An Approach for Selecting Observationally-Constrained Global Climate Model Ensembles for Regional Climate Impacts and Adaptation Studies in Canada
Bibliographic record
Abstract
Given the growing number of global climate models (GCMs) with simulations available for impacts and adaptation studies, methods have been introduced to select models that are ‘fit-for-purpose’. This study applies a GCM selection process to historical and future climate projections from 38 and 43 GCMs contributing to the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Models are selected based on historical performance, with a further selection step targeted at reducing interdependencies between closely related model variants and ensemble members. Ten performance measures are calculated based on climatological statistics (mean, standard deviation, and seasonal cycle) of three climate variables (precipitation, sea level pressure, and surface air temperature (SAT)), as well as SAT warming trend for the 1985–2014 period. Performance is assessed over Canada and six Canadian sub-regions, at both annual and seasonal timescales. As initial-condition members and minor variants of GCMs are not independent, a representative democracy approach – using ensemble averages of initial-condition members and including only the best performance model among minor variants – is employed to reduce redundancy in selected subsets. There is a strong correlation between recent warming trends and future warming projections across Canada; therefore, observed SAT warming trends are recognized as important observational-constraints to aid in model selection. By removing “hot models” that fail to reproduce the historical SAT warming trend, a representative subset of observationally-constrained GCMs projects lower annual SAT than model democracy (using all model runs assuming independence and equal plausibility) over Canada and six Canadian sub-regions for 2071–2100.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".