Sea ice drift in the Arctic since the 1950s

In the global ocean, mesoscale eddies are routinely observed from satellite observation. In the Arctic Ocean, however, their observation is impeded by the presence of sea ice, although there is a growing recognition that eddy may be important for the evolution of the sea ice cover. In this talk, we will present a new method of surface ocean eddy detection based on their signature in sea ice vorticity retrieved from Synthetic Aperture Radar (SAR) images. A combination of Feature Tracking and Pattern Matching algorithm is used to compute the sea ice drift from pairs of SAR images. We will mostly focus on the case of one eddy in October 2017 in the marginal ice zone of the Canadian Basin, which was sampled by mooring observations, allowing a detailed description of its characteristics. Although the eddy could not be identified by visual inspection of the SAR images, its signature is revealed as a dipole anomaly in sea ice vorticity, which suggests that the eddy is a dipole composed of a cyclone and an anticyclone, with a horizontal scale of 80-100 km and persisted over a week. We will also discuss the relative contributions of the wind and the surface current to the sea ice vorticity. We anticipate that the robustness of our method will allow us to detect more eddies as more SAR observations become available in the future.

Download Full-text

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

Elem Sci Anth ◽

10.1525/elementa.226 ◽

2017 ◽

Vol 5 ◽

Cited By ~ 17

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

On the problem of abnormal sea ice drift speed in the Arctic seas

Deep Sea Research Part B Oceanographic Literature Review ◽

10.1016/0198-0254(86)94299-8 ◽

1986 ◽

Vol 33 (12) ◽

pp. 995

Keyword(s):

Sea Ice ◽

The Arctic ◽

Arctic Seas ◽

Drift Speed ◽

Ice Drift ◽

Sea Ice Drift

Download Full-text

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

10.5194/tc-2018-207 ◽

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.

Download Full-text

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

10.5194/tc-2020-324 ◽

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.

Download Full-text

Calibration of sea ice drift forecasts using random forest algorithms

10.5194/egusphere-egu21-14317 ◽

2021 ◽

Author(s):

Cyril Palerme ◽

Malte Müller

Keyword(s):

Random Forest ◽

Sea Ice ◽

Geographical Information ◽

The Arctic ◽

Forecast Errors ◽

Prediction System ◽

Target Variable ◽

Ice Drift ◽

Calibration Methods ◽

Sea Ice Drift

There is a growing demand for accurate sea-ice forecasts in the Arctic due to increasing maritime traffic. Although the capabilities of numerical models steadily improve, sea-ice forecasts produced by numerical prediction systems are affected by biases. In order to reduce forecast errors, statistical methods can be used for calibration.In this study, two calibration methods have been developed for calibrating sea-ice drift forecasts from an operational prediction system (TOPAZ4) in the Arctic. These methods are based on random forest algorithms, a machine learning technique suitable for assessing non-linear relationships between a set of predictors and a target variable. While all the algorithms developed in this study use the same set of predictors, two set of algorithms have been developed using either buoy or synthetic-aperture radar (SAR) observations for the target variable. Furthermore, different algorithms have been developed for predicting the direction and the speed of sea-ice drift, as well as for different lead times. The random forest algorithms use predictor variables from sea-ice concentration observations during the initialization of the forecasts, sea-ice forecasts from the TOPAZ4 prediction system, wind forecasts from the European Centre for Medium-Range Weather Forecasts, and some geographical information.The performances of the calibrated forecasts have been evaluated and compared to those from the TOPAZ4 forecasts using buoy observations from the International Arctic Buoy Programme. Depending on the calibration method, the mean absolute error is reduced, on average, between 5.9 % and 8.1 % for the direction, and between 7.1 % and 9.6 % for the speed of sea-ice drift. However, there is a large spatial variability in the performances of these algorithms, and the random forest algorithms have particularly poor performances in the Canadian Archipelago, an area characterized by narrow channels and the presence of landfast ice.

Download Full-text

Farewell to IceBridge: 10 years of polar sea ice remote sensing

10.5194/egusphere-egu2020-12019 ◽

2020 ◽

Author(s):

Linette Boisvert ◽

Joseph MacGregor ◽

Brooke Medley ◽

Nathan Kurtz ◽

Ron Kwok ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Thickness ◽

Antarctic Sea Ice ◽

Ice Drift ◽

Laser Altimeters ◽

Sea Ice Drift ◽

To Come ◽

The Antarctic

NASA&#8217;s Operation IceBridge (OIB) was a multi-year, multi-platform, airborne mission which took place between 2009-2019. OIB was designed and implemented to continue monitoring the changing sea ice and ice sheets in both the Arctic and Antarctic by &#8216;bridging the gap&#8217; between NASA&#8217;s ICESat (2003&#8211;2009) and ICESat-2 (launched September 2018) satellite missions. OIB&#8217;s instrument suite most often consisted of laser altimeters, radar sounders, gravimeters and multi-spectral imagers. These instruments were selected to study polar sea ice thickness, ice sheet elevation, snow and ice thickness, surface temperature and bathymetry. With the launch of ICESat-2, the final year of OIB consisted of three campaigns designed to under fly the satellite: 1) the end of the Arctic growth season (spring), 2) during the Arctic summer to capture many different types of melting surfaces, and 3) the Antarctic spring to cover an entirely new area of East Antarctica. Over this ten-year period a coherent picture of Arctic and Antarctic sea ice and snow thickness and other properties have been produced and monitored. Specifically, OIB has changed the community&#8217;s perspective of snow on sea ice in the Arctic. Over the decade, OIB has also been used to validate other satellite altimeter missions like ESA&#8217;s CryoSat-2. Since the launch of ICESat-2, coincident OIB under flights with the satellite were crucial for measuring sea ice properties. With sea ice constantly in motion, and the differences in OIB aircraft and ICESat-2 ground speed, there can substantial drift in the sea ice pack over the same ground track distance being measured.Therefore, we had to design and implement sea ice drift trajectories based on low level winds measured from the aircraft in flight, adjusting our plane&#8217;s path accordingly so we could measure the same sea ice as ICESat-2. This was implemented in both the Antarctic 2018 and Arctic 2019 campaigns successfully. Specifically, the Spring Arctic 2019 campaign allowed for validation of ICESat-2 freeboards with OIB ATM freeboards proving invaluable to the success of ICESat-2 and the future of sea ice research to come from these missions.&#160;

Download Full-text

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

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.

Download Full-text