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  • View in gallery
    Fig. 1.

    (top) Topography field (m) and (bottom) land–water fractional mask (%) for (a), (c) 24 and (b), (d) 4 km. Coastlines are represented by black solid lines

  • View in gallery
    Fig. 2.

    Domain of the ocean–ice model on a Mercator chart. The thin lines on the land represent the major rivers. The area of the model domain extends between open boundaries near Cabot Strait, the Strait of Belle Isle, the upper limits of tidal influence near Montréal, and to the head of the Saguenay Fjord. The gridpoint spacing is 5 km

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    Fig. 3.

    Scheme of the coupled atmospheric–ocean-ice model system. The arrow indicates the data-exchange flow. Two processes working in opposite directions (coupler) allow the linking to be simple and efficient between models

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    Fig. 4.

    Coupling scenario 1. The large arrows represent time steps for the atmospheric and ocean-ice model. Atmospheric model: dt = 600 s; dx = 24 km. Oceanic model: dt = 300 s; dx = 5 km. The thin arrows represent the communications between both models. Number 1: ocean model receives air temperature at time (0); 2: atmospheric model receives heat flux at time (0); 3: first time step for the atmospheric model; 4: ocean model receives the updated air temperature; 5: the first oceanic time steps; 6: same as for step 2, but for time (1)

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    Fig. 5.

    Same as Fig. 4 but for the higher-resolution simulation: dt = 50 s and dx = 4 km. The heat flux used by the intermediate time steps (between two coupling; steps 3 and 6) are always calculated on the ocean grid. Steps 4 and 5 represent the connections, between both models, used to calculate the heat flux for the atmospheric model. Number 1: ocean model receives air temperature at time (0); 2: atmospheric model receives heat flux at time (0); 3: the first time step for the atmospheric model; 4: the ocean model receives the updated air temperature; 5: the atmospheric model receives the heat flux computed with the new air temperature; 6: a new intermediate atmospheric time step; 7: the ocean model receives the updated air temperature; 8: the first oceanic time step; 9: same as step 2 but for time (1)

  • View in gallery
    Fig. 6.

    (top) Atmospheric and (bottom) ice analyses over the Gulf of St. Lawrence for mid-Mar. Isobar (solid lines, 4 hPa) and 1000-hPa wind (arrow length, kt) analyses valid respectively at (a) 0000 UTC 12 Mar and (b) 0000 UTC 14 Mar 1997. The ice analysis valid on (c) 12 Mar and (d) 14 Mar

  • View in gallery
    Fig. 7.

    Ice fields (top) observed and (bottom) simulated valid at 2000 UTC 14 Mar 1997. Ice observation [light blue pattern in (a)] deduced from a GOES visible picture with a cloud mask superposition deduced from the infrared channel. The simulated ice coverage (contours: every 10%) obtained with one- and two-way coupling technique are shown in (b) and (c), respectively. The letters identify different ice patterns (red lines) mainly produced by the ice field movement. Pattern B represents a trajectory of ice particles

  • View in gallery
    Fig. 8.

    Difference of the sea ice volume change between the two- and one-way coupling (two-way minus one-way). The solid line (left axis) expresses this sea ice volume change as a percent volume by category. The abscissa gives the sea-ice-coverage categories in percentage

  • View in gallery
    Fig. 9.

    Comparison of low-level clouds, ice, and ice-free water. (a) The satellite picture, (b) the one-way, and (c) two-way simulations for the atmospheric model (4 km). Valid at 1230 UTC 14 Mar 1997. Land: yellow in (a) and white in (b), (c); Ice: gray; clouds: white/blue in (a) and green/blue in (b), (c). The red lines mark the low-level clouds' edge generated by the new open water available in the two-way simulation. M. I. = Madalen Islands; P-E. I. = Prince Edward Island

  • View in gallery
    Fig. 10.

    Surface flow simulated by the high-resolution (4 km) two-way system. Valid at 1230 UTC 14 Mar 1997. The black arrows represent the surface winds (kt) while the shaded (white contours) patterns represent the surface temperatures (°C). The three dashed arrows represent the main trajectories followed by the surface flow

  • View in gallery
    Fig. 11.

    The difference between the two- and one-way coupling simulations (4 km) for the surface temperature averaged over the last 24;chh forecast corresponding to 14 Mar 1997. Labels represent the observations for the same period. Most of the domain has been warmed up by the two-way coupling simulation

  • View in gallery
    Fig. 12.

    Comparison between averaged observed and simulated surface temperature. The average was done over the last 24 h of the simulation corresponding to 14 Mar. (a) At 4 km for two- and one-way simulations, and (b) at 24 and 4 km for two-way simulations. The stations are grouped under three main regions: south coast of Québec, Canadian Maritimes, and Newfoundland

  • View in gallery
    Fig. 13.

    The 48-h accumulated, simulated, and observed precipitation valid at 0000 UTC 15 Mar 1997. Labeled numbers show the observed values from downstream stations

  • View in gallery
    Fig. 14.

    Same as Fig. 12, but for 48-h accumulated precipitation

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Impact of a Two-Way Coupling between an Atmospheric and an Ocean-Ice Model over the Gulf of St. Lawrence

Pierre PellerinRecherche en Prévision Numérique, Service Météorologique du Canada, Dorval, Québec, Canada

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Harold RitchieRecherche en Prévision Numérique, Service Météorologique du Canada, Dorval, Québec, Canada

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François J. SaucierOcean Science Branch, Maurice Lamontagne Institute, Department of Fisheries and Ocean, Mont-Joli, Québec, Canada

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François RoyOcean Science Branch, Maurice Lamontagne Institute, Department of Fisheries and Ocean, Mont-Joli, Québec, Canada

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Serge DesjardinsRecherche en Prévision Numérique, Service Météorologique du Canada, Dorval, Québec, Canada

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Michel ValinRecherche en Prévision Numérique, Service Météorologique du Canada, Dorval, Québec, Canada

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Vivian LeeRecherche en Prévision Numérique, Service Météorologique du Canada, Dorval, Québec, Canada

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Abstract

The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed.

Corresponding author address: Pierre Pellerin, Direction de la Recherche en Météorologie, Meteorological Service of Canada, Environment Canada, 5th Floor, 2121 Trans-Canada Highway, Dorval QC H9P 1J3, Canada. Email: pierre.pellerin@ec.gc.ca

Abstract

The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed.

