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Ocean Science An interactive open-access journal of the European Geosciences Union

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Ocean Sci., 13, 61-75, 2017
http://www.ocean-sci.net/13/61/2017/
doi:10.5194/os-13-61-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
23 Jan 2017
Numerical investigation of the Arctic ice–ocean boundary layer and implications for air–sea gas fluxes
Arash Bigdeli1, Brice Loose1, An T. Nguyen2, and Sylvia T. Cole3 1Graduate School of Oceanography, University of Rhode Island, Rhode Island, 02882, USA
2Institute of Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, 78712, USA
3Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, 02543, USA
Abstract. In ice-covered regions it is challenging to determine constituent budgets – for heat and momentum, but also for biologically and climatically active gases like carbon dioxide and methane. The harsh environment and relative data scarcity make it difficult to characterize even the physical properties of the ocean surface. Here, we sought to evaluate if numerical model output helps us to better estimate the physical forcing that drives the air–sea gas exchange rate (k) in sea ice zones. We used the budget of radioactive 222Rn in the mixed layer to illustrate the effect that sea ice forcing has on gas budgets and air–sea gas exchange. Appropriate constraint of the 222Rn budget requires estimates of sea ice velocity, concentration, mixed-layer depth, and water velocities, as well as their evolution in time and space along the Lagrangian drift track of a mixed-layer water parcel. We used 36, 9 and 2 km horizontal resolution of regional Massachusetts Institute of Technology general circulation model (MITgcm) configuration with fine vertical spacing to evaluate the capability of the model to reproduce these parameters. We then compared the model results to existing field data including satellite, moorings and ice-tethered profilers. We found that mode sea ice coverage agrees with satellite-derived observation 88 to 98 % of the time when averaged over the Beaufort Gyre, and model sea ice speeds have 82 % correlation with observations. The model demonstrated the capacity to capture the broad trends in the mixed layer, although with a significant bias. Model water velocities showed only 29 % correlation with point-wise in situ data. This correlation remained low in all three model resolution simulations and we argued that is largely due to the quality of the input atmospheric forcing. Overall, we found that even the coarse-resolution model can make a modest contribution to gas exchange parameterization, by resolving the time variation of parameters that drive the 222Rn budget, including rate of mixed-layer change and sea ice forcings.

Citation: Bigdeli, A., Loose, B., Nguyen, A. T., and Cole, S. T.: Numerical investigation of the Arctic ice–ocean boundary layer and implications for air–sea gas fluxes, Ocean Sci., 13, 61-75, doi:10.5194/os-13-61-2017, 2017.
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Short summary
We evaluated if numerical model output helps us to better estimate the physical forcing that drives the air–sea gas exchange rate (k) in sea ice zones. We used 36, 9 and 2 km horizontal resolution of regional MITgcm configuration with fine vertical spacing to evaluate the capability of the model to reproduce sea ice velocity, concentration, mixed layer depth and water velocities. We found that even the coarse-resolution model can make a modest contribution to gas exchange parameterization.
We evaluated if numerical model output helps us to better estimate the physical forcing that...
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