Development of a four‐dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations

Abstract

A four‐dimensional variational (4D‐VAR) data assimilation system using a coupled ocean‐atmosphere global model has been successfully developed with the aim of better defining the dynamical states of the global climate on seasonal to interannual scales. The application of this system to state estimations of climate processes during the 1996–1998 period shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector (such as the El Niño, the Indian Ocean dipole, and the Asian summer monsoon) are significantly improved. This fact suggests that our 4D‐VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor on the basis of observational data. In addition, the coupling parameters that control the air‐sea exchange fluxes of mass, momentum, and heat become well adjusted. Such an initialization using the 4D‐VAR CDA approach allows us to make a roughly 1.5‐year lead time prediction of the 1997–1998 El Niño event. These results demonstrate that our 4D‐VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena.