Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea‐Ice Deformation Fields

Bouchat, Amélie; Hutter, Nils; Chanut, Jérôme; Dupont, Frédéric; Dukhovskoy, Dmitry S.; Garric, Gilles; Lee, Younjoo; Lemieux, Jean‐François; Lique, Camille; Lösch, Martin; Maslowski, Wieslaw; Myers, Paul G.; Ólason, Einar; Rampal, Pierre; Rasmussen, Tue Kruse; Talandier, Claude; Tremblay, Bruno; Wang, Qiang

doi:10.1029/2021jc017667

Cited by 30 publications

(28 citation statements)

References 97 publications

(215 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In these simulations, the yield curve parameters have an impact on the LKF quality. For all models we find that simulations with a reduced ellipse ratio show improved LKF densities compared to the simulations with the standard ellipse ratio e = 2, as one could expect from the more localized deformation in these simulations (Bouchat & Tremblay, 2017; Bouchat et al., 2022b). For other LKF statistics the effects vary from model to model and might therefore depend on other configuration parameters that are different between the models, for instance resolution.…”

Section: Relationship Between Model Configuration Parameters and Defo...supporting

confidence: 59%

“… Relationship between skill metrics of the scaling analysis of deformation rates (Sea Ice Rheology Experiment Part I, Bouchat et al., 2022b) and the skill metrics of the Linear kinematic feature statistics. All skill metrics are normalized.…”

Section: Relationship Between Lkf Statistics and The Scaling Analysis...mentioning

confidence: 99%

“…There are indications, however, that the computed scaling exponents are linked to the number of LKFs, but do not provide insights into other LKF statistics such as LKF lifetime or intersection angles (Hutter & Losch, 2020). In this section, we study if the deformation statistics obtained in SIREx Part I (Bouchat et al., 2022b) are linked to the LKF statistics in this study. To this end, we define skill metrics for the deformation rate PDFs (as the sum of the integrated area between the divergence and shear PDF of a SIREx simulation and RGPS) and for all scaling parameters (squared relative error of the parameter compared to RGPS, e.g.,

{\left(\left({\beta }_{model}-{\beta }_{RGPS}\right)/{\beta }_{RGPS}\right)}^{2}

).…”

Section: Relationship Between Lkf Statistics and The Scaling Analysis...mentioning

confidence: 99%

“…The results of this comparison are interpreted in light of the different model parameters and parameterizations (e.g., rheology or spatial resolution) to assess the impact and importance of individual parameters on the quality of resolved LKFs. We link our feature‐based evaluation to the scaling analysis of SIREx Part I (Bouchat et al., 2022b) and to an analysis of sea ice thickness and concentration anomalies along resolved LKFs. This forms an in‐depth comparison of different dynamical modeling frameworks for sea ice and their capabilities for simulating localized deformation along floe boundaries.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations

et al. 2022

Self Cite

View full text Add to dashboard Cite

Simulating sea ice drift and deformation in the Arctic Ocean is still a challenge because of the multiscale interaction of sea ice floes that compose the Arctic Sea ice cover. The Sea Ice Rheology Experiment (SIREx) is a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). In SIREx, skill metrics are designed to evaluate different recently suggested approaches for modeling linear kinematic features (LKFs) to provide guidance for modeling small‐scale deformation. These LKFs are narrow bands of localized deformation that can be observed in satellite images and also form in high resolution sea ice simulations. In this contribution, spatial and temporal properties of LKFs are assessed in 36 simulations of state‐of‐the‐art sea ice models and compared to deformation features derived from the RADARSAT Geophysical Processor System. All simulations produce LKFs, but only very few models realistically simulate at least some statistics of LKF properties such as densities, lengths, or growth rates. All SIREx models overestimate the angle of fracture between conjugate pairs of LKFs and LKF lifetimes pointing to inaccurate model physics. The temporal and spatial resolution of a simulation and the spatial resolution of atmospheric boundary condition affect simulated LKFs as much as the model's sea ice rheology and numerics. Only in very high resolution simulations (≤2 km) the concentration and thickness anomalies along LKFs are large enough to affect air‐ice‐ocean interaction processes.

show abstract

Section: Relationship Between Model Configuration Parameters and Defo...supporting

confidence: 59%

Section: Relationship Between Lkf Statistics and The Scaling Analysis...mentioning

confidence: 99%

{\left(\left({\beta }_{model}-{\beta }_{RGPS}\right)/{\beta }_{RGPS}\right)}^{2}

).…”

Section: Relationship Between Lkf Statistics and The Scaling Analysis...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The role of resolution in reproducing small-scale features in (E)VP models is being actively investigated in the community as well as that of modifying parameters and parameterizations within sea ice models (e.g., J. Zhang, 2021;Bouchat et al, 2022;Hutter et al, 2022).…”

Section: Simulating the 2013 Sea Ice Breakup Eventmentioning

confidence: 99%

Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea

Rheinlænder

Davy

Ólason

et al. 2022

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Arctic sea ice is thinning (Meier, 2017) in conjunction with the decrease in the area covered by thick multiyear ice (MYI) (Kwok, 2018), which is replaced by thinner first-year ice (FYI) that is more mobile and less dynamically stable (Rampal et al., 2009;J. Zhang et al., 2012). This makes the ice cover more vulnerable to intense winds breaking up the sea ice. In the Beaufort Sea in particular, the loss of MYI may contribute to the earlier onset of the melt season in recent years (Johnson & Eicken, 2016).When sea ice breaks up, it exposes the underlying warmer ocean within narrow, linear openings in the ice cover known as leads. This has important consequences for air-sea exchange, ocean eddy generation and dynamics, sea ice production, and Arctic Ocean properties in general (Cohanim et al., 2021;Graham et al., 2019;Nguyen et al., 2009), especially during the winter months when heat fluxes over sea ice are generally small (Andreas & Cash, 1999). In addition, breakup in winter weakens the ice cover, potentially preconditioning the minimum ice extent in summer (Y. Zhang et al., 2018;Babb et al., 2019) and thus creating a positive feedback to Arctic amplification (Dai et al., 2019). Therefore, extreme breakup events are of crucial interest for understanding the seasonal and long-term evolution of Arctic sea ice, which in turn affect weather, ecosystems, and local communities in polar regions and beyond (Forbes et al., 2016;Vihma, 2014).

show abstract

Lake ice simulation using a 3D unstructured grid model

Zhang¹,

Anderson

Danilov

et al. 2023

Ocean Dynamics

Self Cite

View full text Add to dashboard Cite

We develop a single-class ice and snow model embedded inside a 3D hydrodynamic model on unstructured grids and apply it to lake studies using highly variable mesh resolution. The model is able to reasonably capture the ice fields observed in both small and large lakes. For the first time, we attempt simulation of ice processes on very small scales (~ 1 m). Physically sound results are obtained at the expense of moderately increased computational cost, although more rigorous validation nearshore is needed due to lack of observation. We also outline challenges on developing new process-based capabilities for accurately simulating nearshore ice.

show abstract

Sea Ice Rheology Experiment (SIREx): 1. Scaling and Statistical Properties of Sea‐Ice Deformation Fields

Cited by 30 publications

References 97 publications

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations

Sea Ice Rheology Experiment (SIREx): 2. Evaluating Linear Kinematic Features in High‐Resolution Sea Ice Simulations

Driving Mechanisms of an Extreme Winter Sea Ice Breakup Event in the Beaufort Sea

Lake ice simulation using a 3D unstructured grid model

Contact Info

Product

Resources

About