Trends, noise and re-entrant long-term persistence in Arctic sea ice

Abstract

We examine the long-term correlations and multi-fractal properties of daily satellite retrievals of Arctic sea ice albedo and extent, for periods of approximately 23 years and 32 years, respectively. The approach harnesses a recent development called multifractal temporally weighted detrended fluctuation analysis, which exploits the intuition that points closer in time are more likely to be related than distant points. In both datasets, 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 fitted 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 record, the ice extent data exhibit white noise behaviour from seasonal to bi-seasonal time scales, whereas the clear fingerprints of the short (weather) and long (approx. 7 and 9 year) time scales remain, the latter associated with the recent decay in the ice cover. Therefore, long-term persistence is re-entrant beyond the seasonal scale and it is not possible to distinguish whether a given ice extent minimum/maximum will be followed by a minimum/maximum that is larger or smaller in magnitude.

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

“…However, the semi-variogram approach does not account for the non-stationary processes hindering detecting multiple exponents. The MF-TWDFA has previously been utilized to understand long-range correlations and multi-fractal properties in daily satellite retrievals of Arctic sea ice albedo and extent (Agarwal et al 2011(Agarwal et al , 2012. The physical length-scales detected in the MF-TWDFA and the statistical characteristics of fluctuations are the basic information underlying a core mechanism for a given phenomenon.…”

Intrinsic Pink-Noise Multidecadal Global Climate Dynamics Mode

“…In a previous study [11], we showed the effect of the presence of a strong seasonal cycle in the data on fluctuation functions. Due to the strength of the seasonal cycle, the fluctuation function saturates after the seasonal cycle timescale, thereby, masking the longer timescales that may be present in the data.…”

On the existence of stable seasonally varying Arctic sea ice in simple models

“…Finally, it is of broad interest to understand the nature of ice decay in the Arctic. It is extremely difficult to use comprehensive GCMs to understand the qualitative distinction between the range of scenarios proposed [see Eisenman , 2012,and references therein], and extrapolation of observations on seasonal time scales is unwise [ Agarwal et al , 2012]. The simplest theoretical approaches in this field began with the two season model of Thorndike [1992], which predicts that once summer ice vanishes there is an irreversible change to the ice free state.…”