Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere−atmosphere fluxes relevant for ozone air quality
Abstract. Ground-level ozone (O3) is a major air pollutant that adversely affects human health and agricultural productivity. Removal of air pollutants including tropospheric O3 from the atmosphere by vegetation is controlled mostly by the process of dry deposition, an important component of which is plant stomatal uptake that can in turn cause damage to plant tissues with ramifications for ecosystem and crop health. In many atmospheric and land surface models, the openness of plant stomata is represented by a bulk stomatal conductance, which is often semi-empirically parameterized, and highly fitted to historical observations. A lack of mechanistic linkage to ecophysiological processes such as photosynthesis may render models insufficient to represent plant-mediated responses of atmospheric chemistry to long-term changes in CO2, climate and short-lived air pollutant concentrations. A new ecophysiology module was thus developed to mechanistically simulate land−atmosphere exchange of important gas species in GEOS-Chem, a chemical transport model widely used in atmospheric chemistry studies. We adopted the formulations from the Joint UK Land Environmental Simulator (JULES) to couple photosynthesis rate, bulk stomatal conductance and isoprene emission rate dynamically. The implementation not only allows dry deposition to be coupled with plant ecophysiology, but also enables plant and crop productivity and functions to respond dynamically to atmospheric chemical changes. The research questions of this study include: 1) how the new ecophysiology module compares with the prior, semi-empirical parameterization in terms of simulating concentration and dry deposition velocity of O3 with respect to site measurement-based estimates; and 2) whether the ecophysiology module simulates vegetation productivity, dry deposition, isoprene emission rate and O3–vegetation interactions reasonably under a present-day and an elevated CO2 concentration. We conduct simulations to evaluate the effects of the ecophysiology module on simulated dry deposition velocity and concentration of surface O3 against an observation-derived dataset known as SynFlux. Our estimated dry deposition velocity of O3 is close to SynFlux dry deposition velocity with root-mean-squared errors (RMSE) ranging from 0.1 to 0.2 cm s–1 across different plant functional types (PFTs), despite an overall positive bias in surface O3 concentration (by up to 16 ppbv). Representing ecophysiology was found to reduce the simulated biases in deposition fluxes from the prior model, but worsen the positive biases in simulated O3 concentrations. The increase in positive concentration biases is mostly attributable to the ecophysiology-based stomatal conductance being generally smaller (and closer to SynFlux values) than that estimated by the prior semi-empirical formulation, calling for further improvements in non-depositional processes relevant for O3 simulations. Estimated global O3 deposition flux is 864 Tg O3 yr–1 with GEOS-Chem, and the new module decreases this estimate by 92 Tg O3 yr–1. Estimated global gross primary product (GPP) is 119 Pg C yr–1, with an O3-induced damage of 4.2 Pg C yr–1 (3.5 %). An elevated CO2 scenario (580 ppm) yields higher global GPP (+16.8 %) and lower global O3 depositional sink (–3.3 %). Global isoprene emission simulated with a photosynthesis-based scheme is 318 Tg C yr–1, which is 31 Tg C yr−1 (−8.9 %) less than that calculated using the MEGAN emission algorithm. This new model development dynamically represents the two-way interactions between vegetation and air pollutants, and thus provides a unique capability in evaluating pollutant impacts on vegetation health and feedback processes that can shape atmospheric chemistry and air quality especially for any timescales shorter than the multidecadal timescale.
Journal article(s) based on this preprint
Joey C. Y. Lam et al.
Journal article(s) based on this preprint
Joey C. Y. Lam et al.
Joey C. Y. Lam et al.
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In this work, an ecophysiology module was implemented in the GEOS-Chem model. The dry deposition velocity of O3, vegetation productivity, isoprene emission rate, as well as O3 vegetation damage, were simulated under both present-day and elevated CO2 concentration scenarios. The coupling of vegetation processes with CTM is an important update for studying the interactions between ecosystem and atmospheric chemistry. However, the effectiveness of the ecophysiology module was not sufficiently evaluated. Before the possible publication in GMD, I suggest the authors enrich this manuscript in the following aspects to further strengthen the validations and calibrations of key biophysical processes.
