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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.

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

Background In 2019, the German Multiple Sclerosis (MS) Society was certified as a provider for ?Funktionstraining? (FT). FT is an exercise training program developed for people with disabilities or people at risk of disability, takes place weekly and is led by a qualified FT-therapist. It is prescribed by a physician (between 12 and 24 months) and is part of outpatient medical rehabilitation. Due to the COVID-19 pandemic, the tele-rehabilitation concept ‘Funktionstraining-online’ (FT-online) was developed for pwMS. This study examines the FT-online from a participants' point of view within an online survey. Methods We developed an online questionnaire that included data of sample characteristics, participants' experiences with FT-online in terms of usability and training content. Further, the survey captured the quality of FT-online and the FT-therapist and compared carefully FT-online with face-to-face training. The standardised questionnaires Self-Efficacy Scale - Short Form (ASKU), the Multiple Sclerosis Questionnaire for Physiotherapists MSQPT and the Fatigue-Questionnaire (WeiMuS) were included. The 103 interviewed people with MS (pwMS) participated in FT-online at least for three months. Results Data analyses are in process. The response rate was 75% (n = 78). The analysed sample is 53.4±8.1 years old with a disease duration of 18±8.7 years (male=7, female=71). Preliminary results in the ASKU certify a low self-efficacy in the sample (2.25 ±0.67). Qualitative results show the advantages for FT-online in the independence of mobility and infrastructure, flexibility, compatibility within job and family, group feeling in times of social distancing and an increase in quality of life. The disadvantages described by the sample are mainly technical problems and lack of socialising. 35% (n = 27) participants experienced both: FT-online and face-to-face. 44.4% (n = 12) of this sample prefer FT-online, 33.3% (n = 9) prefer the face-to-face training course and 22.2% (n = 6) rate both kinds of training equal. Conclusions The implementation of FT-online was an important step to ensure the exercise-related physical activity in pwMS during the COVID-19 pandemic. The descriptive analyses show high potential for FT-online training in pwMS. We are aware of the bias in current data, because of the pandemic situation both the free choice of FT-offers and the pandemic circumstances held influence in study outcomes. First results lead to the conclusion that FT-online may be beneficial for a broad range of persons with neurologic diseases, movement disorders and fatigue episodes and should be considered for future medical rehabilitation.

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.

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.

The SIN’XS project, led by NOVELTIS in collaboration with AWI, LEGOS and UCL, is a three-year activity (May 2022 – May 2025) funded by ESA in the frame of the Polar Science Cluster, with the objective to foster collaborative research and interdisciplinary networking actions. In light of rapid changes of the Arctic and Antarctic sea ice cover, continued and improved observations, understanding, and predictions of its thickness are particularly important for a range of fields from climate studies to offshore operations in ice. Systematic and accurate ice thickness observations are now available from several satellite missions. However, they differ in used processing algorithms and assumptions, temporal and spatial coverage and resolution, and applicability to stakeholder needs like modelling and assimilation, numerical weather prediction, and ship routing. These differences between products have so far complicated the consistent use of the various data products, and there is little consensus about Arctic and Antarctic Sea ice volume variability and change. The Sea Ice-thickness product iNter-comparison eXerciSe (SIN’XS) will identify some of these gaps by carrying out in-depth intercomparisons of a wide range of satellite ice thickness products from altimetry and other methods, in close collaboration with an international community of scientific and operational sea ice experts, and in partnership with the WMO Global Cryosphere Watch (GCW). It will develop joint protocols for the intercomparison of ice thickness products and their validation, using established approaches from the GEO/CEOS Quality Assurance framework for Earth Observation (QA4EO) and by further developing a framework for Fiducial Reference Measurements (FRMs). SIN’XS will develop an online system to engage the community with data submission and to support scientific analysis and impact assessment of the data sets and intercomparisons. In this poster, we will present the main objectives, the tools and the first outcomes of the project.

