Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard
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...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1588769/full |
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| author | Keguang Wang Caixin Wang Nick Hughes Alfatih Ali |
| author_facet | Keguang Wang Caixin Wang Nick Hughes Alfatih Ali |
| author_sort | Keguang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d2a31eeae73d43eaaedaf7d2796a2610 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Marine Science |
| spelling | doaj-art-d2a31eeae73d43eaaedaf7d2796a26102025-08-20T03:50:11ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-07-011210.3389/fmars.2025.15887691588769Improving short-term forecasts of sea ice edge and marginal ice zone around SvalbardKeguang Wang0Caixin Wang1Nick Hughes2Alfatih Ali3Department of Research and Development, Norwegian Meteorological Institute, Oslo, NorwayDepartment of Research and Development, Norwegian Meteorological Institute, Oslo, NorwayDepartment of Research and Development, Norwegian Meteorological Institute, Tromsø, NorwayDepartment of Research and Development, Norwegian Meteorological Institute, Bergen, NorwaySea 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.https://www.frontiersin.org/articles/10.3389/fmars.2025.1588769/fullshort-term forecastAMSR2 sea ice concentrationsea ice chartOSTIA sea surface temperaturelocal analytical optimal nudgingsea ice edge |
| spellingShingle | Keguang Wang Caixin Wang Nick Hughes Alfatih Ali Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard Frontiers in Marine Science short-term forecast AMSR2 sea ice concentration sea ice chart OSTIA sea surface temperature local analytical optimal nudging sea ice edge |
| title | Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard |
| title_full | Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard |
| title_fullStr | Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard |
| title_full_unstemmed | Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard |
| title_short | Improving short-term forecasts of sea ice edge and marginal ice zone around Svalbard |
| title_sort | improving short term forecasts of sea ice edge and marginal ice zone around svalbard |
| topic | short-term forecast AMSR2 sea ice concentration sea ice chart OSTIA sea surface temperature local analytical optimal nudging sea ice edge |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1588769/full |
| work_keys_str_mv | AT keguangwang improvingshorttermforecastsofseaiceedgeandmarginalicezonearoundsvalbard AT caixinwang improvingshorttermforecastsofseaiceedgeandmarginalicezonearoundsvalbard AT nickhughes improvingshorttermforecastsofseaiceedgeandmarginalicezonearoundsvalbard AT alfatihali improvingshorttermforecastsofseaiceedgeandmarginalicezonearoundsvalbard |