Canada’s extreme wildfires dominate the decline in global land carbon sinks in 2023
Bibliographic record
Abstract
Terrestrial land carbon sinks are strongly influenced by climate extremes, and 2023 is the warmest year on record, accompanied by an El Niño event, extreme wildfires, and extreme precipitation and drought, but their impact on global land sinks in 2023 remains unclear. Here, we used the Global Carbon Assimilation System, version 2, to estimate recent global land sinks by assimilating the OCO-2 ACOS v11.1 XCO2 retrievals. We estimate the global land sink to be -1.63 ± 0.52 PgC/yr in 2023. Compared to 2017-2022, it decreases by 0.59 PgC/yr, in which net ecosystem exchange decreases by only 0.14 PgC/yr, but wildfire emissions increase significantly by 0.45 PgC/yr, mainly in Canada. Our findings suggest that extreme wildfires are an important threat to land sinks under global warming.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".