Impact of Climate Change on Past Indian Monsoon and Circulation: A Perspective Based on Radiogenic and Trace Metal Geochemistry
Bibliographic record
Abstract
The Indian summer monsoon (ISM), one of the dramatic illustrations of seasonal hydrological variability in the climate system, affects billions of lives. The ISM dominantly controls the northern Indian Ocean sea-surface salinity, mostly in the Bay of Bengal and the Andaman Sea, by the Ganga-Brahmaputra-Meghna and Irrawaddy-Salween rivers outflow and direct rainfall. In the past decade, numerous studies have used radiogenic neodymium (εNd) isotopes of seawater to link Indian subcontinent erosion and the ensuing increase in discharge that results in changes in the north Indian Ocean sea surface. Here we synthesized the state of the ISM and ocean circulation using the neodymium and hafnium isotopes from north Indian Ocean deep-sea sediments. Our data suggest that the Bay of Bengal and north Indian Ocean sea-surface conditions were most likely modulated by changes in the ISM strength during the last glacial-interglacial cycle. These findings contrast to the hypothesis that suggests that the bottom water neodymium isotopes of the northern Indian Ocean were modulated by switching between two distant sources, namely North Atlantic Deep Water and Antarctic bottom water. Furthermore, the consistency between the neodymium and hafnium isotopes during the last glacial maximum and Holocene suggests a weak and dry ISM and strong and wet conditions, respectively. These data also indicate that the primary source of these isotopes was the Himalayas. Our results support the previously published paleo-proxy records, indicating weak and strong monsoons for the same periods. Moreover, our data further support the hypothesis that the northern Indian Ocean neodymium isotopes were decoupled from the global ocean neodymium budget due to the greater regional influence by the great Ganga-Brahmaputra-Meghna and Irrawaddy-Salween discharge draining the Indian subcontinent to the Bay of Bengal and the Andaman Sea.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".