Effect of salinity on sea ice motion

We combined large eddy simulation (LES) with a thermodynamic slab ice model to simulate and study the sea ice motion and frazil ice dynamics in the ocean mixed layer in the Arctic winter. To show the accurate representation of leads in models, fluxes distributed laterally beneath leads and sea ice need to be parameterized. The 3-D LES model, which is developed from a 2-D turbulence model, is used to model the convection of beneath leads and sea ice. The experiments were then achived by combining the LES model with the ice model. The concentration of frazil ice was modeled using the Omstedt and Svensson model. The ice crystal radius and growth rates were assumed to be constant and the temperature and salinity changes with depth were taken into account. Salinity distribution and frazil ice concentration were influenced by ice motion, and variations in ocean salinity during freezing and thawing were also investigated. Entrained flow caused by the movement of sea ice has a significant influence on the eddy. Sea ice roughness is also important in the formation of the eddy current, and the values of the ice crystal rise velocity and the ice concentration source term coefficient influence frazil ice dynamics. The effects of sea ice thermodynamic dissipation on the sea is more remarkable, affecting the heat transfer to the atmosphere. The brine rejected during ice crystal formation and dilution of seawater are other important mechanisms of marine cyclical shocks.

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Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model

Annals of Glaciology ◽

10.1017/s026030550001404x ◽

1997 ◽

Vol 25 ◽

pp. 203-207 ◽

Cited By ~ 5

Author(s):

David A. Bailey ◽

Amanda H. Lynch ◽

Katherine S. Hedström

Keyword(s):

Sea Ice ◽

Mixed Layer ◽

Climate Models ◽

Regional Climate ◽

The Arctic ◽

Water Runoff ◽

Polar Regions ◽

Ice Dynamics ◽

Climate System Model ◽

Ice Model

Global climate models have pointed to the polar regions as very sensitive areas in response to climate change. However, these models often do not contain representations of processes peculiar to the polar regions such as dynamic sea ice, permafrost, and Arctic stratus clouds. Further, global models do not have the resolution necessary to model accurately many of the important processes and feedbacks. Thus, there is a need for regional climate models of higher resolution. Our such model (ARCSy M) has been developed by A. Lynch and W. Chapman. This model incorporates the NCAR Regional Climate Model (RegCM2) with the addition of Flato–Hibler cavitating fluid sea-ice dynamics and Parkinson–Washington ice thermodynamic formulation. Recently work has been conducted to couple a mixed-layer ocean to the atmosphere–ice model, and a three-dimensional (3-D) dynamical ocean model, in this case the S-Coordinate Primitive Equation Model (SPEM), to the ice model. Simulations including oceanic circulation will allow investigations of the feedbacks involved in fresh-water runoff from sea-ice melt and sea-ice transport. Further, it is shown that the definition of the mixed-layer depth has significant impact on ice thermodynamics.

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Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model

Annals of Glaciology ◽

10.3189/s026030550001404x ◽

1997 ◽

Vol 25 ◽

pp. 203-207 ◽

Cited By ~ 5

Author(s):

David A. Bailey ◽

Amanda H. Lynch ◽

Katherine S. Hedström

Keyword(s):

Sea Ice ◽

Mixed Layer ◽

Climate Models ◽

Regional Climate ◽

The Arctic ◽

Water Runoff ◽

Polar Regions ◽

Ice Dynamics ◽

Climate System Model ◽

Ice Model

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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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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

The Cryosphere ◽

10.5194/tc-15-3207-2021 ◽

2021 ◽

Vol 15 (7) ◽

pp. 3207-3227

Author(s):

Timothy Williams ◽

Anton Korosov ◽

Pierre Rampal ◽

Einar Ólason

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Forecasting System ◽

Ice Model

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

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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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The Louvain-la-Neuve sea ice model LIM3.5: global and regional capabilities

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3403-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3403-3441 ◽

Cited By ~ 1

Author(s):

C. Rousset ◽

M. Vancoppenolle ◽

G. Madec ◽

T. Fichefet ◽

S. Flavoni ◽

...

Keyword(s):

Sea Ice ◽

Open Water ◽

The Arctic ◽

Small Scale ◽

Solid Base ◽

Ice Dynamics ◽

Svalbard Archipelago ◽

Wide Range ◽

Opening And Closing ◽

Ice Model

Abstract. We present the new 3.5 version of the Louvain-la-Neuve sea ice model (LIM) integrated in NEMO 3.6. The main novelty is the improvement of model robustness and versatility for a wide range of applications, from global to regional scales. Several modifications to the code were required. First, the time stepping scheme of the model was changed from parallel to sequential (ice dynamics first, then thermodynamics). Such a scheme enables to diagnose the different physical processes responsible for exchanges through the air–ice–ocean interfaces, as well as the online inspection of mass, heat and salt conservation properties of the code. In the course of these developments, several minor conservation leaks were found and fixed, so that LIM3.5 is exactly conservative. Second, lateral boundary conditions for regional ice-covered configurations have been implemented. To illustrate the new capabilities, two simulations are performed. One is a global simulation at a nominal 2° resolution forced by atmospheric climatologies and is found reasonably realistic although no specific tuning was done. The other is a regional simulation at 2 km resolution around the Svalbard Archipelago in the Arctic Ocean, with prescribed conditions at the four boundaries including tides. The simulation is able to resolve small-scale features and transient events such as the opening and closing of coastal polynyas. The ice mass budgets for both simulations are illustrated and mostly differ by the strength of ice formation in open water. LIM3.5 now forms a solid base for future scientific studies and model developments.

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Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

Remote Sensing ◽

10.3390/rs13112174 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2174

Author(s):

Lijian Shi ◽

Sen Liu ◽

Yingni Shi ◽

Xue Ao ◽

Bin Zou ◽

...

Keyword(s):

Time Series ◽

Sea Ice ◽

Ocean Circulation ◽

The Arctic ◽

Retrieval Algorithm ◽

Atmospheric Effects ◽

Observation Data ◽

Long Time Series ◽

Ice Concentration ◽

Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Remote Sensing ◽

10.3390/rs13061139 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1139

Author(s):

David Llaveria ◽

Juan Francesc Munoz-Martin ◽

Christoph Herbert ◽

Miriam Pablos ◽

Hyuk Park ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Cost Effective ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Neural Network Approach ◽

Ice Concentration ◽

L Band ◽

Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

Remote Sensing ◽

10.3390/rs13122283 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2283

Author(s):

Hyangsun Han ◽

Sungjae Lee ◽

Hyun-Cheol Kim ◽

Miae Kim

Keyword(s):

Random Forest ◽

Sea Ice ◽

Arctic Ocean ◽

Open Water ◽

The Arctic ◽

Atmospheric Effects ◽

Sea Ice Concentration ◽

Air Temperatures ◽

The Pacific ◽

Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽

10.3390/atmos12020174 ◽

2021 ◽

Vol 12 (2) ◽

pp. 174

Author(s):

Günther Heinemann ◽

Sascha Willmes ◽

Lukas Schefczyk ◽

Alexander Makshtas ◽

Vasilii Kustov ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Open Water ◽

The Arctic ◽

Late Winter ◽

Good Representation ◽

Hourly Temperature ◽

Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

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