Heatwaves are extreme near-surface temperature events that can have substantial impacts on society and biodiversity. Moreover, the intensity, duration, and frequency of heatwaves are increasing at an accelerating rate as a consequence of climate change. Early Warning Systems can help to reduce the impact of heatwaves, as part of climate adaptation programs. However, state-of-the-art prediction systems can often not make accurate predictions of heatwaves more than two weeks in advance, which is required to take action and mitigate the impact of heatwaves. Here, we investigate central European forecasting of summer heatwaves on sub-seasonal timescales of several weeks using statistical and machine learning methods. As a first step, we select a set of atmospheric and surface predictors which are thought to have the largest impact on heatwave prediction based on previous studies and supported by a correlation analysis. Our findings show that at short lead times (1 week) near-surface temperature, 500hPa geopotential, precipitation, and surface soil moisture in central Europe are the most important predictors. At longer lead times (2—6 weeks), Mediterranean and North Atlantic sea surface temperatures, and the North Atlantic jet stream become the most relevant predictors. Secondly, we apply machine learning methods based on these predictors to forecast (1) summer temperature anomalies and (2) the probability of heat waves for 1—6 weeks lead time at weekly resolution. For each of these two types of forecasts (1) and (2) we use both a linear model and a Random Forest model. The performance of these models decays with lead time, as expected, but outperforms persistence and climatology at all lead times. Our machine learning models beat the European Centre for Medium-Range Weather Forecasts (ECMWF) model for lead times of 3 weeks and longer. We show that machine learning models can help extend the forecasting lead time of summer temperature anomalies and heat waves to sub-seasonal scales.
How to cite: Weirich Benet, E., Pyrina, M., Jiménez Esteve, B., Fraenkel, E., Cohen, J., and Domeisen, D.: Predicting Central European summer heatwaves with Machine Learning, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-233, https://doi.org/10.5194/ems2022-233, 2022.