Corresponding author address: Pierre Pellerin, Direction de la Recherche en Météorologie, Meteorological Service of Canada, Environment Canada, 5th Floor, 2121 Trans-Canada Highway, Dorval QC H9P 1J3, Canada. Email: pierre.pellerin@ec.gc.ca

1. Introduction

In attempts to continuously improve atmospheric forecasts, it is becoming increasingly clear that it is important to improve the representation of surface processes. One such process is the representation of sea-ice-cover dynamics. Ice cover and thickness have a significant influence on humidity and heat fluxes. In the operational Canadian atmospheric forecast model, the ice can grow or melt, but for the moment it does not move with the currents and winds. Very frequently, the ice over the Gulf of St. Lawrence (between the Canadian provinces of Newfoundland, Québec, Nova Scotia, Prince Edward Island, and New Brunswick; see Fig. 2, later) can move very rapidly in the presence of strong winds (Saucier et al. 2003). These strong winds are often directly related to a major low pressure system affecting the region. During these events, the sea-ice-cover area diminishes because of convergence, ridging, and fracturing, especially near the coast, giving rise to more open-water areas and thus higher ocean–atmosphere sensible and latent heat fluxes. Hence, it becomes important to include the changing ice conditions in the atmospheric modeling in order to increase the skill of the forecast. In winter, the weather systems move relatively rapidly through these regions, and the inclusion or omission of moving sea ice can significantly change the atmospheric forecasts for the downstream regions. The main objective of this paper is to show the improvements of a fully interactive coupling between an atmospheric and an ocean-ice model over the Gulf of St. Lawrence (GSL). We will also discuss two other points that are important for this experiment: the type of coupling and the coupling technique.

The GSL is a major seaway that is nearly ice covered between January and March. Early in the season, sea ice is produced in the nearshore regions as well as in the highly stratified estuary region and the western part of the GSL. The sea ice circulation is generally westward driven by the dominant currents and winds, leading to sea ice accumulation in the southern Gulf of St. Lawrence and near the coast of Newfoundland. The sea ice cover is seldom complete as currents, strong tides, and wind events continuously make it drift, though it maintains coastal leads and convergent or divergent sea ice circulation throughout (Saucier et al. 2003). The sea-ice-cover dynamics is strongly controlled by surface winds, and the near-surface atmospheric fields are strongly affected by the presence of sea ice during winter (Gachon et al. 2002). Thus in order to improve the predictability of both the sea ice cover and the atmosphere, it becomes clear that models of each component must follow closely the evolution of the other. This paper addresses this issue by fully coupling the model of Saucier et al. (2003) with the atmospheric models developed at the Meteorological Service of Canada. The first experiment was conducted using the operational configuration of the regional Global Environmental Multiscale (GEM) model, which has a horizontal grid spacing of 24 km. It will be shown that the coupled system brings an improvement to the 48-h forecast because the consistent fluxes from the redistributed sea ice cover strongly affect the weather. A second configuration at a higher horizontal resolution (4 km), produced by the Canadian Mesoscale Compressible Community (MC2) limited-area model, permits a more accurate validation of the coupling effects. The increase in the resolution gives a better representation of the topography and thus an improvement of the surface winds and, consequently, should normally improve the ice motions. This higher resolution also produces a better description of the land–sea interface and thus improves the energetic effects produced by the physical fluxes. It is important to have good atmospheric conditions between the islands and in the estuary to produce a good sea ice cover, but a better sea ice cover should also result in better atmospheric effects.

Measurements of ice conditions, cloud cover, air temperature, and precipitation were used to validate this experiment. In order to obtain accurate results on air temperature and precipitation, it is important that the ice conditions in the model are as accurate as possible. The first, but not easy, task is to validate the ice coverage before using it on the new numerical system. The simulated ice coverage is compared directly with a Geostationary Operational Environmental Satellite (GOES) image. Once the ice coverage is validated, it is possible to evaluate the atmospheric conditions. To ensure that the radiation budget and the physical fluxes are well estimated by the system, it is essential to evaluate the clouds generated by the atmospheric model. Then as a second step, the clouds generated directly by interactions with the gulf are also compared directly with the image observed by the Advanced Very High Resolution Radiometer (AVHRR). To complete the validation, the predicted temperature and precipitation are compared with the available observations. It is demonstrated, as in the experiments produced by Gustafsson et al. (1998) on the Baltic Sea, that the sea-state conditions may change considerably within forecasting periods (48 h) and imply the necessity of applying two-way coupling of ocean models with the atmospheric model. A direct impact on the ocean-ice model results is also presented here.

2. Modeling description

a. Atmospheric models

The first atmospheric model used in our experimentation is the Canadian operational model GEM (Côté et al. 1998). The GEM model is used exactly in its regional configuration with a horizontal grid spacing of 24 km (Bélair et al. 2000). The primitive hydrostatic equations are integrated on a variable-resolution grid. The grid spacing in the central uniform domain is 0.22° (∼24 km, 270 × 353 points) and increases uniformly outside the central region with a stretching factor of about 10% (total: 353 × 415 points). Figures 1a and 1c present the topography and the land–water mask used in the model (24 km) for the region of interest.

The second model, used to downscale at a higher resolution (4 km), is the MC2 (Benoit et al. 1997; Thomas et al. 1998). This model is a limited-area model (LAM) running on a polar stereographic grid. Figure 1b shows the entire domain of this LAM and also its topography (400 × 400 points). The surface fields of the 4-km configuration clearly have more details and give a better representation of the land–sea interface (Figs. 1c and 1d). The land–sea mask field uses a land–water representation from 0% (water only) to 100% (land only) instead of a binary one. This more continuous representation of the land–sea interface allows smoother horizontal gradients of physical fluxes and permits generation of physical patterns that can be better resolved by numerical methods. A binary mask representation can cause sharp gradients that are difficult to resolve and are not necessarily desirable in numerical models. With a fourth-order advection scheme such as the one used in our atmospheric models (semi-Lagrangian with cubic interpolation), these types of sharp gradients can cause a loss of mass and energy. A value of 80% means that 80% of the surface fluxes used come from the land scheme and 20% from the ocean model. The land–sea interface of the 4-km configuration (Fig. 1d) fits better with the coastlines compared to the 24-km configuration (Fig. 1c). At low resolution, the numerical land–sea interface is stretched through the coastlines.

Both models use the same unified physics package developed at Recherche en Prévision Numérique (RPN). This package consists of a comprehensive multioption set of physical process parameterizations. A detailed description can be found in Mailhot et al. (1998). The main components used by both configurations are summarized in Table 1. The 4-km resolution configuration allows a better representation of the local winds and physical fluxes while the advanced microphysical equations (Kong and Yau 1997) are used to simulate cloud-scale processes.

