Monsoons: Processes, predictability, and the prospects for prediction
Article first published online: 20 SEP 2012
Copyright 1998 by the American Geophysical Union.
Journal of Geophysical Research: Oceans (1978–2012)
Volume 103, Issue C7, pages 14451–14510, 29 June 1998
How to Cite
1998), Monsoons: Processes, predictability, and the prospects for prediction, J. Geophys. Res., 103(C7), 14451–14510, doi:10.1029/97JC02719., , , , , , and (
- Issue published online: 20 SEP 2012
- Article first published online: 20 SEP 2012
- Manuscript Accepted: 25 SEP 1997
- Manuscript Received: 12 JUL 1996
The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.