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Sea ice detection has played an important role in climate protection and strategic deployment. Due to the autonomous learning characteristics of deep learning, it has been gradually applied to the classification of remote sensing sea ice images. At present, deep learning models are mostly used in sea ice classification of single source remote sensing data. Due to the limitations of single source data and the information loss of deep learning model in the process of feature extraction layer by layer, it is inevitable to encounter a bottleneck in sea ice detection requiring fine classification. To solve the above problems, this paper proposes a sea ice image classification method based on ResNet16-feature pyramid networks-spatial pyramid pooling-gated fusion network (ResFPG) and heterogeneous data fusion. In the feature extraction part, the method uses the improved ResNet16 to extract the multi-level feature information of sea ice in synthetic aperture radar data and optical data, reducing information loss in the feature extraction process, then mines and fuses the low-level spatial information and high-level semantic information through the improved feature pyramid networks (FPN), and then collects and fuses the output features of different scales through the spatial pyramid pooling (SPP) network. In the feature fusion part, a gated feature-level fusion strategy is designed to further improve the overall classification accuracy by adaptively adjusting the feature contribution of two heterogeneous sources of data through the gated fusion network (GFN). In order to verify the effectiveness of this method, we use two sets of heterogeneous sea ice remote sensing data located in Hudson Bay area for experiments. The experimental results show that compared with other image classification methods, the proposed method fully excavates and integrates the multi-scale and multi-level features in heterogeneous data, effectively distinguishes the feature contribution, and achieves better classification results (97.14% and 95.85%). [ABSTRACT FROM AUTHOR]

Sea ice is one of the typical causes of marine disasters. Sea ice image classification is an important component of sea ice detection. Optical data contain rich spectral information, but they do not allow one to easily distinguish between ground objects with a similar spectrum and foreign objects with the same spectrum. Synthetic aperture radar (SAR) data contain rich texture information, but the data usually have a single source. The limitation of single-source data is that they do not allow for further improvements of the accuracy of remote sensing sea ice classification. In this paper, we propose a method for sea ice image classification based on deep learning and heterogeneous data fusion. Utilizing the advantages of convolutional neural networks (CNNs) in terms of depth feature extraction, we designed a deep learning network structure for SAR and optical images and achieve sea ice image classification through feature extraction and a feature-level fusion of heterogeneous data. For the SAR images, the improved spatial pyramid pooling (SPP) network was used and texture information on sea ice at different scales was extracted by depth. For the optical data, multi-level feature information on sea ice such as spatial and spectral information on different types of sea ice was extracted through a path aggregation network (PANet), which enabled low-level features to be fully utilized due to the gradual feature extraction of the convolution neural network. In order to verify the effectiveness of the method, two sets of heterogeneous sentinel satellite data were used for sea ice classification in the Hudson Bay area. The experimental results show that compared with the typical image classification methods and other heterogeneous data fusion methods, the method proposed in this paper fully integrates multi-scale and multi-level texture and spectral information from heterogeneous data and achieves a better classification effect (96.61%, 95.69%). [ABSTRACT FROM AUTHOR]

The study of animal movement provides insights into underlying ecological processes and informs analyses of behaviour and resource use, which have implications for species management and conservation. The tools used to study animal movement have evolved over the past decades, allowing for data collection from a variety of species, including those living in remote environments. Satellite-linked radio and GPS collars have been used to study polar bear (Ursus maritimus) ecology and movements throughout the circumpolar Arctic for over 50 years. However, due to morphology and growth constraints, only adult female polar bears can be reliably collared. Collars have proven to be safe, but there has been opposition to their use, resulting in a deficiency in data across much of the species' range. To bolster knowledge of movement characteristics and behaviours for polar bears other than adult females, while also providing an alternative to collars, we tested the use of fur- and ear-mounted telemetry tags that can be affixed to polar bears of any sex and age. We tested three fur tag designs (SeaTrkr, tribrush and pentagon tags), which we affixed to 15 adult and 1 subadult male polar bears along the coast of Hudson Bay during August–September 2021–2022. Fur tags were compared with ear tags deployed on 42 subadult and adult male polar bears captured on the coast or the sea ice between 2016 and 2022. We used data from the tags to quantify the amount of time subadult and adult males spent resting versus traveling while on land. Our results show the three fur tag designs remained functional for shorter mean durations (SeaTrkr = 58 days; tribrush = 47 days; pentagon = 22 days) than ear tags (121 days), but positional error estimates were comparable among the Argos-equipped tags. The GPS/Iridium-equipped SeaTrkr fur tags provided higher resolution and more frequent location data. Combined, the tags provided sufficient data to model different behavioural states. Furthermore, as hypothesized, subadult and adult male polar bears spent the majority of their time resting while on land, increasing time spent traveling as temperatures cooled. Fur tags show promise as a short-term means of collecting movement data from free-ranging polar bears. [ABSTRACT FROM AUTHOR]

The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3-7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps. [ABSTRACT FROM AUTHOR]