In the polar oceans in winter, fractures and leads are the hotspots of exchange between the ocean and atmosphere which are otherwise well separated by sea ice. By altering the heat, gas, and momentum fluxes they play a crucial role in atmospheric, ecological, and oceanic processes. At the same time, leads represent a part of the present state of strain of the ice cover, opening up the possibility to study ice rheology. The transient nature of leads and their narrow appearance has set limits to the detection of leads from satellites. Different approaches using active and passive sensors from the microwave and infrared spectrum are employed so far to observe leads by means of satellite data. They make use of the strong contrast between leads and the surrounding ice pack in (i) surface temperature, (ii) microwave backscatter, (iii) emission or (iv) a change in ice drift speed. With the increasing availability of high-resolution SAR data for the Arctic, we explored the potential to use SAR derived sea ice deformation to estimate lead fractions. We calculated sea ice drift and divergence with a spatial resolution of 1.4 km from daily Sentinel-1 scenes. We obtained the divergence-based lead fraction of a region by summing up all positive divergence pixels multiplied by the respective time step length. We derived a second lead fraction product from the deformation fields that calculates the position of linear kinematic features (LKFs) first. The advantage is a skilled noise reduction, and a tracking algorithm of the deformation zones. We compared divergence- and LKF-based lead fractions to several other established lead fraction products in the Transpolar Drift along the drift track of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) between October 2019 to April 2020. We used lead fractions from helicopter-borne infrared surveys at a grid resolution of 5 m, classified Sentinel-1 (SAR) scenes at 80 m, MODIS (thermal infrared) at 1 km, AMSR2 (passive microwaves) at 3.25 km, and CryoSat-2 (altimeter in Ku-band) at 12.5 km. Since the methods rely on different physical properties of the water and ice in leads and are affected by different constraints, derived mean lead fractions vary by 1-2 magnitudes between the products. For example, infrared, SAR and microwave radiometer-based algorithms do not only detect open-water leads but also leads with thin ice up to a certain thickness, which differs between the products. Common lead events were identified across products. The time series mostly indicated a phase of increased lead activity during freeze-up in autumn 2019 and spring 2020. We used the different lead fraction time series to estimate new ice formation in the leads and compared the results to ice thickness and oceanographic measurements obtained during the MOSAiC campaign. Results yield lower and upper bounds for ice formation and brine expulsion in and from leads. Due to the wide range of lead fractions obtained from different methods, we conclude that the specific lead fraction product must be chosen depending on research question. Divergence- and LKF-based lead fractions provide valuable information in addition to established lead fraction products at high spatial resolution and independent of cloud coverage.

Arctic sea ice thickness variability and change and their dependence on the atmospheric and oceanic forcing are at the core of research in Subtopic 2.1, Theme: Ongoing and Future Arctic and Antarctic Climate Change. Our research is particularly focused on a better process understanding and representation in models, and observations during MOSAiC play a strong role. The poster gives examples of such process studies focused on Arctic sea ice thickness variability and change. We outline observations of the long-term and regional variability and change of sea ice thickness using satellite remote sensing, airborne surveying, and ice mass balance buoys. Thermodynamic growth and its interaction with the atmosphere over leads and level ice serves as an example for our joint research interests. The poster also gives examples of causes of sea ice thinning, like increased ocean heat flux to the ice due to Atlantification, and consequences, e.g., for reduced sea ice volume transport through Fram Strait.

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.

