Analysis of atmospheric CO2 growth rates at Mauna Loa using CO2 fluxes derived from an inverse model
Bibliographic record
Abstract
Carbon dioxide (CO2) growth rates are estimated for a period 1959–2004 from atmospheric CO2 measurements at Mauna Loa by the Scripps Institute of Oceanography. Only during a few short periods, 1965–1966, 1972–1973, 1987–1988 and 1997–1998, in the last 45 yr have growth rates of atmospheric CO2 been of a similar magnitude or higher than that due to the total emission from burning of fossil fuels. Using results from a time-dependent inverse (TDI) model, based on observations of atmospheric CO2 at 87 stations, we establish that El Nino-induced climate variations in the tropics and large-scale forest fires in the boreal regions are the main causes of anomalous growth rates of atmospheric CO2. The high growth rate of 2.8 ppm yr−1 in 2002 can be predicted fairly successfully by using the correlations between (1) the peak-to-trough amplitudes in the El Nino Southern Oscillation (ENSO) index and tropical flux anomaly, and (2) anomalies in CO2 flux and area burned by fire from the boreal regions. We suggest that the large interannual changes in CO2 growth rates can mostly be explained by natural climate variability. Our analysis also shows that the decadal average growth rate, linked primarily to human activity, has fluctuated around an all-time high value of ~1.5 ppm yr−1 over the past 20 yr. A statistical model analysis is performed to identify the regions which have the maximum influence on the observed growth rate anomaly at Mauna Loa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".