scholarly journals Comparison of elastic-viscous-plastic and viscous-plastic dynamics models using a high resolution Arctic sea ice model

1997 ◽  
Author(s):  
E.C. Hunke ◽  
Y. Zhang
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Dynamics Models ◽  
Ice Model

scholarly journals Characterizing Arctic sea ice topography using high-resolution IceBridge data

2016 ◽  
Vol 10 (3) ◽  
pp. 1161-1179 ◽  
Author(s):  
Alek A. Petty ◽  
Michel C. Tsamados ◽  
Nathan T. Kurtz ◽  
Sinead L. Farrell ◽  
Thomas Newman ◽  
...  
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Feature ◽  
Ice Thickness ◽  
First Year ◽  
Data Set ◽  
Surface Features ◽  
Developed Surface ◽  
Arctic Sea

Abstract. We present an analysis of Arctic sea ice topography using high-resolution, three-dimensional surface elevation data from the Airborne Topographic Mapper, flown as part of NASA's Operation IceBridge mission. Surface features in the sea ice cover are detected using a newly developed surface feature picking algorithm. We derive information regarding the height, volume and geometry of surface features from 2009 to 2014 within the Beaufort/Chukchi and Central Arctic regions. The results are delineated by ice type to estimate the topographic variability across first-year and multi-year ice regimes. The results demonstrate that Arctic sea ice topography exhibits significant spatial variability, mainly driven by the increased surface feature height and volume (per unit area) of the multi-year ice that dominates the Central Arctic region. The multi-year ice topography exhibits greater interannual variability compared to the first-year ice regimes, which dominates the total ice topography variability across both regions. The ice topography also shows a clear coastal dependency, with the feature height and volume increasing as a function of proximity to the nearest coastline, especially north of Greenland and the Canadian Archipelago. A strong correlation between ice topography and ice thickness (from the IceBridge sea ice product) is found, using a square-root relationship. The results allude to the importance of ice deformation variability in the total sea ice mass balance, and provide crucial information regarding the tail of the ice thickness distribution across the western Arctic. Future research priorities associated with this new data set are presented and discussed, especially in relation to calculations of atmospheric form drag.

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.

High resolution simulations of Arctic sea ice, 1979?1993

2003 ◽  
Vol 22 (1) ◽  
pp. 67-74 ◽  
Author(s):  
Wieslaw Maslowski ◽  
William H. Lipscomb
Keyword(s):  
High Resolution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea

Error analysis and assimilation of remotely sensed ice motion within an Arctic sea ice model

2000 ◽  
Vol 105 (C2) ◽  
pp. 3339-3356 ◽  
Author(s):  
Walter N. Meier ◽  
James A. Maslanik ◽  
Charles W. Fowler
Keyword(s):  
Error Analysis ◽  
Sea Ice ◽  
Remotely Sensed ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Ice Model

scholarly journals Nonlinear fractional arctic systems

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Sunday Christian Eze ◽  
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Adomian Decomposition Method ◽  
Arctic Sea Ice ◽  
Ice Melting ◽  
Order Model ◽  
Modified Adomian Decomposition Method ◽  
Adomian Decomposition ◽  
Arctic Sea ◽  
Ice Model

In this contribution, the nonlinear fractional arctic sea ice model is given, and the solution of the model was obtained using a new proposed modified Adomian decomposition method. The result is compared with the integer-order model, and we observed that a model with fractional order gives a better result. We also observed that the effect of climate change on arctic sea ice could lead to a large-scale sea ice melting with sea-level rise for several meters, which could pose a major threat to low-lying island nations and coastal areas.

scholarly journals Sensitivities and uncertainties in a coupled regional atmosphere-ocean-ice model with respect to the simulation of Arctic sea ice

2007 ◽  
Vol 112 (D10) ◽  
Author(s):  
W. Dorn ◽  
K. Dethloff ◽  
A. Rinke ◽  
S. Frickenhaus ◽  
R. Gerdes ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Ice Model

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.

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