Synoptic sea‐level pressure patterns generated by a general circulation model: comparison with types derived from NCEP/NCAR re‐analysis and implications for downscaling
Bibliographic record
Abstract
Abstract A principal component analysis (PCA)‐based synoptic typing scheme is used to assess the ability of the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model (CGCM2) to reproduce daily mean‐sea‐level (MSL) synoptic patterns and their frequencies for the Pacific Northwest region of North America. Model output for the ‘control’ period 1961–1989 is compared against the climatology based on National Center for Environmental Prediction (NCEP) re‐analysis data. Although CGCM2 is able to reproduce the full range and seasonality of 13 synoptic types it significantly under‐represents three cold types and over‐represents (by about 50%) three warm/wet winter types. This effect is most pronounced in winter months. Differences in frequencies between the CGCM2 runs (1961–1989 climatology and 1990–2100 IPCC SRES ‘A2’ greenhouse gases (GHG) and aerosol forcing scenario) are smaller than the differences between NCEP and CGCM2 synoptic type frequencies for the 1961–1989 control. Unresolved orographic influences and atmosphere‐ocean coupling are cited as possible explanations for model deficiencies. Results suggest that application of CGCM2 output in downscaling studies examining regional impacts should take account of these potential biases. The approach adopted provides a methodology for not only assessing progress in emerging generations of more sophisticated higher resolution General Circulation Models (GCMs) (e.g. CGCM4 is under development), but also choosing the most appropriate model for regional downscaling studies. Copyright © 2006 Royal Meteorological Society.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".