Multiscale variations in Arctic sea ice motion and links to atmospheric and oceanic conditions

Abstract. Arctic sea ice drift motion affects the global material balance, energy exchange and climate change and seriously affects the navigational 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 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 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 timescale sequences. We found that the three major drift patterns have four 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 geophysical variables, the results of the correlation analysis show that the first EOF sea ice drift pattern is mainly related to atmospheric environmental factors, the second pattern is related to the joint action of atmospheric and oceanic factors, and the third pattern is mainly related to oceanic factors. Our study suggests that the ocean environment also has a strong correlation with sea ice movement. Especially for some sea ice transport patterns, the correlation even exceeds atmospheric forcing.

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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.

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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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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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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.

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

Annals of Glaciology ◽

10.1017/aog.2021.6 ◽

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.

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neXtSIM: a new Lagrangian sea ice model

The Cryosphere Discussions ◽

10.5194/tcd-9-5885-2015 ◽

2015 ◽

Vol 9 (5) ◽

pp. 5885-5941 ◽

Cited By ~ 4

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.

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

The Cryosphere ◽

10.5194/tc-11-2829-2017 ◽

2017 ◽

Vol 11 (6) ◽

pp. 2829-2846 ◽

Cited By ~ 9

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

The Cryosphere ◽

10.5194/tc-13-627-2019 ◽

2019 ◽

Vol 13 (2) ◽

pp. 627-645 ◽

Cited By ~ 7

Author(s):

Nils Hutter ◽

Lorenzo Zampieri ◽

Martin Losch

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Tracking Algorithm ◽

The Arctic ◽

Surface Drag ◽

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 they also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred to 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 reconnects multiple segments into individual LKFs based on 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, as well as an extensive evaluation of both algorithms against handpicked features in a reference data set, is presented. A 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 stretched exponential distribution of LKF length, an exponential tail in the distribution of LKF lifetimes, and a strong link to atmospheric drivers, here Arctic cyclones, are 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.

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Long-lasting impacts of winds on Arctic sea ice through the ocean’s memory

10.5194/egusphere-egu21-6699 ◽

2021 ◽

Author(s):

Qiang Wang ◽

Sergey Danilov ◽

Longjiang Mu ◽

Dmitry Sidorenko ◽

Claudia Wekerle

Keyword(s):

Sea Ice ◽

Spatial Patterns ◽

Sea Surface Height ◽

Arctic Sea Ice ◽

Sea Surface ◽

The Arctic ◽

Gradient Force ◽

Ice Drift ◽

Arctic Sea ◽

Sea Ice Drift

<p>This modelling study reveals that the changes in the ocean state induced by wind perturbations can significantly influence the Arctic sea ice drift, thickness, concentration and deformation rates even after the wind perturbations have been eliminated for years. Wind perturbations can change the Arctic Ocean liquid freshwater content locally or basin-wide, thus changing the sea surface height and ocean surface geostrophic current accordingly. Such changes in the ocean can last for many years, which enforces long-lasting and strong imprint on sea ice. Both the changes in sea surface height gradient force (due to changes in sea surface height) and ocean-ice stress (due to changes in ocean geostrophic velocity) are found to be important in determining the overall impacts on sea ice. Depending on the preceding atmospheric mode driving the ocean, the ocean&#8217;s memory of wind forcing can lead to changes in Arctic sea ice characteristics with very different spatial patterns. We identified these spatial patterns associated with Arctic Oscillation, Arctic Dipole Anomaly and Beaufort High modes through dedicated numerical simulations in this study. Our results suggest the importance of initial ocean state in sea ice prediction on subseasonal to decadal time scales.</p>

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