Ammonia exchange flux over a tropical dry deciduous forest in the dry season in Thailand

Xu, Mao; Chanonmuang, Phuvasa; Sase, Hiroyuki; Sorimachi, Atsuyuki; Itahashi, Syuichi; Matsuda, Kazuhide

doi:https://doi.org/10.5194/egusphere-2025-2505

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https://doi.org/10.5194/egusphere-2025-2505

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30 Jun 2025

| 30 Jun 2025

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Ammonia exchange flux over a tropical dry deciduous forest in the dry season in Thailand

Mao Xu, Phuvasa Chanonmuang, Hiroyuki Sase, Atsuyuki Sorimachi, Syuichi Itahashi, and Kazuhide Matsuda

Abstract. Ammonia (NH₃) is a significant contributor to total nitrogen deposition in East Asia. However, process-based observations that specifically focus on the air–surface exchange of NH₃ remain limited in this region, especially in Southeast Asia. To clarify the bi-directional exchange process of NH₃ under tropical climatic conditions, we first observed the NH₃ exchange flux over a tropical dry deciduous forest in Thailand during two periods with different canopy and meteorological conditions in the dry season using the aerodynamic gradient method. NH₃ concentrations exhibited strong positive correlations with air temperature and negative correlations with wind speed during the first half of the observation period. However, there was no clear correlation between concentrations and meteorological elements during the second half. Measured NH₃ fluxes fell within the ranges presented in recent studies, with a weighted mean and standard deviation of 0.148 ± 0.240 µg m⁻² s⁻¹, and consistently larger during daytime. During the dry season, the tropical dry deciduous forest acted as an emission source of NH₃. Across both observation periods, NH₃ emissions were governed by air temperature, relative humidity, friction velocity, and solar radiation. While no clear difference in fluxes magnitude was observed between the first half (0.140 ± 0.240 µg m⁻² s⁻¹) and the second half (0.158 ± 0.239 µg m⁻² s⁻¹), the main source of NH₃ emission in the tropical dry deciduous forest probably shifted dynamically from stomata to leaf litter due to the changes in meteorological, canopy, and forest floor conditions.

Received: 28 May 2025 – Discussion started: 30 Jun 2025

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Mao Xu, Phuvasa Chanonmuang, Hiroyuki Sase, Atsuyuki Sorimachi, Syuichi Itahashi, and Kazuhide Matsuda

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RC1:
'Comment on egusphere-2025-2505', Anonymous Referee #1, 09 Jul 2025 reply
The manuscript entitled “Ammonia exchange flux over a tropical dry deciduous forest in the dry season in Thailand”, written by Xu et al., presents a unique study to NH3 exchange between forest and atmosphere in the tropics. It is, to my knowledge, the first to present such data and is valuable to the general NH3 community. They systematically discuss the drivers of both NH3 concentration at different levels and the flux and suggest future studies that could answer questions raised in their own study. Their methodology, however, lacks clarity. As this could potentially affect a major part of their analysis, I recommend major revision. In addition, I have some specific comments and suggestions that could further improve their study.
Comments on methodology
Section 2.2: Flux calculation
As the authors deployed instrument at 26 and 34 m above a forest with mean canopy height of 20 m, they are measuring inside the roughness sublayer (RSL). As they do not mention this, and refer to a paper that is not listed in the bibliography, I cannot verify whether they included this in their calculations and thus they could potentially underestimate their fluxes. (see e.g. Harman and Finnigan (2008), de Ridder (2010) or Duyzer et al. (1994))

At what time interval do the authors calculate the transfer velocity D, and how does that compare to the long measurement interval of dNH3? If possible, flux measurements should be done at 30 minute intervals, as turbulence and gradients are highly variable. I understand that this was not feasible with the given set-up, however, long averages of gradients multiplied with a long average of the transfer velocity will lead to the ‘Schmidt paradox’ (Moene and van Dam, 2014), if there is not accounted for the ‘cross-term’ (see also chapter 2 of https://dx.doi.org/10.21945/RIVM-2022-0202). As I did not see a mention of this, I do not know whether the authors accounted for this. Moreover, the authors should discuss the potential error due to such a long measurement interval, including the effect of averaging over different stability regimes.

Section 2.3: The authors report the used meteorological instruments measure at an interval of 10 minutes. Does that mean that they sample only once every 10 minutes, or that there measurements are averaged/valid over that 10 minutes. Moreover, how do they obtain the meteorological input for flux calculation (by averaging?) and how do they obtain the meteorological conditions that they ultimately compare to the concentrations and fluxes?

