Bibliographic record
Abstract
Accurate assessment of anthropogenic carbon dioxide ( CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO 2 emissions ( E FOS ) are based on energy statistics and cement production data, while emissions from land-use change ( E LUC ), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO 2 concentration is measured directly, and its growth rate ( G ATM ) is computed from the annual changes in concentration. The ocean CO 2 sink ( S OCEAN ) is estimated with global ocean biogeochemistry models and observation-based f CO 2  products. The terrestrial CO 2 sink ( S LAND ) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance ( B IM ), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1 Ï . For the year 2022, E FOS increased by 0.9â% relative to 2021, with fossil emissions at 9.9±0.5 âGtâCâyr â1 ( 10.2±0.5 âGtâCâyr â1 when the cement carbonation sink is not included), and E LUC was 1.2±0.7 âGtâCâyr â1 , for a total anthropogenic CO 2 emission (including the cement carbonation sink) of 11.1±0.8 âGtâCâyr â1 ( 40.7±3.2 âGtâ CO 2 âyr â1 ). Also, for 2022, G ATM was 4.6±0.2 âGtâCâyr â1 ( 2.18±0.1 âppmâyr â1 ; ppm denotes parts per million), S OCEAN was 2.8±0.4 âGtâCâyr â1 , and S LAND was 3.8±0.8 âGtâCâyr â1 , with a B IM of â0.1 âGtâCâyr â1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO 2 concentration averaged over 2022 reached 417.1±0.1 âppm. Preliminary data for 2023 suggest an increase in E FOS relative to 2022 of +1.1 â% (0.0â% to 2.1â%) globally and atmospheric CO 2 concentration reaching 419.3âppm, 51â% above the pre-industrial level (around 278âppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959â2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1âGtâCâyr â1 persist for the representation of annual to semi-decadal variability in CO 2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO 2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023).
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.032 | 0.013 |
| Insufficient payload (model declined to judge) | 0.276 | 0.231 |
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".