Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting?

Hunke, Elizabeth; Allard, Richard A; Blain, Philippe; Blockley, Ed; Feltham, D. L.; Fichefet, Thierry; Garric, Gilles; Grumbine, Robert; Lemieux, Jean‐François; Rasmussen, Tue Kruse; Ribergaard, Mads Hvid; Roberts, Andrew; Schweiger, Axel; Tietsche, Steffen; Tremblay, Bruno; Vancoppenolle, Martin; Zhang, Jinlun

doi:10.1007/s40641-020-00162-y

Cited by 27 publications

(17 citation statements)

References 102 publications

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“…The reproduction of the observed sea ice cover heterogeneity in models remains a challenge (Blockley et al, 2020;Hunke et al, 2020;Hutter et al, 2021), unless they use horizontal resolutions higher than 2 km (Bouchat et al, 2022;Hutter et al, 2021). Using such a high-resolution grid is very costly and therefore not always suitable for simulations over long periods and/or large domains.…”

Section: Introductionmentioning

confidence: 99%

Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework

Boutin

Ólason²,

Rampal³

et al. 2022

Preprint

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Abstract. Sea ice is a key component of the Earth’s climate system as it modulates the energy exchanges and associated feedback processes at the air-sea interface in polar regions. These exchanges strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations, but the importance of these processes remains poorly understood as most numerical models struggle to represent these deformations without using very costly horizontal resolutions ( 2 km). In this study, we present results from a 12 km resolution ocean–sea-ice coupled model, the first that uses a brittle rheology to represent the mechanical behaviour of sea ice. Using this rheology enables the reproduction of the observed characteristics and complexity of fine-scale sea ice deformations with little dependency on the mesh resolution. We evaluate and discuss the Arctic sea ice mass balance of this coupled model for the period 2000–2018. We first assess sea ice quantities relevant for climate (volume, extent and drift) and find that they are consistent with satellite observations. We evaluate components of the mass balance for which observations are available, i.e. sea ice volume export through Fram Strait and winter mass balance in the Arctic marginal seas for the period 2003–2018. The model performs well, particularly for the dynamic contribution to the winter mass balance. We discuss the relative contributions of dynamics and thermodynamics to the sea ice mass balance in the Arctic Basin for 2000–2018. Benefitting from the model's ability to reproduce fine-scale sea ice deformations, we estimate that the formation of sea ice in leads and polynyas contributes to 25 %–35 % of the total ice growth in pack ice from January to March, with a significant increase over 2000–2018. This coupled framework opens up new opportunities to understand and quantify the interplay between small-scale sea ice dynamics and ocean properties.

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Section: Introductionmentioning

confidence: 99%

Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework

Boutin

Ólason²,

Rampal³

et al. 2022

Preprint

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“…We note however that their sea-ice dynamics schemes are all based on Eulerian advection schemes and on variants of the viscous-plastic (VP) rheology (although some solve the rheological equations directly while others solve modified equations as is done with the elastic-viscous-plastic method (EVP)). In this review, Hunke et al (2020) note that the trend is towards fully coupled systems and high resolution. For example, the ECMWF forecast coupled ice and ocean models to their atmospheric model in between the two papers (this system went operational in June 2018) at a resolution of 0.1 • .…”

Section: Introductionmentioning

confidence: 99%

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

et al. 2021

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Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

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“…Such features emerge from sea ice models with isotropic viscous-plastic rheologies, like LIM2, at high resolution (i.e. 4km, Hunke et al, 2020).…”

Section: Discussionmentioning

confidence: 98%

Benefits and challenges of dynamic sea-ice for weather forecasts

Day

Keeley

Arduini

et al. 2022

Preprint

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Abstract. The drive to develop environmental prediction systems that are seamless across both weather and climate timescales has culminated in the development and use of Earth system models, which include a coupled representation of the atmosphere, land, ocean and sea ice, for medium-range weather forecasts. One region where such a coupled Earth system approach has the potential to significantly influence the skill of weather forecasts is in the polar and sub-polar seas, where fluxes of heat, moisture and momentum are strongly influenced by the position of the sea ice edge. In this study we demonstrate that using a dynamically coupled ocean and sea ice model in ECMWF Integrated Forecasting System, results in improved sea ice edge position forecasts in the northern hemisphere in the medium-range. Further, this improves forecasts of boundary layer temperature and humidity downstream of the sea ice edge in some regions during periods of rapid change in the sea ice compared to forecasts in which the sea surface temperature anomalies and sea ice concentration do not evolve throughout the forecasts. Challenges and limitations, such as the quality of ocean and sea ice initial conditions or analyses, and the inability of the coupled system to capture the rate of sea ice concentration change during periods of ice advance and retreat will also be discussed.

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Should Sea-Ice Modeling Tools Designed for Climate Research Be Used for Short-Term Forecasting?

Cited by 27 publications

References 102 publications

Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework

Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework

Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

Benefits and challenges of dynamic sea-ice for weather forecasts

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