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...
Published in: | Journal of Geophysical Research: Oceans |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10037/20932 https://doi.org/10.1029/2019JC015900 |
id |
ftunivtroemsoe:oai:munin.uit.no:10037/20932 |
---|---|
record_format |
openpolar |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/20932 2023-05-15T14:27:10+02:00 A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II - Analyses Stroeve, Julienne C. Liston, Glen E. Buzzard, Samantha Zhou, Lu Mallett, Robbie Barrett, Andrew Tschudi, Mark Tsamados, Michel Itkin, Polona Stewart, Scott 2020-09-02 https://hdl.handle.net/10037/20932 https://doi.org/10.1029/2019JC015900 eng eng Wiley Journal of Geophysical Research (JGR): Oceans Norges forskningsråd: 287871 info:eu-repo/grantAgreement/RCN/ROMFORSK/287871/Norway/Sea Ice Deformation and Snow for an Arctic in Transition// Stroeve, Liston, Buzzard, Zhou, Mallett, Barrett, Tschudi, Tsamados, Itkin, Stewart. A Lagrangian Snow‐Evolution System for Sea Ice Applications (SnowModel‐LG): Part II ‐ Analyses. Journal of Geophysical Research (JGR): Oceans. 2020 FRIDAID 1833647 doi:10.1029/2019JC015900 2169-9275 2169-9291 https://hdl.handle.net/10037/20932 openAccess Copyright 2020 The Author(s) VDP::Technology: 500 VDP::Teknologi: 500 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2020 ftunivtroemsoe https://doi.org/10.1029/2019JC015900 2021-06-25T17:57:56Z 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 Arctic Ocean Greenland Sea ice University of Tromsø: Munin Open Research Archive Arctic Arctic Ocean Greenland Journal of Geophysical Research: Oceans 125 10 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Technology: 500 VDP::Teknologi: 500 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 |
spellingShingle |
VDP::Technology: 500 VDP::Teknologi: 500 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 Stroeve, Julienne C. Liston, Glen E. Buzzard, Samantha Zhou, Lu Mallett, Robbie Barrett, Andrew Tschudi, Mark Tsamados, Michel Itkin, Polona Stewart, Scott A Lagrangian Snow Evolution System for Sea Ice Applications (SnowModel‐LG): Part II - Analyses |
topic_facet |
VDP::Technology: 500 VDP::Teknologi: 500 VDP::Mathematics and natural science: 400 VDP::Matematikk og Naturvitenskap: 400 |
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, Julienne C. Liston, Glen E. Buzzard, Samantha Zhou, Lu Mallett, Robbie Barrett, Andrew Tschudi, Mark Tsamados, Michel Itkin, Polona Stewart, Scott |
author_facet |
Stroeve, Julienne C. Liston, Glen E. Buzzard, Samantha Zhou, Lu Mallett, Robbie Barrett, Andrew Tschudi, Mark Tsamados, Michel Itkin, Polona Stewart, Scott |
author_sort |
Stroeve, Julienne C. |
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 |
Wiley |
publishDate |
2020 |
url |
https://hdl.handle.net/10037/20932 https://doi.org/10.1029/2019JC015900 |
geographic |
Arctic Arctic Ocean Greenland |
geographic_facet |
Arctic Arctic Ocean Greenland |
genre |
Arctic Arctic Arctic Ocean Greenland Sea ice |
genre_facet |
Arctic Arctic Arctic Ocean Greenland Sea ice |
op_relation |
Journal of Geophysical Research (JGR): Oceans Norges forskningsråd: 287871 info:eu-repo/grantAgreement/RCN/ROMFORSK/287871/Norway/Sea Ice Deformation and Snow for an Arctic in Transition// Stroeve, Liston, Buzzard, Zhou, Mallett, Barrett, Tschudi, Tsamados, Itkin, Stewart. A Lagrangian Snow‐Evolution System for Sea Ice Applications (SnowModel‐LG): Part II ‐ Analyses. Journal of Geophysical Research (JGR): Oceans. 2020 FRIDAID 1833647 doi:10.1029/2019JC015900 2169-9275 2169-9291 https://hdl.handle.net/10037/20932 |
op_rights |
openAccess Copyright 2020 The Author(s) |
op_doi |
https://doi.org/10.1029/2019JC015900 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
125 |
container_issue |
10 |
_version_ |
1766300775137411072 |