The impact of ocean observations in seasonal climate prediction (Invited)

The ocean provides the most significant memory for the climate system. Hence, a critical element in climate forecasting with coupled models is the initialization of the ocean with states from an ocean data assimilation system. Remotely-sensed ocean surface fields (e.g., sea surface topography, SST, winds) are now available for extensive periods and have been used to constrain ocean models to provide a record of climate variations. Since the ocean is virtually opaque to electromagnetic radiation, the assimilation of these satellite data is essential to extracting the maximum information content. Altimeter observations provide information for forecasts in areas not sampled by the TAO/TRITON mooring array in the equatorial Pacific. More recently, the Argo drifters have provided unprecedented sampling of the subsurface temperature and salinity. Although the duration of this observation set has been too short to provide solid statistical evidence of its impact, there are indications that Argo improves the forecast skill of coupled systems. The GMAO's Ensemble Kalman Filter has been used to assimilate these observations and initialize our coupled system. This presentation will address the impact these different observations have on seasonal climate predictions with the GMAO's coupled model.