Arctic sea ice and freshwater sensitivity to the treatment of the atmosphere‐ice‐ocean surface layer

Roy, François; Chevallier, Matthieu; Smith, G. C. Moore; Dupont, Frédéric; Garric, Gilles; Lemieux, Jean‐François; Lu, Youyu; Davidson, Fraser

doi:10.1002/2014jc010677

Cited by 40 publications

(44 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mean ice thickness in H05 is closer to the ICESat observations, but there is a region of overestimated thickness in the Beaufort Gyre and an underestimation elsewhere which is consistent with our findings from the ice mass balance and ULS measurements and also with results of Roy et al (2015). Finally, the domain total ice volume of the different model simulations is compared to the estimate of the dataassimilative model PIOMAS (Zhang and Rothrock, 2003).…”

Section: Ice Concentration Thickness and Volumesupporting

confidence: 86%

“…Following Roy et al (2015) for our last two hindcasts and because our first ocean layer thickness is relatively small, the ice-ocean drag coefficient is computed by a log-layer assumption using the oceanic first layer thickness and a roughness length scale of 0.03 m as suggested by Maykut and McPhee (1995) which yields a drag coefficient of 2.32 × 10 −2 . The air-ice stress involves a more sophisticated formulation that takes into account the stability of the atmospheric boundary layer.…”

Section: Lim2 and Cice Parametersmentioning

confidence: 99%

“…The air-ice stress involves a more sophisticated formulation that takes into account the stability of the atmospheric boundary layer. Following again Roy et al (2015), the roughness length scale for ice surface is set in our latest run to the value used in the Canadian GDPS reforecasts (CGRF; Smith et al, 2014) for consistency between the air-ice stress computed in CGRF and in CICE. These modi-fications can be seen as a more objective way of deriving the drag coefficients, as they are not retrieved from a calibration exercise.…”

Section: Lim2 and Cice Parametersmentioning

confidence: 99%

“…Hence H02 and higher are started instead from ORCA12-T321. Changes related to air-ice and ice-ocean drags based on Roy et al (2015) were incrementally implemented in H03 to H05. Parameters are defined in Sect.…”

Section: Simulationsmentioning

confidence: 99%

See 3 more Smart Citations

A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans

et al. 2015

View full text Add to dashboard Cite

Abstract. As part of the CONCEPTS (Canadian Operational Network of Coupled Environmental PredicTion Systems) initiative, a high-resolution (1/12 • ) ice-ocean regional model is developed covering the North Atlantic and the Arctic oceans. The long-term objective is to provide Canada with short-term ice-ocean predictions and hazard warnings in ice-infested regions. To evaluate the modelling component (as opposed to the analysis -or data-assimilation -component, which is not covered in this contribution), a series of hindcasts for the period 2003-2009 is carried out, forced at the surface by the Canadian GDPS reforecasts (Smith et al., 2014). These hindcasts test how the model represents upper ocean characteristics and ice cover. Each hindcast implements a new aspect of the modelling or the ice-ocean coupling. Notably, the coupling to the multi-category ice model CICE is tested. The hindcast solutions are then assessed using a verification package under development, including in situ and satellite ice and ocean observations. The conclusions are as follows: (1) the model reproduces reasonably well the time mean, variance and skewness of sea surface height; (2) the model biases in temperature and salinity show that while the mean properties follow expectations, the Pacific Water signature in the Beaufort Sea is weaker than observed; (3) the modelled freshwater content of the Arctic agrees well with observational estimates; (4) the distribution and volume of the sea ice are shown to be improved in the latest hindcast due to modifications to the drag coefficients and to some degree to the ice thickness distribution available in CICE; (5) nonetheless, the model still overestimates the ice drift and ice thickness in the Beaufort Gyre.