Here are some main concerns:
1. The case 1a experiment is the baseline of this study. It shows some improvements in simulating Vd in Figure 3 compared with case 0. However, the explanation for such changes is almost like no explanations: “The more significant decreases in vd for broadleaf trees and needleleaf trees than for other PFTs are only due to the differences in formulations, but not due to any other physical reasons.” Why it becomes smaller? Differences in what formulations? I think the improvement is limited, as there are still obvious PFT-specific biases in baseline Case 1a. For example, the Vd of needleleaf is much lower than observations. Is it because of the scaling by βt, which turns down the Vd for deciduous trees and consequently decreases the Vd for needleleaf trees as well?
2. In Figure 3, the ecophysiology module seems significantly affected by βt. The larger the parameter, the higher the Vd. This factor is emphasized in the analysis of improvement from Case 0 to Case1a. However, such implementation introduces two problems/uncertainties into the model. First, observations do not always show the dependence of Vd on soil moisture, especially for needleleaf trees and some C3 grassland. Second, the calculation of βt is dependent on data from MERRA2. It’s unclear how accurate are the MERRA2 soil moisture data. For the first point, the updated model will show incorrect responses of Vd to moderate drought. For the second point, both the spatial and temporal biases in the soil moisture of reanalyses data will affect the simulated An, gs, and Vd, but to what extent remains unclear.
3. Figure 4 shows the coupling of the ecophysiology module worsens the simulation of surface ozone. Although the authors tried to explain the causes, these results diminish the meaning of the model improvement with ecophysiology module. Considering that the new module has limited and even negative effects on the ozone simulations, more solid evaluations of carbon cycle modeling is needed rather than three lines of demonstration of “our results demonstrate a seasonal cycle of GPP that peaks at around 130 g C m−2 month−1 in July and falls steadily to around 60 g C m−2 month−1 in February. This resembles with observation-derived datasets like FLUXNET-MTE, as shown in Fig. 3a of Slevin et al. (2017)” (in Line 472-474). For example, site-based evaluations for GPP, stomatal conductance gs, and O3 stomata flux are all crucial. The SynFlux dataset includes these variables in addition O3 concentrations and O3 deposition velocity for further evaluation.
4. The response sensitivity of GPP to CO2 and the damage sensitivity of O3 to GPP highly rely on key parameters originally adapted in JULES rather than the ecophysiology module implemented in this study. Necessary validations or calibrations for these two sensitivities should be conducted within this whole different framework.
5. Line 607: “In particular, LAI does not change dynamically with climatic conditions or O3 damage in the current model”. To what extent the LAI dataset is fixed? Is this a reasonable configuration? LAI is a key parameter regulating carbon fixation, ozone dry deposition, and isoprene emissions. Such omission will likely weaken the interactions between atmosphere chemistry and biosphere especially when CO2 fertilization is considered.
Abstract: The abstract is too lengthy. It can be truncated by half.
Line 139-141: “This approach is particularly useful for examining how ecosystem structure may respond to long-term atmospheric chemical changes over multidecadal timescales, but may be unnecessarily computationally expensive for problems involving shorter timescales…It also introduces extra uncertainties that arise from the computation of ecosystem structure, which involves complex representation of plant phenology and biogeochemistry”. Biospheric calculation is normally not the resource-consuming part in the atmospheric-chemistry-involved simulations. Are there any comparisons in speed and uncertainty with other CTM with a biosphere model?
Equation 11: How is this related to stomatal conductance and how to get the closed relationships among An, Gs, and Cc from this additional equation?
Line 358-359: “Figure 2 shows the locations of 36 SynFlux sites used in our evaluation of the ecophysiology module”. What are the selection criteria for these sites?
Line 398: “resistance” should be conductance.
Line 461: “We note also that such changes in GPP is entirely due to higher photosynthetic rate, and no changes in LAI are simulated”. Isn’t LAI prescribed? “.. changes in GPP is..”, should be “..are..”.
Figures 3 and 4: The inclusion of ozone damage doesn’t cause significant changes to Vd and ozone. I suggest remove the first two columns.
Figure 8d: Why the O3-damage-induced isoprene emission reduction doesn’t match O3 damage in Figure 5c. For example, the high O3 damages in eastern U.S. show limited impacts on the regional isoprene emissions.