Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models

• Main Authors: Weirich-Benet, Elizabeth, Pyrina, Maria, id_orcid:0 000-0002-4890-0732, Jiménez Esteve, Bernat, id_orcid:0 000-0003-2706-5309, Fraenkel, Ernest, Cohen, Judah, Domeisen, Daniela, id_orcid:0 000-0002-1463-929X
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Meteorological Society 2023
• Subjects: Extreme events; Forecasting; Heat wave; Machine learning; Subseasonal variability; Teleconnections; North Atlantic
• Online Access: https://hdl.handle.net/20.500.11850/609655; https://doi.org/10.3929/ethz-b-000609655

Heatwaves are extreme near-surface temperature events that can have substantial impacts on ecosystems and society. Early warning systems help to reduce these impacts by helping communities prepare for hazardous climate-related events. However, state-of-the-art prediction systems can often not make accurate forecasts of heatwaves more than two weeks in advance, which are required for advance warnings. We therefore investigate the potential of statistical and machine learning methods to understand and predict central European summer heatwaves on time scales of several weeks. As a first step, we identify the most important regional atmospheric and surface predictors based on previous studies and supported by a correlation analysis: 2-m air temperature, 500-hPa geopotential, precipitation, and soil moisture in central Europe, as well as Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream. Based on these predictors, we apply machine learning methods to forecast two targets: summer temperature anomalies and the probability of heatwaves for 1–6 weeks lead time at weekly resolution. For each of these two target variables, we use both a linear and a random forest model. The performance of these statistical models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. For lead times longer than two weeks, our machine learning models compete with the ensemble mean of the European Centre for Medium-Range Weather Forecast’s hindcast system. We thus show that machine learning can help improve subseasonal forecasts of summer temperature anomalies and heatwaves. Significance Statement Heatwaves (prolonged extremely warm temperatures) cause thousands of fatalities worldwide each year. These damaging events are becoming even more severe with climate change. This study aims to improve advance predictions of summer heatwaves in central Europe by using statistical and machine learning methods. Machine learning models are shown to compete with ...