Sea ice concentration anomalies as long range predictors of anomalous conditions in the North Atlantic basin
Bibliographic record
Abstract
Long-range empirical forecasts of North Atlantic anomalous conditions are issued, using seaice concentration anomalies in the same region as predictors. Conditions in the North Atlanticare characterized by anomalies of sea surface temperature, of 850 hPa air temperature and ofsea level pressure. Using the Singular Value Decomposition of the cross-covariance matrixbetween the sea ice field (the predictor) and each of the predictand variables, empirical modelsare built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of theair temperature anomalies score the highest levels of the skill, while forecasts of the sea levelpressure anomalies are the less sucessful ones.To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the airtemperature anomalies, the spatial pattern of the skill may be connected to El Ninño SouthernOscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shownin the composite analysis. The composite pattern indicates a higher (lower) sea ice concentrationin the Labrador Sea and the opposite situation in the Greenland’Barents Seas during the warm(cold) phase of ENSO. The forecasts issued under the El Nino conditions show improved skillin the Labrador region, the Iberian Peninsula and south of Greenland for the lead timesconsidered in this paper. For the Great Lakes region the skill increases when the predictor isunder the influence of a cold phase. Some features in the spatial structure of the skill of theforecasts issued in the period of the Great Salinity Anomaly present similarities with thosefound for forecasts made during the cold phase of ENSO. The strength of the dependence onthe Great Salinity Anomaly makes it very difficult to determine the influence of the NorthAtlantic Oscillation.
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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 | 0.000 | 0.000 |
| 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".