Long-term comparison of satellite and in-situ sea surface temperatures around the Korean Peninsula

2015 ◽  
Vol 50 (1) ◽  
pp. 109-117 ◽  
Author(s):  
Myeong-Taek Kwak ◽  
Gwang-Ho Seo ◽  
Yang-Ki Cho ◽  
Bong-Guk Kim ◽  
Sung Hyup You ◽  
...  
1984 ◽  
Vol 35 (4) ◽  
pp. 487 ◽  
Author(s):  
DJ Rochford

Comparison of long-term mean monthly sea surface temperatures of coastal waters at comparable latitudes off south-eastern and south-westem Australia shows that, during the duration of the Leeuwin Current in autumn and winter, sea surface temperatures are 1-3�C higher off south-western Australia.


2012 ◽  
Vol 60 (4) ◽  
pp. 566-569 ◽  
Author(s):  
Woon Seon Jung ◽  
Dong-In Lee ◽  
Young-Jean Choi ◽  
Ki-Ho Chang ◽  
Jae-Won Jung ◽  
...  

2008 ◽  
Vol 9 (4) ◽  
pp. 816-824 ◽  
Author(s):  
Gregory J. McCabe ◽  
David M. Wolock

Abstract Global land surface runoff and sea surface temperatures (SST) are analyzed to identify the primary modes of variability of these hydroclimatic data for the period 1905–2002. A monthly water-balance model first is used with global monthly temperature and precipitation data to compute time series of annual gridded runoff for the analysis period. The annual runoff time series data are combined with gridded annual sea surface temperature data, and the combined dataset is subjected to a principal components analysis (PCA) to identify the primary modes of variability. The first three components from the PCA explain 29% of the total variability in the combined runoff/SST dataset. The first component explains 15% of the total variance and primarily represents long-term trends in the data. The long-term trends in SSTs are evident as warming in all of the oceans. The associated long-term trends in runoff suggest increasing flows for parts of North America, South America, Eurasia, and Australia; decreasing runoff is most notable in western Africa. The second principal component explains 9% of the total variance and reflects variability of the El Niño–Southern Oscillation (ENSO) and its associated influence on global annual runoff patterns. The third component explains 5% of the total variance and indicates a response of global annual runoff to variability in North Atlantic SSTs. The association between runoff and North Atlantic SSTs may explain an apparent steplike change in runoff that occurred around 1970 for a number of continental regions.


Polar Science ◽  
2013 ◽  
Vol 7 (3-4) ◽  
pp. 233-240 ◽  
Author(s):  
Rajkumar Kamaljit Singh ◽  
Megha Maheshwari ◽  
Sandip R. Oza ◽  
Raj Kumar

2020 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Martono Martono ◽  
Fanny Aditya Putri

<p>Absorption of atmospheric CO<sub>2</sub> by the sea through two processes, namely solubility pumps and biological pumps. This study aims to determine the effect of upwelling in the southern waters of Java on atmospheric CO<sub>2</sub> concentrations in Kototabang. The data used are in situ CO<sub>2</sub> concentration, sea surface temperature and chlorophyll-a concentration from 2004-2016. The method used was descriptive analysis. The results showed that upwelling that occurred during JJA-SON caused a decrease in sea surface temperature to 26.8 °C and 27.1 °C respectively, as well as an increase chlorophyll-a concentration to 2.03 mg/m<sup>3</sup> and 2.19 mg/m<sup>3</sup>. In both seasons CO<sub>2</sub> concentration in Kototabang dropped to 385.8 ppm and 385.4 ppm. Meanwhile, when there was no upwelling during DJF-MAM, sea surface temperatures rose to 28.8 °C and 29.0 °C, and chlorophyll-a concentration dropped to 0.32 mg/m<sup>3</sup> and 0.54 mg/m<sup>3</sup>. CO<sub>2</sub> concentration in DJF and MAM increased to 386.3 ppm and 386.5 ppm. Based on these results it is known that when upwelling occurs, CO<sub>2</sub> concentration decrease and vice versa.</p>


2020 ◽  
Vol 12 (20) ◽  
pp. 3326
Author(s):  
Hiroshi Kuroda ◽  
Yuko Toya

Coastal and offshore waters are generally separated by a barrier or “ocean front” on the continental shelf. A basic question arises as to what the representative spatial scale across the front may be. To answer this question, we simply corrected skin sea surface temperatures (SSTs) estimated from Landsat 8 imagery with a resolution of 100 m using skin SSTs estimated from geostationary meteorological satellite Himawari 8 with a resolution of 2 km. We analyzed snapshot images of skin SSTs on 13 October 2016, when we performed a simultaneous ship survey. We focused in particular on submesoscale thermal fronts on the Pacific shelf off the southeastern coast of Hokkaido, Japan. The overall spatial distribution of skin SSTs was consistent between Landsat 8 and Himawari 8; however, the spatial distribution of horizontal gradients of skin SSTs differed greatly between the two datasets. Some parts of strong fronts on the order of 1 °C km−1 were underestimated with Himawari 8, mainly because of low resolution, whereas weak fronts on the order of 0.1 °C km−1 were obscured in the Landsat 8 imagery because the signal-to-noise ratios were low. The widths of the strong fronts were estimated to be 114–461 m via Landsat 8 imagery and 539–1050 m via in situ ship survey. The difference was probably attributable to the difference in measurement depth of the SST, i.e., about 10-μm skin layer by satellite and a few dozen centimeters below the sea surface by the in situ survey. Our results indicated that an ocean model with a grid size of no more than ≤100–200 m is essential for realistic simulation of the frontal structure on the shelf.


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