Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice*

Tsamados, Michel; Feltham, D. L.; Schröder, David; Flocco, Daniela; Farrell, S. L.; Kurtz, N. T.; Laxon, Seymour W.; Bacon, Sheldon

doi:10.1175/jpo-d-13-0215.1

Cited by 187 publications

(306 citation statements)

References 53 publications

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“…We apply the form drag parameterization of Tsamados et al, (2014) in addition to the release of more melt water (CICE-mw-form). Ice thickness increased slightly with respect to CICE-mw due to a reduced ice drift speed resulting in a weaker ocean-ice heat flux and less 10 ice export.…”

Section: Improving Cice Simulation By Varying Model Physicsmentioning

confidence: 99%

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New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone 10 simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of 15 positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50% of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does 20 not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

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Section: Improving Cice Simulation By Varying Model Physicsmentioning

confidence: 99%

“…Y Y CICE-mw-form Instead of a constant drag coefficient for the momentum fluxes between atmosphere and ice (CDa = 1.3 x 10-3) and between ice and ocean (CDo = 5.36 x 10-3), the form drag parametrization of Tsamados et al (2014) is applied accounting for the impact of pressure ridges, keels, ice floe and melt pond edges.…”

Section: Cice-mwmentioning

confidence: 99%

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Feltham¹,

Schröder²,

Tsamados³

2018

Preprint

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“…Calculations of atmospheric form drag require estimates of the surface feature height (as presented in this study), along with the surface feature density (e.g. Arya, 1973;Tsamados et al, 2014). Linear profiling studies calculating atmospheric form drag (e.g.…”

Section: Feature Geometry and The Potential For Additional Feature Chmentioning

confidence: 99%

“…In regions where the sail and keel density is high, the resultant obstructions to fluid flow (form drag) are thought to dominate the total drag on the ice cover over frictional (skin drag) effects (Arya, 1973;Leonardi et al, 2003;Tsamados et al, 2014). Ice deformation also impacts the internal strength of the ice pack, further altering the momentum transfer between the atmosphere and ocean (Martin et al, 2014).…”

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confidence: 99%

Characterizing Arctic sea ice topography using high-resolution IceBridge data

et al. 2016

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Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes.The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

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“…Following steps would be implementing new generation rheologies (e.g., Bouillon et al, 2009;Sulsky and Peterson, 2011;Wilchinsky and Feltham, 2012;Tsamados et al, 2013;Dansereau et al, 550 2014) into climate models and re-apply our methodology to assess whether such new physics improves the ice drift and the ice concentration budget terms in mechanically constrained regions near the coast of Antarctica. Improving the ice-atmosphere boundary layer physics through the use of variable atmospheric drag coefficient parameterizations (e.g., Tsamados et al, 2014), for instance, 555 may on the other hand affect the sea ice dynamics in all Antarctic sea ice covered regions. Regarding models specifically missing ice velocity divergence in winter, a thorough evaluation of local winds in their atmospheric component may also be required.…”

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confidence: 99%

Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

et al. 2016

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Highlights• The ice concentration budget (ICB) is applied to several forced and coupled models.• Ice drift and 10 m wind vectors are evaluated against observations and reanalyses.• Biases in winds are largely responsible for biases in the ICB in free drift regions.• Errors in winds and sea ice model physics are equally important closer to the coast.• The method provides a new tool for climate model intercomparisons. AbstractWe derive the terms in the Antarctic sea ice concentration budget from the output of three models, and compare them to observations of the same terms. Those models include two climate models from the 5th Coupled Model Intercomparison Project (CMIP5) and one ocean-sea ice coupled model with prescribed atmospheric forcing. Sea ice drift and wind fields from those models, in average over April-October 1992-2005, all exhibit large differences with the available observational or reanalysis datasets. However, the discrepancies between the two distinct ice drift products or the two wind reanalyses used here are sometimes even greater than those differences. Two major findings stand out from the analysis. Firstly, large biases in sea ice drift speed and direction in exterior sectors of the sea ice covered region tend to be systematic and consistent with those in winds. This suggests that sea ice errors in these areas are most likely wind-driven, so as errors in the simulated ice motion vectors. The systematic nature of these biases is less prominent in interior sectors, nearer the coast, where sea ice is mechanically constrained and its motion in response to the wind forcing more depending on the model rheology. Second, the intimate relationship between winds, sea ice drift and the sea ice concentration budget gives insight on ways to categorize models with regard to errors in their ice dynamics. In exterior regions, models with seemingly too weak winds and slow ice drift consistently yield a lack of ice velocity divergence and hence a wrong wintertime sea ice growth rate. In interior sectors, too slow ice drift, presumably originating from issues in the physical representation of sea ice dynamics as much as from errors in surface winds, leads to wrong timing of the late winter ice retreat. Those results illustrate that the applied methodology provides a valuable tool for prioritizing model improvements based on the ice concentration budget-ice * Corresponding author

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Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice*

Cited by 187 publications

References 53 publications

New insight from CryoSat-2 sea ice thickness for sea ice modelling

New insight from CryoSat-2 sea ice thickness for sea ice modelling

Characterizing Arctic sea ice topography using high-resolution IceBridge data

Impact of surface wind biases on the Antarctic sea ice concentration budget in climate models

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