Inferring drivers of tropical isoprene: competing effects of emissions and chemistry

Yoon, James; Wells, Kelley C.; Millet, Dylan B.; Frankenberg, Christian; Sanghavi, Suniti; Swann, Abigail L. S.; Thornton, Joel A.; Turner, Alexander J.

doi:10.5194/egusphere-2025-5532

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https://doi.org/10.5194/egusphere-2025-5532

Preprints

02 Dec 2025

| 02 Dec 2025

Inferring drivers of tropical isoprene: competing effects of emissions and chemistry

James Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner

Abstract. Isoprene is the most significant non-methane hydrocarbon by total emissions and is an important control on the tropospheric oxidative capacity. In the atmosphere, isoprene is oxidized by the hydroxyl (OH) radical on the order of hours depending on local OH concentrations. Using isoprene retrievals from the Cross-track infrared sounder (CrIS), we monitor global isoprene column variability and observe differing isoprene column responses to El Niño-Southern Oscillation across three tropical regions: Amazonia, the Maritime Continent, and equatorial Africa. We find correlations between isoprene column variability and temperature over Amazonia, which suggests that isoprene emissions drive Amazonian isoprene variability (“emissions-controlled”). In the Maritime Continent, we find strong correlations between isoprene columns, precipitation and soil moisture, as well as an anti-correlation between isoprene and formaldehyde retrievals. These correlations suggest that isoprene columns may be modulated by non-anthropogenic NO_x emissions, namely soil and biomass burning NO_x (“chemistry-controlled”), although convection and lightning NO_x may also modulate isoprene column retrievals if the lofted isoprene flux is large enough. In equatorial Africa, both biomass burning and temperature can explain isoprene variability during different periods, representing an intermediate regime with contributions from emissions and chemistry. We suggest that these isoprene regimes are caused by differences in the dynamic temperature and oxidant range between the three regions, and we specifically highlight oil palm plantations in the Maritime Continent as an area of co-located isoprene and soil NO_x fluxes. By leveraging CrIS isoprene retrievals, we can study interactions between VOC and NO_x sources over tropical areas with few in-situ observations.

Received: 08 Nov 2025 – Discussion started: 02 Dec 2025

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James Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-5532', Anonymous Referee #1, 14 Jan 2026
In this manuscript, Yoon et al. present analysis of isoprene column satellite data in three different tropical regions: Amazonia, the Maritime Continent, and equatorial Africa. The authors propose three distinct chemical/environmental regimes that are responsible for the observed variability in isoprene concentrations, with Amazonia and the Maritime Continent representing the extremes of emissions- and chemistry-controlled, respectively, while equatorial Africa represents a mixed regime, where emissions and chemistry dominate the isoprene column variability to differing extents.
The presented analysis is interesting, well presented, and appears to be methodologically sound. Though no strong conclusions around the drivers of isoprene column variability in the Maritime continent are made by the authors, the drivers in the other two regions seem convincing. In all, the presented framework is a useful lens through which to interpret the potentially counter-intuitive observations that isoprene concentrations may not correlate well with optimal conditions for isoprene emissions, or with formaldehyde anomalies, in certain locations.
I recommend the article for publication in ACP after the following comments and corrections are addressed.
Comments
Line 81-84: The claims in this paragraph are not clearly supported by Figures 1c and 1d. While the description of the isoprene anomaly for Amazonia is reasonably accurate, the Maritime Continent anomaly regularly drops below 0 during 2019-2020, including at the same time as Amazonia towards the final quarter of 2019. In any case, this does not impact the analysis as the authors do not discuss these 2019-2020 trends further, meaning that this section could probably be removed.

Section 3: The authors do not discuss biomass burning at all in section 3, but then later reference it as a potential contributor later in the paper. I would suggest at least introducing the concept as a potential hypothesis at this point and potentially move some of the analysis regarding the Maritime Continent presented in Section 4 into this section.

Line 165: As the authors note in Lines 21-22 of the introduction, the response of [OH] to changes in [NOx] can be complex and non-linear. The authors should justify why they expect decreased NOx emissions to result in decreased OH in this chemical environment specifically.

Figure 7: For panels (a), (b), and (c), it would be useful to see the entire temperature and GFED4 time series compared against the ISOP column anomalies, even if this is only as additional supplementary figures.

Line 309: It would be interesting for the authors to consider the potential impact of isoprene oxidation by the nitrate radical (NO3) on this analysis, as well as the direct ozonolysis of isoprene. Though both of these processes are predominantly night-time processes, a depletion of isoprene in the night-time could persist into the day-time and reduce background concentrations. This is particularly true given the reduced photolysis that may be provided by a biomass burning smoke plume.

Line 318: As with the reference to the change in [OH] in the maritime continent, the authors should explicitly state why they expect increases in NOx to increase [OH], considering the variability in HOx partitioning mentioned at this line.

Minor Comments
Line 19: [OH] should be defined as OH Concentration.

Line 89: ENSO acronym should be defined.
Citation: https://doi.org/10.5194/egusphere-2025-5532-RC1
RC2: 'Comment on egusphere-2025-5532', Anonymous Referee #2, 26 Jan 2026

General Description and Recommendation:

The authors use novel retrievals of isoprene from the space-based CrIS instrument to evaluate drivers of isoprene abundances in three regions of the tropics. They deduce that Amazonia is emissions controlled, the Maritime continent is chemistry controlled, and equatorial Africa is a mix of both. The manuscript is reasonably well written, though a little meandering in the discussion of the results and in need of a dedicated methods section with a consolidated discussion of the CrIS isoprene dataset used. General and specific comments for the authors to address before publication in ACP are detailed below.

