the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jul 2024
What can we learn about tropospheric OH from satellite observations of methane?
Abstract. The hydroxyl radical (OH) is the main oxidant in the troposphere and controls the lifetime of many atmospheric pollutants including methane. Global annual mean tropospheric OH concentrations ([OH]) have been inferred since the late 1970s using the methyl chloroform (MCF) proxy. However, concentrations of MCF are now approaching the detection limit, and a replacement proxy is urgently needed. Previous inversions of GOSAT satellite measurements of methane in the shortwave infrared (SWIR) have shown success in quantifying [OH] independently of methane emissions, and observing system simulations have suggested that thermal infrared (TIR) measurements may provide additional constraints on OH. Here we combine TIR satellite observations of methane from AIRS with SWIR observations from GOSAT in a three-year (2013–2015) analytical Bayesian inversion optimizing both methane emissions and OH concentrations. We examine how much information can be achieved on the interannual, seasonal, and latitudinal features of the OH distribution using information from MCF data as well as the ACCMIP ensemble of global atmospheric chemistry models to construct a full prior error covariance matrix for OH concentrations for use in the inversion. This is essential to avoid overfit to observations. Our results show that GOSAT alone is sufficient to quantify [OH] and its interannual variability independently of methane emissions, and that AIRS adds little information. The ability to constrain the latitudinal variability of OH is limited by strong error correlations. There is no information on OH at mid-latitudes, but there is some information on the NH/SH interhemispheric ratio, showing this ratio to be lower than currently simulated in models. There is also some information on the seasonal variation of OH concentrations, though it mainly confirms that simulated by models. Future satellite observations of methane will continue to improve our understanding of methane emissions and consequently [OH] and its interannual variability.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(3229 KB)
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Supplement
(496 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3229 KB) - Metadata XML
-
Supplement
(496 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2260', Anonymous Referee #1, 06 Sep 2024
In Penn et al, the authors perform an inversion using GEOS-Chem and methane from GOSAT and AIRS to optimize both methane emissions and OH concentrations. Including both GOSAT and AIRS allows the authors to determine the effect of including methane from both shortwave (GOSAT) and thermal (AIRS) infrared instruments. They find that including AIRS in the inversion provides little additional information beyond that found from using GOSAT alone. While the method cannot resolve information on OH at mid-latitudes due to correlation of errors, it was able to provide information on interannual variability and the interhemispheric ratio. This was an interesting paper that both demonstrates the need for continued methane observations by satellites as well as their utility in constraining OH at the global and hemispheric scales. It is suitable for publication in ACP once the very minor comments listed below are addressed.
Minor Comments:
Line 49: Should be “resulting in a lifetime of ~1 second”. Currently missing “of”.
Line 56: Also differences in UV flux in the troposphere. See, for example, Nicely et al, 2020.
Line 159: You use a uniform correction of 19 ppb but figure 1 shows a clear latitudinal bias in AIRS with respect to the GEOS-Chem simulation you are using as “truth”. Why not use a latitudinally dependent correction? In the end, I imagine this doesn’t really affect your results since including AIRS doesn’t seem to have much benefit.
Line 176: Where do the 50% and 20% numbers come from? Are they just “best guesses”, in which case, how sensitive are your results to these numbers?
Line 211: Similar question here as for line 176. What’s your justification for using an error of 50%?
Line 385: You refer to “Boreal North America” as “East Canada” in Figure 6. Be consistent with labeling.
Line 387: Probably shouldn’t start two consecutive sentences with “remarkably”.
Conclusions: One of the main conclusions from this work seems to be that including information from AIRS doesn’t really seem to improve the inversions and that information from the SWIR retrieval (GOSAT, in this case) is sufficient. It could be helpful to have a discussion on whether this is just a result of limitations of the AIRS instrument, or if you think other TIR instruments could prove more useful. What would need to be done to make the TIR retrievals more useful for these types of studies, either improvements to the retrievals or to the instruments themselves? Similarly, when discussing the inability of this methodology to resolve information on OH across more latitude bands, what could be done to improve this, if anything? Using a different or larger set of independent simulations? Or is this simply a limitation of this methodology and it is unlikely to ever yield spatially resolved information on OH distributions?
