scholarly journals Failure envelope of first-year Arctic sea ice: The role of friction in compressive fracture

2006 ◽  
Vol 111 (C11) ◽  
Author(s):  
E. M. Schulson ◽  
A. L. Fortt ◽  
D. Iliescu ◽  
C. E. Renshaw
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Failure Envelope ◽  
Compressive Fracture ◽  
Arctic Sea

Trends in Arctic sea ice and the role of atmospheric circulation

2013 ◽  
Vol 14 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Masayo Ogi ◽  
Ignatius G. Rigor
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
Arctic Sea Ice ◽  
Arctic Sea

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

scholarly journals Modeling the anisotropic brine microstructure in first-year Arctic sea ice

2012 ◽  
Vol 117 (C2) ◽  
pp. n/a-n/a ◽  
Author(s):  
K. A. Jones ◽  
M. Ingham ◽  
H. Eicken
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
First Year ◽  
Arctic Sea

Arctic sea ice variability and trends in the last four decades: role of ocean-atmospheric forcing

2021 ◽  
pp. 301-324
Author(s):  
Avinash Kumar ◽  
Juhi Yadav ◽  
Rohit Srivastava ◽  
Rahul Mohan
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Atmospheric Forcing ◽  
Arctic Sea

scholarly journals The role of cyclone activity in snow accumulation on Arctic sea ice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
M. A. Webster ◽  
C. Parker ◽  
L. Boisvert ◽  
R. Kwok
Keyword(s):  
Sea Ice ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
The Arctic ◽  
Cyclone Activity ◽  
In Situ Data ◽  
Arctic Sea ◽  
Spatio Temporal

AbstractIdentifying the mechanisms controlling the timing and magnitude of snow accumulation on sea ice is crucial for understanding snow’s net effect on the surface energy budget and sea-ice mass balance. Here, we analyze the role of cyclone activity on the seasonal buildup of snow on Arctic sea ice using model, satellite, and in situ data over 1979–2016. On average, 44% of the variability in monthly snow accumulation was controlled by cyclone snowfall and 29% by sea-ice freeze-up. However, there were strong spatio-temporal differences. Cyclone snowfall comprised ~50% of total snowfall in the Pacific compared to 83% in the Atlantic. While cyclones are stronger in the Atlantic, Pacific snow accumulation is more sensitive to cyclone strength. These findings highlight the heterogeneity in atmosphere-snow-ice interactions across the Arctic, and emphasize the need to scrutinize mechanisms governing cyclone activity to better understand their effects on the Arctic snow-ice system with anthropogenic warming.

scholarly journals Arctic sea-ice albedo derived from RGPS-based ice-thickness estimates

2001 ◽  
Vol 33 ◽  
pp. 225-229 ◽  
Author(s):  
R.W. Lindsay
Keyword(s):  
Sea Ice ◽  
Empirical Relationship ◽  
Early Spring ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
First Year ◽  
Large Set ◽  
Radar Images ◽  
Arctic Sea

AbstractThe RADARSAT geophysical processor system (RGPS) uses sequential synthetic aperture radar images of Arctic sea ice taken every 3 days to track a large set of Lagrangian points over the winter and spring seasons. The points are the vertices of cells, which are initially square and 10 km on a side, and the changes in the area of these cells due to opening and closing of the ice are used to estimate the fractional area of a set of first-year ice categories. The thickness of each category is estimated by the RGPS from an empirical relationship between ice thickness and the freezing degree-days since the formation of the ice. With a parameterization of the albedo based on the ice thickness, the albedo may be estimated from the first-year ice distribution. We compute the albedo for the first spring processed by the RGPS, the early spring of 1997. The data include most of the Beaufort and Chukchi Seas. We find that the mean albedo is 0.79 with a standard deviation of 0.04, with lower albedo values near the edge of the perennial ice zone. The biggest source of error is likely the assumed rate of snow accumulation on new ice.

Summer retreat of Arctic sea ice: Role of summer winds

2008 ◽  
Vol 35 (24) ◽  
Author(s):  
Masayo Ogi ◽  
Ignatius G. Rigor ◽  
Miles G. McPhee ◽  
John M. Wallace
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Arctic Sea
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