Probabilistic downscaling of GCM scenarios over southern India
Bibliographic record
Abstract
Abstract The cumulative distribution function transform (CDF‐t) is used to downscale daily precipitation and surface temperatures from a set of Global climate model (GCM) climatic projections over southern India. To deal with the full annual cycle, the approach has been applied by months, allowing downscaled projections for all seasons. First, CDF‐t is validated over a historical period using observation from the Indian Meteorological Department (IMD). Resulting high resolution fields show substantial improvements compared to original GCM outputs in terms of distribution, seasonal cycle and monsoon means for arid, semi‐arid and wetter regions of the subcontinent. Then, CDF‐t is applied to GCM large‐scale fields to project rainfall and surface temperature changes for the 21st century under the IPCC SRES A2 scenario. The results obtained show an increase of rainfall, mostly during the monsoon season, while winter precipitation is reduced, and suggest a widespread warming especially in the winter and post‐monsoon season. Copyright © 2012 Royal Meteorological Society
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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 | 0.001 | 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) | 0.003 | 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".