the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2024
Seasonal, regional and vertical characteristics of high carbon monoxide plumes along with their associated ozone anomalies as seen by IAGOS between 2002 and 2019
Abstract. In-situ measurements from IAGOS are used to characterise extreme values of carbon monoxide (CO) in the troposphere between 2002 and 2019. The SOFT-IO model, combining the FLEXPART lagrangian dispersion model with emission inventories over the footprint region is used to identify the origins of the CO in the sampled plumes. The impact of biomass burning and anthropogenic emissions on such CO plumes are characterised through CO mixing ratios and simultaneously recorded ozone (O3) ones.
In the Northern Hemisphere, maximum of CO are reached in DJF in the lower troposphere because of the elevated anthropogenic emissions and reduced convective activity of the season. Due to the low photochemistry and the fresh age of the air mass the O3 values of these plumes are low. CO plumes in the upper troposphere result from intense emissions and efficient vertical transport, peaking during JJA. Among the anomalies detected in the UT in JJA, the ones with the higher associated O3 values are the ones associated with biomass burning emissions. The middle troposphere combines the two previous vertical levels with contributions from both local emissions and long-range transport. The emission regimes and meteorological conditions are fundamentally different within the troposphere over Africa. Convection is no longer the limiting factor and the transport of the CO plumes is driven by the ITCZ shift, trade winds and the upper branch of the Hadley cell redistributing the pollution to higher latitudes.
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Status: final response (author comments only)
- CC1: 'Comment on egusphere-2023-2949', Owen Cooper, 04 Mar 2024
- RC1: 'Comment on egusphere-2023-2949', Anonymous Referee #1, 06 Mar 2024
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RC2: 'Comment on egusphere-2023-2949', Anonymous Referee #2, 26 Mar 2024
Review Summary
The paper presents a seasonal analysis of CO pollution plumes (anomalies) sampled by IAGOS commercial aircraft over different regions of the world for the period 2002-2019. Modeled footprints and global emission inventories for CO anthropogenic and biomass burning are used to simulate contributions to CO along each flight track and attribute the observed anomalies to emissions by type and by source region. I assume this study was made possible thanks to a lot of careful work and continued support for the IAGOS program but the paper does not give much details about this although it provides several references for previous analyses of the data. The authors use footprints and emission inventories “semi quantitatively” for emission attribution, I assume previous work has shown this is a reliable approach.
The paper clearly presents graphic summaries for the CO anomaly analysis by region and the text further describes how seasonality in some atmospheric transport processes and emissions can explain the results.
The discussion of ozone levels in the anomaly plumes is mostly descriptive by region. It seems that in only a few cases do CO anomalies correspond to ozone anomalies. Is there another paper that looks more holistically at those ozone anomalies and the processes behind them?
It would be nice to help the reader understand the significance (and maybe limitations) of your analysis and findings for ozone.
The importance of the IAGOS dataset and this work may be made stronger with a more organized argumentation in the introduction. Some of the text there is repetitive and some general statements are not backed up by references.
The conclusion mostly summarizes the findings but could maybe also be more explicit about future work and why continuing and potentially expanding those measurements, adding other tracers… is important for the next decades.
High level comments
- Are the findings new?
- What are some key implications ?
- Why are trends or interannual variability not explored? I think I may be able to guess but you may want to be explicit about it in the paper, ie. if the dataset year to year spatiotemporal coverage does not allow for this type of analysis.
- Be explicit about the nature of the IAGOS dataset for people less familiar with this work: Mention they are measurements on commercial aircraft, in the introduction and mention IAGOS in the conclusion too.
- The consistency of the data calibration and the data quality throughout the period and across instruments is assumed but it may be nice to include a couple of sentences on that.
- Further define the CO anomalies: how many consecutive datapoints above the q95 threshold are needed to become an anomaly plume?
- I found a few typos or small corrections. Another thorough reading would be great to make sure all of these are taken care of. For example, fix a few inconsistencies throughout the article about how you refer to your regions.
Detailed comments
Abstract:
First sentence should be clear the analysis is done for large regions of the globe.
You cover some of the findings for some region but results for India are not mentioned, even though they have their own section.
The much higher CO in anomalies over E Asia may be nice to mention here too.
Introduction section:
- References would be great for statements on model limitations to reproduce or predict extreme weather events and extreme pollution events
- Not clear about the impact of extreme pollution events on climate, maybe expand on what you mean with climate here and add references.
- Pollution is often referring to conditions in the boundary layer. What does it mean for the troposphere?
- The text in the introduction makes it sound like this paper/study can be used to improve model simulations of extreme pollution plumes. How would this be done?
L 27-30: “This compound In the troposphere, ozone is photochemically produced from NOx and VOC (Volatile Organic Compounds)/ or CO (Seinfeld and Pandis, 2008). Hence, a good estimation of its chemical precursors as well as better understanding of the processes leading to their distributions at global scale is of prime importance.”
L 44: Owen et al., 2006 should be Cooper et al., 2006 (Owen is the firstname and Cooper is the lastname of the author).
L 72-75: “We present here a quasi-global overview over almost 20 years of extreme CO mixing ratios and their associated O3 values, as seen by IAGOS.
