Selecting representative climate models for climate change impact studies: an advanced envelope‐based selection approach
Bibliographic record
Abstract
ABSTRACT Climate change impact studies depend on projections of future climate provided by climate models. The number of climate models is large and increasing, yet limitations in computational capacity make it necessary to compromise the number of climate models that can be included in a climate change impact study. The selection of climate models is not straightforward and can be done by following different methods. Usually, the selection is either based on the entire range of changes in climatic variables as projected by the total ensemble of available climate models or on the skill of climate models to simulate past climate. The present study combines these approaches in a three‐step sequential climate model selection procedure: (1) initial selection of climate models based on the range of projected changes in climatic means, (2) refined selection based on the range of projected changes in climatic extremes and (3) final selection based on the climate model skill to simulate past climate. This procedure is illustrated for a study area covering the Indus, Ganges and Brahmaputra river basins. Subsequently, the changes in climate between 1971–2000 and 2071–2100 are analysed, showing that the future climate projections in this area are highly uncertain but that changes are imminent.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".