Season-Dependent Forecast Skill of the Leading Forced Atmospheric Circulation Pattern over the North Pacific and North American Region*
Bibliographic record
Abstract
Abstract Multimodel ensemble (MME) seasonal forecasts are analyzed to evaluate numerical model performance in predicting the leading forced atmospheric circulation pattern over the extratropical Northern Hemisphere (NH). Results show that the time evolution of the leading tropical Pacific sea surface temperature (SST)-coupled atmospheric pattern (MCA1), which is obtained by applying a maximum covariance analysis (MCA) between 500-hPa geopotential height (Z500) in the extratropical NH and SST in the tropical Pacific Ocean, can be predicted with a significant skill in March–May (MAM), June–August (JJA), and December–February (DJF) one month ahead. However, most models perform poorly in capturing the time variation of MCA1 in September–November (SON) with 1 August initial condition. Two possible reasons for the models’ low skill in SON are identified. First, the models have the most pronounced errors in the mean state of SST and precipitation along the central equatorial Pacific. Because of the link between the divergent circulation forced by tropical heating and the midlatitude atmospheric circulation, errors in the mean state of tropical SST and precipitation may lead to a degradation of midlatitude forecast skill. Second, examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance of the model forecasts due to internal dynamics, shows that the atmospheric potential predictability over the North Pacific–North American (NPNA) region is the lowest in SON compared to the other three seasons. The low ratio in SON is due to a low variance associated with external forcing and a high variance related to atmospheric internal processes over this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".