Comment on essd-2022-15
Bibliographic record
Abstract
A global gridded Net Ecosystem Exchange (NEE) of CO2 dataset is vital in global and regional carbon cycle studies. Top-down atmospheric inversion is one of the major methods to estimate the global NEE, however, the existing global NEE datasets generated through inversion from conventional CO2 observations have large uncertainties in places where observational data are sparse. Here, by assimilating the GOSAT ACOS v9 XCO2 product, we generate a ten-year (2010–2019) global monthly terrestrial NEE dataset using the Global Carbon Assimilation System, version 2 (GCASv2), which is named as GCAS2021. It includes gridded (1° × 1°), globally, latitudinally, and regionally aggregated prior and posterior NEE and ocean (OCN) fluxes, and prescribed wildfire (FIRE) and fossil fuel and cement (FFC) carbon emissions. Globally, the decadal mean NEE is −3.73 ± 0.52 PgC yr−1, with interannual amplitude of 2.73 PgC yr−1. Combining the OCN flux, and FIRE and FFC emissions, the net biosphere flux (NBE) and atmospheric growth rate (AGR) as well as their inter-annual variabilities (IAVs) agree well with the estimates of Global Carbon Budget 2020. Regionally, our dateset shows that eastern North America, Amazon, Congo Basin, Europe, boreal forests, southern China and Southeast Asia are carbon sinks, while western US, African grasslands, Brazilian plateaus and parts of South Asia are carbon sources. In the TRANSCOM land regions, the NBEs of temperate N. America, northern Africa and boreal Asia are between the estimates of CMS-Flux NBE 2020 and CT2019B, and those in temperate Asia, Europe, and Southeast Asia are consistent with CMS-Flux NBE 2020 but significantly different from CT2019B. In the RECCAP2 regions, except for Africa and South Asia, the NBEs are comparable with the latest bottom-up estimate of Ciais et al. (2021). Compared with previous studies, the IAVs and seasonal cycles of NEE of this dataset could clearly reflect the impacts of extreme climates and large-scale climate anomalies on the carbon flux. The evaluations also show that the posterior CO2 concentrations at remote sites and in regional scale, as well as on vertical CO2 profiles in the Asia-Pacific region and the Amazon basin, are all consistent with independent CO2 measurements from surface flask and aircraft CO2 observations, indicating that this dataset captures surface carbon fluxes well. We believe that this dataset will contribute to regional or national-scale carbon cycle and carbon neutrality assessment, and carbon dynamics research. The dataset can be accessed at https://doi.org/10.5281/zenodo.5829774 (Jiang, 2022).
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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 | 0.004 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.031 | 0.016 |
| Insufficient payload (model declined to judge) | 0.062 | 0.070 |
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".