the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jul 2024
Multi-model assessment of climatologies in the upper troposphere–lower stratosphere using the IAGOS data
Abstract. The evaluation of global chemistry-climate/transport models in the upper troposphere – lower stratosphere (UTLS) is a important step towards a better understanding of the chemical composition near the tropopause, and therefore towards a more accurate assessment of the impact of NOx emissions in this region of the atmosphere, notably by subsonic aviation. For this purpose, the current study focuses on an evaluation of long-term simulations from five global models based on in-situ measurements on board passenger aircraft (IAGOS). Most simulations span over the 1995–2017 time period, and follow a common protocol among the models. The assessment focuses on climatological averages of ozone (O3), water vapour (H2O), carbon monoxide (CO), and reactive nitrogen compounds (NOy). In the extra-tropics, the models reproduce the seasonality of ozone, water vapour and NOy in both the upper troposphere (UT) and the lowermost stratosphere (LS), but none of them reproduces the CO springtime maximum in the UT. The tropospheric tracers (CO and H2O) tend to be underestimated by the models, which is consistent with an overestimation of the cross-tropopause exchange, but does not exclude other factors as an underestimation of CO emissions, an underestimation of transport from the surface, or an overestimated CO oxidation by the hydroxyl radical (OH). Ozone is systematically overestimated in the UT by most models, and the NOx background appears as the main contributor to the ozone variability across the models. The partitioning between NOy species changes drastically across the models, and acts as a source of uncertainty on the NOx mixing ratio and on its impacts on atmospheric composition and particularly on the response to aviation NOx emissions. However, independently on the mean biases, we highlight some well-reproduced geographical and seasonal distributions, as the ITCZ seasonal shifts above Africa, the upper-tropospheric H2O maximum in the Asian summer monsoon, and the extratropical ozone (H2O) in the LS (UT) that show a high correlation with the observations. These features are encouraging regarding the simulated dynamics in both the troposphere and the stratosphere. The current study confirms the importance of an accurate separation between the UT and LS using a dynamical tracer for model results evaluation but also for model intercomparisons.
Competing interests: At least one of the co-authors is a member of the editorial board of Atmospheric Chemistry and Physics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.-
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RC1: 'Comment on egusphere-2024-2208', Anonymous Referee #1, 17 Aug 2024
Review of Multi-model assessment of climatologies in the upper troposphere–lower stratosphere using the IAGOS data by Cohen et al
This manuscript compares several chemical transport models to in-situ aircraft data from commercial aircraft. The manuscript is generally good, with appropriate methodology for the comparisons. It could be a bit clearer in some points, and is probably publishable with substantive but minor revisions. The current version has a lot of grammar mistakes. I tried to correct the ones that interfere with interpretation but it needs a careful edit.
A few general comments:
1. The paper needs to be a bit more clear about the fact that the models are actually all running as transport models, even those which are described as Chemistry-Climate Models. It's only in one line and the table where this is spelled out.
2. I'm not sure why you don't get PV from the driving meteorology of the MOZART model to use like the other models.
3. Also not sure about the different years and how that is handled, particularly when looking at things like biomass burning or regions subject to a lot of interannual variability. I think the IAGOS data is the same years? But when you put models together it might be a problem
4. I think some discussion of the dynamics of the models are warranted. I know they are nudged or CTMs, but the CCMs nudged can have dynamics which deviate from the driving meteorology and the different analyses may have a biased tropopause. Also the nudging can introduce spurious transport where strong gradients exist in the UTLS if the model is trying to go to another state biased versus the driving meteorology. A more dynamical view might help here (maybe plotting the tropopause height from all the models as a start).
Specific comments:Page 2, L11: Mention the tropopause in the abstract? Is the problem with the tropospheric tracers a tropopause height issue? Or some kind of averaging issue (even across a day, or multiple days in a month)?
Page 2, L23: Reference Gettelman et al 2011
Gettelman, A., P. Hoor, L. L. Pan, W. J. Randel, M. I. Hegglin, and T. Birner. 2011. “The Extratropical Upper Troposphere and Lower Stratosphere.” Rev. Geophys. 49 (RG3003). https://doi.org/10.1029/2011RG000355.
