Understanding Cyclonic Compound “Wet” and “Windy” Extremes in the Eastern Mediterranean through Dynamical Systems Theory
Bibliographic record
Abstract
Cyclonic systems in the Eastern Mediterranean often produce compound extremes of heavy precipitation and strong winds, significantly impacting socio-economic systems. This study leverages traditional atmospheric analysis and dynamical systems theory to investigate these “wet” and “windy” extremes (Vakrat and Hochman, 2023). Using the co-recurrence ratio (α; De Luca et al., 2020) and persistence (1/θ; Faranda et al., 2017), we quantify atmospheric state dynamics and link them to extreme weather events. Results reveal that compound extremes exhibit higher co-recurrence and persistence than individual extremes, with anomalies in these metrics increasing the likelihood of extreme weather events by up to 18-fold. A case study of the mid-February 2012 Eastern Mediterranean compound event highlights the role of persistent upper-level dynamics in driving these extremes. Our findings emphasize the value of dynamical systems metrics in enhancing the predictability of compound extremes and their application to other regions and extreme weather events (Hochman et al., 2019). ReferencesDe Luca P, Messori G, Pons FME, Faranda D. Dynamical systems theory sheds new light on compound climate extremes in Europe and Eastern North  America. Quarterly Journal of the Royal Meteorological Society 146: 1636–1650. https://doi.org/10.1002/qj.3757Faranda D, Messori G, Yiou P. Dynamical proxies of North Atlantic predictability and extremes. Scientific Reports 7: 41278. https://doi.org/10.1038/srep41278Hochman A, Alpert P, Harpaz T, Saaroni H, Messori G. 2019. A new dynamical systems perspective on atmospheric predictability: eastern Mediterranean weather regimes as a case study. Science Advances 5(6): eaau0936.  https://doi.org/10.1126/sciadv.aau0936 Vakrat, E. Hochman, A. 2023.Dynamical systems insights on cyclonic compound “wet” and “windy” extremes in the Eastern Mediterranean. Quarterly  Journal of the Royal Meteorological Society 149(757): 3593–3606. https://doi.org/10.1002/qj.4575  
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".