Sensitivity of Precipitation to Aerosol and Temperature Perturbation over the Foothills of the Nepal Himalayas
Bibliographic record
Abstract
Increasing the amount of anthropogenic aerosols over the Himalayas modulate cloud properties, thereby altering cloud phase and cloud height, consequently influence formation and distribution of orographic precipitation. Moreover, further rises in global temperature may influence cloud properties by the ‘Clausius—Clapeyron effect’, which increases moisture holding capacity of air. This study presents sensitivity of simulated cloud properties to aerosol and temperature perturbations using the Weather Research and Forecasting (WRF) model, coupled with a bulk microphysics scheme, in a convection permitting configuration applied to a complex topographical region, the Nepal Himalayas. We find that the effect of aerosol on the simulated rainfall is nonlinear, ranging from −3% to +4% depending on the investigated aerosol perturbation scenarios. The model results highlight a realistic simulation of the 1st indirect (Twomey) effect. However, the rainfall was not overly sensitive to the aerosol perturbations and not statistically significant at the 95% confidence interval. The oversimplified parameterization of ice phase processes, a dominant cloud formation process over the Himalayas, appears to play a crucial role in buffering the sensitivity to increased aerosol loading. Our results, however, show that aerosol perturbations may modify shape, size and spatial distribution of individual cloud and their precipitation production. In contrast, the impact of temperature perturbations is more than the aerosol effect, ranging from −17% to +93%, which is statistically significant at the 95% confidence interval, suggesting that more intense rain events are likely as the climate warms in this region.
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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 | 0.001 |
| 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 | 0.001 | 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".