show abstract

Section: Ice Concentration Thickness and Volumesupporting

confidence: 86%

Section: Lim2 and Cice Parametersmentioning

confidence: 99%

Section: Lim2 and Cice Parametersmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In addition, higher-resolution regional coupled atmosphere-ocean-ice models are providing improved skill and starting to be used operationally (Pellerin et al, 2004;Smith et al, 2013); while climate and Earth system models are also increasing in resolution and these will all require accurate surface exchange over the MIZ. Recent ocean-ice and atmosphere-ocean-ice modelling studies have demonstrated considerable sensitivity to surface exchange parametrisation over sea ice, particularly in their simulations of sea-ice thickness and extent (Tsamados et al, 2014;Rae et al, 2014) and the polar ocean (Stössel et al, 2008;Roy et al, 2015). Sim-…”

Section: Introductionmentioning

confidence: 99%

Observations of surface momentum exchange over the marginal ice zone and recommendations for its parametrisation

et al. 2016

View full text Add to dashboard Cite

Abstract. Comprehensive aircraft observations are used to characterise surface roughness over the Arctic marginal ice zone (MIZ) and consequently make recommendations for the parametrisation of surface momentum exchange in the MIZ. These observations were gathered in the Barents Sea and Fram Strait from two aircraft as part of the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCA-CIA) project. They represent a doubling of the total number of such aircraft observations currently available over the Arctic MIZ. The eddy covariance method is used to derive estimates of the 10 m neutral drag coefficient (C DN10 ) from turbulent wind velocity measurements, and a novel method using albedo and surface temperature is employed to derive ice fraction. Peak surface roughness is found at ice fractions in the range 0.6 to 0.8 (with a mean interquartile range in C DN10 of 1.25 to 2.85 × 10 −3 ). C DN10 as a function of ice fraction is found to be well approximated by the negatively skewed distribution provided by a leading parametrisation scheme (Lüpkes et al., 2012) tailored for sea-ice drag over the MIZ in which the two constituent components of drag -skin and form drag -are separately quantified. Current parametrisation schemes used in the weather and climate models are compared with our results and the majority are found to be physically unjustified and unrepresentative. The Lüpkes et al. (2012) scheme is recommended in a computationally simple form, with adjusted parameter settings. A good agreement holds for subsets of the data from different locations, despite differences in sea-ice conditions. Ice conditions in the Barents Sea, characterised by small, unconsolidated ice floes, are found to be associated with higher C DN10 values -especially at the higher ice fractions -than those of Fram Strait, where typically larger, smoother floes are observed. Consequently, the important influence of sea-ice morphology and floe size on surface roughness is recognised, and improvement in the representation of this in parametrisation schemes is suggested for future study.

show abstract

The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study

et al. 2016

View full text Add to dashboard Cite

The Arctic sea ice cover is thinning and retreating, causing changes in surface roughness that in turn modify the momentum flux from the atmosphere through the ice into the ocean. New model simulations comprising variable sea ice drag coefficients for both the air and water interface demonstrate that the heterogeneity in sea ice surface roughness significantly impacts the spatial distribution and trends of ocean surface stress during the last decades. Simulations with constant sea ice drag coefficients as used in most climate models show an increase in annual mean ocean surface stress (0.003 N/m2 per decade, 4.6%) due to the reduction of ice thickness leading to a weakening of the ice and accelerated ice drift. In contrast, with variable drag coefficients our simulations show annual mean ocean surface stress is declining at a rate of −0.002 N/m2 per decade (3.1%) over the period 1980–2013 because of a significant reduction in surface roughness associated with an increasingly thinner and younger sea ice cover. The effectiveness of sea ice in transferring momentum does not only depend on its resistive strength against the wind forcing but is also set by its top and bottom surface roughness varying with ice types and ice conditions. This reveals the need to account for sea ice surface roughness variations in climate simulations in order to correctly represent the implications of sea ice loss under global warming.

show abstract

Arctic sea ice and freshwater sensitivity to the treatment of the atmosphere‐ice‐ocean surface layer

Cited by 40 publications

References 65 publications

A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans

A high-resolution ocean and sea-ice modelling system for the Arctic and North Atlantic oceans

Observations of surface momentum exchange over the marginal ice zone and recommendations for its parametrisation

The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study

Contact Info

Product

Resources

About