Dependence of Sea Ice Yield-Curve Shape on Ice Thickness [PDF]
Journal of Physical Oceanography, 2004 In this note, the authors discuss the contribution that frictional sliding of ice floes (or floe aggregates) past each other and pressure ridging make to the plastic yield curve of sea ice.
Alexander V. Wilchinsky, D. L. Feltham
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Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system [PDF]
The Cryosphere, 2019 The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state.
S. Fritzner +4 more
doaj +6 more sources
Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness [PDF]
The Cryosphere, 2018 Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue.
E. W. Blockley, K. A. Peterson
doaj +2 more sources
Assimilation of sea-ice concentration in a global climate model – physical and statistical aspects [PDF]
Ocean Science, 2013 We investigate the initialisation of Northern Hemisphere sea ice in the global climate model ECHAM5/MPI-OM by assimilating sea-ice concentration data. The analysis updates for concentration are given by Newtonian relaxation, and we discuss different ways
S. Tietsche +3 more
doaj +5 more sources
New insight from CryoSat-2 sea ice thickness for sea ice modelling
The Cryosphere, 2018 . Estimates of Arctic sea ice thickness have been 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 five ice thickness categories
David Schröder +4 more
openalex +2 more sources
Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection [PDF]
Remote Sensing, 2016 Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of ...
Hyun-cheol Kim +11 more
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Summertime sea-ice prediction in the Weddell Sea improved by sea-ice thickness initialization. [PDF]
Sci Rep, 2021 AbstractSkillful sea-ice prediction in the Antarctic Ocean remains a big challenge due to paucity of sea-ice observations and insufficient representation of sea-ice processes in climate models. Using a coupled general circulation model, this study demonstrates skillful prediction of the summertime sea-ice concentration (SIC) in the Weddell Sea with ...
Morioka Y +4 more
europepmc +5 more sources
Probabilistic Forecasts of Arctic Sea Ice Thickness [PDF]
Journal of Agricultural, Biological and Environmental Statistics, 2021 AbstractIn recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region.
Peter A. Gao +3 more
openalex +3 more sources
The effects of assimilating a sub-grid-scale sea ice thickness distribution in a new Arctic sea ice data assimilation system [PDF]
The Cryosphere, 2023 In the past decade groundbreaking new satellite observations of the Arctic sea ice cover have been made, allowing researchers to understand the state of the Arctic sea ice system in greater detail than before.
N. Williams +8 more
doaj +1 more source
Assimilating CryoSat-2 freeboard to improve Arctic sea ice thickness estimates [PDF]
The Cryosphere, 2023 In this study, a new method to assimilate freeboard (FB) derived from satellite radar altimetry is presented with the goal of improving the initial state of sea ice thickness predictions in the Arctic.
I. Sievers +3 more
doaj +1 more source