Asymmetrical Impact of El Niño to La Niña Transition in the Tropical Pacific Basin on Hot Extremes over India
Bibliographic record
Abstract
Several studies have identified the significant impact of El Niño on summer season (March to May) temperature extremes over India. The current study identifies an asymmetry in the impact of ENSO on the hot extremes over the Indian region. During some years, the El Niño peak occurs in the November-December-January (NDJ) season and is followed by a transition to a cold phase of ENSO during the summer season. In such years, significantly more Indian districts experience extreme temperature anomalies in summer months compared to other ENSO years. This study focuses on bringing out the geo-spatial characterisation of this asymmetry with few dynamical pointers of its causes. It is found that the asymmetry in heatwave impact is related to two types of ENSO transitions: (a) El Niño followed by La Niña (ELLA) and (b) El Niño not followed by La Niña (ELNLA). It is also found that Eastern Pacific (Central Pacific) El Niño events dominate the ELLA (ELNLA) events. Over the Indian Ocean, distinct warming over the Bay of Bengal and the South Indian Ocean is also noted during ELLA years as compared to ELNLA years. In the ELLA years, the monthly maximum and minimum temperature conditions are much higher, with lower day-night temperature differences over a large number of districts than those of the ELNLA years during the winter and in the summer months, leading to increased discomfort. The uneven impact of ENSO on heatwaves can be used to explore region-specific risk mitigation strategies across the Indian subcontinent.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".