Bibliographic record
Abstract
This research thesis first examines the ability of Community Atmosphere Model (CAM) and Community Climate System Model (CCSM) in simulating the South Asian Monsoon (SAM) summer precipitation in a framework of ensemble. On this basis, the climatic relevant singular vectors (CSVs) perturbation theory is applied to investigate the optimal error growth of SAM seasonal forecast due to the uncertainties in the Pacific and Indian Oceans. Then, the ensemble prediction of SAM constructed by CSVs is evaluated, and further compared with one traditional ensemble method. It is found that CAM4 adequately simulated monsoon precipitation, and considerably reduced systematic errors that occurred in its predecessors, although it tends to overestimate monsoon precipitation when compared with observations. In terms of monsoon interannual variability and its teleconnection with sea surface temperature (SST), CAM4 showed modest skill. In the CCSM4 coupled simulations, several aspects of the monsoon simulation are improved, including the cross-variability of simulated precipitation and SST. A significant improvement is seen in the spatial distribution of monsoon mean climatology where a too-heavy monsoon precipitation, which occurred in CAM4, is rectified. A detailed investigation of precipitation reduction, using sensitivity experiments, showed that the large systematic cold SST errors in the northern Indian Ocean reduces monsoon precipitation and delays the monsoon onset by weakening local evaporation. The CSV analysis using CAM4 revealed that the SST uncertainties in Indian Ocean can result in much larger error growth of SAM seasonal forecast than those in the equatorial Pacific Ocean. It is seen that the CSVs error growth rate changes significantly depending on the initial states whereas the CSVs patterns are insensitive to the initial conditions. The CAM4 comparison with CCSM4 coupled model indicated that the CSVs patterns from CAM4 are similar to those from CCSM4 while the error growth rate is lower in CAM4 than in CCSM4. CAM4
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".