Changes in Oceanic Carbon Storage Due to Anthropogenic Carbon Input Over the Past Three Decades
Bibliographic record
Abstract
While the ocean is known to be an important sink for anthropogenic CO2 emissions, assessing trends in ocean’s uptake and storage of atmospheric CO2 is complicated because changes in the ocean dissolvedinorganic carbon (DIC) concentrations due to natural ocean circulation patterns and flux of anthropogenic CO2 need to be disentangled. In this study, we analyze the interannual and decadal changes in the ocean anthropogenic DIC storage from 1992 to 2022 using data from the physically and biogeochem-ically consistent ECCO-Darwin ocean state estimate model. We use the quasi-conservative tracer C∗ to represent the ocean anthropogenic DIC concentrations and offer several key extensions to previous studies: (1) a longer period of analysis (three decades), (2) analysis including the Arctic Ocean, (3) regular spatio-temporal coverage using annually-averaged data to more accurately estimate the rates of change of C∗. Over the 1992–2022 period, we estimate a total global ocean C∗ increase of 60 Pg C, corresponding to about 28% of total anthropogenic CO2 emissions during this time. The general temporal trend shows a nonlinear increase with accelerating rates of anthropogenic DIC accumulation especially in the last two decades (2002 − 2022), though a slowdown in the increasing rates is found in some parts of the ocean, in particular in high-nutrient low-chlorophyll regions. Empirical Orthogonal Function analysis of the vertically-integrated rates of change of C∗ reveals that the top four modes of interannual variability correspond to the Pacific climate modes, such as El Ni˜no Southern Oscillation, Pacific Decadal Oscillation, and North Pacific Gyre Oscillation.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".