Climate Dynamics

, Volume 36, Issue 9–10, pp 1835–1849 | Cite as

Sensitivity of Hudson Bay Sea ice and ocean climate to atmospheric temperature forcing



A regional sea-ice–ocean model was used to investigate the response of sea ice and oceanic heat storage in the Hudson Bay system to a climate-warming scenario. Projections of air temperature (for the years 2041–2070; effective CO2 concentration of 707–950 ppmv) obtained from the Canadian Regional Climate Model (CRCM 4.2.3), driven by the third-generation coupled global climate model (CGCM 3) for lateral atmospheric and land and ocean surface boundaries, were used to drive a single sensitivity experiment with the delta-change approach. The projected change in air temperature varies from 0.8°C (summer) to 10°C (winter), with a mean warming of 3.9°C. The hydrologic forcing in the warmer climate scenario was identical to the one used for the present climate simulation. Under this warmer climate scenario, the sea-ice season is reduced by 7–9 weeks. The highest change in summer sea-surface temperature, up to 5°C, is found in southeastern Hudson Bay, along the Nunavik coast and in James Bay. In central Hudson Bay, sea-surface temperature increases by over 3°C. Analysis of the heat content stored in the water column revealed an accumulation of additional heat, exceeding 3 MJ m−3, trapped along the eastern shore of James and Hudson bays during winter. Despite the stratification due to meltwater and river runoff during summer, the shallow coastal regions demonstrate a higher capacity of heat storage. The maximum volume of dense water produced at the end of winter was halved under the climate-warming perturbation. The maximum volume of sea ice is reduced by 31% (592 km³) while the difference in the maximum cover is only 2.6% (32,350 km2). Overall, the depletion of sea-ice thickness in Hudson Bay follows a southeast–northwest gradient. Sea-ice thickness in Hudson Strait and Ungava Bay is 50% thinner than in present climate conditions during wintertime. The model indicates that the greatest changes in both sea-ice climate and heat content would occur in southeastern Hudson Bay, James Bay, and Hudson Strait.

1 Introduction

The Hudson Bay marine system is a seasonally ice-covered inland sea (Fig. 1) that plays a significant role in the regional climate variability of Canada (Maxwell 1986). The inter-annual variability of sea ice in Hudson Bay is mainly controlled by large-scale atmospheric variability patterns (Wang et al. 1994b; Mysak et al. 1996; Tivy et al. 2006; Qian et al. 2008). Since the 1950s, the boreal winter index of the North Atlantic Oscillation (NAO) has exhibited an upward trend (Hurrell et al. 2004, 2006). This trend has been correlated with recent observed changes in Canada’s climate (Zhang et al. 2000).
Fig. 1

Bathymetric map (m) of the Hudson Bay marine system. The positions of the two selected locations presented in Figs. 6, 7, 8, and 9 are shown with solid circles. S, Southampton Island; M, Mansell Island; C, Coats Island; N, Nottingham Island; NU, Nunavik; B, Baffin Island; HB, Hudson Bay; FB, Foxe Basin; JB, James Bay; UB, Ungava Bay; HS, Hudson Strait; ON, Ontario

Numerical models provide a useful tool to simulate the response of climate to changing atmospheric forcing. The freshwater budgets and sea-ice variability of Hudson Bay and James Bay were first studied with the aid of numerical models during the 1980s to quantify the oceanic response to runoff modifications associated with the development of hydroelectric facilities in northern Quebec and Manitoba (Prinsenberg 1980, 1991). Later, the impact of global warming on the Hudson Bay marine system was assessed with a first generation Global Climate Model (GCM) (Ingram et al. 1996), followed by several climate change impact studies using projections from different generations of GCMs and different warming scenarios (Gough 1998; Gough and Allakhverdova 1999; Gough and Wolfe 2001; Gagnon and Gough 2005). Future projections derived from the first generation of coupled atmosphere–ocean GCMs include a yearly increase in air temperature over Hudson Bay ranging from 2.5 to 4.5°C in an increased CO2 scenario (from year 2040 to 2069). The largest rise occurs in winter (Gagnon and Gough 2005). Changes in sea-ice cover were highly variable depending on the GCM used. However, these projections were made using a relatively coarse resolution of the oceanic domain, represented as an enclosed sea of from 5 to 31 ocean grid points depending on the GCM used. The coarse resolution of Hudson Bay in GCMs justifies the use of a regional model with higher resolution to resolve regional scales of interest.

High-resolution sea-ice (Wang et al. 1994a, b) and ocean circulation models of Hudson Bay (Saucier and Dionne 1998; Saucier et al. 2004) have been developed. In the first version of the sea-ice–ocean model, simple sensitivity experiments investigated the sea-ice–ocean response to modified atmospheric and hydrologic forcing (Saucier and Dionne 1998). A uniform warming of 2°C produced a 20% reduction in winter sea-ice volume, an increase of 4°C in summer sea-surface temperature, and an increase of 30 days in the ice-free period (Saucier and Dionne 1998). However, the specific bathymetry of Hudson Bay was not considered, and tidal mixing and mesoscale dynamics were not resolved in the model by Saucier and Dionne (1998). Moreover, the prescribed warming did not include spatial and seasonal variability. The latest version of this three-dimensional regional sea-ice–ocean model can produce multi-year simulations with good agreement to observations (Saucier et al. 2004). The mesoscale dynamics observed in Hudson Bay are reproduced with the 10 km horizontal resolution of the model. The objective of the present study is to investigate the impact of a warmer climate scenario on the Hudson Bay marine system using the sea-ice–ocean model presented in Saucier et al. (2004).

2 Method

2.1 Numerical model

The numerical model used in this study is a high-resolution regional oceanic model developed by Saucier et al. (2004). The model uses the hydrostatic shallow-water incompressible formulation from Backhaus (1983, 1985), Stronach et al. (1993), and Saucier et al. (2003). The ocean model is coupled with a dynamic (Hunke and Dukowicz 1997) and thermodynamic (Semtner 1976) two-layer sea-ice model with snow cover as an independent layer. Turbulence is incorporated as a level 2.5 turbulent kinetic energy equation. Heat, salt, and momentum fluxes at the ocean–ice–atmosphere interfaces are represented with the bulk aerodynamic formula of Parkinson and Washington (1979). The conservation equations of momentum and mass are discretized on an Arakawa C grid. The model utilizes a 10 km horizontal resolution projected onto a polar stereographic grid and has a 10 m vertical resolution for up to 36 depth intervals. Surface and bottom layer thickness are a function of local water level and bathymetry that are based on realistic bathymetry data (Jackobsson et al. 1996; Sandwell et al. 2000). The model domain covers Hudson Bay, James Bay, Foxe Basin, and Hudson Strait, with open boundaries at the Hudson Strait–Labrador Sea connection to the east and at Fury and Hecla Strait in the northwest (Fig. 1). Atmospheric forcing includes 2 m air and dew point deficit, cloud cover fraction, precipitation rate, and 10 m winds. Atmospheric, hydrological, and tidal forcing as well as the initial and boundary conditions for temperature and salinity were those used in Saucier et al. (2004). The time step of the ocean model is 300 s, while a 600 s step is used for the sea-ice model.

