[1] The Canadian Ice Service Digital Archive (CISDA) is a compilation of weekly ice charts covering Canadian waters from the early 1960s to present. The main sources of uncertainty in the database are reviewed and the data are validated for use in climate studies before trends and variability in summer averaged sea ice cover are investigated. These data revealed that between 1968 and 2008, summer sea ice cover has decreased by 11.3% ± 2.6% decade −1 in Hudson Bay, 2.9% ± 1.2% decade −1 in the Canadian Arctic Archipelago (CAA), 8.9% ± 3.1% decade −1 in Baffin Bay, and 5.2% ± 2.4% decade −1 in the Beaufort Sea with no significant reductions in multiyear ice. Reductions in sea ice cover are linked to increases in early summer surface air temperature (SAT); significant increases in SAT were observed in every season and they are consistently greater than the pan-Arctic change by up to ∼0.2°C decade −1 . Within the CAA and Baffin Bay, the El Niño-Southern Oscillation index correlates well with multiyear ice coverage (positive) and first-year ice coverage (negative) suggesting that El Niño episodes precede summers with more multiyear ice and less first-year ice. Extending the trend calculations back to 1960 along the major shipping routes revealed significant decreases in summer sea ice coverage ranging between 11% and 15% decade −1 along the route through Hudson Bay and 6% and 10% decade −1 along the southern route of the Northwest Passage, the latter is linked to increases in SAT. Between 1960 and2008, no significant trends were found along the northern western Parry Channel route of the Northwest Passage.

* This only constitutes a subset of the images that were investigated; many images were scrutinized without any ice islands being identified. † These values represent the contents of the database based on digitization that was completed through December 2012. ‡ Ice islands originating from these glaciers were not always monitored from the time of initial calving.

A three-dimensional variational data assimilation (3DVAR) system has been developed to provide analyses of the ice-ocean state and to initialize a coupled ice-ocean numerical model for forecasting sea ice conditions. This study focuses on the estimation of the background-error statistics, including the spatial and multivariate covariances, and their impact on the quality of the resulting sea ice analyses and forecasts. The covariances are assumed to be horizontally homogeneous and fixed in time. The horizontal correlations are assumed to have a Gaussian shape and are modeled by integrating a diffusion equation. A relatively simple implementation of the ensemble Kalman filter is used to produce ensembles of the ice-ocean model state that are representative of background error and from which the 3DVAR covariance parameters are estimated.Data assimilation experiments, using various configurations of 3DVAR and simpler assimilation approaches, are conducted over a 7-month period during the winter of 2006/07 for the Canadian east coast region. The only data assimilated are the gridded daily ice charts and RADARSAT image analyses produced by the Canadian Ice Service. All of the data assimilation experiments produce significantly improved shortterm forecasts as compared with persistence. When assimilating the same data, the forecast quality from the experiments employing either the 3DVAR, direct insertion, or nudging is quite similar. However, assimilation of both the daily ice charts and RADARSAT image analyses in 3DVAR results in significant improvements to the sea ice concentration forecasts. This result supports the use of a data assimilation approach, such as 3DVAR, for combining multiple sources of observational data together with a sophisticated forecast model to provide analyses and forecasts of sea ice conditions.

This paper describes a new regional ice analysis system developed at Environment Canada. It is primarily designed to satisfy the requirements for planning of marine transportation and other marine operations in ice-infested waters around North America, including Canada's two Arctic Metareas; regional sea-ice model initialization; and the needs of regional numerical weather prediction models. A three-dimensional variational approach (3D-Var) is used to assimilate various types of observations. In this first version, only analyses of ice concentration are produced at approximately 5 km resolution using a 6 h persistence forecast from the previous analysis as the background state. The assimilated observations are sea-ice concentrations from two sources of passive microwave satellite data, Advanced Microwave Scanning Radiometer-Earth observing system (AMSR-E) and Special Sensor Microwave/Imager (SSM/I), and manually derived ice charts from the Canadian Ice Service (CIS). Objective verification scores computed from independent data are used to evaluate the accuracy of the analyses in comparison with other available sources of ice information. This comparison demonstrates that the new analyses have consistently more accurate ice extent compared with the currently operational global sea-ice analyses at the Canadian Meteorological Centre. It also shows that the early morning analysis can provide marine transportation clients with a valuable update to the most recently available CIS daily ice chart. RÉSUMÉ [Traduit par la rédaction] Cet article décrit un nouveau système d'analyse régionale des glaces mis au point à Environnement Canada. Il est principalement conçu pour répondre aux exigences de la planification du transport maritime et autres opérations maritimes dans les eaux infestées de glace entourant l'Amérique du Nord, y compris les deux régions météorologiques arctiques du Canada, pour l'initialisation du modèle régional de glaces de mer et pour les besoins des modèles régionaux de prévision météorologique numérique. Nous utilisons une approche variationnelle tridimensionnelle (3D-VAR) pour assimiler différents types d'observations. Dans cette première version, seules des analyses de la concentration de la glace sont produites avec une résolution d'environ 5 km à l'aide d'une prévision de 6 h basée sur la persistance à partir de l'analyse précédente utilisée comme l'ébauche. Les observations assimilées sont les concentrations de la glace de mer de deux sources satellitaires de données hyperfréquences passivesle radiomètre à balayage hyperfréquences de pointe du Système d'observation de la Terre (AMSR-E) et le capteur hyperfréquences spécialisé/imageur (SSM/I)et les cartes des glaces élaborées manuellement du Service canadien des glaces (SCG). Nous utilisons des indices de vérification objectifs calculés à partir de données indépendantes pour évaluer l'exactitude des analyses par comparaison avec d'autres sources d'information sur les glaces disponibles. Il ressort que les nouvelles analyses ont constamment des éten...

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.