Variability and trends of upper-tropospheric aerosols over the Asian summer monsoon region: An AeroCom multi-model study

Chin, Mian; Wright, Jonathon S.; Bian, Huisheng; Tan, Qian; Pan, Xiaohua; Takemura, Toshihiko; Matsui, Hitoshi; Tsigaridis, Kostas; Bauer, Susanne; Ginoux, Paul; Peng, Yiran; Guo, Zengyuan; Fadnavis, Suvarna; Laakso, Anton; Burrows, John P.; Taha, Ghassan; Kar, Jayanta; Rozanov, Alexei; Arosio, Carlo; Rieger, Landon; Bourassa, Adam

doi:10.5194/egusphere-2025-6257

Preprints

https://doi.org/10.5194/egusphere-2025-6257

Preprints

14 Jan 2026

| 14 Jan 2026

Variability and trends of upper-tropospheric aerosols over the Asian summer monsoon region: An AeroCom multi-model study

Mian Chin, Jonathon S. Wright, Huisheng Bian, Qian Tan, Xiaohua Pan, Toshihiko Takemura, Hitoshi Matsui, Kostas Tsigaridis, Susanne Bauer, Paul Ginoux, Yiran Peng, Zengyuan Guo, Suvarna Fadnavis, Anton Laakso, John P. Burrows, Ghassan Taha, Jayanta Kar, Alexei Rozanov, Carlo Arosio, Landon Rieger, and Adam Bourassa

Abstract. Aerosols in the upper troposphere play an important role in Earth’s radiative balance and atmospheric composition. Satellite observations have revealed a recurrent enhancement of aerosol extinction coefficient (AEC) in the upper troposphere and near the tropopause over the Asian summer monsoon (ASM) anticyclone (ASMA) region during July–August. However, substantial uncertainties remain regarding (i) the influence of ASM dynamics and climate variability on these aerosols, (ii) the extent to which the upper-tropospheric aerosol trends reflect changes in surface pollutant emissions, and (iii) the ability of global models to simulate aerosol amounts, variability, and key controlling processes in the upper-tropospheric ASMA region. Here, we present results from an AeroCom-coordinated global multi-model study addressing these issues. Using simulations from nine models for 2000–2018, we find large inter-model differences in non-volcanic AEC over the upper-tropospheric ASMA region, with coefficients of variation ranging from 64 % to 86 %. Approximately half of this spread is attributable to differences in transport and wet removal processes, as diagnosed using common tracers, with discrepancies in wet removal contributing about eight times more than those associated with transport. The multi-model ensemble indicates an overall increase in non-volcanic AEC over the past two decades, consistent with rising anthropogenic emissions in Asia, while interannual variability is linked to climate variability as represented by the Multivariate ENSO Index. Through comparison with satellite observations, we further identify persistent model deficiencies, particularly in the representation of volcanic aerosols, and highlight priorities for future coordinated model developments and evaluation.

Received: 15 Dec 2025 – Discussion started: 14 Jan 2026

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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-6257', Anonymous Referee #1, 02 Feb 2026
Summary
The manuscript titled "Variability and trends of upper-tropospheric aerosols over the Asian summer monsoon region: An AeroCom multi-model study" evaluates nine global models from AeroCom Phase-III UTLS regarding their predictions of aerosol extinction coefficients (AEC) in the Asian Summer Monsoon Anticyclone (ASMA) region.

The inter-comparison shows large discrepancies in volcanic AEC between models. For non-volcanic aerosols, a high coefficient of variation was also found. By using two tracers, the authors diagnosed the extent to which convective transport and wet removal explain these inter-model differences. The analysis suggests that differences in wet removal contribute more significantly to the diversity of AEC in the ASMA region than differences in transport. Based on these results, recommendations are made to improve, constrain, and evaluate global models.

Shifting from model evaluation to physical trends, the study uses the multi-model mean from 2000 to 2018 to investigate trends in atmospheric conditions. A statistically significant increase in AEC of approximately 1.2% per year is found, which correlates with increasing Asian aerosol emissions. Finally, the authors show that the interannual variability of AEC can be linked to climate variability through the Multivariate El Niño/Southern Oscillation (ENSO) Index (MEI.v2).