The variation of polar sea ice is an indicator of polar environmental change, which plays an important role in the study of regional and global climate change. In this paper, the latest sea ice data sets from the European Space Agency Climate Change Initiative were firstly combined to comprehensively analyze the spatial and temporal variation of Arctic sea ice concentration (SIC) and sea ice thickness (SIT) from 2002 to 2017. The results show that during this period, the SIC of the Kara Sea and the Barents Sea decreases the most significantly, reaching −1.11%·year−1. The Arctic annual average sea ice extent (SIE) and sea ice area (SIA) exhibit noticeable decreasing trends, reaching −0.0592 × 106 km·year−1 and −0.0628 × 106 km·year−1, respectively. Both of Arctic SIE and SIA vary seasonally, and they reach the minimum values of 4.97 × 106 km2 and 3.96 × 106 km2 in September and the maximum values of 14.58 × 106 km2 and 13.35 × 106 km2 in March, respectively. Moreover, the Arctic SIE and SIA anomaly series from 2002 to 2017 were analyzed based on the linear regression method. It is found the most noticeable decreasing trends of the Arctic SIE and SIA are from July to October. The maximum variation of the SIE is −1.06 × 106 km2(10a)−1, and the maximum variation of the SIA is −1.09 × 106 km2(10a)−1. The Arctic SIE and SIA present the smallest decreasing trends from April to May, which are −0.19 × 106 km2(10a)−1 and −0.2 × 106 km2(10a)−1, respectively. The Arctic SIT also displays a pronounced decreasing trend with −0.015 m·year−1 in autumn and −0.018 m·year−1 in winter. The annual average SIT reaches a minimum of 1.28 m in 2013 and a maximum of 1.66 m in 2005. Particularly, the SIT of the Hudson Bay decreases the most significantly, reaching −0.051 m·year−1. Finally, the Arctic average SIT was tested by the Mann‐Kendall method. It is found that the SIT has a sudden change in 2007, from a slight increase in 2003–2007 to a significant decrease in 2007–2017. Overall, the decreasing trend of the Arctic sea ice (both SIC and SIT) is significant in the past decade. These findings can contribute to future research on polar regions and global climate change. Key Points: We analyzed the whole and regional variation of Arctic sea ice by combining use of the latest sea ice concentration and sea ice thickness data sets from European Space AgencyThe sea ice concentration in the Barents Sea and the Kara Sea and the sea ice thickness in the Hudson Bay decreased the most obviouslyThe sea ice thickness has a sudden change in 2007 based on Mann‐Kendall mutation test [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the thermodynamic and dynamic forcing of sea ice within the Hudson Bay System, including Hudson Bay, Hudson Strait, and Foxe Basin. Changes in fall and spring sea ice extents (SIEs) are examined in relation to seasonal surface air temperatures (SATs) and winds, as are changes in freeze-up dates and breakup dates. The proportional leverage of the fall (lag1) and spring SATs and winds on ice is statistically examined per basin. Results show SATs have increased significantly since the mid-1990s and that increases in the fall are higher than the spring period. Fall SATs are highly related to fall SIEs ( R2 = 0.79-0.82). For every 1 °C increase in SAT, SIE decreases by 14% (% of basin area) within the Hudson Bay System; a 1 °C increase delays freezeup by 0.7 to 0.9 weeks on average. Spring SIEs and breakup dates are shown to be highly correlated with fall (lag1) and spring SATs, and with U and V component winds. Proportionately, spring and fall SATs combined play a dominant role (70-80%) in SIE, and the remaining leverage is attributed to dynamic forcing (winds). The relative leverage of fall (lag1) SATs and surface winds are shown to be significant and vary by basin. The open water season has on average increased by 3.1 (±0.6) weeks in Hudson Bay, 4.9 (±0.8) weeks in Hudson Strait, and 3.5 (±0.9) weeks in Foxe Basin. [ABSTRACT FROM AUTHOR]

Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7. [ABSTRACT FROM AUTHOR]

Abstract: We address the three main issues raised by Stirling et al. [Stirling, I., Derocher, A.E., Gough, W.A., Rode, K., in press. Response to Dyck et al. (2007) on polar bears and climate change in western Hudson Bay. Ecol. Complexity]: (1) evidence of the role of climate warming in affecting the western Hudson Bay polar bear population, (2) responses to suggested importance of human–polar bear interactions, and (3) limitations on polar bear adaptation to projected climate change. We assert that our original paper did not provide any “alternative explanations [that] are largely unsupported by the data” or misrepresent the original claims by Stirling et al. [Stirling, I., Lunn, N.J., Iacozza, I., 1999. Long-term trends in the population ecology of polar bears in western Hudson Bay in relation to climate change. Arctic 52, 294–306], Derocher et al. [Derocher, A.E., Lunn, N.J., Stirling, I., 2004. Polar bears in a warming climate. Integr. Comp. Biol. 44, 163–176], and other peer-approved papers authored by Stirling and colleagues. In sharp contrast, we show that the conclusion of Stirling et al. [Stirling, I., Derocher, A.E., Gough, W.A., Rode, K., in press. Response to Dyck et al. (2007) on polar bears and climate change in western Hudson Bay. Ecol. Complexity] – suggesting warming temperatures (and other related climatic changes) are the predominant determinant of polar bear population status, not only in western Hudson (WH) Bay but also for populations elsewhere in the Arctic – is unsupportable by the current scientific evidence. The commentary by Stirling et al. [Stirling, I., Derocher, A.E., Gough, W.A., Rode, K., in press. Response to Dyck et al. (2007) on polar bears and climate change in western Hudson Bay. Ecol. Complexity] is an example of uni-dimensional, or reductionist thinking, which is not useful when assessing effects of climate change on complex ecosystems. Polar bears of WH are exposed to a multitude of environmental perturbations including human interference and factors (e.g., unknown seal population size, possible competition with polar bears from other populations) such that isolation of any single variable as the certain root cause (i.e., climate change in the form of warming spring air temperatures), without recognizing confounding interactions, is imprudent, unjustified and of questionable scientific utility. Dyck et al. [Dyck, M.G., Soon, W., Baydack, R.K., Legates, D.R., Baliunas, S., Ball, T.F., Hancock, L.O., 2007. Polar bears of western Hudson Bay and climate change: Are warming spring air temperatures the “ultimate” survival control factor? Ecol. Complexity, 4, 73–84. doi:10.1016/j.ecocom.2007.03.002] agree that some polar bear populations may be negatively impacted by future environmental changes; but an oversimplification of the complex ecosystem interactions (of which humans are a part) may not be beneficial in studying external effects on polar bears. Science evolves through questioning and proposing hypotheses that can be critically tested, in the absence of which, as Krebs and Borteaux [Krebs, C.J., Berteaux, D., 2006. Problems and pitfalls in relating climate variability to population dynamics. Clim. Res. 32, 143–149] observe, “we will be little more than storytellers.” [Copyright &y& Elsevier]

