Enhancing Subseasonal Temperature Prediction by Bridging a Statistical Model With Dynamical Arctic Oscillation Forecasting

Minju Kim, Changhyun Yoo, Jung Choi

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This study proposes a hybrid approach to improving subseasonal prediction skills by bridging a conventional statistical model and a dynamical ensemble forecast system. Based on the perfect prognosis method, the phase of the Arctic Oscillation (AO) from the European Centre for Medium-range Weather Forecasts ensemble forecast system is used as a predictor in a composite based statistical model to predict the wintertime surface air temperature in the Northern Hemisphere. The hybrid model, which employs AO phases predicted by the dynamical model for weeks 1–4, generally outperforms the conventional statistical model for lead times of weeks 2–6. The improved skill score is due to the high accuracy of the AO forecast from the dynamical model and the strong lagged connection between the AO and temperature. This study thus lays the groundwork for the potential use of combining climate variability, statistical relation, and dynamical forecasting.

Original languageEnglish
Article numbere2021GL093447
JournalGeophysical Research Letters
Volume48
Issue number15
DOIs
StatePublished - 16 Aug 2021

Bibliographical note

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© 2021. American Geophysical Union. All Rights Reserved.

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