CR4.2

Year-round observations of the properties and processes that govern the ice pack and its interaction with the atmosphere and the ocean were the key element of the MOSAiC field experiment. The aim was to completely characterize the properties of the snow and ice cover across different spatial scales over an entire annual cycle. This was done by monitoring snow and ice mass balance, observing the evolving energy budget, studying dynamical features, and by documenting snow and ice dynamics over nested spatial scales. We conducted in-situ observations at multiple scales, which will be integrated in numerical models and remote sensing methods. Overall, we performed the most comprehensive snow and sea ice program to date. Here, we summarize the observational snow and sea ice program during the drift from October 2019 to September 2020. We will present improved concepts and diagnostics of the field program and show relationships to satellite retrievals and numerical models. We will highlight individual events and characteristics of the snow and ice pack during the different seasons based on time-series that were obtained from numerous sea-ice programs of the MOSAiC ICE team. We will discuss the various activities with respect to the coupled system and the life cycle of sea ice along the transpolar drift.

How to cite: Nicolaus, M. and Perovich, D. and the MOSAiC Snow and Sea Ice Team: Overview of the MOSAiC expedition – Snow and Sea Ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10136, https://doi.org/10.5194/egusphere-egu21-10136, 2021.

Sea ice drift and deformation shapes the ice cover of the polar oceans, lead opening modulating heat transfer across the ice pack and deformation driven roughness changes affecting momentum transfer from winds and currents. Yet we do not fully understand the seasonal evolution of sea ice deformation. An array of >95 GPS drifting buoys and 11 ice stations was deployed as a Distributed Network around the MOSAiC Central Observatory, capturing scales of sea ice motion between hundreds of meters to up to 200 kilometers. The array drifted across the Arctic in the transpolar drift in less than a year, with an anomalous east-west sea level pressure gradient driving the fast drift. The buoys monitored horizontal deformation of the pack ice from freeze up north of the Laptev Sea to melt in the Greenland Sea. The deformation responds to inertial motion during the freeze up transition to a consolidated ice pack. The fractal dimension of the total deformation changes throughout the year. At smaller scales of about 10 km deformation becomes whiter during the growth season, once the ice pack is consolidated to the coast. There iis an increase in episodic events at the largest scales during the periods the ice pack is consolidated and where it becomes more tidally active during transition through Fram Strait. The MOSAiC distributed network brings improved understanding in the transition of sea ice deformation from freedrift to pack ice, and the response of the ice to changing momentum transfer from the wind and ocean across the Transpolar Drift. The MOSAiC campaign provides unprecedented information about the atmospheric structure and spatial distribution of winds, as well as near surface currents, from which we can deduce the affect of sub-mesoscale deformation in the wind field on the horizontal ice deformation. 

How to cite: Hutchings, J., Bliss, A., Basu, R., Cheng, B., Itkin, P., Krumpen, T., Lei, R., Haapala, J., Haas, C., Hoppmann, M., Hwang, P., Persson, O., von Albedyll, L., and Watkins, D.: Seasonality of sea ice deformation at MOSAiC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6920, https://doi.org/10.5194/egusphere-egu21-6920, 2021.

How to cite: Rabe, B. and the Team MOSAiC Distributed Network: Autonomously observing coupled Arctic processes year-round: the Distributed Network of ice-tethered buoys during MOSAiC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9496, https://doi.org/10.5194/egusphere-egu21-9496, 2021.

The MOSAiC expedition took place in the Arctic from September 2019 to October 2020 while having measurements under, in, and above sea ice for a complete annual cycle. Airborne thermal infrared imaging was conducted during 41 helicopter survey flights along the MOSAiC drift track. We analyze the infrared brightness temperature of snow, sea ice, and ocean water surfaces from October 2019 until May 2020 from the airborne measurements. While the snow-covered sea ice appears very cold, thin ice and open water are significantly warmer. These surface types will be considered with particular attention because they dominate the heat exchange between the ocean, ice, and atmosphere during wintertime. This influences the Arctic Climate and becomes even more important in the currently changing Arctic, where the sea ice gets thinner, moves faster, and breaks up easier. After georeferencing and merging the recorded images to a mosaic, we can provide maps of infrared brightness temperatures in a high spatial resolution of 1 m for each flight. The spatial range of the maps varies from local (~5 km) up to regional (~30 km). This data set provides a basis to study the spatial and temporal variability of sea ice characteristics in the Arctic winter. We derive the physical surface temperature from the brightness temperature, surface emissivity, and downwelling radiation from the sky or clouds. Using the surface temperature, we calculate the heat flux from a local up to a regional scale based on thermodynamic assumptions and atmospheric measurements on the ice floe. From more complex thermodynamic simulations, we estimate ice thickness and ice age based on the airborne measured surface temperatures. The model calculates for each surface temperature a specific ice thickness and heat flux based on the knowledge about the surface’s thermodynamic history. The simulated ice thickness allows a sea ice classification which is compared to our first classification approach which deals with the flight's temperature distribution only. In the future, we will investigate the sub-footprint scale variability of ice surface temperature and thin ice thickness for satellite data, e.g. MODIS and Sentinel-3.