In operational flood forecast systems, the effect of sea ice is typically neglected or parameterized solely in terms of ice concentration. In this study, an efficient way of adding ice effects to the global total water level prediction systems, via the ice–ocean stress, is described and evaluated. The approach features a novel, consistent representation of the tidal relative ice–ocean velocities, based on a transfer function derived from ice and ocean tidal ellipses given by an external ice–ocean model. The approach and its impact are demonstrated over four ice seasons in the Northern Hemisphere, using in situ observations and model predictions. We show that adding ice effects helps the model reproduce most of the observed seasonal modulations in tides (up to 40 % in amplitude and 50 ∘ in phase for M2) in the Arctic and Hudson Bay. The dominant driving mechanism for the seasonal modulations is shown to be the under-ice friction, acting in areas of shallow water (less than 100 m) and its accompanied large shifts in the amphidromes (up to 125 km). Important contributions from baroclinicity and tide–surge interaction due to ice–ocean stress are also found in the Arctic. Both mechanisms generally reinforce the seasonal modulations induced by the under-ice friction. In forecast systems that neglect or rely on simple ice concentration parameterizations, storm surges tend to be overestimated. With the inclusion of ice–ocean stress, surfaces stresses are significantly reduced (up to 100 % in landfast ice areas). Over the four ice seasons covered by this study, corrections up to 1.0 m to the overestimation of surges are achieved. Remaining limitations regarding the overestimated amphidrome shifts and insufficient ice break-up during large storms are discussed. Finally, the anticipated trend of increasing risk of coastal flooding in the Arctic, associated with decreasing ice and its profound impact on tides and storm surges, is briefly discussed. [ABSTRACT FROM AUTHOR]

Multiple factors influence the spatial and temporal chlorophyll‐a concentration of marine systems. The Hudson Bay Complex has historically been seen as a large, low‐production inland sea situated in the north of Canada. However, recent field campaigns, for the BaySys project, have provided new data on primary production in the bay. Due to the Hudson Bay complex's positioning, it experiences seasonal sea‐ice cover and has many rivers draining into it, resulting in a unique estuarine‐like environment. We use the biogeochemical model BLINGv0 + DIC, coupled to the online regional physical oceanographic and sea‐ice models, NEMOv3.6 and LIM2, respectively, forced with two bias‐corrected Coupled Model Intercomparison Project 5 climate forcings (MIROC5 and MRI) to simulate the base of the ecosystem. The simulations were evaluated with chlorophyll‐a satellite imagery and observations collected in 2018 and analyzed with Empirical Orthogonal Functions to understand the underlying physical forcings and key areas of chlorophyll‐a concentration distribution. The evaluation showed that both simulations successfully reproduced the sea‐ice melt, from west to east and formation, from north to south and correlated well with spatial bloom patterns. The main drivers of phytoplankton growth are the seasonal light and nutrient levels (48% and 54%), the mixed layer depth dynamics (18% and 14%), nutrient supply from rivers (13% and 8%), and sea ice production (7%) for the MIROC5 and MRI simulations, respectively. The sea‐ice dynamics and river runoff played a significant role in the system's productivity. Therefore, with future climate change and increased river regulation projects, up to 20% of overall chlorophyll‐a may be negatively impacted. Plain Language Summary: The Hudson Bay Complex (HBC) is an inland sub‐Arctic sea that is covered by sea ice from December to June. Many rivers drain into the bay, making it a relatively fresh marine environment. Due to its size, difficulty to access, and seasonal ice cover, it is a challenging environment to sample. A recent Hudson Bay system study, BaySys, has allowed us to understand that the HBC is more productive than previously thought. Using regional numerical ocean, sea ice, and biological models, we can simulate the biological productivity in this area with two different climate forcings. Comparing the simulations with satellite and in situ observations from 2018, we can see that our models simulate sea‐ice production and the spatial and temporal pattern of chlorophyll a relatively well. Using a statistical method called Empirical Orthogonal Functions, we could identify that the physical components influencing this region's phytoplankton growth are the mixed layer depth, river runoff into the area and sea‐ice melt and freeze cycle. The HBC expects to experience significant climate change and changes in river runoff in the future. Any changes in these two physical forcings can negatively impact up to 20% of the overall chlorophyll a distribution patterns. Key Points: Physical and biogeochemical models can be used to understand the underlying drivers of primary productivityThe biogeochemical model, BLINGv0 + DIC, simulates the spatial and temporal distributions of chlorophyll a within the Hudson Bay ComplexMixed layer depth dynamics, river runoff, and sea‐ice production were observed to have the largest impact on simulated chlorophyll a [ABSTRACT FROM AUTHOR]

Ozone depletion events (ODEs) are a common occurrence in the boundary layer during Arctic spring. Ozone is depleted by bromine species, which are most likely emitted from snow, sea ice, or aerosols in an autocatalytic reaction cycle. Previous three-dimensional modeling studies of ODEs assumed an infinite bromine source at the ground. In the present study, an alternative emission scheme is presented in which a finite amount of bromide in the snow is tracked over time. For this purpose, a modified version of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is used to study ODEs in the Arctic from February to May 2019. The model data are compared to in situ measurements, ozone sonde flights, and satellite data. A simulation of the ODEs in the Arctic spring of 2009 using the infinite-bromide assumption on first-year (FY) ice is transferred to the spring of 2019, which achieves good agreement with the observations; however, there is some disagreement in April 2009 and 2019 with respect to an overestimation concerning both the magnitude and the number of ODEs. New simulations using the finite-bromide assumption greatly improve agreement with in situ observations at Utqiaġvik, Alaska, Zeppelin Mountain, Svalbard, and Pallas, Finland, in April 2019, suggesting that bromide on the sea ice is depleted to an extent that reduces the bromine release. The new simulations also slightly improve the agreement with observations at these sites in February and March. A comparison to measurements near Eureka, Canada, and Station Nord, Greenland, shows that multi-year ice and possibly snow-covered land may be significant bromine sources. However, assuming higher releasable bromide near Eureka does not remove all disagreement with the observations. The numerical results are also compared to tropospheric-BrO vertical column densities generated with a new retrieval method from TROPOspheric Monitoring Instrument (TROPOMI) observations. BrO vertical column densities (VCDs) above 5×1013 molec. cm -2 observed by the satellite agree well with the model results. However, the model also predicts BrO VCDs of around 3×1013 molec. cm -2 throughout the Arctic and patches of BrO VCDs of around 1014 molec. cm -2 not observed by the satellite, especially near Hudson Bay. This suggests that snow at Hudson Bay may be a weaker bromine source in late spring compared to snow in the north. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasts of sea ice conditions are crucial for safe shipping operations in the Canadian Arctic and other ice-infested waters. Given the recent declining trend of Arctic sea ice extent in past decades, seasonal forecasts are often desired. In this study machine learning (ML) approaches are deployed to provide accurate seasonal forecasts based on ERA5 data as input. This study, unlike previous ML approaches in the sea ice forecasting domain, provides daily spatial maps of sea ice presence probability in the study domain for lead times up to 90 d using a novel spatiotemporal forecasting method based on sequence-to-sequence learning. The predictions are further used to predict freeze-up/breakup dates and show their capability to capture these events within a 7 d period at specific locations of interest to shipping operators and communities. The model is demonstrated in hindcasting mode to allow for evaluation of forecasted predication. However, the design allows for the approach to be used as a forecasting tool. The proposed method is capable of predicting sea ice presence probabilities with skill during the breakup season in comparison to both Climate Normal and sea ice concentration forecasts from a leading subseasonal-to-seasonal forecasting system. [ABSTRACT FROM AUTHOR]

