Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift
Since near-term predictions of present-day climate are controlled by both initial condition predictability and boundary condition predictability, initialized prediction experiments aim to augment the external-forcing-related predictability realized in uninitialized projections with initial-condition-related predictability by appropriate observation-based initialization. However, and notwithstanding the considerable effort expended in finding such “good” initial states, a striking feature of current, state-of-the-art, initialized decadal hindcasts is their tendency to quickly drift away from the initialized state, with attendant loss of skill. We derive a dynamical model for such drift, and after validating it we show that including a recalibrated version of the model in a postprocessing framework is able to significantly augment the skill of initialized predictions beyond that achieved by a use of current techniques of postprocessing alone. We also show that the new methodology provides further crucial insights into issues related to initialized predictions.