Reduction of airmass-dependent biases in TCCON XCH <sub>4</sub> retrievals during polar vortex conditions
Bibliographic record
Abstract
Abstract. Trace gas measurements from the Total Carbon Column Observing Network (TCCON) are important for monitoring the global climate system and for validating satellite measurements. In the Arctic, ground-based data coverage is relatively limited due the inherent challenges of conducting measurements in this region (e.g., remoteness, harsh weather) and the polar nights, which prevent solar absorption measurements for half of the year. TCCON measurements from the Arctic sites are of significant value for the validation of satellite data products in this region, as these measurements can extend the spatio-temporal coverage in the Arctic. In this study, we investigate the TCCON methane (CH4) retrieval under polar vortex conditions. The CH4 profile exhibits a distinct shape inside the vortex, which is related to the descent of stratospheric air inside the vortex. We show that the standard TCCON CH4 prior does not sufficiently reproduce this profile shape, leading to airmass dependencies (AMDs), increased spectral residuals and less sensitive averaging kernels. These effects can be explained by the fact that TCCON uses a profile scaling retrieval (PSR) where the prior shape is fixed and only a scaling factor is retrieved. We further show that changes in the prior can improve the retrieval within the polar vortex. This leads to mean differences between 1 and 2 ppb in XCH4 compared to the standard retrieval, and maximum differences up to roughly 17 ppb. This manuscript highlights the importance of understanding the limitations of retrieval methods to avoid misinterpretation of data. Furthermore, it emphasizes the need to investigate the shape of trace gas profiles inside the polar vortex to improve PSR in the Arctic, which could include in situ data campaigns focusing on inside-vortex air.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".