Dynamic processes can contribute more than 50% to the total thickness of deformed sea ice. They gain importance in the context of a changing Arctic, in which reduced ice thickness and increased drift speed enhance deformation events that might compensate the thermodynamic thinning. The case study of an unusual, large polynya that opened and then closed by freezing and convergence north of the coast of Greenland in late winter 2018 offers us the unique chance to study the effects of dynamics on ice thickness in detail. We calculated drift and deformation fields for the closing phase of the polynya from a time series of daily Synthetic Aperture Radar (SAR) satellite images and measured the accumulated effects of dynamic and thermodynamic ice growth with an airborne electromagnetic (AEM) ice thickness survey one month after the closing began. We observed that strong ice convergence decreased the area of the former polynya by a factor of 2.5 while the AEM survey indicated mean and modal thicknesses of the one-month old ice of 1.96 and 0.95 m, respectively. This is in close agreement with the calculated thermodynamic growth and with the dynamic thickening expected from the polynya area decrease. Further, we identified characteristic differences in the shapes of ice thickness distributions in different regions of the closing polynya that are linked to the deformation histories of the ice. We reconstructed the deformation history by combining Lagrangian backward trajectories with the deformation fields derived from high-resolution SAR imagery. We found a linear proportionality between convergence and thickness change in good agreement with ice thickness redistribution theory. Further, we identified a proportionality between the e-folding of the tails of the different ice thickness distributions and the magnitude of the total deformation experienced by the ice. Lastly, we developed a simple volume-conserving model to derive dynamic ice thicknesses change from high-resolution SAR deformation tracking. Model and AEM observations agree reasonably well and the derived ice thickness distribution reproduce main characteristics like mode, width, and e-folding of the observed distribution. Hence, this shows that high-resolution SAR deformation observations are capable of producing realistic ice thickness distributions.

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).

Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from the ADCPs default bottom track (BT) mode measurements of error velocity and range. We demonstrate that this method can be used to obtain daily mean sea ice draft time series with an estimated uncertainty of 0.1 m. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings in the Laptev Sea. This new approach provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data. Applying this method to ADCP data from the sparsely-sampled Laptev Sea allows an extension of mooring-based in situ ice draft measurements from 2 to about 13 years. The Laptev Sea is an important region for net ice production and a major contributor to the Transpolar Drift System. Recent studies show that sea ice area and volume exports from the Laptev Sea are increasing. This increased export accelerates summer sea ice retreat in the Laptev Sea and has far-reaching consequences for the entire Arctic sea ice balance. The newly acquired Laptev Sea ice draft data archive is used to analyse seasonal and interannual changes in sea ice thickness from 2003 to 2016. In addition, it provides unique data for the comparison with sea ice thickness data records derived from ENVISAT, CryoSat-2 and SMOS satellite measurements in an area where large-scale validation data sets are currently unavailable.

In February and March 2018 a unique polynya of open water had opened in the Wandel Sea north of Greenland due to unusually strong southerly winds. The polynya subsequently refroze and the area of the growing young ice strongly decreased by ice convergence due to northerly winds, constituting a natural, well-constrained, full-scale ice deformation experiment. We have carried out an airborne electromagnetic ice thickness survey of the young ice one month after the polynya began to close. It showed a thickness distribution with a modal ice thickness of 1 m and mean thickness of 2 m, representative of the contributions of thermodynamic and dynamic growth in the one month since the ice had begun to grow. We used time series of Sentinel 1 SAR images to back-track the surveyed young ice to its location of initial formation. Results showed that the area of young ice approximately halved between the time of its formation and the time of the ice thickness survey. This is in good qualitative agreement with the result of the thickness survey, showing that its mean thickness was twice its modal thickness, i.e. mean ice thickness doubled while the region's area halved. These results and the SAR observed deformation processes provide valuable information for improving representation of ice rheology and thickness redistribution in sea ice models.

These are the data files for the PLOS ONE journal article "Two-Sided Matching for mentor-mentee allocations - Algorithms and manipulation strategies". Three files are provided: - Data.csv: An overview of the original preferences of mentors and mentee, a data dictionary, and two summary tables used to create figures in the manuscript - MatchingTables.csv: The outcome matching tables for each simulated scenario and repetition - Preferences.csv: The (un)manipulated preferences that were used as input to calculate the solution for each simulated scenario and repetition.

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.

Additional file 1 The file contains eight figures (Figures S1–S8) and four tables (Tables S1-S4) as supplementary results.

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with average pre-melt and snowmelt onset dates of 29 November and 10 December, respectively, without any significant trends over the study period. Related to different signal frequencies, we show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. Combining the observed successive timing of melt events retrieved from radar scatterometers with melt onset dates retrieved from 37 GHz passive microwave radiometers, allows us to develop a conceptual model which illustrates how the evolution of seasonal snow temperature profiles affects different microwave bands with different penetration depths. These suggest that future multi-frequency microwave satellite missions could be used to resolve melt processes throughout the vertical snow column.

