scholarly journals Thermodynamic and Dynamic Ice Thickness Changes in the Canadian Arctic Archipelago in NEMO-LIM2 Numerical Simulations

2017 ◽  
Author(s):  
Xianmin Hu ◽  
Jingfan Sun ◽  
Ting On Chan ◽  
Paul G. Myers
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Numerical Simulations ◽  
Canadian Arctic ◽  
Horizontal Resolution ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
The West ◽  
Sea Ice Thickness ◽  
Frame Work

Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest. In this study, based on the NEMO numerical frame work including the LIM2 sea ice module, simulations at both 1/4° and 1/12° horizontal resolution were conducted from 2002 to 2016. The model captures well the general spatial distribution of ice thickness in the CAA region, with very thick sea ice (∼&rthinsp;4 m and thicker) in the northern CAA, thick sea ice (2.5 m to 3 m) in the west-central Parry Channel and M'Clintock Channel, and thin (

scholarly journals Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations

2018 ◽  
Vol 12 (4) ◽  
pp. 1233-1247 ◽  
Author(s):  
Xianmin Hu ◽  
Jingfan Sun ◽  
Ting On Chan ◽  
Paul G. Myers
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Horizontal Resolution ◽  
Scale Model ◽  
Ice Thickness ◽  
Canadian Arctic Archipelago ◽  
High Concentration ◽  
Baffin Bay ◽  
Ice Growth ◽  
Ice Volume

Abstract. Sea ice thickness evolution within the Canadian Arctic Archipelago (CAA) is of great interest to science, as well as local communities and their economy. In this study, based on the NEMO numerical framework including the LIM2 sea ice module, simulations at both 1∕4 and 1/12∘ horizontal resolution were conducted from 2002 to 2016. The model captures well the general spatial distribution of ice thickness in the CAA region, with very thick sea ice (∼ 4 m and thicker) in the northern CAA, thick sea ice (2.5 to 3 m) in the west-central Parry Channel and M'Clintock Channel, and thin (<2 m) ice (in winter months) on the east side of CAA (e.g., eastern Parry Channel, Baffin Island coast) and in the channels in southern areas. Even though the configurations still have resolution limitations in resolving the exact observation sites, simulated ice thickness compares reasonably (seasonal cycle and amplitudes) with weekly Environment and Climate Change Canada (ECCC) New Ice Thickness Program data at first-year landfast ice sites except at the northern sites with high concentration of old ice. At 1∕4 to 1/12∘ scale, model resolution does not play a significant role in the sea ice simulation except to improve local dynamics because of better coastline representation. Sea ice growth is decomposed into thermodynamic and dynamic (including all non-thermodynamic processes in the model) contributions to study the ice thickness evolution. Relatively smaller thermodynamic contribution to ice growth between December and the following April is found in the thick and very thick ice regions, with larger contributions in the thin ice-covered region. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay while a decline (r2 ≈ 0.6, p < 0.01) is simulated in Parry Channel region. The two main contributors (thermodynamic growth and lateral transport) have high interannual variabilities which largely balance each other, so that maximum ice volume can vary interannually by ±12 % in the northern CAA, ±15 % in Parry Channel, and ±9 % in Baffin Bay. Further quantitative evaluation is required.

scholarly journals A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

2012 ◽  
Vol 6 (6) ◽  
pp. 1507-1526 ◽  
Author(s):  
J. Karvonen ◽  
B. Cheng ◽  
T. Vihma ◽  
M. Arkett ◽  
T. Carrieres
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Added Value ◽  
Ice Thickness ◽  
Motion Field ◽  
Sea Ice Thickness ◽  
Automated Method ◽  
Basin Scale ◽  
Sar Data

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.

scholarly journals Improving the spatial distribution of modeled Arctic sea ice thickness

2005 ◽  
Vol 32 (18) ◽  
pp. n/a-n/a ◽  
Author(s):  
Paul A. Miller ◽  
Seymour W. Laxon ◽  
Daniel L. Feltham
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

scholarly journals Sea ice thickness in the Eastern Canadian Arctic: Hudson Bay Complex & Baffin Bay

2017 ◽  
Vol 200 ◽  
pp. 281-294 ◽  
Author(s):  
Jack C. Landy ◽  
Jens K. Ehn ◽  
David G. Babb ◽  
Nathalie Thériault ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Ice Thickness ◽  
Hudson Bay ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
Eastern Canadian Arctic

scholarly journals A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

2012 ◽  
Vol 6 (3) ◽  
pp. 1871-1914 ◽  
Author(s):  
J. Karvonen ◽  
B. Cheng ◽  
T. Vihma ◽  
M. Arkett ◽  
T. Carrieres
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Added Value ◽  
Ice Thickness ◽  
Motion Field ◽  
Visual Interpretation ◽  
Sea Ice Thickness ◽  
Basin Scale ◽  
Sar Data

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a method for ice concentration and thickness analysis combining modelling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modelling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness.

scholarly journals Assimilating Copernicus SST Data into a Pan-Arctic Ice–Ocean Coupled Model with a Local SEIK Filter

2017 ◽  
Vol 34 (9) ◽  
pp. 1985-1999 ◽  
Author(s):  
Xi Liang ◽  
Qinghua Yang ◽  
Lars Nerger ◽  
Svetlana N. Losa ◽  
Biao Zhao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Coupled Model ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Canadian Arctic Archipelago ◽  
Negative Effects ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Ice

AbstractSea surface temperature (SST) data from the Copernicus Marine Environment Monitoring Service are assimilated into a pan-Arctic ice–ocean coupled model using the ensemble-based local singular evolutive interpolated Kalman (LSEIK) filter. This study found that the SST deviation between model hindcasts and independent SST observations is reduced by the assimilation. Compared with model results without data assimilation, the deviation between the model hindcasts and independent SST observations has decreased by up to 0.2°C at the end of summer. The strongest SST improvements are located in the Greenland Sea, the Beaufort Sea, and the Canadian Arctic Archipelago. The SST assimilation also changes the sea ice concentration (SIC). Improvements of the ice concentrations are found in the Canadian Arctic Archipelago, the Beaufort Sea, and the central Arctic basin, while negative effects occur in the west area of the eastern Siberian Sea and the Laptev Sea. Also, sea ice thickness (SIT) benefits from ensemble SST assimilation. A comparison with upward-looking sonar observations reveals that hindcasts of SIT are improved in the Beaufort Sea by assimilating reliable SST observations into light ice areas. This study illustrates the advantages of assimilating SST observations into an ice–ocean coupled model system and suggests that SST assimilation can improve SIT hindcasts regionally during the melting season.

Sign in / Sign up

Export Citation Format

Share Document