Sea ice, hydrological, and biological processes in the Churchill River estuary region, Hudson Bay

2008 ◽  
Vol 77 (3) ◽  
pp. 369-384 ◽  
Author(s):  
Z.A. Kuzyk ◽  
R.W. Macdonald ◽  
M.A. Granskog ◽  
R.K. Scharien ◽  
R.J. Galley ◽  
...  
Keyword(s):  
Sea Ice ◽  
River Estuary ◽  
Hudson Bay ◽  
Biological Processes

scholarly journals Sediment-laden sea ice in southern Hudson Bay: Entrainment, transport, and biogeochemical implications

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
D. G. Barber ◽  
M. L. Harasyn ◽  
D. G. Babb ◽  
D. Capelle ◽  
G. McCullough ◽  
...  
Keyword(s):  
Sea Ice ◽  
Light Transmission ◽  
Ice Core ◽  
Sediment Resuspension ◽  
River Estuary ◽  
Isotopic Analysis ◽  
Aerial Survey ◽  
Coast Guard ◽  
Hudson Bay ◽  
Areal Extent

During a research expedition in Hudson Bay in June 2018, vast areas of thick (>10 m), deformed sediment-laden sea ice were encountered unexpectedly in southern Hudson Bay and presented difficult navigation conditions for the Canadian Coast Guard Ship Amundsen. An aerial survey of one of these floes revealed a maximum ridge height of 4.6 m and an average freeboard of 2.2 m, which corresponds to an estimated total thickness of 18 m, far greater than expected within a seasonal ice cover. Samples of the upper portion of the ice floe revealed that it was isothermal and fresh in areas with sediment present on the surface. Fine-grained sediment and larger rocks were visible on the ice surface, while a pronounced sediment band was observed in an ice core. Initial speculation was that this ice had formed in the highly dynamic Nelson River estuary from freshwater, but δ18O isotopic analysis revealed a marine origin. In southern Hudson Bay, significant tidal forcing promotes both sediment resuspension and new ice formation within a flaw lead, which we speculate promotes the formation of this sediment-laden sea ice. Historic satellite imagery shows that sediment-laden sea ice is typical of southern Hudson Bay, varying in areal extent from 47 to 118 km2 during June. Based on an average sediment particle concentration of 0.1 mg mL–1 in sea ice, an areal extent of 51,924 km2 in June 2018, and an estimated regional end-of-winter ice thickness of 1.5 m, we conservatively estimated that a total sediment load of 7.8 × 106 t, or 150 t km–2, was entrained within sea ice in southern Hudson Bay during winter 2018. As sediments can alter carbon concentrations and light transmission within sea ice, these first observations of this ice type in Hudson Bay imply biogeochemical impacts for the marine system.

scholarly journals Origins and Levels of Seasonal Forecast Skill for Sea Ice in Hudson Bay Using Canonical Correlation Analysis

2011 ◽  
Vol 24 (5) ◽  
pp. 1378-1395 ◽  
Author(s):  
Adrienne Tivy ◽  
Stephen E. L. Howell ◽  
Bea Alt ◽  
John J. Yackel ◽  
Thomas Carrieres
Keyword(s):  
Correlation Analysis ◽  
Sea Ice ◽  
Canonical Correlation Analysis ◽  
North Atlantic ◽  
Canonical Correlation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Sea Surface ◽  
Hudson Bay ◽  
Ice Concentration

Abstract Canonical correlation analysis (CCA) is used to estimate the levels and sources of seasonal forecast skill for July ice concentration in Hudson Bay over the 1971–2005 period. July is an important transition month in the seasonal cycle of sea ice in Hudson Bay because it is the month when the sea ice clears enough to allow the first passage of ships to the Port of Churchill. Sea surface temperature (quasi global, North Atlantic, and North Pacific), Northern Hemisphere 500-mb geopotential height (z500), sea level pressure (SLP), and regional surface air temperature (SAT) are tested as predictors at 3-, 6-, and 9-month lead times. The model with the highest skill has three predictors—fall North Atlantic SST, fall z500, and fall SAT—and significant tercile forecast skill covering 61% of the Hudson Bay region. The highest skill for a single-predictor model is from fall North Atlantic SST (6-month lead). Fall SST explains 69% of the variance in July ice concentration in Hudson Bay and a possible atmospheric link that accounts for the lagged relationship is presented. CCA diagnostics suggest that changes in the subpolar North Atlantic gyre and the Atlantic multidecadal oscillation (AMO), reflected in sea surface temperature, precedes a deepening/weakening of the winter upper-air ridge northwest of Hudson Bay. Changes in the height of the ridge are reflected in the strength of the winter northwesterly winds over Hudson Bay that have a direct impact on the winter ice thickness distribution; anomalies in winter ice severity are later reflected in the pattern and timing of spring breakup. July ice concentration in Hudson Bay has declined by approximately 20% per decade between 1979 and 2007, and the hypothesized link to the AMO may help explain this significant loss of ice.

