ENSO in a hybrid coupled model: Sensitivity to physical parameterizations and prediction with piggyback data assimilation

Hsin-hsin Syu and J. David Neelin

Abstract. A hybrid coupled model (HCM) for the tropical Pacific ocean-atmosphere is employed for ENSO simulation and prediction, consisting of the Geophysical Fluid Dynamics Laboratory ocean general circulation model coupled to the empirical atmospheric model of Syu, et al., (1995). The standard version of the HCM exhibits spatial and temporal evolution that compare well to observations, with irregular cycles that tend to exhibit 3- and 4-year frequency-locking behavior.

The impacts of ocean vertical mixing schemes and atmospheric spin-up time on ENSO period are investigated. Effects in the vertical mixing parameterization that produce stronger mixing in the surface layer give a longer inherent ENSO period, suggesting model treatment of vertical mixing is crucial to the ENSO problem. Although the atmospheric spin-up time scale is short compared to ENSO time scales, it also has a significant effect in lengthening the ENSO period. This suggests that atmospheric time scales may not be truly negligible in quantitative ENSO theory.

In HCM hindcast experiments, a correlation skill competitive to other prediction models is obtained, so we use this system to examine the effects of several initialization schemes on ENSO forecasts. Initialization with wind stress data and initialization with wind stress reconstructed from SST using the atmospheric model give comparable skill levels. Examination of subsurface heat content anomalies in these cases and a case forced only by the difference between observed and reconstructed winds suggests that at the current level of prediction skill, the crucial wind components for initialization are those associated with the slow ENSO mode, rather than with atmospheric internal variability. A "piggyback" suboptimal data assimilation is tested in which the Climate Prediction Center data assimilation product from a related ocean model is used to correct the ocean initial thermal field. This yields improved skill, suggesting that not all ENSO prediction systems need to invest in costly data assimilation efforts.

Acknowledgements. This work was supported in part by National Oceanographic and Atmospheric Administration grant NA56GP-0451 and National Science Foundation grant ATM-9521389. Computations were carried out at the National Center for Atmospheric Research (NCAR), which is sponsored by the National Science Foundation, and at the Florida State University Computer Center, sponsored by the NOAA Climate and Global Change Program.