A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description. Issue 10 (1st October 2020)

• Record Type: Journal Article
• Authors: Liston, Glen E.; Itkin, Polona; Stroeve, Julienne; Tschudi, Mark; Stewart, J. Scott; Pedersen, Stine H.; Reinking, Adele K.; Elder, Kelly
• Is Part Of: Journal of geophysical research. Volume 125:Issue 10(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-01
• Subjects: snow‐on‐sea‐ice -- Arctic -- SnowModel‐LG -- Lagrangian; Oceanography -- Periodicals; 551.4605
• DOI: 10.1029/2019JC015913
• Languages: English
• ISSNs: 2169-9275

Abstract

Abstract: A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61, 000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution. Plain Language Summary: This study used a high resolution, snow‐evolution model to simulate both snow depth and snow density over Arctic sea ice, filling a critical data gap in polar science. The model was run from 1 August 1980 through 31 July 2018 and produced a new snow‐on‐sea‐ice map every day across the Arctic Ocean. This daily snow depth and snow density data set will be used to improve how Earth‐System models represent Arctic snow and ice processes. Key Points: Lagrangian snow‐on‐sea‐ice simulations revealed high‐resolution, snow property spatial structures associated with ice motion Ice age, associated with ice dynamics, strongly controlled the spatial distributions and temporal evolution of snow properties A new, high resolution snow depth and density product is available for Arctic snow and sea ice studies and applications