The effects of clouds and aerosols on net ecosystem CO <sub>2</sub> exchange over semi-arid Loess Plateau of Northwest China
Bibliographic record
Abstract
Abstract. The impacts of clouds and atmospheric aerosols on the terrestrial carbon cycle at semi-arid Loess Plateau in Northwest China are investigated, by using the observation data obtained at the SACOL (Semi-Arid Climate and Environment Observatory of Lanzhou University) site. Daytime (solar elevation angles of larger than 50°) net ecosystem exchange (NEE) of CO2 obtained during the midgrowing season (July–August) are analyzed with respect to variations in the diffuse radiation, cloud cover and aerosol optical depth (AOD). Results show a significant impact by clouds on the CO2 uptake by the grassland (with smaller LAI values) located in a semi-arid region, quite different from areas covered by forests and crops. The light saturation levels in the canopy are low, with a value of about 434.8 W m−2. Thus, under overcast conditions of optically thick clouds, the CO2 uptake increases with increasing clearness index (the ratio of global solar radiation received at the Earth surface to the extraterrestrial irradiance at a plane parallel to the Earth surface), and a maximum CO2 uptake and light use efficiency of vegetation occur with the clearness index of about 0.37 and lower air temperature. Under other sky conditions, CO2 uptake decreases with cloudiness but light use efficiency is enhanced, due to increased diffuse fraction of PAR. Additionally, under cloudy conditions, changes in the NEE of CO2 also result from the interactions of many environmental factors, especially the air temperature. In contrast to its response to changes in solar radiation, the carbon uptake shows a slightly negative response to increased AOD. The reason for the difference in the response of the semi-arid grassland from that of the forest and crop lands may be due to the difference in the canopy's architectural structure.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".