Numerical simulations of ice thickness redistribution in the Gulf of St. Lawrence

Kubat, Ivana; Sayed, Mohamed; Savage, Stuart B.; Carrières, Tom

doi:10.1016/j.coldregions.2009.07.003

Cited by 12 publications

(10 citation statements)

References 15 publications

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“…Our results support the notion that deformation in shear is a key factor in shaping different ITDs in the Arctic and that stronger ridging caused by shear (i.e., a larger value of C s = 0.85) is required to improve the model simulations with respect to observed ITDs. At the same time, we note that with a redistribution scheme tuned toward direct observations of deformation events as in Kubat et al (2010) it should be possible to constrain uncertain shear parameters such as e with the increasing number of regional ITD observations.…”

Section: Sea Ice Deformationmentioning

confidence: 99%

“…A coastal draft distribution model, forced with high‐precision meteorological observations obtained at the coast, was found to be largely consistent with draft observations from moorings, but produced excessive ridging (Bellchamber‐Amundrud et al, ). A new redistribution model very accurately simulated observed ITDs from high‐resolution field data in the Gulf of St. Lawrence, but the simulation and observation period covered only individual strong deformation events (a storm) over a few days (Kubat et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean

Ungermann

Lösch

2018

JGR Oceans

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A key parameterization in sea ice models describes the subgrid scale ice thickness distribution. Based on only a few observations, the ice thickness distribution model was shown to be consistent with field data and to improve the simulation's large‐scale properties. The available submarine and airborne observations enable to evaluate in greater detail the ability of a pan‐Arctic sea ice‐ocean model with an ice thickness distribution parameterization to reproduce observed thickness distributions in different regions and seasons. Many observations are reproduced accurately. Some cases of poorly simulated modes and tails of the distributions are tentatively attributed to simplified thermodynamics and inaccurate deformation fields. Variability on decadal timescales, however, is generally underestimated. Thickness distributions in individual grid cells of the model show similar differences between regions and seasons as observed regional mean distributions, but the modeled grid‐scale variability is lower than observed. Simulated modal thicknesses of first‐year ice are only insufficiently different from those of multiyear ice. The modal thickness proves to be a useful metric for quantifying model biases in both dynamics and thermodynamics. In addition to improving basin‐wide mean variables, the ice thickness distribution parameterization provides reliable and valuable additional subgrid scale data. At the same time the low climate sensitivity of the parameterization may affect longer simulations with strong climate change aspects.

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Section: Sea Ice Deformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean

Ungermann

Lösch

2018

JGR Oceans

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“…Savage (2008) implemented a twocomponent sea-ice redistribution model for undeformed level ice and deformed (ridged) ice, which is based on the mixture model of Gray and Morland (1994) and formulated for general deformation fields (including shear). As part of the Canadian Ice Service's ice forecasting model, the thickness redistribution after Savage (2008) is coded using an advection method that is based on a particle-in-cell approach and avoids discrete thickness classes (Kubat et al, 2010); the model has then been evaluated in several studies in the Canadian Arctic, for the Gulf of St. Lawrence using 2004 field observations of ridges (Prinsenberg et al, 2006), for Frobisher Bay using observations of besetting events that occurred in 2012, and for Strait of Belle Isle (Kubat et al, 2010(Kubat et al, , 2011(Kubat et al, , 2012(Kubat et al, , 2013. Forecasting of ridging events is important because pressure ridges cause a threat to shipping lines in the Arctic.…”

Section: Summary Of the Approach To Model -Data Comparisonmentioning

confidence: 99%

Sea ice deformation in Fram Strait — Comparison of CICE simulations with analysis and classification of airborne remote-sensing data

Herzfeld

Hunke

McDonald

et al. 2015

Cold Regions Science and Technology

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Complex surface topography is a characteristic especially of older sea ice, and an observed loss of older or multiyear sea ice in the Arctic indicates an imminent transition of the Arctic ice cover from perennial to seasonal. Prediction of this transition in the Arctic system is one of today's "big science" questions. The objective of this paper is to compare model output and data analysis, toward addressing a key problem in sea-ice modeling, the correct representation of ridges and other spatial features that result from deformation. Morphologically complex, ridged ice exists in Fram Strait. High-resolution airborne remote sensing data, including image data and altimeter data showing ridging, collected from unmanned aircraft over Fram Strait during the Characterization of Arctic Sea Ice Experiment (CASIE) in 2009, are analyzed using geostatistical classification. This approach results in parameters that capture deformation characteristics and facilitates comparison to model results. Ridging and other forms of deformation are implemented in the Los Alamos sea-ice model CICE. The main parameters that are compared are freeboard as a proxy of ice thickness and percentages of level versus ridged ice from modeling and laser altimeter data analysis. Results from freeboard analysis indicate that except for the elevation class 0.1 m-0.2 m, models and observations match very well. For concentration of deformed ice, results from CICE using the standard parameter configuration are within 20% of deformed ice area concentration compared to results from altimeter data analysis. Variation of several physical parameters indicates the sensitivity of model results to ridging parameters and provides results that are within 7% of data analysis results in each grid cell, with the parameter that yields the best match depending on geographic location and morphologic province. In general our approach demonstrates an avenue for parameterization on both the data analysis side and the modeling side that allows a direct comparison of results from sea-ice models and data analysis and hence an evaluation of numerical sea-ice models.

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“…where  is the density ( a = 1.2 kg/m 3 ;  w = 1020 kg/m 3 ), C is the drag coefficient (C a ~ 0.002, C w ~ 0.005; see e.g. Kubat et al 2010) and v is the speed. For the Beaufort Sea region, a typical current speed is on the order of 0.15 m/s.…”

Section: The Simple Approachmentioning

confidence: 99%

A critical review of Arctic pack ice driving forces: New sources of data

Timco

Sudom

Frederking

et al. 2017

Cold Regions Science and Technology

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This paper provides the details of ice loading events that can be used to further the understanding of pack ice driving forces in the Beaufort Sea. Several methods have been reviewed and employed including in situ stress measurements, loads on the Molikpaq offshore caisson, shoreline pileup events, pileups and rubble fields on offshore shoals and relic berms, analysis of shear walls on offshore rubble fields, and analysis of deep ridge keels. Over 50 different events are identified with 33 suitable for a pack ice analysis. The data are considered in terms of both the ISO 19906 (2010) Arctic Structures Standard and pack ice pressures that can be exerted across various widths. A new approach is proposed, in which the calculated and measured values from past pack ice pressure events are used to predict limit force.

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Numerical simulations of ice thickness redistribution in the Gulf of St. Lawrence

Cited by 12 publications

References 15 publications

An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean

An Observationally Based Evaluation of Subgrid Scale Ice Thickness Distributions Simulated in a Large‐Scale Sea Ice‐Ocean Model of the Arctic Ocean

Sea ice deformation in Fram Strait — Comparison of CICE simulations with analysis and classification of airborne remote-sensing data

A critical review of Arctic pack ice driving forces: New sources of data

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