Sea ice and snow are key elements of the central Arctic system, and are tightly coupled to changes in the atmosphere and ocean. Today’s sea ice is thinner, younger, and drifts faster than the historical multi-year ice pack. However, modern climate models are still limited in their ability to reproduce the rapid changes this system is undergoing. Progress has been limited by observations of the processes governing the annual evolution.
The MOSAiC campaign 2019-2020 targeted improving understanding of this system with a year-long drifting platform across the central Arctic Ocean, as well as intensive remote sensing and modeling studies. This campaign provided the opportunity to monitor changes in various aspects of the ice and snow cover through the entire annual evolution. This session aims to begin synthesizing data from MOSAiC to better understand processes governing the evolution of snow and sea ice in the Arctic Ocean. We welcome studies using observations from the field campaign, remote sensing, and modeling, and especially that examine the interactions of changes in the sea ice and snow system with atmosphere, ocean, ecological and biogeochemical systems.
The MOSAiC campaign from September 2019 - October 2020 marked the largest polar campaign in history. The goal of the MOSAiC expedition was to take the closest look at the Arctic Ocean as the epicenter of global warming, and to gain fundamental insights to better understanding global climate change. Sea ice is a key component of the Arctic system, and so was a major focus of the scientific approach. Today, the Arctic sea ice is thinner and younger than in past decades. Researchers during MOSAiC had the opportunity to continually monitor various aspects of the sea ice and snow cover over the full annual cycle. Integration of findings with observations by other MOSAiC science teams will provide important insight into changes in the Arctic climate system.
More information on the MOSAiC expedition can be found at https://mosaic-expedition.org.
More information on the MOSAiC expedition can be found at https://mosaic-expedition.org.
vPICO presentations: Fri, 30 Apr
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.
The Arctic Ocean is a region with unique physical processes coupling the atmosphere, sea-ice and ocean. Biogeochemical and ecosystem processes feedback with this physical system not only on a regional scale but also locally around each ice floe. Capturing these processes, both vertically and horizontally from the mesoscale to turbulence scales is essential to understand the Arctic system and to improve model simulations of this region and global climate.
The MOSAiC Distributed Network (DN) of autonomous, ice-tethered measurement systems recorded a full seasonal cycle of atmospheric, surface, sea ice and snow, and oceanic conditions. Physical and biological variables were measured throughout the whole drift of the original MOSAiC ice floe with the icebreaker Polarstern (Central Observatory, CO) from north of Laptev Sea to Fram Strait, covering the seasons from mid-autumn 2019 to mid-summer 2020. In addition, a subset of ice-tethered buoys observed the freeze-up in the central Arctic around the second CO, after the relocation of the ice camp in late summer 2020, and even beyond the drift of Polarstern. These observations form a variety of three-dimensional datasets valuable for analyses across the spectrum of research foci covered by MOSAiC.
We will present the scientific concept of the DN in the context of other MOSAiC observations, and show the success and preliminary scientific results from a whole year of autonomous observations.
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.
Four seasonal ice mass balance buoys were deployed as part of the MOSAiC distributed network. These instruments measured vertical profiles of snow and ice temperature, as well as snow depth and ice thickness every six hours. Ice growth, surface melt, and bottom melt, as well as temporally averaged estimates of ocean heat fluxes, were calculated from these measurements. The buoys were installed in October 2019, with durations ranging from February 2020 to July 2020. Three of the buoys were destroyed in ridging events in February, March, and June 2020. The fourth buoy lasted until floe breakup in July 2020. The sites were separated by tens of kilometers, but had very similar air temperatures. While air temperatures were similar, snow – ice interface temperatures at different buoys varied by as much as 15 C due to differences in snow depth and ice thickness. Initial ice thicknesses ranged from 0.30 to 1.36 meters. During the growth season snow depths typically were around 0.1 to 0.2 meters, except for one case where the buoy was in a snow drift and the snow depth exceeded 0.5 meter. Peak growth rates of about 0.8 cm per day occurred in January. In mid-January there was a rapid increase in ice thickness associated with an aggregation of platelet ice. This aggregation only lasted for two weeks. In mid-April, air temperatures increased to nearly 0 C, almost ending the growth season.
How to cite: Perovich, D., Raphael, I., Moore, R., and Clemens-Sewall, D.: Autonomous observations of sea ice mass balance during MOSAiC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5748, https://doi.org/10.5194/egusphere-egu21-5748, 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.
