Summer Drought Patterns in Canada and the Relationship toGlobal Sea Surface Temperatures
Bibliographic record
Abstract
Canadian summer (June–August) Palmer Drought Severity Index (PDSI) variations and winter (December– February) global sea surface temperature (SST) variations are examined for the 63-yr period of 1940–2002. Extreme wet and dry Canadian summers are related to anomalies in the global SST pattern in the preceding winter season. Large-scale relationships between summer PDSI patterns in Canada and previous winter global SST patterns are then analyzed using singular value decomposition (SVD) analysis. The matrix for the covariance eigenproblem is solved in the EOF space in order to obtain the maximum covariance between the singular values of the SST and the PDSI. The robustness of the relationship is established by the Monte Carlo technique, in which the time expansion of the primary EOF analysis is shuffled 1000 times. Results show that the leading three SVD-coupled modes explain greater than 80% of the squared covariance between the two fields. The interannual El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), and the interrelationship between the two play a significant role in the determination of the summer moisture availability in Canada. These Pacific Ocean processes are reflected in the second and third SVD modes, and together explain approximately 48% of the squared covariance. It is found that the warm ENSO (El Niño) events lead to a summer moisture deficit in the western two-thirds of Canada. Conversely, cold ENSO (La Niña) events produce an abundance of summer moisture, mainly in extreme western Canada and in the southeastern portions of the Canadian Prairies. The first SVD mode strongly relates to the trend in global SSTs and multidecadal variation of the Atlantic SST, explaining approximately one-third of the squared covariance. It is reflective of both the warming trend in the global southern oceans and the influences of the Atlantic multidecadal oscillation (AMO) variability. The 6-month lag relationship between the PDSI and large-scale SSTs provides a basis for developing long-range forecasting schemes for drought in Canada. A two-tier forecast scheme, in which the SST is predicted by an ocean model or a coupled climate model, can potentially further increase the lead time of drought forecasting.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".