Working Group on Compilation and Standardization of Snow on Sea Ice Data (2025-2029)
WG proposal
WG co-chairs
- Ruzica Dadic, WSL-SLF, Davos, Switzerland
- Matthias Bavay, WSL-SLF, Davos, Switzerland
- Polona Itkin, UiT The Arctic University of Norway, Tromsø, Norway
- Petra Heil, Australian Antarctic Division, Kingston, Australia
- Stefanie Arndt, Alfred Wegener Institute, Bremerhaven, Germany
- Ioanna Merkouriadi, Finnish Meteorological Institute, Helsinki, Finland
- Melinda Webster, University of Washington, Seattle, USA
Background
There is a pressing need to improve the model representation of snow on sea ice, and in particular, the representation of snow’s highly complex spatial and temporal variability. As well as being one of Earth’s most insulating naturally occurring materials, snow reflects most incident solar radiation, which has important implications for sea ice mass balance. Snow processes govern the conductive and radiative properties, energy balance, thermodynamics, growth rate, and microstructure of the underlying sea ice, thereby modifying its thickness, temperature, salinity, light transmission, and permeability. Snow can also influence Antarctic sea ice stability and sea ice breakup because the lack of snow can lead to warmer internal ice temperatures and mechanically weaker ice. Due to its high albedo and relatively low thermal conductivity, snow acts as an effective energy barrier between the atmosphere and the ocean, moderating the heat fluxes between them and can significantly affect model simulations of the Arctic Atmospheric Boundary Layer and weather forecasts. Snow also determines light and nutrient availability for polar marine ecosystems. Importantly, snow processes are highly sensitive to changing temperatures and may become increasingly relevant in a changing climate.
Snow stratigraphy over sea ice exhibits a complex microstructural layering, as well as being highly variable in time and space, with wind-slab layers, melt-freeze processes, rain-on-snow events, percolation of snow meltwater and the formation of superimposed ice, snow-ice formation by flooding of the lowermost snow layers due to the high weight of the snow and capillary wicking of salt from the sea ice into the bottom layers of the snow cover. On top of the seemingly 1-D processes, relatively persistent winds are constantly reshaping the spatial patterns of the snowpack and potentially sublimating large fractions of snow, or removing it into leads altogether, making point-measurements extremely difficult to generalize.
The importance of snow stands in stark contrast to our ability to monitor it and determine its physical properties, as direct observations of snow on sea ice have been sparse in space and time. Remote sensing of sea ice can provide such a physically consistent time series, which is needed to ascertain trends and quantify the interaction of sea ice within the climate system37. Accurate remote sensing observations of sea ice are, however, dependent on physical snow properties and the spatially distributed knowledge of snow mass (thickness, density, salinity, liquid water content) and snow microstructure. From a remote sensing perspective, snow thickness and snow physical properties (e.g., snow microstructure, melt layers, rain-on-snow, damp base and flooding, high salinity content, etc.) are critically important for accurate retrievals of ice thickness from sea ice-freeboard.
In light of the upcoming 5th International Polar Year, the timing is perfect for the development of a new snow-data standardization tool is essential to automatically and transparently extract all relevant data and metadata (from diverse sources). Such a tool is critically needed to power an advanced querying interface that can better support snow and climate science.
Objectives
- Compilation of existing snow on sea ice data
- Development of standardizing snow-data tool and advanced querying interface in a data portal
Deliverables
In addition to the compilation and digitalization of existing snow on sea ice data, this project will deliver a Python library and tool (a wrapper around the library) that can read the collected data in one of the supported snow and sea-ice profile formats and convert it to CAAML. The library will be written in a modular and extensible way so more input formats can be supported over time. It will be contributed to a software package for working with snow and avalanche data developed by Avalanche Canada in collaboration with an international operational avalanche forecasting practitioners group (AvaCollabra).