Decoupling of Temporal and Spatial Variability of Greenhouse Gases in a Coastal Upwelling Zone Using High Frequency Ship-Board Measurements and Numerical Analysis
Bibliographic record
Abstract
Upwelling zones associated with eastern boundary currents are regions of intense biological productivity and biogeochemical cycling of climate active gases such as carbon dioxide, methane and nitrous oxide. Sea-air fluxes of these gases have important climate implications, pointing towards a need for understanding the sources of these gases and how they may evolve with future climate change. Coastal upwelling regions exhibit strong temporal and spatial variability in dissolved gas concentrations driven by physical forcing (advection, turbulent mixing and sea-air flux) and biogeochemical processes (primary productivity and microbial activity), and this variability is difficult to quantify based on low-resolution, discrete sampling. To address this challenge, we used high spatial resolution measurements of pCO2, CH4, N2O to map surface water variability of these gases in the California upwelling system, coupled with numerical model output to examine the underlying physical transport mechanism driving observed distributions. Continuous underway measurements revealed a distinct latitudinal gradient in CH4, N2O and pco2 levels in different upwelling plumes, with the highest concentrations (1900% CH4 saturation, 200% N2O saturation and ΔpCO2 of 475 μatm) observed in an upwelling plume north of Cape Blanco, and significantly lower values in the more southerly upwelling region of Cape Mendocino, (average values of 500% CH4 saturation, 120% N2O saturation and ΔpCO2 of 0 μatm). Using backward water mass trajectory analysis, we determined that variability in surface water concentrations primarily reflects differences in the source waters feeding upwelling in the northern and southern regions, rather than surface processes associated with the ‘aging’ of upwelling plumes. Results from our analysis reveal the utility of combining high resolution measurements with numerical models to disentangle coupled spatial and temporal variability in dynamic coastal waters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".