Origin of cold bias over the Arabian Sea in Climate Models
Bibliographic record
Abstract
Almost all climate models in Coupled Model Inter-comparison Project phase five (CMIP5) were found to have a cold bias in Sea Surface Temperature (SST) over the northern Arabian Sea, which is linked to the biases in the Indian Summer Monsoon (ISM). This cold SST bias was attributed to the anomalous cold winds from the north-western part of south Asian landmass during boreal winter. However, the origin of the anomalously strong cold winds over the Arabian Sea and its association with the large-scale circulation is obscure. Here we show that an equatorward bias in subtropical Jetstream during boreal spring season anomalously cools down the northern Arabian Sea and adjoining land regions in CMIP5 models. The models with stronger equatorward bias in subtropical jet are also the ones with stronger cold SST bias over the Arabian Sea. The equatorward shift coupled with enhanced strength of the subtropical jet produce a stronger upper tropospheric convergence, leading to a subsidence and divergence at lower levels over the Arabian deserts. The low entropy air flowing from the Arabian land mass cools the northern Arabian Sea. The weaker meridional temperature gradients in the colder models substantially weaken ISM precipitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".