Abstract: Many estuaries in high latitude regions are subjected to seasonally ice-covered conditions. However, ice effects on estuarine variability have received limited scientific attention and remain poorly understood. In this paper, an 11-month mooring record is used to examine seasonal variation of estuarine hydrodynamics in the Nelson River estuary (NRE), Hudson Bay (HB), in northern Canada. We show that ice cover strongly affects tidal amplitudes, velocities and phases in the NRE. In the mid-winter, the M 2 tidal amplitude and consequently the tidal range are significantly reduced due to under-ice friction in HB, while conversely the M 2 tidal velocity is amplified due to reduction of cross-section of the channel by formation of fast ice. A stronger surface seaward residual flow observed in the winter indicates that the formation of fast ice could also enhance the residual circulation. Suspended sediment concentration in the river mouth is reduced, also possibly due to the formation of fast ice that protects shallow nearshore shoals from erosion. This study demonstrates the importance of ice effects on estuarine variability and the complexity of processes in a seasonally ice-covered estuary. [Copyright &y& Elsevier]

Abstract: The role of coastal seas as either a sink or a source of CO2 is subject to a great deal of uncertainty. This uncertainty largely arises from a lack of observations in the coastal zones. Remote sensing offers an avenue for expanding these observations by allowing for the extrapolation of relatively limited data sets of dissolved CO2 (pCO2sw). In this paper, predictive algorithms for pCO2sw that could be applied to remote sensing products were created from a field data set collected from September–October, 2005 in Hudson Bay, Canada. The field data showed that an effective pCO2sw interpolation algorithm could be created using sea surface temperature (SST) as a predictor, and that a slight improvement of the algorithm could be achieved if measurements of absorption due to coloured dissolved organic material (a CDOM) were included. Unfortunately, satellite retrievals of a CDOM did not match well with in situ observations, and so only SST (obtained from the MODIS Aqua sensor) was used to create monthly maps of pCO2sw for the period of August–October. To estimate fluxes of CO2, constructed surfaces of pCO2sw were combined with estimates of gas transfer velocity derived from QuikSCAT wind retrievals, and pCO2air based on field observations. The results of these calculations revealed that Hudson Bay acts as a source of CO2 during August and September, but reverts to a sink of CO2 in October as the water temperature decreases. Overall, a positive flux of 1.60 TgC was estimated for the region during the ice-free season. This result is in contrast to most Arctic or sub-Arctic continental shelf seas, where usually strong absorptions of CO2 are observed. [Copyright &y& Elsevier]

Abstract: Long-term warming of late spring (April–June) air temperatures has been proposed by Stirling et al. [Stirling, I., Lunn, N.J., Iacozza, J., 1999. Long-term trends in the population ecology of polar bears in western Hudson Bay in relation to climatic change. Arctic 52, 294–306] as the “ultimate” factor causing earlier sea-ice break-up around western Hudson Bay (WH) that has, in turn, led to the poorer physical and reproductive characteristics of polar bears occupying this region. Derocher et al. [Derocher, A.E., Lunn, N.J., Stirling, I., 2004. Polar bears in a warming climate. Integr. Comp. Biol. 44, 163–176] expanded the discussion to the whole circumpolar Arctic and concluded that polar bears will unlikely survive as a species should the computer-predicted scenarios for total disappearance of sea-ice in the Arctic come true. We found that spring air temperatures around the Hudson Bay basin for the past 70 years (1932–2002) show no significant warming trend and are more likely identified with the large-amplitude, natural climatic variability that is characteristic of the Arctic. Any role of external forcing by anthropogenic greenhouse gases remains difficult to identify. We argue, therefore, that the extrapolation of polar bear disappearance is highly premature. Climate models are simply not skilful for the projection of regional sea-ice changes in Hudson Bay or the whole Arctic. Alternative factors, such as increased human–bear interaction, must be taken into account in a more realistic study and explanation of the population ecology of WH polar bears. Both scientific papers and public discussion that continue to fail to recognize the inherent complexity in the adaptive interaction of polar bears with both human and nature will not likely offer any useful, science-based, preservation and management strategies for the species. [Copyright &y& Elsevier]

