Automated Sea Ice Prediction Systems

doi:10.1017/9781108277600.006

5 - Automated Sea Ice Prediction Systems

Published online by Cambridge University Press: 12 October 2017

Tom Carrieres ,

Alain Caya ,

Pam Posey ,

Greg Smith ,

Viatcheslav Kharin and

Adrienne Tivy

Edited by

Tom Carrieres ,

Mark Buehner ,

Jean-Franҫois Lemieux and

Leif Toudal Pedersen

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Tom Carrieres: Affiliation:
Environment and Climate Change Canada
Mark Buehner: Affiliation:
Environment and Climate Change Canada
Jean-Franҫois Lemieux: Affiliation:
Environment and Climate Change Canada
Leif Toudal Pedersen: Affiliation:
Technical University of Denmark, Lyngby

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Summary

A number of sea ice automated prediction systems are currently providing operational output to national ice service organizations. The Los Alamos CICE sea ice model is widely used in these systems but there is more variety in other areas, such as ocean forcing and model initialization. RIPS is a regional implementation of CICE that supports the CIS and includes a sophisticated 3DVar data assimilation system and a number of other innovations. GIOPS is a global ice-ocean prediction system that has been developed in close collaboration with Mercator-Ocean and satisfies a growing need in Canada for a multi-purpose global marine core service. TOPAZ is the Arctic regional component of the Copernicus Marine Environment Monitoring Service and uses the HYCOM ocean model and LIM sea ice model with an Ensemble Kalman Filter (EnKF) data assimilation system. The ACNFS/GOFS system supports the NIC and its global mandate using the CICE sea ice model and the HyCom ocean model. CanSIPS targets Canadian marine clients by providing extended range forecasts using of an global ensemble fully coupled atmosphere-ice-ocean model.

Information

Type: Chapter
Information: Sea Ice Analysis and Forecasting
Towards an Increased Reliance on Automated Prediction Systems
, pp. 109 - 143

DOI: https://doi.org/10.1017/9781108277600.006 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2017

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