Sea ice affects the exchange of energy and matter between the atmosphere and the ocean from local to hemispheric scales. Salt fluxes across the ice-ocean interface that drive thermohaline mixing beneath growing sea ice are important elements of upper ocean nutrient and carbon exchange. Sea-ice melt releases freshwater into the upper ocean and results in formation of melt ponds that affect gas and energy transfer across the atmosphere-ice interface. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provided an opportunity to follow sea-ice evolution and exchange processes over a full seasonal cycle in a rapidly changing ice cover. To this end, approximately 25 sea-ice cores were collected at 2 distinct sites, representing first-year and multi-year ice, to monitor physical, biological and geochemical processes relevant to atmosphere-ice-ocean exchange processes. Here we compare the growth and decay of first-year ice in the Central Arctic during the winter 2019-2020 to that of landfast first-year ice at Utqiaġvik, Alaska, from 1998 to 2016. Ice stratigraphy was similar at both sites with about 15 cm of granular ice on top of columnar ice, with a comparable growth history with a similar maximum ice thickness of 1.6-1.7 m. We aggregated the sea-ice bulk salinity and temperature profiles using a degree-day approach, and examined brine and freshwater fluxes at lower and upper interfaces of the ice, respectively. Preliminary results show lower sea-ice bulk salinity during the growth season and greater desalination at the ice surface during the melt season at the MOSAiC floe in comparison to Utqiaġvik.
How to cite: Oggier, M., Eicken, H., Rember, R., Fong, A., Divine, D. V., Fons, S., Granskog, M. A., Mahoney, A. R., and Salganik, E. and the MOSAiC Sea-Ice Coring Team: Seasonal evolution and salt/freshwater fluxes of first-year sea ice: Comparison between pack ice and landfast sea ice , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10798, https://doi.org/10.5194/egusphere-egu21-10798, 2021.
Pond bathymetry and average pond depth on Arctic sea ice are important for characterizing and quantifying the distribution of surface melt water volume. Melt pond models that take depth into account used to be based on manual in situ measurements; however, the capability of measuring pond depth through other means have increased substantially in recent years .
We take advantage of the extensive sampling and data recorded during the 2019-2020 MOSAiC campaign to compare different melt pond depth retrievals from a unique case study involving a melt pond in the center of the MOSAiC floe. Thus, we are able to present the most recent upscaling cascade of pond depth measurement methods.
The methods we examine in our contribution include in-situ echo sounder and hyperspectral measurements, airborne hyperspectral and photogrammetry-based measurements, as well as spaceborne multispectral measurements. Each method is assessed regarding its spatial resolution, retrieval accuracy, technical prerequisites and limitations.
How to cite: Linhardt, F., Fuchs, N., König, M., Webster, M., von Albedyll, L., Birnbaum, G., and Oppelt, N.: Comparison of complementary methods of melt pond depth retrieval on different spatial scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12860, https://doi.org/10.5194/egusphere-egu21-12860, 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.
Due to logistical challenges, snowfall in the high Arctic has rarely been measured, which is particularly valid for longer time-spans and the polar night. When estimating snowfall with precipitation gauges, a snowfall reference and detailed knowledge of how the precipitated snow accumulated or eroded is required.
To overcome snowfall uncertainties and to improve accumulated and eroded snow estimates, we used data from precipitation gauges, snow particle counters (SPCs) and a Ka-Band ARM Zenith Radar (KAZR) installed on and around research vessel (RV) Polarstern during the snow accumulation season of MOSAiC (October 2019 - May 2020). In addition to this, direct snow water equivalent (SWE) measurements were conducted and SWE estimates were retrieved from SnowMicroPen (SMP) measurements distributed all over the floe. The evolution of accumulated snow mass was finally computed by applying a simple fitted z-SWE function to snow depths that were measured approximately weekly along a fixed transect path with a Magnaprobe. The transects paths were along two distinct ice types: predominantly level remnant ice that at the start of the winter had large refrozen melt ponds, and predominantly deformed thick second year ice (SYI).
We could show that at least 34 mm of snow has accumulated and approximately 9 kg m-2 of snow mass was eroded between 31 October 2019 and 26 April 2020. In the beginning of the winter, the total estimated SWE on level remnant ice was only 42 % of SWE on deformed SYI. By end of April 2020 the values almost equalized as the snow mass on remnant ice reached almost 90 % of the snow mass over deformed SYI.
Based on the SWE evolution of the snowpack, we validated precipitation sensors and the reanalysis ERA5 for their capability to estimate snowfall. Eroded snow mass, among other processes, led to a discrepancy of precipitation- sensor estimated snowfall and computed SWE of the snow cover from 20 February 2020 on. However, for the time period before the first net erosion could be observed we found best agreements of cumulated snowfall and SWE for the Vaisala Present Weather Detector (PWD22) installed on the vessel (RMSE = 2 mm) and for snowfall retrievals from the KAZR (RMSE = 4 mm). Other sensors largely overestimated snowfall (corrected OTT Pluvio2: 14 mm; Vaisala PWD22 on the ice: 26 mm, OTT Parsivel2 on RV Polarstern: 51 mm). ERA5 overestimates snowfall too, with 13 mm and an increasing positive bias from March 2020 on. With horizontal snow mass fluxes derived from SPCs we could show that the Vaisala PWD22 on RV Polarstern was effectively protected against blowing snow. This, however, greatly affected snowfall measurements of instruments collocated on the ice. Further, we investigated a high-wind event in February 2020 resulting in high blowing snow mass fluxes and an average eroded snow mass of 5.5 kg m-2. The lifted blowing snow particles from the surface led to strong overestimation of snowfall from instruments installed on the ice which cannot be corrected with conventional correction methods.
