the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4)
Olivia Elaine Clifton
Donna Schwede
Christian Hogrefe
Jesse O. Bash
Sam Bland
Philip Cheung
Mhairi Coyle
Lisa Emberson
Johannes Flemming
Erick Fredj
Stefano Galmarini
Laurens Ganzeveld
Orestis Gazetas
Ignacio Goded
Christopher D. Holmes
László Horváth
Vincent Huijnen
Qian Li
Paul A. Makar
Ivan Mammarella
Giovanni Manca
J. William Munger
Juan L. Pérez-Camanyo
Jonathan Pleim
Limei Ran
Roberto San Jose
Sam J. Silva
Ralf Staebler
Shihan Sun
Amos P. K. Tai
Timo Vesala
Tamás Weidinger
Zhiyong Wu
Leiming Zhang
Abstract. A primary sink of air pollutants and their precursors is dry deposition. Dry deposition estimates differ across chemical transport models yet an understanding of the model spread is incomplete. Here we introduce Activity 2 of the Air Quality Model Evaluation International Initiative Phase 4 (AQMEII4). We examine dry deposition schemes from regional and global chemical transport models as well as standalone models used for impacts assessments or process understanding. We configure eighteen schemes as single-point models at eight northern hemisphere locations with observed ozone fluxes. Single-point models are driven by a common set of site-specific meteorological and environmental conditions. Five of eight sites have at least three years and up to twelve years of ozone fluxes. The spread across models that de-emphasizes outliers in multiyear mean ozone deposition velocities ranges from a factor of 1.2 to 1.9 annually across sites and tends to be highest during winter compared to summer. No model is within 50 % of observed multiyear averages across all sites and seasons, but some models perform well for some sites and seasons. For the first time, we demonstrate how contributions from depositional pathways vary across models. Models can disagree in relative contributions from the pathways, even when they predict similar deposition velocities, or agree in the relative contributions but predict different deposition velocities. Both stomatal and nonstomatal uptake contribute to the large model spread across sites. Our findings are the beginning of results from AQMEII4 Activity 2, which brings scientists who model air quality and dry deposition together with scientists who measure ozone fluxes to evaluate and improve dry deposition schemes in chemical transport models used for research, planning, and regulatory purposes.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Olivia Elaine Clifton et al.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-465', Anonymous Referee #1, 05 May 2023
This manuscript provides a comprehensive intercomparison between established ozone dry deposition parameterizations. The result is somewhat expected but still highly relevant and valuable for the community, established with robust result. I have only a few minor questions and suggestions:
- The model description is nice and thorough. However, putting them in the main text obstruct the flow of the manuscript. I suggest the authors move the detailed model description to appendix/supplemental material. The authors could also consider using tables to make the model description more organized and readable.
- L 79 – 80: I suggest “land carbon sink” instead of “carbon storage, and more example/elaboration about how ozone affect ecosystem service
- Table 1: What precisely is B? “Parameter related to soil moisture” sounds very vague.
- L 228 – 230: More discussion about how the uncertainty in ra (choice of MOST universal function, h and z0) may (or may not) affect the study can be helpful.
- L 284 – 285: Clarify what is “effective LAI”. What is its physical/biological meaning? How is it calculated?
- L 841: What “other compounds” and why are they “challenging” to be measured in high frequency? Some examples, discussions and citations would be helpful.
- L 1728: Could the authors provide how may we address the over-reliance on LAI to determine seasonality? E.g. Would other ecophysiological parameters (e.g. seasonally-varying leaf nitrogen content/leaf-level photosynthetic capacity) help? What factors other than phenology might contribute to the seasonality of vd, but not yet considered in the parameterizations? Is the seasonality of non-stomatal ozone uptake under-represented?
