14B.2 Impact of new ocean initial conditions on seasonal forecasts with the POAMA coupled model

Thursday, 27 January 2011: 3:45 PM
609 (Washington State Convention Center)
Mei Zhao, Bureau of Meteorology, Melbourne, Australia; and H. H. Hendon, Y. Yin, and O. Alves

We assess the impact of improved ocean initial conditions for predicting ENSO and IOD (Indian Ocean Dipole) using the Bureau of Meteorology's POAMA coupled model for the period 1982-2006. The new ocean initial conditions are provided by an ensemble based system that assimilates sub-surface temperatures and salinity and which is a clear improvement over the previous system which used static error covariances and was univariate (temperature only). Two pairs of hindcasts are assessed, where in each pair the only difference was ocean initial conditions. The first pair used the POAMA version 1 model but with one set based on the old univariate ocean initial conditions and the other based on the new ocean initial conditions. The second pair used the new POAMA2 model, with one set based on the new ocean initial conditions and the other based on an assimilation using the new ensemble system but withholding all observed data. For both pairs of experiments, forecasts based on the new ocean initial conditions have better skill at predicting ocean surface temperature variations associated with ENSO, and this increased skill is associated with improved skill for predicting subsurface temperature variations (the “effective memory” of the system) throughout the tropical Pacific. However, improved ocean initial conditions do not translate into improved skill for predicting the IOD. Improved skill at predicting subsurface temperature variations is demonstrated south of the equator in the Indian Ocean, presumably associated with slow westward propagating Rossby waves, but skill for near-equatorial subsurface temperature variations associated with the IOD skill drops off quickly in both cases. This behavior suggests that potential predicability of IOD is much smaller than for ENSO due to the shorter memory within the subsurface.
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