO) phenomenon, which was reported to achieve a record-breaking positive phase during January–March 2020, explains the unusual timing and magnitude of Arctic haze across the Arctic region compared to longer-term observations. In summer, the aerosol PNCs of the nucleation and Aitken modes are enhanced; however, concentrations were notably lower in the central Arctic over the ice pack than at land-based sites further south. The analysis presented herein provides a current snapshot of Arctic aerosol processes in an environment that is characterized by rapid changes, which will be crucial for improving climate model predictions, understanding linkages between different environmental processes, and investigating the impacts of climate change in future Arctic aerosol studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Measurements targeting mesoscale and smaller-scale processes in the ice-covered part of the Arctic Ocean are sparse in all seasons. As a result, there are significant knowledge gaps with respect to these processes, particularly related to the role of eddies and fronts in the coupled ocean–atmosphere–sea ice system. Here we present a unique observational dataset of upper ocean temperature and salinity collected by a set of buoys installed on ice floes as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network. The multi-sensor systems, each equipped with five temperature and salinity recorders on a 100 m long inductive modem tether, drifted together with the main MOSAiC ice camp through the Arctic Transpolar Drift between October 2019 and August 2020. They transmitted hydrographic in situ data via the iridium satellite network at 10 min intervals. While three buoys failed early due to ice dynamics, five of them recorded data continuously for 10 months. A total of four units were successfully recovered in early August 2020, additionally yielding internally stored instrument data at 2 min intervals. The raw data were merged, processed, quality controlled, and validated using independent measurements also obtained during MOSAiC. Compilations of the raw and processed datasets are publicly available at 10.1594/PANGAEA.937271 and 10.1594/PANGAEA.940320 , respectively. As an important part of the MOSAiC physical oceanography program, this unique dataset has many synergies with the manifold co-located observational datasets and is expected to yield significant insights into ocean processes and to contribute to the validation of high-resolution numerical simulations. While this dataset has the potential to contribute to submesoscale process studies, this paper mainly highlights selected preliminary findings on mesoscale processes. [ABSTRACT FROM AUTHOR]