2.2 Numerical experiments

We conducted a control simulation of the present climate and another simulation of a warmer climate according to regional projections produced by the CRCM (Plummer et al. 2006; Music and Caya 2007). The present climate simulation started on 1 August 2001 and ran until 31 July 2005. Present atmospheric data came from the Global Environmental Multiscale model (Coté et al. 1997a, b). The input frequency ranged from 3 to 12 h depending on the atmospheric variable. Hydrologic forcing was obtained from the Hydat database (Water Survey of Canada–Environment Canada), Hydro-Québec, and the Ministère du Développement Durable, de l’Environnement et des Parcs (Gouvernement du Québec). The simulation of a warmer climate scenario was based on the delta-change approach only for the 2 m air temperature variable. This strategy was used to isolate and quantify the contribution of the temperature signal versus a complete climate change scenario setup. Therefore, the hydrologic forcing in the warmer climate scenario was identical to the one used for the present climate simulation. The radiative forcing used to generate future air temperatures was based on an effective CO2 concentration that varies from 707 to 950 ppmv. Monthly mean changes in 2 m air temperature were generated by the version 4.2 of the Canadian Regional Climate Model (CRCM), which was driven by the CGCM3.1/T47, following the IPCC SRES A2 scenario (member #4). From the CRCM, a recent 30-year climatological monthly mean dataset (1961–1990; run aet) and another future 30-year climatological monthly mean dataset (2041–2070; run aeu) for 2 m air temperature were computed to reconstruct the changes between the two time periods with the spatial variability used in the CRCM grid (45 km horizontal grid-size mesh). For convenience, we introduce a notation \( \psi \) for the 2 m air temperature field
$$ \overline{\psi } (i,j,m) = {\frac{1}{A}}\sum\limits_{y = 1}^{A} {\psi (i,j,m,y)} $$
where \( {\psi (i,j,m,y)} \) is the monthly mean of the variable \( \psi \) at the i and j spatial coordinates for month m of year y. A is 30 and represents the 30-year time period used to calculate the recent (1961–1990) and future (2041–2070) climatologies. Therefore, we obtain the change \( {\Updelta \psi \left( {i,j,m} \right)} \), which represents the difference between the two time periods
$$ \Updelta \psi (i,j,m) = \overline{\psi }^{2041 - 70} (i,j,m) - \overline{\psi }^{1961 - 90} (i,j,m) $$
These computed 30-year monthly mean changes \( {\Updelta \psi \left( {i,j,m} \right)} \) were interpolated on the ocean grid (10 km horizontal grid-size mesh) and were added to the present 2001–2005 time period’s 2 m air temperature field for each simulated year to get a 4-year time slice atmospheric forcing representing a warmer climate, \( {\psi^{\text{warmer}} \left( {i,j,t,d,m,y} \right)} \), defined by
$$ \psi^{\text{warmer}} \left( {i,j,t,d,m,y} \right) = \psi^{\text{present}} \left( {i,j,t,d,m,y} \right) + \Updelta \psi \left( {i,j,m} \right) $$
where \( {\psi^{\text{warmer}} \left( {i,j,t,d,m,y} \right)} \) is the 2m air temperature at the i and j spatial coordinates for the 3 hourly t, day d, month m of year y.
The delta-change approach was used to isolate the influence of temperature and minimize biases in the regional ocean system model. The biases and uncertainties of the atmospheric climate scenarios are treated in de Elía et al. (2007) and Plummer et al. (2006). The use of warmer atmospheric data over an ocean with temperature and salinity conditions of the present climate was needed to bring the sea-ice–ocean model into a type of equilibrium with the new surface forcing before performing the future scenario simulation. Therefore, water temperature and salinity were stabilized through an 8 × 1 year of integration to spin-up the stratification over which the sea-ice–ocean climate reached a new equilibrium over the whole depth. The spin-up experiment started on 1 August and ran until 31 July of the next year; this was repeated eight times. For each simulated run, the initial conditions of temperature and salinity were replaced with the final conditions of the previous spin-up run. The 2 m air temperature forcing used for the spin-up experiment was similar each year and corresponded to
$$ {\psi \left( {i,j,t,d,m} \right) = \psi^{\text{present}} \left( {i,j,t,d,m , 1} \right) + {{\Updelta}}\psi \left( {i,j,t,d,m} \right)} $$
Our experiment to simulate a warmer climate over the Hudson Bay area integrates both monthly and spatial variability of the projected warming computed by the CRCM.

3 Results

Most of the results presented in this study and their interpretations focus on the anomalies between the future scenario and the present simulations for oceanographic conditions in salt and temperature. All the time series data illustrated herein are full domain-averaged, which includes Foxe Basin, James and Hudson bays, and Hudson Strait.

3.1 Temperature and salinity variability

Fig. 2 shows 12 h estimates of the 2 m air temperature (4 years averaged) for the present and warmer climate simulations. The projected change in air temperature varies among seasons and ranges from 0.8°C in July to 10°C in December with a mean warming of 3.9°C. The largest rise in the climate change signal from November to December is directly produced by the coupled GCM. The strong warming during winter is linked to the positive ice–albedo feedback detected in GCMs (Holland and Bitz 2003) as well as in RCMs (Plummer et al. 2006).
Fig. 2

Twelve-hour averages of air temperature for the present climate simulation (blue) and for the warmer climate scenario (red). The change between the two simulations is represented by the black line

Seasonally averaged sea-surface temperature (SST) for the present climate simulation and the anomaly between present and future scenarios are presented in Fig. 3. During winter (Fig. 3a), no significant change in SST under the ice cover is detected between simulations because the freezing point has already been reached. For both simulations, the eastern shore of Hudson Bay is slightly warmer than the western shore. The spatial variability in wintertime SST is mainly due to the influence of salinity on freezing temperatures. During spring (Fig. 3b), an earlier retreat of sea ice produces a 1°C warming in James Bay. The largest warming signal (5°C) occurs during summer (Fig. 3c) and is found along the southeastern shore of Hudson Bay and in James Bay. SSTs increase by more than 3°C on average in central Hudson Bay with decreasing values westward and northward. The waters in northwestern Hudson Bay, where a well-defined recurrent polynya during winter was modelled by Saucier et al. (2004), undergo a warming of less than 2°C, although break-up occurs earlier in this area compared to the central Hudson Bay. SST changes are highest during summer while changes in air temperature are lowest. This additional warming of the surface is achieved through an earlier retreat of the ice cover allowing a higher input of solar radiation. When the mixed layer begins to cool in fall, the surface temperature of the Hudson Bay domain is still higher by about 1–2°C compared to the present climate simulation (Fig. 3d). The strong coastal current that flows cyclonically around James Bay and along the eastern shore of Hudson Bay (Prinsenberg 1986; Saucier et al. 2004) coincides with the highest warming signal (2°C) during fall. This current corresponds to the freshwater outflow originating from meltwater and river runoff. The intensity of the additional surface warming during summer and fall is related to the vertical stability of the water column, mainly established by the freshwater flow. Southern and eastern Hudson Bay are known to store significant heat in fall due to the stability of the water column compared to other regions (Saucier et al. 2004); therefore, we analyzed the spatial variation of the increasing heat through time and its impact on the sea-ice climate.
Fig. 3

