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Trend evaluation in records with long‐term memory: Application to global warming
Author(s) -
Lennartz S.,
Bunde A.
Publication year - 2009
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2009gl039516
Subject(s) - hurst exponent , detrended fluctuation analysis , confidence interval , global temperature , term (time) , null hypothesis , standard deviation , environmental science , climatology , global warming , statistics , linear regression , climate change , statistical physics , mathematics , meteorology , physics , geology , scaling , geometry , oceanography , quantum mechanics
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.