The goal of this paper is to characterise the seasonal, regional and vertical CO mixing ratios anomalies for different regions over the globe for almost 20 years as seen by IAGOS along with the simultaneously recorded O3 between 2002 and 2019.”
These two sentences say the same thing. Please remove repetition.
L 101-109: Is the last paragraph on the model needed in the measurement data set section 2.1? It is mostly repeated in section 2.2. Similarly the first paragraph in 2.2 repeats some of what is in section 2.1. Please revise to focus on what belongs in each section.
L 116: “The Bbiomass Bburning emission inventory used in this version…” remove uppercase letters from biomass burning and check if this should be singular, or plural.
Section 2.2 : In the model, you only look at direct emissions of CO not CO chemically produced?
Figure 1 may have better contrast for the Americas if the oceans were kept white. Could the legend be placed outside of the map to not cover part of it and you can make it a little bigger too? Are the acronyms for the GFED regions defined somewhere in your paper? Especially as you refer to boreal emissions several times, I assume you refer to emissions from BONA and BOAS.
L 165: “At higher altitude, the samples are less influenced by local emissions…”
Figure 2: There are two blue lines, so the CO measurements one would need to be referred to as dark blue. There are horizontal and vertical dashed lines. Are the vertical ones needed? Clarify you refer to the horizontal dashed line for the 95th percentile for the CO for that region/season; you could give the value for q95 in the caption. What altitudes did the measurements in the Figure cover? What happens during the data gaps seen in the Figure?
Table 1: Specify this is for CO and for different seasons in the caption. Put the unit (ppb) in the caption, not the table itself. Explain what “no data” means. Do you need to show results for seasons you will not discuss.
L 172-173: “SOFT-IO is then used as a qualitative tool to assign a source type to each of the detected anomalies. This diagnostic is only applied if the contributions modelled by SOFT-IO are above a detection threshold defined as 5 ppb.” You use 5 ppb for all altitude bins? Does it matter?
Figure 3: c and d could share the legend. You would need to define Low BB in the caption. Legend is covering the a) in the first plot.
Can you comment on the high mean for DJF and JJA CO anomalies in E Asia, plots a and b? What anthropogenic sources contribute the most here?
Figure 5: “At this altitude 24 anomalies over out of the 5341 observed…” The unattributed anomalies in grey are very hard to see. Maybe that text could be in the main text not the Figure caption.
L 217-218: “BB contributions comes in the vast majority from Boreal America and Asia.” plural
L 219-220: “ In JJA, the plumes attributed to BB emissions are the most intense” plural
Figure 6: remove volume from “volume mixing ratio”. You are reporting dry air mole fractions here, is this true?
L 227: keep Figure 7 (and Figure 3) singular to avoid confusion. The figures have 4 subplots.
L 258-260: “In a lot of regions most of the emissions from BB are from the two boreal regions (Boreal America and Boreal Asia), which is probably due to the higher emissions height of those fires increasing the probability of the emitted CO to reach the UT.”
L 267: replace WNam with NWAm. Also simplify by splitting this sentence into two. One is about anomalies attributed to CEAS emissions and the other sentence is about CO anthropogenic emissions (if I understand correctly).
L 302-305: About the 2015 fires in Eq Asia. Can you add references?
For ex: https://www.pnas.org/doi/full/10.1073/pnas.1524888113
Also fix typo: “caracterized” should be “characterized”
L 308: “The anomalies measured during the months MAM have similar characteristics than to the anomalies from DJF but this time…”
L 321: replace “The Gulf of guinea” with “the Gulf of Guinea”
L 326. Remove “Obviously”. It is rarely used in scientific writing, to my knowledge.
L 328: “most of its detected anomalies are attributed to emissions from local fires.”
L 342: “Fig.13 and Fig.14 show…”
L 355 and 367: Replace “wild fires” with “wildfires”
L 372-373: Use uppercase for G in gulf, “gulf of Guinea” appears twice in this sentence. Same for L 387.
L 403: Fix regions acronyms to be consistent with earlier ones. “NWam, NEam and Weur” should be NW Am, NE Am and Eur, I presume.
L 417-418: Fix repetition in the sentence “ This transport of pollution to Northeast Siberia is partly due to the East Asian monsoon, which transports air masses from Southeast Asia to Northeast Siberia.”
L 438: fix typo: “ the emissions (both atnthropogenic and BB)”
L 452 “observed with a thresholds defined as the 75th or 99th”, singular for threshold.
Fix the end of the conclusion:
- Remove the paragraph L 458-461 as it is repeated with an improved sentence for the ozone piece in the last paragraph.
- Remove “obviously”.
- Be more specific. What specifically would you want to further study in those high CO plumes and therefore what measurements or “data” would you need?
Figure A1: fix title and caption “ Number of flights per regions” region should be singular
You have 3 supplementary figures A1, D1 and E1. I do not understand the A1,D1, E1 choice for naming those figures. Fig. B1 and Fig. C1 are showing up after the references so maybe make sure they are in order and the number 1 for A1, B1 etc seems unnecessary, unless it is how the journal asks for these supplementary figures to be labeled.
Citation: https://doi.org/10.5194/egusphere-2023-2949-RC2
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