the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating Brewer-Dobson circulation trends from changes in stratospheric water vapour and methane
- 1Institute of Energy and Climate Research: Stratosphere (IEK-7), Forschungszentrum Jülich, Jülich, Germany
- 2Institute of Bio- and Geosciences: Agrosphere (IBG-3) Forschungszentrum Jülich, Jülich, Germany
- 3Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
- 4Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
- 5Department of Geosciences, Princeton University, Princeton, NJ, USA
- 6University of Wuppertal, Institute for Atmospheric and Environmental Research, Wuppertal, Germany
- 7Department of Meteorology, University of Reading, Reading, UK
- 1Institute of Energy and Climate Research: Stratosphere (IEK-7), Forschungszentrum Jülich, Jülich, Germany
- 2Institute of Bio- and Geosciences: Agrosphere (IBG-3) Forschungszentrum Jülich, Jülich, Germany
- 3Centre for High-Performance Scientific Computing in Terrestrial Systems (HPSC TerrSys), Geoverbund ABC/J, Jülich, Germany
- 4Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
- 5Department of Geosciences, Princeton University, Princeton, NJ, USA
- 6University of Wuppertal, Institute for Atmospheric and Environmental Research, Wuppertal, Germany
- 7Department of Meteorology, University of Reading, Reading, UK
Abstract. The stratospheric meridional overturning circulation, also referred to as the Brewer-Dobson circulation (BDC), controls the composition of the stratosphere, which, in turn, affects radiation and climate. As the BDC cannot be directly measured, one has to infer its strength and trends indirectly. For instance, trace gas measurements allow the calculation of average transit times.
Satellite measurements provide information on the distributions of trace gases for the entire stratosphere, with measurements of particularly long and dense coverage available for stratospheric water vapour (H2O). Although chemical processes and boundary conditions confound interpretation, the influence of CH4 oxidation on H2O is relatively straightforward, and thus H2O is an appealing tracer for transport analysis despite these caveats. In this work, we explore how mean age of air trends can be estimated from the combination of stratospheric H2O and CH4 data. We carry out different sensitivity studies with the Chemical Lagrangian Model of the Stratosphere (CLaMS) and focus on the analysis of the periods of 1990–2006 and 1990–2017. In particular, we assess the methodological uncertainties related to the two commonly-used approximations of (i) instantaneous stratospheric entry mixing ratio propagation, and (ii) constant correlation between mean age and the fractional release factor of methane.
Our results show that the estimated mean age of air trends from the combination of observed stratospheric H2O and CH4 changes may be significantly affected by the assumed approximations. Depending on the investigated stratospheric region and the considered period, the error in estimated mean age of air decadal trends can be large – the discrepancies are up to 10 % per decade or even more at the lower stratosphere. For particular periods, the errors from the two approximations can lead to opposite effects, which may even cancel out. Finally, we propose an improvement to the approximation method by using an idealised age spectrum to propagate stratospheric entry mixing ratios. The findings of this work can be used for improving and assessing the uncertainties in stratospheric BDC trend estimation from global satellite measurements.
Liubov Poshyvailo-Strube et al.
Status: closed
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RC1: 'Comment on acp-2021-934', Anonymous Referee #1, 14 Dec 2021
This study uses CLaMS output to investigate the use of water vapor and methane variability to estimate trends in the stratospheric mean age of air. Estimates based on approximations that have been used in observational studies of this type were compared to the ‘true’ model trends to identify issues in the approach. This revealed significant errors due to the approximations, although the errors were offsetting during one of the time periods. A method of improving one of the approximations was put forth and shown to improve trend estimates.
The analysis is well done and may help with deducing trends in the stratospheric circulation from trace gas observations, which has been notoriously difficult. The topic is appropriate for ACP, the methods are clearly described and the figures are well done. I suggest the manuscript be accepted for publication with consideration of the minor comments below.
Specific comments:
Line 43: ‘CO2 has a seasonal cycle…’
Line 55: awkward phrasing ‘for other than’, maybe ‘compared to’ instead?
Line 77: ‘Also in our consideration is meant that there are no any’ is hard to understand. Perhaps rephrase to ‘The only significant source of H2O considered here is CH4 oxidation (e.g. we neglect all other hydrocarbons…’
Line 179: ‘depths of the BDC’ is unclear. Maybe ‘vertical transport by the BDC’.
Figure 1 caption: I would suggest moving the last two sentences of the caption up into Section 2.1 since they describe important details of the CH4 boundary conditions for the model run. Why is the CH4 boundary condition changed from NOAA to AIRS after 2011?
Line 208: remove ‘used’, ‘…and the method of calculating FRF.’
