Global and Brazilian Carbon Response to El Niño Modoki 2011–2010
Bibliographic record
Abstract
The El Niño Modoki in 2010 led to historic droughts in Brazil. In order to understand its impact on carbon cycle variability, we derive the 2011–2010 annual carbon flux change (δF↑) globally and specifically to Brazil using the NASA Carbon Monitoring System Flux (CMS‐Flux) framework. Satellite observations of CO2, CO, and solar‐induced fluorescence (SIF) are ingested into a 4D‐variational assimilation system driven by carbon cycle models to infer spatially resolved carbon fluxes including net ecosystem production, biomass burning, and gross primary productivity (GPP). The global 2011–2010 net carbon flux change was estimated to be δF↑=−1.60 PgC, while the Brazilian carbon flux change was −0.24 ± 0.11 PgC. This estimate is broadly within the uncertainty of previous aircraft‐based estimates restricted to the Amazon basin. The 2011–2010 biomass burning change in Brazil was −0.24 ± 0.036 PgC, which implies a near‐zero 2011–2010 change of the net ecosystem production (NEP): The near‐zero NEP change is the result of quantitatively comparable increases GPP (0.31 ± 0.20 PgC) and respiration in 2011. Comparisons between Brazilian and global component carbon flux changes reveal complex interactions between the processes controlling annual land‐atmosphere CO2 exchanges. These results show the potential of multiple satellite observations to help quantify and spatially resolve the response of productivity and respiration fluxes to climate variability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".