Convection, Terrestrial Recycling and Oceanic Moisture Regulate the Isotopic Composition of Precipitation at Srinagar, Kashmir
Bibliographic record
Abstract
Abstract The moisture transport and precipitation in the western Himalayas is an interplay of two atmospheric systems: Western Disturbances (WDs) and Indian Summer Monsoon (ISM). WDs primarily transport moisture from the Mediterranean Sea and the Atlantic Ocean, while ISM transports moisture from the Arabian Sea and the Bay of Bengal. Local moisture sources also contribute to the regional precipitation budget. The moisture sources can be distinguished by measuring dual isotopic signatures in precipitation. Oxygen and hydrogen isotopic ratios measured in daily precipitation samples collected at Srinagar, Kashmir from March 2015–April 2017, allowed delineating the role of factors like large‐scale moisture transport processes, and local meteorological factors such as temperature, precipitation amount and relative humidity on the precipitation isotope ratios and its effect on the meteoric water line parameters. Isotopic data were further used to estimate the percentage contributions of distinct moisture sources across different seasons and validation of an isotope enabled general circulation model (iEGCM). We found that the time integrated, large‐scale convection over several days, constitutes a major factor governing the isotopic composition of precipitation, while the role of local meteorological parameters is minimal. A box model to simulate the moisture transport and estimate the oceanic versus terrestrially recycled moisture sources to the regional precipitation reveals Arabian sea to be the key moisture source. Finally, on comparing the observed isotopic composition with iEGCM simulation, discrepancies are noted. Results from the present study will enable improving the interpretation of regional paleo‐climate data derived from various proxy records.
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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 | 0.000 | 0.000 |
| 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".