Évaluation de la vulnérabilité des côtes canadiennes face à la hausse des niveaux d'eau extrêmes basés sur les projections climatiques de CMIP5 et de CMIP6
Bibliographic record
Abstract
RÉSUMÉ: La hausse du niveau de la mer (HNM) causée par les changements climatiques menace les communautés côtières à l’échelle mondiale, dont celles du Canada. Tout d’abord, nous calculons les tendances linéaires paramétriques et non paramétriques de 1993 à 2022 pour les stations canadiennes offrant suffisamment de données. Nous examinons leur répartition spatiale pour mieux comprendre l’hétérogénéité de la HNM à travers le vaste territoire canadien. Puis, nous étudions trois stations marégraphiques, soit Vancouver, Saint John et Churchill, dans le but de représenter les trois côtes canadiennes. Nous projetons les futurs niveaux d’eau totaux extrêmes (NETEs) afin d’estimer les fluctuations possibles ainsi que leurs vulnérabilités. Nous calculons les NETEs en intégrant les estimations extrêmes des marées astronomiques et des ondes de tempête avec les projections de la variation régionale des niveaux d’eau issues des scénarios de forçage radiatif modéré et élevé des phases 5 et 6 du Climate Model Intercomparison Project (CMIP). Un modèle bathtub est utilisé afin d’estimer les zones inondées et asséchées qui sont ensuite évaluées selon plusieurs facteurs socio-économiques. Même avec le scénario le plus conservateur, des milliers de personnes et d’infrastructures pourraient être affectées. Nos résultats soulignent le besoin urgent d’élaborer des stratégies dans le but d’atténuer les impacts de la HNM au Canada. ABSTRACT: Climate change-induced Sea Level Rise (SLR) poses significant threats to coastal communities worldwide. This is particularly the case for Canada, home to the world's longest shoreline. First, we compute water level trends for Canadian stations with sufficient data, focusing on both parametric and non-parametric linear trends from 1993 to 2022. We examine the spatial distribution of trends to better understand the heterogeneity across Canada's vast territory. Then, we study three tide gauge stations – Vancouver, Saint John, and Churchill – to represent the Pacific, Atlantic, and Arctic coasts, respectively. We project future Extreme Total Water Levels (ETWLs) to estimate possible fluctuations along with associated vulnerabilities. We calculate ETWLs by integrating projected Regional Sea Level Rise (RSLR) with estimates of extreme astronomical tides and storm surges during the historical period. Projected RSLRs are obtained from the 5th and 6th phases of the Climate Model Intercomparison Project (CMIP) under moderate and high radiative forcings. We use a bathtub model to estimate flooding and receding extents, which are then used to assess vulnerabilities using multiple socio-economic factors. Even under the most conservative projections, thousands of people and critical infrastructure could be affected. Our findings underscore the need for immediate strategies to mitigate these impacts
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".