Citation: https://doi.org/10.5194/egusphere-2023-465-RC1 -
RC2: 'Comment on egusphere-2023-465', Anonymous Referee #2, 05 Jun 2023
Reviewer summary
This work contributes to Activity 2 of the AQMEII4 framework. This manuscript uses 18 models and model variants to examine how differences in their parameterization of ozone dry deposition drives model bias with respect to observations (of ozone dry deposition). To isolate inter-model variability due to differences in their ozone dry deposition parameterization, the models are used in a single point configuration and driven by observed meteorology and environmental conditions at each of eight sites. The models’ ability to capture seasonal patterns and interannual variability (where possible) in ozone dry deposition is evaluated at the eight sites. The contribution of variability in the simulated deposition pathways (stomatal, soil etc.) to the models’ bias against observations is also evaluated. Overall, this study finds that, broadly, models’ ozone dry deposition may be too strongly linked to LAI, better constraints on wintertime deposition pathways are required and more detailed observations are required to unpick the drivers of model biases in ozone dry deposition. I have some general and technical comments (see below) that should be addressed prior to publication.
General comments:
The authors’ efforts to provide comprehensive descriptions of the ozone dry deposition schemes used in the 18 models and model variants is very commendable. Similarly, their efforts to compile comprehensive multi-annual observational datasets. Both are valuable resources for the community. The manuscript is also clearly laid out and well written.
Given the multitude of sites, models and dry deposition pathways it is difficult to draw overarching conclusions on the drivers of model bias in ozone dry deposition here. While I agree with the authors recommendations for more detailed observational data, which may help with identifying the biases deposition, the time scales required to generate the type of long-term data sets are obviously quite long. I would therefore be keen to hear more about the author’s plans or ideas to identify drivers of model biases using the data sets developed for this manuscript. For example, would more detailed statistical analysis or model sensitivity studies be useful? Or is it that real-world heterogeneity at the site level prevents over-arching conclusions on sources of model bias for ozone dry deposition?
- Section 2.1
Would it be possible to highlight the dry deposition scheme components by group? For example, bold or underlined headings for e.g. ‘Stomatal resistances’, ‘Non-stomatal resistances’, ‘Environmental dependencies’ for each of the models could help readers navigate the schemes for their future reference.
- Figure captions after ‘Figure 3’ need relabelling.
Technical comments:
- Introduction, L140: “…global chemical transport models and used always as standalone models…”
=> Perhaps remove ‘and’ in the above sentence.
- Section 2.1.2, L279: “…then the parameter’s value in Table S6.”
=> “…then the parameter’s value is in Table S6.”
- Section 2.1.2, L287: “…so that nighttime rst values on the single point model more similar to GEOS-Chem.”
=> “…so that nighttime rst values on the single point model are more similar to GEOS-Chem.”
- Section 2.1.7, L553: “van der Walls”
=> van der Waals
5.Figures 2 (or 3):
Would it be possible to indicate the inter-annual variability in the observations here (Fig. 2 might be better), possibly as vertical bars/whiskers? I’m aware that these plots are illustrating inter-model variability, rather than inter-annual model variability – although the latter looks to be encapsulated by the former in Fig. 2. However, in the context of the site specific discussions, I think it would be useful to illustrate the inter-annual variability in the observations.
Citation: https://doi.org/10.5194/egusphere-2023-465-RC2 -
AC1: 'Comment on egusphere-2023-465', Olivia Clifton, 06 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-465/egusphere-2023-465-AC1-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-465', Anonymous Referee #1, 05 May 2023
This manuscript provides a comprehensive intercomparison between established ozone dry deposition parameterizations. The result is somewhat expected but still highly relevant and valuable for the community, established with robust result. I have only a few minor questions and suggestions:
- The model description is nice and thorough. However, putting them in the main text obstruct the flow of the manuscript. I suggest the authors move the detailed model description to appendix/supplemental material. The authors could also consider using tables to make the model description more organized and readable.
- L 79 – 80: I suggest “land carbon sink” instead of “carbon storage, and more example/elaboration about how ozone affect ecosystem service
- Table 1: What precisely is B? “Parameter related to soil moisture” sounds very vague.
- L 228 – 230: More discussion about how the uncertainty in ra (choice of MOST universal function, h and z0) may (or may not) affect the study can be helpful.
- L 284 – 285: Clarify what is “effective LAI”. What is its physical/biological meaning? How is it calculated?
- L 841: What “other compounds” and why are they “challenging” to be measured in high frequency? Some examples, discussions and citations would be helpful.
