Evaluation of Seasonal Climate Predictability Considering the Duration of Climate Indices

Abstract

This study examines the long-term climate predictability in the Seomjin River basin using statistical methods, and explores the effects of incorporating the duration of climate indices as predictors. A multiple linear regression model is employed, utilizing 44 climate indices as predictors, including global climate patterns and local meteorological factors specific to the area. The analysis focuses on teleconnections between the target variables and climate indices, considering the value of each index, not only for the corresponding month, but also for an average value over a duration of 2 and 3 months. The correlation analysis reveals that considering the duration of climate indices allows for the inclusion of predictors with higher correlation, leading to improved forecasting accuracy. The goodness of fit analysis, which compares predicted mean values with observed values on a monthly basis, indicates that neither precipitation nor temperature is significantly affected by the duration. However, the tercile hit rate analysis, comparing the results with historical data, shows a 34.7% hit rate for precipitation, both before and after, reflecting the duration of indices. Notably, for long lead times (10–12 months), the hit rate improves after incorporating the duration. In contrast, for temperature, the tercile hit rate is higher before considering the duration. Nonetheless, both precipitation and temperature exhibit hit rates higher than the baseline probability of 33.3%, affirming the reliability of long-term forecasts in the Seomjin River basin. Incorporating the duration of climate indices enhances the selection of predictors with higher correlation, resulting in a notable impact on long-lead precipitation forecasting. However, since temperature demonstrates little irregularity and displays a consistent pattern according to the month and season, the effect of considering the duration is relatively insignificant compared to precipitation. Future research will explore the decrease in hit rate due to reflecting the duration in temperature by extending the analysis to other regions.

Keywords

• seasonal forecasting
• teleconnection
• multiple linear regression model
• climate indices