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. Around Antarctica, snowmelt on pack ice is weak and very different than in the Arctic, with most snow surviving the summer. It is therefore important to understand the mechanisms that drive snowmelt, both at different times of the year and in different regions around Antarctica. Doing so, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphism of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles in the interior snowpack (snowmelt). We compare these with pan-Antarctic temporary snowmelt onset dates in the uppermost snowpack retrieved from diurnal variations in the brightness temperatures from passive microwave (PMW) observations. Results show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. Snowmelt onset dates from the shortwave PMW observations (37 GHz) are later by 13 and 5 days than those from the scatterometers, respectively. Based on the observed successive timing of melt events retrieved from different sensors and microwave bands, we developed a conceptual model of the temporal evolution of snow temperature and metamorphism and their effect on different microwave wavelengths during the spring/summer transition. These results suggest that future multi-frequency microwave satellite missions could be used to resolve melt processes throughout the vertical snow column. Overall, results show that the magnitude and timing of seasonal and diurnal variations in Antarctic snow on sea ice are highly dependent on latitude, with earlier and more frequent snowmelt in the north. All retrieved melt onset dates show large interannual variability but no significant decadal trends.

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 budget due to physical (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 from small to large scales in the Antarctic snowpack. It is therefore the overall aim of the proposed project to locate and quantify internal snow melt, snow metamorphism, and snow-ice formation in the Antarctic snowpack on different spatial scales. Results will improve our understanding of processes and interactions in the snowpack as well as at the snow/ice interface associated with seasonal and inter-annual variations in the sea-ice mass budget of the Southern Ocean.

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.

Sea ice proxies are important tools to reconstruct the climate and environmental history in polar regions. The novel sea ice proxy for the Southern Ocean is the biomarker IPSO25 (Ice Proxy Southern Ocean with 25 carbon atoms), a highly branched isoprenoid (HBI) diene produced by sea ice diatoms. With this poster we present the first approach of a sea ice index derived from IPSO25, called PIPSO25, for past sea ice reconstructions in Antarctica.

Snowmelt processes on sea ice are the key drivers determining the seasonal sea-ice energy and mass budgets. While there is strong surface melt on Arctic sea ice, snowmelt on Antarctic sea ice is weak with most snow surviving the summer. Here, we compile time series of snowmelt onset dates on perennial Antarctic sea ice from 1992 to 2014 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. Describing snow melt processes, we define two transition stages: A weak backscatter rise indicating the initial warming and metamorphosis of the snowpack (pre-melt), followed by a rapid rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with average pre-melt and snowmelt onset dates of 29 November and 10 December, respectively, without any significant trends over the study period. Related to different signal frequencies, we show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 25 and 11 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. As different signal frequencies result in different penetration depths, we hypothesize that the different sensors respond to typical snowmelt processes in different depths within the snow cover.

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 budget due to comprehensive physical (seasonal) transition processes within the snowpack. It is therefore necessary to locate and quantify internal snowmelt, snow metamorphism, and snow-ice formation in the Antarctic snowpack on different spatial scales. Doing so, we present here, on the one hand, in-situ observations of physical snow properties from point measurements and transect lines during recent expeditions in the Weddell Sea from 2013 to 2018, covering summer and winter conditions. On the other hand, we used passive microwave (19 and 37 GHz signal frequency) as well as scatteroemter observations (5.6 and 13.4 GHz signal frequency) to describe snowmelt processes on Antarctic-wide scales. As different signal frequencies result in different penetration depths, we hypothesize that the different sensors respond to snow melt processes in different depths within the snow cover. The observed differences are consistent with the effects of short- and longwave radiation transmission and absorption, and the related occurrence of strong snow metamorphism, internal snow melt, and superimposed ice formation known to be important on Antarctic sea ice. Results of the study will improve our understanding on processes and interactions in the snowpack as well as at the snow/ice interface associated with seasonal and inter-annual variations in the sea-ice energy and mass budgets of the Southern Ocean.

