Space Observation of Carbon Dioxide Partial Pressure at Ocean Surface
Bibliographic record
Abstract
We have developed and validated a statistical model to estimate the partial pressure (or fugacity) of carbon dioxide at the sea surface from space-based observations of sea surface temperature, chlorophyll, and salinity. More than a quarter million in situ measurements coincident with satellite data were compiled. A portion of the data was randomly selected to train and validate the model. We have produced and made accessible nine years (2002-2011) of the partial pressure at 0.5° and daily resolutions over the global oceans. The outputs are found to be sensitive to variability from intraseasonal to interannual time scales and from the equatorial to high-latitude oceans. They can capture known phenomena, such as, annual spring blooms at high latitudes, interannual episodes of El Niño, and westward propagation of tropical instability waves. The feasibility of using a single algorithm to map the partial pressure over global oceans for all seasons is demonstrated. The result is important for characterizing the sources and sinks of atmospheric greenhouse gas and ocean biogeochemistry. We discuss the significance of the Advanced Scanning Microwave Radiometer in the consistent measurement of sea surface temperature, which is the main driver of ocean carbon dioxide change, in cold and warm waters, under clear and cloudy sky. The continuous and consistent coverage of the partial pressure over global ocean using space-based data is discussed.
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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 | 0.000 | 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".