Page 2, L33: contribute —> contributes significantly to NOx mixing ratios
Page 2, L34: which is not correct grammar here. Awkward phrasing. Also lessor—>lower
Page 3, L40: Please check your use of articles (the). Here it should be accurately UTLS behavior and impact of aviation NOx (remove the). Next sentence: Ozone production (no the), but the background is correct. Chemicals are usually not ‘the’.
Page 3, L45: on OH quantities (no the)
Page 3,L55: … in the model results and helps (but does not make a list in the next line)
Page 4, L76: higher than cruise altitudes.
Page 5, L101: models’ skill
Page 5, L110: what is the typical vertical resolution of models in the UTLS? I see this in Table 1, refer to it here. What actual range of pressure levels are you sampling?
Page 7, L135: I’m unclear the difference between EMAC, MESSy and MECCA. Please clarify. Which one is the modified ECHAM5 atmospheric model? Is one the chemistry module?
Page 10, L216: which year? Most other models are running 2014-2018 at a minimum right?
Page 11, L261: This doesn’t quite overlap most of the model results (until 2018). Are you going to compare the models to the correct years? Some models are nudged and some are not. Is that going to matter? It's not clear from the description exactly how you did this, and how you will do comparisons across models if they are different years.
Page 12, Table 2: Can you indicate dates for IAGOS-CARIBIC and IAGOS-CORE? Since the merge in 2008, which measurements have continued?
Page 12, L282: Are all the models using nudged meteorology so they can get the right comparisons? This is confusing because they are, but several of the models are nudged CCMs.
Also: you are sampling daily and then dividing by a daily averaged PV? How much uncertainty will this introduce if you are off by a few grid boxes? (At 10m/s, that's 900km/day, so could be a few grid boxes off).
Page 13, L289: so you are filtering out stratospheric intrusions? How often does the filtering kick in? Same for models and IAGOS data?
Page 13, L290: Why don’t you just get a PV field from the ERAI data? That seems easy to do. They probably even have a PV field already.
Page 13, L294: define explicitly how the MNMB and FGE are calculated. It is not obvious.
Page 13, L296: What is the IAGOS-DM product? So you use different years for different models?
Page 13 L296: Does your methodology of using monthly data provide the same discrimination as if you used daily or 6 hourly data? I worry that the monthly averaging will smear out the dynamics and the chemistry, and can hide biases in the UT and LS when mixed? Can you show you get the same answers as using daily data with at least one model? I get that you are averaging daily to monthly, but the altitudes will get quite smeared out in the UT and LS.
Page 13, L306: I’ve lost which region is which. Maybe you need a table here to delineate the regions and time periods.
Page 13, L309: How are the UT and LS separated? Are all these nudged simulations? I’m assuming they have to be since you are sampling models at the IAGOS data times right? But again, this information is buried in one comment in the methodology. You might need to repeat it here.
Page 13, L314: The different products are because of different time periods right?
Page 14, L316: That is what is displayed on the far left? Please be explicit.
Page 14, L325: Are you going to show the tropopause altitude/temperature? Seems like that’s important here. What about using tropopause relative coordinates? Would that reduce the biases? I can see how different analyses may have different tropopause heights, and the nudging may also introduce different tropopause heights.
Page 14, L332: But wouldn’t the bias be the opposite across the tropopause if there were two much STE? I.e. too much O3 in the UT and too little in the LS, with the opposite for CO? Your statement implies the same sign bias. Please clarify.
Page 14, L336: That means highs and lows aren’t large enough? I.e. too little variability?
Page 18, L355: Teference figure 6.
Page 19, L365: Again, reference figure 7
Page 19, L370: To what extend does the driving meteorology (e.g. IFS or ERAI/5) have a realistic Brewer-Dobson circulation? Similar to comment about tropopause height above.
Page 19, L384: Are the representations of the stratospheric circulation the same in the different simulations? How much could they differ?
Page 22, L416: Show the tropopause height? Also: meridian —> meridional
Page 26, L419: Again, please provide the formula for FGE and MNMB. The MNMB I can guess, but do not know what you mean by FGE.
Page 26, L424: Can you separate dynamics from chemistry here? Perhaps with another tracer (e.g. H2O).
Page 26, L438: So I’m assuming that EMAC and UKESM are free running models then? No nudging? That seems to be your description but then you are nudging them... what is the nudging timescale? Could the tropopause vary from the driving analysis?
Page 28, L446: What is with UKESM in Figure 11? Why is it an outlier?
