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.
[1] Ice algae are an important component of the carbon cycle in the Arctic. We investigate the dynamics of an ice algae bloom by coupling an ice algae-nutrient model with a multilayer s coordinate thermodynamic sea ice model. The model is tested with the simulation of an algal bloom at the base of first-year ice over the spring. Model output is compared with data from Barrow Strait in the Canadian Arctic Archipelago. Snow cover, through its influence on ice melt, is a dominant factor controlling the decline of the bloom in the model, a finding that supports past studies. The results show that under a higher snow cover (20 cm), biomass in the early stages of the algal bloom is less than expected from the observed data. This discrepancy is due to the severely light-limited algal growth, despite the close match between simulated and observed under-ice photosynthetically active radiation. This result raises issues of how photosynthetic parameters as well as radiative transfer is represented in one-dimensional ice models. This study also shows that for higher algal concentrations, when biomass is split over multiple layers rather than concentrated in one layer at the ice base, there is a reduction in algae accumulation, a result of self shading. In addition, experiments show a sensitivity of total biomass to the oceanic heat flux and ice layer thickness, both of which affect biomass loss at the ice base. Being able to accurately model physical conditions is essential before the seasonal dynamics of ice algae can be accurately modeled, and some recommendations for improvement are discussed.
This study describes the impact from three major modifications to an existing ice-analysis system developed at Environment Canada. The analysis component of the Regional Ice Prediction System currently provides near real-time gridded estimates of ice concentration for all ice-affected areas around North America and Greenland, and is primarily aimed to satisfy the operational requirements of the Canadian Ice Service. The first modification is the assimilation of Special Sensor Microwave Imager/Sounder data from three satellite platforms to complement the already assimilated Special Sensor Microwave Imager data from one platform. The second change is the assimilation of ice-concentration information derived from Advanced Scatterometer data. The third modification is to replace the ice concentration in the analysis with spatially interpolated values for all grid points where an estimated measure of uncertainty is above a specified threshold. Objective verification scores were computed from 1 year experiments spanning all of 2010 using independent verification data to evaluate the accuracy of the analyses. The incremental impact of adding each of the three modifications is examined along with the combined impact from the three modifications. It is demonstrated that the new version of the system produces consistently more accurate ice-concentration analyses than the previous version, especially during the summer period and when the ice is refreezing.
A new tool has been developed to calculate dynamic, state-specific tie points, to aid in the assimilation of various types of satellite data into Environment and Climate Change Canada’s Regional Ice Ocean Prediction System. These tie points are referred to as characteristic values (CVs). In this study, CVs are calculated for RadarSat-2 ScanSAR-Wide-A HH-HV backscatter data from October 2010 to September 2011. In this collection, the mean, standard deviation, and percentile distribution of backscatter at locations and times identified as being either ice or open water are represented over different relevant categories affecting the signal. The resulting water CVs are compared with modeled backscatter values, and are in close agreement at midrange wind speeds (5–14 m s−1), where wind slicks are not present. When compared against previously reported values, the ice CVs correspond best for ice conditions with fairly uniform backscatter distributions, such as the Arctic during the spring. When the ice and water CVs are compared to each other, the best cases for the assimilation of RadarSat-2 data are evident. In these cases, the CV distributions at a given incidence angle and wind speed are well separated from each other, such as in the far range (40°–50°) at midrange wind speeds. This set of CVs will be used for an initial assimilation of binary ice and open water retrievals. Future work will include a more complex treatment of ice CVs to address mixed ice types, and the application of CVs to other types of satellite data, including those from passive microwave sensors.
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