The 24- and 4-km models are run with time steps of 600 and 50 s, respectively, and 28 and 35 vertical levels, respectively. Analyses at a 24-km horizontal grid spacing were generated via a 3D variational data assimilation scheme (Gauthier et al. 1996) using the GEM model. These analyses were used as initial conditions by both the 24- and 4-km models. The boundary conditions used by the LAM came directly from the 24-km simulation. The results presented here are from 48-h forecasts starting at 0000 UTC 13 March 1997.

b. Ocean-ice model

The ocean-ice model is the Gulf of St. Lawrence (Fig. 2) model (Saucier et al. 2003). The gulf is treated as a semienclosed sea opened to the Atlantic Ocean through Cabot Strait and the Strait of Belle Isle. The model domain extends between open boundaries near Cabot Strait and the Strait of Belle Isle, and the upper limits of tidal influence near Montréal and at the head of the Saguenay Fjord. It runs on a 5-km horizontal resolution grid for the region between the outer straits and Ile d'Orléans and uses a one-dimensional model for the momentum transfer from Québec City to Montréal. The surface and bottom layers adjust to the local water level and depth, respectively. The vertical resolution is uniform at 5 m from the surface down to 300-m depth and then becomes uniform at 10 m below. The time step used by the model is 300 s. The GSL ocean model is coupled to a Multi-category Particle-In-Cell (McPIC) sea ice model from Flato (1994). The initial ice conditions were obtained from charts produced each day by the Canadian Ice Service (CIS) incorporating Radar Satellite (RADARSAT) images, aerial reconnaissance, and ship-delivered data. This coupled model is developed for daily experimental sea ice and ocean forecasting at the Canadian Department of Fisheries and Oceans.

The initial conditions for the ocean have been generated by running the coupled system from 10 to 12 March 1997. The initial conditions for temperature, salinity, and currents used to start the ocean model on 10 March came from a combination of observations and climatological data (Petrie et al. 1996). The spinup is produced only for the motion, which is enough to obtain a quasi-stationary regime. In March the water is near the freezing point from the surface until approximately 100 m. The ice analysis produced by the CIS is used as the initial ice condition. The GSL ocean model is run in a continuous mode from 10 to 12 March. The 24-km configuration of the atmospheric model is coupled for this initialization period. The atmospheric and ice conditions (for the sea ice model) have been reinitialized from the analysis after 24 and 48 h of ocean simulation, that is, for 11 and 12 March. Thus, this initialization period is a combination of three atmospheric and ice forecasts of 24 h each and of one ocean simulation of 72 h (10–13 March). The purpose is to generate the best possible ocean conditions for 13 March. All the simulations presented in this paper are started using ocean conditions obtained this way.

3. Coupling strategy

a. The coupler

The RPN coupler has been designed to transfer data between models without modifying their model codes too much. Since this tool is a connector modulus between many environmental models that are developed by different research centers and universities, it is important that the coupler is a general application, is easy to implement on different computer systems, and is computationally efficient.

Figure 3 schematically represents the two-way coupled atmosphere–ocean-ice model system where two processes work in opposite directions to allow simple and efficient linking between the models. The coupler is divided into two main components. The first is the communication package that allows the physical linkage between the models and the coupler. This Globally Organized System for Simulation Information Passing (GOSSIP) package (Lee and Vallin 2000) is a relatively short C code using the pipefile technique. Many options are included to ease the use of the coupler. The code is well documented and is now open source. The second component of the coupler is the package that transfers physical information from one grid to another by method of interpolation or aggregation. Many popular types of grids used in atmospheric science are already supported by the package. The main idea is to minimize the related modifications or dependencies in the codes of the numerical models that are being coupled together. In this way it becomes easier to support, in parallel, the evolution of many numerical models by many organizations that are collaborating in the coupled modeling activity. Only a couple of additional subroutines have to be included in the numerical models to allow direct connection with the coupler. Each model passes and receives the physical information from the coupler directly onto its numerical grid. Once the coupling mechanism is installed in a model, it is then easy to migrate it into future versions and use it with other various external models. For example, the same coupler allows our atmospheric models to communicate with land surface schemes, hydrological models, and wave models (e.g., Desjardins et al. 2000). The RPN coupler is very similar to the Ocean Atmosphere Sea Ice and Soil (OASIS) coupler (Valcke et al. 2003). The main differences are located in the techniques used to exchange the data. OASIS uses message passing interface (MPI) while the RPN coupler uses GOSSIP.

b. Relationship between the time–space strategy and physical aspects

The two-way interactive coupling between two different numerical models requires a certain level of approximation in the representation of the physical connections. To maximize the effects of the coupling between both models, an attempt was made to respect these three principles: 1) to conserve energy and mass, 2) to represent as much as possible all the physical details produced by the models, and 3) to reduce the possible numerical discrepancies. Different techniques can be used to couple many models together, but they do not necessarily satisfy these goals. Here are two different coupling scenarios that strive to meet them.

In the first scenario, the atmospheric model has a spatial grid resolution (24 km) that is coarser than the resolution of the ocean-ice model (5 km). The time step of the atmospheric model (600 s) is larger than the time step of the ocean model (300 s). Even if the average motion is faster in air than in water (∼100 times), the efficiency of the semi-Lagrangian advection scheme, together with the lower grid resolution, allows the atmospheric model (Courant number ∼ 0.8) to have a time step that is 2 times larger than the one required by the Eulerian scheme of the ocean model (Courant number ∼ 0.3). Figure 4 schematically presents this first case for heat flux exchanges only. The large arrows represent a time step for the atmospheric (up) and the ocean-ice model (down). The thin black arrows represent the different exchanges, and the numbers represent the order in which the different processes (steps) are executed. Given that the numerical grid of the ocean-ice model is at a finer resolution, all the information coming from the atmospheric model is interpolated by the coupler to the ocean-ice model grid. Conversely, the heat flux computed on the ocean grid is aggregated by the coupler onto the atmospheric model grid. For more information, see section 3a. To simplify the following description, the models are considered to communicate directly together instead of through the coupler. The atmospheric model passes the field of air temperature near the surface to the ocean model (step 1 in Fig. 4, thin black arrow), and the latter computes the heat flux on its own grid using the air temperature and the surface temperature. To keep the maximum amount of physical information, the heat fluxes are computed on the grid having the higher resolution (principles 1 and 2). It is more natural to aggregate energy than to aggregate ice. For example, the latent heat flux computed directly on a high-resolution grid of an ocean-ice model and aggregated onto a low-resolution atmospheric grid will not necessarily be equivalent to the flux computed on the low-resolution grid using aggregated ice and ocean fields. In an attempt to conserve energy (principle 1), the models need to exchange energy rather than variables. The atmospheric model receives the heat flux [Fc(0), step 2 in Fig. 4] and then makes its first time step. The ocean model receives the updated air temperature and can then make its first time step (steps 4, 5 in Fig. 4). After these time steps, the ocean model computes the heat flux field that will be used for the next atmospheric time step. The energy amount passed to the atmospheric model [Fc(1) in Fig. 4] is the amount used to complete the two oceanic time steps. With this technique, the coupling is accomplished between both models by a time and process splitting that reduces as much as possible the temporal and spatial approximations (principle 3).

