Increase in European summer heatwaves driven by greenhouse gases and amplified by aerosol emission reductions

Abstract More frequent heatwaves in Europe are posing considerable risks to human health, infrastructure,
and ecosystems. However, the contributions of external forcing factors such as well-mixed greenhouse
gases (GHGs) and aerosols remain to be better quantified. Here, using model outputs from the Large
Ensemble Single Forcing Model Intercomparison Project (LESFMIP), a recent atmospheric reanalysis
and a machine learning method – self-organising maps (SOMs), we attribute European heatwave
trends during 1940-2020 to various external forcings. The Europe-averaged heatwave trend during
1940-2020 (0.87 days per decade) is well captured by the multi-model mean (MMM) response with
GHGs dominating the trend. The positive heatwave trend in GHGs and ozone is more than cancelled
out by the effects of aerosols during 1940-1979, leading to weak negative heatwave trends. In contrast,
the increase in GHGs has driven about half (53±17%%) of the strong heatwave trends in 1980-2020 (2.5
days per decade), amplified by the reduction in aerosols (23±15%%). This highlights the increasing risk of
heatwave increase in Europe if GHG emissions continue to rise without significant mitigation measures.
Analysis of atmospheric circulation by SOMs reveals that four major atmospheric circulation patterns,
dominated by a blocking high anomaly, are linked to the most spatially-intense European summer
heatwaves. A relatively large increase in the occurrence of blocking-like atmospheric circulation has
likely exacerbated heatwave trends in Southern and Eastern Europe in 1980-2020. However, this
atmospheric circulation trend is much weaker in the model response, and also seems to be outside the
internal variability in most of the models. This may partly explain the underestimated heatwave trends
in Southern and Eastern Europe. Constraining and further understanding of the thermodynamic and
dynamic response in the LESFMIP models is important for attributing and predicting the multi-annual
and decadal variability of climate and weather extremes.