Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework
This work addresses climate prediction for regional and global futures, representing an incremental advancement in combining existing techniques.
The authors tackled multi-year-to-decadal temperature prediction by developing a machine learning model-analog framework that combines neural networks with analog forecasting, resulting in improved performance over traditional analog methods and initialized decadal predictions.
Multi-year-to-decadal climate prediction is a key tool in understanding the range of potential regional and global climate futures. Here, we present a framework that combines machine learning and analog forecasting for predictions on these timescales. A neural network is used to learn a mask, specific to a region and lead time, with global weights based on relative importance as precursors to the evolution of that prediction target. A library of mask-weighted model states, or potential analogs, are then compared to a single mask-weighted observational state. The known future of the best matching potential analogs serve as the prediction for the future of the observational state. We match and predict 2-meter temperature using the Berkeley Earth Surface Temperature dataset for observations, and a set of CMIP6 models as the analog library. We find improved performance over traditional analog methods and initialized decadal predictions.