In the second scenario, the atmospheric model has a spatial grid resolution (∼4 km) approximately equivalent to the ocean-ice model (∼5 km). The increased spatial resolution is associated with a decrease of the atmospheric model time step to 50 s. Thus the atmospheric model executes six time steps (50 s) for one oceanic time step (300 s). The coupling is at each oceanic time step. To simplify Fig. 5 and the following description, consider that the atmospheric model produces two time steps during one oceanic time step. The description of this second scenario is similar to the previous one (Fig. 4). The difference is that for the same real time the atmospheric model executes more time steps than the ocean model. The heat fluxes used by the intermediate time steps (between two coupling; steps 3 and 6 in Fig. 5) are always calculated on the ocean grid (principles 1 and 3). This will maintain complete consistency with the ocean model and the coupling time steps as well as consider the maximum physical information produced by the models. Steps 4 and 5 in Fig. 5 represent the connections, between both models, used to calculate the heat flux for the atmospheric model. In the beginning, the atmospheric model communicates with the ocean-ice model (steps 1 and 2 in Fig. 5) to pass the air temperature and to obtain the corresponding heat flux calculated from the initial air and surface temperatures: Fc(0) = σ[Ts(0) − T(0)], where σ is the transfer coefficient. Afterward, the atmospheric model makes its first time step (step 3 in Fig. 5). Even if it is not a coupling time step, the atmospheric model communicates with the ocean model to obtain an updated heat flux [Fc(1/2)] computed from the updated air temperature [T(1/2)]. The surface temperature used for the intermediate time step is unchanged: Fc(1/2) = σ[Ts(0) − T(1/2)]. With this technique, the fluxes are definitely calculated, not only with the maximum information available, but also exactly in the same manner for all the atmospheric time steps. Keeping the fluxes unchanged between two coupling time steps could induce numerical discrepancies or instabilities. In this case, the gridpoint spacings are approximately the same in both models; thus, calculation of the fluxes is permitted directly in the atmospheric model. The goal is to keep as close as possible the same setup as in the previous configuration (24 km) and thus facilitate the physical comparisons. The similarities in resolutions of both atmospheric and ocean-ice models require using the interpolation technique for all exchanges of physical fields.

In both coupling scenarios, the fields passed to the ocean-ice model are the surface temperature, humidity, precipitation, short- and longwave radiation coming from the atmosphere, and surface winds. The ocean model transfers the heat and humidity fluxes, the longwaves radiation coming from the surface, and the averaged ice coverage to the atmospheric model. For this experiment the surface wind stress transferred to the atmosphere is calculated within the atmospheric model using the ice coverage. In future experiments, the wind stress used by the atmospheric model will come directly from the ocean-ice model. A more consistent treatment will allow conservation of the momentum fluxes but could also improve the winds forecasted by the 24-km atmospheric configuration.

4. Case study

The case chosen is particularly interesting given that a cold and relatively quiet period was followed by an intense atmospheric circulation that changed the ice conditions dramatically in only 48 h. In the preceding days (10–12 March 1997), a large double upper cutoff low system, composed of a quasi-stationary cold low over Hudson Bay and a major quasi-stationary surface low pressure center east of Newfoundland, affected eastern Canada. This system established a blockage in the upper circulation that deflected all potential development farther south. It also established a weak wind regime with average speeds of 13 km h−1 and a mean temperature of −10°C over the Gulf of St. Lawrence. During this period a complete ice coverage was present in the gulf and in the St. Lawrence estuary. Figures 6a and 6c present the meteorological and ice analysis valid at 0000 UTC 12 March 1997. Conditions were propitious to ice formation. The generation of the ice analysis over this region was not an easy task, as different observations are available at different times during the day and the RADARSAT usually requires 3 days to cover the ocean grid. In this case study, the meteorological situation remained relatively stationary for a few days; thus, the ice analysis can be considered to be a very good and realistic representation of the situation at the end of 12 March.

At 0000 UTC 12 March, a frontal wave, south of 40°N latitude, developed into a low pressure system that created strong cold advection behind it. The cold advection allowed the eastern displacement of a large ridge of high pressure over the northeastern United States. On 13 March, the eastern progression of the ridge was stopped by the broad-surface eastern Newfoundland low system that was being moved westward by the active low pressure system. From this complex synoptic situation, a very strong northwesterly flow prevailed over the Gulf of St. Lawrence, as shown by Fig. 6b. On 14 March, the northwesterlies reached maximum values of 60 km h−1. Figures 6b and 6d show the ice conditions valid at 0000 UTC 14 March 1997.

Note that the ice cover conditions changed dramatically during the 48-h period. By 15 March, many large regions of the St. Lawrence became free of ice. As seen in Fig. 6d, the north coast of Québec is now completely free of ice, as well as the waters south of Anticosti Island, where downstream airflow generated a large ice-free area.

5. Experiments: Comparisons and validations

a. Observed sea ice patterns

Given that the ice coverage changes and moves very rapidly in only 24 h (13–14 March), it is very important for the validation to compare the real and the simulated ice coverage for a specific and common time. However, the different observation tools presently used to generate an analysis on the St. Lawrence produce only one analysis per day (Canadian Ice Service). These analyses combine observations acquired in different areas at different times of the day, making it difficult to examine the subdaily variability. As mentioned earlier, it takes approximately 3 days for RADARSAT to cover the simulation domain entirely, and the flight and ship observations are not necessarily valid at the same time. It is possible to obtain a complete picture of the sea ice cover on the St. Lawrence valid for a specific time by using a visible satellite image. The challenge with the visible image is to be able to distinguish between clouds and sea ice or snow. Fortunately, a satellite picture without clouds over the major part of the ocean grid (valid at 2000 UTC 14 March 1997) corresponded to 42 h into the forecast of the simulation. It is, however, difficult to distinguish between snow/sea ice or open water using the infrared picture given approximately the same surface temperature for both water phases (melting period, ∼0°C). It is easier, given the large difference of temperature between clouds and snow/sea ice, to use the infrared picture to produce a cloud mask for the visible picture. This deduced mask has been superimposed on the visible picture (red regions in Fig. 7a). Only the mid- to high-level clouds have been masked. The infrared picture allows us to clearly detect the regions free of mid- to high-level clouds. Most of the gulf and estuary of the St. Lawrence are free of low-level clouds except for the Magdalen Islands and Cape Breton, where thin layers of low-level clouds are noticed.

