Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O)  profile data sets

Dhomse, Sandip S.; Chipperfield, Martyn P.

doi:https://doi.org/10.5194/essd-15-5105-2023

Articles | Volume 15, issue 11

https://doi.org/10.5194/essd-15-5105-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-5105-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 11

Data description paper

|

24 Nov 2023

Data description paper |

| 24 Nov 2023

Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets

Sandip S. Dhomse and Martyn P. Chipperfield

Supplement

https://doi.org/10.5194/essd-15-5105-2023-supplement

Data sets

TCOM-CH4: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric methane profile dataset [1991-2021] constructed using machine-learning Sandip S. Dhomse https://doi.org/10.5281/zenodo.7293740

TCOM-N2O: TOMCAT CTM and Occultation Measurements based daily zonal stratospheric nitrous oxide profile dataset [1991-2021] constructed using machine-learning Sandip S. Dhomse https://doi.org/10.5281/zenodo.7386001

SPARC Data Initiative monthly zonal mean composition measurements from stratospheric limb sounders (1978-2018) Michaela I. Hegglin, Susann Tegtmeier, John Anderson, Adam E. Bourassa, Samuel Brohede, Doug Degenstein, Lucien Froidevaux, Bernd Funke, John Gille, Yasuko Kasai, Erkki Kyrölä, Jerry Lumpe, Donal Murtagh, Jessica L. Neu, Kristell Pérot, Ellis Remsberg, Alexey Rozanov, Matt Toohey, Thomas von Clarmann, Kaley A. Walker, Hsiang Jiu Wang, Robert Damadeo, Ryan Fuller, Gretchen Lingenfelser, Chris Roth, Niall J. Ryan, Christopher Sioris, Lesley Smith, and Katja Weigel https://doi.org/10.5281/zenodo.4265393

ARS Halogen Occultation Experiment (HALOE) Level 2 V019 J. M. Russell III, and M. James https://acdisc.gesdisc.eosdis.nasa.gov/data//UARS_HALOE_Level2/

Atmospheric Chemistry Experiment SciSat Level 2 Processed Data P. Bernath, J. Steffen, J. Crouse, and C. Boone https://doi.org/10.20383/101.0291

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Short summary

There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH₄) and nitrous oxide (N₂O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH₄ and N₂O. We aim to construct similar data sets for other important trace gases (e.g. O₃, Cl_y, NO_y species).