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Record W2945433043 · doi:10.1080/07055900.2019.1605974

Modelling Sea Surface Temperature (SST) in the Hudson Bay Complex Using Bulk Heat Flux Parameterization: Sensitivity to Atmospheric Forcing, and Model Resolution

2019· article· en· W2945433043 on OpenAlexafffundvenueabout
Shabnam Jafarikhasragh, Jennifer V. Lukovich, Xianmin Hu, Paul G. Myers, Kevin Sydor, David G. Barber

Bibliographic record

VenueATMOSPHERE-OCEAN · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of AlbertaManitoba HydroUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridManitoba HydroCompute CanadaUniversity of Manitoba
KeywordsForcing (mathematics)Environmental scienceSea surface temperatureBayHeat fluxSensitivity (control systems)ClimatologyFlux (metallurgy)Atmospheric sciencesResolution (logic)OceanographyGeologyHeat transferPhysicsMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Sea surface temperature (SST) from four Nucleus for European Modelling of the Ocean (NEMO) model simulations is analyzed to study the bulk flux parameterization to compute SST over the Hudson Bay Complex (HBC) for the summer months (August and September) from 2002 to 2009. The NEMO simulation was forced with two atmospheric forcing sets with different resolutions: the Coordinated Ocean-ice Reference Experiment, version 2 (COREv2), as the lower resolution and the Canadian Meteorological Centre's Global Deterministic Prediction System Reforecasts (CGRF) as the higher resolution. The CGRF forcing is also implemented in the third and fourth runs using different runoff data and different NEMO resolutions (1/12° versus 1/4°). Results show that all four modelled SSTs followed observed SST patterns, with regional differences in SST bias between simulations with different atmospheric forcing. The SST differences are small between simulations forced with the same atmospheric forcing but with different model resolution or runoff. This implies that the model resolution and runoff have a small effect on the simulated SST in the HBC. Moreover, to better capture the effect of near-surface temperature (Tair) on simulated SST, we conducted three analyses using the Haney flux linearization formula. Results from these assessments did not indicate any direct influence on the model-simulated SSTs by Tair. Looking at the heat flux as a signature for SST showed that both averaged spatial distribution and time series of net heat flux produced by the three CGRF forcing simulations were higher than the net heat flux generated by the CORE 2 simulation. This was generally true for all four components of the total heat flux (sensible, latent, shortwave, and longwave) individually as well. Total heat flux in summer is governed by the shortwave heat flux, with values up to 120 W m−2 in August, and the longwave heat flux is the main contributor to the total heat flux differences. These heat flux differences lead to corresponding colder model SSTs for the CGRF runs and warmer SSTs for the CORE 2 simulations.

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 categoriesnone
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 score0.199
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.219
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2019
Admission routes4
Has abstractyes

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