Climate models can exhibit systematic errors in their mean precipitation over the Maritime Continent of the Indonesian archipelago at the heart of the tropical warm pool. These can often be traced back to an erroneous simulation of the diurnal cycle, and can lead to errors in global climate, through planetary wave propagation. Here, we examine the simulation of the diurnal cycle over the Maritime Continent in a series of high-resolution integrations of the UK Met Office atmospheric model, with horizontal resolutions of 40 and 12 km (where the convection is parametrised) and 4 km (where the convection is explicitly resolved), as part of the Cascade project. In these models, the vertical heating profile over the islands changes from a convective profile with a mid-tropospheric maximum in the early afternoon to a more stratiform profile with upper-tropospheric heating and mid-tropospheric cooling later. The convective heating profile forces a first internal mode gravity wave that propagates rapidly offshore; the deep warm anomalies behind its downwelling wavefront suppress convection offshore during early afternoon. The stratiform heating profile forces a gravity wave with a higher-order vertical mode that propagates slowly offshore later in the afternoon. This mode has a negative, destabilising temperature anomaly in the mid-troposphere. Together with the convergence zone between the wave fronts of the two modes, favourable conditions are created for offshore convection. In the 4 km explicit convection model, the offshore convection responds strongly to this gravity wave forcing, in agreement with observations, supporting a gravity wave-convection paradigm for the diurnal cycle over the Maritime Continent. However, the convective response in the lower-resolution models is much less coherent, leading to errors in the diurnal cycle and mean precipitation.
A simple guide to the new technique of empirical mode decomposition (EMD) in a meteorologicalclimate forecasting context is presented. A single application of EMD to a time series essentially acts as a local high-pass filter. Hence, successive applications can be used to produce a bandpass filter that is highly efficient at extracting a broadband signal such as the Madden-Julian oscillation (MJO). The basic EMD method is adapted to minimize end effects, such that it is suitable for use in real time. The EMD process is then used to efficiently extract the MJO signal from gridded time series of outgoing longwave radiation (OLR) data.A range of statistical models from the general class of vector autoregressive moving average (VARMA) models was then tested for their suitability in forecasting the MJO signal, as isolated by the EMD. A VARMA (5, 1) model was selected and its parameters determined by a maximum likelihood method using 17 yr of OLR data from 1980 to 1996. Forecasts were then made on the remaining independent data from 1998 to 2004. These were made in real time, as only data up to the date the forecast was made were used. The median skill of forecasts was accurate (defined as an anomaly correlation above 0.6) at lead times up to 25 days.
ABSTRACT:Existing statistical forecast models of the Madden-Julian Oscillation (MJO) are generally of very low order and predict the evolution of a small number (typically two) of principal components (PCs). While such models are skilful up to 25 days lead time, by design they only predict the very largest-scale features of the MJO. Here we present a higherorder MJO statistical forecast model that is able to predict MJO variability on smaller, more localised scales, that will be of more direct benefit to national weather agencies and regional government planning. The model is based on daily outgoing long-wave radiation (OLR) data that are intraseasonally filtered using a recently developed technique of empirical mode decomposition that can be used in real time. A standard truncated PC analysis is then used to isolate the maximum amount of variance in a finite number of modes. The evolution of these modes is then forecast using a neural network model, which does not suffer from the parametrisation problems of other statistical forecast techniques when applied to a higher number of modes. Compared to a standard 2-PC model, the higher-order PC model showed improved skill over the whole MJO domain, with substantial improvements over the western Pacific, Arabian Sea, Bay of Bengal, South China Sea and Phillipine Sea.
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