Changes in Arctic Stratification and Mixed Layer Depth Cycle: A Modeling Analysis

Abstract. In this study, a new method to assimilate satellite radar altimetry derived freeboard instead of sea ice thickness is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic. In order to quantify the improvement in sea ice thickness gained by assimilating freeboard, we compare three different model runs. One reference run (refRun), one that assimilates only SIC (sicRun) and one that assimilates both SIC and FB (fbRun). It is shown that, estimates for both SIC and FB can be improved by assimilation, but only the fbRun improved the sea ice thickness estimates. The resulting sea ice thickness is evaluated by comparing it to Alfred Wegener Institute's (AWI) weekly CryoSat-2 sea ice thickness data product, which is based on the same FB observations as were assimilated in this study. It is shown that the sea ice thickness from the fbRun is closer to the traditional CryoSat-2 sea ice thickness than sea ice thickness from refRun or sicRun. Additionally, we compare independent sea ice draft measurements from the Beaufort Gyre Exploration Project to both fbRun sea ice thickness and observed CryoSat-2 sea ice thickness. This comparison shows that our new method provides equally good results as the AWI weekly CryoSat-2 product; in two of three locations even better results.

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“…The model domain is pan Arctic as shown by the red area in figure 1. NEMO is set up following (Hordoir et al, 2022).…”

Section: Coupled Ocean and Sea Ice Modelmentioning

confidence: 99%

Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates

Sievers

Rasmussen

Stenseng

2023

Preprint

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“…The Beaufort Gyre has been in a persistent anticyclonic state since 1997, resulting in an accumulation of 6,400 km 3 of liquid freshwater from 2003 to 2018 alone (the period of high-quality oceanographic observations; Proshutinsky et al, 2019). There has been an associated increase in Beaufort Gyre stratification, contrasting with the decrease in Eurasian Basin stratification due to Atlantification (Hordoir et al, 2022). J. studied the potential impacts of releasing this freshwater by looking at past analogs of freshwater accumulation and release episodes by the Beaufort Gyre.…”

Section: Impacts Of the Arctic On The Amocmentioning

confidence: 99%

Interactions between the Arctic Mediterranean and the Atlantic Meridional Overturning Circulation: A Review

Weijer

Haine

Siddiqui

et al. 2022

Oceanog

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The Atlantic Meridional Overturning Circulation (AMOC) plays a significant role in the global climate system, and its behavior in a warming climate is a matter of significant concern. The AMOC is thought to be driven largely by ocean heat loss in the subpolar North Atlantic Ocean, but recent research increasingly emphasizes the importance of the Arctic Mediterranean for the AMOC. In turn, the AMOC may influence the Arctic heat budget through its impact on poleward heat transport. Hence, understanding the processes that link the AMOC and the Arctic is critical for our ability to project how both may evolve in a warming climate. In this paper we review some of the recent research that is shaping our thinking about the AMOC and its two-way interactions with the Arctic.

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“…The ocean model NEMO (Madec et al, 2017) is widely used in the modelling community for both regional (Le Sommer et al, 2019) and global ocean simulations, climate prediction, hind casts (Hazeleger et al, 2012) and modelling of marginal seas (Hordoir et al, 2019) to only name a few applications. The set up used in this paper closely follows Hordoir et al (2022) and uses similar forcing and namelist settings. Changes were made to the namelist due to a version update and the atmospheric forcing differs.…”

Section: Ocean Modelmentioning

confidence: 99%

Arctic sea ice and snow from different ice models: A CICE–SI3 intercomparison study

Sievers

Gierisch

Rasmussen

et al. 2022

Preprint

Self Cite

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Abstract. Sea ice models fill many purposes. They are used in global climate models or for short term forecasts to plan shipping routes. No matter what their output is used for, understanding the cause for there variability is crucial. In the this study two commonly used sea-ice models, CICE and SI3 were compared after running both models with similar boundary condition, on the same grid, with the same forcing and initialised with the same data, with the aim to understand how the two models differ from each other when forced equally. The set-up also allows linking certain model biases observed in both models to the external forcing. We found that the models compare well to sea ice concentration, sea ice thickness and snow thickness observations, with small regional differences, which could be linked to different model processes. The processes with the highest influence are the drag formulation, the albedo, and the treatment of snow. We find that the treatment of snow has a significant influence on the difference in sea ice thickness between the models, even though their forcing is equal.

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Changes in Arctic Stratification and Mixed Layer Depth Cycle: A Modeling Analysis

Cited by 38 publications

References 57 publications

Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates

Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates

Interactions between the Arctic Mediterranean and the Atlantic Meridional Overturning Circulation: A Review

Arctic sea ice and snow from different ice models: A CICE–SI3 intercomparison study

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