Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting

TL;DR

This paper introduces machine learning post-processing methods that leverage ensemble forecast data and observations to improve subseasonal climate predictions of temperature and precipitation over the US, outperforming traditional baselines.

Contribution

It presents novel ML models that utilize ensemble forecast information and spatial data to enhance subseasonal climate predictions, including extreme events, surpassing standard methods.

Findings

ML models outperform climatological and ensemble mean baselines.

Incorporating spatial information improves forecast accuracy.

Model stacking mitigates trade-offs between different approaches.

Abstract

Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and two-meter temperature two weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multi-model approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.