Atmospheric teleconnection patterns and severity of winters in the Laurentian Great Lakes basin
Bibliographic record
Abstract
We analyzed the relationship between an index of Great Lakes winter severity (winters 1950–1998) and atmospheric circulation characteristics. Classification and Regression Tree analysis methods allowed us to develop a simple characterization of warm, normal and cold winters in terms of teleconnection indices and their combinations. Results are presented in the form of decision trees. The single most important classifier for warm winters was the Polar/Eurasian index (POL). A majority of warm winters (12 out of 15) occurred when this index was substantially positive (POL > 0.23). There were no cold winters when this condition was in place. Warm winters are associated with a positive phase of the Western Pacific pattern and El Niño events in the equatorial Pacific. The association between cold winters and La Niña events was much weaker. Thus, the effect of the El Niño/Southern Oscillation (ENSO) on severity of winters in the Great Lakes basin is not symmetric. The structure of the relationship between the index of winter severity and teleconnection indices is more complex for cold winters than for warm winters. It takes two or more indices to successfully classify cold winters. In general, warm winters are characterized by a predominantly zonal type of atmospheric circulation over the Northern Hemisphere (type W1). Within this type of circulation it is possible to distinguish two sub‐types, W2 and W3. Sub‐type W2 is characterized by a high‐pressure cell over North America, which is accompanied by enhanced cyclonic activity over the eastern North Pacific. Due to a broad southerly “anomalous” flow, surface air temperatures (SATs) are above normal almost everywhere over the continent. During the W3 sub‐type, the polar jet stream over North America, instead of forming a typical ridge‐trough pattern, is almost entirely zonal, thus effectively blocking an advection of cold Arctic air to the south. Cold winters tend to occur when the atmospheric circulation is more meridional (type C1). As with warm winters, there are two sub‐types of circulation, C2 and C3. In the case of C2, the jet stream loops southward over the western part of North America, but its northern excursion over the eastern part is suppressed. In this situation, the probability of a cold winter is higher for Lake Superior than for the lower Great Lakes. Sub‐type C3 is characterized by an amplification of the climatological ridge over the Rockies and the trough over the East Coast. The strongest negative SAT anomalies are located south of the Great Lakes basin, so that the probability of a cold winter is higher for the lower Great Lakes than for Lake Superior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".