A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses
Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response...
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ftucl:oai:eprints.ucl.ac.uk.OAI2:10109600 2023-12-24T10:13:22+01:00 A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses Stroeve, J Liston, GE Buzzard, S Zhou, L Mallett, R Barrett, A Tschudi, M Tsamados, M Itkin, P Stewart, JS 2020-10 text https://discovery.ucl.ac.uk/id/eprint/10109600/7/Buzzard_2019JC015900.pdf https://discovery.ucl.ac.uk/id/eprint/10109600/ eng eng American Geophysical Union (AGU) https://discovery.ucl.ac.uk/id/eprint/10109600/7/Buzzard_2019JC015900.pdf https://discovery.ucl.ac.uk/id/eprint/10109600/ open Journal of Geophysical Research: Oceans , 125 (10) , Article e2019JC015900. (2020) snow on sea ice Arctic climate change Article 2020 ftucl 2023-11-27T13:07:36Z Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow depth, density, and grain size on a pan‐Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective‐Analysis for Research and Applications, Version 2 and European Centre for Medium‐Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first‐year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel‐LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole. Article in Journal/Newspaper Arctic Arctic Ocean Climate change Greenland Sea ice University College London: UCL Discovery Arctic Arctic Ocean Greenland |
institution |
Open Polar |
collection |
University College London: UCL Discovery |
op_collection_id |
ftucl |
language |
English |
topic |
snow on sea ice Arctic climate change |
spellingShingle |
snow on sea ice Arctic climate change Stroeve, J Liston, GE Buzzard, S Zhou, L Mallett, R Barrett, A Tschudi, M Tsamados, M Itkin, P Stewart, JS A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
topic_facet |
snow on sea ice Arctic climate change |
description |
Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow depth, density, and grain size on a pan‐Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective‐Analysis for Research and Applications, Version 2 and European Centre for Medium‐Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first‐year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel‐LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole. |
format |
Article in Journal/Newspaper |
author |
Stroeve, J Liston, GE Buzzard, S Zhou, L Mallett, R Barrett, A Tschudi, M Tsamados, M Itkin, P Stewart, JS |
author_facet |
Stroeve, J Liston, GE Buzzard, S Zhou, L Mallett, R Barrett, A Tschudi, M Tsamados, M Itkin, P Stewart, JS |
author_sort |
Stroeve, J |
title |
A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
title_short |
A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
title_full |
A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
title_fullStr |
A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
title_full_unstemmed |
A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II—Analyses |
title_sort |
lagrangian snow evolution system for sea ice applications (snowmodel‐lg): part ii—analyses |
publisher |
American Geophysical Union (AGU) |
publishDate |
2020 |
url |
https://discovery.ucl.ac.uk/id/eprint/10109600/7/Buzzard_2019JC015900.pdf https://discovery.ucl.ac.uk/id/eprint/10109600/ |
geographic |
Arctic Arctic Ocean Greenland |
geographic_facet |
Arctic Arctic Ocean Greenland |
genre |
Arctic Arctic Ocean Climate change Greenland Sea ice |
genre_facet |
Arctic Arctic Ocean Climate change Greenland Sea ice |
op_source |
Journal of Geophysical Research: Oceans , 125 (10) , Article e2019JC015900. (2020) |
op_relation |
https://discovery.ucl.ac.uk/id/eprint/10109600/7/Buzzard_2019JC015900.pdf https://discovery.ucl.ac.uk/id/eprint/10109600/ |
op_rights |
open |
_version_ |
1786181896628076544 |