A Diachronic Assessment of Advances in Seasonal Forecasting: Evolution of the APCC Multi‐Model Ensemble Prediction System Over the Last Two Decades
Bibliographic record
Abstract
Abstract Since its establishment in 2005, the APEC Climate Center (APCC) has pioneered advancements in seasonal climate prediction through its Multi‐Model Ensemble (MME) system, integrating the world's most diverse range of dynamical climate models. Over the past two decades, APCC has incorporated long‐range forecasts from more than 60 model versions, contributed by 21 institutions across 11 countries. This study presents the first diachronic assessment of the APCC MME system evolution, focusing on operational model transitions and associated substantial 34% improvement in global forecast skill. Despite these significant advances, challenges persist, particularly in predicting precipitation over land areas in the northern extratropics and in overcoming the spring predictability barriers. Our findings underscore the crucial role of model innovation, increased diversity, and international collaboration in advancing seasonal prediction. As the first study of its kind, this work provides key insights into the evolution of climate modeling, providing a foundation for future forecasting improvements.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".