Finite-Element Sea Ice Model (FESIM), version 2

Danilov, Sergey; Wang, Qiang; Timmermann, Ralph; Iakovlev, Nikolay; Sidorenko, Dmitry; Kimmritz, Madlen; Jung, Thomas; Schröter, Jens

doi:10.5194/gmd-8-1747-2015

Cited by 138 publications

(176 citation statements)

References 24 publications

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“…It works with unstructured triangular meshes, so variable grid resolution can be conveniently applied without the necessity of using traditional nesting. It is coupled to a dynamicthermodynamic sea ice model (Timmermann et al, 2009;Danilov et al, 2015), which is based on the Parkinson and Washington (1979) thermodynamics and uses an updated version of the elastic-viscous-plastic (EVP; Hunke and Dukowicz, 1997) rheology. The sea ice model is discretized on the same surface mesh as the ocean model by using an unstructured-mesh method too.…”

Section: Model Setupmentioning

confidence: 99%

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

et al. 2018

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Abstract. In the framework of developing a global modeling system which can facilitate modeling studies on Arctic Ocean and high-to midlatitude linkage, we evaluate the Arctic Ocean simulated by the multi-resolution Finite Element Sea ice-Ocean Model (FESOM). To explore the value of using high horizontal resolution for Arctic Ocean modeling, we use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 km vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer, in terms of both Atlantic Water (AW) mean state and variability. The deepening and thickening bias of the AW layer, a common issue found in coarse-resolution simulations, is significantly alleviated by using higher resolution. The topographic steering of the AW is stronger and the seasonal and interannual temperature variability along the ocean bottom topography is enhanced in the high-resolution simulation. The high resolution also improves the ocean surface circulation, mainly through a better representation of the narrow straits in the Canadian Arctic Archipelago (CAA). The representation of CAA throughflow not only influences the release of water masses through the other gateways but also the circulation pathways inside the Arctic Ocean. However, the mean state and variability of Arctic freshwater content and the variability of freshwater transport through the Arctic gateways appear not to be very sensitive to the increase in resolution employed here. By highlighting the issues that are independent of model resolution, we address that other efforts including the improvement of parameterizations are still required.

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Section: Model Setupmentioning

confidence: 99%

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

et al. 2018

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“…It comprises a dynamic-thermodynamic sea ice model (Danilov et al, 2015). The ice-shelf component goes back to the Hellmer and Olbers (1989) three-equation model of ice-shelf-ocean interaction with a velocity-dependent parameterization of boundary layer heat and salt fluxes according to Holland and Jenkins (1999).…”

Section: The Ocean Component: Fesommentioning

confidence: 99%

Response to Filchner–Ronne Ice Shelf cavity warming in a coupled ocean–ice sheet model – Part 1: The ocean perspective

Timmermann¹,

Goeller²

2017

Ocean Sci.

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Abstract. The Regional Antarctic ice and Global Ocean (RAnGO) model has been developed to study the interaction between the world ocean and the Antarctic ice sheet. The coupled model is based on a global implementation of the Finite Element Sea-ice Ocean Model (FESOM) with a mesh refinement in the Southern Ocean, particularly in its marginal seas and in the sub-ice-shelf cavities. The cryosphere is represented by a regional setup of the ice flow model RIMBAY comprising the Filchner-Ronne Ice Shelf and the grounded ice in its catchment area up to the ice divides. At the base of the RIMBAY ice shelf, melt rates from FESOM's ice-shelf component are supplied. RIMBAY returns ice thickness and the position of the grounding line. The ocean model uses a pre-computed mesh to allow for an easy adjustment of the model domain to a varying cavity geometry.RAnGO simulations with a 20th-century climate forcing yield realistic basal melt rates and a quasi-stable grounding line position close to the presently observed state. In a centennial-scale warm-water-inflow scenario, the model suggests a substantial thinning of the ice shelf and a local retreat of the grounding line. The potentially negative feedback from ice-shelf thinning through a rising in situ freezing temperature is more than outweighed by the increasing water column thickness in the deepest parts of the cavity. Compared to a control simulation with fixed ice-shelf geometry, the coupled model thus yields a slightly stronger increase in ice-shelf basal melt rates.

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“…Finite Element Sea Ice Model version 2 (FESOM v.2, Danilov et al, 2015) is a dynamic-thermodynamic sea ice model on unstructured meshes. It uses the same triangular meshes as its counterpart ocean model (FESOM, Wang et al, 2014).…”

Section: Fesim V2mentioning

confidence: 99%

An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea ice and solid freshwater

et al. 2016

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The Arctic Ocean simulated in fourteen global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE II) is analyzed. The focus is on the Arctic sea ice extent, the solid freshwater (FW) sources and solid freshwater content (FWC). Available observations are used for model evaluation. The variability of sea ice extent and solid FW budget is more consistently reproduced than their mean state in the models. The descending trend of September sea ice extent is well simulated in terms of the model ensemble mean. Models overestimating sea ice thickness tend to underestimate the descending trend of September sea ice extent. The models underestimate the observed sea ice thinning trend by a factor of two. When averaged on decadal time scales, the variation of Arctic solid FWC is contributed by those of both sea ice production and sea ice transport, which are out of phase in time. The solid FWC decreased in the recent decades, caused mainly by the reduction in sea ice thickness. The models did not simulate the acceleration of sea ice thickness decline, leading to an underestimation of solid FWC trend after 2000. The common model behaviour, including the tendency to underestimate the trend of sea ice thickness and March sea ice extent, remains to be improved. Dear editor,We now submit the revised manuscript of CORE-II Arctic Part 1. The minor comments by the reviewers, including some of the optional suggestions, were considered in the revision.Thank you for your help during the review process. Yours sincerely the authors Cover LetterDear reviewer, Thank you for your helpful comments.In the revised version we add the reference to the new paper of Carmack et al. 2015. (line 45 and footnote 2).The typos like in line 342 of the original version are due to typos in a citation. The citation has been updated. The authorsreply to reviewer 1 Dear reviewer, We addressed most of your optional suggestions in our revision. Thanks for your help.(1)Concerning not using the full record for the trend, although the record to 2007 has acceleration at the end, the subsequent record (to 2015) shows this was a transient. I still recommend using the full record for trend analysis Reply: The trends are calculated and discussed for both periods (1979-2003 and 1979-2007). Table 2 and lines 241-274.(2) Table 1: It would be helpful to have more information on the sea ice models--how many layers, how many thickness categories, is there a ridging parameterization, how is albedo calculated? Also might make sense to group the models in the plots by ice model rather than ocean model.Reply: We have an appendix (Appendix A) now to describe the sea ice models used in the CORE-II simulations. For the ordering of models in tables and figures, we try to keep the same order in both the CORE-II Arctic papers (on solid and liquid freshwater) for a close linkage of the two parts. Reply: We did the analysis and the results are briefly mentioned in the paper now (lines 271-274).(4) Line 282: Not sure that th...

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Finite-Element Sea Ice Model (FESIM), version 2

Cited by 138 publications

References 24 publications

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

Response to Filchner–Ronne Ice Shelf cavity warming in a coupled ocean–ice sheet model – Part 1: The ocean perspective

An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea ice and solid freshwater

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Finite-Element Sea Ice Model (FESIM), version 2

Cited by 138 publications

References 24 publications

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

Response to Filchner–Ronne Ice Shelf cavity warming in a coupled ocean–ice sheet model – Part 1: The ocean perspective

An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea ice and solid freshwater

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Product

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A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4

A 4.5 km resolution Arctic Ocean simulation with the global multi-resolution model FESOM 1.4