Enhanced Himalayan Glacial Melting During YD and H1 Recorded in the Northern Bay of Bengal
Bibliographic record
Abstract
Abstract Ocean‐land thermal feedback mechanisms in the Indian Summer Monsoon (ISM) domain are an important but not well understood component of regional climate dynamics. Here we present a δ18O record analyzed in the mixed‐layer dwelling planktonic foraminifer Globigerinoides ruber (sensu stricto) from the northernmost Bay of Bengal (BoB). The δ18O time series provides a spatially integrated measure of monsoonal precipitation and Himalayan meltwater runoff into the northern BoB and reveals two brief episodes of anomalously low δ18O values between 16.3±0.4 and 16±0.5 and 12.6±0.4 and 12.3±0.4 thousand years before present. The timing of these events is centered at Heinrich event 1 and the Younger Dryas, well‐known phases of weak northern hemisphere monsoon systems. Numerical climate model experiments, simulating Heinrich event‐like conditions, suggest a surface warming over the monsoon‐dominated Himalaya and foreland in response to ISM weakening. Corroborating the simulation results, our analysis of published moraine exposure ages in the monsoon‐dominated Himalaya indicates enhanced glacier retreats that, considering age model uncertainties, coincide and overlap with the episodes of anomalously low δ18O values in the northernmost BoB. Our climate proxy and simulation results provide insights into past regional climate dynamics, suggesting reduced cloud cover, increased solar radiation, and air warming of the Himalaya and foreland areas and, as a result, glacier mass losses in response to weakened ISM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".