The influence of climate change on wildlife disease dynamics is a burgeoning conservation and human health issue, but few long‐term studies empirically link climate to pathogen prevalence. Polar bears (Ursus maritimus) are vulnerable to the negative impacts of sea ice loss as a result of accelerated Arctic warming. While studies have associated changes in polar bear body condition, reproductive output, survival, and abundance to reductions in sea ice, no long‐term studies have documented the impact of climate change on pathogen exposure. We examined 425 serum samples from 381 adult polar bears, collected in western Hudson Bay (WH), Canada, for antibodies to selected pathogens across three time periods: 1986–1989 (n = 157), 1995–1998 (n = 159) and 2015–2017 (n = 109). We ran serological assays for antibodies to seven pathogens: Toxoplasma gondii, Neospora caninum, Trichinella spp., Francisella tularensis, Bordetella bronchiseptica, canine morbillivirus (CDV) and canine parvovirus (CPV). Seroprevalence of zoonotic parasites (T. gondii, Trichinella spp.) and bacterial pathogens (F. tularensis, B. bronchiseptica) increased significantly between 1986–1989 and 1995–1998, ranging from +6.2% to +20.8%, with T. gondii continuing to increase into 2015–2017 (+25.8% overall). Seroprevalence of viral pathogens (CDV, CPV) and N. caninum did not change with time. Toxoplasma gondii seroprevalence was higher following wetter summers, while seroprevalences of Trichinella spp. and B. bronchiseptica were positively correlated with hotter summers. Seroprevalence of antibodies to F. tularensis increased following years polar bears spent more days on land, and polar bears previously captured in human settlements were more likely to be seropositive for Trichinella spp. As the Arctic has warmed due to climate change, zoonotic pathogen exposure in WH polar bears has increased, driven by numerous altered ecosystem pathways. [ABSTRACT FROM AUTHOR]

Arctic ecosystems are changing rapidly in response to climate warming. While Arctic mammals are highly evolved to these extreme environments, particularly with respect to their stress axis, some species may have limited capacity to adapt to this change. We examined changes in key components of the stress axis (cortisol and its carrier protein—corticosteroid binding globulin [CBG]) in polar bears (Ursus maritimus) from western Hudson Bay (N = 300) over a 33 year period (1983–2015) during which time the ice‐free period was increasing. Changing sea ice phenology limits spring hunting opportunities and extends the period of onshore fasting. We assessed the response of polar bears to a standardized stressor (helicopter pursuit, darting, and immobilization) during their onshore fasting period (late summer–autumn) and quantified the serum levels of the maximum corticosteroid binding capacity (MCBC) of CBG, the serum protein that binds cortisol strongly, and free cortisol (FC). We quantified bear condition (age, sex, female with cubs or not, fat condition), sea ice (breakup in spring–summer, 1 year lagged freeze‐up in autumn), and duration of fasting until sample collection as well as cumulative impacts of the latter environmental traits from the previous year. Data were separated into "good" years (1983–1990) when conditions were thought to be optimal and "poor" years (1991–2015) when sea ice conditions deteriorated and fasting on land was extended. MCBC explained 39.4% of the variation in the good years, but only 28.1% in the poor ones, using both biological and environmental variables. MCBC levels decreased with age. Changes in FC were complex, but more poorly explained. Counterintuitively, MCBC levels increased with increased time onshore, 1 year lag effects, and in poor ice years. We conclude that MCBC is a biomarker of stress in polar bears and that the changes we document are a consequence of climate warming. [ABSTRACT FROM AUTHOR]

Habitat fragmentation occurs when continuous habitat gets broken up as a result of ecosystem change. While commonly studied in terrestrial ecosystems, Arctic sea ice ecosystems also experience fragmentation, but are rarely studied in this context. Most fragmentation analyses are conducted using patch‐based metrics, which are potentially less suitable for sea ice that has gradual changes between sea ice cover, than distinct "long‐term" patches. Using an integrated step selection analysis, we compared the descriptive power of a patch‐based metric to a more novel metric, the variation in local spatial autocorrelation over time. We used satellite telemetry data from 39 adult female polar bears (Ursus maritimus) in Hudson Bay to examine their sea ice habitat using Advanced Microwave Scanning Radiometer 2 data during sea ice breakup in May through July from 2013–2018. Spatial autocorrelation resulted in better model fits across 64% of individuals, although both metrics were more effective in describing movement patterns than habitat selection. Variation in local spatial autocorrelation allows for the visualization of sea ice habitat at complex spatial and temporal scales, condensing a targeted time period of habitat that would otherwise have to be analyzed daily. [ABSTRACT FROM AUTHOR]

The spatiotemporal changes in the sea ice region albedo over the entire Arctic region and in eight subregions (the Central Arctic Ocean [CAO], the Beaufort and Chukchi Seas [BC], the East Siberian and Laptev Seas [ESL], Baffin Bay and Labrador Sea [BL], the Canadian Archipelago [CA], the Greenland Sea [GS], Hudson Bay [HB] and the Kara and Barents Seas [KB]) in the summer of 1982–2015 are analysed with CLARA‐A2‐SAL data. The results indicate that in the summer of 1982–2015, the Arctic Sea ice region albedo fluctuated with a downward trend of −1.6% per decade (significance level of 99%). The BC had the largest decline in the albedo trend of −2.7% per decade (significance level of 99%), and most other subregions had downward trends except the GS, which exhibited a slight upward trend. The mean Arctic Sea ice region albedo was 44%. The high albedo areas were mainly concentrated in the CAO and the vicinity of Greenland. The albedo decreased with decreasing latitude, while the low‐value areas were mainly concentrated in the outer sea ice area. In the Arctic region, both the sea ice concentration (SIC) and the sea ice extent (SIE) showed a decreasing trend, while the near‐surface air temperature (NSAT) and the summer Central Arctic Index (CAI) showed an increasing trend. The Arctic Sea ice region albedo was positively correlated with the SIC and the SIE (0.84, 0.78) and negatively correlated with the NSAT (−0.72), all with a statistical significance level of 99%. The correlation between sea ice region albedo and the summer CAI revealed different relationships in these regions. The BC and ESL had a significant negative correlation, and the GS showed a significant positive correlation. These findings indicated that the decrease in albedo is closely related to the reduction in Arctic Sea ice and the increase in air temperature. In addition, the sea ice region albedo variations in the BC, ESL and GS are also greatly influenced by atmospheric circulation. [ABSTRACT FROM AUTHOR]