The snow cover on Antarctic sea ice impacts the energy, mass, and momentum balance of the sea ice cover, which in turn strongly influences fluxes between ocean, sea ice, and atmosphere. Despite the fact that snow depth is qualified as an Essential Climate Variable, the knowledge of the snow cover distribution and properties is still mostly vague and no large-scale (e.g. Antarctic wide) snow depth data product is available. Snow on Antarctic sea ice is characterized through a high spatial and temporal variability, and shows a highly heterogeneous internal stratification. This poses a challenge to air or space borne snow depth retrieval algorithms. Similarly, sea ice models are not yet able to resolve snow processes with enough accuracy. Here we present measurements of snow depth and physical snow properties along drift trajectories of autonomous Snow Buoys, which were deployed during several Polarstern cruises in the Weddell Sea since 2014. Resulting time series of snow depth show an event driven snow accumulation even during austral summer, whereas melting and a significant decrease of snow depth is only observed along the marginal sea ice zone. Additional analysis with the 1D multi-layer thermodynamic snow model SNOWPACK provides insights into internal processes such as snow to ice development cover along the trajectories. For these studies, SNOWPACK, which was previously used for land-based snow, has been extended with a sea ice module and is forced with re-analysis data. Comparisons between model and in-situ measurements show the capability of the model to reproduce the prevalent snow stratigraphy.

The significant loss of Arctic sea ice during the last decades shows the sensitivity of the sea-ice system to changes in global climate. To distinguish between natural variability and the impact of global warming, an understanding of processes and feedbacks is necessary, and for that, consistent and comprehensive measurements of the most important sea-ice properties are required. While sea-ice concentration is observed routinely year-round since the beginning of the satellite era, strategies to investigate the sea-ice thickness distribution have been developed only recently. These are crucially required for an examination of sea-ice mass changes. Presently, ice thickness observations are mainly based on freeboard measurements by means of satellite laser and radar altimetry. To contribute to the interpretation of these sea-ice thickness products, available airborne thickness and freeboard data were collected within the Sea Ice Downstream Services for Arctic and Antarctic Users and Stakeholders (SIDARUS) EU-Project, and have been analyzed with respect to their usability for the validation of the large-scale satellite products. One major challenge in comparing satellite and airborne measurements is the different footprint area of these methods. Therefore, statistical parameters like the variability of freeboard within the common footprint areas have been analyzed from measurements made during the PAMARCMIP 2011 campaign in order to determine the differences between point measurements and areal averages. It turned out that mean freeboard is less dependent of the freeboard areas than the modal values are. Furthermore, differences in modal and mean values for the range of chosen footprint areas have been related to their dependency on different ice characteristics and length scales of freeboard and thickness profiles. Also for this comparison, mean values are found to be more representative, as the results are mostly independent from the footprint but depend more on the length scales. However, there was no length scale that was representative for all the observed regions. Finally, results will be used for the interpretation of the comparison between freeboard and sea-ice thickness data derived from different data sources using laser and radar altimetry.

The sea-ice thickness distribution is one of the most important sea-ice properties, but also one of the less frequently observed ones so far. Satellite retrievals of Antarctic sea-ice thickness are currently limited to laser and radar altimetry observations of snow freeboard with large uncertainties, and electromagnetic measurements have been obtained only sporadically. For the investigation of the variability and changes in the sea-ice thickness distribution over the last decades, data are mainly available from very basic methods such as drilling or ship-based observations following the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol. Thereby it is an advantage that those data also include information on the snow-depth and partly on the sea-ice freeboard distribution, which are as sparse as information on the sea-ice thickness distribution, in particular during winter conditions. The most recent data based on those methods were obtained during austral winter 2013, when various sea-ice parameters were measured in the Weddell Sea as part of the Antarctic Winter Ecosystem Climate Study (AWECS). Here, we present first results of the sea-ice thickness, freeboard and snow-depth distribution obtained by drill-hole measurements from this expedition. The new data set is compared with results from three previous winter campaigns done in 1989, 1992 and 2006 in the Weddell Sea in order to determine the long-term variability of sea-ice thickness, snow depth and freeboard. A challenge in comparing all those data is that measurement sites are based only on individual floes, which are expected to be representative for an entire region. In addition, sampling rates differ between the considered field experiments. Therefore, drill-hole thicknesses are cross-correlated with ground-based EM-measurements in order to identify for the newest data set, how representative the chosen study areas have been for the respective sea-ice floes and which consequences different measurement spacing has for the comparison of data from different years.

