Increase in gross primary production of boreal forests balanced out by increase in ecosystem respiration
Bibliographic record
Abstract
Changes in the net carbon sink of boreal forests constitute a major source of uncertainty in the future global carbon budget and, hence, climate change projections. The annual net ecosystem exchange of carbon dioxide (CO2) controlling the terrestrial carbon stock results from the small difference between respiratory CO2 release and the photosynthetic CO2 uptake by vegetation. The boreal forest, and the boreal biome in general, is regarded as a persistent and even increasing net carbon sink. However, decreases in photosynthetic CO2 uptake and/or concurrent increases in respiratory CO2 release under a changing climate may turn boreal forests from a net sink to a net source of CO2. Here, we assessed the interannual variability of the boreal forest net CO2 sink-source strength and its two component fluxes from 1981 to 2018. Our remote sensing approach - trained by net CO2 flux observations at eddy covariance sites across the circumpolar boreal forests - employs satellite-derived retrievals of snowmelt timing, landscape freeze-thaw status, and yearly maximum estimates of the normalized difference vegetation index as a proxy for peak vegetation productivity. Our results suggest that for the period 2000–2018, the mean annual evergreen boreal forest CO2 photosynthetic uptake (gross primary productivity) was 0.2 Pg C y−1 (0.1 Pg C y−1 for Eurasia and 0.1 Pg C y−1 for North America). In contrast to earlier studies results obtained here do not indicate a clear increasing trend in the circumpolar evergreen boreal forest CO2 sink. The increase in photosynthetic CO2 uptake is compensated by increasing respiratory releases with both component fluxes showing considerable interannual variabilities.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".