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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 m

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry-derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual-frequency, fully polarized Ku- and Ka-band radar was deployed in “stare” nadir-looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual-frequency, dual-polarization and waveform shape, and compared to independent snow depth measurements. Novel dual-polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub-banded to CryoSat-2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co-polarized dual-frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

Comparing helicopter-borne surface temperature maps in winter and optical orthomosaics in summer from the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate expedition, we find a strong geometric correlation between warm anomalies in winter and melt pond location the following summer. Warm anomalies are associated with thinner snow and ice, that is, surface depression and refrozen leads, that allow for water accumulation during melt. Warm surface temperature anomalies in January were 0.3–2.5 K warmer on sea ice that later formed melt ponds. A one-dimensional steady-state thermodynamic model shows that the observed surface temperature differences are in line with the observed ice thickness and snow depth. We demonstrate the potential of seasonal prediction of summer melt pond location and coverage from winter surface temperature observations. A threshold-based classification achieves a correct classification for 41% of the melt ponds.

Oral presentation at the 2nd MOSAiC science conference showing the MOSAiC airborne laser scanner product overview and first results of seasonal surface roughness

Relating the sea-ice surface to the under-ice topography is a timely scientific effort in the Arctic. This relation is crucial for estimating the ice thickness distribution for large‐scale modelling, for assessing the mechanical force that ships need to overcome, for risk evaluation of offshore structures, for determining roughness characteristics to derive wind and water drag coefficients for dynamics modelling, for sound scattering, and for the confinement of under-ice oil spills. Existing relations are based on numerical modelling assuming estimates of snow depth, snow density, and ice density or are based on field observations confined to specific areas and short time periods. MOSAiC provided the first year-long, high-resolution dataset of sea-ice draft derived from a multibeam echosounder. In combination with co-located freeboard estimates from airborne mapping of the surface, we construct the 3D sea-ice topography to study the evolution of sea-ice geometry both at the surface and underside. We can obtain direct and high precision relations between draft and freeboard on an almost weekly basis for an ice floe continuously drifting from the North Pole to Fram Strait during winter, spring, and summer. A precise evaluation of total ice thickness, ice density, freeboard, draft and their respective relations on small scales is crucial information to future satellite remote sensing ice thickness retrievals, a key asset of climate monitoring in the Arctic.

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.

IceBird Winter 2023 is part of a long-term sea ice observation program within the IceBird aircraft campaign series. IceBird was initiated in 2018 with the objective to ensure the long-term availability of a unique data record of direct sea-ice thickness observations to understand the role of the sea ice component for the causes and consequences of Arctic change, but is built on the heritage of airborne sea-ice thickness observations that date back to 2004. Compared to earlier airborne programs, IceBird has been enhanced with an improved sensor setup that also allows measuring snow depth on sea ice, fully collocated with sea-ice thickness and surface roughness at high resolution. The objectives of IceBird Winter 2023 include the continued quantification of trends, the separation of variability and extreme events of sea ice thickness and its snow cover in the Western Seas of the Arctic Ocean. The continuation of airborne sea-ice observation programs fulfils the requirement of consistent and long-term observations of key climate parameters. The data will be used to improve understanding of the response of sea ice and its snow cover to the ongoing warming of the Arctic and to improve snow models. Airborne data of snow and sea-ice thickness are also critically needed for the evaluation of sea-ice remote sensing products as well as for the evolution of algorithms for current and future satellite missions. Surveys from IceBird Winter 2023 will target the validation of sea-ice freeboard and snow depth estimates from CryoSat-2, ICESat-2, Sentinel-3A/B and AltiKa altimeters.

Knowledge of sea-ice thickness and volume depends on freeboard observations from satellite altimeters and in turn on information of snow mass and sea-ice density required for the freeboard-to-thickness conversion. These parameters, especially sea-ice density, are usually based on climatologies constructed from in situ observations made in the 1980s and earlier while contemporary and representative measurements are lacking. Our aim with this paper is to derive updated sea-ice bulk density estimates suitable for the present Arctic sea-ice cover and a range of ice types to reduce uncertainties in sea-ice thickness remote sensing. Our sea-ice density measurements are based on over 3000 km of high-resolution collocated airborne sea-ice and snow thickness and freeboard measurements in the western Arctic Ocean in 2017 and 2019. Sea-ice bulk density is derived assuming isostatic equilibrium for different ice types. Our results show higher average bulk densities for both first-year ice (FYI) and especially multi-year ice (MYI) compared to previous studies. In addition, we find a small difference between deformed and possibly unconsolidated FYI and younger MYI. We find a negative-exponential relationship between sea-ice bulk density and sea-ice freeboard and apply this parameterisation to one winter of monthly gridded CryoSat-2 sea-ice freeboard data. We discuss the suitability and the impact of the derived FYI and MYI bulk densities for sea-ice thickness retrievals and the uncertainty related to the indirect method of measuring sea-ice bulk density. The results suggest that retrieval algorithms be adapted to changes in sea-ice density and highlight the need of future studies to evaluate the impact of density parameterisation on the full sea-ice thickness data record.

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.

Arctic sea ice is a mosaic of ice floes whose distribution greatly impacts the interaction of sea ice with the atmosphere and the ocean. However, we are still lacking physics to describe the complex interplay of ice floes that are a key characteristic of sea ice. Kilometer-scale satellite and buoy observations have been used to describe the deformation of sea-ice as a continuum of floes, whereas ice fracture of individual floes has been studied in laboratory and field experiments. In our contribution, we aim to bridge this gap and outline a framework to characterize sea-ice deformation at the floe-scale from observational data by studying the mechanical interaction of multiple identifiable floes. We use Sentinel SAR imagery and airborne laser scanner surveys acquired during the MOSAiC expedition to map ice floes in the larger area around Polarstern. This combination of data products allows us to describe the floe-size distribution from hundreds of kilometers down to the meter-scale. With the repeated coverage of both data products, ice motion is tracked and deformation estimates are derived. By combining both floe-size estimates and deformation rates we provide insights into how the floe composition changes in regions that were exposed to deformation. This relationship between floe sizes and mechanical redistribution could constrain parameterizations to improve the representation of sea-ice deformation and floe sizes in large-scale continuum models. Finally, we highlight the potential to use the presented floe and deformation information to initialise and evaluate floe-resolving sea-ice simulations using discrete element models.

