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The Regional Ice Prediction System (RIPS): verification of forecast sea ice concentration

Authors :
Christiane Beaudoin
Pierre Pellerin
Mark Buehner
Jack Chen
Frédéric Dupont
Gilles Garric
Tom Carrieres
François Roy
Gregory C. Smith
Alain Caya
Anna Shlyaeva
André Plante
Hal Ritchie
Nicolas Ferry
Jean-François Lemieux
Paul Pestieau
Lynn Pogson
Patricia DeRepentigny
Source :
Quarterly Journal of the Royal Meteorological Society
Publication Year :
2015
Publisher :
Wiley, 2015.

Abstract

In recent years, the demand for improved environmental forecasts in the Arctic has intensified as maritime transport and offshore exploration increase. As a result, Canada has accepted responsibility for the preparation and issuing services for the new Arctic MET/NAV Areas XVII and XVIII. Environmental forecasts are being developed based on a new integrated Arctic marine prediction system. Here, we present the first phase of this initiative, a short-term pan-Arctic 1/12° resolution Regional Ice Prediction System (RIPS). RIPS is currently set to perform four 48 h forecasts per day. The RIPS forecast model (CICE 4.0) is forced by atmospheric forecasts from the Environment Canada regional deterministic prediction system. It is initialized with a 3D-Var analysis of sea ice concentration and the ice velocity field and thickness distribution from the previous forecast. The other forcing (surface current) and initialization fields (mixed-layer depth, sea surface temperature and salinity) come from the 1/4° resolution Global Ice Ocean Prediction System. Three verification methods for sea ice concentration are presented. Overall, verifications over a complete seasonal cycle (2011) against the Ice Mapping System ice extent product show that RIPS 48 h forecasts are better than persistence during the growth season while they have a lower skill than persistence during the melt period. A better representation of landfast ice, oceanic processes (wave–ice interactions, upwelling events, etc.) in the marginal ice zone and better initializing fields should lead to improved forecasts.

Details

ISSN :
1477870X and 00359009
Volume :
142
Database :
OpenAIRE
Journal :
Quarterly Journal of the Royal Meteorological Society
Accession number :
edsair.doi.dedup.....04e1ea1511e7e13a96e13d65d4534bd7
Full Text :
https://doi.org/10.1002/qj.2526