Empirical Orthogonal Function (EOF) analysis of sea-surface temperature and chlorophyll in the eastern Bering Sea

2010 ◽  
Author(s):  
Puneeta Naik ◽  
Eurico J. D'Sa
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Function ◽  
Bering Sea ◽  
Sea Surface ◽  
Eof Analysis ◽  
Orthogonal Function ◽  
Eastern Bering Sea

scholarly journals Seasonal to interannualvariability of Chlorophyll-<i>a</i> and sea surface temperature in the Yellow Sea using MODIS satellite datasets

2017 ◽  
Author(s):  
Chunli Liu ◽  
Qiwei Sun ◽  
Sufen Wang ◽  
Qianguo Xing ◽  
Lixin Zhu ◽  
...  
Keyword(s):  
Power Spectrum ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Function ◽  
Yellow Sea ◽  
Seasonal Cycle ◽  
Sea Surface ◽  
Wavelet Coherence ◽  
The Yellow Sea ◽  
Orthogonal Function

Abstract. The spatial and temporal variability of Chlorophyll-a concentration (CHL) and sea surface temperature (SST) in the Yellow Sea (YS) were examined using Empirical Orthogonal Function (EOF) analysis, which was based on the monthly, cloud-free Data INterpolating Empirical Orthogonal Function (DINEOF) reconstruction datasets for 2003–2015. The variability and oscillation periods on an inter-annual timescale were also confirmed using the Morlet wavelet transform and wavelet coherence analyses. At a seasonal time scale, the CHL EOF1 mode was dominated by a seasonal cycle of a spring and a fall bloom, with a spatial distribution that was modified by the strong mixing of the water column of the Yellow Sea Cold Warm Mass (YSCWM) that facilitated nutrient delivery from the ocean bottom. The EOF2 mode was likely associated with a winter bloom in the southern region, where it was affected by the Yellow Sea Warm Current (YSWC) that moved from southeast to north in winter. The SST EOF1 explained 99 % of the variance in total variabilities, which was dominated by an obvious seasonal cycle (in response to net surface heat flux) that was inversely proportional to the water depth. At the inter-annual scale, the wavelet power spectrum and global power spectrum of CHL and SST showed significant similar periods of variations. The dominant periods for both spectra were 2–4 years during 2003–2015. A significant negative cross-correlation existed between CHL and SST, with the largest correlation coefficient at time lags of 4 months. The wavelet coherence further identified a negative relationship that was significant statistically between CHL and SST during 2008–2015, with periods of 1.5–3 years. These results provided insight into how CHL might vary with SST in the future.

scholarly journals Identification of Global Warming Contribution to the El Niño Phenomenon Using Empirical Orthogonal Function Analysis

Agromet ◽  
2021 ◽  
Vol 35 (1) ◽  
pp. 11-19
Author(s):  
Mochamad Tito Julianto ◽  
Septian Dhimas ◽  
Ardhasena Sopaheluwakan ◽  
Sri Nurdiati ◽  
Pandu Septiawan
Keyword(s):  
Global Warming ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Function ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
El Nino Southern Oscillation ◽  
Orthogonal Function

Sea surface temperature (SST) is identified as one of the essential climate/ocean variables. The increased SST levels worldwide is associated with global warming which is due to excessive amounts of greenhouse gases being released into the atmosphere causing the multi-decadal tendency to warmer SST. Moreover, global warming has caused more frequent extreme El Niño Southern Oscillation (ENSO) events, which are the most dominant mode in the coupled ocean-atmosphere system on an interannual time scale. The objective of this research is to calculate the contribution of global warming to the ENSO phenomenon.  SST anomalies (SSTA) variability rosed from several mechanisms with differing timescales. Therefore, the Empirical Orthogonal Function in this study was used to analyze the data of Pacific Ocean sea surface temperature anomaly. By using EOF analysis, the pattern in data such as precipitation and drought pattern can be obtained. The result of this research showed that the most dominant EOF mode reveals the time series pattern of global warming, while the second most dominant EOF mode reveals the El Niño Southern Oscillation (ENSO). The modes from this EOF method have good performance with 95.8% accuracy rate.

Holocene variability in sea surface temperature and sea ice extent in the northern Bering Sea: A multiple biomarker study

2017 ◽  
Vol 113 ◽  
pp. 1-9 ◽  
Author(s):  
Jiaping Ruan ◽  
Yuanhui Huang ◽  
Xuefa Shi ◽  
Yanguang Liu ◽  
Wenjie Xiao ◽  
...  
Keyword(s):  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Bering Sea ◽  
Sea Surface ◽  
Sea Ice Extent ◽  
Northern Bering Sea
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