Atmospheric blocking is a recurring phenomenon in midlatitudes, causing winter cold spells and summer heat waves. Current models underestimate it, hindering understanding of global warming's impact on extremes. In this paper, we investigate whether stochastic parameterizations can improve blocking representation. We find that blocking frequency representation slightly deteriorates, following a change in midlatitude winds. We conclude by suggesting a direction for future model development.

This study introduces a novel deep learning (DL) approach to analyze how regional radiative forcing in Europe impacts the Arctic climate. By integrating atmospheric poleward energy transport with DL-based clustering of atmospheric patterns and attributing anomalies to specific clusters, our method reveals crucial, nuanced interactions within the climate system, enhancing our understanding of intricate climate dynamics.

Latent heating due to condensation can influence atmospheric circulation by strengthening or weakening horizontal temperature contrasts. Strong temperature contrasts intensify storms and imply the existence of strong upper tropospheric winds called jets. It remains unclear whether latent heating preferentially reinforces or abates the existing jet. We show that this disagreement is attributable to how the jet is defined, confirming that latent heating reinforces the jet.