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

Abstract

Concentrations of fine particulate matter smaller than 2.5 mu m in diameter (PM2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM2.5 concentrations at 1-3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005-2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM2.5 concentrations. For the wintertime (December-January-February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March-April-May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM2.5 concentration and an extreme metric, i.e., seasonal number of high PM2.5 concentration days.