The NCEP GODAS Ocean Analysis of the Tropical Pacific Mixed Layer Heat Budget on Seasonal to Interannual Time Scales

Abstract

Abstract The mixed layer heat budget in the tropical Pacific is diagnosed using pentad (5 day) averaged outputs from the Global Ocean Data Assimilation System (GODAS), which is operational at the National Centers for Environmental Prediction (NCEP). The GODAS is currently used by the NCEP Climate Prediction Center (CPC) to monitor and to understand El Niño and La Niña in near real time. The purpose of this study is to assess the feasibility of using an operational ocean data assimilation system to understand SST variability. The climatological mean and seasonal cycle of mixed layer heat budgets derived from GODAS agree reasonably well with previous observational and model-based estimates. However, significant differences and biases were noticed. Large biases were found in GODAS zonal and meridional currents, which contributed to biases in the annual cycle of zonal and meridional advective heat fluxes. The warming due to tropical instability waves in boreal fall is severely underestimated owing to use of a 4-week data assimilation window. On interannual time scales, the GODAS heat budget closure is good for weak-to-moderate El Niños. A composite for weak-to-moderate El Niños suggests that zonal and meridional temperature advection and vertical entrainment/diffusion all contributed to the onset of the event and that zonal advection played the dominant role during decay of the event and the transition to La Niña. The net surface heat flux acts as a damping during the development stage, but plays a critical role in the decay of El Niño and the transition to the following La Niña. The GODAS heat budget closure is generally poor for strong La Niñas. Despite the biases, the GODAS heat budget analysis tool is useful in monitoring and understanding the physical processes controlling SST variability associated with ENSO. Therefore, it has been implemented operationally at CPC in support of NOAA’s ENSO forecasting.