Substantial cold bias during wintertime cold extremes in the southern Cascadia region in historical CMIP6 simulations
Bibliographic record
Abstract
Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document the magnitude and extent of minimum temperature biases in the CMIP6 model ensemble, relative to ERA5. Bias in the southern Cascadia region (i.e. Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains) stands out relative to the rest of North America, with some models showing a bias in excess of -10°C in the 1st percentile of daily winter minimum temperature. During the coldest minimum temperature days, the CMIP6 models show an anomalous high in mean sea level pressure in the Northeast Pacific – an atmospheric blocking pattern that is also present in ERA5. While this atmospheric blocking pattern is typically concurrent with cold temperatures across much of North America, terrain barriers such as the Rockies and Cascades prevent the cold air from reaching the Pacific Northwest in observation and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from the interior of the continent.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".