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Abstract
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.
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1 Division of Earth and Environmental System Sciences, Major of Environmental Atmospheric Sciences, Pukyong National University , Busan 48513, Republic of Korea
2 Ocean Climate Prediction Center, Korea Institute of Ocean Science & Technology , Busan 49111, Republic of Korea
3 Center for Climate/Environment Change Prediction Research, Ewha Womans University , Seoul 03760, Republic of Korea
4 Faculty of Earth and Environmental Sciences, Chonnam National University , Gwangju 61186, Republic of Korea