the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 May 2025
Quantifying Forest Canopy Shading and Turbulence Effects on Boundary Layer Ozone over the United States
Abstract. The presence of dense forest canopies significantly alters the near-field dynamical, physical, and chemical environment, with implications for atmospheric composition and air quality variables such as boundary layer ozone (O₃). Observations show profound vertical gradients in O3 concentration beneath forest canopies; however, most chemical transport models (CTMs) used in the operational and research community, such as the Community Multiscale Air Quality (CMAQ) model, cannot account for such effects due to inadequate canopy representation and lack of sub-canopy processes. To address this knowledge gap, we implemented detailed forest canopy processes—including in-canopy photolysis attenuation and turbulence—into the CMAQv5.3.1 model, driven by the Global Forecast System and enhanced with high-resolution vegetation datasets. Simulations were conducted for August 2019 over the contiguous U.S. The canopy-aware model shows substantial improvement, with mean O₃ bias reduced from +0.70 ppb (Base) to −0.10 ppb (Canopy), and fractional bias from +9.71% to +6.37%. Monthly mean O₃ in the lowest model layer (~0–40 m) decreased by up to 9 ppb in dense forests, especially in the East. Process analysis reveals a 75.2% drop in first-layer O₃, with daily surface production declining from 673 to 167 ppb d⁻¹, driven by suppressed photolysis and vertical mixing. This enhances NOₓ titration and reduces O₃ formation under darker, stable conditions. The results highlight the critical role of canopy processes in atmospheric chemistry and demonstrate the importance of incorporating realistic vegetation-atmosphere interactions in CTMs to improve air quality forecasts and health-relevant exposure assessments.
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RC1: 'Comment on egusphere-2025-485', Anonymous Referee #1, 22 May 2025
Summary: This paper applies the canopy physics (Makar et al., 2017) in CMAQ. Apparently, the science has already been done by Makar et al. (2017). But the implementation in a more open-sourced and widely used community model (CMAQ) and the use the process analysis tool for deeper insights provides a lot of extra scientific value on top of Makar et al. (2017), and worthy of publication in Atmospheric Chemistry and Physics after some corrections and clarifications.
Major comments:
However, this work has one potentially important conceptual/theoretical issue. The presence of canopy creates segregation between in-canopy and above-canopy air, and this work indeed shows its importance. However, the presence of plant canopy also enhances the mixing immediately above the canopy by generating extra eddies (Harman and Finnigan, 2007). Ignoring this effect leads to underestimation of overall near-surface (z ~ 0 – 2h) vertical mixing. If redesigning and rerunning the numerical schemes/experiments are not feasible, some quantitative arguments are required to explore the size of its potential effects on simulation ozone.
On a similar note, stating dry deposition as “a second effect that’s not covered” (L 139 - 140) is confusing, and contradictory to the fact that the authors keep referencing changes in deposition rates (e.g. L 558, table 4). Please clarify and provide quantitative arguments/references about why this is ignored, and how much would that affect the result.
Minor comments
- L32: Unclear. What is 75.2% drop in first-layer O3?
- L 420 – 425: The word “variability” is vague and confusing, making the whole paragraph hard to understand, especially the final sentence. Please rewrite with more precise and understandable terminologies.
- L 462: what is “lowering O3 diurnal profile”?
- L 511: “within the canopy” or “within the first model layer”?
- L 531: “larger”
- L 535: notation on left hand side is sloppy
- Table 4/5: The explanation could be much clearer if total Ox budget is analyzed in addition
- L723: Does more NOx become NO2 matter through promoting NOx deposition, since NO2 deposits much more rapidly than NO?
Reference
Harman, I. N. and Finnigan, J. J.: A simple unified theory for flow in the canopy and roughness sublayer, Boundary-Layer Meteorol, 123, 339–363, https://doi.org/10.1007/s10546-006-9145-6, 2007.
Makar, P. A., Staebler, R. M., Akingunola, A., Zhang, J., McLinden, C., Kharol, S. K., Pabla, B., Cheung, P., and Zheng, Q.: The effects of forest canopy shading and turbulence on boundary layer ozone, Nat Commun, 8, 15243, https://doi.org/10.1038/ncomms15243, 2017.
