High-resolution Neural Network Demonstrates Strong CO2 Source-Sink Juxtaposition in the Coastal Zone
Bibliographic record
Abstract
Coastal oceans may play an important role in regulating the concentration of carbon dioxide in the atmosphere. Quantification of carbon fluxes at this highly dynamic land-ocean interface will aid in monitoring, reporting, and verification for marine carbon dioxide removal. Here, we use a two-step neural network approach to generate basin-wide estimates from sparse observational data in the coastal Northeast Pacific Ocean at an unprecedented spatial resolution of 1/12° with coverage in the nearshore (0 - 25 km offshore). We compiled partial pressure of carbon dioxide (pCO2) observations as well as a range of predictor variables including satellite-based and physical oceanographic reanalysis products. With the predictor variables representing processes affecting pCO2, we created non-linear relationships to interpolate observations from 1998-2019. Compared to in situ shipboard and mooring observations, our coastal pCO2 product captures broad spatial patterns and seasonal cycle variability well. A sensitivity analysis identifies that the parameters responsible for the neural network’s ability to capture regional pCO2 variability agrees with mechanistic processes. Using wind speed and atmospheric CO2, we calculated air-sea CO2 fluxes. We report an anticorrelation between net annual air-sea CO2 flux and air-sea CO2 flux seasonal amplitude and suggest the relationship is driven by regional processes. We show the inclusion of nearshore net outgassing fluxes lowers the overall regional net flux. Overall, our results suggest that the region is a net sink (-0.7 mol m-2 yr-1) for atmospheric CO2 with trends indicating increasing oceanic uptake due to strong connectivity to subsurface waters.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".