Large-Amplitude Quasi-Stationary Rossby Wave Events in ERA5 and the CESM2: Features, Precursors, and Model Biases in Northern Hemisphere Winter
Bibliographic record
Abstract
Abstract High-amplitude quasi-stationary Rossby waves (QSWs) have been connected to extreme weather events. By identifying particularly high-amplitude QSW events (QWEs) over Europe and North America, we study their characteristics in ERA5 data and in ensemble simulations from the CESM2 general circulation model. The CESM2 reproduces the overall statistics of QWEs, with ERA5 results within the ensemble spread. The ensemble spread is large, indicating a strong influence of internal variability. Composites of meridional wind anomalies for QWEs show a phase preference in both ERA5 and CESM2, resembling the climatological wave pattern. This is partly due to the definition of QSWs; with the day-of-year climatological meridional wind removed when identifying QSWs, the phase preference remains, albeit with a weaker signal. Significant tropical Pacific precipitation anomalies are seen 5–15 days before observed QWEs; the location of these anomalies is broadly reproduced in CESM2, but the magnitude is substantially underestimated and the time scale is biased. We find a narrowed and strengthened jet stream over the Pacific at the early stage of European QWEs, which may create enhanced waveguidability; this signal is generally reproduced in the models. Overall, the CESM2 can simulate QWEs; differences between the model ensemble mean and the reanalysis could result from model bias or internal variability, although biases are not reduced in CESM2 simulations forced with observed SSTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".