Long-Lead Predictions of Warm Season Droughts in South Korea Using North Atlantic SST

Abstract

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 (r2 = 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.

Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula

“…S1 – S2 ). This may be surprising given that some previous studies have shown these two factors to be good predictors of springtime rainfall over the Korean Peninsula (Myoung et al 2020 ), which is an important wet removal mechanism of PM 2.5 concentrations. Moreover, there are studies that find evidence of linkage between the first mode wintertime PM 10 concentrations over the Korean Peninsula and Arctic sea ice (Kim et al 2019 ; Lee et al 2020 ).…”

“…4 ), with a hint of tripole-like SST mode in the North Atlantic including the area south of Greenland. The North Atlantic is known to influence East Asia through changes in atmospheric circulation that exhibit poleward and then equatorward wave trains (e.g., Ok et al 2017 ; Myoung et al 2020 ). In addition, due to the slowly varying nature of the ocean, the correlation patterns remain largely unchanged from September to November (i.e., 3 months ahead) through October–December (2 months ahead) and November to January (1 month ahead), suggesting that the equatorial Atlantic SST exerts a significant influence at seasonal time scale.…”

“…Multiple linear regression model is built for predictions of seasonal mean PM 2.5 concentrations and numbers of high PM 2.5 concentration days during DJF and MAM at lead times of 1–3 months. To identify the predictors, we apply the forward selection stepwise regression approach, which is described in Section 3 (see also Myoung et al 2020 ). Note that the regression model is built after the linear trends are removed respectively from predictors and predictands for each season.…”

Comparison of the opposite behaviours of Korean heatwaves with extreme hot sea surface temperatures in August 2016 and 2022

“…The year 2016 is characterized by a heatwave extreme in Korea with arid and hot weather (Figure 5a,c), which is also well described in Yeh et al (2018). Based on a statistical analysis by Myoung et al (2020), SST conditions over the Atlantic Ocean are associated with more precipitation in 2022 and less precipitation in 2016. However, this study does not fully explain extreme behaviour in both years, especially in August, because it is conducted in 6‐month averaged precipitation.…”

Stochastic Generation of Multisite Streamflow for Future Water Resources Vulnerability Assessments: Application over South Korea

“…A trend line is also presented if the p value of the slope is significant (< 0.10) based on student's t-test. South Korea has experienced a multiyear drought in 2014-2016 (Bae et al, 2019;Myoung et al, 2020). The dry period is properly represented in variations in the frequencies of the node (1,1), but is also manifested in other frequencies.…”