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A targeted correction for arctic oscillation biases in seasonal forecasts.

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Title: A targeted correction for arctic oscillation biases in seasonal forecasts.
Authors: Sim, Ji-Han1 (AUTHOR), Kim, Baek-Min2,3 (AUTHOR) baekmin@pknu.ac.kr, Kim, Ha-Rim1 (AUTHOR), Kim, Gaeun4 (AUTHOR), Lim, A-Young4 (AUTHOR), Jung, Yoo-Rim4 (AUTHOR), Kim, Ju Heon2 (AUTHOR), Kim, Geun Young2 (AUTHOR), Cocke, Steven5 (AUTHOR)
Source: Climate Dynamics. May2026, Vol. 64 Issue 5, p1-10. 10p.
Abstract: Seasonal prediction of Eurasian winter surface air temperature (SAT) is fundamentally limited by systematic errors in representing Arctic Oscillation (AO) teleconnections. Across the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC) Multi-Model Ensemble (MME), most component models misrepresent the impact of the AO on SAT, leading to degraded skill in Eurasian forecasts. To address this deficiency, we applied an AO correction method that replaces the distorted AO–SAT relationship simulated by models with one constrained by observations. The correction systematically enhanced winter SAT prediction skill across all APCC MME models, demonstrating that the improvement is not model-specific but generalizable across the ensemble. Importantly, the magnitude of the SAT anomaly correlation coefficient improvement showed a strong inverse relationship with intrinsic AO skill: models with the weakest ability to capture AO variability experienced the greatest gains (correlation r = − 0.65). This finding highlights that AO correction functions not as a uniform adjustment, but as a targeted remedy for a structural deficiency common to current generation seasonal forecast models. By demonstrating that a targeted correction of the AO yields systematic skill improvements—particularly in models with the poorest representation of Arctic variability—this study establishes a generalizable and physically-motivated post-processing framework for enhancing seasonal climate prediction. [ABSTRACT FROM AUTHOR]
Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A targeted correction for arctic oscillation biases in seasonal forecasts.
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  Data: <searchLink fieldCode="AR" term="%22Sim%2C+Ji-Han%22">Sim, Ji-Han</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Baek-Min%22">Kim, Baek-Min</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> baekmin@pknu.ac.kr</i><br /><searchLink fieldCode="AR" term="%22Kim%2C+Ha-Rim%22">Kim, Ha-Rim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Gaeun%22">Kim, Gaeun</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lim%2C+A-Young%22">Lim, A-Young</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jung%2C+Yoo-Rim%22">Jung, Yoo-Rim</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Ju+Heon%22">Kim, Ju Heon</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kim%2C+Geun+Young%22">Kim, Geun Young</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cocke%2C+Steven%22">Cocke, Steven</searchLink><relatesTo>5</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Climate+Dynamics%22">Climate Dynamics</searchLink>. May2026, Vol. 64 Issue 5, p1-10. 10p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Seasonal prediction of Eurasian winter surface air temperature (SAT) is fundamentally limited by systematic errors in representing Arctic Oscillation (AO) teleconnections. Across the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC) Multi-Model Ensemble (MME), most component models misrepresent the impact of the AO on SAT, leading to degraded skill in Eurasian forecasts. To address this deficiency, we applied an AO correction method that replaces the distorted AO–SAT relationship simulated by models with one constrained by observations. The correction systematically enhanced winter SAT prediction skill across all APCC MME models, demonstrating that the improvement is not model-specific but generalizable across the ensemble. Importantly, the magnitude of the SAT anomaly correlation coefficient improvement showed a strong inverse relationship with intrinsic AO skill: models with the weakest ability to capture AO variability experienced the greatest gains (correlation r = − 0.65). This finding highlights that AO correction functions not as a uniform adjustment, but as a targeted remedy for a structural deficiency common to current generation seasonal forecast models. By demonstrating that a targeted correction of the AO yields systematic skill improvements—particularly in models with the poorest representation of Arctic variability—this study establishes a generalizable and physically-motivated post-processing framework for enhancing seasonal climate prediction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2026
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