Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020

Wang, Hao; Liu, Xiaohong; Wu, Chenglai; Lin, Guangxing

doi:10.5194/acp-24-3309-2024

Articles | Volume 24, issue 5

https://doi.org/10.5194/acp-24-3309-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-24-3309-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 5

Research article

|

18 Mar 2024

Research article |

| 18 Mar 2024

Regional to global distributions, trends, and drivers of biogenic volatile organic compound emission from 2001 to 2020

Hao Wang, Xiaohong Liu, Chenglai Wu, and Guangxing Lin

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Final revised paper (published on 18 Mar 2024)
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Preprint (discussion started on 11 Aug 2023)
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Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2023-1830', Anonymous Referee #1, 13 Sep 2023
BVOCs are important precursors to ozone and secondary organic aerosols in the atmosphere. In this manuscript, the authors focused on a comprehensive analysis of trends in BVOC emissions from 2001-2020 on a regional to global scales and identified the contribution of various driving factors to these trends. The manuscript is well written, and I suggested the acceptance after addressing the comments below.
The authors used a newer version of MEGAN. Could the authors elaborate the major advancement compared to previous versions such as MEGAN 2.1?

Are the observations only in 2013? The seasonal variations of isoprene flux in MEGAN appears to be very small, which seems to be quite different from the observations. Any explanations?

Line 274: These discrepancies are mainly ascribed to the differences in vegetation emission factors between the two versions of MEGAN. Could the authors add some explanations the emission factors from which vegetation are more accurate?

Line 367: Could the authors explain why for monoterpene emissions, only the effects of vegetation and meteorological factors are considered?
Citation: https://doi.org/10.5194/egusphere-2023-1830-RC1
- AC1: 'Reply on RC1', Hao Wang, 10 Oct 2023
  
  We thank the reviewers for their valuable comments and suggestions; our responses are attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1830-AC1
RC2:
'Comment on egusphere-2023-1830', Anonymous Referee #2, 18 Sep 2023
This is a study of great relevance to the atmospheric pollution modeling community. The trends presented and the sensitivity of MEGAN 3.2 to various parameters and its geographical distribution are of great interest to all of us who model both emissions and air quality. It provides interesting insights into how BVOC emissions can change at specific regions and highlights the significant impact of land-use changes and global warming.
I would like to ask the authors if they have reviewed or analyzed the uncertainty associated with the new emission factors used in MEGAN. Have they utilized those with the highest confidence index (denoted as 'J' in the code)? Or do they consider it worthwhile to address these factors or their spatial distribution in the future? Are these emission factors the default values in MEGAN for tree, shrub, grass, and crop categories?
Was there any anomaly in the reference year of 2001 that could potentially bias the study in specific regions? Did the authors find any unexpected anomalies that they did not anticipate?
Possible issues I have detected:
In line 70, based on my reading of the rest of the manuscript, shouldn't the range '0.04-0.33% yr-1' be negative?

Please review the use of capitalization for the acronyms 'LAI' and 'VCF' in both the text and figure captions.

In line 545, it should be corrected with “activity factors”.
Citation: https://doi.org/10.5194/egusphere-2023-1830-RC2
- AC2: 'Reply on RC2', Hao Wang, 10 Oct 2023
  
  We thank the reviewers for their valuable comments and suggestions; our responses are attached.
  
  Citation: https://doi.org/10.5194/egusphere-2023-1830-AC2

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Hao Wang on behalf of the Authors (10 Oct 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (30 Oct 2023) by Eduardo Landulfo

RR by Anonymous Referee #2 (30 Oct 2023)

RR by Anonymous Referee #3 (04 Jan 2024)

Suggestions for revision or reasons for rejection

This work provides a comprehensive analysis of the different trends impacting BVOC emissions at global and at regional scale. Wang et al. use the state-of-the-art emission model MEGANv3.2 to evaluate BVOC emission trends by varying specific drivers, and keeping other variables fixed in time. These simulations provide an important insight into the importance of these variables, such as temperature changes, land cover changes, and CO2 concentration changes in the past two decades.

The paper is well written and features an array of interesting figures and graphs. I would recommend this paper to be published after minor revisions.

