Future projections of precipitation and temperature extremes at Sohra (Cherrapunji) using Statistical Downscaling Model
Bibliographic record
Abstract
The Statistical Downscaling Model (SDSM 4.2) is used to project the future precipitation and maximum and minimum temperatures at Sohra, one of the extreme places on earth, using the predictors of the Second-Generation Canadian Earth System Model (CanESM2). The SDSM was calibrated with daily precipitation and temperature data from 1979 to 2005 and validated from 2006 to 2020. Future scenarios generated under the three Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 are divided into three future periods, Near Future (2021-2040), Mid Future (2041-2071), and Far Future (2071-2100). It is found that the precipitation and maximum/minimum temperature at Sohra are influenced mainly by the major global predictors; specific humidity at 850 hPa height (s850) and mean temperature at 2 m (temp)/near surface specific humidity (shum), respectively. The downscaled result reveals an increase in Monsoon precipitation in the range of 266-1543 mm under various RCPs compared with the base periods 1985-2005 during the Near Future and 1979-2008 during the Mid and Far Future. Also, annual maximum and minimum temperature increases in the range of 1-2.8 °C and 1.2-3.6 °C for all RCPs in the future.
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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 | 0.001 |
| 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) | 0.001 | 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".