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Long-Lead Predictions of Warm Season Droughts in South Korea Using North Atlantic SST
- Long-Lead Predictions of Warm Season Droughts in South Korea Using North Atlantic SST
- Myoung, Boksoon; Rhee, Jinyoung; Yoo, Changhyun
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- JOURNAL OF CLIMATE
- JOURNAL OF CLIMATE vol. 33, no. 11, pp. 4659 - 4677
- North Atlantic Ocean; Sea ice; Teleconnections; Drought; Statistical forecasting; Decadal variability
- AMER METEOROLOGICAL SOC
- SCIE; SCOPUS
- Document Type
- Understanding and predicting warm season (May-October) droughts is critically important in South Korea for agricultural productivity and water resource management. Using a 6-month standardized precipitation index ending in October (SPI6_Oct), we investigate the interannual variability of warm season droughts and the related large-scale atmospheric circulations for the most recent 20-yr period (1995-2014). Cyclonic (anticyclonic) circulations to the east of Japan (in the North Pacific) tend to induce warm season droughts (wetness) by suppressing (enhancing) moist water transport from the south of the Korean Peninsula. These circulation patterns to the east of Japan are linked to a barotropic Rossby wave-like teleconnection pattern from the North Atlantic to East Asia, which is found to be responsible for the interannual variability of SPI6_Oct. This teleconnection pattern is highly correlated with the difference in sea surface temperature (SST) between the Norwegian Sea and the Barents Sea (referred to as NA_dipole) in January-March (r = 0.68), which modulates the snow depth over the Ural Mountains in spring and the sea ice concentration over the Barents Sea during the entire warm season. Two drought prediction models, an empirical model and a hybrid machine learning model, are developed and tested for their predictive skills for South Korea. An empirical prediction model using NA_dipole as one of the predictors is found to accurately capture the interannual variability of SPI6_Oct (r(2) = 53%). NA_dipole is found to improve the predictive skills of the hybrid machine learning drought prediction model, especially for longer lead times. Our results emphasize the significant role of North Atlantic SST anomalies in warm season medium-range droughts in South Korea.
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- 엘텍공과대학 > 기후·에너지시스템공학전공 > Journal papers
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