Description: 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.

Description: 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 (FMCW) radar is integrated to the instrument configuration of the Alfred Wegener Institute's (AWI) IceBird sea-ice campaigns. Snow depth retrievals with a custom algorithm based on signal peakiness from the radar measurements at a low altitude of 200 ft show good consistency against high altitude measurements at 1500 ft, which are comparable to previous acquisitions. At the nominal low altitude of the IceBird surveys, the small, two-metre radar footprint increases the spatial resolution and reduces the effect of off-nadir targets. Validation against ground measurements reveal a sub-centimetre mean bias, which is below the sensor resolution. As the main result of this step, the AWI IceBird surveys are now capable of discriminating between the snow and sea-ice layers. In the third step, the full AWI IceBird sensor configuration, including airborne laser, radar, and electromagnetic induction sounding instruments, is exploited by collocating the coincident thickness and freeboard measurements and tracking the locations of air–snow, snow–sea-ice, and sea-ice-water interfaces for more than 3000 km along survey paths over different sea-ice types. Assuming values for snow and sea-water densities and that the sea-ice cover is in isostatic equilibrium, it is possible to derive sea-ice bulk density. The results show that the ice-type averaged densities for first-year and multi-year ice are higher than and do not differ as much as widely used values from previous studies. This highlights the demand of algorithms to adapt to changing sea-ice density in satellite altimetry retrievals of sea-ice thickness. Finally, a negative-exponential parametrisation of sea-ice bulk density is derived using sea-ice freeboard as the predictor variable for future applications. In conclusion, this dissertation takes important advancing steps in characterising the snow and sea-ice layers. Previously, the airborne AWI IceBird surveys carried out in late-winter were only able to measure the combined thickness of the snow and sea-ice layers but now, after successful integration of the FMCW radar and in combination with the airborne laser scanner measurements, it is possible to track the locations of all three interfaces bounding the snow–sea-ice system. Such airborne multi-instrument measurements of snow depth, sea-ice thickness, and freeboard are important data sets in their own right to complement the scarce observations of sea-ice related parameters in remote areas of the polar regions, but a joint analysis allows deriving further key parameters like sea-ice density. The results of this dissertation can be applied to improve retrievals of geophysical sea-ice parameters from the soon 30-year long satellite altimetry data record, which in turn will contribute to enhance monitoring the climate-sensitive sea-ice cover and modelling future projections of the changing global climate.

Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: 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.

Description: Sea-ice thickness is a key factor and indicator in understanding the impact of the global climate change. Deriving basin-wide sea-ice thickness estimates from satellite laser and radar altimetry relies on freeboard measurements. The freeboard-to-thickness conversion in turn requires information of snow mass and the density of the sea-ice layer that have unknown spatio-temporal variabilities and trends directly translating into the uncertainty of decadal sea-ice thickness data records. In addition, inter-mission biases arise from, e.g., different sensor types and frequencies as well as varying footprint sizes affected by surface roughness across regions and seasons. Therefore, carrying out validation and inter-calibration studies is crucial for reliable and continuous observation of the Earth’s cryosphere. To achieve this, it is beneficial to have simultaneous measurements of freeboard, snow depth, and sea-ice thickness, which provide reference data for both direct satellite observations and geophysical target parameters. Here, we present Alfred Wegener Institute’s (AWI) IceBird program, which is a series of fixed-wing aircraft campaigns to measure Arctic sea ice and to monitor its change. During two late-winter campaigns in the western Arctic Ocean in 2017 and 2019, we have carried out surveys with the unique scientific instrument configuration including an airborne laser scanner (ALS) for surface topography and freeboard measurements, a tethered electromagnetic induction sounding instrument (EM-Bird) for total (snow+ice) thickness measurements, and an ultrawideband frequency-modulated continuous-wave microwave radar to measure snow thickness. Therefore, we are able to observe all three bounding interfaces in the sea-ice–snow system in high resolution along survey tracks on regional scales. During the ship-based drift expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 and September 2020, helicopter surveys were carried out in high spatio-temporal resolution throughout the year, including the polar night, to measure freeboard and roughness with the ALS both in local grid pattern and in larger scale. Coincident EM-Bird ice thickness data and information from snow measurements on the ground will help linking these parameters and monitor them and their effect on satellite retrievals for a full seasonal cycle. 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 characterise sea ice. By assuming isostatic equilibrium, we are able to estimate up-to-date bulk density values for different sea-ice types from the IceBird data and to derive a parametrisation of sea-ice bulk density based on sea-ice freeboard. These data allow us to explore spatio-temporal variations in sea-ice parameters observable from space and to evaluate the validity of the freeboard-to-thickness conversion in satellite altimetry through comparison against dedicated satellite overpasses and orbit collections.

Description: The sea ice surface temperature is important to understand the Arctic winter heat budget. We conducted 35 helicopter flights with an infrared camera in winter 2019/2020 during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The flights were performed from a local, 5 to 10 km scale up to a regional, 20 to 40 km scale. The infrared camera recorded thermal infrared brightness temperatures, which we converted to surface temperatures. More than 150000 images from all flights can be investigated individually. As an advanced data product, we created surface temperature maps for every flight with a 1 m resolution. We corrected image gradients, applied an ice drift correction, georeferenced all pixels, and corrected the surface temperature by its natural temporal drift, which results in time-fixed surface temperature maps for a consistent analysis of one flight. The temporal and spatial variability of sea ice characteristics is an important contribution to an increased understanding of the Arctic heat budget and, in particular, for the validation of satellite products.