Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-6207-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-6207-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regime-dependence when constraining a sea ice model with observations: lessons from a single-column perspective
University of Washington, Department of Atmospheric Sciences, Seattle, WA, USA
NSF National Center for Atmospheric Research, Advanced Study Program, Boulder, CO, USA
Cecilia M. Bitz
University of Washington, Department of Atmospheric Sciences, Seattle, WA, USA
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We tested a new way to improve Arctic sea ice forecasts by adding satellite-based surface brightness, or albedo, into a sea ice model. This approach captures key surface changes like melting and snowfall that affect ice loss. We found it often gives better results than using standard data like ice coverage or thickness, especially during the melt season. This method offers a powerful tool for tracking Arctic sea ice in a changing climate.
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Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2540, https://doi.org/10.5194/egusphere-2025-2540, 2025
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We tested a new way to improve Arctic sea ice forecasts by adding satellite-based surface brightness, or albedo, into a sea ice model. This approach captures key surface changes like melting and snowfall that affect ice loss. We found it often gives better results than using standard data like ice coverage or thickness, especially during the melt season. This method offers a powerful tool for tracking Arctic sea ice in a changing climate.
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Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.
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Sea ice models suffer from large uncertainties arising from multiple sources, among which parametric uncertainty is highly under-investigated. We select a key ice albedo parameter and update it by assimilating either sea ice concentration or thickness observations. We found that the sea ice albedo parameter is improved by data assimilation, especially by assimilating sea ice thickness observations. The improved parameter can further benefit the forecast of sea ice after data assimilation stops.
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Short summary
Integrating observations into complex sea ice models improves model estimates, but the impact of specific kinds of observations may vary in space and time. By modeling sea ice at single locations, this work quantifies the impact of four different observation kinds on sea ice at three characteristic locations in the Arctic. The results indicate that this simplified experimental framework is a useful tool for developing methods to meld new and existing observations with modern sea ice models.
Integrating observations into complex sea ice models improves model estimates, but the impact of...