Projected Trends of Wintertime North American Surface Mean and Extreme Temperatures over the Next Half-century in Two Generations of Canadian Earth System Models
Bibliographic record
Abstract
Based on two single-model initial-condition 50-member ensembles of climate simulations conducted with two generations of Canadian Earth System Models (CanESM2 and its successor CanESM5), we analyze the ensemble mean and spread of the projected trends of wintertime North American surface air temperature (SAT) and extreme indices of cold (TX10) and warm (TX90) days over the next half-century (2021–2070) and explore the contribution of internal climate variability to these trends. The ensemble mean of future climate simulations forced by the high-emissions scenario Representative Concentration Pathway 8.5 (RCP8.5) in CanESM2 and the Shared Socioeconomic Pathway 8.5 (SSP5-8.5) in CanESM5 reveals a poleward intensified warming, high risk of severe warm days over the west coast of North America and northern Canada, and a weakening belt of extreme cold days extending from Alaska to the northeastern United States. The warming trend is stronger in CanESM5 than in CanESM2, likely because of higher climate sensitivity and slightly higher CO2 emissions in CanESM5. Large ensemble spreads are apparent in the SAT trend and in the historical simulations and future projections of extreme temperatures, especially for the TX10 index. Individual realizations differ from the ensemble mean in both spatial pattern and magnitude of the projected trends. The signal-to-noise ratio reveals strong signals of the SAT and TX90 trends primarily over the west coast of North America and northern Canada, along with relatively strong signals of the TX10 trend over most of the central to eastern parts of North America in CanESM2 and western Canada and the southwestern and eastern United States in CanESM5. The components of the mean and extreme temperature trends generated by internal climate variability exhibit large-scale spatial coherences and are comparable to the externally anthropogenic-forced components of the trends, mostly in the central parts of North America. Overall, similar ensemble mean patterns of North American mean and extreme temperature trends are evident in the two models; CanESM5 tends to be less uncertain in projecting those trends than CanESM2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".