scholarly journals Climate change and sea ice: Shipping in Hudson Bay, Hudson Strait, and Foxe Basin (1980–2016)

Elem Sci Anth ◽  
2018 ◽  
Vol 6 ◽  
Author(s):  
Jonathan Andrews ◽  
David Babb ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Total Population ◽  
Open Water ◽  
Hudson Bay ◽  
Sea Ice Concentration ◽  
Foxe Basin ◽  
Significant Relationships ◽  
Ice Concentration ◽  
Shipping Traffic ◽  
Main Barrier

The seasonally ice-covered waters of Hudson Bay, James Bay, Foxe Basin, and Hudson Strait (“the study area”) are bordered by 39 communities with a total population of roughly 50,000 people, most of whom are Inuit or Cree. Sea ice is a cornerstone of the environment and culture of the study area but is also the main barrier to shipping traffic, which has been growing in the area. This paper presents a review of sea ice and shipping in the study area and an analysis of shipping accessibility as represented by the timing of breakup, freeze-up, and the open water season in its offshore and local waters. Offshore ice timing was analyzed using passive microwave-based data for 1980–2014; local ice timing near Rankin Inlet, Churchill, Kuujjuarapik/Whapmagoostui, and Salluit was examined using Canadian Ice Service ice charts for 1996–2016. Open water was defined using sea ice concentration thresholds of ≤15% (offshore) or <20% (local) in an attempt to represent accessible conditions for open water shipping vessels. The results for both offshore and local waters display considerable spatial variability. For offshore waters, breakup currently occurs between 17 May and 19 August and freeze-up occurs between 22 October and 30 December, with overall trends (1980–2014) of +0.58 days year–1 towards an earlier breakup, +0.47 days year–1 towards a later freeze-up, and +0.97 days year–1 towards a longer open water season. Also, significant relationships amongst breakup and freeze-up were observed. For local waters, the 1996–2016 average open water season at the four communities varied between 112.7 days (Churchill) and 154.7 days (Kuujjuarapik/Whapmagoostui). Ultimately, shipping accessibility to Rankin Inlet, Churchill, and Salluit appears to be limited by their local ice timing, while accessibility to Kuujjuarapik/Whapmagoostui appears to be limited by ice timing in northeastern Hudson Bay.

scholarly journals Climate change and sea ice: Shipping accessibility on the marine transportation corridor through Hudson Bay and Hudson Strait (1980–2014)

Elem Sci Anth ◽  
2017 ◽  
Vol 5 ◽  
Author(s):  
Jonathan Andrews ◽  
David Babb ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Average Length ◽  
Open Water ◽  
Hudson Bay ◽  
Shipping Industry ◽  
Sea Ice Concentration ◽  
Breakup Date ◽  
Transportation Corridor ◽  
Linear Relationships ◽  
Ice Concentration

Shipping traffic has been increasing in Hudson Strait and Hudson Bay and the shipping route through these waters to the Port of Churchill may soon become a federally-designated transportation corridor. A dataset on passive microwave-based sea ice concentration was used to characterize the timing of the ice on the shipping corridor to the Port between 1980 and 2014. Efforts were made to produce results in a readily accessible format for stakeholders of the shipping industry; for example, open water was defined using a sea ice concentration threshold of ≤ 15% and results are presented in terms of real dates instead of anomalies. Between 1980 and 2014, the average breakup date on the corridor was July 4, the average freeze-up date was November 25, and the average length of the open water season was 145 days. However, each of these three variables exhibited significant long-term trends and spatial variability over the 34-year time period. Regression analysis revealed significant linear trends towards earlier breakup (–0.66 days year–1), later freeze-up (+0.52 days year–1), and a longer open water season (+1.14 days year–1) along the shipping corridor between 1980 and 2014. Moreover, the section of the corridor passing through Hudson Strait displayed significantly stronger trends than the two sections in Hudson Bay (i.e., “Hudson Islands” and “Hudson Bay”). As a result, sea ice timing in the Hudson Strait section of the corridor has diverged from the timing in the Hudson Bay sections. For example, the 2010–2014 median length of the open water season was 177 days in Hudson Strait and 153 days in the Hudson Bay sections. Finally, significant linear relationships were observed amongst breakup, freeze-up, and the length of the open water season for all sections of the corridor; correlation analysis suggests that these relationships have greatest impact in Hudson Strait.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

scholarly journals Simulated impacts of relative climate change and river discharge regulation on sea ice and oceanographic conditions in the Hudson Bay Complex

