Landfast ice zoning from SAR imagery

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.  In order to obtain a comprehensive information on landfast ice state we use a time series of wide swath SAR imagery.  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 сan 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>

scholarly journals Tracing Atlantic Water Signature in the Arctic Sea Ice Cover East of Svalbard

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir V. Ivanov ◽  
Vladimir A. Alexeev ◽  
Irina Repina ◽  
Nikolay V. Koldunov ◽  
Alexander Smirnov
Keyword(s):  
Heat Flux ◽  
Time Series ◽  
Sea Ice ◽  
Ice Cover ◽  
Atlantic Water ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Ice Concentration ◽  
Ice Conditions

We focus on the Arctic Ocean between Svalbard and Franz Joseph Land in order to elucidate the possible role of Atlantic water (AW) inflow in shaping ice conditions. Ice conditions substantially affect the temperature regime of the Spitsbergen archipelago, particularly in winter. We test the hypothesis that intensive vertical mixing at the upper AW boundary releases substantial heat upwards that eventually reaches the under-ice water layer, thinning the ice cover. We examine spatial and temporal variation of ice concentration against time series of wind, air temperature, and AW temperature. Analysis of 1979–2011 ice properties revealed a general tendency of decreasing ice concentration that commenced after the mid-1990s. AW temperature time series in Fram Strait feature a monotonic increase after the mid-1990s, consistent with shrinking ice cover. Ice thins due to increased sensible heat flux from AW; ice erosion from below allows wind and local currents to more effectively break ice. The winter spatial pattern of sea ice concentration is collocated with patterns of surface heat flux anomalies. Winter minimum sea ice thickness occurs in the ice pack interior above the AW path, clearly indicating AW influence on ice thickness. Our study indicates that in the AW inflow region heat flux from the ocean reduces the ice thickness.

scholarly journals Sea ice drift in the Southern Ocean: Regional patterns, variability, and trends

Elem Sci Anth ◽  
2017 ◽  
Vol 5 ◽  
Author(s):  
Ron Kwok ◽  
Shirley S. Pang ◽  
Sahra Kacimi
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Sea Level ◽  
Ice Cover ◽  
The Arctic ◽  
Positive Trend ◽  
Amundsen Sea ◽  
Regional Patterns ◽  
Ice Drift ◽  
Sea Ice Drift

Understanding long-term changes in large-scale sea ice drift in the Southern Ocean is of considerable interest given its contribution to ice extent, to ice production in open waters, with associated dense water formation and heat flux to the atmosphere, and thus to the climate system. In this paper, we examine the trends and variability of this ice drift in a 34-year record (1982–2015) derived from satellite observations. Uncertainties in drift (~3 to 4 km day–1) were assessed with higher resolution observations. In a linear model, drift speeds were ~1.4% of the geostrophic wind from reanalyzed sea-level pressure, nearly 50% higher than that of the Arctic. This result suggests an ice cover in the Southern Ocean that is thinner, weaker, and less compact. Geostrophic winds explained all but ~40% of the variance in ice drift. Three spatially distinct drift patterns were shown to be controlled by the location and depth of atmospheric lows centered over the Amundsen, Riiser-Larsen, and Davis seas. Positively correlated changes in sea-level pressures at the three centers (up to 0.64) suggest correlated changes in the wind-driven drift patterns. Seasonal trends in ice edge are linked to trends in meridional winds and also to on-ice/off-ice trends in zonal winds, due to zonal asymmetry of the Antarctic ice cover. Sea ice area export at flux gates that parallel the 1000-m isobath were extended to cover the 34-year record. Interannual variability in ice export in the Ross and Weddell seas linked to the depth and location of the Amundsen Sea and Riiser-Larsen Sea lows to their east. Compared to shorter records, where there was a significant positive trend in Ross Sea ice area flux, the longer 34-year trends of outflow from both seas are now statistically insignificant.

scholarly journals neXtSIM: a new Lagrangian sea ice model

2016 ◽  
Vol 10 (3) ◽  
pp. 1055-1073 ◽  
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.

