A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

Mapping Intimacies ◽

10.32942/osf.io/hxbc7 ◽

2022 ◽

Author(s):

Volodymyr Tytar

Keyword(s):

Sea Ice ◽

Chlorophyll A ◽

Southern Hemisphere ◽

Species Distribution ◽

Sea Ice Concentration ◽

Chlorophyll A Concentration ◽

Antarctic Minke Whale ◽

Ice Concentration ◽

Balaenoptera Bonaerensis ◽

The Antarctic

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60°S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (ºC), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/m³), primary productivity (g/m3/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia–Weddell Confluence. Given the association of AMWs with sea ice the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

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A SPECIES DISTRIBUTION MODEL OF THE ANTARCTIC MINKE WHALE (BALAENOPTERA BONAERENSIS)

10.32942/osf.io/jrc8v ◽

2022 ◽

Author(s):

Volodymyr Tytar

Keyword(s):

Sea Ice ◽

Chlorophyll A ◽

Southern Hemisphere ◽

Species Distribution ◽

Sea Ice Concentration ◽

Chlorophyll A Concentration ◽

Antarctic Minke Whale ◽

Ice Concentration ◽

Balaenoptera Bonaerensis ◽

The Antarctic

The Antarctic minke whale (Balaenoptera bonaerensis) is regarded a Southern Hemisphere endemic found throughout the Southern Hemisphere, generally south of 60 degrees S in austral summer. Here they have been routinely observed in highest densities adjacent to and inside the sea ice edge, and where they feed predominantly on krill. Detecting abundance trends regarding this species by employing visual monitoring is problematic. Partly this is because the whales are frequently sighted within sea ice where navigational safety concerns prevent ships from surveying. In this respect species-habitat models are increasingly recognized as valuable tools to predict the probability of cetacean presence, relative abundance or density throughout an area of interest and to gain insight into the ecological processes affecting these patterns. The objective of this study was to provide this background information for the above research needs and in a broader context use species distribution models (SDMs) to establish a current habitat suitability description for the species and to identify the main environmental covariates related to its distribution. We used filtered 464 occurrences to generate the SDMs. We selected eight predictor variables with reduced collinearity for constructing the models: mean annuals of the surface temperature (degrees C), salinity (PSS), current velocity (m/s), sea ice concentration (fraction, %), chlorophyll-a concentration (mg/cub. m), primary productivity (g/cub.m/day), cloud cover (%), and bathymetry (m). Six modeling algorithms were test and the Bayesian additive regression trees (BART) model demonstrated the best preformance. Based on variable importance, those that best explained the environmental requirements of the species, were: sea ice concentration, chlorophyll-a concentration and topography of the sea floor (bathymetry), explaining in sum around 62% of the variance. Using the BART model, habitat preferences have been interpreted from patterns in partial dependence plots. Areas where the AMW have particularly high likelihood of occurrence are East Antarctica, NE of the Weddell Sea, areas around the northern tip of the Antarctica Peninsula, areas bordering the Scotia-Weddell Confluence. Given the association of AMWs with sea ice, the pagophilic character of their biology makes them particularly vulnerable to climate change and a perfect biological indicator for tracking these changes.

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Cyclone behaviour response to changes in winter southern hemisphere sea-ice concentration

Quarterly Journal of the Royal Meteorological Society ◽

10.1002/qj.49711951313 ◽

1993 ◽

Vol 119 (513) ◽

pp. 1121-1148 ◽

Cited By ~ 53

Author(s):

Ian Simmonds ◽

Xingren Wu

Keyword(s):

Sea Ice ◽

Southern Hemisphere ◽

Sea Ice Concentration ◽

Ice Concentration

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Comparison between AMSR-E ASI sea-ice concentration product, MODIS and pseudo-ship observations of the Antarctic sea-ice edge

Annals of Glaciology ◽

10.3189/2015aog69a588 ◽

2015 ◽

Vol 56 (69) ◽

pp. 45-52 ◽

Cited By ~ 8

Author(s):

Xi Zhao ◽

Haoyue Su ◽

Alfred Stein ◽

Xiaoping Pang

Keyword(s):

Sea Ice ◽

Average Distance ◽

Sea Ice Concentration ◽

Earth Observing System ◽

Antarctic Sea Ice ◽

Spatial Coverage ◽

Ice Concentration ◽

Ice Edge ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Antarctic

AbstractThe performance of passive microwave sea-ice concentration products in the marginal ice zone and at the ice edge draws much attention in accuracy assessments. In this study, we generated 917 pseudo-ship observations from four Moderate Resolution Imaging Spectroradiometer (MODIS) images based on the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol to assess the quality of the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI) concentrations at the ice edge in Antarctica. The results indicate that the ASI pixels in the pseudo-ASPeCt observations have a mean ice concentration of 13% and are significantly different from the well-established 15% threshold. The average distance between the pseudo-ice edge and the 15% threshold contour is ~10 km. The correlation between the sea-ice concentration (SIC), SICASI and SICMODIS values at the ice edge was considerably lower than the high coefficients obtained from a transect analysis. Underestimation of SICASI occurred in summer, whereas no clear bias was observed in winter. The proposed method provides an opportunity to generate a new source of reference data in which the spatial coverage is wider and more flexible than in traditional in situ observations.