Specific comments:
Line 43: How did NOx change during recent years?

Line 80: Is the range the difference over space or over time? And how heterogeneous is the surroundings? What is the standard deviation on the LAI measurements.

Line 99: first halve and second halve imply that the observation periods are consecutive. Therefore, I suggest to replace it with period 1 and period 2, or something alike.

Line 104: Matsuda et al. (2010) is not listed in the bibliography

Line 137: Is there any information on how these numbers compare to long term values of the site? Are they relatively high or low?

Line 171: Please specify what is meant by ‘a high category’

Figure 4 and Figure A1. the WD seems to have a diurnal cycle during a distinct amount of time, with different WD during night and daytime. I wonder what could cause such conditions and whether there are some submesoscale processes that could explain this.

Section 3.2: There are more factors that could control the NH3 concentration, such as boundary layer height and entrainment, see also Schulte et al., 2020 (https://doi.org/10.1016/j.atmosenv.2020.118153).

Figures 5 and 6: The different relation between the two different levels implicitly say something about the footprint of the two levels. It would be nice if the authors could add a discussion on this.

Line 226: Is there any scientific argument to exclude 1^st February? Why are the concentration on this day so different?

Line 254: Do the authors have any information on the amount of emission from the source area during the measurement campaign?

Line 276-277: please specify what you mean with ‘roughly patterned’.

Lines 277 – 279. I assume that the authors note on possible measurement errors due to these causes. Should be replaced to discussion on errors.

Line 280: A paired t-test is usually used to compare different groups. Does that mean that you are here comparing e.g. all night samples at 26m to all night samples at 34 m? And if so, why did the authors do that? A difference could be significant at group level, but still be insignificant on a single instance. I think it would be better here to judge each gradient measurement individually by comparing the uncertainty/error on the measurements, unless the authors are only interested in an average flux (but the rest of the article addresses individual measurements). Also, how does this relate to the previous sentence? I.e., if the two levels are significantly different, how would that relate to the presence of emission sources or meteorological elements? I don’t think I fully understand the intention of this sentence.

Lines 298-299 I don’t understand this. Figure 9 shows a quite large difference between D1 and D2, and N for all panels (including the transfer velocity)?

Lines 329-349: The authors extensively discuss the stomatal conductance gs, and use measurements of August 2020 and from 1996 to explain their strongest emissions during D1. However, gs is ultimately controlled by ecosystem health and meteorological conditions, which might have changed over this time, especially when comparing over such a long period or over different seasons. The authors could make a much stronger case here by calculating the gs themselves using e.g. Embserson or an A-gs model (https://doi.org/10.1016/j.envpol.2006.04.007), which they could train on the CO2/H2O fluxes (if available) or otherwise on the previous measurements of gs. If the authors decide against this, they should better discuss the validity of their comparison.

Lines 340-341: I assume the authors refer to different timing of emissions in Xu et al., 2023 and this study, please clarify the timings in the main text.

Lines 373-375: But then why the negative correlation with temperature during the second halve (especially during nighttime). Wouldn’t you expect it the other way around?

Line 396: what do you mean with ‘obviously’

Figure 10: Please consider to use constant binsizes for the two different observation periods, as this would allow for an easier comparison.

Lines 434 – 459: This reads more as a results and discussion section. Please consider moving it to Section 3.5

Technical comments
Line 15: (…) we present the first observations of NH3 exchange (…)

Line 44: therefor >> Therefore

Figure 1: Please use a white font for more clarity

Line 80: (…) at the beginning of the observation period (…)

Line 275: (…) dC, because (…)

Line 295: please rephrase “about four times”

Line 307: (…) acted as a source of NH3 (…)

Figure 9: The authors could consider boxplots for more clarity

Lines 314-319. The authors could consider to uniform the units of all previous studies (i.e. all to ug/m2/s), as that would allow for an easier comparison.

Line 336: please rephrase “And there was no change during this”

Line 364: closed >> close

Line 442 & 456: Wentwortth >> Wentworth

Line 456: (…) conditions. Notably (…)

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Citation: https://doi.org/10.5194/egusphere-2025-2505-RC1

Mao Xu, Phuvasa Chanonmuang, Hiroyuki Sase, Atsuyuki Sorimachi, Syuichi Itahashi, and Kazuhide Matsuda

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Short summary

We first observed the exchange flux of ammonia over a tropical forest in Thailand. Measurements were taken during two periods in the dry season with different environmental conditions. This data improves our understanding of how ammonia behaves in forests under tropical climates and helps refine models that estimate nitrogen deposition and assess its impact across East Asia.


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