A New Discrete Element Sea-Ice Model for Earth System Modeling

Sea ice forms a frozen crust of sea water oating in high-latitude oceans. It is a critical component of the Earth system because its formation helps to drive the global thermohaline circulation, and its seasonal waxing and waning in the high north and Southern Ocean signi cantly affects planetary al...

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Bibliographic Details
Main Author: Turner, Adrian Keith
Language:unknown
Published: 2021
Subjects:
Online Access:http://www.osti.gov/servlets/purl/1346837
https://www.osti.gov/biblio/1346837
https://doi.org/10.2172/1346837
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Summary:Sea ice forms a frozen crust of sea water oating in high-latitude oceans. It is a critical component of the Earth system because its formation helps to drive the global thermohaline circulation, and its seasonal waxing and waning in the high north and Southern Ocean signi cantly affects planetary albedo. Usually 4{6% of Earth's marine surface is covered by sea ice at any one time, which limits the exchange of heat, momentum, and mass between the atmosphere and ocean in the polar realms. Snow accumulates on sea ice and inhibits its vertical growth, increases its albedo, and contributes to pooled water in melt ponds that darken the Arctic ice surface in the spring. Ice extent and volume are subject to strong seasonal, inter-annual and hemispheric variations, and climatic trends, which Earth System Models (ESMs) are challenged to simulate accurately (Stroeve et al., 2012; Stocker et al., 2013). This is because there are strong coupled feedbacks across the atmosphere-ice-ocean boundary layers, including the ice-albedo feedback, whereby a reduced ice cover leads to increased upper ocean heating, further enhancing sea-ice melt and reducing incident solar radiation re ected back into the atmosphere (Perovich et al., 2008). A reduction in perennial Arctic sea-ice during the satellite era has been implicated in mid-latitude weather changes, including over North America (Overland et al., 2015). Meanwhile, most ESMs have been unable to simulate observed inter-annual variability and trends in Antarctic sea-ice extent during the same period (Gagne et al., 2014).