Seasonally averaged sea-surface temperature (SST) for the present climate simulation (T) and SST anomaly between the present and warmer simulations (dT). JFM (a), AMJ (b), JAS (c), and OND (d). Note the different scales for the colour bars

The effect of a warmer atmosphere not only results in SST changes but also influences the water column at depth. Because the increasing heat inside the water column cannot be detected by analyzing surface temperature, we computed the column-averaged heat content, Q (J m−3), defined as the energy that must be extracted from the water column to bring it to its freezing temperature at surface pressure (Maykut and McPhee 1995)
$$ {Q = {\frac{1}{z}}\int\limits_{z}^{0} \rho \left( z \right)C_{p} \;\delta T\;{\text{d}}z} $$
where \( \rho \) is the water density at the depth z, \( {C_{p} } \)is the specific heat capacity of seawater, and \( {\delta T} \) is the temperature difference from the freezing point. Fig. 4 shows the heat content anomaly between the warmer climate scenario and the present climate simulation for each season. During winter, although SSTs are similar in both simulations (Fig. 3a), we found additional heat stored along the eastern shores of James and Hudson bays, with values exceeding 3 MJ m−3 (Fig. 4a); there is no difference in the water column heat content between simulations for the western shore of Hudson Bay and Foxe Basin. This spatial difference in heat storage during winter between eastern and western Hudson Bay is related to the differing vertical stability (stratification) of the water column (Saucier et al. 2004). The region located between Southampton Island and the tip of the Nunavik, also surrounded by Coats, Mansel, and Nottingham islands (all identified on Fig. 1), stores additional heat year-round. Modelled temperature profiles show that the highest warming occurs at depths below 100 m (data not shown), and these waters appear to be advected from Hudson Strait at the end of fall. During spring (Fig. 4b), the heat content anomaly in western James Bay exceeds 5 MJ m−3. This anomaly is related to an earlier melting of sea ice that allows an increase in shortwave heat input. The water column in Ungava Bay also shows an increase in the heat content along the shore, which reaches a maximum in summer and persists until the end of fall. During summer (Fig. 4c), the higher warming signal for coastal compared to central regions is directly explained by the bathymetry. During fall, the storage of additional heat is also concentrated in coastal regions. This contrasting pattern of coastal versus central regions has been related to the mean sea-surface elevation generated by winds (Wang et al. 1994c); however, this is not the case for summer in our simulation. Warmer surface waters are advected toward the shore by winds, leading to a deepening of the isopycnals in coastal regions. The warmer climate enhances the warming of surface waters, and the additional heat is redistributed alongshore and at depth. These warmer waters at the end of fall delay surface freezing and slow sea-ice growth if the excess heat is redistributed upward through turbulent mixing.
Fig. 4

Seasonally averaged heat content anomaly (in MJ m−3) normalized by depth between the warmer climate scenario and the present climate simulation for JFM (a), AMJ (b), JAS (c), and OND (d). Note the different scales for the colour bars for the (a, b) and (c, d) plates

Figure 5 presents domain-averaged surface salinity (4 years averaged) for the present climate simulation and the warmer climate scenario. In the warmer climate, the surface layer is saltier from July to February and fresher from March through June compared to the present climate simulation. This positive salt anomaly during the ice-free season results from a decreased amount of meltwater from sea ice and a higher evaporation rate in the warmer climate scenario (not shown). The rise in surface salinity during fall due to reduced runoff, the deepening of the mixed layer (Saucier et al. 2004), and the export of freshwater out of the system are not as large as in the present climate simulation. Once freezing begins, the lower surface salinity in the warmer climate scenario has lower brine rejection compared to the present climate simulation. The lower amount of freshwater in the surface layer during summer and fall in the warmer climate scenario also leads to lower surface stratification (not shown), which tends to enhance the vertical diffusion of heat accumulated during summer compared to the present climate conditions. The seasonal variability of surface salinity is reduced due to a lower amount of meltwater as well as lower brine release during sea-ice growth.
Fig. 5

Domain-averaged daily surface salinity for the present climate simulation (blue) and for the warmer climate scenario (red)

We illustrate the changes in stratification and changes in the mixed-layer depth between the present and warmer climate simulations using daily averaged salinity as function of depth at station W1 (northwestern Hudson Bay, 61°34′ N, 91°38′ W; Fig. 1) (Fig. 6). This location was chosen due to its proximity to the recurrent polynya in northwestern Hudson Bay. This region presents the highest sea-ice production rate and strong haline convection events (Saucier et al. 2004). Comparing the isohaline distribution between the two panels, we note a mixed layer that is about 20 m deeper in January for the warmer climate scenario. Brine rejection increases the salt content in surface waters. As a consequence, salt is transported downward through turbulent mixing, thus increasing the salinity of bottom waters for the present climate simulation (Fig. 6a) from about 33 (February) to 33.5 (April). In the warmer climate scenario, this core of dense water has a lower salinity (Fig. 6b) that is due to lower sea-ice production from the polynya. At the beginning of the melt season, the sea-surface freshening is advanced by 1 month and occurs as early as May in the warmer climate. During summer, the deficit of meltwater for the warmer climate scenario lowers the vertical salinity gradient in the surface layer. This alteration of surface stratification then enhances the downward mixing of heat (Fig. 7), which contributes to the deeper storage of additional heat below the seasonal thermocline during the ice-free period. Although atmosphere–ocean coupling reaches the bottom of the water column, no additional heat remains at depth at the end of winter for the warmer climate scenario.
Fig. 6

Daily averaged salinity as function of depth (in m) at a selected station (W1) in western Hudson Bay (61°34′ N, 91°38′ W) for the present climate simulation (a) and the warmer climate scenario (b). The colour range is bounded with narrowed values in order to better see the smaller changes through the water column. Values outside the prescribed ranges have been set to the bounding values

Fig. 7

Daily averaged temperature as function of depth (in m) at a selected station (W1) in western Hudson Bay (61°34′ N, 91°38′ W) for the present climate simulation (a) and the warmer climate scenario (b)

