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Extracting climate memory using Fractional Integrated Statistical Model: a new perspective on climate prediction
Naiming Yuan1, Zuntao Fu2, Shida Liu2
11] Lab for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China [2] Chinese Academy of Meteorological Science, Beijing, 100081, China [3] Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Senckenbergstrasse 1, 35390 Giessen, Germany.
Scientific Reports
|October 11, 2014
Summary
This study introduces a new method to quantify climate memory, separating long-lasting influences from short-term weather variations. This approach enhances understanding of climate variability and prediction.
Area of Science:
- Climate Science
- Statistical Modeling
- Time Series Analysis
Background:
- Climate variability is influenced by long-term memory effects.
- Quantifying these memory signals is crucial for accurate climate prediction.
Purpose of the Study:
- To develop and validate a novel method for estimating and extracting climate memory signals.
- To decompose climate variations into cumulative climate memory and weather-scale excitation.
Main Methods:
- Utilizing fractional integral techniques and the Fractional Integral Statistical Model (FISM).
- Applying the method to analyze Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation (PDO).
Main Results:
- Successfully extracted climate memory signals from NHTA and PDO data.
- Decomposed variations into cumulative climate memory (CCM) and weather-scale excitation (WSE).
- Demonstrated that stronger long-term memory (LTM) correlates with a larger proportion of memory signals.
Conclusions:
- The proposed method provides a quantitative way to assess the impact of historical climate states.
- Extracted climate memory signals offer insights into the basis of future climate time series changes.
- This work presents a new perspective for improving climate prediction models.
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