Citation: https://doi.org/10.5194/egusphere-2025-485-RC1 -
RC2: 'Comment on egusphere-2025-485', Anonymous Referee #2, 27 Jun 2025
General Comments
Wang and coauthors present an advancement in regional scale chemical transport modeling by implementing in-canopy photolysis attenuation and turbulence into the CMAQ model. The authors provide a thorough evaluation of the canopy parameterization’s impact on O3 concentrations and above/below canopy processing rates across multiple sites, and the study is a valuable step toward improving surface air quality predictions in forested environments and beyond. The comments below are intended to clarify certain methodological choices and their defense, and suggest additional context where relevant to further strengthen the manuscript. After addressing these comments, I believe that this manuscript is suitable for publication in ACP.
My major comment concerns the treatment of dry deposition and sub-canopy O3 processing rates. Surface O3 predictions are known to be sensitive to dry deposition parameterizations, which are constrained by time-varying environmental drivers that influence both the accuracy and variability of modeled O3 (e.g., Hardacre et al., 2015; Visser et al., 2021). It would be valuable to discuss whether improvements in O3 representation in the canopy-parameterized model might also be achieved through alternative or updated dry deposition schemes. In particular, could a change in the dry deposition parameterization alone, or the application of a surrogate vertical treatment (e.g., Silva et al., 2020) similar to what is used in Kcan(z), lead to comparable impacts on model–observation agreement? If a dedicated sensitivity analysis is not feasible within the current scope of the study, I recommend at least acknowledging this potential source of uncertainty and its implications for interpreting the canopy model’s performance. I also suggest including the specific dry deposition scheme used in this study in Table 1 to aid reader clarity.
Similarly, do you anticipate that sub-grid vertical resolution of in-canopy chemistry and VOC emissions would meaningfully affect first-layer concentrations and processing rates? For example, you cite articles that show that VOCs and NOx have a vertical gradient (due to changes in leaf area and proximity to soil) and thus the chemical processing rate of O3 in the lower half of the canopy will be different than the upper half (e.g., Vermeuel, et al. 2024). It is unclear whether CMAQ’s first-layer treatment captures this integrated vertical effect or whether the vertical averaging of Kcan(z), in the absence of a corresponding treatment for dry deposition and chemistry, could introduce systematic biases, particularly in grid cells with relatively short canopy heights. While the effect of chemistry is touched upon briefly in Section 3.3, a more explicit acknowledgment of this potential limitation, and an estimate or qualitative discussion of the scale of uncertainty it could introduce, would provide important context for readers assessing the robustness of the chemical representation.
References
Hardacre et al. (2015) - https://doi.org/10.5194/acp-15-6419-2015
Visser et al. (2021) - https://acp.copernicus.org/articles/21/18393/2021/acp-21-18393-2021.html
Silva et al. (2020) - https://gmd.copernicus.org/articles/13/2569/2020/
Vermeuel et al. (2024) - https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JD042092
Specific Comments
Line 153: Adding in a short definition of what forest clumping index is would help clarify how threshold criteria #5 is calculated.
Lines 171–186: The phrasing of this section is somewhat confusing. The text defines canopy threshold criteria by describing conditions under which grid cells do not qualify, while italicizing the qualifying metrics. To improve clarity, consider consistently framing each criterion in terms of what qualifies as a canopy. For example: “1. The LAI exceeds a minimal threshold for forest cover (i.e., more likely to have canopy shading or turbulence changes): LAI > 0.1."
Line 250: The frequent references to Makar et al. (2017) may reduce accessibility for readers unfamiliar with that study, such as those who are interested in just CMAQ improvements. To improve clarity, I recommend including Eqs. 4–9 from Makar et al. (2017) in the Supplementary Information.
Line 260-261: Please clarify how the sub-grid Kcan(z) values are incorporated into CMAQ’s first model layer. Is a vertical average used to replace K(z1)? How sensitive is this approach to canopy height, particularly when canopy heights fall well below the vertical extent of the first layer?
Line 358-359: The term “indirect effect” is mentioned a few times. It would be helpful to define a quantitative or operational threshold for when a site is considered indirectly impacted by canopy processes.
Table 2: Across the U.S. Domain, ME, NME, and R do not change for Hourly O3 and MDA8 O3. Does this mean that the variability in O3 is dominated by non-canopy sites? R for hourly O3 also does not change for the Canopy scenario. You mention improvements in variability in the daytime diel profile but is that compensated for by poorer nighttime values?
Line 421-422: Visual inspection of Figure 4 does not clearly support the claim that variability predictions have improved. Please provide a statistical metric, such as coefficient of variation or standard deviation, to demonstrate this more convincingly.
