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Based on counts of record highs and lows, and employing reversibility in time, an approach to examining natural variability is proposed. The focus is on intrinsic variability; that is, variance separated from the trend in the mean. A variability index α is suggested and studied for an ensemble of monthly temperature time series around the globe. Deviation of 〈α〉 (mean α) from zero, for an ensemble of time series, signifies a variance trend in a distribution-independent manner. For 15 635 monthly temperature time series from different geographical locations (Global Historical Climatology Network), each time series about a century-long, 〈α〉 = −1.0, indicating decreasing variability. This value is an order of magnitude greater than the 3σ value of stationary simulations. Using the conventional best-fit Gaussian temperature distribution, the trend is associated with a change of about −0.2°C (106 yr)−1 in the standard deviation of interannual monthly mean temperature distributions (about 10%).

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© 2010 American Meteorological Society. Article deposited here in compliance with publisher policies. Publisher’s version of record:

Publication Title

Journal of Applied Meteorology and Climatology


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Physics Commons



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