the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
- 1Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- 2University of Chinese Academy of Sciences, Beijing, 100029, China
- 3Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- 4Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4RJ, UK
- 5Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, P.O. Box 461, 40530, Sweden
- 6UK Centre for Ecology and Hydrology, Benson Lane, Wallingford, OX10 8BB, UK
- 7State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- 8School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- 9Livelihoods and Institutions Department, Natural Resources Institute, University of Greenwich, Kent, ME4 4TB, UK
- 10Chinese Academy of Meteorological Sciences, Beijing, 100081, China
- 11Centre for Tropical Environmental and Sustainability Science, College of Science & Engineering, James Cook University, Cairns, Queensland, 4870 Australia
- 12College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, EX4 4PY, UK
- 1Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- 2University of Chinese Academy of Sciences, Beijing, 100029, China
- 3Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- 4Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4RJ, UK
- 5Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, P.O. Box 461, 40530, Sweden
- 6UK Centre for Ecology and Hydrology, Benson Lane, Wallingford, OX10 8BB, UK
- 7State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
- 8School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, 210044, China
- 9Livelihoods and Institutions Department, Natural Resources Institute, University of Greenwich, Kent, ME4 4TB, UK
- 10Chinese Academy of Meteorological Sciences, Beijing, 100081, China
- 11Centre for Tropical Environmental and Sustainability Science, College of Science & Engineering, James Cook University, Cairns, Queensland, 4870 Australia
- 12College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, EX4 4PY, UK
Abstract. A major limitation in modeling global ozone (O3) vegetation damage has long been the reliance on empirical O3 sensitivity parameters derived from a limited number of species and applied at the level of plant functional types (PFTs), which ignore the large interspecific variations within the same PFT. Here, we present a major advance in large-scale assessments of O3 plant injury by linking the trait leaf mass per area (LMA) and plant O3 sensitivity in a broad and global perspective. Application of the new approach and a global LMA map in a dynamic global vegetation model reasonably represents the observed interspecific responses to O3 with a unified sensitivity parameter for all plant species. Simulations suggest a contemporary global mean reduction of 4.8 % in gross primary productivity by O3, with a range of 1.1 %–12.6 % for varied PFTs. Hotspots with damages > 10 % are found in agricultural areas in the eastern U.S., western Europe, eastern China, and India, accompanied by moderate to high levels of surface O3. Furthermore, we simulate the distribution of plant sensitivity to O3, which is highly linked with the inherent leaf trait trade-off strategies of plants, revealing high risks for fast-growing species with low LMA, such as crops, grasses and deciduous trees.
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Yimian Ma et al.
Status: final response (author comments only)
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RC1: 'Comment on gmd-2022-227', Anonymous Referee #1, 06 Oct 2022
Review for GMD-2022-227 by Yimian Ma et al.
-------------------------------------------The authors present a submodel for ozone damage to different plant types inserted into an existing biosphere model (YIBs). This new ozone model calculates plant damage, expressed as GPP penalties, based on a unified sensitivity interacting with leaf mass per area. They conclude that approx. 5% of global GPP is not materialized due to ozone damage.
I perceive the study as a major advance over previous approaches to model ozone damage to plants, taking into account latest findings on leaf mass rather than area as defining factors for ozone sensitivity across plant types. The manuscript is well written and the steps taken to develop and integrate the ozone model into YIBs are sound. All conclusions are grounded on the presented evidence. Nonetheless, I see several places where the study could be amended; they are detailed in the following. While I strongly suggest to consider these, none of them questions the relevance and overall validity of the approach, though.
In conclusion, I recommend a major revision of the article. The 'major' is a sum of many 'minor' elements. If the authors are able to address my concerns, I clearly support a publication of this article.
*** General comments ***
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-> While the new mass-based approach may prevail over area-based calculations, a crucial factor of ozone sensitivity is also the abiotic environment of growth, e.g. water availability, temperature or CO2 concentration. A change in these parameters, all others being equal, may strongly modify the ozone response of plants. It remains unclear how much these confounding factors are already considered in the model by shaping the actual LMA, since the global LMA data are prescribed from data sets. The authors are encouraged to discuss this and, if necessary, amend the model to also consider climatic parameters in their ozone module.
-> There are several unclear points in the methods; these are detailed below in the specific comments. I mention them here as they sum up to a general comment.
-> The calibration partly remains unclear (see below). Most importantly, though, an out-of-sample calibration is missing where each PFT is removed from calibration - for both the unified and the supporting PFT-specific calibration - and the resulting estimate compared particularly for this omitted PFT. This is relevant especially for crops, as they are well apart from the other plant types (e.g. in Figure 2), suggesting that this difference could largely drive calibration and thus the resulting performance be overly optimistic. The perfect fit of S_S to S_O for crops in Figure 6b corroborates this hypothesis.
-> An additional, similar exercise could include another year of ozone data. The current study only uses 2010, for calibration and validation. Another year will have another ozone distribution and thus would be useful to validate the findings.
-> All of these new suggestions, once implemented, should then also be considered in the discussion section.
*** Specific comments ***
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-> Methods, 2.1: if F is the UNdamaged fraction, why is there a ozone penalty in equation 1?
-> Methods, 2.1: explain f_O3 at first mention and add units to all variables
-> Methods, 2.1, eq 2: f_O3 depends, in turn, on F. Please explain this circular dependence in the text and also what it means for calculation - do you need an optimizing routine?
-> Methods, 2.1: how do water availability, temperature, CO2 et al. interact with the ozone uptake?
-> Methods, 2.2, eq 6: what happens with negative values of f_O3 - y in the integral?
-> Methods, 2.2: is every PFT dominant somewhere?
-> Methods, 2.3: the exact recipe for the calibration is missing. It remains partly elusive how you did the calibration - how many runs, which parameters were tuned, which step size, which algorithm, which target variables etc. Please augment, for all runs.
-> Methods, 2.3: a sensitivity towards environmental parameters would be useful to add
-> Results, 3.1, l199+: is the higher agreement between observations and mass-based simulations (R2 = 0.77), when compared to area-based simulations (R2 = 0.54), expectable already in the uncalibrated version given the design towards mass-based traits?
-> Results, 3.2: can you justify the use of S2007 as a reference, i.e. why is the new model good if it agrees with the old?
-> Results, 3.2, l232+: can you provide numbers on the difference components ([O3], LMA variation, land-use intensity etc.)?
-> Figure 3h (crops): a linear fit does not seem to be the best choice here, in contrast to all other PFTs. How to account for that or interpret this levelling off?
*** Technical corrections ***
*****************************
-> Methods, 2.2: explain POD at first mention (abbreviation & what does it mean)
-> Methods, 2.2, l131: what do you mean with 'bio-indicators'?
-> Results, 3.3: this section requires language proof-reading
-> Results, 3.3, l250: the values (-0.2 and 1.7) are not %, but percentage points - the difference in % would be much larger
-> Figure 2: please add the 1:1 line and the out-of-sample line once it is calculated
-> Figure 3: add the grey simulated dots to the legend
-> Figure 6a: CRO is missing here? -
RC2: 'Comment on gmd-2022-227', Anonymous Referee #2, 30 Dec 2022
This paper addresses the importance issue of properly representing interspecific variations of plant sensitivity to ozone damage in global ecosystem or Earth system models, by taking advantage of the observed relationships between leaf-based traits (such as leaf mass per area) and ozone sensitivity. The methodology and analysis are scientifically rigorous and valid, and potentially important implications for all future studies of plant-ozone interactions. I recommend the publication of this manuscript as long as the following suggestions have been addressed.
Section 1:
Overall, the introduction is too short, thus the motivation and justification for the importance of their work are relatively weak. It is also not as informative as what an introduction section should be like. The authors are thus recommended to lengthen the introduction, especially to:
- How exactly are the different kinds of plant sensitivities currently used in models measured/determined? What are the differences between the different approaches (e.g., Felzer vs. Lombardozzi vs. Sitch)? Based on experimental values or field observations? A discussion on the methodological and theoretical basis of the current approaches should be included. Moreover, a comparative analysis of the numerical results from the different approaches and studies should be included to highlight the uncertainties and justify the need to revise the current approach.
- In addition to semi-mechanistic representation of sensitivity of photosynthesis to ozone exposure, there have also been other more empirical approaches to quantify plant sensitivity to ozone, including the concentration-based approach (e.g., AOT40) and flux-based approach (e.g., DO3SE, POD). These approaches have been mostly applied to crops but also to some extent to natural vegetation. A paragraph should be devoted to discuss the merits and shortfalls of these various approaches, so as to justify the importance of mechanistically representing photosynthetic responses to ozone exposure. Some references that should be discussed include Tai et al. (2021) and Emberson et al. (2018).
