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Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

dc.contributor.authorSmith, G.C.
dc.contributor.authorRoy, F.
dc.contributor.authorReszka, M.
dc.contributor.authorSurcel Colan, D.
dc.contributor.authorHe, Z.
dc.contributor.authorDeacu, D.
dc.contributor.authorBelanger, J.-M.
dc.contributor.authorSkachko, S.
dc.contributor.authorLiu, Y.
dc.contributor.authorDupont, F.
dc.contributor.authorLemieux, J.-F.
dc.contributor.authorBeaudoin, C.
dc.contributor.authorTranchant, B.
dc.contributor.authorDrévillon, M.
dc.contributor.authorGarric, G.
dc.contributor.authorTestut, C.-E.
dc.contributor.authorLellouche, J.-M.
dc.contributor.authorPellerin, P.
dc.contributor.authorRitchie, H.
dc.contributor.authorLu, Y.
dc.contributor.authorDavidson, F.
dc.contributor.authorBuehner, M.
dc.contributor.authorCaya, A.
dc.contributor.authorLajoie, M.
dc.date2016
dc.date.accessioned2016-03-31T12:16:17Z
dc.date.available2016-03-31T12:16:17Z
dc.identifier.urihttps://orfeo.belnet.be/handle/internal/3219
dc.descriptionRecent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4°-resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea-level anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill. © 2015 Royal Meteorological Society May 2015.
dc.languageeng
dc.titleSea ice forecast verification in the Canadian Global Ice Ocean Prediction System
dc.typeArticle
dc.subject.frascatiEarth and related Environmental sciences
dc.audienceScientific
dc.subject.freeAtmospheric temperature
dc.subject.freeForecasting
dc.subject.freeOceanography
dc.subject.freeSea ice
dc.subject.freeSea level
dc.subject.freeSurface waters
dc.subject.freeVerification
dc.subject.freeCanadian
dc.subject.freeData assimilation
dc.subject.freeGlobal
dc.subject.freeOcean modelling
dc.subject.freeOperational oceanography
dc.subject.freeIce
dc.source.titleQuarterly Journal of the Royal Meteorological Society
dc.source.volume142
dc.source.issue695
dc.source.page659-671
Orfeo.peerreviewedYes
dc.identifier.doi10.1002/qj.2555
dc.identifier.scopus2-s2.0-84929430917


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