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

2006 ◽  
Vol 44 ◽  
pp. 418-428 ◽  
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.

scholarly journals Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm

2018 ◽  
Author(s):  
Nils Hutter ◽  
Lorenzo Zampieri ◽  
Martin Losch
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Tracking Algorithm ◽  
The Arctic ◽  
Power Law Distribution ◽  
Data Set ◽  
Extensive Evaluation ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift

Abstract. Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This paper introduces two methods that detect and track LKFs in sea ice deformation data and establish an LKF data set for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). Both algorithms are available as open-source code and applicable to any gridded sea-ice drift and deformation data. The LKF detection algorithm classifies pixels with higher deformation rates compared to the immediate environment as LKF pixels, divides the binary LKF map into small segments, and re-connects multiple segments into individual LKFs based their distance and orientation relative to each other. The tracking algorithm uses sea-ice drift information to estimate a first guess of LKF distribution and identifies tracked features by the degree of overlap between detected features and the first guess. An optimization of the parameters of both algorithms is presented, as well as an extensive evaluation of both algorithms against hand-picked features in a reference data set. An LKF data set is derived from RGPS deformation data for the years from 1996 to 2008 that enables a comprehensive description of LKFs. LKF densities and LKF intersection angles derived from this data set agree well with previous estimates. Further, a power-law distribution of LKF length, an exponential distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, is derived from the data set. Both algorithms are applied to output of a numerical sea-ice model to compare the LKF intersection angles in a high-resolution Arctic sea-ice simulation with the LKF data set.

scholarly journals Multiscale Variations in Arctic Sea Ice Motion, Links to Atmospheric and Oceanic Conditions

2021 ◽  
Author(s):  
Dongyang Fu ◽  
Bei Liu ◽  
Yali Qi ◽  
Guo Yu ◽  
Haoen Huang ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Interdecadal Variation ◽  
Environmental Data ◽  
The Arctic ◽  
Northern Coast ◽  
Ice Drift ◽  
Arctic Sea ◽  
Variation Characteristics ◽  
Sea Ice Drift

Abstract. Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigation safety of ships along certain channels. Due to the Arctic's special geographical location and harsh natural conditions, observations and broad understanding of the Arctic sea ice motion of sea ice are very limited. In this study, sea ice motion data released by the National Snow and Ice Data Center (NSIDC) were used to analyze the climatological, spatial and temporal characteristics of the Arctic sea ice drift from 1979 to 2018 and to understand the multiscale variation characteristics of the three major Arctic sea ice drift patterns. The results show that the sea ice drift velocity is greater in winter than in summer. The empirical orthogonal function (EOF) analysis method was used to extract the three main sea ice drift patterns, which are the anticyclonic sea ice drift circulation pattern on the scale of the Arctic basin, the average sea ice transport pattern from the Arctic Ocean to the Fram Strait and the transport pattern moving ice between the Kara Sea (KS) and the northern coast of Alaska. By using the ensemble empirical mode decomposition (EEMD) method, each temporal coefficient series extracted by the EOF method was decomposed into multiple time-scale sequences. We found that the three major drift patterns have 4 significant interannual variation periods of approximately 1, 2, 4 and 8 years. Furthermore, the second pattern has a significant interdecadal variation characteristic with a period of approximately 19 years, while the other two patterns have no significant interdecadal variation characteristics. Combined with the atmospheric and oceanic physical environmental data, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly affected by atmospheric environmental factors, the second pattern is affected by the joint action of atmospheric and oceanic factors, and the third pattern is mainly affected by oceanic factors. Our study suggests that the ocean environment also has a significant influence on sea ice movement. Especially for some sea ice transport patterns, the influence even exceeds atmospheric forcing.

scholarly journals Higher-order statistical moments to analyse Arctic sea-ice drift patterns

