Abstract
A data-driven, high-order vector auto-regressive (VAR) model is evaluated for predicting the Northern Hemisphere summer time (May through September) low frequency (>10 days or so) variability. The VAR model is suitable for linear stationary time series, similar to the commonly used linear inverse model (LIM), with additional temporal information incorporated to improve forecast skill. The intraseasonal forecast skill of the 250/750 hPa streamfunction is investigated using observational data since 1979, which shows significant improvements in high-order VAR models than the first-order model LIM. Furthermore, the tropical diabatic heating is found to significantly improve the forecast skill of the atmospheric low frequency circulation when included in the VAR model. The forecast skill of 250 hPa streamfunction at Arabian Peninsula is particularly enhanced for up to 5 weeks lead-time through circumglobal wave propagation associated with the persistent tropical eastern Pacific and equatorial Atlantic heating anomalies and the intraseasonal evolution of the tropical Indian Ocean and western Pacific heating anomalies.
Highlights
The atmospheric extra-tropical flow is characterized as chaotic motions that are sensitive to initial conditions and is merely predictable by operational weather forecast models 2 weeks in advance given the current observational and modelling accuracy (e.g., Lorenz 1969; Leith 1971; Tribbia and Baumhefner 2004)
To examine the spatial distribution of the persistence and forecast skill, we show in Fig. 5 the 14-day lead anomaly correlation in the reanalysis and vector auto-regressive (VAR) model forecasts
We examine the role of tropical heating in northern summer low-frequency predictability with the VAR model
Summary
The atmospheric extra-tropical flow is characterized as chaotic motions that are sensitive to initial conditions and is merely predictable by operational weather forecast models 2 weeks in advance given the current observational and modelling accuracy (e.g., Lorenz 1969; Leith 1971; Tribbia and Baumhefner 2004). Winkler et al (2001) and Newman et al (2003) illustrated that by including tropical heating in a linear inverse model (LIM) for the streamfunction fields, the predictability of northern winter and summer low-frequency circulation is much improved in the intermediate range (i.e., week 2 and week 3). One expects the high-order VAR model to better satisfy the assumption of the data driven statistical model such as Eq 1 that the residual noise is closer to being white compared to that in the LIM (Chapman et al 2015), when taking into account the long range dependence of the atmosphere than one single time step. We examine the role of tropical heating in northern summer low-frequency predictability with the VAR model
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