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

TitleEnhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift
Publication TypeJournal Article
Year of Publication2019
JournalGeophysical Research Letters
Date Published08/2019
Abstract / Summary

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.

URLhttp://dx.doi.org/10.1029/2019gl084223
DOI10.1029/2019gl084223
Journal: Geophysical Research Letters
Year of Publication: 2019
Date Published: 08/2019

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.

DOI: 10.1029/2019gl084223
Citation:
Nadiga, B, T Verma, W Weijer, and B Nadiga.  2019.  "Enhancing Skill of Initialized Decadal Predictions Using a Dynamic Model of Drift."  Geophysical Research Letters.  https://doi.org/10.1029/2019gl084223.