the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Upper tropospheric pollutants observed by MIPAS: geographic and seasonal variations
Abstract. We present a global climatology of upper tropospheric hydrogen cyanide (HCN), carbon monoxide (CO), acetylene (C2H2), ethane (C2H6), peroxyacetyl nitrate (PAN) and formic acid (HCOOH), obtained from MIPAS/Envisat observations between 2002 and 2012. At northern mid- and high latitudes the biomass burning tracer HCN as well as CO, PAN and HCOOH exhibit maxima during spring and/or summer and minima during winter. On the contrary, maximum northern extra-tropical C2H2 and C2H6 amounts were measured during winter and spring and minimum values during summer and fall. In the tropics and subtropics, enhanced amounts of all pollutants were observed during all seasons, especially widespread and up to southern mid-latitudes during austral spring. Other characteristic features are eastward transport of anthropogenic C2H6 and of biogenic HCOOH from Central and North America in boreal summer, accumulation of pollutants in the Asian Monsoon Anticyclone and enhanced C2H2 over South-East Asia in boreal winter. Clear indication of biogenic release of HCOOH was also found above tropical South America and Africa. A global correlation analysis of the other pollutants with HCN corroborates common release by biomass burning as source of the widespread southern hemispheric pollution during austral spring. Further, high correlation with HCN points to biomass burning as major source of tropical and subtropical C2H2 and PAN during most of the year. In the northern extra-tropics there are generally low correlations with HCN during spring and early summer, indicating the influence of anthropogenic and biogenic sources. However, in August there are stronger correlations above Siberia and boreal North America, which points to common release by boreal fires. This is confirmed by the respective enhancement ratios (ERs). The ERs measured above North-East Africa fit well to the emission ratios of the dominant local fire type (savanna burning) for C2H2, while those for CO, C2H6 and HCOOH rather indicate tropical forest fires or additional anthropogenic or biogenic sources. The southern hemispheric ΔC2H6/ΔHCN ERs obtained during August to October are in good agreement with the emission ratio for savanna fires. The same applies for ΔC2H2/ΔHCN in August and for ΔHCN/ΔCO as well as for ΔHCOOH/ΔHCN in October.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2024-1793', Chris Boone, 23 Jul 2024
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AC1: 'Reply on RC1', Norbert Glatthor, 30 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1793/egusphere-2024-1793-AC1-supplement.pdf
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AC1: 'Reply on RC1', Norbert Glatthor, 30 Sep 2024
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RC2: 'Comment on egusphere-2024-1793', Anonymous Referee #2, 25 Jul 2024
Overall, this is an interesting study making use of long-term MIPAS retrievals to investigate the potential sources controlling the spatio-temporal distribution of upper tropospheric pollutants. This study exploits information from enhancement ratios (ER) in comparison to emission ratios from known source types (e.g. forest fires) to determine the likely source of the pollutants. The manuscript is generally well written, figure presentation is good and would be an interesting addition to ACP. Therefore, subject to some minor comments, it is suitable for publication in ACP.
- The GFED data used in Figure 3 is from an older version (3.1). Therefore, it makes sense to exploit a newer version e.g. (vn4.1s or vn5).
- The authors use the ERs to infer source information about the pollutants retrieved by MIPAS. However, in places these statements are overall “conclusive” and need to be weakened (e.g. Page 17 Line 33, Page 20 Line 7 and Page 9 Line 20, Page 12 Lines 21-22) without the support of e.g. a model.
- The methods used in Section 4.3.2 need some more detail. From reading the text (Page 13 Lines 15-22), it is not overly clear what ESDs are and how they are calculated and why λ is needed in the regression model.
- In Section 4.3.2, why focus on individual months instead of the seasons as used earlier in the manuscript and why choice those specific months (i.e. Feb, Apr, Jul, Oct)?
Page 9 Line 17: Reword “even better visible” to something like “more evident”.
Page 9 Line 18: Can you provide a motivation for looking at 11 km (e.g. why not 10 km)?
Page 16 Lines 14-19: Suggest rewording as not overly easy to follow.
Page 16 Line 28: Add “a” between “as” and “possible”.
Page 17 Line 28: This line needs rewording as not clearly written. Also, what is the “two times larger relative retrieval error” based on? Is this from the literature, the data product, do you calculate this?
Page 18 Line 33: Add “any” between “hardly” and “information”.
Page 21 Line 8-9: This sentence needs rewording as difficult to follow.
Page 21 Line 22: Add “an” between “as” and “important”.
Page 21 Line 26: Replace “better” with e.g. “stronger”.
Page 22 Line 24: Replace “become better” with “becomes more”.
Table 2: For “a” why give the value as ppbv. For consistency and presentation, keep as pptv.
Table 3: Same as Table 2, why use “a”? Best to use consistent units and not a %.
Figure 6: Panel title and x/y-axes are missing for PAN.
Figure 8: I do not understand the delta HCN / delta CO units of 0.01 pptv/pptv while the colour bars show ER %.
Figure 3: For the colour bar units, what is 10 to the power of? Just says “10^”.
Citation: https://doi.org/10.5194/egusphere-2024-1793-RC2 -
AC2: 'Reply on RC2', Norbert Glatthor, 30 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1793/egusphere-2024-1793-AC2-supplement.pdf
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