Characterizing sudden changes in Arctic sea ice drift and deformation on synoptic timescales

Abstract. In this study, we develop a framework for the assessment of sudden changes in sea ice drift and associated deformation processes in response to atmospheric forcing and ice–coastal interactions, based on analysis of ice buoy triplet centroids and areas. Examined in particular is the spatiotemporal evolution in sea ice floes that are tracked with GPS beacons deployed in triplets in the southern Beaufort Sea at varying distances from the coastline in fall, 2009 – triplets A to D, with A (D) located closest to (furthest from) the coastline. This study illustrates the use of shock-response diagnostics to evaluate eight identified sudden changes or shock events on daily timescales. Results from this analysis show that shock events in the southern Beaufort Sea occur in at least one of two forms: (1) during a reversal in winds, or (2) sustained north/easterly winds, with response mechanisms governed by ice conditions and interactions with the coastline. Demonstrated also is the emergence of a shear-shock event (SSE) that results in reduced ice concentrations for triplets B, C, and D, one, three and five days following the SSE, respectively and loss of synchronicity in ice-atmosphere interactions. The tools developed in this study provide a unique characterization of sea ice dynamical processes in the southern Beaufort Sea, with implications for quantifying "shock-response" systems relevant for ice hazard assessments and forecasting applications required by oil and gas, marine transportation, and indigenous use of near shore Arctic areas.

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Landfast ice zoning from SAR imagery

10.5194/egusphere-egu2020-22541 ◽

2020 ◽

Author(s):

Valeria Selyuzhenok ◽

Denis Demchev ◽

Thomas Krumpen

Keyword(s):

Time Series ◽

Sea Ice ◽

Ice Cover ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Drift ◽

Landfast Ice ◽

Sea Ice Drift ◽

Ice Conditions ◽

Sar Imagery

<p>Landfast sea ice is a dominant sea ice feature of the Arctic coastal region. As a part of Arctic sea ice cover, landfast ice is an important part of coastal ecosystem, it provides functions as a climate regulator and platform for human activity. Recent changes in sea ice conditions in the Arctic have also affected landfast ice regime. At the same time, industrial interest in the Arctic shelf seas continue to increase. Knowledge on local landfast ice conditions are required to ensure safety of on ice operations and accurate forecasting.&#160; In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.&#160; An automatic sea ice tracking algorithm was applied to the sequential SAR images during the development stage of landfast ice cover. The analysis of resultant time series of sea ice drift allows to classify homogeneous sea ice drift fields and timing of their attachment to the landfast ice. In addition, the drift data allows to locate areas of formation of grounded sea ice accumulation called stamukha. This information &#1089;an be useful for local landfast ice stability assessment. The study is supported by the Russian Foundation for Basic Research (RFBR) grant 19-35-60033.</p>

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Method to characterize directional changes in Arctic sea ice drift and associated deformation due to synoptic atmospheric variations using Lagrangian dispersion statistics

The Cryosphere ◽

10.5194/tc-11-1707-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1707-1731 ◽

Cited By ~ 1

Author(s):

Jennifer V. Lukovich ◽

Cathleen A. Geiger ◽

David G. Barber

Keyword(s):

Sea Ice ◽

Single Particle ◽

Weather Forecasting ◽

Particle Dispersion ◽

Arctic Sea Ice ◽

Maximum Pressure ◽

Dynamical Regime ◽

Scaling Exponent ◽

Ice Drift ◽

Sea Ice Drift

Abstract. A framework is developed to assess the directional changes in sea ice drift paths and associated deformation processes in response to atmospheric forcing. The framework is based on Lagrangian statistical analyses leveraging particle dispersion theory which tells us whether ice drift is in a subdiffusive, diffusive, ballistic, or superdiffusive dynamical regime using single-particle (absolute) dispersion statistics. In terms of sea ice deformation, the framework uses two- and three-particle dispersion to characterize along- and across-shear transport as well as differential kinematic parameters. The approach is tested with GPS beacons deployed in triplets on sea ice in the southern Beaufort Sea at varying distances from the coastline in fall of 2009 with eight individual events characterized. One transition in particular follows the sea level pressure (SLP) high on 8 October in 2009 while the sea ice drift was in a superdiffusive dynamic regime. In this case, the dispersion scaling exponent (which is a slope between single-particle absolute dispersion of sea ice drift and elapsed time) changed from superdiffusive (α ∼ 3) to ballistic (α ∼ 2) as the SLP was rounding its maximum pressure value. Following this shift between regimes, there was a loss in synchronicity between sea ice drift and atmospheric motion patterns. While this is only one case study, the outcomes suggest similar studies be conducted on more buoy arrays to test momentum transfer linkages between storms and sea ice responses as a function of dispersion regime states using scaling exponents. The tools and framework developed in this study provide a unique characterization technique to evaluate these states with respect to sea ice processes in general. Application of these techniques can aid ice hazard assessments and weather forecasting in support of marine transportation and indigenous use of near-shore Arctic areas.

