scholarly journals Atmospheric vorticity sets the basin-scale circulation in Hudson Bay

Elem Sci Anth ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Igor A. Dmitrenko ◽  
Paul G. Myers ◽  
Sergei A. Kirillov ◽  
David G. Babb ◽  
Denis L. Volkov ◽  
...  
Keyword(s):  
Water Circulation ◽  
Sea Surface ◽  
Water Dynamics ◽  
Labrador Sea ◽  
Hudson Bay ◽  
Atmospheric Forcing ◽  
Riverine Water ◽  
Freshwater Transport ◽  
Basin Scale ◽  
Western Hudson Bay

Hudson Bay of northern Canada receives upward of 700 km3 of river discharge annually. Cyclonic water circulation in Hudson Bay transports this massive volume of riverine water along the coast toward Hudson Strait and into the Labrador Sea. However, synoptic, seasonal and interannual variability of the freshwater transport in Hudson Bay remains unclear. Using yearlong observations of current velocity profiles, collected from oceanographic moorings deployed in western Hudson Bay from September 2016 to September/October 2017, we examined the role of atmospheric forcing on circulation and freshwater transport in the Bay. Our analysis reveals that the along-shore southeastward current through western Hudson Bay was amplified through the entire water column in response to winds generated by cyclones passing over Hudson Bay toward Baffin Bay and/or the Labrador Sea. An atmospheric vorticity index was used to describe the atmospheric forcing and found to correlate with sea surface height and along-shore currents. We showed that a surface Ekman on-shore transport increases sea surface heights along the coast, producing a cross-slope pressure gradient that drives an along-shore southeastward flow, in the same direction as the wind. Expanding our observations to the bay-wide scale, we confirmed this process of wind-driven water dynamics with (1) satellite altimetry measurements and (2) ocean model simulations. Ultimately, we find that cyclonic wind forcing amplifies cyclonic water circulation in Hudson Bay facilitating the along-shore freshwater transport to Hudson Strait. During periods of positive atmospheric vorticity, this forcing can reduce the residence time of riverine water in Hudson Bay.

scholarly journals Understanding past and future sea surface temperature trends in the Baltic Sea

2021 ◽  
Author(s):  
C. Dutheil ◽  
H. E. M. Meier ◽  
M. Gröger ◽  
F. Börgel
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Baltic Sea ◽  
Heat Fluxes ◽  
Sea Surface ◽  
Atmospheric Forcing ◽  
The Baltic Sea ◽  
Climate Simulations ◽  
Basin Scale ◽  
The Baltic

AbstractThe Baltic Sea is one of the fastest-warming semi-enclosed seas in the world over the last decades, yielding critical consequences on physical and biogeochemical conditions and on marine ecosystems. Although long-term trends in sea surface temperature (SST) have long been attributed to trends in air temperature, there are however, strong seasonal and sub-basin scale heterogeneities of similar magnitude than the average trend which are not fully explained. Here, using reconstructed atmospheric forcing fields for the period 1850–2008, oceanic climate simulations were performed and analyzed to identify areas of homogenous SST trends using spatial clustering. Our results show that the Baltic Sea can be divided into five different areas of homogeneous SST trends: the Bothnian Bay, the Bothnian Sea, the eastern and western Baltic proper, and the southwestern Baltic Sea. A classification tree and sensitivity experiments were carried out to analyze the main drivers behind the trends. While ice cover explains the seasonal north/south warming contrast, the changes in surface winds and air-sea temperature anomalies (along with changes in upwelling frequencies and heat fluxes) explain the SST trends differences between the sub-basins of the southern part of the Baltic Sea. To investigate future warming trends climate simulations were performed for the period 1976–2099 using two RCP scenarios. It was found that the seasonal north/south gradient of SST trends should be reduced in the future due to the vanishing of sea ice, while changes in the frequency of upwelling and heat fluxes explained the lower future east/west gradient of SST trend in fall. Finally, an ensemble of 48 climate change simulations has revealed that for a given RCP scenario the atmospheric forcing is the main source of uncertainty. Our results are useful to better understand the historical and future changes of SST in the Baltic Sea, but also in terms of marine ecosystem and public management, and could thus be used for planning sustainable coastal development.

Modelling Sea Surface Temperature (SST) in the Hudson Bay Complex Using Bulk Heat Flux Parameterization: Sensitivity to Atmospheric Forcing, and Model Resolution

2019 ◽  
Vol 57 (2) ◽  
pp. 120-133 ◽  
Author(s):  
Shabnam JafariKhasragh ◽  
Jennifer V. Lukovich ◽  
Xianmin Hu ◽  
Paul G. Myers ◽  
Kevin Sydor ◽  
...  
Keyword(s):  
Heat Flux ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Hudson Bay ◽  
Model Resolution ◽  
Atmospheric Forcing ◽  
Flux Parameterization

Atmospheric Forcing of Ocean Convection in the Labrador Sea

1997 ◽  
Author(s):  
Peter S. Guest
Keyword(s):  
Labrador Sea ◽  
Atmospheric Forcing ◽  
Ocean Convection

Measurable Levels of Short‐Chain Chlorinated Paraffins (SCCPs) in Western Hudson Bay Fishes, But Limited Biomagnification from Fish to Ringed Seals

2021 ◽  
Author(s):  
Nadia Facciola ◽  
Sara Pedro ◽  
Magali Houde ◽  
Aaron T. Fisk ◽  
Steven H. Ferguson ◽  
...  
Keyword(s):  
Short Chain ◽  
Hudson Bay ◽  
Ringed Seals ◽  
Chlorinated Paraffins ◽  
Western Hudson Bay

scholarly journals A sea change in our view of overturning in the subpolar North Atlantic

