MétaCan
Menu
Back to cohort
Record W4406627368 · doi:10.5194/gmd-18-211-2025

HOTSSea v1: a NEMO-based physical Hindcast of the Salish Sea (1980–2018) supporting ecosystem model development

2025· article· en· W4406627368 on OpenAlexafffund
Greig Oldford, Tereza Jarníková, Villy Christensen, Michael Dunphy

Bibliographic record

VenueGeoscientific model development · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of British ColumbiaFisheries and Oceans Canada
FundersNatural Sciences and Engineering Research Council of CanadaFisheries and Oceans CanadaMitacsPacific Salmon FoundationUniversity of British ColumbiaGovernment of Canada
KeywordsHindcastEnvironmental scienceEcosystemClimatologyMeteorologyGeographyEcologyGeologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.215
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueGeoscientific model developmentSame topicOceanographic and Atmospheric ProcessesFrench-language works237,207