How to cite: Thielke, L., Huntemann, M., Spreen, G., Hendricks, S., Jutila, A., and Ricker, R.: Thermal sea ice classification during the MOSAiC expedition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3907, https://doi.org/10.5194/egusphere-egu21-3907, 2021.

Sea ice plays a critical role in the Arctic climate system, regulating much of the energy transfer between the ocean and the atmosphere. Repeat measurements of ice mass balance at discrete points allow us to determine the direct response of sea ice mass to environmental conditions. We installed a network of mass balance measurement sites across the MOSAiC Central Observatories, distributed over a diverse range of ice types and features. The sites were composed of gridded arrays of 9-17 hotwire thickness gauges, each paired with a surface ablation stake. Seven sites were installed on first year ice, and seven on second or multi year ice, with a total of 120+ individual measurement stations. The sites were operational over different periods throughout the year; several were destroyed or became inaccessible during ridging events. Initial ice thicknesses ranged from 0.13-3.50 m. We made measurements of ice and snow interfaces and thicknesses with 1 cm precision at each station, at intervals of 2-3 weeks during the growth season and as few as 1-2 days during the melt season. From these measurements, we infer ice growth, ice bottom melt, ice surface melt, snow deposition, snow erosion, and snow melt. The time series spans October 2019–September 2020, with a five-week measurement gap beginning mid-May 2020. We present an overview of the measurements and preliminary analysis, partitioning results by ice type and comparing mass balance to concurrent atmosphere and ocean measurements. We identify trends in the seasonal evolution of different ice types, and give particular attention to notable events in the time series. As true point-measurements, the data are especially relevant in improving one-dimensional thermodynamic sea ice models. The results also provide validation for satellite and electromagnetic induction ice-thickness measurements made during MOSAiC, which offer higher areal coverage but lower measurement- and spatial-precision.

How to cite: Raphael, I., Perovich, D., Polashenski, C., Clemens-Sewall, D., Itkin, P., Jaggi, M., Regnery, J., Smith, M., Hutchings, J., Nicolaus, M., Matero, I., Wagner, D., Oggier, M., Demir, O., Macfarlane, A., and Fons, S.: Manual point-measurements of sea ice mass balance during the MOSAiC Expedition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3757, https://doi.org/10.5194/egusphere-egu21-3757, 2021.

During the melt season in the Arctic, freshwater ponds can accumulate under ice floes as a result of local snow and sea ice melt, far from terrestrial freshwater inputs. Under-ice freshwater ponds have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from salty, warmer water below, and by driving formation of ice ‘false bottoms’ at the interface of the under-ice pond and the underlying ocean. 

The MOSAiC drift expedition in the Central Arctic observed the presence of under-ice ponds and false bottoms beginning early in the melt season (June - July) at primarily first-year ice locations on the floe. We examine the prevalence and drivers of these ponds and resulting false bottoms during this period. Additionally, we explore the impact for mass balance using observations from ablation stakes and a 1D model, where freshwater ponds can not only delay summer melt but also result in growth. We speculate that the unique history of the MOSAiC floe likely led to a relatively high occurrence of these features, but the results also suggest that freshwater under-ice ponds and false bottoms may be more common and more persistent in early summer in the Arctic than previously thought. Both have implications for the broader ice-ocean system, for example by reducing fluxes between the ice and the ocean, isolating the primary producers in ice from pelagic nutrient sources, and altering the optical properties.

How to cite: Smith, M., von Albedyll, L., Raphael, I., Matero, I., and Lange, B. A.: Freshwater under the MOSAiC floe: implications of under-ice melt ponds for mass balance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3783, https://doi.org/10.5194/egusphere-egu21-3783, 2021.

We undertook a lead survey during the international drift campaign MOSAiC, Leg 5 (from 22 August to 17 September 2020) to understand the effects of lead width variation, re-freezing, and mixing events on lead water vertical structure. At the beginning of the survey period, the freshwater layer was occupied for the top 1 m depth and there were strong vertical gradients in temperature, salinity, and dissolved oxygen (DO) within 1 m depth: from 0.0°C to –1.6°C for temperature, from 0.0 to 31.4 psu for salinity, and 10.5 to 13.5 mg L^–1 for DO. A strong DO minimum layer corresponded with a salinity of 25 psu, and usually occurred at the freshwater–seawater interface at approx. 1 m depth, most likely as a result of an accumulation of organic matter and ongoing degradation/respiration processes at this interface. However, during the survey period, these strong gradients weakened and reduced the freshwater layer thickness (FLT). In the first half of the sampling period (until 4 September), FLT changed due to variations in lead width: as lead width increased, FLT decreased due to a stretching of the freshwater layer. In the second half of the sampling period, FLT was controlled by the surface ice formation (re-freezing) and mixing processes along the lower boundary of the freshwater layer. Surface ice formation removed freshwater and the formation of surface ice (about 0.2 m thick) explains 20% of the reduction of FLT. The remaining 80% of the reduction of FLT was due to the mixing process within the water column that was initiated by cooling and re-freezing. This mixing process diluted the salinity from 31.6 to 29.3 psu in the water below freshwater layer towards the end of the survey period. Our results indicate that lead water structure can change rapidly and dynamically and that this has significant effects on the biogeochemical exchange between lead systems and the atmosphere.

