Summer intraseasonal variability forecasting: Analysis of a serial numerical experiment and evaluation of slow manifold modeling

Forecasting the observed high amplitude tropical intraseasonal activity has proven to be a difficult task for the state of the art climate models. A serial modeling experiment was designed in order to evaluate the skill of the ECMWF coupled ocean-atmosphere model in simulating the observed intraseasonal variability in the Indo-Pacific basin during summer. The model was run in ensemble mode for 30 day periods initialized daily for 15 days before to 15 days after major intraseasonal oscillations, allowing the examination of the skill of the model relative to the phase of the oscillation. Two different summer cases were studied: the onset of the 2002 and 2004 wet season. Different parametric and non-parametric statistics show that the model skillfully represents the observations for about 8 days after which the magnitude of the errors is greater than the signal itself.

Based on the relatively high skill of the intraseasonal forecasts using a wavelet banding empirical scheme (Webster and Hoyos 2004: BAMS), in which the evolution of the intraseasonal signal is isolated from higher frequency variability and noise, a Slow Manifold Modeling (SMM) scheme was designed in order to minimize the high frequency error growth associated with poorly simulated convection by the model's parameterization, which in turns erodes the intraseasonal signal. The SMM mimics the philosophy of the banded wavelet empirical scheme by continuously smoothing out the errors at every time step of the integration in a moving average fashion. The modified model is used for the two series of 30-day forecasts described above. The magnitude and propagation features associated with intraseasonal activity in the Indo-Pacific basin are considerably improved in the SMM runs.