General comments
Overall, this is a scientifically sound and well-written manuscript. It is relevant to evaluate inter-model differences and their causes for understanding the effects of different model parameters and improving the reliability of aerosol simulations. The manuscript furthermore provides good recommendations for improving, constraining, and evaluating future models. I also appreciate that the study extends the scope beyond the model inter-comparison to analyze physical trends in atmospheric conditions. The writing is clear, supported by effective figures and tables. The study fits well within the aims and scope of Atmospheric Chemistry and Physics. I believe this manuscript is suitable for publication in Atmospheric Chemistry and Physics after addressing the minor comments listed below.

Minor comments
In subsection 2.1, the authors mention the use of several emission datasets to cover the full simulation period of 2000 to 2018. Specifically, anthropogenic emissions from 2015–2018 are taken to be constant based on 2014 data, and volcanic emissions switch from EP-TOMS to Aura-OMI in 2004. While I recognize that this is a standard and necessary strategy given data availability, it is good practice to briefly discuss how these discontinuities might influence the results or cite references that discuss this. Given that a key finding of the manuscript is a statistically significant trend in AEC, could the authors clarify how holding anthropogenic emissions constant for the final four years might influence that trend? A brief comment on the potential artifacts introduced by switching satellite instruments for volcanic data would also be beneficial.

On lines 304–307, the authors state that R²≈0.50 is "consistent with a simple summation of the CV values... which is roughly half of the 79% CV." This comparison is mathematically difficult to justify. Coefficients of variation, which are derived from standard deviations, do not sum linearly. For independent variables, variances add linearly, while standard deviations add as the square root of the sum. R² represents the fraction of explained variance, whereas CV represents a fraction of standard deviation. An R² of 0.50 implies that ~71% of the standard deviation is explained, which contradicts that the summed CVs are only half of the total. I suggest removing this specific sentence as the result of R²=0.505 stands strong enough on its own without this non-rigorous comparison.

There is no clear description of which statistical test was used to determine the statistical significance of the increasing AEC trend in subsection 3.4. Based on the caption of Fig. 10, I conclude that a Student's t-test was used. The standard Student t-test relies on assumptions that may not hold for this data, particularly the assumption of independence. Time series of aerosol extinction coefficients often have autocorrelation. Could the authors clarify exactly which type of t-test was used and if any adjustments were made to account for autocorrelation? If a standard Student t-test was used, I suggest performing a more robust test, such as the Mann-Kendall test, to confirm the results.

The authors correctly note in subsection 3.1 that satellite retrievals below the tropopause are limited by cloud presence. However, deep convection in the ASMA region can also reach the tropopause (100 hPa). I would thus expect that even at the studied height of Fig. 2 the satellite data misses some times when clouds are present, whereas the models likely span the full period. Was any filtering applied to the model data to match the satellite data availability? Otherwise, this discrepancy in data availability could bias the comparison.
Citation: https://doi.org/10.5194/egusphere-2025-6257-RC1
RC2: 'Comment on egusphere-2025-6257', Daniele Visioni, 03 Mar 2026

