Cumulative and Synergistic Effects of Physical, Biological, and Acoustic Signals on Marine Mammal Habitat Use Physical Oceanography Component: Soundscapes Under Sea Ice: Can We Listen for Open Water?

2013 ◽  
Author(s):  
Jeffrey A. Nystuen
Keyword(s):  
Habitat Use ◽  
Sea Ice ◽  
Marine Mammal ◽  
Synergistic Effects ◽  
Open Water ◽  
Acoustic Signals ◽  
Physical Oceanography

Segregation of Beaufort Sea beluga whales during the open-water season

2006 ◽  
Vol 84 (12) ◽  
pp. 1743-1751 ◽  
Author(s):  
L.L. Loseto ◽  
P. Richard ◽  
G.A. Stern ◽  
J. Orr ◽  
S.H. Ferguson
Keyword(s):  
Habitat Use ◽  
Sea Ice ◽  
Resource Selection ◽  
Open Water ◽  
Beaufort Sea ◽  
Reproductive Status ◽  
The Arctic ◽  
Habitat Segregation ◽  
Beluga Whales ◽  
Distance Analysis

Population segregation by habitat use occurs because energy requirements and survival strategies vary with age, sex, size, and reproductive stage. From late summer to early fall in 1993, 1995, and 1997, relative length (age), sex, and reproductive status of satellite-tagged beluga whales ( Delphinapterus leucas (Pallas, 1776)) in the eastern Beaufort Sea were tested for habitat segregation. We used (i) resource selection function models to evaluate how belugas used areas of varying sea ice concentration and shelf habitat and (ii) distance analysis to measure the selection of areas varying in distance to mainland and island coastlines. Resource selection functions and distance analysis established that habitat selection differed with length, sex, and reproductive status of whales: (i) females with calves and smaller males selected open-water habitats near the mainland; (ii) large males selected closed sea ice cover in and near the Arctic Archipelago; and (iii) smaller males and two females with calves (not newborn) selected habitat near the ice edge. The segregation of habitat use according to sex, age, and reproductive status relates to the different resources required at different life stages and may represent characteristics of beluga social structure. We discuss our results in the context of two common sexual segregation hypotheses and conclude that summer habitat segregation of belugas reflects differences in foraging ecology, risk of predation, and reproduction.

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 Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

scholarly journals Ice-edge detection from Japanese C-band radar and high-frequency radar coastal stations

2013 ◽  
Vol 54 (62) ◽  
pp. 59-64 ◽  
Author(s):  
K. Shirasawa ◽  
N. Ebuchi ◽  
M. Leppäranta ◽  
T. Takatsuka
Keyword(s):  
Sea Ice ◽  
Edge Detection ◽  
High Frequency ◽  
Open Water ◽  
Radar Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Geometrical Structures ◽  
Overlapping Data ◽  
Ice Edge

AbstractA C-band sea-ice radar (SIR) network system was operated to monitor the sea-ice conditions off the Okhotsk Sea coast of northern Hokkaido, Japan, from 1969 to 2004. The system was based on three radar stations, which were capable of continuously monitoring the sea surface as far as 60 km offshore along a 250 km long coastal section. In 2004 the SIR system was closed down and a sea surface monitoring programme was commenced using high-frequency (HF) radar; this system provides information on surface currents in open-water conditions, while areas with ‘no signal’ can be identified as sea ice. The present study compares HF radar data with SIR data to evaluate their feasibility for sea-ice remote sensing. The period of overlapping data was 1.5 months. The results show that HF radar information can be utilized for ice-edge mapping although it cannot fully compensate for the loss of the SIR system. In particular, HF radar does not provide ice concentration, ice roughness and geometrical structures or ice kinematics. The probability of ice-edge detection by HF radar was 0.9 and the correlation of the ice-edge distance between the radars was 0.7.

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