Optimizing simulated oxygen variability, circulation, and export in the subpolar North Atlantic Ocean using BGC-Argo & ship-based observations
Bibliographic record
Abstract
The subpolar North Atlantic (SPNA) transports surface-ocean properties deep into the interior via deep convection and is one of the most intense regions of air-sea gas exchange globally. Deep convection in the SPNA exports highly-oxygenated water masses to depth, which subsequently ventilate intermediate and deep waters throughout the North Atlantic. The SPNA thus plays a critical role in setting the oxygen inventory of the global ocean. Due to intensifying ocean warming, many climate models predict substantial global-ocean oxygen loss — albeit at magnitudes which vary widely by model. Therefore, there is a need to better understand the impacts of SPNA convective variability on oxygen saturation in intermediate and deep water masses. Here we use a physical-biogeochemical model, ASTE-BGC, which couples the Arctic Subpolar gyre sTate Estimate (ASTE) with the Biogeochemistry with Light, Iron, Nutrients, and Gas (BLING) model to quantify oxygen cycling and deep ventilation in the SPNA. ASTE utilizes the MIT General Circulation Model (MITgcm) and assimilates physical in-situ and satellite data using tools developed by the Estimating the Circulation and Climate of the Ocean (ECCO) consortium. We use a Green’s Functions approach to optimize ASTE-BGC biogeochemistry using BGC-Argo and GLODAPv2 ship-based profiles of O2 and NO3. The Green’s Functions approach allows us to adjust the biogeochemical parameters of the BLING ecosystem towards O(106) in-situ data constraints over the 2002–2017 model period. We then evaluate the optimized simulation against independent data and construct an oxygen budget for the central Labrador Sea to assess the interannual variability of SPNA oxygen.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".