Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift

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AbstractSince 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.