the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Sep 2025
Observed Impacts of Aerosol Regimes on Energy and Carbon Fluxes in the Amazon Forest
Abstract. Atmospheric aerosols play a crucial role in modulating the energy available to the Earth’s surface, influencing the hydrological cycle, ecosystems, and climate. In the Amazon, previous studies have mainly examined how aerosols scatter and absorb radiation, enhancing diffuse radiation and influencing gross primary productivity. However, little is known about their interactions with energy partitioning (i.e., sensible and latent heat fluxes). Here, we investigate how regimes of high (AOD > 0.40) and low (AOD < 0.13) aerosol optical depth (AOD) affect surface energy and carbon dioxide (CO2) fluxes in an undisturbed Amazon rainforest. For this, we used long-term meteorological measurements from the Amazon Tall Tower Observatory (ATTO) collected between 2016 and 2022. We find that enhanced aerosol presence reduces both sensible heat flux and energy available for evapotranspiration by approximately 10 %, while decreasing CO2 fluxes by about 58 %, which suggests enhanced carbon assimilation by the forest. The impact of aerosols on turbulent surface fluxes is reflected in a cooling of approximately 0.5 °C at the canopy top, caused by a 5.6 % reduction in incoming shortwave radiation. These results demonstrate that aerosols modify turbulent energy exchange, with consequences for the forest microclimate and the coupled carbon and water cycles. It highlights the critical role of aerosols in the functioning of the ecosystem.
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Status: open (until 05 Nov 2025)
- RC1: 'Comment on egusphere-2025-4278', Anonymous Referee #1, 20 Oct 2025 reply
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General comment
The manuscript investigates how contrasting aerosol optical depth (AOD) regimes affect surface energy and carbon fluxes over an undisturbed Amazon rainforest using long-term in situ data (2016–2022) from the Amazon Tall Tower Observatory (ATTO). The authors focus on differences between “clean” (AOD < 0.13) and “polluted” (AOD > 0.40) regimes and assess impacts on radiation, sensible and latent heat fluxes, and CO₂ exchange. The topic is highly relevant to ACP because it addresses aerosol–biosphere–atmosphere interactions in one of the planet’s key ecosystems. The study provides new observational insights from a unique long-term dataset and uses appropriate statistical tools (Spearman correlation, Pillai’s trace, Random Forest) to assess nonlinear relationships. I think the paper is well written and it is neatly exposed. The literature cited is adequate.
The manuscript presents an interesting empirical analysis of aerosol effects on energy and carbon fluxes in the Amazon. However, the methodology lacks quantitative robustness in defining aerosol pollution regimes and in assessing statistical significance of differences between them and improved discussion. The structure and figures are generally clear, but the discussion often repeats background concepts and lacks a mechanistic synthesis connecting radiation, energy partitioning, and ecosystem carbon exchange.
In its present form, I recommend major revisions according to my specific comments before the manuscript can be considered for publication in ACP.
Specific comments
The study contributes observational evidence from a rare, pristine tropical forest site. The long-term dataset and the combination of aerosol and flux measurements are strengths. Nevertheless, the novelty is moderate, as the main conclusions - reduction of net radiation and turbulent fluxes under high AOD, accompanied by enhanced CO₂ assimilation - are qualitatively consistent with previous literature (e.g., Cirino et al. 2014; Braghiere et al. 2020; Palácios et al. 2022). The novelty would be strengthened by including a quantitative analysis of diffuse versus direct radiation, or by exploring seasonally resolved patterns rather than aggregating all data into two AOD categories. Defining “clean” (AOD < 0.13) and “polluted” (AOD > 0.40) purely from percentiles is arbitrary. Include a sensitivity test or physical rationale for these cutoffs. To increase the scientific value of the study, the authors should demonstrate, through appropriate statistical testing, whether the observed reductions (≈10%) are robust across years and not driven by interannual variability.
Line 94-97. Only 523 valid half-hour periods (370 dry season, 153 wet) are quite small relative to the six-year period. The statistical representativeness and interannual variability need further discussion.
Table 2. The authors should include appropriate statistical tests reporting p-values or confidence intervals when comparing fluxes between regimes, to demonstrate that the observed differences are statistically significant rather than due to random variability.
Line 173-174. The section on radiative fluxes should include a graph of the full diurnal cycle of SW, LW, and Rn to visually demonstrate the 10:00 - 14:00 LT maximum. This would strengthen the rationale for focusing on that time window.
Line 203-204. The physical interpretation of the longwave radiation components (LWatm and LWterr) is interesting, but it would benefit from quantitative support - for instance, by including a vertical temperature profile or an estimate of surface and atmospheric emissivity.
Line 227-232. The manuscript would benefit from a discussion of the energy balance closure, specifically addressing the discrepancy between Rn and the sum of H, LE, and G. Reporting the residuals for both clean and polluted regimes would provide a clearer evaluation of data quality and potential systematic biases.
Figure 6. The fourth-order polynomial fits to the diurnal cycles provide a useful visual comparison, but the authors should complement them with statistical analyses to confirm that the apparent differences between regimes are statistically significant.
Line 250-255. The connection between aerosol effects and water-use efficiency (WUE) is largely speculative because WUE is not quantitatively evaluated in the manuscript. The authors should consider calculating WUE (for example, as GPP/ET using FCO₂ and LE data) or presenting an appropriate proxy to substantiate this aspect of the discussion.
Line 242-245. It seems to me that there is some inconsistency throughout the manuscript regarding the sign convention of CO₂ flux. The authors should clearly state that CO₂ uptake by the ecosystem corresponds to a negative flux, while positive flux values indicate a CO₂ emission to the atmosphere. Accordingly, a “drop” or decrease in FCO₂ should represent reduced carbon uptake, not enhanced assimilation. In the Abstract, for example, Authors should clarify the meaning of “decrease in CO₂ fluxes by 58%” (does this mean more negative flux, i.e., greater uptake?). Clarifying this point is essential for avoiding misinterpretation of the results and ensuring consistency across figures, tables, and the discussion.
The figures are generally clear and well designed, but they would benefit from the inclusion of confidence intervals or error bars to convey the statistical variability of the data. Adding uncertainty information would allow readers to better assess the robustness of the observed differences between regimes and the reliability of the fitted curves.
Minor comments
All physical variables (Rn, H, LE, FCO₂, AOD, etc.) should be written in italics or formatted with the equation editor for consistency and readability.
Throughout the manuscript, several acronyms are not explicitly defined (ARF24h, LWterr), which may affect readability. I recommend defining each acronym upon first use.
Line 191. “ARF24h”, did Authors refer to daily mean? It should be clarified
Line 287-188. The phrase “In contrast” seems used incorrectly; the studies cited do not contradict one another, showing similar ARF values (within the estimated errors). The Authors should revise wording.
Table3. Caption. FCO should be replaced by FCO2
Line 299. As before, CO -> CO2