Water column stratification and temperature profiles in eastern Hudson Bay have very different annual cycles compared to the western shore of Hudson Bay. Fig. 8 presents the annual cycle of salinity as a function of depth at station E1 in the eastern part of Hudson Bay (58°37′ N, 79°47′ W; Fig. 1). This location is representative of the eastern shore of Hudson Bay, where the strong coastal baroclinic current drives the freshwater outflow originating from meltwater and river runoff. At this location, isohaline positions reveal year-round strong stratification that contrasts with station W1 in western Hudson Bay. In both simulations, we identify increases and oscillations in surface salinity from September to December. This variability is explained by the outflow of the seasonal freshwater pulse and by storm and tidal mixing that brings salt into the surface layer. Compared to the present climate simulation, the transient rise in surface salinity from January to the end of April is lower (by about 0.5 at the surface) in the warmer climate scenario. The freshening of the surface layer begins 1 month earlier (end of April) compared to the present climate simulation. In contrast to western Hudson Bay, strong stratification and low thermodynamic sea-ice growth rate at this eastern station prevent the deepening of the winter mixed layer. The temperature profile (Fig. 9) shows that the heating season increases by approximately 2 months for the warmer climate scenario. In both simulations, the cold winter layer remains confined to the upper 20 m and a significant amount of heat is stored below this stable layer. A longer ice-free season in the warmer climate scenario increases the heat input not only below the seasonal thermocline, but also at depth, from 60 m to the bottom, where the water column is warmed by 0.6°C. This generalized warming at depth throughout the whole year is likely not only the result of enhanced downward diffusion of heat during summer: lateral advection probably plays an important role. The stronger eastern stratification limits the vertical heat exchange compared to other regions. The atmosphere–ocean coupling is a function of stratification and its seasonal variability.
Fig. 8

Daily averaged salinity as function of depth (in m) at a selected station (E1) in eastern Hudson Bay (58°37′ N, 79°47′ W) for the present climate simulation (a) and the warmer climate scenario (b)

Fig. 9

Daily averaged temperature as function of depth (in m) at a selected station (E1) in eastern Hudson Bay (58°37′ N, 79°47′ W) for the present climate simulation (a) and the warmer climate scenario (b)

The domain-averaged annual cycle of dense water volume is presented in Fig. 10. Brine-enriched dense water is seasonally produced through intense sea-ice formation inside polynyas. Dense water, defined as having temperatures ≤–1.69°C and salinities ≥33.4, is produced by the model at the surface and then sinks to the bottom of the polynya in northwestern Hudson Bay. The computed volume of dense water is a robust result since there is no significant variation when small modifications are made to salinity and temperature threshold values. The maximum volume of dense water appears earlier for the warmer climate scenario. The amount of dense water calculated with the selected threshold values is 50% lower in the warmer climate scenario. The formation of dense water is reduced due to lower sea-ice formation, and these dense waters are less saline in the warmer climate scenario, which is confirmed by the lower salinity of bottom waters in April–May in Fig. 6b. A modification in the density of these dense waters alters the renewal of bottom waters. Further investigations are needed to quantify how the ventilation of bottom waters over the whole domain is affected.
Fig. 10

Daily averaged volume (in m3) of dense water computed with prescribed thresholds of temperature and salinity (T ≤ –1.69°C and S ≥ 33.4) for the present climate simulation (blue) and the warmer climate scenario (red)

3.2 Sea-ice sensitivity and spatio-temporal variability

The variability of sea-ice cover in Hudson Bay is strongly impacted by a warmer atmosphere. The freeze-up and break-up dates for Foxe Basin, Hudson Bay, and James Bay as well as the changes associated with a warmer climate are listed in Table 1. These dates correspond to the median sea-ice concentration (Markham 1986). The sea-ice season is reduced by 7–9 weeks in the warmer climate scenario. In Hudson Bay, the delay in surface freezing (25 days) and the advance in melting (24 days) are similar, whereas the change is longer for melting than for surface freezing in James Bay. In Foxe Basin, the change is more notable during freezing.
Table 1

Median dates of freeze-up and break-up for Foxe Basin, Hudson Bay, and James Bay for the present climate simulation and the warmer climate scenario




New sea-ice season length

Present climate

Warmer climate

Present climate

Warmer climate

Foxe Basin

November 4

+31 days

July 13

−22 days

~6 ½ months

Hudson Bay

December 4

+25 days

July 8

−24 days

~5 ½ months

James Bay

December 18

+26 days

June 22

−39 days

4 months

Figure 11 presents mean sea-ice area and volume for both simulations (4 years averaged). The right vertical axis scale presents the relative surface coverage of the whole Hudson Bay domain. The shift observed in sea-ice cover at the beginning of the sea-ice season is caused by a higher heat content of the mixed layer in the warmer climate. Maximum sea-ice coverage reaches 1.152 × 106 km2 in the warmer climate scenario compared to 1.185 × 106 km2 in the present climate simulation. The difference of 32,350 km2 in the maximum cover is relatively small, only 2.6% of the domain. At this latitude, winter severity and the confined geography of this inland sea are sufficient to keep an almost completely ice-covered sea even in the warmer climate scenario. However, the sea-ice extent is greatly reduced during freezing and melting periods. During spring, when shortwave radiation increases, any change in mean sea-ice thickness and concentration generated by a warmer climate would change the amount of solar radiative energy absorbed by sea ice and transmitted into the ocean. The melting of sea ice would thus begin earlier through a higher heat input from the positive ice–albedo feedback. The earlier decrease of sea-ice coverage observed in the future scenario suggests strong changes in sea-ice thickness compared to the present climate simulation. These changes in thickness are illustrated in Fig. 11b through changes in sea-ice volume between simulations. While the difference in ice cover between scenarios is relatively low (Fig. 11a), the warmer climate generates a larger depletion in terms of sea-ice volume (Fig. 11b). The delayed freezing produces a deficit of 592 km3 (–31%) in maximum sea-ice volume, which corresponds to a freshwater layer of 45 cm. The maximum volume of sea ice for the warmer climate occurs 2 weeks earlier (mid-April). The earlier decrease of sea-ice volume indicates an earlier end to the net sea-ice production, which corresponds to the time when the extraction of heat from ocean to sea ice becomes equivalent to or surpasses the transfer of heat from sea ice to the atmosphere. Although air temperature is still below the freezing point at the beginning of spring, the increasing solar radiative flux that penetrates the mixed layer through the sea-ice cover transfers heat into the sea-ice–water system. The input of solar energy is negatively correlated with the thickness of the sea-ice cover (Jin et al. 1994); therefore, the thinner sea-ice cover in the future scenario is predisposed to melt earlier through a higher input of solar radiation.
Fig. 11

Twelve-hour averaged (a) sea-ice cover area (in m2) and (b) sea-ice volume (in km3) for the present climate simulation (blue) and the warmer climate scenario (red). Note in (a) that we include the right y-axis for the relative sea-ice coverage of the whole Hudson Bay domain

The sea-ice growth rate is also strongly affected in the warmer climate scenario in both thermodynamic (Fig. 12a) and dynamic (Fig. 12b) growth. The mean thermodynamic growth rate decreases from 0.24 to 0.13 cm day−1 while the maximum value decreases from 1.42 to 1.24 cm day−1 (Fig. 12a). This 46% drop in the mean rate corresponds to a depletion of about 40 cm in mean sea-ice thickness. The largest difference occurs during the first months of winter, from early December to February, when sea ice thickens rapidly. Sea-ice growth is still slowed in the warmer climate scenario for the rest of the freezing season, although the difference between simulations is reduced. This observation confirms that sea-ice production in the warmer climate is greatly reduced through changes in the preconditioning of the mixed layer. Dynamic growth (mechanical ridging) is also reduced in the warmer climate (Fig. 12b). The mean dynamic growth rate decreases from 0.79 to 0.46 cm day−1. This decrease is a direct consequence of the thinner pack ice that converges for ridging rather than a reduction in the frequency of the ridging events. As stated by Saucier et al. (2004), there is a significant difference in the sea-ice regime between western and eastern Hudson Bay. Therefore, the spatial variability in the thinning of ice cover is a function of the spatial variability in both dynamic and thermodynamic growth.
Fig. 12