Eddy covariance flux measurements of momentum, heat and carbon dioxide in Hudson Bay at the onset of sea ice melt 

2021 ◽  
Author(s):  
Richard Sims ◽  
Brian Butterworth ◽  
Tim Papakyriakou ◽  
Mohamed Ahmed ◽  
Brent Else
Keyword(s):  
Carbon Dioxide ◽  
Gas Exchange ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Open Water ◽  
The Arctic ◽  
Gas Transfer ◽  
Hudson Bay ◽  
Flux Measurements ◽  
Ice Fraction

<p>Remoteness and tough conditions have made the Arctic Ocean historically difficult to access; until recently this has resulted in an undersampling of trace gas and gas exchange measurements. The seasonal cycle of sea ice completely transforms the air sea interface and the dynamics of gas exchange. To make estimates of gas exchange in the presence of sea ice, sea ice fraction is frequently used to scale open water gas transfer parametrisations. It remains unclear whether this scaling is appropriate for all sea ice regions. Ship based eddy covariance measurements were made in Hudson Bay during the summer of 2018 from the icebreaker CCGS Amundsen. We will present fluxes of carbon dioxide (CO<sub>2</sub>), heat and momentum and will show how they change around the Hudson Bay polynya under varying sea ice conditions. We will explore how these fluxes change with wind speed and sea ice fraction. As freshwater stratification was encountered during the cruise, we will compare our measurements with other recent eddy covariance flux measurements made from icebreakers and also will compare our turbulent CO<sub>2 </sub>fluxes with bulk fluxes calculated using underway and surface bottle pCO<sub>2</sub> data. </p><p> </p>

scholarly journals Evaluation of the representation of Arctic sea ice in the U.K. Hadley Centre GCM

1997 ◽  
Vol 25 ◽  
pp. 423-428
Author(s):  
Douglas M. Smith ◽  
Claire Cooper ◽  
Duncan J. Wingham ◽  
Seymour W. Laxon
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Seasonal Cycle ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
Sea Surface ◽  
Hudson Bay ◽  
The Sea Of Okhotsk ◽  
Arctic Sea ◽  
Hadley Centre

The amount of Arctic sea ice predicted by the Hadley Centre Global Cilimate Model (GCM) is evaluated using 15 years of passive-microwave data. While the Hadley model reproduces the seasonal cycle reasonably well, it underestimates the total area of sea ice by more than 3 × 106km2for most of the year. In the winter months, most of the underestimate in ice area results from the prediction of far too little ice in Hudson Bay and the Sea of Okhotsk, leading to an excess of up to 0.2 PW heat input to the atmosphere from Hudson Bay alone. The surface-energy budget of Hudson Bay is investigated using a mixture of surface observations (POLES), satellite data (ATSR, SSM/I and ISCCP) and output from the Goddard Data Assimilation Office analysis. Flux adjustments of the order of 200 Wm−2, resulting from anomalously high sea-surface temperatures in the Levitus (1982) climatology, are found to be the cause of the model’s underestimation of sea ice in both Hudson Bay and the Sea of Okhotsk. The fact that flux adjustments based on an inaccurate climatology will produce errors, even if the model physics is correct, underlines the need both for improved climatologies and for models accurate enough not to require flux adjustment.

scholarly journals On the Response of a Sea-Ice Cover to Changes in Surface Temperature

1966 ◽  
Vol 6 (45) ◽  
pp. 439-442 ◽  
Author(s):  
Peter Schwerdtfeger
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Surface Temperature ◽  
Ice Cover ◽  
Hudson Bay ◽  
Time Separation ◽  
The Mean ◽  
Temperature And Growth

The time separation between related extremes in the values of surface temperature and growth rate of a floating ice cover are shown to depend on the mean ice temperature and thickness. A quantity termed the lag coefficient is introduced for which observations from Churchill, Hudson Bay, and Davis, Antarctica, suggest a dependence on temperature but not on geography.

scholarly journals Contribution of Biological Effects to the Carbon Sources/Sinks and the Trophic Status of the Ecosystem in the Changjiang (Yangtze) River Estuary Plume in Summer as Indicated by Net Ecosystem Production Variations

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1264 ◽  
Author(s):  
Yifan Zhang ◽  
Dewang Li ◽  
Kui Wang ◽  
Bin Xue
Keyword(s):  
Mixed Layer ◽  
Yangtze River ◽  
River Estuary ◽  
Co2 Exchange ◽  
Atmospheric Co2 ◽  
Yangtze River Estuary ◽  
Biological Processes ◽  
Ecosystem Production ◽  
Near Shore ◽  
Exchange Flux

We conducted 24-h real-time monitoring of temperature, salinity, dissolved oxygen, and nutrients in the near-shore (M4-1), front (M4-8), and offshore (M4-13) regions of the 31° N section of the Changjiang (Yangtze) River estuary plume in summer. Carbon dioxide partial pressure changes caused by biological processes (pCO2bio) and net ecosystem production (NEP) were calculated using a mass balance model and used to determine the relative contribution of biological processes (including the release of CO2 from organic matter degradation by microbes and CO2 uptake by phytoplankton) to the CO2 flux in the Changjiang River estuary plume. Results show that seawater in the near-shore region is a source of atmospheric CO2, and the front and offshore regions generally serve as atmospheric CO2 sinks. In the mixed layer of the three regions, pCO2bio has an overall positive feedback effect on the air–sea CO2 exchange flux. The contribution of biological processes to the air–sea CO2 exchange flux (Cont) in the three regions changes to varying extents. From west to east, the daily means (±standard deviation) of the Cont are 32% (±40%), 34% (±216%), and 9% (±13%), respectively. In the front region, the Cont reaches values as high as 360%. Under the mixed layer, the daily means of potential Conts in the near-shore, front, and offshore regions are 34% (±43%), 8% (±13%), and 19% (±24%), respectively. The daily 24-hour means of NEP show that the near-shore region is a heterotrophic system, the front and offshore regions are autotrophic systems in the mixed layer, and all three regions are heterotrophic under the mixed layer.

scholarly journals The Effect of Finite Heat Content and Thermal Diffusion on the Growth of a Sea-Ice Cover

1964 ◽  
Vol 5 (39) ◽  
pp. 315-324 ◽  
Author(s):  
Peter Schwerdtfeger
Keyword(s):  
Thermal Diffusivity ◽  
Sea Ice ◽  
Basic Equation ◽  
Heat Content ◽  
Ice Cover ◽  
Hudson Bay ◽  
Growth Equation ◽  
Ice Growth ◽  
Classical Work ◽  
Practical Analysis

AbstractThe practical analysis of the growth of a sea-ice cover is discussed with initial reference to the classical work of Stefan, whose basic equation connecting surface temperature with the growth of a uniform ice cover of negligible specific heat and hence infinite diffusivity is extended to cover “real” cases. The separate effects of a finite heat content and thermal diffusivity are derived theoretically and semi-empirically respectively, and combined in a more general ice-growth equation which is then tested in the analysis of annual sea-ice growth on Hudson Bay.

Future sea ice conditions in Western Hudson Bay and consequences for polar bears in the 21st century

2013 ◽  
Vol 19 (9) ◽  
pp. 2675-2687 ◽  
Author(s):  
Laura Castro de la Guardia ◽  
Andrew E. Derocher ◽  
Paul G. Myers ◽  
Arjen D. Terwisscha van Scheltinga ◽  
Nick J. Lunn
Keyword(s):  
Sea Ice ◽  
21St Century ◽  
Hudson Bay ◽  
Polar Bears ◽  
Ice Conditions ◽  
Western Hudson Bay

scholarly journals IcePAC – a probabilistic tool to study sea ice spatio-temporal dynamics: application to the Hudson Bay area

2019 ◽  
Vol 13 (2) ◽  
pp. 451-468 ◽  
Author(s):  
Charles Gignac ◽  
Monique Bernier ◽  
Karem Chokmani
Keyword(s):  
Sea Ice ◽  
Temporal Dynamics ◽  
Numerical Models ◽  
Temporal Distribution ◽  
Grid Cell ◽  
Hudson Bay ◽  
Sea Ice Concentration ◽  
Temporal Behaviour ◽  
Ice Conditions ◽  
Spatio Temporal

Abstract. 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.

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