2020 ◽  
Vol 61 (83) ◽  
pp. 464-471
Author(s):  
Satwant Kaur ◽  
Jennifer V. Lukovich ◽  
Jens K. Ehn ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Drift Time ◽  
Higher Order ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Winter Period ◽  
Higher Order Moments ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift

AbstractGeophysical systems are often assumed to follow Gaussian probability density functions; however, deviations from Gaussian behaviour can shed light on the underlying dynamics. For the large-scale motion of the Arctic sea ice, such deviations have been interpreted as signatures of structure in dynamic flow fields. In this study, we use higher-order moments (skewness and kurtosis) to identify spatiotemporal changes in the Beaufort Gyre (BG) and the Transpolar Drift (TPD) sea-ice drift patterns. Higher-order moments of satellite-derived ice drift speeds are examined over the winter period of 2006–2017 to describe the persistence of features like the BG and TPD, and their variation over time. Index patterns indicate that the periphery of the BG can be identified by a combination of high positive skewness and high kurtosis in the ice drift time series on an annual basis.

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

2016 ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Cathleen A. Geiger ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Oil And Gas ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Ice Drift ◽  
Shock Response ◽  
Near Shore ◽  
Sea Ice Drift ◽  
Ice Conditions ◽  
Ice Floes

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.

scholarly journals neXtSIM: a new Lagrangian sea ice model

2015 ◽  
Vol 9 (5) ◽  
pp. 5885-5941 ◽  
Author(s):  
P. Rampal ◽  
S. Bouillon ◽  
E. Ólason ◽  
M. 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 in order to address this. neXtSIM is a continuous and fully Lagrangian model, and the equations are discretised with the finite-element method. In this model, sea ice physics are driven by a synergic combination of two core components: a model for sea ice dynamics built on a new mechanical framework using an elasto-brittle rheology, and a model for sea ice thermodynamics providing damage healing for the mechanical framework. The results of a thorough evaluation of the model performance for the Arctic are presented for the period September 2007 to October 2008. They show 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 a very promising tool for simulating the sea ice over a wide range of spatial and temporal scales.

scholarly journals Relationships between Arctic sea ice drift and strength modelled by NEMO-LIM3.6

2017 ◽  
Vol 11 (6) ◽  
pp. 2829-2846 ◽  
Author(s):  
David Docquier ◽  
François Massonnet ◽  
Antoine Barthélemy ◽  
Neil F. Tandon ◽  
Olivier Lecomte ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Global Ocean ◽  
Model Intercomparison ◽  
Drift Speed ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift ◽  
Ice Strength

Abstract. Sea ice cover and thickness have substantially decreased in the Arctic Ocean since the beginning of the satellite era. As a result, sea ice strength has been reduced, allowing more deformation and fracturing and leading to increased sea ice drift speed. We use the version 3.6 of the global ocean–sea ice NEMO-LIM model (Nucleus for European Modelling of the Ocean coupled to the Louvain-la-Neuve sea Ice Model), satellite, buoy and submarine observations, as well as reanalysis data over the period from 1979 to 2013 to study these relationships. Overall, the model agrees well with observations in terms of sea ice extent, concentration and thickness. The seasonal cycle of sea ice drift speed is reasonably well reproduced by the model. NEMO-LIM3.6 is able to capture the relationships between the seasonal cycles of sea ice drift speed, concentration and thickness, with higher drift speed for both lower concentration and lower thickness, in agreement with observations. Model experiments are carried out to test the sensitivity of Arctic sea ice drift speed, thickness and concentration to changes in sea ice strength parameter P*. These show that higher values of P* generally lead to lower sea ice deformation and lower sea ice thickness, and that no single value of P* is the best option for reproducing the observed drift speed and thickness. The methodology proposed in this analysis provides a benchmark for a further model intercomparison related to the relationships between sea ice drift speed and strength, which is especially relevant in the context of the upcoming Coupled Model Intercomparison Project 6 (CMIP6).

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