General Comments:

Given the prominence of ENSO in the abstract and results, the introduction should include a paragraph detailing how ENSO affects isoprene in the tropics.
Methods detailed are peppered throughout the manuscript (specifically for CrIS isoprene and treatment of these data) or relegated to an appendix section. Given the journal format, these can be more conveniently consolidated to a Methods section, thus enhancing the reproducibility and traceability of the study. This dedicated section should include details of the CrIS instrument used, the retrieval method, priors used in the retrieval, data screening, and any quantitative assessment of the retrieved product so that the reader has context for the core data used throughout the study. For filters, is there any screening for contamination from biomass burning isoprene and what proportion of data is removed with the cloud masking? The latter is important for assessing whether there is potential for representation error in a region that is often cloudy.
Not discussed is the potential effect of vertical sensitivity of the instrument on the analysis. A major disadvantage of the retrieval method used is that it does not output averaging kernels, so it is not possible to assess the information content to determine whether the retrieved product is being informed mostly by the prior or the observations. The IASI NH₃ product that first used the retrieval method applied to retrieve CrIS isoprene has developed an approach to calculate averaging kernels (Clarisse et al., 2023). The same might not be the case yet for CrIS isoprene. Some discussion is needed regarding the implications of the quality and information content of the CrIS data used and the potential impact on the study findings.
It is not apparent that assessment of a representation error due to screening for clouds is suitably tested, as described in Section 3.2. The effect on the CrIS isoprene retrieval is tested, but not the effect on only considering scenes that are cloud masked. It would be worthwhile to evaluate whether any of the findings could potentially be affected by this cloud masking, especially in the tropics where clouds are abundant and dense and so may induce effects like relatively low temperatures and limited incoming radiation, impacting isoprene emissions.
Past literature targeting these regions is not sufficiently sourced, in particular the literature using satellite observations of formaldehyde to derive isoprene emissions and evaluate temporal trends and environmental drivers in emissions. Though these focus on emissions and the current study on abundances, there are findings and insights related to emissions that could be supported by these past studies. For example, Barkley et al. (2013) for estimating isoprene emissions in South America, Marais et al. (2012) in support of extensive transport of biomass burning across Africa affecting NO_x, Marais et al. (2014) in assessing relationships between isoprene emissions and environmental drivers, and Stavrakou et al. (2015) for all 3 regions.
Specific Comments:

L2: Fix order to “hydroxyl radical (OH)”.
L47: “especially as anthropogenic NO_x emissions decrease” maybe true for most of the northern hemisphere, but can such a statement be made for the tropics? See for example, positive trends in urban NO_x from Vohra et al. (2022) and decline in burned area in the tropics from Andela et al. (2017) affecting biomass burning NO_x.
L49: “novel isoprene retrievals” is overstated and without context. This is an established retrieval method already applied to IASI NH₃ (Whitburn et al., 2016), so including a reference to this methodology would make it clearer that this is a standard retrieval method for IASI NH₃ that is now being successfully applied to CrIS isoprene and/or adapted to suit CrIS isoprene if this is the case.
L61: What is the source of the “50%” value? Past bottom-up or top-down studies? Or your own simulation of MEGAN?
L113: provide quantitative values to demonstrate how much smaller the range in temperatures is in the Maritime Continent and equatorial Africa.
L187: Rather starting the sentence as “According to the measurements, …” would ensure it’s not written as though the measurements were responsible for providing this insight.
L272-273: is “input” correct here? If so, is this coming from the prior? And would this hold value to the user because it serves an indication of the skill of the retrieval?
L286: “end-of-the-year” is stated, but biomass burning peaks in Amazonia and The Maritime continent in September.
L290-300: Need to be specific that this is the timing of the dry season south of the Equator, as Sub-Saharan Africa has 2 distinct dry seasons.
L308: What is the logic of “which is consistent with high NO_x emission factors from these fires.”? Comparison to another region might help clarify the point being made.
Figure 9 caption: Move portion of caption interpreting the content of the figure to the manuscript text.
References:

Andela et al. (2017), doi:10.1126/science.aal4108.

Barkley et al. (2013), doi:10.1002/jgrd.50552.

Clarisse et al. (2023), doi:10.5194/amt-16-5009-2023.

Marais et al. (2012), doi:10.5194/acp-12-6219-2012.

Marais et al. (2014), doi:10.5194/acp-14-7693-2014.

Stavrakou et al. (2015), doi:10.5194/acp-15-11861-2015.

Vohra et al. (2022), doi:10.1126/sciadv.abm4435.

Whitburn et al. (2016), doi:10.1002/2016JD024828.

Citation: https://doi.org/10.5194/egusphere-2025-5532-RC2

James Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner

Supplement

https://doi.org/10.5194/egusphere-2025-5532-supplement

Model code and software

vSmartMOM with Isoprene James (Young Suk) Yoon, Suniti Sanghavi, Christian Frankenberg https://github.com/james-y-yoon/vSmartMOM.jl

James Yoon, Kelley C. Wells, Dylan B. Millet, Christian Frankenberg, Suniti Sanghavi, Abigail L. S. Swann, Joel A. Thornton, and Alexander J. Turner

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Short summary

Isoprene is a molecule emitted by trees that is oxidized in the atmosphere within hours. Much of the isoprene globally is emitted in the remote tropics, where we have few direct observations of isoprene. Here, we use new satellite retrievals of isoprene to infer drivers of tropical isoprene variability. Across different regions, isoprene column variability is controlled by different factors, namely changes in emissions or changes in natural nitrogen oxide sources, like soils and fires.


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