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Abstract Summer heatwaves over Europe, which can cause many deaths and severe damage, have become increasingly frequent over central and eastern Europe and western Russia in recent decades. In this paper, we estimate the contributions of the warming due to increased greenhouse gases (GHG) and nonlinear variations correlated with the Atlantic Multidecadal Oscillation (AMO) to the observed heatwave trend over Europe during 1980–2021, when the GHG‐induced warming over Europe exhibits a linear trend. It is found that GHG‐induced warming contributes to ∼57% of the European heatwave trend over 1980–2021, while the cold‐to‐warm phase shift of the AMO‐like variations accounts for ∼43% of the trend via the intensification of midlatitude North Atlantic jet. The recent trend of heatwaves over western and northern Europe is mainly due to GHG‐induced warming, while that over central and eastern Europe and western Russia is primarily related to the combined effect of the AMO‐like variations and GHG‐induced warming. To some extent, GHG‐induced warming is an amplifier of the increasing trend of recent AMO‐related European heatwaves. Moreover, European blocking (Ural blocking, UB) is shown to contribute to 55% (42%) of the AMO‐related heatwave trend via the influence of midlatitude North Atlantic jet. In the presence of a strong North Atlantic jet during the recent warm AMO phase, UB events concurrent with positive‐phase North Atlantic Oscillation can cause intense, persistent and widespread heatwaves over Europe such as that observed in the summer of 2022.
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Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models
Abstract 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 StatementHeatwaves (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 conventional physics-based models for forecasting heatwaves more than two weeks in advance. These early warnings can be used to activate effective and timely response plans targeting vulnerable communities and regions, thereby reducing the damage caused by heatwaves.
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- Award ID(s):
- 2115072
- PAR ID:
- 10406713
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Artificial Intelligence for the Earth Systems
- Volume:
- 2
- Issue:
- 2
- ISSN:
- 2769-7525
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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