HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development
Bibliographic record
Abstract
Abstract. Decadal-scale oceanographic, environmental, and ecological changes have been reported in the Salish Sea, an ecologically productive inland sea in the northeast Pacific that supports the economies and cultures of millions of people. However, there are substantial data gaps related to physical water properties that make it difficult to evaluate trends and the pathways of effects between physical ocean water properties and the productivity of marine ecosystems. With the aim of addressing these gaps, we present the Hindcast of the Salish Sea (HOTSSea) v1, a 3D physical oceanographic model developed using the Nucleus for European Modelling of the Ocean (NEMO) ocean engine, with temporal coverage from 1980–2018. We used an experimental approach to incrementally assess sensitivity to atmospheric and ocean reanalysis products used for boundary forcings and to the horizontal discretisation of the model grid (∼ 1.5 km). Biases inherited from forcings were quantified, and a simple temperature bias correction factor applied at one ocean boundary was found to substantially improve model skill. Evaluation of salinity and temperature indicates performance is best in the Strait of Georgia. Relatively large biases occur in near-surface waters, especially in subdomains with topography narrower than the model grid's horizontal resolution. However, we demonstrated that the model simulates temperature anomalies and a secular warming trend over the entire water column in general agreement with observations. HOTSSea v1 provided a first look at spatially and temporally heterogenous ocean temperature trends throughout the northern and central part of the domain where observations are sparse. Overall, despite the biases inherited from forcings and a relatively coarse horizontal discretisation, HOTSSea v1 performs well at representing temperature and salinity at the spatial–temporal scales needed to support research related to decadal-scale climate effects on marine ecosystems, fish, and fisheries. We conclude by underscoring the need to further extend the hindcast to capture a regime shift that occurred in the 1970s.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".