Sea Ice Deformation and Snow for an Arctic in Transition

SIDRiFT will estimate the role of sea ice deformation and snow on ice in the changing Arctic climate. The amount and distribution of sea ice ridges, leads and snow have a large effect not only sea ice growth in the winter but also on the sea ice melt, extent and thickness in the summer. This means that winter sea ice and snow dynamics have a significant impact on climate and navigability that lasts beyond the winter season. Arctic sea ice became thinner and more vulnerable to the impacts of winds and ocean currents. Sea ice is drifting faster and is pilled up in ridges or moved apart in leads more frequently. Recent and ongoing satellite remote sensing data sets offer an opportunity to establish a better understanding of these processes on a detailed regional scale. In SIDRiFT we will combine the historic and new mid-scale spatial resolution (40-100m) of radars mounted on polar-orbiting satellites to estimate the increase in the sea ice deformation, occurrence of leads and pressure ridges. Rough surfaces such as ridges and lead edges capture large amounts of snow and leave the rest of the ice with correspondingly fewer snow. Because snow is an excellent thermal insulator surfaces with thinner snow will grow thicker ice through the winter. Such ice will be more resilient to melt in summer. In turn, the broken up ice in ridges and thin ice in leads will melt faster. Because snow on sea ice detection from satellite sensors is still under development, we will instead use high resolution numerical models and field observations (e.g. from MOSAiC: https://mosaic-expedition.org/) to estimate the snow thickness on level and deformed ice. The development of these sophisticated models in SIDRiFT will help to improve our understanding of the Earth climate system and help develop simplified (lower resolution) global climate models and Arctic weather prediction models. SIDRiFT started in May 2019 by launching a pre-study of sea ice drift and deformation data collected by Sentinel-1 satellite and a field work conducted in 2015. Such study will help us understand the relationship between the different spatial and temporal scales of the processes. The initial results of the study were presented at the International Glaciology Society symposium on sea ice in Winnipeg, Canada, 18.-23. August 2019. We also made new adoptions of the numerical model of snow - SnowModel, so that it can be better used in the context of ever-moving sea ice. This is an ongoing work initiated by a 2-month overseas research stay at the Colorado State University (CSU), USA. The research grant was funded by the UiT and CSU. Already in May we started the hiring process for the SIDRiFT post-doc, which has been successfully recruited and Wenkai Guo joined our team in November 2019. Wenkai?s main task is to adopt the existing sea ice classifications of synthetic aperture radar (SAR) to better detect deformed sea ice. Another large commitment of the project already in 2019 was involvement into the MOSAiC expedition planning, training and logistics. MOSAiC is an international research expedition that stared end of September 2019 from Tromsø and is now in a finishing state with the research vessel arrival date to Bremerhaven, Germany on 12 October 2020. The main objective of the expedition was to collect the data on sea ice, ocean, atmosphere, bio-geo-chemical cycles and ecosystem across a full seasonal cycle of the Arctic Ocean. These data will facilitate process studies of Arctic climate system that is undergoing a major climate change. The same data will also support validation and development of new satellite remote sensing sensors, techniques as well as numerical models. Location-wise, the research was concentrated around German research ice breaker Polarstern that has been frozen into the Arctic pack ice in October 2019 in the Northern Laptev Sea and then drifted with the ice across the Transpolar drift towards the Fram Strait. From 15 December 2019 to 6 March 2020, Polona Itkin from SIDRiFT project joint the expedition. During that time Polarstern drifted along a 500 km-long track over the Arctic Ocean. In the meanwhile the consortium of international partners in MOSAiC ordered large volumes of SAR images of various spatial resolution, polarimetry and band width over sea ice surrounding the central observatory of MOSAiC. As part of this effort we collaborated with other Norwegian partners (Norwegian Polar institute, Nansen Environmental Research Center and MET Norway) and ordered around 100 high resolution (approx. 5 m) SAR images. Already during the final stage of the MOSAiC expedition, we started preparing and publishing peer-reviewed papers that include the expedition and satellite remote sensing data. In July 2021 Wenkai Guo successfully started his overseas research stay at the German Aerospace Agency (DLR) in Bremen, Germany, where he will expand his research for improving sea ice classification.

-

Arctic sea ice internal strength is reduced due to its general thinning. Simultaneously, the wind forcing of winter storms has increased due to their greater frequency. As a consequence ice drift speed has increased and evidences from buoy data show an increase in sea ice deformation. Satellite remote sensing datasets offer an opportunity to establish a better understanding of these processes on a detailed regional scale. In SIDRiFT we will combine the historic and new Synthetic Aperture Radar (SAR) satellite data (including Sentinel-1,A,B of European Space Agency) to estimate the increase in the sea ice deformation, occurrence of leads and deformed ice fraction. Indirectly, increased deformation alters the accumulation of snow cover on rough surface compared to level sea ice and consequently the heat fluxes through it. Snow on sea ice detection methods from space are still under development and to fill this knowledge-gap, we will use high resolution numerical models. Current Earth System Models (ESMs) are not able to capture the recent trends in Arctic sea ice decline or represent the snow cover accurately. The spatial scales of sea ice deformation features and snow inhomogeneity are both below ESMs resolution are one of the heat transfer mechanisms that are inadequately represented in ESMs. To account for this problem SIDRiFT will provide regional scale SAR validation datasets and high resolution snow depth and sea ice thickness model output. Both are highly sought after by model developers and teams developing satellite and air-borne remote sensing of snow. Furthermore, improving the numerical models (climate and forecasting) and remote sensing of environment will support the knowledge-based management that Norway as well as international decision-makers are striving for.