Twelve-hour averaged (a) thermodynamic sea-ice growth rate (in cm day−1) and (b) dynamic sea-ice growth rate (in cm day−1) for the present climate simulation (blue) and the warmer climate scenario (red)

The spatial integration of the combined effects of lower dynamic and thermodynamic sea-ice growth under the warmer climate scenario is presented on Fig. 13. This figure presents the relative change in sea-ice thickness computed during the high ice-covered season (1 January to 30 April). The relative change of sea-ice thickness is defined as
Fig. 13

Relative winter-averaged (JFMA) sea-ice thickness anomaly between the warmer climate scenario and the present climate simulation

$$ \Updelta \bar{H} = {\frac{{\bar{H}_{w} - \bar{H}_{p} }}{{\bar{H}_{p} }}} \times 100 $$
where \( \bar{H}_{w} \) and \( \bar{H}_{p} \) are the January to April 4-year averaged sea-ice thickness for the warmer and present climate simulations, respectively. The regions of Ungava Bay, Hudson Strait, and James Bay present the largest decreases. The reduction in sea-ice cover exceeds 50% during the winter season. In the present climate scenario, the north shore of Hudson Strait is partially covered with sparse and thin ice and is ice-free in the future climate scenario. Therefore, the reduction reaches almost 100% in this area. The main feature is the northwest–southeast gradient in the reduced sea-ice thickness. In the middle of Hudson Bay, sea-ice thickness is reduced by 35%. The region south of 57°N, which includes southeastern Hudson Bay and James Bay, shows higher reductions compared to the centre of Hudson Bay, where thinning ranges from 40 to 55%. This result comes from the specific sea-ice regime in this region since mechanical ridging is maximal in southern Hudson Bay (see Fig. 13 in Saucier et al. 2004). In Foxe Basin, the sea-ice response to a warmer climate is rather moderate compared to other regions. Another region of interest is the polynya in northwestern Hudson Bay, where sea ice undergoes a thinning of 30% in the future scenario.
As the domain is almost totally ice-covered during winter for both simulations (Fig. 11a), two single-month periods, December and June, illustrate the time-dependent changes in sea-ice cover induced by a warmer climate. Figure 14a shows the mean sea-ice concentration in December for the present climate simulation and Fig. 14b the warmer climate scenario. A sharp rise in open-water freezing is noted in December (Figs. 11a and 12a). The difference in ice coverage corresponds to the delay due to the time required to extract the additional heat stored inside the water column. The southern and eastern shores of Hudson Bay, James Bay, and Hudson Strait are the last regions to be covered by sea ice in December. This spatial feature matches both the heat content anomaly (Fig. 4d) and the changes of SST during fall (Fig. 3d). The surface layer of the strong coastal current that drives the freshwater pulse toward Hudson Strait is warm enough to inhibit the propagation of surface freezing during December. The other interesting feature is the polynya in northwestern Hudson Bay. The expansion and drift of sea ice from this area becomes obvious in the warmer scenario. Mean sea-ice concentrations in June are shown for the present climate simulation (Fig. 14c) and for the future scenario (Fig. 14d). Sea-ice cover begins to disappear rapidly in June, decreasing from 900,000 to 600,000 km2 in only 30 days (see Fig. 11a). Figure 14d illustrates the almost ice-free conditions under the warmer climate for James Bay and northwestern Hudson Bay. Hudson Strait is completely clear of sea ice in June in the future scenario. A patch of fragmented mobile sea-ice subject to drift under the influence of wind during early summer is still present in the middle of Hudson Bay.
Fig. 14

December (a, b) and June (c, d) mean sea-ice concentration for (a, c) the present climate simulation and (b, d) the warmer climate scenario

4 Discussion

We performed a coupled sea-ice–ocean simulation of the Hudson Bay marine system for a current and a warmer climate following a RCM-derived climate scenario, as used in Music and Caya (2007). Our objective was to quantify the response of Hudson Bay sea-ice cover and its variability to a warmer atmospheric forcing keeping all other forcings such as wind or river run-off unchanged. The use of a regional sea-ice–ocean model was necessary to integrate the specific estuarine circulation and the local thermohaline processes of a freezing estuary into the global response of the Hudson Bay system.

Regional climate change scenarios of subarctic environments such as the Baltic Sea have been widely studied (Meier 2002a, b, 2006; Meier and Döscher 2002; Döscher and Meier 2004; Meier et al. 2004). These earlier works provide a reliable methodology to develop a robust climate change scenario. In Meier (2002a), the introduction of a spin-up strategy to initialize future oceanic conditions is discussed. The spin-up time scale is closely related to the residence time of the modelled system. In addition, the spin-up experiment produces new conditions of stratification that integrate future climate variability. In Meier (2006), the simulation period (96 years) was long enough to allow almost 60 years of integration to spin-up the stratification in their scenario simulations. However, their surface atmospheric forcing for the 1902–1998 reference period has been reconstructed using a statistical model that produces monthly mean atmospheric fields except for the high frequency input of sea-level pressure (Kauker and Meier 2003). This approach to reconstruct a 96-year time period has not yet been performed for Hudson Bay. Our coupled sea-ice–ocean model uses high spatial and temporal resolution, atmospheric forcing, and river runoff data for the 2001–2005 period. The delta-change approach has been successfully used to transfer the signal of climate change either to the ocean system (Meier 2006) or to the drainage basin (Graham 2004) of the Baltic Sea. Meier (2006) calculated monthly mean changes of atmospheric forcing from present and future 30-year time slice experiments. The same approach was successfully applied in our study.

The simulation of a warmer climate over the Hudson Bay marine system shows a lengthening of the ice-free season by more than 2 months. A simple analysis of the internal mass and energy structure of seawater revealed that these temporal changes are associated with a modification in the seasonal variability of water stratification. These simultaneous changes revealed the strong coupling between sea ice and the surface layer stratification (Manak and Mysak 1989; Backhaus and Kämpf 1999; Steele and Boyd 1999). It is worth noting that a perturbation of the atmospheric forcing results in a significant change of the freshwater balance in terms of both timing and intensity. In an estuarine environment, such as the Hudson Bay system, where density stratification is primarily controlled by salinity, stabilization of the mixed layer through a large seasonal input of freshwater is a dominant mechanism for controlling atmospheric heat loss and sea-ice freezing (Rudels et al. 1999). In Hudson Bay, a lower amount of spring meltwater weakens the halocline, thus enhancing the downward mixing of heat during summer and fall. Haline convection at the beginning of winter takes place within a deeper mixed layer, which delays surface freezing. Moreover, the heat content analysis shows a warmer water column year-round. The model shows that heat storage at depth occurs in stable waters along the eastern shores of James and Hudson bays. Thermodynamic growth of sea ice, which lowers surface buoyancy fluxes through brine rejection, is also delayed and reduced in the warmer climate scenario. The simulated atmospheric perturbation sustains a positive feedback that decreases sea-ice production. Results from the present study suggest that the preconditioning of the mixed layer is a dominant factor influencing the thermodynamic growth of sea ice in Hudson Bay. This evidence supports previous findings on the positive correlation between river runoff and sea-ice extent (Manak and Mysak 1989; Wang et al.1994b) or thickness (Saucier and Dionne 1998) inside the Hudson Bay system.

