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 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 (∼&amp;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 (

Sea ice melt and meteoric water distributions in Nares Strait, Baffin Bay, and the Canadian Arctic Archipelago

2010 ◽  
Vol 68 (6) ◽  
pp. 767-798 ◽  
Author(s):  
Matthew B. Alkire ◽  
Kelly K. Falkner ◽  
Timothy Boyd ◽  
Robie W. Macdonald
Keyword(s):  
Sea Ice ◽  
Meteoric Water ◽  
Canadian Arctic ◽  
Canadian Arctic Archipelago ◽  
Baffin Bay ◽  
Ice Melt ◽  
Nares Strait

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

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

&lt;p&gt;The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.&lt;/p&gt;&lt;p&gt;We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.&lt;/p&gt;&lt;p&gt;The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.&lt;/p&gt;

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

Sea ice deformation and thickness in the Western Ross Sea

2021 ◽  
Author(s):  
Wolfgang Rack ◽  
Daniel Price ◽  
Christian Haas ◽  
Patricia J. Langhorne ◽  
Greg H. Leonard
Keyword(s):  
Sea Ice ◽  
Satellite Images ◽  
Ross Sea ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Ice Dynamics ◽  
Sea Ice Thickness ◽  
Ice Volume ◽  
Terra Nova Bay ◽  
Western Ross Sea

&lt;p&gt;Sea ice cover is arguably the longest and best observed climate variable from space, with over four decades of highly reliable daily records of extent in both hemispheres. In Antarctica, a slight positive decadal trend in sea ice cover is driven by changes in the western Ross Sea, where a variation in weather patterns over the wider region forced a change in meridional winds. The distinguishing wind driven sea ice process in the western Ross Sea is the regular occurrence of the Ross Sea, McMurdo Sound, and Terra Nova Bay polynyas. Trends in sea ice volume and mass in this area unknown, because ice thickness and dynamics are particularly hard to measure.&lt;/p&gt;&lt;p&gt;Here we present the first comprehensive and direct assessment of large-scale sea-ice thickness distribution in the western Ross Sea. Using an airborne electromagnetic induction (AEM) ice thickness sensor towed by a fixed wing aircraft (Basler BT-67), we observed in November 2017 over a distance of 800 km significantly thicker ice than expected from thermodynamic growth alone. By means of time series of satellite images and wind data we relate the observed thickness distribution to satellite derived ice dynamics and wind data. Strong southerly winds with speeds of up to 25 ms&lt;sup&gt;-1&lt;/sup&gt; in early October deformed the pack ice, which was surveyed more than a month later.&lt;/p&gt;&lt;p&gt;We found strongly deformed ice with a mean and maximum thickness of 2.0 and 15.6 m, respectively. Sea-ice thickness gradients are highest within 100-200 km of polynyas, where the mean thickness of the thickest 10% of ice is 7.6 m. From comparison with aerial photographs and satellite images we conclude that ice preferentially grows in deformational ridges; about 43% of the sea ice volume in the area between McMurdo Sound and Terra Nova Bay is concentrated in more than 3 m thick ridges which cover about 15% of the surveyed area. Overall, 80% of the ice was found to be heavily deformed and concentrated in ridges up to 11.8 m thick.&lt;/p&gt;&lt;p&gt;Our observations hold a link between wind driven ice dynamics and the ice mass exported from the western Ross Sea. The sea ice statistics highlighted in this contribution forms a basis for improved satellite derived mass balance assessments and the evaluation of sea ice simulations.&lt;/p&gt;

scholarly journals Monitoring evolution of melt ponds on first-year and multiyear sea ice in the Canadian Arctic Archipelago with optical satellite data

2020 ◽  
Vol 61 (82) ◽  
pp. 154-163
Author(s):  
Qing Li ◽  
Chunxia Zhou ◽  
Lei Zheng ◽  
Tingting Liu ◽  
Xiaotong Yang
Keyword(s):  
Sea Ice ◽  
Canadian Arctic ◽  
Arctic Sea Ice ◽  
First Year ◽  
Landsat 8 ◽  
Canadian Arctic Archipelago ◽  
Melt Pond ◽  
Melt Ponds ◽  
Validation Experiment ◽  
Multiyear Ice

AbstractThe evolution of melt ponds on Arctic sea ice in summer is one of the main factors that affect sea-ice albedo and hence the polar climate system. Due to the different spectral properties of open water, melt pond and sea ice, the melt pond fraction (MPF) can be retrieved using a fully constrained least-squares algorithm, which shows a high accuracy with root mean square error ~0.06 based on the validation experiment using WorldView-2 image. In this study, the evolution of ponds on first-year and multiyear ice in the Canadian Arctic Archipelago was compared based on Sentinel-2 and Landsat 8 images. The relationships of pond coverage with air temperature and albedo were analysed. The results show that the pond coverage on first-year ice changed dramatically with seasonal maximum of 54%, whereas that on multiyear ice changed relatively flat with only 30% during the entire melting period. During the stage of pond formation, the ponds expanded rapidly when the temperature increased to over 0°C for three consecutive days. Sea-ice albedo shows a significantly negative correlation (R = −1) with the MPF in melt season and increases gradually with the refreezing of ponds and sea ice.

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