Dynamical-statistical method for seasonal forecasting of wintertime PM10 concentration in South Korea using multi-model ensemble climate forecasts

Climate conditions and emissions are among the primary influences on seasonal variations in air quality. Consequently, skillful climate forecasts can greatly enhance the predictability of air quality seasonal forecasts. In this study, we propose a dynamical-statistical method for seasonal forecasting of particulate matter (PM 10 ) concentrations in South Korea in winter using climate forecasts from the Asian Pacific Climate Center (APCC) multi-model ensemble (MME). We identified potential climate predictors that potentially affect the wintertime air quality variability in South Korea in the global domain. From these potential climate predictors, those that can be forecasted skillfully by APCC MME were utilized to establish a multiple-linear regression model to predict the winter PM 10 concentration in South Korea. As a result of evaluating the forecast skill through retrospective forecasts for the past 25 winters (1995/96-2019/20), this model showed statistically significant forecast skill at a lead time of a month to a season. The skill of PM 10 forecast from the MME was overall better than that from a single model. We also found that it is possible to improve forecast skills through optimal MME combinations.