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"identifiers": [ { "identifier": "0000-0002-1878-3731", "scheme": "orcid" } ], "name": "Merkouriadi, Ioanna", "type": "personal" } }, { "affiliations": [ { "name": "Colorado State University" } ], "person_or_org": { "family_name": "Liston", "given_name": "Glen", "name": "Liston, Glen", "type": "personal" } }, { "affiliations": [ { "name": "Finnish Meteorological Institute" } ], "person_or_org": { "family_name": "Jutila", "given_name": "Arttu", "name": "Jutila, Arttu", "type": "personal" } } ], "description": "

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.

\n

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:

\n
    \n
  1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS
    2. \n
  2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS
    3. \n
  3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS
    4. \n
  4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS
    5. \n
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