Systematic approach for global identification of extreme weather events associated with atmospheric blockings and subtropical ridges

It is known that some extreme weather events are associated with the appearance of large-scale blocking patterns (e.g. heatwaves and droughts), while others are linked to cut-off low systems that often occur on the southern flanks of the blocking patterns (e.g. extreme precipitation, intense snow storms). These quasi-stationary high-pressure systems disrupt the atmospheric flow, producing significant extreme weather and influencing surface impacts. However, identifying and tracking atmospheric blocks is challenging due to their diverse dynamics.

BLOCS (Blocking Location and Obstruction Cataloguing System) is an open-source, Python-based framework designed to systematically identify, classify, and track atmospheric blocking events. It is based on the state-of-the-art geopotential height gradient methodology (e.g., Sousa et al., 2021) and provides a robust tool applicable to different regular-grid datasets, such as NCEP-NCAR and ERA5. The method captures blocking subtypes (e.g., ridge, omega, Rex) and their life cycles, enabling detailed analyses of their spatial and temporal variability. By integrating customizable parameters, BLOCS can be adapted for studying atmospheric blocking and subtropical ridges under changing climate conditions across diverse datasets (e.g. Coupled Model Intercomparison Project, CMIP; Paleoclimate Modelling Intercomparison Project, PMIP).

Outputs from BLOCS include daily- and event-based catalogues, facilitating the study of blocking dynamics and their influence on extreme weather conditions, such as temperature anomalies and precipitation extremes. BLOCS has been used to analyze historic events like the 2003 European heatwave and the 2010 Russian mega-heatwave, demonstrating its ability to connect blocking patterns to surface impacts. Its applications extend to regional and global studies, enabling users to systematically explore blocking-driven socio-economic and environmental impacts.

By offering a user-friendly community-driven tool, BLOCS can be used to bridge traditional meteorological approaches with data-driven methods, provide a benchmark for assessing the prediction of extreme weather events, address critical gaps in atmospheric blocking research, and ultimately advance our understanding of these phenomena and their role in shaping extreme events.

Acknowledgements: This work is supported by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC): UIDB/50019/2025 and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). M. M. Lima was supported through the PhD MIT Portugal MPP2030-FCT programme grant PRT/BD/154680/2023. Additional support comes from the EU-funded H2020 project CLINT (Grant Agreement No. 101003876), and MALONE (PID2021-122252OB-I00), funded by MICIU/AEI/10.13039/501100011033 and ERDF, EU.