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 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 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.

Remotely-sensed and simulated variability of Arctic sea-ice concentrations in response to atmospheric synoptic systems

1995 ◽  
Vol 16 (17) ◽  
pp. 3325-3342 ◽  
Author(s):  
J. A. MASLANK ◽  
C. FOWLER ◽  
J. HEINRICHS ◽  
R. G. BARRY ◽  
W. J. EMERY
Keyword(s):  
Sea Ice ◽  
Remotely Sensed ◽  
Arctic Sea Ice ◽  
Arctic Sea

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.

scholarly journals Insights on Sea Ice Data Assimilation from Perfect Model Observing System Simulation Experiments

2018 ◽  
Vol 31 (15) ◽  
pp. 5911-5926 ◽  
Author(s):  
Yong-Fei Zhang ◽  
Cecilia M. Bitz ◽  
Jeffrey L. Anderson ◽  
Nancy Collins ◽  
Jonathan Hendricks ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Simulation Experiments ◽  
Sea Ice Concentration ◽  
Perfect Model ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ice Model

Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance.

scholarly journals Simulating Arctic sea ice variability with a coupled regional atmosphere-ocean-sea ice model

2005 ◽  
Vol 14 (6) ◽  
pp. 793-800 ◽  
Author(s):  
Uwe Mikolajewicz ◽  
Dmitry V. Sein ◽  
Daniela Jacob ◽  
Torben Königk ◽  
Ralf Podzun ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea ◽  
Ice Model

scholarly journals Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model

2015 ◽  
Vol 373 (2052) ◽  
pp. 20140167 ◽  
Author(s):  
Michel Tsamados ◽  
Daniel Feltham ◽  
Alek Petty ◽  
David Schroeder ◽  
Daniela Flocco
Keyword(s):  
Sea Ice ◽  
Research Priorities ◽  
Sensitivity Study ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Future Research ◽  
Melt Ponds ◽  
Equation Boundary ◽  
Arctic Sea ◽  
Ice Model

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice–atmosphere and ice–ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice–ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

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