Page 28, L450: I read it that the gradient for LS O3 is okay except for the sub-tropics, while for LS NOy the subtropics are slightly less biased.
Page 28, L452: Not sure what this last sentence ‘smaller scales are hardly captured’ means. How do you know that from this scatterplot?
Page 32, L461: Do these models have more realistic stability? Presumably the CTMs do, but what about the CCMs? They might not interact well with the nudging?
Page 32, L470: it would be useful to look at tropopause relative gradients to try to understand the model UTLS separation.
Page 32, L481: You don’t really comment much on the O3 and NOy UT scatterplots. Think this needs another paragraph.
Page 32, L487: overview of model skill.
Page 33, L488: what does “sometimes correlates” mean?
Page 33, L491: This last sentence needs some significant grammar corrections. ‘Lessened’, ‘impossibility to’ and ‘meridian’ are not correct.
Page 33, L498: Delete ‘their’
Page 33, L502: What is a wind shear area?
Page 33, L504: these CO peaks originated…
Page 33, L508: convective systems.
Page 33, L511: about representation of convective systems
Page 36, L517: most? Do you mean most (delete the article then) or moist? Not sure what ‘favored’ means. Pick a different verb to clarify please.
Page 36, L520: peaks’ intensity (the possessive I think is needed here).
Page 36, L5235: also in the southward CO shift? What is this referring to? biomass burning? Awkward phrasing.
Page 36, L539: Are the models being compared to the same years of the observations? Could interannual variability being different explain the difference? Or are you matching years so it is missing biomass burning? I think different models have different years? That seems problematic.
Page 37, L532: lessened —> lower
Page 37, L549: do you mean that overestimating of lightning is a known bias in LMDZ-INCA? Reference?
Page 37, L573: you don’t mean directly comparable across models right? Just that for each model you compare DM and M? Clarify.
Page 38, L576: skills—>skill
Page 38, L577: Do the models have the right representation of the Extratropical Tropopause Layer? Why would they over estimate mixing? You might need to comment on this.
Page 40, L609: I think you need a bit more dynamical assessment. How much of the bias or extra cross tropopause transport assumed could be just your analysis method smearing out the tropopause? Is the tropopause height okay in the models?
Citation: https://doi.org/10.5194/egusphere-2024-2208-RC1 -
AC1: 'Reply on RC1', Yann Cohen, 04 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2208/egusphere-2024-2208-AC1-supplement.pdf
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AC1: 'Reply on RC1', Yann Cohen, 04 Feb 2025
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RC2: 'Comment on egusphere-2024-2208', Anonymous Referee #2, 03 Sep 2024
Cohen et al. processed long-term IAGOS measurements to generate climatologies of CO, O3, H2O, and NOy in the UTLS region, and then utilized these observations to evaluate five different chemical transport/chemistry-climate models. They also highlighted dynamic features observed in the IAGOS data, such as the seasonal shifts of the ITCZ above Africa. The manuscript is quite comprehensive, perhaps containing more results than can be effectively presented in a single paper. I hope the authors could address the following comments before being published in ACP.
Major comments: The manuscript is challenging to follow from beginning to end, as each section feels somewhat disconnected. For example, while the introduction emphasizes assessing the impact of aviation emissions, the paper itself does not present results specifically related to aviation emissions. Additionally, Sections 3.1 and 3.2 both discuss biases due to cross-tropopause transport, yet they are presented separately. Figures 10 and 11-13 seem redundant. The manuscript could be significantly improved by simplifying the introduction and focusing on presenting the most important results.
Other comments:
Line 40: How do you differentiate the impact of aviation NOx emissions from lightning NO emissions?
Line 54-67: This paragraph describes the impact of individual processes on measured species but does not explain how the IAGOS dataset can assess the sensitivity of model responses to aircraft emissions.
Line 75: Do most measurements during the cruise occur in the LS? Are measurements in the UT primarily taken during departure and landing, potentially limiting the spatial representation of observed climatology in the UT?
Table 1: It would be helpful to indicate whether these models simulate chemistry in the stratosphere.
Line 175: What is meant by “online model” here? Does it imply no interaction between chemistry and meteorology?
Line 283-293: How do model simulated tropopause heights compare to layers defined by PV fields? Could differences in tropopause height affect the comparison between IAGOS data and model results?