References:
Nicely, J. M., Duncan, B. N., Hanisco, T. F., Wolfe, G. M., Salawitch, R. J., Deushi, M., et al. (2020). A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1. Atmospheric Chemistry and Physics, 20(3), 1341-1361.
Citation: https://doi.org/10.5194/egusphere-2024-2260-RC1 - RC2: 'Comment on egusphere-2024-2260', Anonymous Referee #2, 24 Sep 2024
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AC1: 'Comment on egusphere-2024-2260', Elise Penn, 12 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2260/egusphere-2024-2260-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2260', Anonymous Referee #1, 06 Sep 2024
In Penn et al, the authors perform an inversion using GEOS-Chem and methane from GOSAT and AIRS to optimize both methane emissions and OH concentrations. Including both GOSAT and AIRS allows the authors to determine the effect of including methane from both shortwave (GOSAT) and thermal (AIRS) infrared instruments. They find that including AIRS in the inversion provides little additional information beyond that found from using GOSAT alone. While the method cannot resolve information on OH at mid-latitudes due to correlation of errors, it was able to provide information on interannual variability and the interhemispheric ratio. This was an interesting paper that both demonstrates the need for continued methane observations by satellites as well as their utility in constraining OH at the global and hemispheric scales. It is suitable for publication in ACP once the very minor comments listed below are addressed.
Minor Comments:
Line 49: Should be “resulting in a lifetime of ~1 second”. Currently missing “of”.
Line 56: Also differences in UV flux in the troposphere. See, for example, Nicely et al, 2020.
Line 159: You use a uniform correction of 19 ppb but figure 1 shows a clear latitudinal bias in AIRS with respect to the GEOS-Chem simulation you are using as “truth”. Why not use a latitudinally dependent correction? In the end, I imagine this doesn’t really affect your results since including AIRS doesn’t seem to have much benefit.
Line 176: Where do the 50% and 20% numbers come from? Are they just “best guesses”, in which case, how sensitive are your results to these numbers?
Line 211: Similar question here as for line 176. What’s your justification for using an error of 50%?
Line 385: You refer to “Boreal North America” as “East Canada” in Figure 6. Be consistent with labeling.
Line 387: Probably shouldn’t start two consecutive sentences with “remarkably”.
Conclusions: One of the main conclusions from this work seems to be that including information from AIRS doesn’t really seem to improve the inversions and that information from the SWIR retrieval (GOSAT, in this case) is sufficient. It could be helpful to have a discussion on whether this is just a result of limitations of the AIRS instrument, or if you think other TIR instruments could prove more useful. What would need to be done to make the TIR retrievals more useful for these types of studies, either improvements to the retrievals or to the instruments themselves? Similarly, when discussing the inability of this methodology to resolve information on OH across more latitude bands, what could be done to improve this, if anything? Using a different or larger set of independent simulations? Or is this simply a limitation of this methodology and it is unlikely to ever yield spatially resolved information on OH distributions?
References:
Nicely, J. M., Duncan, B. N., Hanisco, T. F., Wolfe, G. M., Salawitch, R. J., Deushi, M., et al. (2020). A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1. Atmospheric Chemistry and Physics, 20(3), 1341-1361.
Citation: https://doi.org/10.5194/egusphere-2024-2260-RC1 - RC2: 'Comment on egusphere-2024-2260', Anonymous Referee #2, 24 Sep 2024
-
AC1: 'Comment on egusphere-2024-2260', Elise Penn, 12 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2260/egusphere-2024-2260-AC1-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
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- 1
Elise Penn
Daniel J. Jacob
Zichong Chen
James D. East
Melissa P. Sulprizio
Lori Bruhwiler
Joannes D. Maasakkers
Hannah Nesser
Yuzhong Zhang
John Worden
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3229 KB) - Metadata XML
-
Supplement
(496 KB) - BibTeX
- EndNote
- Final revised paper