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Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2

10.5194/tc-2021-367 ◽

2021 ◽

Author(s):

Jill Brouwer ◽

Alexander D. Fraser ◽

Damian J. Murphy ◽

Pat Wongpan ◽

Alberto Alberello ◽

...

Keyword(s):

Sea Ice ◽

Significant Wave Height ◽

Wave Attenuation ◽

Physical Processes ◽

Sea Ice Concentration ◽

New Techniques ◽

Ice Concentration ◽

Marginal Ice Zone ◽

Wave Height Attenuation ◽

The Antarctic

Abstract. The Antarctic marginal ice zone (MIZ) is a highly dynamic region where sea ice interacts with ocean surface waves generated in ice-free areas of the Southern Ocean. Improved large-scale (satellite-based) estimates of MIZ width and variability are crucial for understanding atmosphere-ice-ocean interactions and biological processes, and detection of change therein. Legacy methods for defining the MIZ width are typically based on sea ice concentration thresholds, and do not directly relate to the fundamental physical processes driving MIZ variability. To address this, new techniques have been developed to determine MIZ width based on the detection of waves and calculation of significant wave height attenuation from variations in ICESat-2 surface heights. The poleward MIZ limit (boundary) is defined as the location where significant wave height attenuation equals the estimated satellite height error. Extensive automated and manual acceptance/rejection criteria are employed to ensure confidence in MIZ width estimates, due to significant cloud contamination of ICESat-2 data or where wave attenuation was not observed. Analysis of 304 MIZ width estimates retrieved from four months of 2019 (February, May, September and December) revealed that sea ice concentration-derived MIZ width estimates were far narrower (by a factor of ~7) than those from the new techniques presented here. These results suggest that indirect methods of MIZ estimation based on sea ice concentration are insufficient for representing physical processes that define the MIZ. Improved measurements of MIZ width based on wave attenuation will play an important role in increasing our understanding of this complex sea ice zone.

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NEMO-ICB (v1.0): interactive icebergs in the NEMO ocean model globally configured at coarse and eddy-permitting resolution

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-5661-2014 ◽

2014 ◽

Vol 7 (4) ◽

pp. 5661-5698 ◽

Cited By ~ 2

Author(s):

R. Marsh ◽

V. O. Ivchenko ◽

N. Skliris ◽

S. Alderson ◽

G. R. Bigg ◽

...

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Ocean Model ◽

Coastal Current ◽

Density Gradients ◽

Surface Salinity ◽

Sea Ice Concentration ◽

Freshwater Forcing ◽

Ice Concentration ◽

The Antarctic

Abstract. NEMO-ICB features interactive icebergs in the NEMO ocean model. Simulations with coarse (2°) and eddy-permitting (0.25°) global configurations of NEMO-ICB are undertaken to evaluate the influence of icebergs on sea-ice, hydrography and transports, through comparison with control simulations in which the equivalent iceberg mass flux is applied as coastal runoff, the default forcing in NEMO. Comparing a short (14 year) spin-up of the 0.25° model with a computationally cheaper 105 year spin-up of the 2° configuration, calving, drift and melting of icebergs is evidently near equilibrium in the shorter simulation, justifying closer examination of iceberg influences in the eddy-permitting configuration. Freshwater forcing due to iceberg melt is most pronounced in southern high latitudes, where it is locally dominant over precipitation. Sea ice concentration and thickness in the Southern Ocean are locally increased with icebergs, by up to ~ 8 and ~ 25% respectively. Iceberg melting reduces surface salinity by ~ 0.2 psu around much of Antarctica, with compensating increases immediately adjacent to Antarctica, where coastal runoff is suppressed. Discernible effects on salinity and temperature extend to 1000 m. At many locations and levels, freshening and cooling indicate a degree of density compensation. However, freshening is a dominant influence on upper ocean density gradients across much of the high-latitude Southern Ocean, leading to weaker meridional density gradients, a reduced eastward transport tendency, and hence an increase of ~ 20% in westward transport of the Antarctic Coastal Current.