Line 327: In Section 3.1, the O3/CO ratio is used to indicate biases in cross-tropopause exchanges, while Section 3.2 attributes H2O variation to cross-tropopause mixing as well. These two sections could either be combined or require additional explanation to clarify the differences.
Figure 11-13: where is the figure for the comparison of CO?
Citation: https://doi.org/10.5194/egusphere-2024-2208-RC2 -
AC2: 'Reply on RC2', Yann Cohen, 04 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2208/egusphere-2024-2208-AC2-supplement.pdf
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AC2: 'Reply on RC2', Yann Cohen, 04 Feb 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2208', Anonymous Referee #1, 17 Aug 2024
Review of Multi-model assessment of climatologies in the upper troposphere–lower stratosphere using the IAGOS data by Cohen et al
This manuscript compares several chemical transport models to in-situ aircraft data from commercial aircraft. The manuscript is generally good, with appropriate methodology for the comparisons. It could be a bit clearer in some points, and is probably publishable with substantive but minor revisions. The current version has a lot of grammar mistakes. I tried to correct the ones that interfere with interpretation but it needs a careful edit.
A few general comments:
1. The paper needs to be a bit more clear about the fact that the models are actually all running as transport models, even those which are described as Chemistry-Climate Models. It's only in one line and the table where this is spelled out.
2. I'm not sure why you don't get PV from the driving meteorology of the MOZART model to use like the other models.
3. Also not sure about the different years and how that is handled, particularly when looking at things like biomass burning or regions subject to a lot of interannual variability. I think the IAGOS data is the same years? But when you put models together it might be a problem
4. I think some discussion of the dynamics of the models are warranted. I know they are nudged or CTMs, but the CCMs nudged can have dynamics which deviate from the driving meteorology and the different analyses may have a biased tropopause. Also the nudging can introduce spurious transport where strong gradients exist in the UTLS if the model is trying to go to another state biased versus the driving meteorology. A more dynamical view might help here (maybe plotting the tropopause height from all the models as a start).
Specific comments:Page 2, L11: Mention the tropopause in the abstract? Is the problem with the tropospheric tracers a tropopause height issue? Or some kind of averaging issue (even across a day, or multiple days in a month)?
Page 2, L23: Reference Gettelman et al 2011
Gettelman, A., P. Hoor, L. L. Pan, W. J. Randel, M. I. Hegglin, and T. Birner. 2011. “The Extratropical Upper Troposphere and Lower Stratosphere.” Rev. Geophys. 49 (RG3003). https://doi.org/10.1029/2011RG000355.
Page 2, L33: contribute —> contributes significantly to NOx mixing ratios
Page 2, L34: which is not correct grammar here. Awkward phrasing. Also lessor—>lower
Page 3, L40: Please check your use of articles (the). Here it should be accurately UTLS behavior and impact of aviation NOx (remove the). Next sentence: Ozone production (no the), but the background is correct. Chemicals are usually not ‘the’.
Page 3, L45: on OH quantities (no the)
Page 3,L55: … in the model results and helps (but does not make a list in the next line)
Page 4, L76: higher than cruise altitudes.
Page 5, L101: models’ skill
Page 5, L110: what is the typical vertical resolution of models in the UTLS? I see this in Table 1, refer to it here. What actual range of pressure levels are you sampling?
Page 7, L135: I’m unclear the difference between EMAC, MESSy and MECCA. Please clarify. Which one is the modified ECHAM5 atmospheric model? Is one the chemistry module?
Page 10, L216: which year? Most other models are running 2014-2018 at a minimum right?
Page 11, L261: This doesn’t quite overlap most of the model results (until 2018). Are you going to compare the models to the correct years? Some models are nudged and some are not. Is that going to matter? It's not clear from the description exactly how you did this, and how you will do comparisons across models if they are different years.
Page 12, Table 2: Can you indicate dates for IAGOS-CARIBIC and IAGOS-CORE? Since the merge in 2008, which measurements have continued?
Page 12, L282: Are all the models using nudged meteorology so they can get the right comparisons? This is confusing because they are, but several of the models are nudged CCMs.
Also: you are sampling daily and then dividing by a daily averaged PV? How much uncertainty will this introduce if you are off by a few grid boxes? (At 10m/s, that's 900km/day, so could be a few grid boxes off).
