The Madden-Julian Oscillation (MJO, Madden-Julian 1971, 1994) interacts with, and influences, a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, mid-latitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale (Lau and Waliser, 2005). The Monsoon Intraseasonal Variation (MISV), which is more complex in nature because of intrinsic monsoon variability as well as due to interaction between monsoon circulations and MJO, is one of the dominant short-term climate variability in global monsoon system (Webster et al. 1998, Wang 2006). The wet and dry spells of the MISV strongly influence extreme hydro-meteorological events, which composed of about 80% of natural disaster, thus the socio-economic activities in the World's most populous monsoon region.
About a decade ago, dynamical forecasts of MJO made using the atmospheric-only model of the NCEP reanalysis vintage had a useful skill only up to about 7 days for boreal winter season (Hendon et al. 1999). Dynamical models have improved greatly in the past decade (Sperber and Waliser 2008) and a few models have produced rather credible simulations of MJO, with evidence of useful prediction skill of the principal characteristics of MJO out to a lead-time comparable to empirical-statistical schemes (~ 2 weeks) (Kim et al. 2007; Vitart et al. 2008). Air-sea coupling can further extend the MJO predictability perhaps by up to a week (Fu et al. 2007; Woolnough et al. 2007).
Through thedevelopment of a common MJO forecast metric, the US CLIVAR MJO Working Group (hereafter MJOWG), in conjunction with the Working Group on Numerical Experimentation (WGNE) has fostered the development of a multi-institution/model operational MJO prediction framework anchored at the Climate Prediction Center (CPC) of the National Center for Environmental Prediction (NCEP) (Gottschalck et al. 2008). The MJOWG has also developed a set of diagnostics for evaluating model simulations of the MJO (CLIVAR MJO Working Group, 2009) and applied them to a set of contemporary climate simulations (Kim et al. 2009).
Despite the significant societal and environmental demands for accurate prediction of MJO/MISV and notable improvements in our ability to simulate the MJO over the past decade, operational prediction of MJO is still in its infancy and its achievement is seen as a great challenge faced by operational weather forecast centers.
The multi-model ensemble (MME) approach has proven to be one of the most effective ways to improve seasonal prediction by reducing model errors and better quantifying forecast uncertainties (Krishnamurti et al. 1999; Doblas-Reyes et al. 2000; Shukla et al. 2000; Palmer 2000, Wang et al. 2008). Give the recent growth in interest and expected benefits in MJO prediction; it is meaningful to develop the MME techniques for determination of ISO predictability and prediction of ISO. However, it is not known to what extent the MME approach can improve the skill of MJO and MISV prediction.
While the establishment of the MJO forecast metric and the coordination of operational forecast activity is a great advance, there is an outstanding challenge and urgent need to exploit these efforts to full potential and produce an MME forecast. However, underlying the development of an MME is the intrinsic need for lead-dependent model climatologies (i.e. multi-decade hindcast data sets) to properly quantify and combine the independent skill of each model as a function of lead-time and season. Moreover, there are still great uncertainties regarding the level of predictability that can be ascribed to the MJO, other subseasonal phenomena and the weather/climate components that they interact with and influence. The development and analysis of a multi-model hindcast experiment is needed to address the above questions and challenges (e.g., Sperber and Waliser 2008). The ISVHE is the first attempt to produce the long-term hindcast data.