In this study, a novel climatological analysis of the linkage between clear air turbulence and synoptic weather features is presented based on a combination of aircraft measurements and feature detection in reanalysis data. Detailed hypotheses are put forward on the physical processes leading to the generation of turbulence in the vicinity of Rossby wave breaking and warm conveyor belts. The results can be useful for aviation safety and turbulence forecasting in operational settings, but also for future research into the fundamental mechanisms behind clear air turbulence and its spatio-temporal variability.

This study adopts the concept of atmospheric rivers, which is often applied in the extratropics, in an innovative manner to study the synoptic moisture fluxes in tropical East Africa, a region that stands out in terms of its inter-annual rainfall variability with large socio-economic impacts. In doing so, the authors are able to provide critical insights into the mechanisms underlying this rainfall variability and connect inter-annual changes to shorter time scales and specific synoptic events, including tropical cyclones and phases of the Madden-Julian Oscillation.

This study introduces an original methodology combining ensemble boosting and conditional probability theory to estimate return periods of rare climate extremes without relying on long climate simulations. The method is rigorously developed, validated on a red-noise process, and applied to CESM2 simulations, including an analysis of the 2021 Pacific Northwest heatwave. Importantly, this framework enables linking probability estimates to specific climate storylines, allowing an assessment of the odds that an extreme event like the one examined will occur in the future.

The occurrence and predictability of extreme events is often modulated by regional dynamical drivers, but their identification is not straightforward. This paper by Fiona Spuler et al. presents an innovate approach to dimensionality reduction targeted on regionally occurring phenomena. Their approach is based on a machine-learning method demonstrating, in a broad sense, that these method can identify physically interpretable drivers of targeted phenomena. The authors discuss the tradeoff between regime informativeness of local precipitation extremes and predictability of the regimes at subseasonal lead times. The novel machine-learning-based approach may find useful application beyond atmospheric and climate science.