Page 13, L289: so you are filtering out stratospheric intrusions? How often does the filtering kick in? Same for models and IAGOS data?
Page 13, L290: Why don’t you just get a PV field from the ERAI data? That seems easy to do. They probably even have a PV field already.
Page 13, L294: define explicitly how the MNMB and FGE are calculated. It is not obvious.
Page 13, L296: What is the IAGOS-DM product? So you use different years for different models?
Page 13 L296: Does your methodology of using monthly data provide the same discrimination as if you used daily or 6 hourly data? I worry that the monthly averaging will smear out the dynamics and the chemistry, and can hide biases in the UT and LS when mixed? Can you show you get the same answers as using daily data with at least one model? I get that you are averaging daily to monthly, but the altitudes will get quite smeared out in the UT and LS.
Page 13, L306: I’ve lost which region is which. Maybe you need a table here to delineate the regions and time periods.
Page 13, L309: How are the UT and LS separated? Are all these nudged simulations? I’m assuming they have to be since you are sampling models at the IAGOS data times right? But again, this information is buried in one comment in the methodology. You might need to repeat it here.
Page 13, L314: The different products are because of different time periods right?
Page 14, L316: That is what is displayed on the far left? Please be explicit.
Page 14, L325: Are you going to show the tropopause altitude/temperature? Seems like that’s important here. What about using tropopause relative coordinates? Would that reduce the biases? I can see how different analyses may have different tropopause heights, and the nudging may also introduce different tropopause heights.
Page 14, L332: But wouldn’t the bias be the opposite across the tropopause if there were two much STE? I.e. too much O3 in the UT and too little in the LS, with the opposite for CO? Your statement implies the same sign bias. Please clarify.
Page 14, L336: That means highs and lows aren’t large enough? I.e. too little variability?
Page 18, L355: Teference figure 6.
Page 19, L365: Again, reference figure 7
Page 19, L370: To what extend does the driving meteorology (e.g. IFS or ERAI/5) have a realistic Brewer-Dobson circulation? Similar to comment about tropopause height above.
Page 19, L384: Are the representations of the stratospheric circulation the same in the different simulations? How much could they differ?
Page 22, L416: Show the tropopause height? Also: meridian —> meridional
Page 26, L419: Again, please provide the formula for FGE and MNMB. The MNMB I can guess, but do not know what you mean by FGE.
Page 26, L424: Can you separate dynamics from chemistry here? Perhaps with another tracer (e.g. H2O).
Page 26, L438: So I’m assuming that EMAC and UKESM are free running models then? No nudging? That seems to be your description but then you are nudging them... what is the nudging timescale? Could the tropopause vary from the driving analysis?
Page 28, L446: What is with UKESM in Figure 11? Why is it an outlier?
Page 28, L450: I read it that the gradient for LS O3 is okay except for the sub-tropics, while for LS NOy the subtropics are slightly less biased.
Page 28, L452: Not sure what this last sentence ‘smaller scales are hardly captured’ means. How do you know that from this scatterplot?
Page 32, L461: Do these models have more realistic stability? Presumably the CTMs do, but what about the CCMs? They might not interact well with the nudging?
Page 32, L470: it would be useful to look at tropopause relative gradients to try to understand the model UTLS separation.
Page 32, L481: You don’t really comment much on the O3 and NOy UT scatterplots. Think this needs another paragraph.
Page 32, L487: overview of model skill.
Page 33, L488: what does “sometimes correlates” mean?
Page 33, L491: This last sentence needs some significant grammar corrections. ‘Lessened’, ‘impossibility to’ and ‘meridian’ are not correct.
Page 33, L498: Delete ‘their’
Page 33, L502: What is a wind shear area?
Page 33, L504: these CO peaks originated…
Page 33, L508: convective systems.
Page 33, L511: about representation of convective systems
Page 36, L517: most? Do you mean most (delete the article then) or moist? Not sure what ‘favored’ means. Pick a different verb to clarify please.
Page 36, L520: peaks’ intensity (the possessive I think is needed here).
Page 36, L5235: also in the southward CO shift? What is this referring to? biomass burning? Awkward phrasing.
Page 36, L539: Are the models being compared to the same years of the observations? Could interannual variability being different explain the difference? Or are you matching years so it is missing biomass burning? I think different models have different years? That seems problematic.
