Flux of CO2, CH4 and N2O from temperate woodland soil under elevated CO2
Bibliographic record
Abstract
Atmospheric fluxes of greenhouse gases (GHG’s) in the form of CO2, CH4 and N2O from temperate forest soils are an important aspect of the net global warming potential and climate change mitigation function of forests. However, it remains unclear how the magnitude of these atmospheric fluxes of GHG’s will respond to rising atmospheric CO2 concentrations in mature temperate forests. An increase in carbon capture by temperate forests under elevated atmospheric CO2 concentration (eCO2) and its subsequent storage in biomass and soils can have direct impact on the activities of soil microbes. In addition to indirect effects through shifting soil moisture regimes, potentially altering GHG production and consumption processes and hence net emissions from temperate forests. The Birmingham Institute of Forest research established a Free Air Carbon Enrichment Facility (BIFoR-FACE) whereby a mature temperate forest in the UK is exposed to +150 ppm CO2 above the ambient (aCO2), mimicking future CO2 conditions. Understanding GHG exchange from soils under elevated atmospheric CO2 levels is critical for addressing this component of the systems response to eCO2. Fumigation started in 2017 and continues to date, where the ecological and biogeochemical responses of the forest is being studied. In this abstract, the focus is placed on quantifying ~5-years (2019 – 2024) of GHG flux response to eCO2 to elucidate shifts in fluxes as influenced by eCO2 and local microclimatic conditions.The flux of CO2 from the soil has been continuously measured within fumigated treatment (eCO2) and ambient control (aCO2) arrays since 2017 via LI-COR 8100A long-term measurement systems. With capabilities to additionally measure CH4 and N2O being added in 2020 through a coupled Picarro-G2508 analyser. Initial trends from 2017 - 2020 indicated that eCO2 arrays had a higher efflux of CO2 relative to paired aCO2 arrays by +20%. However, from 2020 – 2022 a significant decline of -46.6% in the efflux of CO2 was detected, in addition to a -76.6% reduction in N2O effluxes and a -44.3% decline in the CH4 uptake by the soil component. This period corresponds to a significant decline in soil moisture across the soil profile from the surface (0.05m) to a depth of 0.4m, equivalent to a -36% decline in volumetric water content under eCO2 relative to aCO2. Which when coupled with the prevalence of drought periods during the growing seasons of 2021 and 2022 suggest an enhanced drying of soil under eCO2, which is in turn exacerbated by drought events. During 2023 and the wettest July on record for the UK, the moisture deficit between eCO2 and aCO2 shrunk, reducing the variance in the efflux of CO2 to just ~4.5%. Therefore, it is possible that a functional change in the heterotrophic and autotrophic mediated flux dynamic could be occurring, driven by significant soil drying under eCO2, an affect which is exacerbated during drought events. Inter and intra-seasonal patterns of GHG fluxes will be examined in further detail, whilst also partitioning between autotrophic and heterotrophic contributions.
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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 | 0.001 |
| 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) | 0.002 | 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".