Abstract
AbstractThis paper proposes a new method for assessing potential predictability of seasonal means using a single realization of daily time series. Potential predictability is defined as variability in seasonal means that exceeds the variability due to weather stochastic processes. The proposed method is based on analysis of covariance and accounts for autocorrelation in daily time series and uncertainties in statistical parameters. The method is applied to reanalyzed daily surface air temperature and detects significant potential predictability over the oceans and equatorial land areas. Potential predictability is weaker and varies significantly with season over extratropical land areas, with the fraction of potentially predictable variance rarely exceeding 60%. The proposed method also produces an estimate of the potentially predictable component of seasonal means, which can be used to investigate the relation between potential predictability and possible boundary forcings. The results are generally consistent with previous studies, although a more detailed study will be made in a future paper.
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