We found that southern and coastal regions as well as Hudson Strait show greater sensitivity to a warmer climate in terms of sea ice and water column heat content. The storage of additional heat along the coast during fall is explained by winds. The pattern of the heat content anomaly in fall perfectly matches the mean sea-surface slope generated by wind stress in Hudson and James bays (Wang et al. 1994c). Warm surface waters are advected toward the coast, removing additional heat from central regions. Our results are different from those of a similar regional climate study conducted over the Baltic Sea (Meier 2002a, b, 2006)—a European Inland Sea located at a similar latitude. The highest changes in summer SST for the Baltic Sea were projected in the northernmost basins (see Fig. 9 in Meier 2002a) and mostly in the central Bothnian Sea compared to shallow coastal areas (see Fig. 13 in Meier 2006). Coastal areas in the Baltic Sea were found to be less sensitive to the warming signal than were central parts of the basins (Meier 2006). This author also concluded that the sensitivity of sea ice to salinity was relatively small in the Baltic Sea (Meier 2002b). In our study, the greatest oceanic warming signal observed is correlated to the wind-driven and buoyancy-driven circulation along the coast of Nunavik. Sea-ice redistribution by the winds in Hudson Bay (the polynya to the northwest and accumulation of sea ice to the southeast (Saucier et al. 2004)) is a reason why the Hudson Bay is different from the Baltic Sea.

Southern regions of Hudson Bay undergo the largest changes in the simulations. The strong decline in sea-ice thickness in James Bay and southeastern Hudson Bay can be explained by a reduction of both thermodynamic and dynamic growth of sea ice. The southward drift of sea ice by winds (Maxwell 1986) leads to the accumulation of pack ice in southern Hudson Bay along the Ontario coast (Wu et al. 2006) and particularly in James Bay, where ridging was found to be important during April (Saucier et al. 2004). Pressure ridges act as stores for ice mass and constitute additional sources of freshwater at the onset of melting. The confined geography of James Bay facilitates the formation of pressure ridges. The warmer climate lowers the ridge production rate in terms of dynamic height increase. However, the relative decrease in ridging is not as large as the relative thinning of the mean sea-ice cover. Because thin ice is preferentially used before thicker ice as a primer in ridging schemes (Thorndike et al. 1975; Amundrud et al. 2004; Lipscomb et al. 2007), one could hypothesize that ridging events in our simulation are more frequent with thinner pack ice although the net volume production is lower. This hypothesis is difficult to demonstrate with a field survey since the observed thinning of the Arctic sea-ice cover is controlled by large-scale atmospheric and oceanic processes that act simultaneously (Rothrock et al. 1999). Ridging has more often been studied as a causal mechanism to explain changes in Arctic sea-ice thickness (Shoutilin et al. 2005) rather than the reverse. In modelling results of the Arctic basin, Makshtas et al. (2003) found that changes in atmospheric circulation decreased the concentration of ridges, which in turn was responsible for the observed thinning of Arctic sea ice (Rothrock et al. 1999).

The production of cold, brine-enriched waters occurs inside wind-driven coastal polynyas mainly located in western Foxe Basin (Defossez et al. 2008) and northwestern Hudson Bay (Markham 1986). These areas exhibit high rates of sea-ice formation and brine rejection that produce convecting cold and salty water masses, as demonstrated by observations and model simulations (Saucier et al. 2004; Defossez et al. 2008). Our results suggest that a warmer atmosphere slows the ventilation of bottom waters in Hudson Bay via the slowdown of the polynya activity. While the overall 3.9°C warming simulated in our study reduces the maximum volume of dense water produced by 50%, a sensitivity study conducted on a coastal polynya in Antarctic showed that dense water export was reduced by 40% for a surface temperature increase of 2°C (Marsland et al. 2007). These similar results from a shallow Canadian Inland Sea and the east Antarctic continental shelf illustrate the strong connection between atmospheric surface processes and the buoyancy-driven ventilation of bottom waters. Although the ventilation of bottom waters inside a shallow basin such as the Hudson Bay marine system differ significantly from deep ocean ventilation, the seasonal variability of vertical salt fluxes is greatly affected, which in turn altered the baroclinic circulation at all depths.

The decrease in maximum sea-ice volume by 31% contrasted greatly with the 2.6% change in maximum sea-ice extent. The greater impact on sea-ice thickness rather than extent reveals the strong polar influence that prevents a large reduction in the ice sheet during winter. It also reflects the sea-ice thickness sensitivity from water column preconditioning. These results also demonstrate the relative protection of the Hudson Bay marine system from large lateral oceanic heat input compared to the Arctic Ocean, which underwent an accelerated decline of its ice cover faster than was anticipated by numerical models (Stroeve et al. 2007).

This numerical experiment has shown that changes in sea-surface temperature could exceed the change from air temperature forcing. The input of thermal energy from shortwave radiation is greatly increased through the lengthening of the ice-free season. In the present climate simulations, the persistence of the ice cover around summer solstice is sufficient to reflect a large proportion of incident solar radiation. Solar heat input is therefore inhibited through a decoupling between the peak of solar radiative forcing and the highest proportion of low albedo surface. Sea ice plays a significant role in the absorption and partitioning of solar radiative energy in the atmosphere, sea ice, and ocean (Jin et al. 1994). The simulated warmer climate produces thinner sea ice, but this thinning trend allows higher input of solar energy (Jin et al. 1994; Perovich 2005). The increasing absorption of solar energy through an extended ice-free period and the thinning of sea ice increase the positive ice–albedo feedback. Maykut and McPhee (1995) demonstrated that shortwave radiation, absorbed below the bottom of the pack ice, is the main energy source in the oceanic heat flux between water and ice. Therefore, the increase of solar heat input into the ocean unbalances the fragile thermodynamic equilibrium and accelerates warming at high latitudes. We have provided a general pattern of changes in the sea-ice climate associated with the variability in temperature and salinity profiles for the Hudson Bay marine system. However, we note in closing that the impact of global warming at a regional scale requires further investigation, specifically regarding the freshwater export toward the Labrador Sea in the perspective of changing river discharge for the Hudson Bay drainage system (Déry et al. 2005).



The authors wish to thank James Caveen and François Roy for technical support and Dr. Christopher-John Mundy for helpful advice and encouragement. Hydrological data were provided by HydroQuébec, Water Survey of Canada (Environment Canada), and the Ministère du Développement Durable, de l’Environnement et des Parcs (Government of Québec). The authors are members of the Canadian ArcticNet Program. This work is a contribution to the ArcticNet Program, funded in part by the Network Centres of Excellence (NCE) Canada.