Hudson Bay is a large seasonally ice-covered Canadian inland sea connected to the Arctic Ocean and North Atlantic through Foxe Basin and Hudson Strait. This study investigates zooplankton distribution, dynamics, and factors controlling them during open-water and ice cover periods (from September 2016 to October 2017) in Hudson Bay. A mooring equipped with two acoustic Doppler current profilers (ADCPs) and a sediment trap was deployed in September 2016 in Hudson Bay ∼190 km northeast from the port of Churchill. The backscatter intensity and vertical velocity time series showed a pattern typical for zooplankton diel vertical migration (DVM). The sediment trap collected five zooplankton taxa including two calanoid copepods (Calanus glacialis and Pseudocalanus spp.), a pelagic sea snail (Limacina helicina), a gelatinous arrow worm (Parasagitta elegans), and an amphipod (Themisto libellula). From the acquired acoustic data we observed the interaction of DVM with multiple factors including lunar light, tides, and water and sea ice dynamics. Solar illuminance was the major factor determining migration pattern, but unlike at some other polar and subpolar regions, moonlight had little effect on DVM, while tidal dynamics are important. The presented data constitute the first-ever observed DVM in Hudson Bay during winter and its interaction with the tidal dynamics. [ABSTRACT FROM AUTHOR]

At the centres of previously glaciated regions such as Hudson Bay in Canada and the Gulf of Bothnia in Fennoscandia, it has been observed that the sea level history follows an exponential form and that the associated decay time is relatively insensitive to uncertainty in the ice loading history. We revisit the issue of decay time sensitivity by computing relative sea level histories for Richmond Gulf and James Bay in Hudson Bay and Ångerman River in Sweden for a suite of reconstructions of the North American and Fennoscandian Ice Sheets and Earth viscosity profiles. We find that while some Earth viscosity models do indeed show insensitivity in computed decay times to the ice history, this is not true in all cases. Moreover, we find that the location of the study site relative to the geometry of the ice sheet is an important factor in determining ice sensitivity, and based on our set of ice sheet reconstructions, conclude that the location of James Bay is not well-suited to a decay time analysis. We describe novel corrections to the RSL data to remove the effects associated with the spatial distribution of sea level indicators as well as for other signals unrelated to regional ice loading (ocean loading, rotation and global mean sea level changes) and demonstrate that they can significantly affect the inference of viscosity structure. We performed a forward modelling analysis based on a commonly adopted 2-layer, sublithosphere viscosity structure to determine how the solution space of viscosity models changes with the input ice history at the three study sites. While the solution spaces depend on ice history, for both Richmond Gulf and Ångerman River there are regions of parameter space where solutions are common across all or most ice histories, indicating low ice load sensitivity for these mantle viscosity parameters. For example, in Richmond Gulf, upper mantle viscosity values of (0.3–0.5)x1021 Pa s and lower mantle viscosity values of (5–50)x1021 Pa s tend to satisfy the data constraint consistently for most ice histories considered in this study. Similarly, the Ångerman River solution spaces contain a solution with an upper mantle viscosity of 0.3 × 1021 Pa s and lower mantle viscosity values of (5–50)x1021 Pa s common to 9 of the 10 ice histories considered there. However, the dependence of the viscosity solution space on ice history suggests that joint estimation of ice and Earth parameters is the optimal approach. [ABSTRACT FROM AUTHOR]

A reliable knowledge and assessment of the sea ice conditions and their evolution in time is a priority for numerous decision makers in the domains of coastal and offshore management and engineering as well as in commercial navigation. As of today, countless research projects aimed at both modelling and mapping past, actual and future sea ice conditions were completed using sea ice numerical models, statistical models, educated guesses or remote sensing imagery. From this research, reliable information helping to understand sea ice evolution in space and in time is available to stakeholders. However, no research has, until present, assessed the evolution of sea ice cover with a frequency modelling approach, by identifying the underlying theoretical distribution describing the sea ice behaviour at a given point in space and time. This project suggests the development of a probabilistic tool, named IcePAC, based on frequency modelling of historical 1978–2015 passive microwave sea ice concentrations maps from the EUMETSAT OSI-409 product, to study the sea ice spatio-temporal behaviour in the waters of the Hudson Bay system in northeast Canada. Grid-cell-scale models are based on the generalized beta distribution and generated at a weekly temporal resolution. Results showed coherence with the Canadian Ice Service 1981–2010 Sea Ice Climatic Atlas average freeze-up and melt-out dates for numerous coastal communities in the study area and showed that it is possible to evaluate a range of plausible events, such as the shortest and longest probable ice-free season duration, for any given location in the simulation domain. Results obtained in this project pave the way towards various analyses on sea ice concentration spatio-temporal distribution patterns that would gain in terms of information content and value by relying on the kind of probabilistic information and simulation data available from the IcePAC tool. [ABSTRACT FROM AUTHOR]

Previous work has found Hudson Bay seasonal sea ice particularly sensitive to climate change with a strong signal of earlier breakup dates. This work extends the previous analysis by including eight additional years of recent sea ice data. The expanded sea ice record, 1971 to 2011, revealed stronger and more statistically significant trends than the earlier work, most strikingly for the later freeze up. The average magnitude of the temporal trend for all 36 locations studied is 0.50 days/year for earlier breakup, 0.46 days/year for the later freeze-up, and 0.91 days/year for longer ice-free period. Of the 36 points, 12 points for the breakup period, 30 points for the freeze-up period, and 22 points for the ice-free season have accelerating temporal trends during the past decade. [ABSTRACT FROM AUTHOR]