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Review of Evaluation of Arctic sea-ice drift and its dependency on near-surface wind and sea-ice concentration and thickness in the coupled regional climate model HIRHAM-NAOSIM

10.5194/tc-2019-183-rc2 ◽

2019 ◽

Author(s):

Anonymous

Keyword(s):

Sea Ice ◽

Climate Model ◽

Regional Climate ◽

Surface Wind ◽

Arctic Sea Ice ◽

Sea Ice Concentration ◽

Near Surface ◽

Ice Drift ◽

Ice Concentration ◽

Sea Ice Drift

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IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline

Journal of Geophysical Research Atmospheres ◽

10.1029/2011jc007110 ◽

2011 ◽

Vol 116 ◽

Cited By ~ 79

Author(s):

P. Rampal ◽

J. Weiss ◽

C. Dubois ◽

J.-M. Campin

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

Ice Drift ◽

Arctic Sea ◽

Sea Ice Drift

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Reassessing Sea Ice Drift and Its Relationship to Long‐Term Arctic Sea Ice Loss in Coupled Climate Models

Journal of Geophysical Research Oceans ◽

10.1029/2017jc013697 ◽

2018 ◽

Vol 123 (6) ◽

pp. 4338-4359 ◽

Cited By ~ 6

Author(s):

Neil F. Tandon ◽

Paul J. Kushner ◽

David Docquier ◽

Justin J. Wettstein ◽

Camille Li

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

Coupled Climate Models ◽

Ice Drift ◽

Arctic Sea ◽

Sea Ice Drift ◽

Arctic Sea Ice Loss

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neXtSIM: a new Lagrangian sea ice model

The Cryosphere ◽

10.5194/tc-10-1055-2016 ◽

2016 ◽

Vol 10 (3) ◽

pp. 1055-1073 ◽

Cited By ~ 39

Author(s):

Pierre Rampal ◽

Sylvain Bouillon ◽

Einar Ólason ◽

Mathieu Morlighem

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

The Arctic ◽

Dynamical Response ◽

Ice Dynamics ◽

Ice Drift ◽

Wide Range ◽

Arctic Sea ◽

Sea Ice Drift ◽

Ice Model

Abstract. The Arctic sea ice cover has changed drastically over the last decades. Associated with these changes is a shift in dynamical regime seen by an increase of extreme fracturing events and an acceleration of sea ice drift. The highly non-linear dynamical response of sea ice to external forcing makes modelling these changes and the future evolution of Arctic sea ice a challenge for current models. It is, however, increasingly important that this challenge be better met, both because of the important role of sea ice in the climate system and because of the steady increase of industrial operations in the Arctic. In this paper we present a new dynamical/thermodynamical sea ice model called neXtSIM that is designed to address this challenge. neXtSIM is a continuous and fully Lagrangian model, whose momentum equation is discretised with the finite-element method. In this model, sea ice physics are driven by the combination of two core components: a model for sea ice dynamics built on a mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The evaluation of the model performance for the Arctic is presented for the period September 2007 to October 2008 and shows that observed multi-scale statistical properties of sea ice drift and deformation are well captured as well as the seasonal cycles of ice volume, area, and extent. These results show that neXtSIM is an appropriate tool for simulating sea ice over a wide range of spatial and temporal scales.

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Performance of a multiscale correlation algorithm for the estimation of sea-ice drift from SAR images: initial results

Annals of Glaciology ◽

10.3189/172756411795931462 ◽

2011 ◽

Vol 52 (57) ◽

pp. 311-317 ◽

Cited By ~ 22

Author(s):

Thomas Hollands ◽

Wolfgang Dierking

Keyword(s):

Sea Ice ◽

Displacement Vector ◽

Winter Season ◽

Image Resolution ◽

Displacement Fields ◽

Sar Images ◽

High Entropy ◽

Ice Drift ◽

Sea Ice Drift ◽

Ice Conditions

AbstractSea-ice drift fields were obtained from sequences of synthetic aperture radar (SAR) images using a method based on pattern recognition. the accuracy of the method was estimated for two image products of the Envisat Advanced SAR (ASAR) with 25 m and 150 m pixel size. For data from the winter season it was found that 99% of the south–north and west–east components of the determined displacement vector are within ±3–5 pixels of a manually derived reference dataset, independent of the image resolution. For an image pair with 25 m resolution acquired during summer, the corresponding value is 12 pixels. Using the same resolution cell dimensions for the displacement fields in both image types, the estimated displacement components differed by 150–300 m. the use of different texture parameters for predicting the performance of the algorithm dependent on ice conditions and image characteristics was studied. It was found that high entropy values indicate a good performance.

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Modeling M2 tidal variability in Arctic Sea-ice drift and deformation

Annals of Glaciology ◽

10.3189/172756406781811178 ◽

2006 ◽

Vol 44 ◽

pp. 418-428 ◽

Cited By ~ 14

Author(s):

W.D. Hibler ◽

A. Roberts ◽

P. Heil ◽

A.Y. Proshutinsky ◽

H.L. Simmons ◽

...

Keyword(s):

Sea Ice ◽

Arctic Basin ◽

Ocean Model ◽

Arctic Sea Ice ◽

The Arctic ◽

Ocean Coupling ◽

Ice Drift ◽

Arctic Sea ◽

Sea Ice Drift ◽

Tidal Variability

AbstractSemi-diurnal oscillations are a ubiquitous feature of polar Sea-ice motion. Over much of the Arctic basin, inertial and Semi-diurnal tidal variability have Similar frequencies So that periodicity alone is inadequate to determine the Source of oscillations. We investigate the relative roles of tidal and inertial variability in Arctic Sea ice using a barotropic ice–ocean model with Sea ice embedded in an upper boundary layer. Results from this model are compared with ‘levitated’ ice–ocean coupling used in many models. In levitated models the mechanical buoyancy effect of Sea ice is neglected So that convergence of ice, for example, does not affect the oceanic Ekman flux. We use rotary Spectral analysis to compare Simulated and observed results. This helps to interpret the rotation Sense of Sea-ice drift and deformation at the Semi-diurnal period and is a useful discriminator between tidal and inertial effects. Results indicate that the levitated model generates an artificial inertial resonance in the presence of tidal and wind forcing, contrary to the embedded Sea-ice model. We conclude that Sea-ice mechanics can cause the rotational response of ice motion to change Sign even in the presence of Strong and opposing local tidal forcing when a physically consistent dynamic ice–ocean coupling is employed.

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