Comparisons Between CMIP5 and CMIP6 Models: Simulations of Climate Indices Influencing Food Security, Infrastructure Resilience, and Human Health in Canada
Bibliographic record
Abstract
Abstract The warming climate can considerably affect socioeconomic activities and environmental health conditions in Canada. Climate models play a key role in evaluating the impact of climate change and developing adaption and mitigation strategies corresponding to Canadian regions. This study compares the behavior of climate models participating in the Climate Model Intercomparison Project Phase 6 (CMIP6) with their CMIP5 predecessors in representing a set of climate indices relevant to Canada’s agricultural productivity, infrastructure resilience, and environmental health. Our results show that although CMIP5 and CMIP6 multi‐model ensemble mean values for the considered indices are almost similar, the behavior of individual CMIP5 and CMIP6 models or even the model pairs of the same modeling center can be different across Canada. Moreover, the CMIP6 models do not necessarily outperform CMIP5 in comparison to NOAA and NCEP reanalysis datasets in simulating the annual mean and coefficient of variation values for the indices during the historical period. The comparisons between models’ simulations also reveal that the envelope of estimated values based on individual CMIP6 models does not cover or overlap with their CMIP5 counterparts or even with other CMIP6 models. Therefore, the CMIP6 models with higher number of simulations do not necessarily provide a larger range of projections over Canadian regions. The divergence between CMIP5 and CMIP6 models’ behavior is more obvious under the 8.5 W/m 2 forcing in the long‐term horizon. Overall, the estimated sign, magnitude, and spatial pattern of changes in the climate indices depend on the considered climate model and forcing scenario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".