Reconciling estimates of the contemporary North American carbon balance among terrestrial biosphere models, atmospheric inversions, and a new approach for estimating net ecosystem exchange from inventory‐based data
Bibliographic record
Abstract
Abstract We develop an approach for estimating net ecosystem exchange ( NEE ) using inventory‐based information over North America ( NA ) for a recent 7‐year period (ca. 2000–2006). The approach notably retains information on the spatial distribution of NEE , or the vertical exchange between land and atmosphere of all non‐fossil fuel sources and sinks of CO 2 , while accounting for lateral transfers of forest and crop products as well as their eventual emissions. The total NEE estimate of a −327 ± 252 TgC yr −1 sink for NA was driven primarily by CO 2 uptake in the Forest Lands sector (−248 TgC yr −1 ), largely in the Northwest and Southeast regions of the US, and in the Crop Lands sector (−297 TgC yr −1 ), predominantly in the Midwest US states. These sinks are counteracted by the carbon source estimated for the Other Lands sector (+218 TgC yr −1 ), where much of the forest and crop products are assumed to be returned to the atmosphere (through livestock and human consumption). The ecosystems of Mexico are estimated to be a small net source (+18 TgC yr −1 ) due to land use change between 1993 and 2002. We compare these inventory‐based estimates with results from a suite of terrestrial biosphere and atmospheric inversion models, where the mean continental‐scale NEE estimate for each ensemble is −511 TgC yr −1 and −931 TgC yr −1 , respectively. In the modeling approaches, all sectors, including Other Lands, were generally estimated to be a carbon sink, driven in part by assumed CO 2 fertilization and/or lack of consideration of carbon sources from disturbances and product emissions. Additional fluxes not measured by the inventories, although highly uncertain, could add an additional −239 TgC yr −1 to the inventory‐based NA sink estimate, thus suggesting some convergence with the modeling approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".