Modelling the impacts of marine heatwaves on plankton in the Salish Sea
Bibliographic record
Abstract
Abstract. Marine heatwaves are discrete events of prolonged anomalously warm ocean temperatures caused by a combination of atmospheric forcing and ocean processes. The Northeast Pacific Marine Heatwave (NEP-MHW) was first detected in the Salish Sea in 2014 and persisted in the region until 2017. Here, we used a three-dimensional coupled biophysical model, SalishSeaCast, to examine the impacts of the NEP-MHW on the physics and the plankton in the Salish Sea. Sixteen years (2007–2022) of model results were used to follow the trajectory of the NEP-MHW into key regions of the Salish Sea. Model results were compared to observation data collected over the same period. We resolved the specific impacts of the NEP-MHW versus the impacts of warming via other large-scale climate indices operating on longer time scales. Model results showed that the strongest physical signatures of the NEP-MHW were evident in the Juan de Fuca region wherein warming was favourable for the growth of both diatoms and nanoflagellates. In comparison, the direct warming from the NEP-MHW impacted the Strait of Georgia (SoG) to a lesser degree but warm water anomalies persisted in this region until the end of our study period in 2022. Both temperature and nitrate in the upper layer of the SoG were strongly linked to the North Pacific Gyre Oscillation and diatom biomass decreased during this prolonged warming period. Our results highlight the need to recognize that multiple types of marine heatwaves associated with different large-scale climate indices can occur simultaneously, even within a single waterbody such as the Salish Sea, each with distinct impacts on the local food web.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".