A new Asian/North American teleconnection linking clustered extreme precipitation from Indian to Canada
Bibliographic record
Abstract
Abstract Three consecutive precipitation extremes emerged in November 2021, including India-Sri Lanka flooding, East Asian blizzard, and Canadian floods. Why the catastrophic events occurred successively and whether they will become more frequent as global warming continues are unknown. Here we show they are organized by an intraseasonal Asian/North American (ANA) teleconnection consisting of two cross-Pacific wave trains fortified by dipolar diabatic heating anomalies (“wet India-dry Philippines”). The dipolar heating anomaly is shaped by multi-scale interaction between a quasi-stationary Madden-Julian Oscillation (MJO) episode and a rapidly developed La Niña over the tropical Asian monsoon region. Numerical experiments suggest that the off-equatorial heating dipole can generate the ANA pattern resembling observations, distinct from the equatorial MJO-induced teleconnection and the La Niña-induced Pacific/North American teleconnection. Philippine cooling stimulates the circum-Pacific wave train, while Indian heating produces the eastward-propagating subtropical wave train. These wave trains persistently steered cross-Pacific atmospheric rivers channeling warm-moisture-laden air to the extratropics. We suggest that the ANA teleconnection could be a new route by which multi-scale interaction between the La Niña and quasi-stationary MJO over the tropical Asian monsoon affects extratropical East Asia and North America. This work provides a unique perspective on understanding the origins of increasing collisions of extremes worldwide within a short time as the global climate warms.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".