An evaluation of East Asian summer monsoon forecast with the North American Multimodel Ensemble hindcast data
Bibliographic record
Abstract
Abstract The abilities of three models (Climate Forecast System version 2 [CFSv2], Canadian Coupled Climate Model version 3 [CanCM3] and Canadian Coupled Climate Model version 4 [CanCM4]) in the North American Multimodel Ensemble for the East Asian summer monsoon (EASM) forecast were evaluated with their 29‐year hindcast data (1982–2010). Many EASM features including monsoon precipitation centres, large‐scale monsoon circulations and monsoon onset and retreat are generally captured by the three models and their ensemble mean, and the multimodel ensemble has the best performance. Since the East Asian domain includes the tropical western North Pacific summer monsoon (WNPSM) and the subtropical continent monsoon, two well‐known monsoon indices, the WNPSM index (WNPSMI) and EASM index (EASMI), and their associated low‐level winds and precipitation anomalies are well forecasted by the models. However, the forecast performance generally decreases as the leads increase, and the performance of EASMI is not as good as that of WNPSMI. CFSv2 forecasts well at leads up to 6 months whereas the skill of CanCM3 (CanCM4) decreases rapidly when the lead increases to 2 months (3 months). The failure of CanCM3 is mainly attributed to the poor forecast of the relationship of EASMI with the El Niño‐Southern Oscillation and Northern Indian Ocean‐western North Pacific (WNP) sea surface temperature anomaly. However, the causes of the poor forecast of CanCM4 for EASMI require further investigation. Sources of the forecast error (FE), which is the difference between model and observation for monsoon precipitation, are more significant than those of the predictability error (PE), which originates from the initial condition error, indicating that model deficiency plays a dominant role in limiting the EASM precipitation forecast. However, the PE cannot be neglected over the tropical western Pacific in CFSv2, over the WNP in CanCM3 and over the Tibetan Plateau in CanCM4. As the lead time increases, the FE does not remarkably change whereas the PE decreases significantly.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".