Estimating the intermonth covariance between rainfall and the atmospheric circulation

Carsten Segerlund Frederiksen, Xiaogu Zheng, Simon Grainger


The seasonal mean of a climate variable consists of a slow and intraseasonal component. Existing methods for deriving coupled patterns between intraseasonal components assume stationarity and first order autoregressive processes. This does not hold for a variable such as rainfall where the daily data consists of dichotomous (on/off) events. It is possible to formulate a more general method for such two-state climate variables but it requires an estimate of the intermonth covariance. We use a stochastic two-state first-order Markov chain model fitted to daily Australian rainfall data to provide an estimate of the intermonth covariance with daily 500hPa atmospheric geopotential height anomalies. We show that the estimate of the intermonth covariance is much smaller than the within-month covariance between rainfall and the 500hPa height intraseasonal component.

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