Importance of Tropospheric Wave Breaking for Subseasonal Forecasts of the February 2021 North American Cold Air Outbreak
Bibliographic record
Abstract
<!--!introduction!--> The February 2021 North American Cold Air Outbreak (CAO) featured historic cold and other winter hazards over a two-week period across the Central United States (US), leading to major socioeconomic impacts. Leading up to the event, model forecasts diverged on the evolution of critical features that ultimately affected their forecast accuracy. In particular, the CAO event was preceded by two wave breaks at high latitudes -- one in the East Siberian Sea (2-4 February 2021) and another in the Labrador Sea (9-11 February 2021). In this talk, we illustrate how these wave breaks were important for the ultimate evolution and intensity of the CAO in subseasonal model forecasts. We use a combination of ERA5 and realtime forecasts from two models from the Subseasonal-to-Seasonal (S2S) Prediction Project Database for the analysis: ECMWF and NCEP. We select model initialization dates with 2-3 weeks lead time to isolate ensemble members that simulated the wave breaks well and poorly and then evaluate their subsequent forecasts for the event. In both models, ensemble members that successfully simulated these wave-break features produce higher chances for widespread negative temperature anomalies across the Central US, corresponding to strong anomalous Arctic ridging, as seen in reanalysis. By contrast, ensemble members with either no or weak wave breaking forecast neutral to even positive temperature anomalies for the Central US over the target period, with less amplified flow. These results reinforce the need to simulate better blocking regimes in S2S operational models for improved accuracy of extreme winter weather events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".