Unveiling the multiscale teleconnection between Pacific Decadal Oscillation and global surface temperature using time-dependent intrinsic correlation analysis

2016 ◽  
Vol 37 (1) ◽  
pp. 548-558 ◽  
Author(s):  
Adarsh Sankaran
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Pacific Decadal Oscillation ◽  
Time Dependent ◽  
Global Surface Temperature ◽  
Time Dependent Intrinsic Correlation ◽  
Intrinsic Correlation ◽  
Global Surface

THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (02) ◽  
pp. 233-265 ◽  
Author(s):  
XIANYAO CHEN ◽  
ZHAOHUA WU ◽  
NORDEN E. HUANG
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Sliding Window ◽  
Time Dependent ◽  
New Approach ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Time Dependent Intrinsic Correlation ◽  
Intrinsic Correlation

A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This new method treats the selection of the sliding window sizes as an adaptive process determined by the data itself, not a "tuning" process. Therefore, it gives an intrinsic correlation analysis of the data. Furthermore, the multi-window approach makes the new method applicable to complicated data from multi-scale phenomena. The synthetic and time series from real world are used to demonstrate conclusively that the new approach is far more superior over the traditional method in its ability to reveal detailed and subtle correlations unavailable through any other methods in existence. Thus, the TDIC represents a major advance in statistical analysis of data from nonlinear and nonstationary processes.

scholarly journals Updated Temperature Data Give a Sharper View of Climate Trends

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Huai-Min Zhang ◽  
Jay Lawrimore ◽  
Boyin Huang ◽  
Matthew Menne ◽  
Xungang Yin ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Climate Trends ◽  
Historical Changes ◽  
Global Surface Temperature ◽  
Global Surface

The latest version of NOAA’s Global Surface Temperature Dataset improves coverage over land and sea and improves the treatment of historical changes in observational practices.

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