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

1993 ◽  
Vol 119 (513) ◽  
pp. 1121-1148 ◽  
Author(s):  
Ian Simmonds ◽  
Xingren Wu
Keyword(s):  
Sea Ice ◽  
Southern Hemisphere ◽  
Sea Ice Concentration ◽  
Ice Concentration

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

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.

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

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.

scholarly journals Satellite Passive Microwave Sea-Ice Concentration Data Set Inter-comparison using Landsat data

2021 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe ◽  
Louisa Bell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Southern Hemisphere ◽  
Passive Microwave ◽  
Late Winter ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Comparison Results ◽  
Ice Concentration ◽  
The Impact

Abstract. We report on results of an inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations. For this we use SIC estimated from > 300 images acquired in the visible / near-infrared frequency range by joint the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat sensor during years 2003–2011 and 2013–2015. Conditions covered are late winter / early spring in the Northern Hemisphere and from late winter through fall freeze-up in the Southern Hemisphere. Among the products investigated are the four products of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Ocean and Sea Ice Satellite Application Facility (OSI SAF) and European Space Agency (ESA) Climate Change Initiative (CCI) algorithms: SICCI-2 and OSI-450. We stress the importance to consider inter-comparison results across the entire SIC range instead of focusing on overall mean differences, and to take in account known biases in PMW SIC products, e.g. for thin ice. We find superior linear agreement between PMW SIC and Landsat SIC for the 25 km and the 50 km SICCI-2 products in both hemispheres. We discuss quantitatively various uncertainty sources of the evaluation carried out. First, depending on the number of mixed ocean-ice Landsat pixels classified erroneously as ice only, our Landsat SIC is found to be biased high. This applies to some of our Southern Hemisphere data, promotes an overly large fraction of Landsat SIC under-estimation by PMW SIC products, and renders PMW SIC products overestimating Landsat SIC particularly problematic. Secondly, our main results are based on SIC data truncated to the range 0 % to 100 %. We demonstrate using non-truncated SIC values, where possible, can considerably improve linear agreement between PMW and Landsat SIC. Thirdly, we investigate the impact of filters often used to clean up the final products from spurious SIC over open water due to weather effects and along coastlines due to land spillover. Benefiting from the possibility to switch on or off certain filters in the SICCI-2 and OSI-450 products we quantify the impact land spillover filtering can have on evaluation results as shown in this paper.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

2021 ◽  
Vol 13 (6) ◽  
pp. 1139
Author(s):  
David Llaveria ◽  
Juan Francesc Munoz-Martin ◽  
Christoph Herbert ◽  
Miriam Pablos ◽  
Hyuk Park ◽  
...  
Keyword(s):  
Sea Ice ◽  
Microwave Radiometer ◽  
Cost Effective ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Neural Network Approach ◽  
Ice Concentration ◽  
L Band ◽  
Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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.

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