Quantifying statistical associations among persistent events: Interval coincidence analysis between Northern hemisphere heatwaves and different types of circulation anomalies
Bibliographic record
Abstract
Statistical associations between variables of interest are commonly assessed by applying similarity measures like Pearson correlation to corresponding observational time series. Most traditional measures focus on continuous variables and their associated complete variability, while there is a vast amount of practical examples where only times with specific conditions (e.g. extreme events) are of interest. For the latter cases, concepts like event synchronization strength or event coincidence rates have been introduced as proper similarity measures, and have proven their broad applicability across many areas of research. However, recent work has shown that such event based similarity measures may have conceptual as well as practical limitations when studying co-occurrence statistics between temporally clustered or extended events that do not meet the common assumption of serially uncorrelated point processes.In this work, we introduce and discuss a straightforward extension of event coincidence analysis (ECA) to studying statistical associations between sequences of persistent events, which we tentatively call interval coincidence analysis (InCA). Here, each event of interest corresponds to a well-defined time interval, and the discrete counts of event co-occurrences in ECA are replaced by the fractions of time during which event intervals in two sequences mutually overlap. A statistical significance test for the obtained interval coincidence rates is realized by block bootstrapping event and non-event intervals, retaining the event duration and waiting time distributions of the persistent events in both sequences.We demonstrate the practical potentials of InCA, as well as its similarities and differences with ECA, for a specific case study on atmospheric dynamics. Specifically, we apply both methods to studying the likelihood of co-occurrences between boreal summer (June to August) heatwaves in different parts of the Northern hemisphere and hemispheric anomalies of the atmospheric circulation, such as a jet stream pattern exhibiting two distinct wind bands known as double-jet. Our analysis reveals large-scale regions of markedly elevated likelihood of co-occurrences over Northern Europe, Central to Eastern Siberia, Northeastern Canada as well as the Middle East, Eastern China, the Southwestern and Northeastern United States and Northwest Africa, indicating a particular vulnerability of those regions to the presence of double-jet patterns.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".