The trends and interannual variations of summer sea ice extents ( SIEs) in both Hudson Bay and the Arctic Ocean are investigated in association with variations in atmospheric circulation and air temperature. The summer SIE variabilities of both Hudson Bay and the Arctic Ocean are well correlated and have a strong negative trend. The negative SIE trends are associated with a summer atmospheric circulation pattern that is characterized by positive anomalies over the Arctic Ocean and negative anomalies over mid-latitudes. The trends are also connected with warm air temperatures over mid- and high-latitudes. The atmospheric circulation and air temperature regressed on the summer SIEs in both Hudson Bay and the Arctic Ocean are quite similar to these trend patterns. On the other hand, the year-to-year variations of the detrended summer SIEs in both Hudson Bay and the Arctic Ocean are not correlated. The summer atmospheric circulation and air temperature over the Arctic Ocean have contributed to the detrended summer Arctic Ocean SIE. In contrast, the detrended summer Hudson Bay SIE is controlled by atmospheric circulation and air temperature in the previous spring. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Earth Sciences is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sea ice in Hudson Bay is melting earlier and freezing later as the climate warms, resulting in declines in the condition, survival, and population size of polar bears ( Ursus maritimus) in the Western Hudson Bay population. The objective of this study was to analyse temporal variation in polar bear distribution on the sea ice in Hudson Bay to determine how home range size and location may be responding to changing sea ice conditions and to examine the current population boundary. Between 1990 and 2012, 153 satellite collars were deployed on 141 adult females yielding 67,495 usable locations. We examined annual minimum convex polygons and seasonal utilization distributions. Home ranges in the 1990s (mean = 264,356 ± 30,551 km) did not differ significantly (t = −1.96, P = 0.07) from those in 2004 to 2012 (mean = 353,557 ± 33,719 km). Home range distribution of individuals differed between seasons and across years, with most variation in the freeze-up and break-up seasons. Home range size was predicted by season, ice break-up date, and individual in a multiple regression, though R was low. Solitary females had smaller home ranges and were closer to land compared to females with offspring. While on the sea ice, the population boundary often encompassed only half of the 2004-2012 polar bear locations and should be reassessed. The distribution of polar bears has shifted both annually and seasonally since 2004, but the consequences remain unclear as the system is extremely variable. [ABSTRACT FROM AUTHOR]

Over the last few decades, the period of ice cover in Hudson Bay has decreased, owing to climate warming, with breakup occurring approximately 3 weeks earlier than it did 30 years ago. The trend towards lengthening of the open water season has led to speculation that ringed seal numbers would decline, but then harbour seals might become numerous enough to replace ringed seals in the diet of polar bears. The movement patterns of 18 harbour seals equipped with satellite-linked transmitters in the Churchill River estuary (western Hudson Bay) were examined, as well as the dive behaviour of 11 of these seals. During the ice-free period, seals followed a general central place-foraging strategy, making repeated trips between their haul-out site in the Churchill River estuary and nearshore areas (<20 km) near the river mouth and estuary. Seal behaviour changed significantly as ice started to form along the coast of western Hudson Bay: animals remained significantly farther from the Churchill River haul-out site and from the coast and performed longer and deeper dives. However, throughout the entire tracking period, whether ice was present or not, all animals restricted their movements to a narrow band of shallow coastal waters (<50 m depth) along a 600-km stretch of the western Hudson Bay coastline, centred on the Churchill River estuary haul-out site. This natural self-limitation to nearshore shallow waters could restrict the potential for the population to increase in size and replace ringed seals as a primary energy resource for polar bears. [ABSTRACT FROM AUTHOR]

Gaston A. J., Smith, P. A., and Provencher, J. F. 2012. Discontinuous change in ice cover in Hudson Bay in the 1990s and some consequences for marine birds and their prey. – ICES Journal of Marine Science, 69: .Arctic ice cover has changed strikingly since the mid-1990s, with the minimum ice extent in the northern hemisphere diminishing by 8.5% per decade since 1981. In the Canadian Arctic, ice cover in June and November showed a step change in the mid-1990s, with little reduction before that. There was a similar step change in northern Hudson Bay. A long-term dataset on marine birds at Coats Island, Nunavut, revealed that many changes in seabird biology also exhibited an abrupt change at, or soon after, the change in ice conditions. This applied to their diet that switched in the 1990s from one dominated by Arctic cod, Boreogadus saida, to one dominated by capelin, Mallotus villosus. Evidence from the proportion of Arctic cod in adult diets suggested that the length of the open-water season may be a good predictor of the switch between Arctic cod and capelin. Other changes, in nestling growth and population trend, may relate to the same ecosystem changes that led to the switch in diet. Abrupt changes, as in the breeding biology of murres at Coats Island, would seem to be characteristic of ecosystem alterations driven by climate change. [ABSTRACT FROM PUBLISHER]

Primary production, the basic component of the food web and a sink for dissolved inorganic carbon, is a major unknown in Arctic seas, particularly ice algal production, for which detailed and comprehensive studies are often limited in space and time. We present here a simple ice alga model and its coupling with a regional 3D ice-ocean model of the Hudson Bay system (HBS), including Hudson Strait and Foxe Basin, as a first attempt to estimate ice algal production and its potential contribution to the pelagic ecosystem on a regional scale. The ice algal growth rate is forced by sub-ice light and nutrient availability, whereas grazing and ice melt control biomass loss from the underside of the ice. The simulation shows the primary role of sea-ice dynamics on the distribution and production of ice algae with a high spatio-temporal variability in response to the great variability of ice conditions in different parts of the HBS. In addition to favourable light and nutrient conditions, there must be a sufficient time lag between the onset of sufficient light and ice melt to ensure significant ice algal production. This suggests that, in the context of enhanced warming in Arctic and sub-Arctic regions, earlier melt could be more damaging for ice algal production than later freezing. The model also includes a particulate organic matter (POM) variable, fed by ice melting losses to the water column, and shows a large redistribution of the POM produced by the ice ecosystem on a regional scale. [ABSTRACT FROM AUTHOR]

Sea-ice cover over the Hudson Bay (HB) exhibits large variability in the freeze-up season normally starting in November. Its influence on the climate over eastern Canada has been studied with the Canadian Regional Climate Model (CRCM) in three steps. First, a 30-year continuous simulation from 1970 to 1999 was performed as a control run to evaluate the simulated climate variability over eastern Canada, in particularly variability associated with the North Atlantic oscillation (NAO). Then, 50 additional 1 month experiments were performed with modified sea-surface conditions prescribed over the HB. These integrations allowed us to quantify the contribution of HB sea-ice anomalies versus large scale NAO atmospheric variability (as defined by prescribed lateral boundary conditions) in inducing climate variability over eastern Canada. Results show that the NAO is the dominant factor controlling climate variability over eastern Canada. The contribution of HB sea-ice anomalies is significant only in the immediate coastal region. Under the influence of different phases of NAO, HB sea-ice anomalies do co-vary with temperature and precipitation anomalies downstream of the HB over eastern Canada. The ultimate cause of this co-variability is NAO variability which forces variability in both HB sea-ice cover as well as temperature/precipitation over eastern Canada. [ABSTRACT FROM AUTHOR]

