Wave heights over Canadian oceans: Tempo-spatial variations and climate-oscillation impacts based on macroscale spatially-extrapolative retrieval from altimetric ensembles
Bibliographic record
Abstract
Estimation and analyses of significant wave heights (SWHs) are crucial to climate research, ocean engineering and other applications, with satellite retrieval serving as a fundamental approach. However, few studies attempt to extrapolate SWH models across buoy grids to retrieve ungauged-grid SWHs from multiple altimeters at macroscales, or examine variations of extreme SWHs in relation to climate oscillations, particularly in the Canadian context. To fill these gaps, we develop a macroscale spatially-extrapolative ensemble wave-height retrieval and analysis (MEERA) method to retrieve SWHs from multi-mission satellite altimetry and reveal tempo-spatial characteristics of SWHs means and extremes as well as their variations with climate oscillations. The method is applied across all Canadian waters. According to modeling results, MEERA significantly enhances consistency and accuracy of retrieved SWHs (especially in coastal areas), e.g., reducing biases of conventional methods by over 98%. From 1985 to 2020, waves were strongly seasonal and regional, which drop from winter (1.45 m) to summer (1.17 m) and tend to decline northward. SWHs tend to decrease in mid-eastern regions (e.g., Hudson Bay, Davis Strait and Gulf of St Lawrence) and increase in Canadian Atlantic, Pacific, and Arctic. Across all Canadian waters, climate indices regarding precipitation, e.g., the NBRA (Northeast Brazil Rainfall Anomaly) index, pose the strongest impacts on extreme SWHs compared with others. In Pacific and Atlantic, spatial patterns of winter SWH extremes are associated with negative NAO (North Atlantic Oscillation). El Niño might increase SWHs extremes over the Pacific and Arctic, while decreasing them over mid-eastern regions. This study advances macroscale SWH estimation and analysis, enhancing the understanding of SWH characteristics and their variations across Canada under climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".