Interannual variability of rainfall over the Arabian Peninsula using the <scp>IPCC AR4 Global Climate Models</scp>
Bibliographic record
Abstract
ABSTRACT The interannual rainfall variability derived from the 22 Global Climate Model (GCM) simulations of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) for the duration 1979–2000 is analysed and compared with the gridded observed dataset over the Arabian Peninsula. The annual cycle of the rainfall derived from these models is validated for the entire Arabian Peninsula, and separately for its two sub‐regions, named northern and southern Arabian Peninsula. The spatial patterns of the rainfall and the mean sea level pressure are analysed to judge the ability of the models to simulate the mean climatology of the Peninsula. This analysis reveals that out of the 22 IPCC AR4 GCM multi‐model datasets, only one group (composed of 5 models) is relatively better than all the others in simulating the interannual variability of the wet season rainfall for the northern sub‐region, and another group (also composed of 5 models) is likewise for the dry season rainfall climatology of the southern sub‐region, compared with the gridded dataset. The above two groups of models tend to fall within one‐sigma standard deviation of the mean seasonal rainfall derived from the gridded dataset. Moreover, only one model [CCCMA‐CGCM3 (T47) from Canada] is found to be relatively better in simulating the rainfall climatology for both the wet and the dry seasons (i.e. for the northern and the southern sub‐regions) simultaneously, compared with the observed data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".