Lines 424–425: It would be helpful to further explain how “The larger variability in the canopy model is impacted by the more discrete nature of the turbulent eddies using Raupach’s approach to K diffusivities.” by reading lines 419-425 or looking at Figure 4 alone. An additional sentence or two clarifying the mechanism would benefit readers.
Technical Corrections
Line 69: I suggest changing big leaf to “big-leaf” to stay consistent with your naming.
Line 81: Is there an extra “and” in “…may drive and chemical processes…”?
Line 524: “More negative” could be replaced with “decreases” for clarity.
Line 531: Typo: “la7rger” should be corrected to “larger”.
Citation: https://doi.org/10.5194/egusphere-2025-485-RC2 - AC1: 'Comment on egusphere-2025-485', Chi-Tsan Wang, 08 Aug 2025
Status: closed
-
RC1: 'Comment on egusphere-2025-485', Anonymous Referee #1, 22 May 2025
Summary: This paper applies the canopy physics (Makar et al., 2017) in CMAQ. Apparently, the science has already been done by Makar et al. (2017). But the implementation in a more open-sourced and widely used community model (CMAQ) and the use the process analysis tool for deeper insights provides a lot of extra scientific value on top of Makar et al. (2017), and worthy of publication in Atmospheric Chemistry and Physics after some corrections and clarifications.
Major comments:
However, this work has one potentially important conceptual/theoretical issue. The presence of canopy creates segregation between in-canopy and above-canopy air, and this work indeed shows its importance. However, the presence of plant canopy also enhances the mixing immediately above the canopy by generating extra eddies (Harman and Finnigan, 2007). Ignoring this effect leads to underestimation of overall near-surface (z ~ 0 – 2h) vertical mixing. If redesigning and rerunning the numerical schemes/experiments are not feasible, some quantitative arguments are required to explore the size of its potential effects on simulation ozone.
On a similar note, stating dry deposition as “a second effect that’s not covered” (L 139 - 140) is confusing, and contradictory to the fact that the authors keep referencing changes in deposition rates (e.g. L 558, table 4). Please clarify and provide quantitative arguments/references about why this is ignored, and how much would that affect the result.
Minor comments
- L32: Unclear. What is 75.2% drop in first-layer O3?
- L 420 – 425: The word “variability” is vague and confusing, making the whole paragraph hard to understand, especially the final sentence. Please rewrite with more precise and understandable terminologies.
- L 462: what is “lowering O3 diurnal profile”?
- L 511: “within the canopy” or “within the first model layer”?
- L 531: “larger”
- L 535: notation on left hand side is sloppy
- Table 4/5: The explanation could be much clearer if total Ox budget is analyzed in addition
- L723: Does more NOx become NO2 matter through promoting NOx deposition, since NO2 deposits much more rapidly than NO?
Reference
Harman, I. N. and Finnigan, J. J.: A simple unified theory for flow in the canopy and roughness sublayer, Boundary-Layer Meteorol, 123, 339–363, https://doi.org/10.1007/s10546-006-9145-6, 2007.
Makar, P. A., Staebler, R. M., Akingunola, A., Zhang, J., McLinden, C., Kharol, S. K., Pabla, B., Cheung, P., and Zheng, Q.: The effects of forest canopy shading and turbulence on boundary layer ozone, Nat Commun, 8, 15243, https://doi.org/10.1038/ncomms15243, 2017.
Citation: https://doi.org/10.5194/egusphere-2025-485-RC1 -
RC2: 'Comment on egusphere-2025-485', Anonymous Referee #2, 27 Jun 2025
General Comments
Wang and coauthors present an advancement in regional scale chemical transport modeling by implementing in-canopy photolysis attenuation and turbulence into the CMAQ model. The authors provide a thorough evaluation of the canopy parameterization’s impact on O3 concentrations and above/below canopy processing rates across multiple sites, and the study is a valuable step toward improving surface air quality predictions in forested environments and beyond. The comments below are intended to clarify certain methodological choices and their defense, and suggest additional context where relevant to further strengthen the manuscript. After addressing these comments, I believe that this manuscript is suitable for publication in ACP.
