Multi-model forecast quality assessment of CMIP6 decadal predictions
Bibliographic record
Abstract
Decadal climate predictions are a new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and lead times) with skill that can be used to develop a climate service and inform users in several sectors. Also, it can help to monitor improvements in current forecast systems. The Decadal Climate Prediction Project Component A (DCPP-A) of the Coupled Model Intercom-parison Project Phase 6 (CMIP6) now provides the most comprehensive set of retrospective decadal predictions from multiple forecast systems. The increasing availability of these simulations leads to the question of how to best post-process the raw output from the forecast systems so that the most useful and reliable information is provided to users. This work evaluates the quality of deterministic and probabilistic forecasts for spatial fields of near-surface air temperature and precipitation, and time series of the Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available decadal predictions contributing to CMIP6/DCPP-A (169 members from 13 forecast systems). The predictions generally show high skill in predicting temperature and the AMV and GSAT time series, while the skill is more limited for precipitation. Also, different approaches for building a multi-model forecast are compared (pooling all ensemble members versus combining the averages from individual forecast systems), finding small differences. Besides, the multi-model ensemble is compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the uninitialized historical climate simulations (195 members from the same forecast systems as the decadal prediction members) to estimate the impact of initialization. An added value is found for temperature over several ocean and land regions, and for the AMV and GSAT time series, while it is more reduced for precipitation. Moreover, the full DCPP-A ensemble is compared to a sub-ensemble of predictions that could be provided in near real-time for a potential operational product generation. The comparison shows a benefit of using a large ensemble over several regions, especially for temperature. Finally, the implications of these results in a climate services context are discussed.
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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 | 0.012 | 0.026 |
| 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 | 0.002 |
| 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".