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Long-Range Correlations of Global Sea Surface Temperature.
Lei Jiang1,2, Xia Zhao3, Lu Wang4
1School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, China.
Plos One
|April 22, 2016
Summary
Global sea surface temperature (SST) exhibits strong long-range correlations, with higher scaling exponents in the tropics. These findings suggest potential climate predictability clues.
Area of Science:
- Climatology and Oceanography
- Time Series Analysis
- Geophysics
Background:
- Sea surface temperature (SST) is a critical climate indicator.
- Understanding long-range correlations in SST is vital for climate modeling.
- Previous studies have explored SST variability, but detailed scaling behavior requires further investigation.
Purpose of the Study:
- To investigate the scaling behaviors of global monthly sea surface temperature (SST) using historical data.
- To quantify long-range correlations (LRCs) in SST fluctuations across different latitudes.
- To explore the relationship between SST variability and its predictability.
Main Methods:
- Utilized detrended fluctuation analysis (DFA) on global monthly SST data from 1870-2009 (HadISST dataset).
- Calculated scaling exponents (α) for global, hemispheric, and latitudinal bands.
- Introduced a new index (χ = α * σ) incorporating standard deviation to analyze spatial distributions.
Main Results:
- Global SST fluctuations demonstrate strong positive long-range correlations across all time scales.
- Scaling exponents (α) are higher in the tropics (0.90) compared to intermediate latitudes (0.81-0.84).
- A negative correlation was observed between standard deviation and LRCs, particularly in upwelling regions, with notable SST changes in the Pacific and Atlantic.
Conclusions:
- Monthly SST anomalies exhibit persistent long-term correlated behaviors.
- The tropics show stronger long-range correlations in SST compared to higher latitudes.
- The introduced index (χ) highlights significant SST changes in specific oceanic regions, offering potential insights into climate predictability.
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