Narrowing uncertainties in projected warming by constraining using the past global warming trend with the pattern effect removed
Bibliographic record
Abstract
Observational constraint methods based on emergent relationships between observable predictors and future projected warming across multi-model ensembles enable us to constrain multi-model projections. Unforced internal variability in predictors can weaken such emergent relationships. Assessing the Sixth Coupled Model Intercomparison Project (CMIP6) with all accessible realizations, we find that there of sea surface temperature (SST) trend over the eastern tropical pacific (ETP) which is well correlated with the global warming trend. The strong cooling in the ETP in observations induces a global-scale cooling, yet most realizations in the CMIP6 multi-model ensemble cannot reproduce it. Using the observed raw historical global mean near surface air temperature (GSAT) trend as a constraint therefore results in a relatively lower projected 21st century warming. However, by removing the unforced internal variability associated with variation in the ETP in observed and simulated GSAT trends, we find an enhanced correlation between GSAT trends and projected warming and improved results in an imperfect model test. This approach results in a relatively higher 21st century warming than a constrained projection based on the raw GSAT trend, and brings constrained projections into much closer agreement with projections constrained using climatological cloud metrics.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".