Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework

• URL: http://arxiv.org/abs/2502.17583v1
• Date: Mon, 24 Feb 2025 19:07:39 GMT
• Title: Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework
• Authors: M. A. Fernandez, Elizabeth A. Barnes,
• Abstract summary: Multi-year-to-decadal climate prediction is a key tool in understanding the range of potential regional and global climate futures.<n>We present a framework that combines machine learning and analog forecasting for predictions on these timescales.
• Score: 0.9096537342780928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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