With this combined infrared/visible picture, it is now easier to see the regions of the St. Lawrence estuary where a direct comparison with the observed and the simulated ice is possible. Efforts were made to examine the position of the sea ice margin. Nevertheless, this comparison is a very good indicator of the quality of the forecast, given the rapid change in the ice coverage during these 42 h of simulation. The red lines delineate, in Fig. 7a, eight segments (A–H) of the sea ice margin identified in the satellite image. These will be used for validation.

b. Differences between the one-way and two-way coupling

As discussed in the previous section, Fig. 7a is the observed ice field valid at 2000 UTC 14 March 1997. Figures 7b and 7c show the 42-h forecasts of simulated ice cover valid at the same time. Figure 7b shows the results from one-way coupling compared to those from two-way coupling in Fig. 7c. The integrations for the one-way version have exactly the same configuration as those for the two-way coupling except for the physical information received by the atmospheric model. On the first time step, the atmospheric model receives the physical information related with the initial ocean-ice conditions. With the one-way coupling these related ocean physical fields (water and ice temperature, ice thickness, and ice coverage) remain invariant for the rest of the simulation, while the ocean model continues to receive the updated atmospheric conditions at each time step. In the one-way coupling the ocean model does not influence the atmospheric model. This is very similar to the operational configuration of the Canadian atmospheric forecast model. The experiments presented here have been produced with the higher-resolution configuration of the atmospheric model. In Fig. 7, the dark blue areas represent water (0%) and the other colors indicate ice coverage (light blue: 20%–40%; green: 41%–60%; gray: 61%–80%; white: 81%–100%). The model has reproduced the segment labeled A (Fig. 7c), which is generated by the stronger wind regime due to the large-scale wind channeling and funneling effect. As the melting is not so efficient in the one-way coupling case (segment A, Fig. 7b), the ice edge does not seem to propagate southward rapidly enough compared with the observations (Fig. 7a) and the two-way experiment (Fig. 7c). The “V” shape (segment A) is also better reproduced by the interactive experiment. But, in general, both configurations have relatively well reproduced the observed segment A. The right position of the ice edge after 42 h of forecast indicates a very good approximation of the real surface wind produced by the atmospheric model. The segments C, D, and E clearly demonstrate the advantages of the two-way simulation. The larger ice-free areas generated between Québec and New Brunswick (D and E) have clearly been underestimated by the one-way coupling (Fig. 7b). The ice edges from the two-way simulation (red lines D, E in Fig. 7c) correspond better with the observations (red lines D, E in Fig. 7a), although the eastward advection of the two ice margin segments D and E is still underestimated. The same statement can be made about the ice segment C located along the western Newfoundland coast (red line C, Fig 7a). The ice coverage produced by the one-way coupling between Anticosti Island and Newfoundland is clearly more extensive than in the two-way simulation. The supplementary energy input in the two-way simulation gives a better reproduction of the shape and the location of the segment C (wave segment, Fig. 7c).

Another very interesting pattern allows validation of the two-way simulation. Good prediction of pattern C is essential to simulate well the trajectory represented by pattern B. In Fig. 7a, the red arrow (pattern B) represents the trajectory followed by sea ice floes along the north shore of Anticosti Island and exiting toward Cabot Strait. An animation of the ice field shows that the ice floes coming from the coast of Québec are accumulated on the north coast of the island and follow the coast in a southward direction until they move into the Laurentian Channel. The observed dimension, location, and amount of ice floes along this trajectory are very similar to those produced by the two-way simulation (Fig. 7c). It is more difficult to find the correspondence in the one-way coupling (Fig. 7b). As the ice located between the island and Newfoundland does not melt rapidly enough (without any two-way interactions, pattern C), the floes accumulated on the north coast and at the end of the island are larger than observed. In the one-way coupling, the ice coverage is too extensive (pattern C) and the floes cannot follow exactly the same trajectory with the same speeds as simulated by the two-way coupling. The floes cannot easily pass to the south of the island. The ice trajectory in the one-way coupling is thus shorter (pattern B) than in the two-way simulation, which seems to adopt an ice-edge shape more similar to the observed ones.

All the other patterns (F to H) have been relatively well reproduced by both coupled simulations. The fact that these patterns are relatively small has reduced the thermodynamic effects of the two-way coupling. These small openings in the ice field have essentially been produced by advection. It is, however, somewhat difficult to compare the pattern G produced near the Magdalen Islands, given the presence of low-level clouds. These clouds are noticed in the infrared picture (section 5a). By studying the ice thickness field (not shown here), it can be seen that thicker ice particles have been moved farther south in the two-way coupled run than in the one-way coupled run. The supplementary energy input obtained by the two-way coupling seems to generate a more realistic reduction in the simulated ice coverage and have an important impact near the ice edge.

Figure 8 shows the ice volume difference between the two-way and the one-way coupled runs as a function of ice coverage categories. Positive difference means that ice production is also positive while negative values indicate a loss in the ice field mass. The solid line represents this difference due to the coupling by expressing it as a percentage of the volume of each category. Note that the effect of the two-way coupling is to reduce the ice volume of the full coverage region (−10% for category 90%–100%) and to increase the other categories (+20% for category 50%–60%). In allowing a reduction of the ice thickness, the simulation with two-way coupling increases the regions with ice coverage between 0% and 75% and decreases the regions between 76% and 100%. This effect results in a thinner ice pack near the coasts with the two-way coupling.

In summary, the two-way coupling seems to have a positive impact on the prediction of the ocean-ice simulations. It is warmer than the one-way simulation that causes a reduction of 8% in the total ice volume when compared to the resulting ice from the one-way simulation. The following sections explore the impact of the coupling on the atmospheric aspect.

c. Observed and simulated clouds patterns

As seen in the previous section, the two-way coupling seems to improve the sea ice distribution forecast. This result was essentially associated with higher temperature due to a reduced sea ice cover seen by the atmospheric model during the simulation. The details of the impact on the atmospheric conditions are presented using a dynamical sea ice cover. The first atmospheric evaluation is rather objective and tries to indirectly evaluate the physical fluxes at the base of the atmospheric model. The low-level clouds generated directly over ice and water have been used to produce this evaluation. Clouds directly affect the energy reaching the surface and thus have an important impact on the predicted temperature, precipitation, and ice. Figure 9a shows a low-level cloud cover obtained from a satellite image (AVHRR). It is presented as a satellite view angle of the middle southern area of Fig. 7 rotated counterclockwise by 90°. The image is valid at 1230 UTC 14 March 1997. Dark blue areas represent ice-free water, gray ones correspond to ice, and yellow ones to land, while blue and white colors represent the low-level clouds. The geographical contours (white lines) are shown to better distinguish the coastlines of New Brunswick, Prince Edward Island, and the Magdalen Islands, which are covered by scattered low-level clouds. The red line represents the location of the cloud edge. The arrows (A, B, and C) indicate the three main trajectories for the surface winds. The gravity waves produced by Cape Breton Island and the Magdalen Islands are clearly visible in the clouds.

