Heatwaves are among the deadliest natural hazards, yet their evolving atmospheric drivers remain unclear. We apply an unsupervised machine learning model to reanalysis data to identify multivariate heatwave patterns over Europe. The model captures seasonally distinct regimes and reveals significant shifts between historical and recent events across all seasons, highlighting changes in heatwave dynamics over time.

The El Niño-Southern Oscillation (ENSO) drives short-term global plant changes, but its influence on decadal changes is not fully known. Using global climate models, we found that plants show pronounced ENSO-driven changes on decadal and longer timescales. Slow plant responses are influenced by soil water content and plant dynamics but only have a weak effect on the plant’s net carbon uptake. Our work shows that vegetation memory could be an important source of decadal climate predictability.

Decadal forecasts use models to predict near-term natural and human-induced climate changes. Large scale pressure patterns affect surface impacts, but in summer they are hard to predict. North Atlantic surface temperatures are known to influence these patterns in summer. We find that a large set of retrospective decadal forecasts can predict this ocean-atmosphere interaction but also underestimates it, consistent with other predictable North Atlantic pressure patterns in climate forecasts.

This research investigates the interactions between the Atlantic Meridional Overturning Circulation (AMOC) and the Antarctic Ice Sheet, which are both recognized as critical climate tipping elements in the Earth system. We conduct a computer simulation with artificial freshwater input in the North Atlantic to collapse the AMOC for 1500 years. Our findings show no destabilization of the Antarctic Ice Sheet induced by changes in the Southern Ocean during this period.