The ASTE-BGC Data-Assimilative Regional Ocean Biogeochemistry Model
Bibliographic record
Abstract
We present a data-assimilative regional ocean biogeochemical model, ASTE-BGC, which simulates the physical and biogeochemical state of the North Atlantic Ocean from 2002 to 2017. Model physics are provided by a physical state estimate (ASTE), which assimilates O(10 9 ) in-situ and satellite-based observations over the model domain and time period. Model biogeochemistry is simulated by a medium-complexity biogeochemical module (BLING), which simulates nine prognostic biogeochemical tracers and three ecological classes. We minimize model-data misfit between a “spun-up” simulated biogeochemical state and O(10 5 ) BGC-Argo and ship-based bottle data of O 2 , NO 3 , PO 4 , DIC, and alkalinity. First, we modify the baseline ASTE-BGC configuration to initialize assimilated variables directly from the GLODAPv2.2016b 1° × 1° mapped climatology. Second, we adjust the empirical relationship between solar irradiance and photosynthesis in the Labrador Sea. Finally, we use a Green’s Functions approach to optimize seven BLING biogeochemical parameters using sensitivity experiments. The optimization leads to demonstrable improvements in all regions, particularly in the subpolar North Atlantic. We compare the unconstrained and constrained model versions against independent satellite data and observational products to further demonstrate the improvements to correlation, root mean square error, and bias in modeled O 2 , surface ocean pCO 2 , and chlorophyll-a in the Labrador Sea. We conclude by offering our perspective on the challenges inherent to biogeochemical data assimilation and the future work needed to advance this field.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".