An Assessment of the South Asian Summer Monsoon Variability for Present and Future Climatologies Using a High Resolution Regional Climate Model (RegCM4.3) under the AR5 Scenarios
Bibliographic record
Abstract
We assessed the present and future climatologies of mean summer monsoon over South Asia using a high resolution regional climate model (RegCM4) with a 25 km horizontal resolution. In order to evaluate the performance of the RegCM4 for the reference period (1976–2005) and for the far future (2070–2099), climate change projections under two greenhouse gas representative concentration pathways (RCP4.5 and RCP8.5) were made, the lateral boundary conditions being provided by the geophysical fluid dynamic laboratory global model (GFDL-ESM2M). The regional climate model (RCM) improves the simulation of seasonal mean temperature and precipitation patterns compared to driving global climate model (GCM) during present-day climate conditions. The regional characteristic features of South Asian summer monsoon (SASM), like the low level jet stream and westerly flow over the northern the Arabian Sea, are well captured by the RegCM4. In spite of some discrepancies, the RegCM4 could simulate the Tibetan anticyclone and the direction of the tropical easterly jet reasonably well at 200 hPa. The projected temperature changes in 2070–2099 relative to 1976–2005 for GFDL-ESM2M show increased warming compared to RegCM4. The projected patterns at the end of 21st century shows an increase in precipitation over the Indian Peninsula and the Western Ghats. The possibilities of excessive precipitation include increased southwesterly flow in the wet period and the effect of model bias on climate change. However, the spatial patterns of precipitation are decreased in intensity and magnitude as the monsoon approaches the foothills of the Himalayas. The RegCM4-projected dry conditions over northeastern India are possibly related to the anomalous anticyclonic circulations in both scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".