What aspect of model performance is the most relevant to skillful future projection on regional scale?
Bibliographic record
Abstract
Weighting models according to their performance has been used in constructing multi-model regional climate change scenarios. But the added value of model weighting is not always examined. Here we apply an imperfect model framework to examine the added value of model weighting in projecting summer temperature changes over China. Members of large ensemble initial condition simulations by three climate models of different climate sensitivities under the historical forcing and future scenarios are used as pseudo-observations. Performance of the models participating in the 6 th phase of the coupled model intercomparison project (CMIP6) in simulating past climate are evaluated against the pseudo-observations based on climatology, trends in global, regional and local temperatures. The performance along with model’s independence are used to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations for the future. We find that regional trend as a metric of model performance yields the best skill for future projection while past climatology as performance metric does not improve future projection. Trend at the grid-box scale is also not a good performance indicator as small scale trend is highly uncertain. Projected summer warming based on model weighting is similar to that of unweighted projection, at 2.3°C increase relative to 1995-2014 by the middle of the 21 st century under SSP8.5 scenario, but the 5 th -95 th uncertainty range of the weighted projection is 18% smaller with the reduction mainly in the upper bound, with the largest reduction in the northern Tibetan Plateau.
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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 | 0.006 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".