The physical patterns presented in Fig. 10 allow a good understanding of the mechanisms producing these low-level cloud patterns. This surface flow has been produced by the high-resolution (4 km) two-way system and has the same valid time as the satellite image. The black arrows represent the surface winds (knots), and the shaded (white contours) patterns represent the surface temperatures (degrees Celsius). The three dashed arrows represent the main trajectories followed by the surface flow and correspond to the arrows presented on the satellite image (Fig. 9a). The air parcels following the trajectory A came from the Québec lower north shore and are deviated by the Chic Chocs Mountains (∼1100 m; see Fig. 1b). They have an important interaction with the large ice-free area extending from the area west of Anticosti Island to the northwest of the Magdalen Islands clearly visible in Fig. 7a. Note that the cold air (−6°, −4°C contours, Fig. 10) moved over this large ice-free water area and gained heat and moisture (by heat and latent flux). It then reached the condensation threshold to produce the clouds clearly visible on the satellite image (Fig. 9a). The air parcels followed the trajectory B pass over the Chic Chocs Mountains before reaching the domain of interest. They are affected by a cooling and a drying (precipitation) during the ascending portion and are heated (adiabatic) on the downslope portion of their trajectory. They arrived at the ocean warmer and dryer than those following the previous trajectory (A). The pattern of temperature corresponding with trajectory B is clearly warmer (−4°, −2°C, Fig. 10). This dryer (Chic Chocs chinook) effect explains the region without cloud observed around trajectory B of the satellite image (Fig. 9a). The air parcels of the trajectory C originated directly from land (red portion of the arrow C, New Brunswick) with conditions relatively cold (−6°C, Fig. 10) and humid (∼91% RH). By passing over the ice-free waters (blue portion of the arrow C), the air parcels increased their temperature and specific humidity by sensible and latent heat flux. These same parcels continued their trajectory over the ice (green portion of the arrow, Fig. 9a), and on contact with this colder surface, they transferred a part of this new energy to the ice. This transfer changed the energy budget of the ice coverage that allowed a reduction of the ice thickness. The air parcels near the surface rapidly reached saturation (∼109%) and then generated the low-level cloud coverage located between the Magdalen Islands and Prince Edward Island (Fig. 9a). Figures 9b and 9c show the clouds and ice simulated for one-way and two-way coupling (4-km configuration) valid at the same time as the satellite image (Fig. 9a). The contours of simulated ice coverage shown are 0% (water: dark blue),>20% (light gray), and <20% (gray). The observed low-level clouds over the ice and on Prince Edward Island (red line in Fig. 9a) are relatively well reproduced by the simulation with two-way coupling (red line in Fig. 9c), while the simulation with one-way coupling is unable to generate them since it used an invariant ice field with no real ice-free area (see Fig. 6b). The shape of the simulated pattern is very similar to the observed pattern. These low-level clouds are caused by the presence of open waters during the simulation. The cloud and no-cloud patterns described by the three trajectories on the satellite image are relatively well reproduced by the atmospheric model (Fig. 9b). The same positive impact of the two-way coupling is also visible in comparing the clouds generated by the 24-km simulations (not shown here).

It is also very interesting to compare smaller features such as the observed limit of cloud formation captured by the satellite image with the model outputs. The satellite image (Fig. 9a) shows clearly that cloud forms very rapidly over the ice. The location of the cloud edge (red line, near the green arrow C) corresponds approximately to the location of the ice edge. This edge is shifted downstream in the two-way simulation (Fig. 9c). The numerical physical exchanges (fluxes) seem to be less efficient compared to reality. A second interesting feature to compare is the small line of ice produced near the coast of New Brunswick (letter G in Fig. 9c). This line can be relatively well distinguished on the satellite image (letter G in Fig. 9a). These small features bring complementary information that will be very useful in future studies.

This validation of low-level clouds demonstrates that the two-way coupled simulation has produced physical exchanges (fluxes) that correspond better with reality.

d. Observed and simulated temperature and precipitation

1) Temperature

In the previous section, it can be seen that the very fine scales of cloud generated by the system with two-way coupling seem to be a reasonable approximation to what was observed. This validation suggests that the 4-km grid spacing for the atmospheric model is sufficient to represent well the physical exchanges between the models. To reinforce this validation, this section will compare two important variables directly to surface observations. Figure 11 presents the mean surface temperature difference between the two-way and one-way coupled simulations at a resolution of 4 km. The surface temperatures were averaged over the last 24-h period (14 March) of the simulation to focus more on the coupling effect. Note in Fig. 11 that most of the domain is warmer in the simulation that uses two-way coupling. The regions experiencing the greatest impact (>2°C) are not found directly over the open-water regions, but essentially downstream from them. This influence of open-water area (see area A on Fig. 7) on surface temperature extends quite far, as demonstrated by the large elongated northwest to southeast axis crossing the Gulf of St. Lawrence. It is noteworthy that the width of influence on surface temperature is about the same as the width of open-water area. This shows that the persistency in the wind direction combined with its strength acts as a heat conveyor belt bringing warming over remote downstream regions. Fetch also plays a role, as indicated by the largest maxima along the western coast of Newfoundland. The air accumulating heat and moisture over the open-water area B (see Fig. 7a) is displaced on a shorter distance before reaching the western coast of Newfoundland. The largest difference of temperature in the last 24 h of the simulation between the two different coupling was 8°C.

In Fig. 11, the affected observation stations valid for the same last 24-h period are plotted. Observed temperatures are also averaged for the period for comparison. The stations located upstream of the flow are not included in the comparison. The observation stations located in these coastal regions are most often located near the water. In numerical models, since land–sea mask and topography accuracy are dependant on the model grid resolutions, it is unlikely that the representation of such fields in the numerical models will be close enough to reality (unless resolution is below a kilometer). Therefore, this imperfect comparison makes the use of these coastal observations difficult in the evaluation of the atmospheric forecasts.

