The representation of synoptic-scale cyclone climatology in CMIP6 models over Atlantic Canada using self-organizing maps
Bibliographic record
Abstract
This study compares the ability of selected CMIP6 Earth System Models (ESMs) to simulate synoptic-scale cyclone climatology over Atlantic Canada by examining their representation of pressure anomalies against that of ERA5 reanalysis data. A Self-Organizing Map (SOM) was trained using daily mean sea level pressure (MSLP) anomalies from both historical model simulations and ERA5 reanalysis data for the warm and cold seasons over a 45 year period. The ESMs are assessed based on their ability to reproduce the frequency and spatial structure of synoptic pressure anomaly patterns using both statistical and visual inspection techniques. Results indicate notable inter-model variability, with MPI-ESM1-2-HR and MRI-ESM2-0 showing relatively stable performance across both seasons, NorESM2-MM excelling in the cold season but declining in the warm season, and CMCC-CM2-HR4 exhibiting the highest biases overall. In general, models captured cold-season patterns more faithfully than warm-season ones. This research contributes an understanding on how to compare ESM biases in simulating cyclone climatology and also informs future efforts in regional climate impact assessment in the region surrounding Newfoundland.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".