- L 1728: Could the authors provide how may we address the over-reliance on LAI to determine seasonality? E.g. Would other ecophysiological parameters (e.g. seasonally-varying leaf nitrogen content/leaf-level photosynthetic capacity) help? What factors other than phenology might contribute to the seasonality of vd, but not yet considered in the parameterizations? Is the seasonality of non-stomatal ozone uptake under-represented?
Citation: https://doi.org/10.5194/egusphere-2023-465-RC1 -
RC2: 'Comment on egusphere-2023-465', Anonymous Referee #2, 05 Jun 2023
Reviewer summary
This work contributes to Activity 2 of the AQMEII4 framework. This manuscript uses 18 models and model variants to examine how differences in their parameterization of ozone dry deposition drives model bias with respect to observations (of ozone dry deposition). To isolate inter-model variability due to differences in their ozone dry deposition parameterization, the models are used in a single point configuration and driven by observed meteorology and environmental conditions at each of eight sites. The models’ ability to capture seasonal patterns and interannual variability (where possible) in ozone dry deposition is evaluated at the eight sites. The contribution of variability in the simulated deposition pathways (stomatal, soil etc.) to the models’ bias against observations is also evaluated. Overall, this study finds that, broadly, models’ ozone dry deposition may be too strongly linked to LAI, better constraints on wintertime deposition pathways are required and more detailed observations are required to unpick the drivers of model biases in ozone dry deposition. I have some general and technical comments (see below) that should be addressed prior to publication.
General comments:
The authors’ efforts to provide comprehensive descriptions of the ozone dry deposition schemes used in the 18 models and model variants is very commendable. Similarly, their efforts to compile comprehensive multi-annual observational datasets. Both are valuable resources for the community. The manuscript is also clearly laid out and well written.
Given the multitude of sites, models and dry deposition pathways it is difficult to draw overarching conclusions on the drivers of model bias in ozone dry deposition here. While I agree with the authors recommendations for more detailed observational data, which may help with identifying the biases deposition, the time scales required to generate the type of long-term data sets are obviously quite long. I would therefore be keen to hear more about the author’s plans or ideas to identify drivers of model biases using the data sets developed for this manuscript. For example, would more detailed statistical analysis or model sensitivity studies be useful? Or is it that real-world heterogeneity at the site level prevents over-arching conclusions on sources of model bias for ozone dry deposition?
- Section 2.1
Would it be possible to highlight the dry deposition scheme components by group? For example, bold or underlined headings for e.g. ‘Stomatal resistances’, ‘Non-stomatal resistances’, ‘Environmental dependencies’ for each of the models could help readers navigate the schemes for their future reference.
- Figure captions after ‘Figure 3’ need relabelling.
Technical comments:
- Introduction, L140: “…global chemical transport models and used always as standalone models…”
=> Perhaps remove ‘and’ in the above sentence.
- Section 2.1.2, L279: “…then the parameter’s value in Table S6.”
=> “…then the parameter’s value is in Table S6.”
- Section 2.1.2, L287: “…so that nighttime rst values on the single point model more similar to GEOS-Chem.”
=> “…so that nighttime rst values on the single point model are more similar to GEOS-Chem.”
- Section 2.1.7, L553: “van der Walls”
=> van der Waals
5.Figures 2 (or 3):
Would it be possible to indicate the inter-annual variability in the observations here (Fig. 2 might be better), possibly as vertical bars/whiskers? I’m aware that these plots are illustrating inter-model variability, rather than inter-annual model variability – although the latter looks to be encapsulated by the former in Fig. 2. However, in the context of the site specific discussions, I think it would be useful to illustrate the inter-annual variability in the observations.
Citation: https://doi.org/10.5194/egusphere-2023-465-RC2 -
AC1: 'Comment on egusphere-2023-465', Olivia Clifton, 06 Jul 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-465/egusphere-2023-465-AC1-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Olivia Elaine Clifton et al.
Olivia Elaine Clifton et al.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(5815 KB) - Metadata XML
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Supplement
(488 KB) - BibTeX
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- Final revised paper