- A proper representation of ozone-vegetation interactions is important in Earth system and atmospheric modeling as much as in ecosystem modeling, because ozone damage on plants can subsequently affects land surface fluxes and thus atmospheric chemistry and climate. Some discussion should be done on these aspects, with references to, e.g., Zhou et al. (2018), Gong et al. (2020), and Zhu et al. (2022).
- The possible theoretical basis behind the connection between LMA and ozone sensitivity should be discussed. Possible uses of equations are recommended.
Section 2.2:
The authors describe the POD approach here. As mentioned above, a due discussion comparing the various approaches including POD should be given in the introduction section.
Section 2.3:
Since the calibration exercise is so crucial to this study, the authors are recommended to include at least a table or two for the calibration (from Table S3–S7), ideally the most important one or a consolidated one, in the main paper. Details of the calibration method (e.g., Monte Carlo? Or simply varying the value manually until it fits the best?) should be given in the text or table caption.
Section 2.4:
Since the global distribution of ozone concentration is so crucial in evaluating the resulting GPP reductions, the global map of ozone concentration should be given in the main text instead of in the supplementary materials.
Section 3:
The use of tenses seems to be inconsistent across the paper. Section 2 mostly uses the present tense, but the past tense is sometimes used in Section 3. The authors are recommended to consistently use tenses throughout the paper (i.e., using the past tense for the research tasks and actions they did for their study and for the actions done by previous researchers, but the present tense whenever the results are presented and discussed).
Section 3.2:
- It is not surprising that “the simulation with the optimal a =3.5 nmol-1 s g predicted a global GPP reduction of 4.8% (Fig. 4a), which was similar to the value estimated with the area-based S2007 scheme”, because ultimately the LMA approach is derived from the area-based approach. This then comes to an important question – why do we need to use the LMA approach after all, if the resulting GPP is similar? This should be addressed. I suspect that using the LMA approach may better capture the regional differences and intra-PFT variations, but these are not explicitly shown or analyzed by the authors, who are thus recommended to address these issues (e.g., by elaborately comparing the PFT-specific and/or regional differences of ozone damage from the area-based approach vs. the LMA approach). This is done in part in Fig. 4, but the attribution to PFT or regional variations are lacking. It may be important to show how each PFT behaves differently under the two approaches.
- A more elaborate discussion should be given to how “the differences in LMA and simulated O3 sensitivities of these PFTs were the main cause of discrepancies in GPP damage at the large scale”.
Section 4:
The authors have described the possible mechanisms behind the LMA-ozone damage relationships here. As suggested above, some of these should be devoted to the introduction section (at least discussed briefly), and here the authors may discuss how their model development and simulations verify them and allow them to derive a fuller picture.
Section 4.3:
The authors well justify the merits of their LMA-based approach. Indeed, this can bring potentially significant unification and simplification of global modeling. I would further recommend an additional merit is that the LMA-based approach can even address the intra-PFT (not just inter-PFT) variations in ozone sensitivity because species in the same PFT can have largely varying LMA. Even though for now each PFT may have a fixed LMA in many models, this LMA-based approach provides a unifying way to model ozone damage as more spatially resolved LMA data become available in the future.
References to cite:
- Emberson, L. D., Pleijel, H., Ainsworth, E. A., van den Berg, M., Ren, W., Osborne, S., …, & Van Dingenen, R. (2018). Ozone effects on crops and consideration in crop models. European Journal of Agronomy, 100, 19-34.
- Gong, C., Lei, Y., Ma, Y., Yue, X., & Liao, H. (2020). Ozone–vegetation feedback through dry deposition and isoprene emissions in a global chemistry–carbon–climate model. Atmospheric Chemistry and Physics, 20(6), 3841–3857.
- Tai, A. P. K., Sadiq, M., Pang, J. Y. S., Yung, D. H. Y., & Feng, Z. (2021). Impacts of surface ozone pollution on global crop yields: Comparing different ozone exposure metrics and incorporating co-effects of CO2. Frontiers in Sustainable Food Systems, 5, 534616.
- Zhou, S. S., Tai, A. P. K., Sun, S., Sadiq, M., Heald, C. L., & Geddes, J. A. (2018). Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health. Atmospheric Chemistry and Physics, 18(19), 14133-14148.
- Zhu, J., Tai, A. P. K., & Yim, S. H. L. (2022). Effects of ozone-vegetation interactions on meteorology and air quality in China using a two-way coupled land-atmosphere model. Atmospheric Chemistry and Physics, 22(2), 765-782.
Yimian Ma et al.
Yimian Ma et al.
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