The development of a coupled data assimilation system using only surface pressure observations: potential for decadal climate prediction initialization

Initializing climate models for decadal prediction is a major challenge, in part due to the lack of long-term subsurface ocean observations and the changing nature of observing systems. In order to overcome these limitations, we have developed a novel method for initializing a climate model for decadal prediction. Using GFDL’s next-generation prediction system, we developed a coupled ensemble data assimilation system, which assimilated only surface pressure observations, since the surface pressure measurements have been made since the late 1800s. Physically, by assimilating high-frequency surface pressure observations we constrain the model to experience a sequence of wind and storms, and thus surface fluxes, that is very similar to what is observed. The hypothesis is that by having the ocean component of the coupled model experience a very similar sequence of surface fluxes as observations, the ocean component of the coupled model will gradually reproduce the same variations as the observed system.

We evaluate the potential of such a system using a perfect model framework, in which surface pressure “observations” from one model simulation (the control) were assimilated into an independent simulation (the assimilation run). The method is successful if the assimilation run gradually moves closer to the control. In coupled assimilation experiments we find this method to be very successful. Major decadal climate variations in the assimilation run, including the Pacific Decadal Oscillation and the Atlantic Multi-Decadal Oscillation (AMO), converge to the control simulation, solely as a result of the information coming from surface pressure assimilation. Interestingly, decadal variations of the Atlantic Meridional Overturning Circulation (AMOC) in the assimilation run were able to reproduce those in the control simulation. The feasibility of using this method to initialize coupled climate models for realistic decadal predictions will be discussed in the talk.