Science ◽  
2019 ◽  
Vol 363 (6426) ◽  
pp. 516-521 ◽  
Author(s):  
M. S. Lozier ◽  
F. Li ◽  
S. Bacon ◽  
F. Bahr ◽  
A. S. Bower ◽  
...  
Keyword(s):  
North Atlantic ◽  
Meridional Overturning Circulation ◽  
Labrador Sea ◽  
Climate Change Projections ◽  
Overturning Circulation ◽  
Intergovernmental Panel ◽  
Freshwater Transport ◽  
Deep Waters ◽  
Meridional Overturning ◽  
Prevailing View

To provide an observational basis for the Intergovernmental Panel on Climate Change projections of a slowing Atlantic meridional overturning circulation (MOC) in the 21st century, the Overturning in the Subpolar North Atlantic Program (OSNAP) observing system was launched in the summer of 2014. The first 21-month record reveals a highly variable overturning circulation responsible for the majority of the heat and freshwater transport across the OSNAP line. In a departure from the prevailing view that changes in deep water formation in the Labrador Sea dominate MOC variability, these results suggest that the conversion of warm, salty, shallow Atlantic waters into colder, fresher, deep waters that move southward in the Irminger and Iceland basins is largely responsible for overturning and its variability in the subpolar basin.

Distribution and abundance of ringed (Phoca hispida) and bearded seals (Erignathus barbatus) in western Hudson Bay

1997 ◽  
Vol 54 (4) ◽  
pp. 914-921 ◽  
Author(s):  
N J Lunn ◽  
I Stirling ◽  
S N Nowicki
Keyword(s):  
Polar Bear ◽  
Hudson Bay ◽  
Phoca Hispida ◽  
Current Estimate ◽  
Ringed Seals ◽  
Erignathus Barbatus ◽  
The Mean ◽  
Western Hudson Bay ◽  
The Relationship ◽  
Bearded Seals

We flew a medium-altitude, systematic, strip-transect survey for ringed (Phoca hispida) and bearded seals (Erignathus barbatus) over western Hudson Bay in early June 1994 and 1995. The mean density (per square kilometre) of ringed seals hauled out on the ice was four times higher in 1995 (1.690) than in 1994 (0.380). The 1994 survey appeared to underestimate seal abundance because it was flown too late. Ringed seals preferred high ice cover habitat (6 + /8 ice) and, within this habitat, favoured cracking ice and large floes. We found no consistent effect of either wind or cloud cover on habitat preference. We estimated a total of 1980 bearded seals and 140<|>880 ringed seals hauled out on the sea ice in June 1995. A recent review of the relationship between ringed seal and polar bear (Ursus maritimus) populations suggests that a visible population of this size should support a population of up to 1300 polar bears, which is in general agreement with the current estimate of 1250-1300 bears in western Hudson Bay.

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.

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

scholarly journals Neural network-based estimates of Southern Ocean net community production from in situ O<sub>2</sub> / Ar and satellite observation: a methodological study

2014 ◽  
Vol 11 (12) ◽  
pp. 3279-3297 ◽  
Author(s):  
C.-H. Chang ◽  
N. C. Johnson ◽  
N. Cassar
Keyword(s):  
Neural Network ◽  
Organic Carbon ◽  
Southern Ocean ◽  
Mixed Layer ◽  
Sea Surface ◽  
Carbon Export ◽  
Net Community Production ◽  
Basin Scale ◽  
Community Production

Abstract. Southern Ocean organic carbon export plays an important role in the global carbon cycle, yet its basin-scale climatology and variability are uncertain due to limited coverage of in situ observations. In this study, a neural network approach based on the self-organizing map (SOM) is adopted to construct weekly gridded (1° × 1°) maps of organic carbon export for the Southern Ocean from 1998 to 2009. The SOM is trained with in situ measurements of O2 / Ar-derived net community production (NCP) that are tightly linked to the carbon export in the mixed layer on timescales of one to two weeks and with six potential NCP predictors: photosynthetically available radiation (PAR), particulate organic carbon (POC), chlorophyll (Chl), sea surface temperature (SST), sea surface height (SSH), and mixed layer depth (MLD). This nonparametric approach is based entirely on the observed statistical relationships between NCP and the predictors and, therefore, is strongly constrained by observations. A thorough cross-validation yields three retained NCP predictors, Chl, PAR, and MLD. Our constructed NCP is further validated by good agreement with previously published, independent in situ derived NCP of weekly or longer temporal resolution through real-time and climatological comparisons at various sampling sites. The resulting November–March NCP climatology reveals a pronounced zonal band of high NCP roughly following the Subtropical Front in the Atlantic, Indian, and western Pacific sectors, and turns southeastward shortly after the dateline. Other regions of elevated NCP include the upwelling zones off Chile and Namibia, the Patagonian Shelf, the Antarctic coast, and areas surrounding the Islands of Kerguelen, South Georgia, and Crozet. This basin-scale NCP climatology closely resembles that of the satellite POC field and observed air–sea CO2 flux. The long-term mean area-integrated NCP south of 50° S from our dataset, 17.9 mmol C m−2 d−1, falls within the range of 8.3 to 24 mmol C m−2 d−1 from other model estimates. A broad agreement is found in the basin-wide NCP climatology among various models but with significant spatial variations, particularly in the Patagonian Shelf. Our approach provides a comprehensive view of the Southern Ocean NCP climatology and a potential opportunity to further investigate interannual and intraseasonal variability.

scholarly journals Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 403-419 ◽  
Author(s):  
C. Skandrani ◽  
J.-M. Brankart ◽  
N. Ferry ◽  
J. Verron ◽  
P. Brasseur ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Sea Surface ◽  
Global Ocean ◽  
Atmospheric Forcing ◽  
Ocean Reanalysis ◽  
Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

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