Simulated wave evolution and coastal erosion in the Arctic and the Canadian Arctic Archipelago (2002-2022)
Bibliographic record
Abstract
In the Arctic, ocean surface waves are becoming more energetic. This is due to the larger wind fetch caused by decreased sea ice cover in summer and delayed sea ice formation in fall. These changes, driven by global climate change and regional warming, are projected to be more extreme in the future. Surface gravity waves are a key factor in coastal erosion and flooding, which are already negatively affecting coastlines in the Arctic (Casas-Prat & Wang 2020). Understanding and quantifying surface waves evolution is therefore particularly important for the communities that live along the coasts of the Canadian Arctic Archipelago (CAA), yet it has not been investigated with modeling.We used the spectral wave model Wavewatch III® (Tolman 1997, 1999a, 2009) to simulate gravity waves formation and propagation for the entire Arctic and the North Atlantic over 2002-2022, using output from a regional 1/4° NEMO simulation. Simulations reveal a positive wave height trend in Baffin Bay and locations near the sea ice margin in the Barents, Kara and East Greenland Seas. A positive trend is found in Baffin Bay from June to October (max 0.25 m/y), where peak wave heights of 4-6 m are also observed during fall, in the second decade of the run. This highlights the importance of combined delay in seasonal sea ice formation and storm activity in the CAA, with storms more likely to produce high waves conditions during fall.Further ongoing work will: 1) analyze the impact of waves on coastal erosion; 2) project ocean and wave conditions under CMIP6 forcing: the numerical ocean model NEMO, at 1/4° resolution, and a nested grid over the CAA will allow WW3 wave simulations to be projected over 2100.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".