Potential seasonal predictability: Comparison between empirical and dynamical model estimates

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Abstract

[1] Methods for estimating potential seasonal predictability from a single realization of daily data are validated against an ensemble of simulations from an atmospheric model driven by observed sea surface temperature, sea ice extent, and greenhouse gas concentration. The methods give surprisingly good estimates of potential predictability of seasonal precipitation despite the fact that the methods assume Gaussian distributions. For temperature, the methods systematically underestimate weather noise variance over land, often by a factor of 2 or more. This bias can be reduced by taking account of precipitation-induced variability. These conclusions may be model dependent, and hence, confirmation in other models would be of interest. Nevertheless, for the state-of-the-art atmospheric model used in this study, the results strongly support the validity of the single time series approach to estimating potential predictability and enhances our confidence in previous estimates of potential predictability based on observations alone.

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