Sea level responses to climatic variability and change in northern British Columbia
Bibliographic record
Abstract
Sea level responses to climatic variability (CV) and change (CC) signals at multiple temporal scales (interdecadal to monthly) are statistically examined using long‐term water level records from Prince Rupert (PR) on the north coast of British Columbia. Analysis of observed sea level data from PR, the longest available record in the region, indicates an annual average mean sea level (MSL) trend of +1.4±0.6 mm yr−1 for the period (1939–2003), as opposed to the longer term trend of 1±0.4 mm yr−1 (1909–2003). This suggests a possible acceleration in MSL trends during the latter half of the twentieth century. According to the results of this study, the causes behind this acceleration can be attributed not only to the effects of global warming but also to cyclic climate variability patterns such as the strong positive Pacific Decadal Oscillation (PDO) phase that has been present since the mid‐1970s. The linear regression model based on highest sea levels (MAXSL) of each calendar year showed a trend exceeding twice that (3.4 mm yr−1) of MSL. Previous work shows that the influence of vertical crustal motions on relative sea level are negligible at PR. Relations between sea levels and known CV indices (e.g., the Multivariate ENSO Index (MEI), PDO, Northern Oscillation Index (NOI), and Aleutian Low Pressure Index (ALPI)) are explored to identify potential controls of CV phenomena (e.g., the El Niño Southern Oscillation (ENSO), PDO) on regional MSL and MAXSL. Linear and non‐linear statistical methods including correlation analyses, multiple regression, Cumulative Sum (CumSum) analysis, and Superposed Epoch Analysis (SEA) are used. Results suggest that ENSO forcing (as shown by the MEI and NOI indices) exerts significant influence on winter sea level fluctuations, while the PDO dominates summer sea level variability. The observational evidence at PR also shows that, during the period 1939–2003, these cyclic shorter temporal scale sea level fluctuations in response to CV were significantly greater than the longer term sea‐level rise trend by as much as an order of magnitude and with trends over twice that of MSL. Such extreme sea level fluctuations related to CV events should be the immediate priority for the development of coastal adaptation strategies, as they are superimposed on long‐term MSL trends, resulting in greater hazard than longer term MSL rise trends alone.
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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 | 0.002 |
| 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".