Recent progress in simulating two types of ENSO – from CMIP5 to CMIP6
Bibliographic record
Abstract
The new emerging type of El Niño brings challenges to the state-of-the-art coupled models to simulated features of El Niño - Southern Oscillation (ENSO) diversity. The Coupled Model Intercomparison Project (CMIP), containing the advanced worldwide coupled models, has recently released the model outputs in phase 6. In this paper, the characteristics of two types of ENSO in 19 models from CMIP phase 5 and their counterparts in phase 6 are assessed regarding the spatial and temporal features and the seasonal cycle features. The weaker amplitude of Eastern Pacific (EP) and Central Pacific (CP) ENSO in CMIP5 is increased and the spatial structure of CP ENSO is better depicted in CMIP6. However, no significant improvement in the ENSO periodicity and the ENSO phase-locking behavior compared to CMIP5. A synthetic ENSO score, containing eight metrics, is defined and employed to evaluate the performance of each CMIP model. The average ENSO score for CMIP6 is 2.375, indicating modest improvement compared to the average score of 2.441 for CMIP5. Furthermore, the slight improvement in the ENSO score is partly related to the reduced climatology bias of sea surface temperature in the Niño4 region. The overall evaluation provides necessary information for future investigation about the mechanism exploration of the ENSO diversity based on the models with better performance.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".