Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: CMIP5 to CMIP6
Bibliographic record
Abstract
Abstract This study evaluates and compares historical simulations of daily sea-level pressure circulation types over 6 continental-scale regions (North America, South America, Europe, Africa, East Asia, and Australasia) by 15 pairs of global climate models from modeling centers that contributed to both Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Atmospheric circulation classifications are constructed using two different methodologies applied to two reanalyses. Substantial improvements in performance, taking into account internal variability, are found between CMIP5 and CMIP6 for both frequency (24% reduction in global error) and persistence (12% reduction) of circulation types. Improvements between generations are robust to different methodological choices and reference datasets. A modest relationship between model resolution and skill is found. While there is large intra-ensemble spread in performance, the best performing models from CMIP6 exhibit levels of skill close to those from the reanalyses. In general, the latest generation of climate models should provide less biased simulations for use in regional dynamical and statistical downscaling efforts than previous generations.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".