The Detection and Attribution Model Intercomparison Project: CMIP6 highlights and plans for CMIP7
Bibliographic record
Abstract
The Detection and Attribution Model Intercomparison Project (DAMIP) coordinates single forcing climate model simulations for detection and attribution analysis and other applications. DAMIP simulations were carried out with fifteen climate models as part of CMIP6, and these simulations were used in at least 270 published articles. These simulations were also used directly in at least five chapters of the IPCC Sixth Assessment Working Group I Report, and they underpinned the estimate of anthropogenic attributable warming highlighted in the Summary for Policymakers of that report, and quoted directly in the UNFCCC Glasgow Climate Pact. For CMIP7, natural-only, well-mixed greenhouse gas-only, and aerosol-only simulations have been proposed as fast track DAMIP simulations, and planning of a broader set of experiments is currently underway. This talk will highlight key DAMIP results from CMIP6, and will discuss plans for the CMIP7 version of DAMIP. Comments and suggestions regarding the CMIP7 DAMIP experimental design will be welcomed.
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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 | 0.038 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".