Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges

We investigate the ability of viscous‐plastic (VP) sea‐ice models with an elliptical yield curve and normal flow rule to reproduce the shear and divergence distributions derived from the RADARSAT Geophysical Processor System (RGPS). In particular, we reformulate the VP elliptical rheology to allow independent changes in the ice compressive, shear and isotropic tensile strength parameters ( P∗, S∗, and T∗, respectively) in order to study the sensitivity of the deformation distributions to changes in the ice mechanical strength parameters. Our 10 km VP simulation with standard ice mechanical strength parameters P∗ = 27.5 kN m−2, S∗ = 6.9 kN m−2, and T∗ = 0 kN m−2 (ellipse aspect ratio of e = 2) does not reproduce the large shear and divergence deformations observed in the RGPS deformation fields, and specifically lacks well‐defined, active linear kinematic features (LKFs). Probability density functions (PDFs) for the shear and divergence of are nonetheless not Gaussian. Reducing the ice compressive strength (with constant S∗ and T∗) or increasing the ice shear strength (with constant P∗ and T∗) both results in shear and divergence PDFs in better agreement with RGPS distributions. The isotropic tensile strength of sea ice does not significantly affect the shear and divergence distributions. When considering additional metrics such as the ice drift error, mean ice thickness fields, and spatial scaling of the total deformations, our results suggest that reducing the ice compressive strength P∗ (while keeping S∗ constant, i.e. reducing the ellipse aspect ratio) is a better solution than increasing the shear strength to improve simulations of the Arctic sea‐ice cover with the VP elliptical rheology.

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“…These fractions are representative of values used in other studies (e.g., k T = 0.2 by Lemieux et al . [] and

k_{T} = 0.5 - 0.8

by Tremblay and Hakakian []).…”

Section: Resultsmentioning

confidence: 99%

Using sea‐ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice

Bouchat

Tremblay

2017

JGR Oceans

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“…The sea ice model utilized here does produce zero-motion sea ice (e.g., Fig. 4d), but more realistic physical parameterizations (e.g., Lemieux et al, 2016) are not applied in our simulations yet. With such parameterizations, we expect great improvements in simulating the widely existing landfast ice in the CAA region (Melling, 2002;Galley et al, 2012;Haas and Howell, 2015;Howell et al, 2016).…”

Section: Summary and Discussionmentioning

confidence: 99%

“…A no-slip boundary condition is applied for sea ice in the simulations, which means the ice can have zero velocity along the coast. However, it should be noted that the sea ice module used in this study does not include a representation of landfast ice (e.g., Lemieux et al, 2016), which may negatively impact the sea ice simulation where landfast ice exists. Two simulations, ANHA4-CGRF and ANHA12-CGRF, are integrated from 1 January 2002 to 31 December 2016.…”

Section: Numerical Model Setupmentioning

confidence: 99%

Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations

Sun

Chan

et al. 2018

The Cryosphere

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Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest to science, as well as local communities and their economy. In this study, based on the NEMO numerical framework including the LIM2 sea ice module, simulations at both 1/4 and 1/12 • horizontal resolution were conducted from 2002 to 2016. The model captures well the general spatial distribution of ice thickness in the CAA region, with very thick sea ice (∼ 4 m and thicker) in the northern CAA, thick sea ice (2.5 to 3 m) in the west-central Parry Channel and M'Clintock Channel, and thin (< 2 m) ice (in winter months) on the east side of CAA (e.g., eastern Parry Channel, Baffin Island coast) and in the channels in southern areas. Even though the configurations still have resolution limitations in resolving the exact observation sites, simulated ice thickness compares reasonably (seasonal cycle and amplitudes) with weekly Environment and Climate Change Canada (ECCC) New Ice Thickness Program data at first-year landfast ice sites except at the northern sites with high concentration of old ice. At 1/4 to 1/12 • scale, model resolution does not play a significant role in the sea ice simulation except to improve local dynamics because of better coastline representation. Sea ice growth is decomposed into thermodynamic and dynamic (including all non-thermodynamic processes in the model) contributions to study the ice thickness evolution. Relatively smaller thermodynamic contribution to ice growth between December and the following April is found in the thick and very thick ice regions, with larger contributions in the thin ice-covered region. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay while a decline (r 2 ≈ 0.6, p < 0.01) is simulated in Parry Channel region. The two main contributors (thermodynamic growth and lateral transport) have high interannual variabilities which largely balance each other, so that maximum ice volume can vary interannually by ±12 % in the northern CAA, ±15 % in Parry Channel, and ±9 % in Baffin Bay. Further quantitative evaluation is required.

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“…The sea ice model utilized here does produce zero-motion sea ice (e.g., Fig. 3d), however, more realistic physical parameterizations (e.g., Lemieux et al, 2016) are not applied in our simulations yet. Improvements are expected in both the sea ice thickness and 20 dynamics when including such parameterizations in the future.…”

Section: May-10mentioning

confidence: 99%

“…A no-slip boundary condition is applied for sea ice in the simulations, which means the ice can have zero velocity along the coast. However, it should be noted that the sea ice module used in this study does not include a representation of landfast ice (e.g., Lemieux et al, 2016), which may negatively impact the sea ice simulation where 20 landfast ice exists. Two simulations, ANHA4-CGRF and ANHA12-CGRF, are integrated from January 1st 2002 to December 31 2016.…”

Section: Numerical Model Setupmentioning

confidence: 99%

Thermodynamic and Dynamic Ice Thickness Changes in the Canadian Arctic Archipelago in NEMO-LIM2 Numerical Simulations

Hu¹,

Sun²,

Chan³

et al. 2017

Preprint

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Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest. In this study, based on the NEMO numerical frame work including the LIM2 sea ice module, simulations at both 1/4• and 1/12 model resolution does not play a significant role in the sea ice simulation except to improve local dynamics because of better coastline representation. Sea ice growth is decomposed into thermodynamic and dynamic (including all non-thermodynamic 10 processes in the model) contributions to study the ice thickness evolution. Relatively smaller thermodynamic contribution to ice growth between December and the following April is found in the thick and very thick ice regions, with larger contributions in the thin ice covered region. Wavelet analysis of the hourly simulated ice fields clearly shows the thermodynamic contribution have seasonal and diurnal cycles while only the seasonal cycle is significant for the total ice thickness. High frequency changes are found in both fields during the sea ice melting and formation process, particularly in the melting season.

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Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges

Cited by 61 publications

References 30 publications

Using sea‐ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice

Using sea‐ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice

Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations

Thermodynamic and Dynamic Ice Thickness Changes in the Canadian Arctic Archipelago in NEMO-LIM2 Numerical Simulations

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