How to cite: Wagner, D. N., Shupe, M. D., Persson, O. G., Uttal, T., Frey, M., Kirchgaessner, A., Schneebeli, M., Jaggi, M., Macfarlane, A. R., Itkin, P., Arndt, S., Hendricks, S., Krampe, D., Regnery, J., Ricker, R., Kolabutin, N., Shimanchuck, E., Oggier, M., Raphael, I., and Lehning, M.: Snowfall and snow accumulation processes during MOSAiC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12692, https://doi.org/10.5194/egusphere-egu21-12692, 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.
Sea ice albedo is both a driver and a consequence of summer melt evolution. The ability to collect comprehensive observations and develop accurate, general, and consistent physics-based models is central to our quantitative understanding of sea ice mass and heat budgets and a variety of associated feedback processes. Sea ice albedos recorded during the MOSAiC field campaign have extended our knowledge of the optical properties of specific ice types as well as their seasonal evolution. This new dataset complements and extends observations made during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign in the Beaufort Sea in 1998. It also presents an opportunity to improve numerical treatment of shortwave radiation partitioning by sea ice covers in climate models. Specifically, the observations include spectral and broadband albedo measurements made by observers on the surface for two classes of measurement: 1) individual ice types including snow covered ice (prior to and during melt), bare melting ice, ponded ice, and sediment-laden ice, and 2) time series measured over the full seasonal cycles. The MOSAiC and SHEBA data sets show remarkable similarity with respect to the steady spectral albedo of bare, melting summer ice and the seasonal evolution measured over representative survey lines. Both data sets include coordinated physical property characterization, which is key to the development and refinement of radiative transfer treatment in climate modeling.
In this work, we compare the observational record with results generated from runs of the CESM2 model. The Community Earth System Model (CESM2) is a coupled climate model that includes a physics-based radiative transfer treatment for sea ice. This model relies on a 2-stream delta-Eddington solution with prescribed ice-type-specific inherent optical properties. Specifically, we consider newly available sub-gridscale diagnostics in the model that detail the radiative partitioning for individual surface types and thickness categories. Comparisons between observed and modeled values are considered for the albedo of individual surface types, aggregate albedo estimates, and their seasonal progression. In particular, we use these comparisons to derive a quantitative picture of the overall partitioning of shortwave radiation by the ice cover, and how it has changed over past decades. These results can help pinpoint where the most substantial model upgrades can be accomplished as well as where the best observational investments should be made.
How to cite: Light, B., Holland, M., Smith, M., Perovich, D., Webster, M., Clemens-Sewell, D., Linhardt, F., Raphael, I., and Bailey, D.: The MOSAiC sea ice albedo record: its context and role for informing improved surface radiative budgets in a climate model , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8585, https://doi.org/10.5194/egusphere-egu21-8585, 2021.
Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.
Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 – 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.
Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.
The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.
The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.
How to cite: Pirazzini, R., Hannula, H.-R., Brus, D., Dadic, R., and Scnheebeli, M.: Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11377, https://doi.org/10.5194/egusphere-egu21-11377, 2021.
Snow cover dominates the thermal and optical properties of sea ice and the energy fluxes between the ocean and the atmosphere, yet data on the physical properties of snow and its effects on sea ice are limited. This lack of data leads to two significant problems: 1) significant biases in model representations of the sea ice cover and the processes that drive it, and 2) large uncertainties in how sea ice influences the global energy budget and the coupling of climate feedback. The MOSAiC research initiative enabled the most extensive data collection of snow and surface scattering layer (SSL) properties over sea ice to date. During leg 5 of the MOSAiC expedition, we collected multi-scale (microscale to 100-m scale) measurements of the surface layer (snow/SSL) over first year ice (FYI) and MYI on a daily basis. The ultimate goal of our measurements is to determine the spatial distribution of physical properties of the surface layer. During leg 5 of the MOSAiC expedition, that surface layer changed from the surface scattering layer (SSL), characteristic for the melt season, to an early autumn snow pack. Here, we will present data showing both a) the physical properties and the spatial distribution of the SSL during the late melt season and b) the transition of the sea ice surface from the SSL to the fresh autumn snowpack. The structural properties of this transition period are poorly documented, and this season is critical for the initialization of sea ice and snow models. Furthermore, these data are crucial to interpret simultaneous observations of surface energy fluxes, surface optical and remote sensing data (microwave signals in particular), near-surface biochemical activity, and to understand the sea ice processes that occur as the sea ice transitions from melting to freezing.
How to cite: Dadic, R., Schneebeli, M., Hannula, H.-R., Macfarlane, A., and Pirazzini, R.: Physical properties and spatial distribution of the sea ice surface layer (SSL/snow) during the autumn phase of the MOSAiC expedition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1467, https://doi.org/10.5194/egusphere-egu21-1467, 2021.
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