My major comment concerns the treatment of dry deposition and sub-canopy O3 processing rates. Surface O3 predictions are known to be sensitive to dry deposition parameterizations, which are constrained by time-varying environmental drivers that influence both the accuracy and variability of modeled O3 (e.g., Hardacre et al., 2015; Visser et al., 2021). It would be valuable to discuss whether improvements in O3 representation in the canopy-parameterized model might also be achieved through alternative or updated dry deposition schemes. In particular, could a change in the dry deposition parameterization alone, or the application of a surrogate vertical treatment (e.g., Silva et al., 2020) similar to what is used in Kcan(z), lead to comparable impacts on model–observation agreement? If a dedicated sensitivity analysis is not feasible within the current scope of the study, I recommend at least acknowledging this potential source of uncertainty and its implications for interpreting the canopy model’s performance. I also suggest including the specific dry deposition scheme used in this study in Table 1 to aid reader clarity.
Similarly, do you anticipate that sub-grid vertical resolution of in-canopy chemistry and VOC emissions would meaningfully affect first-layer concentrations and processing rates? For example, you cite articles that show that VOCs and NOx have a vertical gradient (due to changes in leaf area and proximity to soil) and thus the chemical processing rate of O3 in the lower half of the canopy will be different than the upper half (e.g., Vermeuel, et al. 2024). It is unclear whether CMAQ’s first-layer treatment captures this integrated vertical effect or whether the vertical averaging of Kcan(z), in the absence of a corresponding treatment for dry deposition and chemistry, could introduce systematic biases, particularly in grid cells with relatively short canopy heights. While the effect of chemistry is touched upon briefly in Section 3.3, a more explicit acknowledgment of this potential limitation, and an estimate or qualitative discussion of the scale of uncertainty it could introduce, would provide important context for readers assessing the robustness of the chemical representation.
References
Hardacre et al. (2015) - https://doi.org/10.5194/acp-15-6419-2015
Visser et al. (2021) - https://acp.copernicus.org/articles/21/18393/2021/acp-21-18393-2021.html
Silva et al. (2020) - https://gmd.copernicus.org/articles/13/2569/2020/
Vermeuel et al. (2024) - https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2024JD042092
Specific Comments
Line 153: Adding in a short definition of what forest clumping index is would help clarify how threshold criteria #5 is calculated.
Lines 171–186: The phrasing of this section is somewhat confusing. The text defines canopy threshold criteria by describing conditions under which grid cells do not qualify, while italicizing the qualifying metrics. To improve clarity, consider consistently framing each criterion in terms of what qualifies as a canopy. For example: “1. The LAI exceeds a minimal threshold for forest cover (i.e., more likely to have canopy shading or turbulence changes): LAI > 0.1."
Line 250: The frequent references to Makar et al. (2017) may reduce accessibility for readers unfamiliar with that study, such as those who are interested in just CMAQ improvements. To improve clarity, I recommend including Eqs. 4–9 from Makar et al. (2017) in the Supplementary Information.
Line 260-261: Please clarify how the sub-grid Kcan(z) values are incorporated into CMAQ’s first model layer. Is a vertical average used to replace K(z1)? How sensitive is this approach to canopy height, particularly when canopy heights fall well below the vertical extent of the first layer?
Line 358-359: The term “indirect effect” is mentioned a few times. It would be helpful to define a quantitative or operational threshold for when a site is considered indirectly impacted by canopy processes.
Table 2: Across the U.S. Domain, ME, NME, and R do not change for Hourly O3 and MDA8 O3. Does this mean that the variability in O3 is dominated by non-canopy sites? R for hourly O3 also does not change for the Canopy scenario. You mention improvements in variability in the daytime diel profile but is that compensated for by poorer nighttime values?
Line 421-422: Visual inspection of Figure 4 does not clearly support the claim that variability predictions have improved. Please provide a statistical metric, such as coefficient of variation or standard deviation, to demonstrate this more convincingly.
Lines 424–425: It would be helpful to further explain how “The larger variability in the canopy model is impacted by the more discrete nature of the turbulent eddies using Raupach’s approach to K diffusivities.” by reading lines 419-425 or looking at Figure 4 alone. An additional sentence or two clarifying the mechanism would benefit readers.
Technical Corrections
Line 69: I suggest changing big leaf to “big-leaf” to stay consistent with your naming.
Line 81: Is there an extra “and” in “…may drive and chemical processes…”?
Line 524: “More negative” could be replaced with “decreases” for clarity.
Line 531: Typo: “la7rger” should be corrected to “larger”.
Citation: https://doi.org/10.5194/egusphere-2025-485-RC2 - AC1: 'Comment on egusphere-2025-485', Chi-Tsan Wang, 08 Aug 2025
Model code and software
GMU-SESS-AQ/CMAQ: GMU Canopy Tag for CMAQv5.3.1 Patrick Campbell et al. https://zenodo.org/records/14502375
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