Predictability of weather and climate

Abstract:

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 offset 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%; MMM and model-spread) 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 more frequent heatwaves 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.

Abstract:

Abstract We correct an error relating to the comparison of precipitation variability in coupled models versus models run with prescribed SSTs (“AMIP models”) and discuss what conclusions to draw from the corrected result.

Abstract:

Combining climate models with statistical learning allows an assessment of the relative contributions of different factors to trends in winter precipitation at mid-latitudes. Thermodynamic (non-circulation-related) effects are mostly consistent between models and observations, but whether circulation-related changes are forced or unforced remains unclear.

Abstract:

Understanding changes in precipitation is crucial for society and ecosystems. Studies have documented the respective contributions of anthropogenic forcing and internal variability to precipitation trends, yet discrepancies persist between observed and simulated patterns. In Northern Hemisphere winter, these mismatches are often attributed to unforced internal variability that dominates observed trends. However, growing evidence also indicates that climate models underestimate the total response of precipitation to human forcings. Here we show that the thermodynamic contribution is broadly reproduced by climate models, whereas the dynamic contribution can diverge more substantially. Our approach disentangles the anthropogenic forced thermodynamic and dynamic components from internal variability in winter precipitation trends (1950-2022) to investigate their contribution to the trend discrepancies. In the Mediterranean, the forced dynamic signal from model simulations explains only about 10% of the observed dynamic trend, making detection challenging. Under continued anthropogenic emissions, the projected circulation response intensifies and more closely resembles observed trend patterns. Although internal variability in the observed record may contribute to this similarity, the results indicate an uncertain yet potentially emerging role of dynamic response in shaping regional winter precipitation trends. A reliable representation of the forced large-scale circulation response in climate models remains key for increasing confidence in regional precipitation projections.