The understanding and predictability of the observed decline of Arctic sea ice depends on the knowledge of its mass balance in a warming environment. While sea ice extent is monitored by passive microwave sensors for decades, only recently its volume is measured by basin-scale sea ice thickness observations of satellite altimetry missions. The current sea ice thickness sensor is SIRAL, a synthetic aperture radar altimeter on-board ESAs CryoSat-2. Altimetry missions measure freeboard, the height of the ice surface above the local water level, which can be converted into sea ice thickness. The conversion is very sensitive to errors in the freeboard retrieval and uncertainties in input parameters for the freeboard-tothickness conversion. Snow on Arctic sea ice plays a crucial role, since its regional variable physical parameters control the penetration of the Ku-Band radar waves and snow depth is required for the sea ice thickness retrieval algorithm, but not measured Arctic-wide. We present Arctic sea ice freeboard and thickness maps from CryoSat-2. The results are compared to available airborne validation datasets, which have been collected in an international validation program (CryoVEx). Sea ice thickness datasets obtained by airborne electromagnetic inductions sounding are available in Arctic spring of 2011 and 2012 and late summer of 2012 to quantify the uncertainties of the CryoSat-2 Arctic sea ice thickness data product of the Alfred Wegener Institute. Compared to the Arctic, only few validation datasets exist for CryoSat-2 sea ice data in the southern Ocean. We present the layout and first results of CryoSat-2 validation campaigns in the Weddell and Bellingshausen Seas between June and October 2013. The complicated snow properties of Antarctic sea ice may increase the uncertainty of CryoSat-2 sea ice thickness data, however sea ice volume estimates in the southern hemisphere are needed as well to understand the contrasting increase of Antarctic sea ice area.

Senior Thesis Research of Igneous Rocks near Dillon, MT. Geochem data, petrographic descriptions and thin section images of common intrusive and volcanic rocks from Badger Pass, Burma Road (Block Mountain) Area and Rochester Creek Area.

The significant loss of Arctic sea ice during the last decades shows the sensitivity of the sea ice system to changes in the global climate. To distinguish between natural variability and the impact of global warming, an understanding of processes and feedbacks is necessary and for that, consistent and comprehensive measurements of the most important sea ice properties are required. While sea ice concentration is observed routinely year-round since the beginning of the satellite era, strategies to investigate the sea ice thickness distribution, crucially needed for an investigation of ice mass changes, has only recently been developed. To contribute to the interpretation of the remotely sensed sea ice thickness products, which are mainly based on freeboard determination from altimeter measurements, available airborne sea ice thickness and freeboard data have been collected within the Sea Ice Downstream Services for Arctic and Antarctic Users and Stakeholders (SIDARUS) EU-Project, and have been analyzed with respect to their usability for validation of the large scale satellite products. Thus, statistical parameters like the variability of freeboards within the common footprint areas of satellites have been analyzed from measurements made during the PAMARCMIP 2011 campaign to determine the differences between point measurements and areal averages. Also, impacts on the deviated sea ice thicknesses have been studied by means of a cross validation of freeboard-based sea ice thicknesses with airborne thickness measurements with electromagnetic induction sounding. Finally, since during the PAMARCMIP campaign few flights were performed in conjunction with CryoSat-2 overpasses, the airborne-based freeboards can finally be used for a comparison with satellite-derived data.

