On the origins of temporal power‐law behavior in the global atmospheric circulation

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Climate variations on timescales longer than a year are often characterized by temporal scaling (“power‐law”) behavior for which spectral power builds up at low frequencies, in contrast to red‐noise behavior for which spectral power saturates at low frequencies. Checks on the ability of climate prediction models to simulate temporal scaling behavior represent stringent performance tests on the models. We here estimate temporal power‐law exponents (“Hurst exponents”) for the global atmospheric circulation of the stratosphere and troposphere during the 20th century. We show that current generation climate models generally simulate the spatial distribution of the Hurst exponents well. We then use simulations with different climate forcings to explain the Hurst exponent distribution and to account for discrepancies in scaling behavior between different observational products. We conclude that characterization of temporal power‐law behavior provides a valuable tool for cross‐validating low‐frequency variability in various datasets, for elucidating the physical mechanisms underlying this variability, and for statistical testing of trends and periodicities in climate time series.