Figure 2 caption: ‘lapse’ is misspelled
Figure 6 caption: The text in the parenthesis after ‘FULL’ and ‘APPROX’ isn’t necessary since it’s partly repetitive from the Figure 5 caption and is explained in the text.
Line 367: ‘…stems from the differences in the AoA-FRF correlations used in each method…’
Line 373: ‘…discussed earlier in the paper…’
Figure 7: The constant FRF-AoA correlation approximation appears to bias the AoA trend too positive over both time periods and nearly all the stratosphere. Although you don’t try to improve the APPROX method with an improved treatment of the FRF-AoA correlation it seems there might be a simple correction made for the positive trend bias. The interesting aspect is that the age trend biases are largest at the youngest ages, whereas the correlations shown in Figure 4 have no discernable differences either seasonally or latitudinally at ages younger than 3-4 years. This would be worth discussing further.
Line 413, Appendix B: I don’t really understand the partitioning of the age into constant values in seven zones. Why not just use the actual age at each location? Is it too computationally expensive?
Line 429: Remove second ‘method’ in this line.
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AC1: 'Reply on RC1', Liubov Poshyvailo-Strube, 06 Mar 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-934/acp-2021-934-AC1-supplement.pdf
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AC1: 'Reply on RC1', Liubov Poshyvailo-Strube, 06 Mar 2022
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RC2: 'Comment on acp-2021-934', Anonymous Referee #2, 30 Dec 2021
Review of ACP-2-21-934
“Estimating Brewer-Dobson circulation trends from changes in stratospheric water vapour and methane” by Liubov Poshyvailo-Strube, Rolf MuÌller, Stephan Fueglistaler, Michaela I. Hegglin, Johannes, C. Laube, C. Michael Volk, and Felix Ploeger
Paper Review
This is an interesting and well written paper, but overly detailed. I recommend that the authors take some time to significantly reduce the content to the salient points, briefly summarizing the experiments and driving toward the main conclusions (which are a little nebulous). Concensing and consolidating the paper would improve the focus and make it more accessible to the reader.
Summary
The basic idea, as I understand it, is that the authors want to use measurements of water vapor and methane to determine trends in the BDC. If there is a trend in the BDC it should show up as a change in the age of air in the upper stratosphere (faster BDC –> younger air). This paper is about how to accurately diagnose any trend using those tracers. Accurately reconstructing the age spectrum from two tracers will be problematic since there probably won’t be enough information to characterize the whole spectrum (see Schoeberl et al., 2005), but it seems likely that the AoA estimation from these long-lived gases should be ok. This paper is about trying different techniques to diagnose changes in BDC using AoA constructed using ERAi and CLaMS.
No actual observations (except boundary forcing of methane and water) are used in this paper.
The authors assess various methods of using model water vapor and methane to determine changes in the BDC, or basically AoA trends and associated errors. I liked the evaluations they produce and an analysis of various errors (Fig. 5), but I think there is WAY too much detail, and the paper could use more of a reminder of the goals in the results section. For example, near the end perhaps you should show only 3 cases – True, Full and Improve Approx. Discussion of the other cases can be put in an Appendix since the average reader will give up while wading through this material. I think about 30% of this paper could be deleted with no loss of information content.
It was interesting that if you assume a simple age spectrum (Eq. 7) rather than try and reconstruct it, the methodology might work (Fig. 8) pretty well. I look forward to the authors applying this technique to real data, and I wonder how observational uncertainty will impact the results given the size of the existing errors.
As an aside, the authors mentioned a number of times that their analysis won’t work in the polar regions, yet they show these regions in the figures which is distracting. Perhaps cutting the figures at ±50° might be reasonable.
-
AC2: 'Reply on RC2', Liubov Poshyvailo-Strube, 06 Mar 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-934/acp-2021-934-AC2-supplement.pdf
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AC2: 'Reply on RC2', Liubov Poshyvailo-Strube, 06 Mar 2022
Status: closed
-
RC1: 'Comment on acp-2021-934', Anonymous Referee #1, 14 Dec 2021
This study uses CLaMS output to investigate the use of water vapor and methane variability to estimate trends in the stratospheric mean age of air. Estimates based on approximations that have been used in observational studies of this type were compared to the ‘true’ model trends to identify issues in the approach. This revealed significant errors due to the approximations, although the errors were offsetting during one of the time periods. A method of improving one of the approximations was put forth and shown to improve trend estimates.
The analysis is well done and may help with deducing trends in the stratospheric circulation from trace gas observations, which has been notoriously difficult. The topic is appropriate for ACP, the methods are clearly described and the figures are well done. I suggest the manuscript be accepted for publication with consideration of the minor comments below.