General comments:
- It should be noted that there are large uncertainties in models of bottom-up inventories, such as in the treatment of drought stress, emission factors of certain tree species, etc. Although MEGAN3 provides an improved model of some important emission drivers, an in-depth discussion on model uncertainties is somewhat lacking.
- Introduction: I think it would be good to introduce the main BVOC compounds in the introduction (i.e. isoprene, monoterpenes, methanol, …).
- Section 2.2.1: It is not entirely clear how the vegetation dataset was constructed. The authors write that the high-resolution MODIS vegetation map was mapped onto the four PFTs of MEGANv3.2. I presume the interpolation is used to calculate the growth form fractions used by the Emission Factor Processor in Eq. 2? Furthermore, it is unclear to me where the ecotype dataset mentioned in Sect. 2.1 comes from.
- Section 3.1.1: The comparison of in situ isoprene measurements with the model is not always representative. Isoprene has a very short lifetime in the atmosphere, which implies that its measured fluxes depend on the local tree or plant species near the observation site. Are the assigned vegetation types shown in Fig. 1 based on the local vegetation of the observation site? And how do they differ from the average PFT derived from Sect. 2.2.1?
- Figure 9: Do the simulations of the individual drivers refer to the simulations in Table 1? The trends are described as contributions of the individual meteorological factors in panel 9c, but from Table 1 I understand that only one of the drivers is fixed and the two others are allowed to vary over the simulations. Consequently, if the T2m simulation implies T2m is fixed, why is the trend large for Central Africa and Europe? And the same goes for soil moisture and solar radiation trends.

Specific comments:
- Line 38: This sentence is unclear, as BVOC emissions are almost entirely determined by vegetation and meteorology, whereas CO2 concentrations play a much smaller, secondary effect due to its homogeneous distribution.
- Line 44: Add reference for the CO2 inhibition effect. Why does it only impact isoprene?
- Line 240-243: The authors attribute the potential reasons for the differences between their study and IASB-TD-OMI to different model parameters and different meteorological datasets used. Although these factors indeed influence the top-down emissions fluxes, they are mainly constrained by formaldehyde column measurements. Consequently, the reasons for the difference are more complex, as these are two very different methods.
- Line 289: Which new BVOC components were added to MEGANv3.2? Does any on these new components contribute significantly to the total of 279.6 Tg yr-1?
- Line 321: The study of Sindelarova et al. (2014) shows a peak in October, not December.
- Line 368: Technically, the increase of CO2 concentration is also partly responsible for global warming and thus higher temperatures (see e.g. Monson et al. 2007). Additionally, increased CO2 concentration can potentially lead to a larger LAIv (also Monson et al. 2007). Consequently, this sentence could be rephrased to specifically the CO2 inhibition effect resulting in the decrease of -0.20% yr-1 of isoprene emissions.
- Line 371: In Malik et al. (2023), the authors also argue that evidence for the effect of soil moisture stress on monoterpene emissions is inconclusive. Is there a reason why this effect is included for monoterpenes and not for CO2 inhibition?
- The title of Sect. 3.3 is the same as of Sect. 3.3.2.

Lay-out comments: write percentage changes in mathematical format to get a better minus-sign and to avoid splitting at the end of a line.

References:
Monson, R. K., et al. (2007), Isoprene emission from terrestrial eco systems in response to global change: Minding the gap between models and observations, Philos. Trans. R. Soc. A,365, 1677–1695, doi:10.1098/rsta.2007.2038.

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ED: Publish subject to minor revisions (review by editor) (04 Jan 2024) by Eduardo Landulfo

AR by Hao Wang on behalf of the Authors (14 Jan 2024) Author's response Author's tracked changes Manuscript

ED: Publish as is (05 Feb 2024) by Eduardo Landulfo

AR by Hao Wang on behalf of the Authors (06 Feb 2024) Manuscript

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Short summary

We quantified different global- and regional-scale drivers of biogenic volatile organic compound (BVOC) emission trends over the past 20 years. The results show that global greening trends significantly boost BVOC emissions and deforestation reduces BVOC emissions in South America and Southeast Asia. Elevated temperature in Europe and increased soil moisture in East and South Asia enhance BVOC emissions. The results deepen our understanding of long-term BVOC emission trends in hotspots.