Post-peak trend of upper stratospheric hydrogen chloride derived from ground-based FTIR solar spectra and model simulations
Bibliographic record
Abstract
After several decades of sustained increase, hydrogen chloride (HCl, the main reservoir for stratospheric chlorine) reached a maximum abundance around 1997. Since then, its decrease has been documented, characterized by short-term variability which was attributed to atmospheric circulation changes, affecting mainly the lower stratosphere (Mahieu et al., 2014). This notably led to a temporary increase of HCl over 2007-2011, complicating the determination of the long-term HCl trend and the accurate verification of the success of the Montreal Protocol for the protection of the stratospheric ozone layer. Studies have used other long-lived tracers to remove the effects of dynamical variability in the lower stratosphere (e.g., Stolarski et al., 2018), while other investigations have suggested that trends in the upper stratosphere were potentially more appropriate for the long-term characterization of the HCl decrease (e.g., Froidevaux et al., 2015; Bernath and Fernando, 2018), especially when dealing with satellite height-resolved data. In this contribution, we use FTIR (Fourier Transform InfraRed) data from the Jungfraujoch station (Swiss Alps, 3580 m a.s.l.), a site of the NDACC network (http://www.ndacc.org), to study the evolution of HCl in some detail. The SFIT-4 retrieval algorithm implementing the Optimal Estimation Method of Rodgers (2000) is employed, providing HCl columns with good sensitivity from the tropopause up to about 40 km altitude. Moreover, the vertical resolution is sufficient to determine independent partial columns for the lower and upper stratosphere. With the support of model simulations performed with the 3D-Chemistry Transport Model of the Belgian Assimilation System for Chemical ObsErvations (BASCOE; Chabrillat et al., 2018), driven by the ERA-Interim meteorological reanalysis, we investigate the post-peak trend of HCl in the lower and upper stratosphere. We also determine the magnitude of the uncertainties affecting the various trends, using bootstrap tools which are specifically developed to take into account the auto-correlation present in our geophysical data sets. Acknowledgments This research has been primarily supported by the F.R.S. - FNRS (Brussels), under Grants T.0040.16 and J.0147.18. The vital supports from the Fédération Wallonie Bruxelles and the Swiss GAW-CH programme are further acknowledged. We thank the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern) for supporting the facilities needed to perform the observations. We gratefully acknowledge support by the Deutsche Forschungsgemeinschaft DFG within the SFB/Transregio 172 (AC)3. References Bernath, P. and Fernando, A. M.: Trends in stratospheric HCl from the ACE satellite mission, J. Quant. Spectrosc. Radiat. Transf., 217, 126-129, doi:10.1016/j.jqsrt.2018.05.027, 2018. Chabrillat, S., Vigouroux, C., Christophe, Y., et al.: Comparison of mean age of air in five reanalyses using the BASCOE transport model, Atmos. Chem. Phys., 18(19), 14715-14735, doi:10.5194/acp-18-14715-2018, 2018. Froidevaux, L., Anderson, J., Wang, H. J., et al.: Global OZone Chemistry and Related trace gas Data records for the Stratosphere (GOZCARDS): Methodology and sample results with a focus on HCl, H2O, and O3, Atmos. Chem. Phys., 15(18), 10471-10507, doi:10.5194/acp-15-10471-2015, 2015. Mahieu, E., Chipperfield, M. P., Notholt, J., et al.: Recent Northern Hemisphere stratospheric HCl increase due to atmospheric circulation changes, Nature, 515(7525), 104-107, doi:10.1038/nature13857, 2014. Rodgers, C.D.: Inverse methods for atmospheric sounding: theory and practice (Series on atmospheric, oceanic and planetary physics), Vol. 2. Singapore, 2000. Stolarski, R. S., Douglass, A. R. and Strahan, S. E.: Using satellite measurements of N2O to remove dynamical variability from HCl measurements, Atmos. Chem. Phys., 18(8), 5691-5697, doi:10.5194/acp-18-5691-2018, 2018.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".