However, the station located on St. Paul Island (directly between Newfoundland and Cape Breton; Fig. 11) is the most exposed observation station to these persistent northwesterlies. Moreover, the fact that this island is very small and that the observations are taken at the north tip of the island allows the consideration that the observed weather is essentially influenced by conditions over sea (at least during windy periods). One can say that St. Paul Island is essentially a sea point as numerical models see it, which makes it a very good choice for the validation. The mean temperature observed on 14 March at St. Paul is −4.9°C (see Fig. 11). In the noninteractive system (one-way coupling), a colder temperature than the observed one was obtained. The numerical temperature showed a mean of −7.6°C for the 4 km and of −7.2°C for the 24 km. The forecast with two-way coupling, however, has exactly reproduced the observation in the 4-km resolution and has also produced a good value of −4.6°C in the 24 km. The comparison of the daily mean temperatures has helped to reproduce exactly the observation, but nevertheless this validation makes it clear that the two-way interactive coupling has a positive impact on the atmospheric forecast. Moreover, good results for the St. Paul Island station were obtained independent of grid resolution, which means that this location can be considered a sea point for the numerical models (Fig. 11). It reinforces the hypothesis that it is very difficult and probably even impossible to use an observation station located on any coast (e.g., on the south coast of Québec; Fig. 11) to validate an atmospheric simulation that does not have enough resolution to correctly resolve the land– water interface (e.g., with 24-km grid spacing).

The same comparison done at St. Paul Island has been carried out for all observing stations, and the results are presented in Fig. 12. In Fig. 12a, the observations are represented by the black line, and the simulated values for the two- and one-way coupled systems are respectively represented by a plus sign and a triangle. The observing stations are grouped under three main regions: south coast of Québec, Canadian Maritimes, and Newfoundland. The two-way coupled simulation has improved the forecasts for 82% of the stations. Both configurations have produced the same forecast for 4% of the stations. The mean improvements are 1.8°C when the two-way is better than the one-way, and only 0.8°C when the one-way is better. This indicates that if a station is located in a region downstream of the new open water generated by the simulation, it has a greater chance of being correctly simulated by an interactive numerical system. The rms error for the two-way coupling is smaller by 15% compared to the one-way coupled simulation.

Figure 12b presents the same comparison as Fig. 12a except that it shows the effects of increasing the horizontal resolution of the atmospheric model from 24 to 4 km. Both simulations are two-way coupled with the ocean-ice model. With a finer resolution, 60% of the stations have improved forecast temperatures because there is a resolving of the physical events and transfers as well as an increase in the definition of the land–sea mask in the atmospheric model. The rms error for the 4-km simulation is also smaller (15%) than that of the 24 km.

2) Precipitation

Another important variable simulated by the atmospheric model is the precipitation field. Precipitation is very sensitive and depends on all the atmospheric variables. To reproduce the streamer precipitation patterns well, it is necessary to handle at least the atmospheric circulation well and also to have a good representation of the humidity field. In this validation so far, it seems that the ice, clouds, and temperature are relatively well reproduced, or at least are positively influenced by the two-way coupling. But the final validation is to directly compare the predicted and observed precipitation values. Figure 13 presents the 48-h precipitation accumulation generated by the atmospheric model at 4-km resolution. The field is superimposed with the observation stations located downstream from the atmospheric flow. The maximum amounts are located on the south coast of Québec, in Cape Breton, and along the west coast of Newfoundland where wind has significant fetch over open water. Figure 14a presents the comparison between both techniques for each station. The same conclusions are found for precipitation as for the other variables. The interactive simulation improves the forecasts for 50% of the stations with a mean difference of 4.5 mm, which is significant. The maximum amount located on the west coast of Newfoundland (Fig. 13) has been better reproduced by the two-way simulation (Fig. 14a). An observation of 25 mm (located on the coast) has been relatively well reproduced by the two-way coupling with a value of 21 mm. The one-way technique has clearly underestimated the water amount by producing only 9 mm. The one-way technique is better for 25% of the stations, but these stations are essentially in areas with smaller amounts of precipitation (mean differences ∼ 1.5 mm). For the other 25% of the stations both techniques have a perfect forecast of 0 mm. The absolute error of only −0.2 mm for the two-way technique compared to −2.1 mm for the one-way technique clearly shows the positive impact of the supplementary humidity fluxes obtained by taking into account the motion and the evolution of the ice field during the full interactive simulation. The rms error is also 2 times smaller for the fully coupled run. In Fig. 14b it can be seen that the increase of the atmospheric resolution results in a direct improvement in the precipitation amounts. The 4-km simulation is better than the 24 km for 62% of the stations, and the rms error is reduced from 8.4 to 3.1 mm (2.7 times smaller).

These validations of the physical fields of temperature and precipitation clearly show that the two-way coupling with an ocean-ice model and the increasing of numerical resolution are two different factors that improve the atmospheric forecasts of the surface fields.

6. Conclusions

The main purpose of this study was to evaluate the utility of a fully interactive coupling between an atmospheric and an ocean-ice model over the Gulf of St. Lawrence. It was demonstrated that the two-way interaction processes can be relatively important in the presence of persistent strong winds. In this situation the ice is mostly influenced by the atmospheric conditions, and the fluxes between the atmosphere and the open water are maximized. Thus it becomes important to include the evolution of ice conditions in the atmospheric modeling in order to increase the skill of the forecast. In winter, the weather systems move rapidly through these regions, and the inclusion or omission of moving ice effects can significantly change the atmospheric forecasts for the downstream regions.

The combination of an infrared picture and visible picture to deduce the most likely ice cover valid for a specific time during the storm was very useful. It allowed a validation of not only the ocean-ice simulation, but also a part of the interaction with the atmospheric model. The observed ice edges were better reproduced by the two-way coupled version compared to the one-way coupled version. The supplementary energy input obtained by the two-way coupling gives a more realistic reduction of the simulated ice coverage. The fact that the model seems to respond well to the evolution of the ice field suggests a good representation of the physical fluxes exchanged between both models, as well as a very good approximation of the simulated surface winds. However, it is clear that these validations need to be extended for longer periods of simulation in following projects.

The objective of the second validation with clouds was to indirectly evaluate the physical fluxes at the base of the atmospheric model, essentially to see if the fluxes produced by the two-way coupling were more realistic than those generated by the one-way technique. The possibility of distinguishing the low-levels clouds on an AVHRR satellite image has allowed us to meet this objective. The low-level cloud patterns observed on the ice and on Prince Edward Island were relatively well reproduced by the simulation with two-way coupling, while the simulation with one-way coupling did not generate these features. These low-level clouds are a direct effect of the open water available during the simulation. The first two validation techniques, used to evaluate the ice conditions and the clouds, were more subjective, given the lack of accurate observed quantities. Nevertheless, these first two validations were essential to reinforce the third validation technique, which compares gridpoint model outputs with averaged surface observations.

