Improving the seasonal forecast by utilizing the observed relationship between the Arctic Oscillation and Northern Hemisphere surface air temperature

Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from obs...

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Bibliographic Details
Published in:Environmental research letters 2024-07, Vol.19 (7), p.74039
Main Authors: Sim, Ji-Han, Kwon, MinHo, Jang, Yeon-Soo, Kim, Ha-Rim, Kim, Ju Heon, Yang, Gun-Hwan, Jeong, Jee-Hoon, Kim, Baek-Min
Format: Article
Language:English
Subjects:
Air temperature
Arctic Oscillation
Autumn
Climate models
Climate prediction
Correlation coefficient
Correlation coefficients
Dynamic models
multiple linear regression
Northern Hemisphere
Prediction models
Regression models
Sea ice
Sea surface temperature
seasonal forecast
Skills
Snow cover
surface air temperature
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Summary:Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: (1) correction of the AO impact on SAT pattern, and (2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively.
ISSN:1748-9326
1748-9326
DOI:10.1088/1748-9326/ad545b