How to cite: Nomura, D., Web, A., Li, Y., Dall’osto, M., Schmidt, K., Droste, E., Chamberlain, E., Kawaguchi, Y., Inoue, J., Damm, E., and Delille, B.: Effects of lead width variation, re-freezing and mixing events on lead water structure in the central Arctic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12470, https://doi.org/10.5194/egusphere-egu21-12470, 2021.

The spatial heterogeneity of the snow cover on Arctic sea ice impacts the coupled Ice-Ocean-Atmosphere system. This spatial heterogeneity manifests in both the spatial distribution of snow thickness and the material properties of that snow (e.g. density, specific surface area [SSA], thermal conductivity, salinity, etc). This presents a challenge for observing the aggregate snow cover properties. Most material properties can only be measured in-situ and it is logistically difficult to measure material properties at a large number of sites. Here, we address this challenge by integrating repeat Terrestrial Laser Scan (TLS) data and in-situ observations of snow properties on an area several hundred meters across. We used TLS to map the topography of this area at cm-scale vertical resolution on approximately a biweekly basis throughout the winter during MOSAiC. By comparing successive scans, we map the spatial extent of snow layers as they build up the snow cover. Concurrently, we made in-situ penetration resistance force measurements using a SnowMicroPen (SMP) to quantify the snow properties at sites within the measurement area. These weekly point measurements, with 3mm vertical resolution, provide details of the grain type, snow density and SSA stratigraphy. Combining the TLS and SMP observations enables us to extrapolate the layer-wise properties of the snow cover throughout the measurement area. We examine how consistent snow properties are within layers and use this information to quantify aggregate snow cover properties for the entire region. For example, by integrating SMP-derived density with TLS-derived layers we estimate aggregate change in snow mass for this region for selected periods of the winter.

How to cite: Clemens-Sewall, D., Macfarlane, A., Polashenski, C., Perovich, D., Jaggi, M., Raphael, I., Schneebeli, M., and Wagner, D.: Improving Observations of Aggregate Snow Cover Properties on MOSAiC by Integrating Repeat Terrestrial Laser Scanning and In-Situ Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6720, https://doi.org/10.5194/egusphere-egu21-6720, 2021.

Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic.

The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples.  This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges.

First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instruments.

How to cite: Macfarlane, A. R., Arndt, S., Dadic, R., Gabarró, C., Light, B., Mahmud, M., Naderpour, R., Scharien, R., Smith, M., Spreen, G., Stroeve, J., Tavri, A., Wagner, D. N., and Schneebeli, M.: Snow microstructure on sea ice: Importance for remote sensing applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7626, https://doi.org/10.5194/egusphere-egu21-7626, 2021.

Retrieving the thickness of sea ice, and its snow cover, on long time- and length-scales is critical for studying climate. Satellite altimetry has provided estimations of sea ice thickness spanning nearly three decades, and more recently altimetry techniques have provided estimations of snow depth, using dual-band satellite altimetry data. These approaches are based on assumptions about the main scattering surfaces of the radiation. The dominant scattering surface is often assumed to be the snow/ice interface at Ku-band frequencies and the air/snow interface at Ka-band and laser frequencies. It has previously been shown that these assumptions do not always hold, but field data to investigate the dominant scattering surfaces and investigate how these relate to the physical snow and ice characteristics were spatially and temporally limited. The MOSAiC expedition provided a unique opportunity to gather data using a newly-developed Ku- and Ka-band radar 'KuKa' deployed over snow-covered sea ice, along with coincident field measurements of snow and ice properties. We present transect data gathered with the instrument looking at nadir to demonstrate how the scattering characteristics vary spatially and temporally in the Ku- and Ka-bands, and discuss implications for interpretation of dual-frequency satellite radar altimetry data. We compare KuKa data with field measurements to demonstrate snow depth retrieval using Ku- and Ka-band data.

How to cite: Willatt, R., Stroeve, J., Nandan, V., Tonboe, R., Hendricks, S., Ricker, R., Mead, J., Newman, T., Itkin, P., Liston, G., Mallett, R., Zhou, L., Schneebeli, M., Krampe, D., Tsamados, M., Demir, O., Oggier, M., Buehner Gattis, E., and Wilkinson, J.: KuKa altimeter mode data gathered during MOSAiC: scattering from snow covered sea ice and snow depth determination using dual-frequency and polarimetric approaches, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16150, https://doi.org/10.5194/egusphere-egu21-16150, 2021.