On the statistical modeling of persistence in total ozone anomalies

Geophysical time series sometimes exhibit serial correlations that are stronger than can be captured by the commonly used first‐order autoregressive model. In this study we demonstrate that a power law statistical model serves as a useful upper bound for the persistence of total ozone anomalies on m...

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Bibliographic Details
Published in:Journal of Geophysical Research: Atmospheres 2010-08, Vol.115 (D16), p.n/a
Main Authors: Vyushin, D. I., Shepherd, T. G., Fioletov, V. E.
Format: Article
Language:English
Subjects:
Anomalies
Atmospheric sciences
Chlorine
Correlation
Earth sciences
Earth, ocean, space
Evolution
Exact sciences and technology
Exponents
Geophysics
Hurst exponent
long-range correlations
Mathematics
Ozone
Serials
Statistical analysis
Statistical models
Time series
total ozone
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Summary:Geophysical time series sometimes exhibit serial correlations that are stronger than can be captured by the commonly used first‐order autoregressive model. In this study we demonstrate that a power law statistical model serves as a useful upper bound for the persistence of total ozone anomalies on monthly to interannual timescales. Such a model is usually characterized by the Hurst exponent. We show that the estimation of the Hurst exponent in time series of total ozone is sensitive to various choices made in the statistical analysis, especially whether and how the deterministic (including periodic) signals are filtered from the time series, and the frequency range over which the estimation is made. In particular, care must be taken to ensure that the estimate of the Hurst exponent accurately represents the low‐frequency limit of the spectrum, which is the part that is relevant to long‐term correlations and the uncertainty of estimated trends. Otherwise, spurious results can be obtained. Based on this analysis, and using an updated equivalent effective stratospheric chlorine (EESC) function, we predict that an increase in total ozone attributable to EESC should be detectable at the 95% confidence level by 2015 at the latest in southern midlatitudes, and by 2020–2025 at the latest over 30°–45°N, with the time to detection increasing rapidly with latitude north of this range.
ISSN:0148-0227
2169-897X
2156-2202
2169-8996
DOI:10.1029/2009JD013105