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Shabnam Jafarikhasragh ◽  
Paul G. Myers ◽  
Natasha A. Ridenour ◽  
Laura Castro de la Guardia ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
River Discharge ◽  
Hudson Bay ◽  
Sea Ice Concentration ◽  
Changing Climate ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
Discharge Regulation

In this analysis, we examine relative contributions from climate change and river discharge regulation to changes in marine conditions in the Hudson Bay Complex using a subset of five atmospheric forcing scenarios from the Coupled Model Intercomparison Project Phase 5 (CMIP5), river discharge data from the Hydrological Predictions for the Environment (HYPE) model, both naturalized (without anthropogenic intervention) and regulated (anthropogenically controlled through diversions, dams, reservoirs), and output from the Nucleus for European Modeling of the Ocean Ice-Ocean model for the 1981–2070 time frame. Investigated in particular are spatiotemporal changes in sea surface temperature, sea ice concentration and thickness, and zonal and meridional sea ice drift in response to (i) climate change through comparison of historical (1981–2010) and future (2021–2050 and 2041–2070) simulations, (ii) regulation through comparison of historical (1981–2010) naturalized and regulated simulations, and (iii) climate change and regulation combined through comparison of future (2021–2050 and 2041–2070) naturalized and regulated simulations. Also investigated is use of the diagnostic known as e-folding time spatial distribution to monitor changes in persistence in these variables in response to changing climate and regulation impacts in the Hudson Bay Complex. Results from this analysis highlight bay-wide and regional reductions in sea ice concentration and thickness in southwest and northeast Hudson Bay in response to a changing climate, and east-west asymmetry in sea ice drift response in support of past studies. Regulation is also shown to amplify or suppress the climate change signal. Specifically, regulation amplifies sea surface temperatures from April to August, suppresses sea ice loss by approximately 30% in March, contributes to enhanced sea ice drift speed by approximately 30%, and reduces meridional circulation by approximately 20% in January due to enhanced zonal drift. Results further suggest that the offshore impacts of regulation are amplified in a changing climate.

scholarly journals The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations

2016 ◽  
Vol 10 (5) ◽  
pp. 2217-2239 ◽  
Author(s):  
Stefan Kern ◽  
Anja Rösel ◽  
Leif Toudal Pedersen ◽  
Natalia Ivanova ◽  
Roberto Saldo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Sea Ice Concentration ◽  
Melt Pond ◽  
Surface Fraction ◽  
Brightness Temperatures ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Ice Floes

Abstract. Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea-ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near90GHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt-pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced.

scholarly journals Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

2019 ◽  
Vol 11 (17) ◽  
pp. 2009 ◽  
Author(s):  
Qingkai Wang ◽  
Peng Lu ◽  
Yongheng Zu ◽  
Zhijun Li ◽  
Matti Leppäranta ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Moving Average ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Melt Ponds ◽  
Ice Concentration ◽  
Arctic Navigation

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.

scholarly journals Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters

2020 ◽  
Vol 12 (16) ◽  
pp. 2552
Author(s):  
Walter N. Meier ◽  
J. Scott Stewart
Keyword(s):  
Sea Ice ◽  
Correlation Method ◽  
Open Water ◽  
Circulation Pattern ◽  
Time Of Day ◽  
Passive Microwave ◽  
Sea Ice Concentration ◽  
Microwave Brightness Temperature ◽  
Enhanced Resolution ◽  
Ice Concentration

A new enhanced resolution gridded passive microwave brightness temperature (TB) product is used to estimate sea ice concentration and motion. The effective resolution of the TBs is found to be roughly twice that of the standard 25 km resolution, though the gridded resolution of the distributed product is higher. Enhanced resolution sea ice concentrations from the Bootstrap algorithm show more detail in the sea ice, including relatively small open water regions within the ice pack. Sea ice motion estimates from the enhanced resolution TBs using a maximum cross-correlation method show a smoother motion circulation pattern; in comparison to buoys, RMS errors are 15–20% lower than motion estimates from the standard resolution fields and the magnitude of the bias is smaller as well. The enhanced resolution product includes other potentially beneficial characteristics, including twice-daily grids based on local time of day and a complete timeseries of data from nearly all multi-channel passive microwave radiometers since 1978. These enhanced resolution TBs are potential new source for long-term records of sea ice concentration, motion, age, melt, as well as salinity and ocean-atmosphere fluxes.

scholarly journals Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay

Elem Sci Anth ◽  
2020 ◽  
Vol 8 ◽  
Author(s):  
Madison L. Harasyn ◽  
Dustin Isleifson ◽  
Wayne Chan ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Liquid Water ◽  
Thermophysical Properties ◽  
Passive Microwave ◽  
Hudson Bay ◽  
Strong Positive Correlation ◽  
Sea Ice Concentration ◽  
Multi Scale ◽  
Ice Concentration

Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.

scholarly journals The EUMETSAT sea ice climate record

2016 ◽  
Author(s):  
R. T. Tonboe ◽  
S. Eastwood ◽  
T. Lavergne ◽  
A. M. Sørensen ◽  
N. Rathmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Open Water ◽  
The Arctic ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
Microwave Imager

Abstract. An Arctic and Antarctic sea ice area and extent dataset has been generated by EUMETSAT's Ocean and Sea Ice Satellite Application Facility (OSISAF) using the record of American microwave radiometer data from Nimbus 7 Scanning Multichannel Microwave radiometer (SMMR) and the Defense Meteorological satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) satellite sensors. The dataset covers the period from 1978 to 2014 and updates and further developments are planned for the next phase of the project. The methodology is using: 1) numerical weather prediction (NWP) input to a radiative transfer model (RTM) for correction of the brightness temperatures for reduction of atmospheric noise, 2) dynamical algorithm tie-points to mitigate trends in residual atmospheric, sea ice and water emission characteristics and inter-sensor differences/biases, 3) and a hybrid sea ice concentration algorithm using the Bristol algorithm over ice and the Bootstrap algorithm in frequency mode over open water. A new algorithm has been developed to estimate the spatially and temporally varying sea ice concentration uncertainties. A comparison to sea ice charts from the Arctic and the Antarctic shows that ice concentrations are higher in the ice charts than estimated from the radiometer data at intermediate ice concentrations. The sea ice climate dataset is available for download at (www.osisaf.org) including documentation.

scholarly journals Abrupt transitions in Arctic open water area

2016 ◽  
Author(s):  
Michael A. Goldstein ◽  
Amanda H. Lynch ◽  
Todd E. Arbetter ◽  
Florence Fetterer
Keyword(s):  
Sea Ice ◽  
Structural Breaks ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Atlantic Sector ◽  
Water Fraction ◽  
Ice Concentration

Abstract. September open water fraction in the Arctic is analyzed using the satellite era record of ice concentration (1979–2014). This analysis suggests that there is a statistically significant breakpoint (shift in the mean) and increase in the variance around 1988 and another breakpoint around 2007 in the Pacific sector. These structural breaks are robust to the choice of algorithm used for deriving sea ice concentration from satellite data, and are also apparent in other measures of open water, such as operational ice charts and the record of navigable days from Barrow to Prudhoe Bay. Breakpoints in the Atlantic sector record of open water are evident in 1988 and 2007 but more weakly significant. The breakpoints appear to be associated with concomitant shifts in average ice age, and tend to lead change in Arctic circulation regimes. These results support the thesis that Arctic sea ice may have critical points beyond which a return to the previous state is less likely.

Polynya area and frequency in the Weddell Sea in CMIP6 climate models

2020 ◽  
Author(s):  
Martin Mohrmann ◽  
Céline Heuzé ◽  
Sebastiaan Swart
Keyword(s):  
Sea Ice ◽  
Deep Water ◽  
Climate Models ◽  
Numerical Models ◽  
Open Water ◽  
Weddell Sea ◽  
Threshold Method ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ice Concentration

&lt;p&gt;The presence of polynyas has a large effect on air-sea fluxes and deep water production, therefore impacting climate-relevant properties such as heat and carbon exchange between the atmosphere and ocean interior. One of the key areas of deep water formation is in the Weddell Sea, where much attention has recently been placed in the reoccurance of the open ocean Maud Rise polynya. In this study, two methods are presented to track the number, area and spatial distribution of polynyas with a focus on the Weddell Sea. The analysis is applied to a set of 10 Coupled Model Intercomparison Project phase 6 (CMIP6) models and to satellite sea ice concentration data. The first approach is a sea ice threshold method applied to the CMIP6 sea ice data at the original model grid. Open water areas surrounded by sea ice are classified as polynyas. Without requiring any remapping or interpolation, this method preserves the area information of all grid cells and is well suited to compute the combined area of the polynyas in the Weddell Sea. The second approach makes use of an image analysis technique to outline areas with low sea ice concentration. This method is preferable for counting the absolute number of polynyas and obtaining statistical information about their position. Satellite sea ice concentration is used as a reference to compare the performance of the models representing polynya area and to indicate model biases in the location of polynyas. All analyzed CMIP6 models show coastal polynyas, while only about half of the models regularly form open water polynyas. The resolution (about one degree for most models) sets a limit for the number of the polynyas in the numerical models.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document