Trend evaluation in records with long-term memory: Application to global warming

Physics – Geophysics

Scientific paper

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Mathematical Geophysics: Persistence, Memory, Correlations, Clustering (3265, 7857), Atmospheric Processes: Climate Change And Variability (1616, 1635, 3309, 4215, 4513)

Scientific paper

Previous statistical detection methods indicate that, on a global scale, the observed warming cannot be attributed solely to natural fluctuations. Here we estimate the probability W(Δ) that an observed trend Δ occurs naturally, and determine the anthropogenic part A Q (Δ) of the temperature increase within a given confidence interval Q. To obtain these quantities, we do not use climate simulations, but assume as statistical null hypothesis that monthly temperature records are long-term correlated with a Hurst exponent α > 0.5 (including also nonstationary records with α values above 1). We show that for confidence intervals with Q above 80% analytical expressions for W(Δ) and A Q (Δ) can be derived, which request as input solely the Hurst exponent, as well as the temperature increase Δ obtained from the linear regression line and the standard deviation σ t around it. We apply this approach to global and local temperature data and discuss the different results.

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