Machine Learning Models for the Seasonal Forecast of Winter Surface Air Temperature in North America

Abstract

In this study, two machine learning (ML) models (support vector regression (SVR) and extreme gradient boosting (XGBoost)) are developed to perform seasonal forecasts of the surface air temperature (SAT) in winter (December-January-February, DJF) in North America (NA). The seasonal forecast skills of the two ML models are evaluated via cross validation. The forecast results from one linear regression (LR) model, and two dynamic climate models are used for comparison. In the take-one-out hindcast experiment, the two ML models and the LR model show reasonable seasonal forecast skills for winter SAT in NA. Compared to the two dynamic models, the two ML models and the LR model have better forecast skill for the winter SAT over central NA, which is mainly derived from a skillful forecast of the second empirical orthogonal function (EOF) mode of winter SAT over NA. In general, the SVR model and XGBoost model hindcasts show better forecast performances than the LR model. However, the LR model shows less dependence on the size of the training data set than the SVR and XGBoost models. In the real forecast experiments during the period of 2011–2017, the two ML models exhibit better forecasting skills for the winter SAT over northern and central NA than do the two dynamic models. The results of this study suggest that the ML models may provide improved forecasting skill for seasonal forecasts of the winter climate in NA.