Some of the most pronounced ecological responses to climatic warming are expected to occur in polar marine regions, where temperature increases have been the greatest and sea ice provides a sensitive mechanism by which climatic conditions affect sympagic (i.e., with ice) species. Population-level effects of climatic change, however, remain difficult to quantify. We used a flexible extension of Cormack-Jolly-Seber capture-recapture models to estimate population size and survival for polar bears (Ursus maritimus), one of the most ice-dependent of Arctic marine mammals. We analyzed data for polar bears captured from 1984 to 2004 along the western coast of Hudson Bay and in the community of Churchill, Manitoba, Canada. The Western Hudson Bay polar bear population decfined from 1,194 (95% CI = 1,020-1,368) in 1987 to 935 (95% CI = 794-1,076) in 2004. Total apparent survival of prime-adult polar bears (5-19 yr) was stable for females (0.93; 95% CI = 0.91-0.94) and males (0.90; 95% CI = 0.88-0.91). Survival of juvenile, subadult, and senescent-adult polar bears was correlated with spring sea ice breakup date, which was variable among years and occurred approximately 3 weeks earlier in 2004 than in 1984. We propose that this correlation provides evidence for a causal association between earlier sea ice breakup (due to climatic warming) and decreased polar bear survival. It may also explain why Churchill, like other communities along the western coast of Hudson Bay, has experienced an increase in human-polar bear interactions in recent years. Earlier sea ice breakup may have resulted in a larger number of nutritionally stressed polar bears, which are encroaching on human habitations in search of supplemental food. Because western Hudson Bay is near the southern limit of the species' range, our findings may foreshadow the demographic responses and management challenges that more northerly polar bear populations will experience if climatic warming in the Arctic continues as projected. [ABSTRACT FROM AUTHOR]

Despite recent reductions in Arctic sea ice extent and the associated increase in both the recreational and commercial use of ice-infested waters, long-range prediction of operationally relevant sea ice parameters is an area of seasonal forecasting that has received little attention. Statistical methods that isolate and exploit empirical relationships between antecedent low-frequency climate variability and specific variables of interest are often used to solve seasonal forecasting problems. In this study, simple multiple linear regression (MLR) techniques are used to improve the skill of the seasonal (3-month lead) forecast of the breakup and clearing of sea ice along the shipping route through Hudson Bay that is issued each March by the Canadian Ice Service of Environment Canada. Using sea ice and climate data from 1972 to 2002, predictive MLR models are developed for the spring opening date of the shipping route and the latest expected opening date. A success rate of 77% over the 1972–2002 period for the opening date, from an MLR model that explains 76% of the variability in the original time series with a mean absolute error (MAE) of 0.38, is a marked improvement over the 48% success rate of the current analog methodology. The success rate of the model for the latest expected date is 87%; the modeled time series adequately represented interannual variability in the observed time series (r = 0.71) with a low MAE (MAE = 0.51). Results from a series of model diagnostics that include Monte Carlo simulations, cross validation, and analysis of residuals, suggest the final models are statistically valid and are not influenced by artificial skill. The main source of predictive skill in the model is winter low-frequency variability in North Atlantic sea surface temperatures and 500-mb geopotential heights; physical processes that may explain this link are presented. It is concluded that simple multiple linear regression techniques can be applied to generate use-specific seasonal forecasts of sea ice conditions and that the empirical knowledge gained in the model development may help elucidate or identify physical processes in the climate system. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Zoology is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Examines the impact of model tuning on modelled climate change response in Hudson Bay sea ice. Pairs of values for the thermal conductivity of sea ice and thermal diffusivity of sea water; Identification on the diffusion of upstream scheme using a proxy model; Relation of ice thickness to the choice of pairs of values and the seasonal distribution of the warming.

Examines the limitations of using a coarse resolution model to determine the impact of climate change on sea ice in Hudson Bay in Canada. Comparison of sea-ice distribution and duration with climatological values; Effect of below ice heat fluxes on Foxe Basin and Baffin Bay; Tunability and sensitivity of the model.

Hudson Bay (HB) is the largest semi-inland sea in the Northern Hemisphere, connecting with the Arctic Ocean through the Foxe Basin and the northern Atlantic Ocean through the Hudson Strait. HB is covered by ice and snow in winter, which completely melts in summer. For about six months each year, satellite remote sensing of sea surface salinity (SSS) is possible over open water. SSS links freshwater contributions from river discharge, sea ice melt/freeze, and surface precipitation/evaporation. Given the strategic importance of HB, SSS has great potential in monitoring the HB freshwater cycle and studying its relationship with climate change. However, SSS retrieved in polar regions (poleward of 50°) from currently operational space-based L-band microwave instruments has large uncertainty (~ 1 psu) mainly due to sensitivity degradation in cold water (<5°C) and sea ice contamination. This study analyzes SSS from NASA Soil Moisture Active and Passive (SMAP) and European Space Agency (ESA) Soil Moisture and Ocean Salinity(SMOS) missions in the context of HB freshwater contents. We found that the main source of the year-to-year SSS variability is sea ice melting, in particular, the onset time and places of ice melt in the first couple of months of open water season. The freshwater contribution from surface forcing P-E is smaller in magnitude comparing with sea ice contribution but lasts on longer time scale through the whole open water season. River discharge is comparable with P-E in magnitude but peaks before ice melt. The spatial and temporal variations of freshwater contents largely exceed the remote sensed SSS uncertainty. This fact justifies the use of remote sensed SSS for monitoring the HB freshwater cycle. [ABSTRACT FROM AUTHOR]