The article of record as published may be found at http://dx.doi.org/10.1029/2011JC007257 Six Arctic Ocean Model Intercomparison Project model simulations are compared with estimates of sea ice thickness derived from pan-Arctic satellite freeboard measurements (2004-2008); airborne electromagnetic measurements (2001-2009); ice draft data from moored instruments in Fram Strait, the Greenland Sea, and the Beaufort Sea (1992-2008) and from submarines (1975-2000); and drill hole data from the Arctic basin, Laptev, and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than approximately 2 mand underestimate the thickness of ice measured thicker than about approximately 2m. In the regions of flat immobile landfast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than 4 times and more than one standard deviation, respectively. The models do not reproduce conditions of fast ice formation and growth. Instead, the modeled fast ice is replaced with pack ice which drifts, generating ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the Estimating the Circulation and Climate of the Ocean, Phase II and Pan-Arctic Ice Ocean Modeling and Assimilation System models. ARC-0804180 (M.J.); ARC-0804010 (A.P.); ARC-0805141 (W.M.); ARC080789; ARC0908769 (J.Z.); ARC-0804010; 09-05-00266; 09-05-01231 Approved for public release; distribution is unlimited.

To investigate the morphology and distR(i)bution of pressure R(i)dges in the northwestern Weddell Sea, ice surface elevation profiles were measured by a helicopter-borne laser altimeter duR(i)ng Winter Weddell Outflow Study with the German R/V Polarstern in 2006. An optimal cutoff height of 0.62 m, deR(i)ved from the best fits between the measured and theoretical R(i)dge height and spacing distR(i)butions, was first used to separate pressure R(i)dges from other sea ice surface undulations. It was found that the measured R(i)dge height distR(i)bution was well modeled by a negative exponential function, and the R(i)dge spacing distR(i)bution by a lognormal function. Next, based on the R(i)dging intensity R-i (the ratio of mean R(i)dge sail height to mean spacing), all profiles were clustered into three regimes by an improved k-means clusteR(i)ng algoR(i)thm: R-i 0.026 (denoted as C-1, C-2, and C-3 respectively). Mean (and standard deviation) of sail height was 0.99 (+/- 0.07) m in Regime C1, 1.12 (+/- 0.06) m in C-2, and 1.17 (+/- 0.04) m in C-3, respectively, while the mean spacings (and standard deviations) were 232 (+/- 240) m, 54 (+/- 20) m, and 31 (+/- 5.6) m. These three ice regimes coincided closely with distinct sea ice regions identified in a satellite radar image, where C-1 corresponded to the broken ice in the marginal ice zone and level ice formed in the Larsen Polynya, C-2 corresponded to the deformed first-and second-year ice formed by dynamic action in the center of the study region, and C-3 corresponded to heavily deformed ice in the outflowing branch of the Weddell Gyre. The results of our analysis showed that the relationship between the mean R(i)dge height and frequency was well modeled by a logaR(i)thmic function with a correlation coefficient of 0.8, although such correlation was weaker when consideR(i)ng each regime individually. The measured R(i)dge height and frequency were both greater than those reported by others for the Ross Sea. Compared with reported values for other parts of the Antarctic, the present R(i)dge heights were greater, but the R(i)dge frequencies and R(i)dging intensities were smaller than the most extreme of them. Meanwhile, average thickness of R(i)dged ice in our study region was significantly larger than that of the Coastal Ross Sea showing the importance of deformation and ice age for ice conditions in the northwestern Weddell Sea.

We compare results from six AOMIP model simulations with estimates of sea ice thickness obtained from ICESat, moored and submarine-based upward looking sensors, airborne electromagnetic measurements and drill holes. Our goal is to find patterns of model performance to guide model improvement. The satellite data is pan-arctic from 2004-2008, ice-draft data is from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea from 1992-2008 and from submarines from 1975-2000. The drill hole data are from the Laptev and East Siberian marginal seas from 1982-1986 and from coastal stations from 1998-2009. While there are important caveats when comparing modeled results with measurements from different platforms and time periods such as these, the models agree well with moored ULS data. In general, the AOMIP models underestimate the thickness of measured ice thicker than about 2 m and overestimate thickness of ice thinner than 2 m. The simulated results are poor over the fast ice and marginal seas of the Siberian shelves. Averaging over all observational data sets, the better correlations and smaller differences from observed thickness are from the ECCO2 and UW models. NSF ARC-0804180; NSF ARC-0804010; NSF ARC-0805141; NSF ARC080789; NSF ARC0908769; NSF ARC-0804010 Approved for public release; distribution is unlimited.