Page 37, L532: lessened —> lower
Page 37, L549: do you mean that overestimating of lightning is a known bias in LMDZ-INCA? Reference?
Page 37, L573: you don’t mean directly comparable across models right? Just that for each model you compare DM and M? Clarify.
Page 38, L576: skills—>skill
Page 38, L577: Do the models have the right representation of the Extratropical Tropopause Layer? Why would they over estimate mixing? You might need to comment on this.
Page 40, L609: I think you need a bit more dynamical assessment. How much of the bias or extra cross tropopause transport assumed could be just your analysis method smearing out the tropopause? Is the tropopause height okay in the models?
Citation: https://doi.org/10.5194/egusphere-2024-2208-RC1 -
AC1: 'Reply on RC1', Yann Cohen, 04 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2208/egusphere-2024-2208-AC1-supplement.pdf
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AC1: 'Reply on RC1', Yann Cohen, 04 Feb 2025
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RC2: 'Comment on egusphere-2024-2208', Anonymous Referee #2, 03 Sep 2024
Cohen et al. processed long-term IAGOS measurements to generate climatologies of CO, O3, H2O, and NOy in the UTLS region, and then utilized these observations to evaluate five different chemical transport/chemistry-climate models. They also highlighted dynamic features observed in the IAGOS data, such as the seasonal shifts of the ITCZ above Africa. The manuscript is quite comprehensive, perhaps containing more results than can be effectively presented in a single paper. I hope the authors could address the following comments before being published in ACP.
Major comments: The manuscript is challenging to follow from beginning to end, as each section feels somewhat disconnected. For example, while the introduction emphasizes assessing the impact of aviation emissions, the paper itself does not present results specifically related to aviation emissions. Additionally, Sections 3.1 and 3.2 both discuss biases due to cross-tropopause transport, yet they are presented separately. Figures 10 and 11-13 seem redundant. The manuscript could be significantly improved by simplifying the introduction and focusing on presenting the most important results.
Other comments:
Line 40: How do you differentiate the impact of aviation NOx emissions from lightning NO emissions?
Line 54-67: This paragraph describes the impact of individual processes on measured species but does not explain how the IAGOS dataset can assess the sensitivity of model responses to aircraft emissions.
Line 75: Do most measurements during the cruise occur in the LS? Are measurements in the UT primarily taken during departure and landing, potentially limiting the spatial representation of observed climatology in the UT?
Table 1: It would be helpful to indicate whether these models simulate chemistry in the stratosphere.
Line 175: What is meant by “online model” here? Does it imply no interaction between chemistry and meteorology?
Line 283-293: How do model simulated tropopause heights compare to layers defined by PV fields? Could differences in tropopause height affect the comparison between IAGOS data and model results?
Line 327: In Section 3.1, the O3/CO ratio is used to indicate biases in cross-tropopause exchanges, while Section 3.2 attributes H2O variation to cross-tropopause mixing as well. These two sections could either be combined or require additional explanation to clarify the differences.
Figure 11-13: where is the figure for the comparison of CO?
Citation: https://doi.org/10.5194/egusphere-2024-2208-RC2 -
AC2: 'Reply on RC2', Yann Cohen, 04 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2208/egusphere-2024-2208-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Yann Cohen, 04 Feb 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
IAGOS time series D. Boulanger, R. Blot, U. Bundke, C. Gerbig, M. Hermann, P. Nédélec, S. Rohs, and H. Ziereis https://doi.org/10.25326/06
Model code and software
Interpol-IAGOS Yann Cohen, Valérie Thouret, Virginie Marécal, and Béatrice Josse https://doi.org/10.25326/81
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Cited
2 citations as recorded by crossref.
- The contribution of transport emissions to ozone mixing ratios and methane lifetime in 2015 and 2050 in the Shared Socioeconomic Pathways (SSPs) M. Mertens et al. 10.5194/acp-24-12079-2024
- Validation of 12 years (2008–2019) of IASI-A CO with IAGOS aircraft observations B. Barret et al. 10.5194/amt-18-129-2025
Didier Hauglustaine
Nicolas Bellouin
Marianne Tronstad Lund
Sigrun Matthes
Agnieszka Skowron
Robin Thor
Ulrich Bundke
Andreas Petzold
Susanne Rohs
Valérie Thouret
Andreas Zahn
Helmut Ziereis
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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