  1. Amundrud TL, Melling H, Ingram RG (2004) Geometrical constraints on the evolution of ridged sea ice. J Geophys Res 109:C06005. doi:10.1029/2003JC002251 CrossRefGoogle Scholar
  2. Backhaus JO (1983) A semi-implicit scheme for the shallow water equations for application to shelf sea modeling. Cont Shelf Res 2:234–254Google Scholar
  3. Backhaus JO (1985) A three-dimensional model for the simulation of shelf-sea dynamics. Dtsch Hydrogr Z 38:165–187CrossRefGoogle Scholar
  4. Backhaus JO, Kämpf J (1999) Simulations of sub-mesoscale oceanic convection and ice-ocean interactions in the Greenland Sea. Deep Sea Res II 46:1427–1455CrossRefGoogle Scholar
  5. Coté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (1997a) The operational CMC/MRB Global Environmental Multiscale (GEM) model: part I—design considerations and formulation. Mon Wea Rev 126:1373–1395CrossRefGoogle Scholar
  6. Coté J, Gravel S, Méthot A, Patoine A, Roch M, Staniforth A (1997b) The operational CMC/MRB Global Environmental Multiscale (GEM) model: part II—results. Mon Wea Rev 126:1397–1418CrossRefGoogle Scholar
  7. de Ramón Elía, Caya Daniel, Côté Hélène, Frigon Anne, Biner Sébastien, Giguère Michel, Paquin Dominique, Harvey Richard, Plummer David (2007) Uncertainty study of an ensemble of CRCM regional climate simulations over North America. Clim Dyn 30:113–132Google Scholar
  8. Defossez M, Saucier FJ, Myers PG, Caya D, Dumais JF (2008) Multi-year observations of deep water renewal in Foxe Basin, Canada. Atmos Ocean 46(3):377–390CrossRefGoogle Scholar
  9. Déry SJ, Stieglitz M, McKenna EC, Wood EF (2005) Characteristics and trends of river discharge into Hudson, James, and Ungava Bays, 1964–2000. J Climate 18:2540–2557CrossRefGoogle Scholar
  10. Döscher R, Meier HEM (2004) Simulated sea surface temperature and heat fluxes in different climates of the Baltic Sea. Ambio 33:242–248Google Scholar
  11. Gagnon AS, Gough WA (2005) Climate change scenarios for the Hudson Bay region: an intermodel comparison. Clim Change 69:269–297CrossRefGoogle Scholar
  12. Gough WA (1998) Projections of sea-level change in Hudson and James Bays, Canada, due to global warming. Arct Alp Res 30:84–88CrossRefGoogle Scholar
  13. Gough WA, Allakhverdova T (1999) Limitations of using a coarse resolution model to assess the impact of climate change on sea ice in Hudson Bay. Can Geographer 43:415–422CrossRefGoogle Scholar
  14. Gough WA, Wolfe E (2001) Climate change scenarios for Hudson Bay, Canada, from general circulation models. Arctic 54:142–148Google Scholar
  15. Graham LP (2004) Climate change effects on river flow to the Baltic Sea. Ambio 33:235–241Google Scholar
  16. Holland MM, Bitz CM (2003) Polar amplification of climate change in coupled models. Clim Dyn 21:221–232CrossRefGoogle Scholar
  17. Hunke EC, Dukowicz JK (1997) An elastic-viscous-plastic model for sea ice dynamics. J Phys Oceanogr 27:1849–1867CrossRefGoogle Scholar
  18. Hurrell JW, Hoerling MP, Phillips AS, Xu T (2004) Twentieth century North Atlantic climate change. Part I: assessing determinism. Clim Dyn 23:371–389CrossRefGoogle Scholar
  19. Hurrell JW et al (2006) Atlantic climate variability and predictability: a CLIVAR perspective. J Climate 19:5100–5121CrossRefGoogle Scholar
  20. Ingram RG, Wang J, Lin C, Legendre L, Fortier L (1996) Impact of freshwater on a subarctic coastal ecosystem under seasonal sea-ice cover (Southeastern Hudson Bay, Canada), I, Interannual variability and predicted global warming influence on river plume dynamics and sea ice. J Mar Syst 7:221–231CrossRefGoogle Scholar
  21. Jackobsson M, Cherkis NZ, Woodward J, Macnab R, Coakley B (1996) New grid of Arctic bathymetry aids scientists and mapmakers. Eos Transaction Am Geophys Union 81:89–93CrossRefGoogle Scholar
  22. Jin Z, Stammes K, Weeks W, Tsay S (1994) The effect of sea ice on the solar energy budget in the atmosphere-sea ice-ocean system: a model study. J Geophys Res 99((C12)):25281–25294CrossRefGoogle Scholar
  23. Kauker F, Meier HEM (2003) Modeling decadal variability of the Baltic Sea: 1. Reconstructing atmospheric surface data for the period 1902–1998. J Geophys Res 108(C8):3267. doi:10.1029/2003JC001797 CrossRefGoogle Scholar
  24. Lipscomb WH, Hunke EC, Maslowski W, Jakacki J (2007) Ridging, strength, and stability in high-resolution sea-ice models. J Geophys Res 112:C03S91. doi:10.1029/2005JC003355 CrossRefGoogle Scholar
  25. Makshtas A, Shoutilin S, Romanov V (2003) Possible dynamic and thermal causes for the recent decrease in sea-ice in the Arctic. J Geophys Res 108:3232. doi:10.1029/2001JC000878 CrossRefGoogle Scholar
  26. Manak DK, Mysak LA (1989) On the relationship between Arctic sea-ice anomalies and fluctuations in northern Canadian air temperature and river discharge. Atmos Ocean 27:682–691Google Scholar
  27. Markham WE (1986) The ice cover. In: Martini EP (ed) Canadian Inland Seas, Oceanogr Ser 44. Elsevier, New York, pp 101–116CrossRefGoogle Scholar
  28. Marsland SJ, Church JA, Bindoff NL, Williams GD (2007) Antarctic coastal polynya response to climate change. J Geophys Res 112:C07009. doi:10.1029/2005JC003291 CrossRefGoogle Scholar
  29. Maxwell JB (1986) A climate overview on the Canadian Inland Seas. In: Martini EP (ed) Canadian Inland Seas, Oceanogr Ser 44. Elsevier, New York, pp 79–99CrossRefGoogle Scholar
  30. Maykut GA, McPhee MG (1995) Solar heating of the Arctic mixed layer. J Geophys Res 100(C12):24691–24703CrossRefGoogle Scholar
  31. Meier HEM (2002a) Regional ocean climate simulations with a 3-D ice-ocean model for the Baltic Sea. Part 1: model experiments and results for temperature and salinity. Clim Dyn 19:237–253CrossRefGoogle Scholar
  32. Meier HEM (2002b) Regional ocean climate simulations with a 3-D ice-ocean model for the Baltic Sea. Part 2: results for sea ice. Clim Dyn 19:255–266CrossRefGoogle Scholar
  33. Meier HEM (2006) Baltic Sea climate in the late twenty-first century: a dynamical downscalling approach using two global models and two emission scenarios. Clim Dyn 27:39–68CrossRefGoogle Scholar