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Variability in Antarctic sea ice from 1998-2017

10.7287/peerj.preprints.27121v1 ◽

2018 ◽

Author(s):

Zhankai Wu ◽

Xingdong Wang

Keyword(s):

Sea Ice ◽

First Year ◽

Concentration Data ◽

Sea Ice Concentration ◽

Ice Melt ◽

Antarctic Sea Ice ◽

Ice Concentration ◽

The Antarctic ◽

Melt Duration ◽

Multiyear Ice

This study was based on the daily sea ice concentration data from the National Snow and Ice Data Center (Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA) from 1998 to 2017. The Antarctic sea ice was analysed from the total sea ice area (SIA), first year ice area, first year ice melt duration, and multiyear ice area. On a temporal scale, the changes in sea ice parameters were studied over the whole 20 years and for two 10-year periods. The results showed that the total SIA increased by 0.0083×106 km2 yr-1 (+2.07% dec-1) between 1998 and 2017. However, the total SIA in the two 10-year periods showed opposite trends, in which the total SIA increased by 0.026×106 km2 yr-1 between 1998 and 2007 and decreased by 0.0707×106 km2 yr-1 from 2008 to 2017. The first year ice area increased by 0.0059×106 km2 yr-1 and the melt duration decreased by 0.0908 days yr-1 between 1998 and 2017. The multiyear ice area increased by 0.0154×106 km2 yr-1 from 1998 to 2017, and the increase in the last 10 years was about 12.1% more than that in the first 10 years. On a spatial scale, the Entire Antarctica was divided into two areas, namely West Antarctica (WA) and East Antarctica (EA), according to the spatial change rate of sea ice concentration. The results showed that WA had clear warming in recent years; the total sea ice and multiyear ice areas showed a decreasing trend; multiyear ice area sharply decreased and reached the lowest value in 2017, and accounted for only about 10.1% of the 20-year average. However, the total SIA and multiyear ice area all showed an increased trend in EA, in which the multiyear ice area increased by 0.0478×106 km2 yr-1. Therefore, Antarctic sea ice presented an increasing trend, but there were different trends in WA and EA. Different sea ice parameters in WA and EA showed an opposite trend from 1998 to 2007. However, the total SIA, first year ice area, and multiyear ice area all showed a decreasing trend from 2008-2017, especially the total sea ice and first year ice, which changed almost the same in 2014-2017. In summary, although the Antarctic sea ice has increased slightly over time, it has shown a decreasing trend in recent years.

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Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

10.5194/tc-2019-120 ◽

2019 ◽

Cited By ~ 2

Author(s):

Stefan Kern ◽

Thomas Lavergne ◽

Dirk Notz ◽

Leif Toudal Pedersen ◽

Rasmus Tage Tonboe ◽

...

Keyword(s):

Sea Ice ◽

The Arctic ◽

Data Sets ◽

Sea Ice Concentration ◽

Data Set ◽

Group I ◽

Ice Concentration ◽

Data Products ◽

Group Ii ◽

The Antarctic

Abstract. Accurate sea-ice concentration (SIC) data are a pre-requisite to reliably monitor the polar sea-ice covers. Over the last four decades, many algorithms have been developed to retrieve the SIC from satellite microwave radiometry, some of them applied to generate long-term data products. We report on results of a systematic inter-comparison of ten global SIC data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global winter-time near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the observed inter-product consistency and differences of the inter-comparison results. Group I consists of data sets using the self-optimizing EUMETSAT-OSISAF – ESA-CCI algorithms. Group II includes data using the NASA-Team 2 and Comiso-Bootstrap algorithms, and the NOAA-NSIDC sea-ice concentration climate data record (CDR). The standard NASA-Team and the ARTIST Sea Ice (ASI) algorithms are put into a separate group III because of their often quite diverse results. Within group I and II evaluation results and intra-product differences are mostly very similar. For instance, among group I products, SIA agrees within ±100 000 km2 in both hemispheres during maximum and minimum sea-ice cover. Among group II products, satellite- minus ship-based SIC differences agree within ±0.7 %. Standing out with large negative differences to other products and evaluation data is the standard NASA-Team algorithm, in both hemispheres. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to the 100 % reference SIC with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic only range from −0.2 to +0.9 %. The standard deviation is smaller in the Arctic for the quoted group I products: 1.9 % to 2.9 % and Antarctic: 2.5 % to 3.1 %, than for group II products: Arctic: 3.6 % to 5.0 %, Antarctic: 4.5 % to 5.4 %. Products of group I exhibit larger overall satellite- minus ship-based SIC differences than group II in both hemispheres. However, compared to group II, group I products’ standard deviations are smaller, correlations higher and evaluation results are less sensitive to seasonal changes. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to re-construct the un-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for, e.g., surface heat-flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and suggest advancing studies about the influence of these weather filters on SIA and SIE time-series and their trends.

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Trends in the Stability of Antarctic Coastal Polynyas and the Role of Topographic Forcing Factors

Remote Sensing ◽

10.3390/rs12061043 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1043

Author(s):

Liyuan Jiang ◽

Yong Ma ◽

Fu Chen ◽

Jianbo Liu ◽

Wutao Yao ◽

...

Keyword(s):

Sea Ice ◽

Polar Regions ◽

Sea Ice Concentration ◽

Dynamic Factors ◽

Ice Concentration ◽

Two Factors ◽

Microwave Imager ◽

The Stability ◽

Relationship Of ◽

The Antarctic

Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them.

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