In this work the authors present results from nine models that participated in AeroCom Phase-III UTLS, focused on the Asian Summer Monsoon region. While I think the results are robust and generally well presented, and I support publication in ACP, there are a few areas of improvement that I list below that would help with the overall presentation.
I found some sections of the introduction a bit disjointed, and the authors could do a better job offering an overview of the topic, expanding on them a bit. For instance, the phrase “Aerosols in this region affect radiative forcing, cloud microphysics, and chemical composition” is a very generic and bland statement. It’s true, but it’s also true for aerosols everywhere! A better argument would highlight the difference compared to lower tropospheric aerosols better (for instance, discussing lifetime and sources better), as well as to stratospheric aerosols.
Similarly, the section about AeroCom could be better explained, with some references and examples to the finding it produced [as well as, for instance, including some references and discussion to similar model intercomparison projects. For instance, the ISAMIP background sulfur budget analyses in Brodowsky et al. 2024 might be particularly relevant for this work, see the comment in line 385 and 467].
Line 121: “The prescribed sources for the tracers were used repeatedly for all simulated years (2000-2018)” Not sure what this means?
Line 136: non-permanent links such as the two in this section should not be used in a paper that will be permanent. What if the link changes 5 years from now? If you want a link to external info, it will either have to be permanently archived somewhere, or included as supplementary here. Or just repeat the information somehow.
Table 2: I was surprised that there is no mention in the methodology or in the discussion about the modeling differences in the meteorology. A replay is very different from a nudged simulation, and those are very different in terms of “realistic” conditions ffrom fixed SST, AMIP style simulations, especially when comparing to observations. The authors should make sure to acknowledge these differences and integrate them in the discussion phase of the manuscript (see for instance Orbe et al., 2016). On a similar note, it would be useful to have information in the table about the aerosol microphysics scheme used, since that is then discussed in 318-320.
Figure 2-3-4-6: These figures are all too small, and make it very hard to distinguish the results, especially in a quantitative way. Some of them (especially the maps) could be more zoomed in, whereas the full map could stay in the supplementary.
I strongly agree with reviewer 1 about lines 304–307.
Section 3.4: Considering some models in Figures 2 and 3 do not capture aerosols trends and contributions at all, I wonder if the authors could show also, and comment on, how the trends look like if such models are de-weighted or removed altogether from the multi-model average. Some comments also in the conclusions about how to leverage the model-observation evaluation to differentially weight the results could be of interest to the community (even in the presence of observational uncertainties).
Line 405: I found this part quite vague. Yes, the tropopause height can be diagnosed in different ways, but are you saying that different models have different ways to output it? In the Chemistry-Climate Model Intercomparison (CCMI), this was often solved by re-calculating the tropopause in a coherent way (see for instance Section 2.2 in Pisoft et al., 2021).
Data Availability statement: “The AeroCom UTLS model output is stored in the AeroCom repository, which can be accessed on request, as described at https://aerocom.met.no/data/” I couldn’t find this when reading the page linked, and considering the last update to that page is from 2021, I would suggest updating it to make it more usable. I would also note that, as is, this kind of statement is not in line with ACP editorial policies (https://www.atmospheric-chemistry-and-physics.net/policies/data_policy.html). At the very list, the authors should make the final output as analyzed here available in a repository with a DOI.
Brodowsky, C. V., Sukhodolov, T., Chiodo, G., Aquila, V., Bekki, S., Dhomse, S. S., ... & Peter, T. (2024). Analysis of the global atmospheric background sulfur budget in a multi-model framework. Atmospheric Chemistry and Physics, 24(9), 5513-5548.
Orbe, C., D. W. Waugh, H. Wang, D. E. Kinnison, J-F Lamarque, Simone Times, Tropospheric Transport Differences Between Models Using the Same Large-Scale Meteorological Fields Geophys. Res. Lett., doi:10.1002/2016GL071339, 2016.
Pisoft, P., Sacha, P., Polvani, L. M., Añel, J. A., De La Torre, L., Eichinger, R., ... & Rieder, H. E. (2021). Stratospheric contraction caused by increasing greenhouse gases. Environmental Research Letters, 16(6), 064038.

Citation: https://doi.org/10.5194/egusphere-2025-6257-RC2

Viewed

Total article views: 368 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
234	115	19	368	20	24

HTML: 234
PDF: 115
XML: 19
Total: 368
BibTeX: 20
EndNote: 24

Views and downloads (calculated since 14 Jan 2026)

Month	HTML	PDF	XML	Total
Jan 2026	113	48	12	173
Feb 2026	52	26	6	84
Mar 2026	69	41	1	111

Cumulative views and downloads (calculated since 14 Jan 2026)

Month	HTML	PDF	XML	Total
Jan 2026	113	48	12	173
Feb 2026	52	26	6	84
Mar 2026	69	41	1	111

Viewed (geographical distribution)

Total article views: 331 (including HTML, PDF, and XML) Thereof 331 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 21 Mar 2026

Short summary

Aerosols in the upper troposphere influence weather and climate. The Asian summer monsoon efficiently transports surface pollutants upward, shaping aerosol amounts and variability in the upper troposphere. Using multiple global models, this study finds a summertime increase in upper-tropospheric aerosols of about 1.2 % per year from 2000 to 2018 over Asia, consistent with rising pollutant emissions, while year-to-year changes are mainly driven by climate variability affecting monsoon dynamics.


Total:	0
HTML:	0
PDF:	0
XML:	0