Enhancing Forecast Skill of Winter Temperature of East Asia Using Teleconnection Patterns Simulated by GloSea5 Seasonal Forecast Model

• Published in: Atmosphere Vol. 14; no. 3; p. 438
• Main Authors: Lee, Yejin, Kim, Ha-Rim, Noh, Namkyu, Kim, Ki-Young, Kim, Baek-Min
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Atmospheric temperature; Climate; Correlation coefficient; Correlation coefficients; Dynamic models; Environmental aspects; GloSea5; Ice; Methods; Modelling; multiple linear regression; Performance evaluation; Regression analysis; Regression models; REOF; Root-mean-square errors; Seasonal forecasting; Statistical analysis; Statistics; Surface temperature; Teleconnection patterns; Teleconnections; Teleconnections (Climatology); Temperature; Variables; Weather forecasting; Winter; Winter temperatures; East Asia; United Kingdom > UK
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14030438

GloSea5, a seasonal forecast system of the UK Met Office, shows reasonable skill among state-of-the-art operational seasonal forecast systems. However, the average surface temperature (T2m) in winter (December–February) of GloSea5 is particularly low in East Asia. To improve the seasonal forecast skill over East Asia, we focused on the high skill score of global teleconnection patterns simulated by GloSea5. Among the well-predicted teleconnection patterns, we selected those highly correlated with the East Asian T2m: East Atlantic (EA), Polar/Eurasia (PE), East Atlantic/Western Russia (EAWR), and West Pacific (WP) patterns. A multiple linear regression model was constructed using the selected teleconnection indices as predictors. These results are promising. The statistical skill-score evaluation of the constructed linear regression model using the anomaly correlation coefficient (ACC), root mean squared error (RMSE), and mean-squared skill score (MSSS) showed an improvement in the predicted T2m of East Asia, where the values of ACC and MSSS increased by 0.25 and 0.37, respectively, and the RMSE decreased by 0.63 compared to the dynamic forecast model results. These results suggest that a well-designed combined statistical and dynamical approach for seasonal prediction can be beneficial for some regions where the predictability of the dynamic model exhibits a low value.