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Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
by
Ali, Alfatih
, Wang, Keguang
, Hughes, Nick
, Wang, Caixin
in
AMSR2 sea ice concentration
/ local analytical optimal nudging
/ OSTIA sea surface temperature
/ sea ice chart
/ sea ice edge
/ short-term forecast
2025
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Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
by
Ali, Alfatih
, Wang, Keguang
, Hughes, Nick
, Wang, Caixin
in
AMSR2 sea ice concentration
/ local analytical optimal nudging
/ OSTIA sea surface temperature
/ sea ice chart
/ sea ice edge
/ short-term forecast
2025
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Do you wish to request the book?
Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
by
Ali, Alfatih
, Wang, Keguang
, Hughes, Nick
, Wang, Caixin
in
AMSR2 sea ice concentration
/ local analytical optimal nudging
/ OSTIA sea surface temperature
/ sea ice chart
/ sea ice edge
/ short-term forecast
2025
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Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
Journal Article
Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
2025
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Overview
Sea ice is a major threat to marine operations around Svalbard, and accurate short-term (1–5 days) forecasts of sea ice edge (SIE) and marginal ice zone (MIZ) are crucial for safe marine operations. In this paper, we investigate the effects of assimilating the AMSR2 sea ice concentration (SIC), the Norwegian sea ice chart, and the OSTIA sea surface temperature (SST) on the short-term forecasts of SIE and MIZ around Svalbard. The used model, Barents-LAON, is based on the coupled ROMS-CICE model with the Local Analytical Optimal Nudging (LAON) for data assimilation. The assimilation effects are evaluated through seven model experiments, from Free run to the full assimilation of OSTIA SST, AMSR2 SIC, and ice chart. The results show that the Free run of Barents-LAON contains a large cold bias, which significantly overestimates the sea ice extent and underestimates the SST. Assimilation of SST mildly improves the analyses of SIE and MIZ, and additional assimilations of AMSR2 SIC and ice chart considerably improve the analyses and forecasts. We show that 1–3 days of forecasts of SIE and MIZ with assimilations of both SIC and SST outperform the CMEMS operational forecasts TOPAZ5 and neXtSIM, the US Navy GOFS3.1 system, and the Norwegian Meteorological Institute’s Barents-EPS. The assimilation of both ice chart and OSTIA SST is shown to have the largest improvement for MIZ analysis and forecasts. All the Barents-LAON short-term SIE forecasts with assimilations of SIC and SST outperform the sea ice chart persistence forecasts after the first day. However, all the MIZ forecasts, regardless of using the operational models or the current model experiments, are shown to have lower skills than the sea ice chart persistence. This suggests two possible defects: 1) the present AMSR2 SIC is not sufficiently accurate for separating MIZ from dense pack ice, and 2) some important physical processes may be lacking for the transformation between dense pack ice and MIZ in the present coupled ocean and sea ice models.
Publisher
Frontiers Media S.A
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