Intrinsic Predictability of the Madden-Julian Oscillation

The 20-90-day Madden-Julian oscillation (MJO) dominates the tropical atmospheric intraseasonal variability while interacting with tropical and extra-tropical weather and climate systems. It exhibits multiscale characters in space and time as a result of nonlinear and stochastic interactions among its component cloud systems and hierarchical regulations imposed by the atmospheric and oceanic environment. This talk provides an overview of the recent advancement in quantifying the dynamical and stochastic components of the MJO by applying nonlinear temporal and spatio-temporal analysis methods to decade-long satellite observations and reanalysis data in the global tropics. The choices of data were made to elucidate the multiscale convection-coupled MJO, including the CLAUS satellite infrared brightness temperature and TRMM rain rates, dynamic and thermodynamic fields in the ECMWF Interim reanalysis, as well as the heat and moisture budget residuals calculated from the reanalysis. The methods, largely rooted in dynamical systems, fractal, and information theories, have made it possible to address statistically the intrinsic predictability of the underlying multiscale dynamical and stochastic processes associated with the MJO. The findings impose fundamental constraints on the simulation and prediction of the MJO and set the parameter space for the observation-driven parsimonious modeling of the MJO.