Impacts of Air–sea Coupling on Systematic Errors in Medium-Range Winter Forecasts over the North Pacific and North Atlantic
Bibliographic record
Abstract
Abstract. The impact of air-sea coupling in North Pacific and North Atlantic medium-range forecasts during winter is assessed using 20 years (1998–2017) of hindcasts produced by the Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada (ECCC). We compare an uncoupled atmospheric model (versions 5, GEPS5) with an atmosphere-ocean coupled model (version 6, GEPS6) alongside European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) as the verification dataset. We find that by the third pentad, or days 11–15, coupling weakens the Aleutian Low, the Icelandic Low, and the Atlantic Subtropical High. This produces less integrated vapor transport (IVT) over the Pacific and Atlantic Oceans, whose spatial patterns are modulated by phases of Madden-Julian oscillation (MJO). Coupling also results in colder sea surface temperature (SST) over the Kuroshio Current Extension region and produces a weaker Aleutian Low due to less upward latent heat fluxes. The weaker Aleutian Low further reinforces its weakening through a positive feedback loop. Lastly, the air-sea coupling reduces the latent heat flux bias variance by 10–20 %, thus improving the IVT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".