Statistical Mechanics and the Climatology of the Arctic Sea Ice Thickness Distribution. [PDF]
J Stat Phys, 2017 We study the seasonal changes in the thickness distribution of Arctic sea ice, $g(h)$, under climate forcing. Our analytical and numerical approach is based on a Fokker-Planck equation for $g(h)$ (Toppaladoddi \& Wettlaufer \emph{Phys. Rev. Lett.} {\bf 115}, 148501, 2015), in which the thermodynamic growth rates are determined using observed ...
Toppaladoddi S, Wettlaufer JS.
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Improving the spatial distribution of modeled Arctic sea ice thickness [PDF]
, 2005 The spatial distribution of ice thickness/draft in the Arctic Ocean is examined using a sea ice model. A comparison of model predictions with submarine observations of sea ice draft made during cruises between 1987 and 1997 reveals that the model has the
Paul Miller +2 more
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Seasonal evolution of the Arctic sea ice thickness distribution [PDF]
Journal of Geophysical Research: Oceans, 2022 The Thorndike et al., (\emph{J. Geophys. Res.} {\bf 80} 4501, 1975) theory of the ice thickness distribution, $g(h)$, treats the dynamic and thermodynamic aggregate properties of the ice pack in a novel and physically self-consistent manner. Therefore, it has provided the conceptual basis of the treatment of sea-ice thickness categories in climate ...
S. Toppaladoddi +2 more
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Thickness distribution of Antarctic sea ice [PDF]
Journal of Geophysical Research: Oceans, 2008 Shipâbased observations are used to describe regional and seasonal changes in the thickness distribution and characteristics of sea ice and snow cover thickness around Antarctica. The data set comprises 23,373 observations collected over more than 2 decades of activity and has been compiled as part of the Scientific Committee on Antarctic Research ...
A. P. Worby +5 more
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Discrimination Algorithm and Procedure of Snow Depth and Sea Ice Thickness Determination Using Measurements of the Vertical Ice Temperature Profile by the Ice-Tethered Buoys [PDF]
Sensors, 2018 Snow depth and sea ice thickness in the Polar Regions are significant indicators of climate change and have been measured over several decades by ice-tethered buoys.
Guangyu Zuo, Yinke Dou, Ruibo Lei
doaj +2 more sources
Sea ice deformation and thickness in the Western Ross Sea [PDF]
, 2021 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.
Haas, Christian +4 more
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A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data [PDF]
The Cryosphere, 2017 Sea-ice thickness on a global scale is derived from different satellite sensors using independent retrieval methods. Due to the sensor and orbit characteristics, such satellite retrievals differ in spatial and temporal resolution as well as in the ...
R. Ricker +7 more
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Near-real-time Arctic sea ice thickness and volume from CryoSat-2 [PDF]
The Cryosphere, 2016 Timely observations of sea ice thickness help us to understand the Arctic climate, and have the potential to support seasonal forecasts and operational activities in the polar regions.
R. L. Tilling, A. Ridout, A. Shepherd
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Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness [PDF]
The CryosphereSea ice thickness is an essential climate variable, which is often derived from satellite altimetry freeboard estimates, e.g., by CryoSat-2. In order to convert freeboard to sea ice thickness, assumptions are needed for snow thickness, snow density, sea ...
I. Sievers +2 more
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Sea Ice Brightness Temperature as a Function of Ice Thickness: Computed curves for AMSR-E and SMOS (frequencies from 1.4 to 89 GHz) [PDF]
arXiv, 2012 The relationship between sea ice thickness and microwave brightness temperature is explored. Parameterized ice profiles are fed to a radiative-transfer-based sea ice emissivity model (Microwave Emmission of Layered Snowpack, MEMLS). Complex permittivities, required as input for the simulation, are determined using a semi-empirical mixture model.
Peter Mills, Georg Heygster
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