Sea ICE has been visibly changing in recent decades; most notably the annual minimum extent, which has shown a distinct downward, and recently accelerating, trend. Should this loss continue, there…

This is a $720,771.62 research grant awarded by the National Aeronautics and Space Administration (NASA) to the University of Colorado's Office of Contracts and Grants. The grant, titled "Bridging the Snow - Sea Ice Gap: A Snow on Sea Ice Assimilation System for the Arctic", aims to develop daily and monthly snow depth and density products to support improved satellite-based measurements of sea ice thickness. This information is critical for understanding and predicting changes in Arctic sea ice cover, which impacts marine ecosystems, coastal communities, and weather. The grant does not have a set-aside designation. The University of Colorado will serve as the prime contractor, leveraging its expertise in climate science, physics, and engineering to create the snow on sea ice assimilation system over the 5-year performance period.

Sea ICE has been visibly changing in recent decades; most notably the annual minimum extent, which has shown a distinct downward, and recently accelerating, trend. Should this loss continue, there will be wide-ranging impacts on marine ecosystems, coastal communities, prospects for shipping and resource extraction, and weather conditions within and possibly beyond the arctic. Key to understanding the long-term evolution of the sea ICE cover and improving seasonal predictions of ICE condition is knowledge of ICE thickness. A fundamental impediment to obtaining thickness from satellite missions (e.g. NASA icesat/2 or ESA cryosat-2) is the lack of knowledge of snow depth atop the sea ICE and snow density. We propose to meet this need through development of the snow on sea ICE assimilation system (snosas). Snosas will develop daily and monthly snow depth and density products, initially focusing on the icesat and cryosat-2 time-periods. Snowfall on sea ICE will be determined using an improved lagrangian sea ICE tracking approach in conjunction with snowfall data from a suite of atmospheric reanalyses, observations of solid precipitation from arctic coastal stations and drifting stations, snow depth from icebridge and data from field campaigns (e.g. cryovex). Snowmelt will be determined from an energy budget approach, making use of surface fluxes from satellite data and reanalyses. A lagrangian-modified optimal interpolation data assimilation scheme will be used to combine available snow observations and the modeling system. While snosas has the primary aim of enabling improved sea ICE thickness retrievals from satellite sensors, IT will also be used for process studies of seasonal and interannual variability of snow depth. Estimates of ICE thickness across the arctic ocean have become available over the past 20 years based on data from ERS-1/2, envisat, icesat, cryosat-2 and operation icebridge. While there is potential to combine data sources into consistent time-series, the variety of measurement approaches, sensor technologies and spatial coverage (among other factors) present formidable challenges. By providing daily and monthly snow depths and densities, with error estimates, we can better understand the observed changes in sea ICE both from existing data streams and upcoming missions such as icesat-2.