The comparisons using the traditional observations confirmed results from the first two validations. The two-way coupled simulation improved the atmospheric forecasts (4 km) of the influenced downstream stations: 82% for the temperature and 50% for the precipitation. The rms errors for the simulation with two-way coupling were reduced by 15% and 50% compared to the one-way coupled simulation. Another interesting aspect evaluated was the impact of increasing the atmospheric grid resolution. The experiments showed that in changing the grid spacing from 24 to 4 km, the gain of increased skill of the forecast is as important as the gain between the one-way to the two-way coupling. It seems that increasing the resolution allows a better representation of the local winds influenced by topographical effects and physical fluxes. A more accurate definition of the land–sea mask improves the representation of the coastal regions where the majority of observation stations are found. The validations of these physical fields of temperature and precipitation clearly show that the two-way coupling with an ocean-ice model and the increase of numerical resolution are two different factors that improve the atmospheric forecasts of the surface fields.

It is now clearer, with these positive impacts obtained, that the operational Canadian Global Environmental Multiscale (GEM) model would benefit from a two-way coupling with an ocean-ice model. A dynamic evolution of the ice field has to be present to fully capture the complex air–sea exchange in winter over the Gulf of St. Lawrence. By improving the weather forecast in the Canadian Maritimes, this new numerical system may have a direct impact on the Gulf of St. Lawrence marine forecast and could help to increase the efficiency and security of the St. Lawrence seaway.

Acknowledgments

We would like to thank B. Bilodeau and M. Desgagné for support with the atmospheric models (MSC/RPN). This work also benefited from precious discussions with S. Bélair, C. Girard, J. Mailhot, and Y. Delage (MSC/RPN). The help and advice of Y. Chartier for the preparation of this document are very appreciated (MSC/RPN). The NOAA Satellite Active Archive system has been very usefull for obtaining the AVHRR images. We would like to thank the anonymous reviewers for their comments that contributed to improving our article.

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Fig. 1.
Fig. 1.

(top) Topography field (m) and (bottom) land–water fractional mask (%) for (a), (c) 24 and (b), (d) 4 km. Coastlines are represented by black solid lines

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 2.
Fig. 2.

Domain of the ocean–ice model on a Mercator chart. The thin lines on the land represent the major rivers. The area of the model domain extends between open boundaries near Cabot Strait, the Strait of Belle Isle, the upper limits of tidal influence near Montréal, and to the head of the Saguenay Fjord. The gridpoint spacing is 5 km

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 3.
Fig. 3.

Scheme of the coupled atmospheric–ocean-ice model system. The arrow indicates the data-exchange flow. Two processes working in opposite directions (coupler) allow the linking to be simple and efficient between models

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 4.
Fig. 4.

Coupling scenario 1. The large arrows represent time steps for the atmospheric and ocean-ice model. Atmospheric model: dt = 600 s; dx = 24 km. Oceanic model: dt = 300 s; dx = 5 km. The thin arrows represent the communications between both models. Number 1: ocean model receives air temperature at time (0); 2: atmospheric model receives heat flux at time (0); 3: first time step for the atmospheric model; 4: ocean model receives the updated air temperature; 5: the first oceanic time steps; 6: same as for step 2, but for time (1)

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 5.
Fig. 5.

Same as Fig. 4 but for the higher-resolution simulation: dt = 50 s and dx = 4 km. The heat flux used by the intermediate time steps (between two coupling; steps 3 and 6) are always calculated on the ocean grid. Steps 4 and 5 represent the connections, between both models, used to calculate the heat flux for the atmospheric model. Number 1: ocean model receives air temperature at time (0); 2: atmospheric model receives heat flux at time (0); 3: the first time step for the atmospheric model; 4: the ocean model receives the updated air temperature; 5: the atmospheric model receives the heat flux computed with the new air temperature; 6: a new intermediate atmospheric time step; 7: the ocean model receives the updated air temperature; 8: the first oceanic time step; 9: same as step 2 but for time (1)

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 6.
Fig. 6.

(top) Atmospheric and (bottom) ice analyses over the Gulf of St. Lawrence for mid-Mar. Isobar (solid lines, 4 hPa) and 1000-hPa wind (arrow length, kt) analyses valid respectively at (a) 0000 UTC 12 Mar and (b) 0000 UTC 14 Mar 1997. The ice analysis valid on (c) 12 Mar and (d) 14 Mar

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 7.
Fig. 7.

Ice fields (top) observed and (bottom) simulated valid at 2000 UTC 14 Mar 1997. Ice observation [light blue pattern in (a)] deduced from a GOES visible picture with a cloud mask superposition deduced from the infrared channel. The simulated ice coverage (contours: every 10%) obtained with one- and two-way coupling technique are shown in (b) and (c), respectively. The letters identify different ice patterns (red lines) mainly produced by the ice field movement. Pattern B represents a trajectory of ice particles

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 8.
Fig. 8.

Difference of the sea ice volume change between the two- and one-way coupling (two-way minus one-way). The solid line (left axis) expresses this sea ice volume change as a percent volume by category. The abscissa gives the sea-ice-coverage categories in percentage

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 9.
Fig. 9.

Comparison of low-level clouds, ice, and ice-free water. (a) The satellite picture, (b) the one-way, and (c) two-way simulations for the atmospheric model (4 km). Valid at 1230 UTC 14 Mar 1997. Land: yellow in (a) and white in (b), (c); Ice: gray; clouds: white/blue in (a) and green/blue in (b), (c). The red lines mark the low-level clouds' edge generated by the new open water available in the two-way simulation. M. I. = Madalen Islands; P-E. I. = Prince Edward Island

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 10.
Fig. 10.

Surface flow simulated by the high-resolution (4 km) two-way system. Valid at 1230 UTC 14 Mar 1997. The black arrows represent the surface winds (kt) while the shaded (white contours) patterns represent the surface temperatures (°C). The three dashed arrows represent the main trajectories followed by the surface flow

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 11.
Fig. 11.

The difference between the two- and one-way coupling simulations (4 km) for the surface temperature averaged over the last 24;chh forecast corresponding to 14 Mar 1997. Labels represent the observations for the same period. Most of the domain has been warmed up by the two-way coupling simulation

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 12.
Fig. 12.

Comparison between averaged observed and simulated surface temperature. The average was done over the last 24 h of the simulation corresponding to 14 Mar. (a) At 4 km for two- and one-way simulations, and (b) at 24 and 4 km for two-way simulations. The stations are grouped under three main regions: south coast of Québec, Canadian Maritimes, and Newfoundland

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 13.
Fig. 13.

The 48-h accumulated, simulated, and observed precipitation valid at 0000 UTC 15 Mar 1997. Labeled numbers show the observed values from downstream stations

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Fig. 14.
Fig. 14.

Same as Fig. 12, but for 48-h accumulated precipitation

Citation: Monthly Weather Review 132, 6; 10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2

Table 1.

Summary of the RPN physics options used

Table 1.
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