Correction of Systematic Errors in Coupled GCM Forecasts

Abstract

The prognostic tendency (PT) correction method is applied in an attempt to reduce systematic errors in coupled GCM seasonal forecasts. The PT method computes the systematic initial tendency error (SITE) of the coupled model and subtracts it from the discrete prognostic equations. In this study, the PT correction is applied only to the three-dimensional ocean temperature. The SITE is computed by calculating a climatologically averaged difference between coupled model initial conditions and resulting forecasts at very short lead times and removing the observed mean seasonal tendency. Two sets of coupled GCM forecasts, one using an annual mean SITE correction and the other using a SITE correction that is a function of season, are compared with a control set of uncorrected forecasts. Each set consists of 17 12-month forecasts starting on 1 January from 1980 through 1996. The PT correction is found to be an effective method for maintaining a more realistic forecast climatology by reducing systematic ocean temperature errors that lead to a relaxation of the tropical Pacific thermocline slope and a weak tropical SST annual cycle in the control set. The annual mean PT correction, which allows the model to freely generate its own seasonal cycle, leads to increased prediction skill for tropical Pacific SSTs while the seasonally varying PT correction has no impact on this skill. Physical mechanisms responsible for improvements in the coupled model?s annual cycle and forecast skill are investigated. The annual mean structure of the tropical Pacific thermocline is found to be essential for producing a realistic SST annual cycle. The annul mean PT correction helps to maintain a realistic thermocline slope that allows surface winds to impact the annual cycle of SST in the eastern Pacific. Forecast skill is increased if the coupled model correctly captures dynamical modes related to ENSO. The annual mean correction leads to a model ENSO that is best characterized as a delayed oscillator mode while the control model appears to have a more stationary ENSO mode; this apparently has a positive impact on ENSO forecast skill in the PT corrected model.