Specific comments:
Line 43: ‘CO2 has a seasonal cycle…’
Line 55: awkward phrasing ‘for other than’, maybe ‘compared to’ instead?
Line 77: ‘Also in our consideration is meant that there are no any’ is hard to understand. Perhaps rephrase to ‘The only significant source of H2O considered here is CH4 oxidation (e.g. we neglect all other hydrocarbons…’
Line 179: ‘depths of the BDC’ is unclear. Maybe ‘vertical transport by the BDC’.
Figure 1 caption: I would suggest moving the last two sentences of the caption up into Section 2.1 since they describe important details of the CH4 boundary conditions for the model run. Why is the CH4 boundary condition changed from NOAA to AIRS after 2011?
Line 208: remove ‘used’, ‘…and the method of calculating FRF.’
Figure 2 caption: ‘lapse’ is misspelled
Figure 6 caption: The text in the parenthesis after ‘FULL’ and ‘APPROX’ isn’t necessary since it’s partly repetitive from the Figure 5 caption and is explained in the text.
Line 367: ‘…stems from the differences in the AoA-FRF correlations used in each method…’
Line 373: ‘…discussed earlier in the paper…’
Figure 7: The constant FRF-AoA correlation approximation appears to bias the AoA trend too positive over both time periods and nearly all the stratosphere. Although you don’t try to improve the APPROX method with an improved treatment of the FRF-AoA correlation it seems there might be a simple correction made for the positive trend bias. The interesting aspect is that the age trend biases are largest at the youngest ages, whereas the correlations shown in Figure 4 have no discernable differences either seasonally or latitudinally at ages younger than 3-4 years. This would be worth discussing further.
Line 413, Appendix B: I don’t really understand the partitioning of the age into constant values in seven zones. Why not just use the actual age at each location? Is it too computationally expensive?
Line 429: Remove second ‘method’ in this line.
-
AC1: 'Reply on RC1', Liubov Poshyvailo-Strube, 06 Mar 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-934/acp-2021-934-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Liubov Poshyvailo-Strube, 06 Mar 2022
-
RC2: 'Comment on acp-2021-934', Anonymous Referee #2, 30 Dec 2021
Review of ACP-2-21-934
“Estimating Brewer-Dobson circulation trends from changes in stratospheric water vapour and methane” by Liubov Poshyvailo-Strube, Rolf MuÌller, Stephan Fueglistaler, Michaela I. Hegglin, Johannes, C. Laube, C. Michael Volk, and Felix Ploeger
Paper Review
This is an interesting and well written paper, but overly detailed. I recommend that the authors take some time to significantly reduce the content to the salient points, briefly summarizing the experiments and driving toward the main conclusions (which are a little nebulous). Concensing and consolidating the paper would improve the focus and make it more accessible to the reader.
Summary
The basic idea, as I understand it, is that the authors want to use measurements of water vapor and methane to determine trends in the BDC. If there is a trend in the BDC it should show up as a change in the age of air in the upper stratosphere (faster BDC –> younger air). This paper is about how to accurately diagnose any trend using those tracers. Accurately reconstructing the age spectrum from two tracers will be problematic since there probably won’t be enough information to characterize the whole spectrum (see Schoeberl et al., 2005), but it seems likely that the AoA estimation from these long-lived gases should be ok. This paper is about trying different techniques to diagnose changes in BDC using AoA constructed using ERAi and CLaMS.
No actual observations (except boundary forcing of methane and water) are used in this paper.
The authors assess various methods of using model water vapor and methane to determine changes in the BDC, or basically AoA trends and associated errors. I liked the evaluations they produce and an analysis of various errors (Fig. 5), but I think there is WAY too much detail, and the paper could use more of a reminder of the goals in the results section. For example, near the end perhaps you should show only 3 cases – True, Full and Improve Approx. Discussion of the other cases can be put in an Appendix since the average reader will give up while wading through this material. I think about 30% of this paper could be deleted with no loss of information content.
It was interesting that if you assume a simple age spectrum (Eq. 7) rather than try and reconstruct it, the methodology might work (Fig. 8) pretty well. I look forward to the authors applying this technique to real data, and I wonder how observational uncertainty will impact the results given the size of the existing errors.
As an aside, the authors mentioned a number of times that their analysis won’t work in the polar regions, yet they show these regions in the figures which is distracting. Perhaps cutting the figures at ±50° might be reasonable.
-
AC2: 'Reply on RC2', Liubov Poshyvailo-Strube, 06 Mar 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-934/acp-2021-934-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Liubov Poshyvailo-Strube, 06 Mar 2022
Liubov Poshyvailo-Strube et al.
Liubov Poshyvailo-Strube et al.
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