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

2017 ◽  
Vol 11 (4) ◽  
pp. 1707-1731 ◽  
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.

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

2018 ◽  
Vol 123 (6) ◽  
pp. 4338-4359 ◽  
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

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 Increased Arctic sea ice drift alters adult female polar bear movements and energetics

2017 ◽  
Vol 23 (9) ◽  
pp. 3460-3473 ◽  
Author(s):  
George M. Durner ◽  
David C. Douglas ◽  
Shannon E. Albeke ◽  
John P. Whiteman ◽  
Steven C. Amstrup ◽  
...  
Keyword(s):  
Sea Ice ◽  
Adult Female ◽  
Polar Bear ◽  
Arctic Sea Ice ◽  
Ice Drift ◽  
Arctic Sea ◽  
Sea Ice Drift ◽  
Female Polar Bear

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 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

2019 ◽  
Author(s):  
Xiaoyong Yu ◽  
Annette Rinke ◽  
Wolfgang Dorn ◽  
Gunnar Spreen ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Arctic Sea Ice ◽  
Near Surface ◽  
Drift Speed ◽  
Ice Drift ◽  
Sea Ice Drift

Abstract. We examine the simulated Arctic sea-ice drift speed for the period 2003–2014 in the coupled Arctic regional climate model HIRHAM-NAOSIM 2.0. In particular, we evaluate the dependency of the drift speed on the near-surface wind speed and sea-ice conditions. Considering the seasonal cycle of Arctic basin averaged drift speed, the model reproduces the summer-autumn drift speed well, but significantly overestimates the winter-spring drift speed, compared to satellite-derived observations. Also, the model does not capture the observed seasonal phase lag between drift and wind speed, but the simulated drift speed is more in phase with near-surface wind. The model calculates a realistic negative relationship between drift speed and ice thickness and between drift speed and ice concentration during summer-autumn when concentration is relatively low, but the correlation is weaker than observed. A daily grid-scale diagnostic indicates that the model reproduces the observed positive relationship between drift and wind speed. The strongest impact of wind changes on drift speed occurs for high and moderate wind speeds, with a low impact for calm conditions. The correlation under low-wind conditions is overestimated in the simulations, compared to observation/reanalysis. A sensitivity experiment demonstrates the significant effects of sea-ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice. However, this does not improve the agreement of the modelled drift speed/wind speed ratio with observations based on reanalysis for wind and remote sensing for sea ice drift. An improvement might be possible, among others, by tuning the open parameters of the parameterization in future.

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.

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