Snow is a key factor in the sea-ice and Earth's climate systems that modifies the physical, climatic, and biogeochemical processes taking place. One of its most important impacts is in regulating sea-ice growth and melt. Despite its importance, little is known about the spatial and temporal distribution of snow depth on sea ice on the regional to global scales. Snow is tightly coupled to the highly dynamic sea-ice and atmospheric conditions and it is, therefore, very heterogeneous and constantly evolving both in space and in time. As a spatially and temporally representative, global, year-round product of snow depth observations on sea ice does not exist to this date, applications often have to rely on climatological values that do not necessarily hold true in the rapidly warming global climate. The unknown properties directly translate into the uncertainty of the result. This dissertation takes on the ambitious goal of working toward full characterisation of the snow and sea-ice layers. To achieve that, the focus is on advancing microwave radar retrievals of snow depth on sea ice. Enhanced snow depth observations will enable improving other measurements of sea-ice related parameters, most importantly sea-ice thickness, and in joint analysis of coincident sea-ice measurements estimating sea-ice bulk density becomes possible. In the first step, field experiments with ground-based C and K band pulse radars are carried out to investigate microwave penetration into the snow cover. The results show the K band microwaves expectedly reflect from the snow surface while the C band microwaves penetrate closer to the snow–sea-ice interface potentially enabling dual-frequency snow depth retrieval in less than half of the studied cases and only on first-year ice. In the second step, radar measurements of snow depth on sea ice are upscaled by using an airborne radar in the western Arctic Ocean in 2017–2019. A high-sensitivity, ultra-wideband, frequency-modulated continuous-wave (

An integrated sensor platform including an inertial navigation system (INS) and a commercial airborne laser scanner (ALS) among other sensor was mounted in the cargo compartment in one of the Polarstern helicopters during MOSAiC. ALS data was acquired from more than 60 flights between October 2019 and September 2020 with a range of survey types intended to map changes of the sea ice surface during the full annual cycle at high spatial resolution and coverage. Here, we provide an overview of the collected data, the challenge of achieving centimeter elevation accuracy with a helicopter platform at high polar latitudes as well as the content and specifications of ALS data products. The high spatial resolution and repeated coverage of the larger area around Polarstern allow studying various surface features (e.g. pressure ridges, floes, melt ponds, snow drifts, etc.), their seasonal evolution, and their impact on atmosphere and ocean. Finally, we outline methods for planned applications, such as identifying individual floes and surface types using both measured freeboard and surface reflectance. Collocated helicopter-based optical and infrared imagery allow analyzing sea ice properties in further applications and to upscale comparable in-situ observations.

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.

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.

An integrated sensor platform including an inertial navigation system (INS) and a commercial airborne laser scanner (ALS) among other sensor was mounted in the cargo compartment in one of the Polarstern helicopters during MOSAiC. ALS data was acquired from more than 60 flights between October 2019 and September 2020 with a range of survey types intended to map changes of the sea ice surface during the full annual cycle at high spatial resolution and coverage. Here we provide an overview of the collected data and the challenge of achieving centimeter elevation accuracy with a helicopter platform at high polar latitudes. The high spatial resolution and repeated coverage of the larger area around Polarstern allow to study various surface features (e.g. pressure ridges, floes, melt ponds, snow drifts, etc.), their seasonal evolution, and their impact on atmosphere and ocean. We outline methods to identify individual floes and surface types using both measured freeboard and surface reflectance. The identified surface features can be followed in time using automated feature tracking for consecutive flights to study the dynamic and thermodynamic processes that shape the sea-ice surface. Finally, we introduce a framework to train novel data methods with the transect snow and ice measurements to predict the snow and sea-ice thickness based on the surface structure in the ALS freeboard data.

Alfred Wegener Institute’s (AWI) IceBird program is a series of airborne campaigns carried out in winter and summer using a fixed-wing Basler BT-67 research aircraft to measure Arctic sea ice and to monitor its change. In 2017, the primary scientific instrument configuration including an airborne laser scanner (ALS) for surface topography and freeboard measurements and a tethered electromagnetic induction sounding instrument (EM-Bird) for total (snow+ice) thickness measurements was complemented with an ultrawideband frequency-modulated continuous-wave microwave radar to measure snow thickness. With the unique instrumentation onboard the IceBird campaigns, we are able to observe the respective thicknesses of the snow and sea-ice layers in high resolution along survey tracks on regional scale. Here, we describe the IceBird program concept and focus on the winter campaigns that take advantage of the full instrument configuration. We present recent data of high-resolution, collocated, airborne sea-ice and snow thickness and freeboard measurements based on over 3000 km of profiles collected in the western Arctic Ocean in April 2017 and 2019. The individual parameters are important for describing and monitoring the state of the Arctic sea ice and validating retrievals from satellite data, but combined they offer further possibilities to characterize sea ice. By assuming isostatic equilibrium, we are able to derive up-to-date estimates for sea-ice bulk density for first-year and multi-year ice, including deformed ice. As an outlook, we derive a parametrization of sea-ice bulk density based on sea-ice freeboard for further applications, such as evaluating the freeboard-to-thickness conversion for satellite altimetry.

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2-18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017-2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales.