Description: Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice, and ocean. As Antarctic snow cover persists during most of the year, it contributes significantly to the sea-ice mass and energy budgets due to comprehensive physical (seasonal) transition processes within the snowpack. However, field studies reveal not only a strong seasonality but especially spatial variations from local to regional scales. It is therefore necessary to quantify seasonal snow processes, such as internal snowmelt, snow metamorphism, and snow-ice formation at multiple spatial scales on Antarctic sea ice. Doing so, we present here in-situ observations of physical snow properties from point measurements (snow pits) and transect lines (SnowMicroPen, SMP) during recent expeditions in the Weddell Sea from 2013 to 2019, covering summer and winter conditions. Results from a case study of snow pit analyses in the Weddell Sea during austral winter reveal a high spatiotemporal variability of snow parameters highlighting the need to distinguish between seasonal and perennial snow regimes. Also, it is shown that snow grain size dominates the spatial variability of the snowpack while snow density variability can be neglected. In order to extend local snow pit analysis towards the description of snow layer evolution on small scales (up to 500 meters), SMP measurements are added. An applied layer tracking algorithm to the vertical density profiles throughout the snowpack allows to quantify length-scale variabilities of snow properties in different ice regimes. Overall, results will improve our understanding of seasonal processes in the snowpack and will guide us towards upscaling approaches of vertical snow layers on Antarctic sea ice.

Description: During the 2018 Multidisciplinary Arctic Program‐Last Ice in the Lincoln Sea, we sampled 45 multiyear ice (MYI) and 34 first‐year ice (FYI) cores, combined with snow depth, ice thickness, and transmittance surveys from adjacent level FYI and undeformed MYI. FYI sites show a decoupling between bottom‐ice chlorophyll a (chl a) and snow depth; however, MYI showed a significant correlation between ice‐algal chl a biomass and snow depth. Topographic control of the snow cover resulted in greater spatiotemporal variability of the snow over the level FYI, and consequently transmittance, compared to MYI with an undulating surface. The coupled patterns of snow depth, transmittance, and chl a indicate that MYI provides an environment with more stable light conditions for ice algal growth. The importance of sea ice surface topography for ice algal habitat underpins the potential ecological changes associated with projected increased ice dynamics and deformation.

Description: Light transmission through Arctic sea ice and snow has an important impact on energy partitioning at the atmosphere-ice-ocean interface and the ice-associated ecosystem. Thus, it is crucial to understand which parameters determine the temporal and spatial variation of sea ice transmission. Ice and snow imprint characteristic features on the spectral shape of transmitted light. Here, we aim to use these spectral features to retrieve snow depth from hyperspectral under-ice light measurements. Transmitted spectral radiance was measured underneath a 100 m long transect on level landfast First-Year-Ice (FYI) using a remotely operated vehicle (ROV). Measurements took place off the northern coast of Ellesmere Island close to the Canadian Forces Station Alert in May 2018. Co-located measurements of ice and snow thickness were acquired with an electromagnetic induction device, a Magna-Probe and a terrestrial laser scanner. The small variation in FYI thickness allows separating the spectral effect of snow depth on transmitted radiance spectra. We retrieve snow depth from spectral transflectance data using an inverse algorithm based on normalized difference indices (NDI). We further fit multiplicative exponential functions to the measured spectra to retrieve wavelength-dependent extinction coefficients of sea ice and snow. Fitted values of broadband bulk extinction coefficients range from 1.8 to 3.5 m-1 for sea ice and from 7.4 to 17.2 m-1 for snow. Mean differences in fitted/calculated and measured modal snow depths are 6 cm for the multiplicative exponential functions and 5 cm using NDIs. 41% of the fitted snow depths lie within 5 cm of the measured snow depths using the multiplicative exponential functions and 42% for the NDI-method. The accuracy of snow depth retrieved from our optical approaches is limited to +/-5 cm, as the variation of snow depth within the optical sensor footprint is between 2.5 and 5.0 cm. Our results show how this optical approach allows to derive large-scale snow depth information from under-ice optical spectra e.g. during autonomous long-range under-ice missions, despite its limited spatial resolution and absolute accuracy.

Description: Light transmittance through Arctic sea ice has an important impact on both the ocean heat content and the ice associated ecosystem. Thus, it is crucial to investigate the optical properties of sea ice to assess the role of the surface energy budget and its change due to climate change. Measurements of spectral transmittance can be used to investigate the influence of surface and ice properties regulating radiative transfer, especially on a larger horizontal scale. Here, we concentrate on categorizing snow and sea ice based on spectral transmittance data. Transmitted radiance and irradiance are measured at the underside of sea ice using a remotely operated vehicle (ROV). The scientific payload also includes CTD, fluorometer, pH-, nitrate-, oxygen-, attenuation sensor, upward-looking single-beam sonar, and periodically a surface and under ice trawl for assessing the spatio-temporal variability of sea ice algae. Thus, data for all disciplines in sea ice research can be recorded. The main benefits using the ROV compared to point measurements are the larger spatial coverage in comparably short times and the undisturbed sampling even under very thin sea ice, with parameters all collected during the same time. Snow depth is derived from a combination of terrestrial laser scanner data and manual measurements, while ice draft is measured using the single-beam sonar. Here, we present first data from the Last Ice campaign off Alert in May 2018. This region is dominated by sea ice with a larger thickness due to dynamic thickening. We investigated different ice regimes, such as First Year Ice with a continuous thickness of about 1.5 m and structured Multi Year Ice with thicknesses up to 6 m over the duration of four weeks to study the differences between various ice types.

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: Light transmittance through Arctic sea ice and snow has an important impact on both the ocean heat content and the ice-associated ecosystem. The partitioning of the radiation is a key factor of the mass and energy balance of Arctic sea ice. It is therefore crucial to measure sea ice transmittance and understand which parameters determine its variation on temporal and spatial scales. Ice and snow imprint characteristic features in the spectral shape of transmitted light. Transmitted spectral irradiance was recorded at the underside of levelled landfast First-Year-Ice (FYI) in a refrozen lead using a hyper-spectral radiometer mounted on a remotely operated vehicle (ROV) during the Last Ice Area campaign off Alert in the Lincoln Sea in May 2018. The main benefits of using the ROV are large spatial coverage in comparably short survey times and non-destructive measurements under sea ice. Snow depth was obtained using a Magna Probe and a Terrestrial Laser Scanner measured the surface topography. The total ice thickness was recorded with a ground-based electromagnetic induction sounding device whereas an upward-looking single-beam sonar also mounted on the ROV recorded ice draft. This unique co-located data set enables to categorize groups of spectral transmittances. Due to the relatively constant FYI thickness it was possible to separate the spectral effect of snow depth on the light transmittance. Further we discuss how to retrieve snow depth and ice thickness based only on spectral transmittance data by developing a new observation-based inverse algorithm. Three methods are envisioned: First, to fit a multiplicative exponential function to the spectra which includes wavelength-dependent extinction coefficients of snow and sea ice. Second, to follow a statistical approach using normalized difference indices (NDIs) to construct spectral correlation coefficients between the NDIs with snow depth and ice thickness. Third, to generate synthetic spectra from snow depth and ice thickness using the radiative transfer model AccuRT and compare those with the observed spectra. Expected results are accurate snow depth and sea ice thickness (as well as melt pond depth and coverage).

Description: Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice, and ocean. It contributes significantly to the sea-ice mass and energy budgets due to comprehensive seasonal transition processes within the snowpack. Therefore, several studies have shown the importance of comprehensive understanding of snow properties for large-scale estimates in the ice-covered oceans. However, field studies reveal not only a strong seasonality but especially spatial variations on floe-size scales. It is therefore necessary to locate and quantify seasonal snow processes, such as internal snowmelt, snow metamorphism, and snow-ice formation in the Arctic and Antarctic snowpack on small scales. Doing so, we present here in-situ observations of physical snow properties from point measurements (snow pits) and transect lines (SnowMicroPen, SMP) during recent expeditions in the Weddell Sea and off the northeastern coast of Ellesmere Island, Canada, from 2013 to 2019, covering summer and winter conditions. Results from a case study of snow pit analyses in the Weddell Sea during austral winter reveal a high variability of snow parameters throughout the snowpack. It is shown that snow grain size dominates the spatial variability of the snow pack while snow density variability can be neglected. The additional use of the SMP allows to even quantify length-scale variabilities of snow properties in different ice regimes in both hemispheres. Overall, results will improve our understanding of seasonal processes in the snowpack and will guide us towards upscaling approaches of vertical snow layers on Arctic and Antarctic sea ice.

Description: This technical report provides information on validation activities of ICESat-2 sea ice products in April 2019 by airborne observations within the IceBird aircraft campaign series of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. The airborne data acquisition includes direct observations of snow freeboard, snow depth on sea ice and sea-ice thickness by a set of sensors. The following sections specify the extent of airborne data collocated with an ICESat-2 orbit, the specification of the sensors and the file format of the validation data.

Description: The snow layer on sea ice has high importance for polar climate as it affects heat, radiation, and fresh-water budgets. Additionally, snow loading is a critical parameter for the sea-ice freeboard-to-thickness conversion for satellite radar and laser altimeters. Despite its importance, there is a lack of snow observations spanning different spatial and temporal scales, thus introducing a significant source of uncertainty to altimetric sea-ice thickness retrievals. The ultra-wideband microwave radar (UWBM) Snow Radar, a 2–18 GHz airborne frequency-modulated continuous-wave (FMCW) radar developed by the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas, can accurately detect the air/snow and snow/ice interfaces to measure snow thickness. Since 2009, an airborne Snow Radar has been operated onboard NASA’s Operation IceBridge (OIB) campaigns. In 2017, the UWBM Snow Radar was operated for the first time on an Alfred Wegener Institute (AWI) research aircraft, together with an airborne laser scanner for surface topography and freeboard measurements and an electromagnetic induction sounding instrument (EM Bird) to measure total ice thickness. The AWI airborne surveys operate at a low survey altitude (60 m a.g.l.) and slow aircraft speed, enabling fine-resolution mapping of the snow layer. Furthermore, the unique instrument setup on board the AWI research aircraft and the concurrent measurements of snow freeboard, total sea-ice thickness and snow depth allow us to directly investigate the freeboard-to-thickness conversion on regional scales for the first time. Here, we evaluate the performance of the radar installation and present radar-derived snow depth retrieved with a wavelet technique from recent airborne campaigns, PAMARCMiP2017 and IceBird winter 2019, over Arctic sea ice in the Greenland, Lincoln, Beaufort and Chukchi Seas and the central Arctic Ocean in March–April of the respective years.

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.