Study of the daily and seasonal atmospheric CH <sub>4</sub> mixing ratio variability in a rural Spanish region using <sup>222</sup> Rn tracer
Bibliographic record
Abstract
Abstract. The ClimaDat station at Gredos (GIC3) has been continuously measuring atmospheric (dry air) mixing ratios of carbon dioxide (CO2) and methane (CH4), as well as meteorological parameters, since November 2012. In this study we investigate the atmospheric variability of CH4 mixing ratios between 2013 and 2015 at GIC3 with the help of co-located observations of 222Rn concentrations, modelled 222Rn fluxes and modelled planetary boundary layer heights (PBLHs). Both daily and seasonal changes in atmospheric CH4 can be better understood with the help of atmospheric concentrations of 222Rn (and the corresponding fluxes). On a daily timescale, the variation in the PBLH is the main driver for 222Rn and CH4 variability while, on monthly timescales, their atmospheric variability seems to depend on emission changes. To understand (changing) CH4 emissions, nocturnal fluxes of CH4 were estimated using two methods: the radon tracer method (RTM) and a method based on the EDGARv4.2 bottom-up emission inventory, both using FLEXPARTv9.0.2 footprints. The mean value of RTM-based methane fluxes (FR_CH4) is 0.11 mg CH4 m−2 h−1 with a standard deviation of 0.09 or 0.29 mg CH4 m−2 h−1 with a standard deviation of 0.23 mg CH4 m−2 h−1 when using a rescaled 222Rn map (FR_CH4_rescale). For our observational period, the mean value of methane fluxes based on the bottom-up inventory (FE_CH4) is 0.33 mg CH4 m−2 h−1 with a standard deviation of 0.08 mg CH4 m−2 h−1. Monthly CH4 fluxes based on RTM (both FR_CH4 and FR_CH4_rescale) show a seasonality which is not observed for monthly FE_CH4 fluxes. During January–May, RTM-based CH4 fluxes present mean values 25 % lower than during June–December. This seasonal increase in methane fluxes calculated by RTM for the GIC3 area appears to coincide with the arrival of transhumant livestock at GIC3 in the second half of the year.
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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 | 0.000 |
| 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 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".