  We examine the long-term correlations and multifractal properties of daily
satellite retrievals of Arctic sea ice albedo and extent, for periods of $\sim$
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 record, the ice extent data
exhibits white noise behavior from seasonal to bi-seasonal time scales, whereas
the clear fingerprints of the short (weather) and long ($\sim$ 7 and 9 year)
time scales remain, the latter associated with the recent decay in the ice
cover. Therefore, long term persistence is reentrant 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.
