Trends, noise and reentrant long-term persistence in Arctic sea ice
S. Agarwal, W. Moon, J. S. Wettlaufer

TL;DR
This study analyzes long-term correlations in Arctic sea ice data using multifractal analysis, revealing complex persistence patterns and the masking effect of seasonal cycles on long-term correlations.
Contribution
It introduces the use of Multifractal Temporally Weighted Detrended Fluctuation Analysis to characterize Arctic sea ice variability over multiple time scales, surpassing traditional single-scale models.
Findings
Long-term persistence reenters beyond seasonal scales.
Seasonal cycle masks long-term correlations.
Decadal-scale decay in ice cover is identified.
Abstract
We examine the long-term correlations and multifractal properties of daily satellite retrievals of Arctic sea ice albedo and extent, for periods of 23 years and 32 years respectively. The approach harnesses a recent development called Multifractal Temporally Weighted Detrended Fluctuation Analysis (MF-TWDFA), which exploits the intuition that points closer in time are more likely to be related than distant points. In both data sets we extract multiple crossover times, as characterized by generalized Hurst exponents, ranging from synoptic to decadal. The method goes beyond treatments that assume a single decay scale process, such as a first-order autoregression, which cannot be justifiably fit to these observations. Importantly, the strength of the seasonal cycle "masks" long term correlations on time scales beyond seasonal. When removing the seasonal cycle from the original…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Climate variability and models · Complex Network Analysis Techniques