  34. Meier HEM, Döscher R (2002) Simulated water and heat cycles of the Baltic Sea using a 3D coupled atmosphere-ice-ocean model. Boreal Environ Res 7:327–334Google Scholar
  35. Meier HEM, Döscher R, Halkka A (2004) Simulated distributions of Baltic Sea-ice in warming climate and consequences for the winter habitat of the Baltic ringed seal. Ambio 33:249–256Google Scholar
  36. Music B, Caya D (2007) Evaluation of the hydrological cycle over the Mississippi River basin as simulated by the Canadian Regional Climate Model (CRCM). J Hydromet 8(5):969–988CrossRefGoogle Scholar
  37. Mysak LA, Ingram RG, Wang J, Van der Baaren A (1996) The anomalous sea-ice extent in Hudson Bay, Baffin Bay and the Labrador Sea during three simultaneous NAO and ENSO episodes. Atmos Ocean 34:313–343Google Scholar
  38. Parkinson CL, Washington WM (1979) A large scale numerical model of sea ice. J Geophys Res 84:311–337CrossRefGoogle Scholar
  39. Perovich DK (2005) On the aggregate-scale partitioning of solar radiation in the Arctic sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment. J Geophys Res 110:C03002. doi:10.1029/2004JC002512 CrossRefGoogle Scholar
  40. Plummer DA, Caya D, Frigon A, Côté H, Giguère M, Paquin D, Biner S, Harvey R, De Elia R (2006) Climate and climate change over North America as simulated by the Canadian RCM. J Climate 19:3112–3132CrossRefGoogle Scholar
  41. Prinsenberg SJ (1980) Man-made changes in the freshwater input rates of Hudson and James Bays. Can J Fish Aquat Sci 37:1101–1110CrossRefGoogle Scholar
  42. Prinsenberg SJ (1986) The circulation pattern and current structure of Hudson Bay. In: Martini EP (ed) Canadian Inland Seas, Oceanogr Ser 44. Elsevier, New York, pp 187–203CrossRefGoogle Scholar
  43. Prinsenberg SJ (1991) Effects of hydro-electric projects on Hudson Bay’s marine and ice environments. James Bay Publication, James Bay, Series No. 2, pp 1–8Google Scholar
  44. Qian M, Jones C, Laprise R, Caya D (2008) The influences of NAO and Hudson Bay sea-ice on the climate of eastern Canada. Clim Dyn 31(2–3):169–182. doi:10.1007/s00382-007-0343-9 Google Scholar
  45. Rothrock DA, Yu Y, Maykut GA (1999) Thinning of the Arctic sea-ice cover. Geophys Res Lett 26:3469–3472CrossRefGoogle Scholar
  46. Rudels B, Friedrich HJ, Hainbucher D, Lohmann G (1999) On the parameterisation of oceanic sensible heat loss to the atmosphere and to ice in an ice-covered mixed layer in winter. Deep Sea Res II 46:1385–1425CrossRefGoogle Scholar
  47. Sandwell DT, Walter H, Smith F (2000) Bathymetric estimation. In: Fu LL, Cazenave A (eds) Satellite altimetry and earth sciences: a handbook of techniques and applications. International Geophysics Series, vol 69. Academic Press, San Diego, USAGoogle Scholar
  48. Saucier FJ, Dionne J (1998) A 3-D coupled ice-ocean model applied to Hudson Bay, Canada: the seasonal cycle and time-dependent climate response to atmospheric forcing and runoff. J Geophys Res 103:27689–27705CrossRefGoogle Scholar
  49. Saucier FJ, Roy F, Gilbert D, Pellerin P, Ritchie H (2003) The formation and circulation processes of water masses in the Gulf of St. Lawrence. J Geophys Res 108:3269–3289CrossRefGoogle Scholar
  50. Saucier FJ, Senneville S, Prinsenberg SJ, Roy F, Smith G, Gachon P, Caya D, Laprise R (2004) Modelling the sea ice-ocean seasonal cycle in Hudson Bay, Foxe Basin and Hudson Strait, Canada. Clim Dyn 23:303–326CrossRefGoogle Scholar
  51. Semtner AJ Jr (1976) A model for the thermodynamic growth of sea ice in numerical investigations of climate. J Phys Oceanogr 6:379–389CrossRefGoogle Scholar
  52. Shoutilin SV, Makshtas AP, Ikeda M, Marchenko AV, Bekryaev RV (2005) Dynamic-thermodynamic sea ice model: ridging and its application to climate study and navigation. J Climate 18:3840–3855CrossRefGoogle Scholar
  53. Steele M, Boyd T (1999) Retreat of the cold halocline layer in the Arctic Ocean. J Geophys Res 103(C05):10419–10435Google Scholar
  54. Stroeve J, Holland MM, Meier W, Scambos T, Serreze M (2007) Arctic sea ice decline: faster than forecast. Geophys Res Lett 34:L09501. doi:10.1029/2007GL029703 CrossRefGoogle Scholar
  55. Stronach JA, Backhaus JO, Murty TS (1993) An update on the numerical simulation of oceanographic processes in the waters between Vancouver Island and the mainland: the GF8 model. Oceanogr Mar Biol Annu Rev 31:1–86Google Scholar
  56. Thorndike AS, Rothrock DA, Maykut GA, Colony R (1975) The thickness distribution of sea ice. J Geophys Res 80:4501–4513CrossRefGoogle Scholar
  57. Tivy A, Alt B, Howell S, Wilson K, Yackel J (2006) On the relationship between ENSO, the NAO, the PNA and anomalous spring ice cover in Hudson Bay. Proceedings of the ArcticNet Annual Conference, Banff, 13–16 December 2005Google Scholar
  58. Wang J, Mysak LA, Ingram RG (1994a) A numerical simulation of sea-ice cover in Hudson Bay. J Phys Oceanogr 24:2515–2533CrossRefGoogle Scholar
  59. Wang J, Mysak LA, Ingram RG (1994b) Interannual variability of sea-ice cover in Hudson Bay, Baffin Bay and the Labrador Sea. Atmos Ocean 32:421–447Google Scholar
  60. Wang J, Mysak LA, Ingram RG (1994c) A three-dimensional numerical simulation of Hudson Bay summer ocean circulation: topographic gyres, separations, and coastal jets. J Phys Oceanogr 24:2496–2514CrossRefGoogle Scholar
  61. Wu W, Barber D, Iacozza J, Mosscrop D (2006) Trends of spatial and temporal patterns of sea ice concentrations in Hudson Bay region over the period 1971 to 2004. Proceedings of the ArcticNet Annual Conference, Banff, 13–16 December 2005Google Scholar
  62. Zhang X, Vincent LA, Hogg WD, Niitsoo A (2000) Temperature and precipitation trends in Canada during the 20th century. Atmos Ocean 38:395–429Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • S. Joly
    • 1
  • S. Senneville
    • 1
  • D. Caya
    • 2
  • F. J. Saucier
    • 1
  1. 1.Institut des Sciences de la Mer de RimouskiUniversité du Québec à RimouskiRimouskiCanada
  2. 2.Consortium OuranosTour Ouest, MontréalCanada

Personalised recommendations