scholarly journals Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent Waters Using Satellite Data

2020 ◽  
Author(s):  
Dmitry Lozhkin
Keyword(s):  
Surface Temperature ◽  
Satellite Data ◽  
Heat Content ◽  
Aquatic Organisms ◽  
Spatiotemporal Variability ◽  
Okhotsk Sea ◽  
Winter Convection ◽  
Sea Surface Temperature Anomalies ◽  
Periodic Fluctuations ◽  
Linear Trends

The chapter is divided into 5 parts. The first part describes what the satellite data is and describes the levels of its processing. In the second part, attention is paid to sea surface temperature anomalies, the conditions for the appearance of the most significant anomalies and their influence on the behavior and survival of aquatic organisms are described. The third part is devoted to calculating linear trends in ocean surface temperature from a 20-year series of satellite data. It is shown that the heat content of the surface layer of Okhotsk Sea decreases, most significantly in its northern and western parts. This trend is especially pronounced in the spring, which may be due to a decrease in ice cover and a more significant cooling of the waters due to winter convection. In the fourth part, periodic fluctuations in the temperature of the surface of the ocean are considered. It is demonstrated how, using the calculated trend and several basic harmonics, one can try to predict the temperature next year. And the last part concludes the chapter.

scholarly journals Trends in sea surface temperature of the Sea of Okhotsk and adjacent water areas by satellite data in 1998–2017

2019 ◽  
pp. 55-61 ◽  
Author(s):  
D. M. Lozhkin ◽  
G. V. Shevchenko
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Of Okhotsk ◽  
Linear Trend ◽  
Heat Content ◽  
Water Area ◽  
Sea Surface ◽  
The Sea Of Okhotsk ◽  
Adjacent Water ◽  
Winter Convection

For the Sea of Okhotsk and the adjacent water areas, a series of mean monthly sea surface temperature values were computed from satellite measurements of 20 years (1998–2017). In each space cell, the coefficients of the linear trend are determined by the method of least squares. Such coefficients were calculated for each month separately, for the whole series as a whole, and also for average values of the temperature for the season. The relationship of these coefficients to the observed decrease in ice extent in the water area of the Sea of Okhotsk during the last twenty years has been analyzed. It is shown that the heat content of the surface layer in this basin decreases, most significantly in its northern and western parts. This trend is especially pronounced in the spring, which may be due to a decrease in ice cover and a more significant cooling of the waters due to winter convection.

scholarly journals Detection of an Ice-Forming Area by Radar and Satellite

1985 ◽  
Vol 6 ◽  
pp. 252-253 ◽  
Author(s):  
Masaaki Aota ◽  
Masayuki Oi ◽  
Masao Ishikawa ◽  
Hiroki Fukushi
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Satellite Data ◽  
Okhotsk Sea ◽  
Sea Clutter ◽  
Thermal Images ◽  
Radar Echoes ◽  
Pack Ice ◽  
Sea Coast ◽  
Infrared Radiometer

This paper describes a method of distinguishing between pack ice and sea clutter in radar echoes, an attempt to roughly estimate the thickness of sea ice from measurement of surface temperature by air-borne infrared radiometer, and an application of thermal images from satellite data to estimate the concentration of sea ice off the Okhotsk Sea coast of Hokkaido.

scholarly journals Detection of an Ice-Forming Area by Radar and Satellite

1985 ◽  
Vol 6 ◽  
pp. 252-253
Author(s):  
Masaaki Aota ◽  
Masayuki Oi ◽  
Masao Ishikawa ◽  
Hiroki Fukushi
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Satellite Data ◽  
Okhotsk Sea ◽  
Sea Clutter ◽  
Thermal Images ◽  
Radar Echoes ◽  
Pack Ice ◽  
Sea Coast ◽  
Infrared Radiometer

This paper describes a method of distinguishing between pack ice and sea clutter in radar echoes, an attempt to roughly estimate the thickness of sea ice from measurement of surface temperature by air-borne infrared radiometer, and an application of thermal images from satellite data to estimate the concentration of sea ice off the Okhotsk Sea coast of Hokkaido.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

Copepod and monogenean infection of fish under sea surface temperature anomalies

2021 ◽  
pp. 102098
Author(s):  
F. Neptalí Morales-Serna ◽  
Lorenia Olivas-Padilla ◽  
Emigdio Marín-Enriquez ◽  
Juan M. Osuna-Cabanillas ◽  
Hugo Aguirre-Villaseñor ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Sea Surface Temperature Anomalies ◽  
Monogenean Infection

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

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