Investigating the effect of snow-ice formation on snow depth and density over Arctic sea ice

• Main Authors: Merkouriadi, Ioanna, Liston, Glen, Jutila, Arttu
• Format: Dataset
• Language: unknown
• Published: Finnish Meteorological Institute
• Subjects: snow depth; snow density; snow-ice; Arctic Ocean; Arctic; National Snow and Ice Data Center; Sea ice
• Online Access: http://hdl.handle.net/11304/1b4edfed-ae56-4b05-9b86-d8fec79701e4; https://doi.org/10.23728/fmi-b2share.e4b0307d7dc74b449d03a41b6cae65d8; https://fmi.b2share.csc.fi/api/records/e4b0307d7dc74b449d03a41b6cae65d8

We investigated the effect of snow-ice formation on snow depth and density over Arctic sea ice. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. The data set includes daily, pan-Arctic information of snow depth, snow density, snow-ice thickness and thermodynamically grown sea ice thickness from 1.8.1980 until 31.7.2022, based on SnowModel-LG_HS. Specifically, it includes the following (4) netcdf files: 1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS 2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS 3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS 4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS