Ozone–vegetation feedback through dry deposition and isoprene emissions in a global chemistry–carbon–climate model

Ozone–vegetation feedback is essential to tropospheric ozone (O₃) concentrations. The O₃ stomatal uptake damages leaf photosynthesis and stomatal conductance and, in turn, influences O₃ dry deposition. Further, O₃ directly influences isoprene emissions, an important precursor of O₃. The effects of O₃ on vegetation further alter local meteorological fields and indirectly influence O₃ concentrations. In this study, we apply a fully coupled chemistry–carbon–climate global model (ModelE2-YIBs) to evaluate changes in O₃ concentrations caused by O₃–vegetation interactions. Different parameterizations and sensitivities of the effect of O₃ damage on photosynthesis, stomatal conductance, and isoprene emissions (IPE) are implemented in the model. The results show that O₃-induced inhibition of stomatal conductance increases surface O₃ on average by +2.1 ppbv (+1.2 ppbv) in eastern China, +1.8 ppbv (−0.3 ppbv) in the eastern US, and +1.3 ppbv (+1.0 ppbv) in western Europe at high (low) damage sensitivity. Such positive feedback is dominated by reduced O₃ dry deposition in addition to the increased temperature and decreased relative humidity from weakened transpiration. Including the effect of O₃ damage on IPE slightly reduces surface O₃ concentrations by influencing precursors. However, the reduced IPE weaken surface shortwave radiative forcing of secondary organic aerosols, leading to increased temperature and O₃ concentrations in the eastern US. This study highlights the importance of interactions between O₃ and vegetation with regard to O₃ concentrations and the resultant air quality.

1 Introduction

Tropospheric ozone (O₃) is generated by photochemical reactions involving nitrogen oxides (NO_x) and volatile organic compounds (VOCs) under strong solar radiation (Sillman, 1999; Atkinson, 2000; Jacob and Winner, 2009). It is one of the most important air pollutants and has been of widespread concern (Wang et al., 2017; Li et al., 2019). High O₃ concentrations at the surface can not only injure human respiratory health (Gauderman et al., 2004; Lelieveld et al., 2015) but also lead to considerable damage to plants and crops, which further changes the land carbon budget (Fuhrer et al., 1997; Yue and Unger, 2014; Lombardozzi et al., 2015). In turn, vegetation can modulate O₃ concentrations via influencing dry deposition processes, precursor emissions (such as those of isoprene, monoterpene, and sesquiterpene), and meteorological fields. Studying O₃–vegetation interactions is of great importance to better understand the variations in O₃ concentrations as well as the ecosystem carbon cycle, particularly for regions with high O₃ levels and vegetative cover.

Ground-level O₃ reduces vegetation photosynthesis by stomatal uptake (Fuhrer et al., 1997; Ainsworth et al., 2012). Through a globally statistical meta-analysis, Wittig et al. (2007) showed that the elevated O₃ since the preindustrial period depressed photosynthesis and stomatal conductance of trees by 9 %–13 % and 11 %–15 %, respectively. A recent global meta-analysis on poplar showed that current O₃ concentrations reduced the CO₂ assimilation rate and stomatal conductance by 33 % and 25 %, respectively, compared to that of charcoal-filtered air (Feng et al., 2019a). In model studies, an offline process-based vegetation model (the Yale Interactive Terrestrial Biosphere model, or YIBs) estimated that the present-day effect of O₃ damage reduced gross primary productivity (GPP) by 4 %–8 % on average over the eastern US during the summer (Yue and Unger, 2014) and annual net primary productivity (NPP) by approximately 14 % in China (Yue et al., 2017). Lombardozzi et al. (2015) also showed that the present-day O₃ exposure reduces GPP globally by 8 %–12 % using the Community Land Model (CLM).

Isoprene emissions (IPE) from vegetation can be affected by surface O₃. Isoprene is the most dominant species among biogenic VOCs (BVOCs) and accounts for approximately one-half of global BVOC emissions (Guenther et al., 2012). The effect of O₃ on IPE is complex. Calfapietra et al. (2009) reviewed observational experiments in Italy and proposed a hypothesis that there might be a detoxification effect resulting from O₃–IPE interactions. Vegetation under a low accumulated O₃ dose can be simulated to increase the levels of IPE to reduce oxidative damage, but months of O₃ exposure are harmful to metabolism and reduce IPE. Several studies have showed that O₃ fumigation over a short time (days to weeks) but at high concentrations (100–300 ppbv) led to increased IPE (Velikova et al., 2005; Fares et al., 2010), while some other experiments conducted over an entire growing season (at least 3 months) under controlled O₃ concentrations (approximately 80 ppbv) showed that O₃ reduced IPE (Calfapietra et al., 2008; Yuan et al., 2016, 2017). A recent global meta-analytic review showed that IPE negatively responded to elevated O₃ (91 ppbv on average) by −8 % (Feng et al., 2019b). Overall, consecutive exposure to high O₃ levels has a negative impact on IPE, although there are large uncertainties resulting from vegetation type (Tiiva et al., 2007; Ryan et al., 2009), temperature (Hartikainen et al., 2009), and CO₂ concentration (Calfapietra et al., 2008).

O₃ dry deposition is one of the important sinks of tropospheric O₃ and mainly occurs over vegetation (Wesely, 1989). The stomatal uptake of vegetation plays an important role in this removal process. (Wesely and Hicks, 2000). Val Martin et al. (2014) showed that the O₃ dry deposition velocity in the Community Earth System Model (CESM) significantly increased and was more reasonable when the original scheme (Wesely, 1989), which assumed that stomatal resistance was only related to temperature and water vapor, was replaced with a scheme coupled to vegetation (Collatz et al., 1991; Sellers et al., 1996). In addition, BVOC emissions can change the local NO_x∕VOC ratio and, in turn, influence O₃ concentrations. For example, Fu and Liao (2012) showed that the interannual variations in BVOCs alone can lead to 2 %–5 % differences in simulated O₃ over China during the summer using the Model of Emissions of Gases and Aerosols from Nature (MEGAN; Guenther et al., 2006) module embedded within the global three-dimensional chemical transport model (GEOS-Chem). Calfapietra et al. (2013) reviewed the role of BVOCs emitted by urban trees in O₃ concentrations in cities and showed that BVOCs generally promoted O₃ formation because of the VOC-limited condition. Furthermore, the modifications of meteorological fields caused by vegetation (Liu et al., 2006; Wu et al., 2011) may also potentially have an effect on O₃ formation as well as vegetation growth. As a result, O₃ stomatal uptake (O₃ dry deposition via stomata), BVOC emissions, and changes in meteorological fields are connected and jointly affect O₃ concentrations.

Thus far, very few studies have comprehensively investigated the O₃–vegetation feedback at a global scale. Sadiq et al. (2017) investigated the effect of O₃ damage on the photosynthesis rate and stomatal conductance as well as potential meteorological feedback on surface O₃ concentrations using the CESM. They found that O₃–vegetation interactions led to increased O₃ concentrations mainly in Europe, the northern US, and northern China. However, the effect of O₃ on BVOCs was not directly considered but was indirectly simulated by the increased temperature resulting from O₃–vegetation interactions. The O₃ damage sensitivities for photosynthesis and stomatal conductance were calculated by using two decoupled linear regressions with accumulated O₃ concentrations. However, the linear slope of the photosynthetic rate and stomatal conductance to O₃ was zero for some vegetation types (such as broadleaf forests), showing the significant effect of O₃ damage even at zero O₃ concentrations. Based on the same flawed O₃ damage scheme, Zhou et al. (2018) calculated responses of the leaf area index (LAI) to surface O₃ and implemented steady-state results for the GEOS-Chem model to simulate O₃ perturbations. Such asynchronous coupling may underestimate O₃ changes caused by the full pollution–biosphere interactions, not to mention the omission of feedback of O₃ to BVOC emissions and meteorology. More comprehensive work utilizing a validated O₃ damage scheme and considering the direct effect of O₃ on BVOCs is necessary to reasonably predict O₃–vegetation feedback on O₃ concentrations.

In this study, we apply a semi-mechanistic O₃ damage scheme (Sitch et al., 2007) to the YIBs dynamic vegetation model coupled with the global Earth system model NASA ModelE2 (ModelE2-YIBs) to explore O₃-induced changes in stomatal conductance and evaluate the consequences of such changes on surface O₃ concentrations (O₃–vegetation feedback via O₃ dry deposition). Then, two schemes are proposed to estimate the contributions of O₃ damage to IPE based on the existing scheme generated by Sitch et al. (2007), and observations are made. The feedback of O₃ damage to both stomatal conductance and IPE and the resultant effect on surface O₃ concentrations is calculated by using ModelE2-YIBs. Finally, the associated meteorological feedback to O₃ concentrations is discussed. We found that the O₃–vegetation feedback enhanced surface O₃ concentrations particularly in O₃-polluted regions.

2 Method

2.1 The NASA ModelE2-YIBs model

NASA ModelE2-YIBs is a fully coupled chemistry–carbon–climate global model with a horizontal resolution of 2^∘ latitude × 2.5^∘ longitude and 40 vertical layers up to 0.1 hPa. The dynamic and physical processes are calculated every 30 min. Gas-phase chemistry in the troposphere includes basic NO_x–HO_x–O_x–CO–CH₄ chemistry as well as peroxyacyl nitrates and the following hydrocarbons: terpenes, isoprene, alkyl nitrates, aldehydes, alkenes, and paraffins. Chlorine-containing and bromine-containing compounds, chlorofluorocarbons (CFCs), and N₂O source gases are all included in the stratospheric gas-phase chemistry. Dry deposition of gases is calculated by using a resistance-in-series scheme, which was updated to include coupling to stomatal resistance (Val Martin et al., 2014). In addition, the model interactively simulates aerosols such as sulfate, nitrate, elemental and organic carbon, sea salt, and dust considering the climate through direct (Koch et al., 2006) and indirect effects (Menon et al., 2008, 2010) and gas-phase chemistry by affecting photolysis rates (Bian et al., 2003). Meteorological and hydrological variables in this model have been fully validated via observations and a reanalysis dataset (Schmidt et al., 2014). The anthropogenic emission inventory for the present-day (2010) from the IPCC RCP8.5 scenario (van Vuuren et al., 2011) is utilized in this study.

The YIBs model is a dynamic vegetation model that includes nine plant functional types (PFTs; Table S1 in the Supplement) and can simulate biophysical processes of photosynthesis, transpiration, and respiration with variations in meteorological fields. Since the higher leaf photosynthesis requires larger stomatal conductance to allow more CO₂ enter the leaves, leaf photosynthesis and stomatal conductance are closely related and calculated using the Farquhar and Ball–Berry models (Farquhar et al., 1980; Ball et al., 1987) as follows:

$$\begin{array}{}\text{(1)}& {\displaystyle }& {\displaystyle }{A}_{\text{tot}}=min\left(J_{\mathrm{c}},J_{\mathrm{e}},J_{\mathrm{s}}\right),\text{(2)}& {\displaystyle }& {\displaystyle }{g}_{\mathrm{s}}=m{\displaystyle \frac{\left(A_{\text{tot}}-R_{\mathrm{d}}\right)\times \text{RH}}{c_{\mathrm{s}}}}+b,\end{array}$$

where the total leaf photosynthesis (A_tot) is the minimum value of the ribulose-1,5-bisphosphate carboxylase-limited (RuBisCO-limited) rate of carboxylation (J_c), light-limited rate (J_e), and export-limited rate (J_s). Stomatal conductance for H₂O (g_s) is calculated by the A_tot, dark respiration rate (R_d), relative humidity (RH), and CO₂ concentration at the leaf surface (c_s). The values of m and b are different for different PFTs (Table S1). A canopy radiation scheme is applied in YIBs to separate diffuse and direct light for sunlit and shaded leaves (Spitters et al., 1986). The LAI and tree growth are dynamically simulated with the allocation of carbon assimilation. The emissions of isoprene are calculated online as a function of J_e photosynthesis (Eq. 1), canopy temperature, intercellular CO₂, and CO₂ compensation point (Arneth et al., 2007; Unger, 2013) and have been fully validated by Unger et al. (2013). Carbon fluxes, phenology, LAI, GPP, and net ecosystem exchange (NEE), as well as other parameters of vegetation in ModelE2-YIBs, have been previously extensively evaluated and agree well with the observations (Yue and Unger, 2015). In addition, ModelE2-YIBs shows good performance in simulating O₃–vegetation interactions such as O₃–GPP and O₃–g_s relationships (Yue et al., 2016; Yue et al., 2018).

The O₃ dry deposition velocity (V_d) in ModelE2-YIBs is calculated following the multiple-resistance approach originally described by Wesely (1989):

$$\begin{array}{}\text{(3)}& V_{\mathrm{d}}={\displaystyle \frac{\mathrm{1}}{R_{\mathrm{a}}+R_{\mathrm{b}}+R_{\mathrm{c}}}},\end{array}$$

where R_a, R_b, and R_c are the aerodynamic resistance, quasi-laminar sublayer resistance above the canopy, and surface resistance, respectively. R_c is computed as follows:

$$\begin{array}{}\text{(4)}& {\displaystyle \frac{\mathrm{1}}{R_{\mathrm{c}}}}={\displaystyle \frac{\mathrm{1}}{R_{\mathrm{s}}}}+{\displaystyle \frac{\mathrm{1}}{R_{\mathrm{lu}}}}+{\displaystyle \frac{\mathrm{1}}{R_{\mathrm{cl}}}}+{\displaystyle \frac{\mathrm{1}}{R_{\mathrm{g}}}},\end{array}$$

where R_s, R_lu, R_cl, and R_g represent the stomatal resistance, leaf cuticle resistance, lower-canopy resistance, and the ground resistance, respectively. In this study, the original parameterization for R_s, which is empirically expressed by solar radiation, surface air temperature, and the molecular diffusivities for water vapor, has been substituted by the reciprocal of g_s from Eq. (2) following Val Martin et al. (2014). In this case, O₃ dry deposition can be interactively influenced by the stomatal O₃ uptake process for vegetation.

Isoprene and α-pinene are considered to be the precursors for biogenic secondary organic aerosols (SOAs) in ModelE2-YIBs, which are computed online based on the two-product scheme developed by Chung and Seinfeld (2002). Isoprene can be oxidized by O₃ as follows:

$$\begin{array}{}\text{(5)}& {\mathrm{C}}_{\mathrm{5}}{\mathrm{H}}_{\mathrm{8}}+{\mathrm{O}}_{\mathrm{3}}\to \mathrm{HCHO}+A_{\mathrm{1}}{P}_{\mathrm{1}}+A_{\mathrm{2}}{P}_{\mathrm{2}}.\end{array}$$

Changes for semivolatile product P_i ($i=\mathrm{1},\mathrm{2}$) at each time step (dt) are calculated by

$$\begin{array}{}\text{(6)}& {\displaystyle \frac{\mathrm{d}{P}_i}{\mathrm{d}t}}=A_i\cdot \text{rr}\cdot \left[{\mathrm{O}}_{\mathrm{3}}\right]\cdot \left[{\mathrm{C}}_{\mathrm{5}}{\mathrm{H}}_{\mathrm{8}}\right],\end{array}$$

where rr is the chemical reaction rate of O₃ and isoprene calculated by the Arrhenius equation. [O₃] and [C₅H₈] are the O₃ and isoprene concentrations, respectively. A_i is the molar-based stoichiometric coefficient depending on SOA formation pathways (high or low NO_x; Lane et al., 2008). Temperature (T) dependence on the partitioning coefficient (K_p) is given by the Clausius–Clapeyron equation:

$$\begin{array}{}\text{(7)}& K_{\mathrm{p}}=K_{\text{sc}}{\displaystyle \frac{T}{T_{\text{sc}}}}\exp \left[{\displaystyle \frac{\mathrm{\Delta }H}{R}}\left({\displaystyle \frac{\mathrm{1}}{T}}-{\displaystyle \frac{\mathrm{1}}{T_{\text{sc}}}}\right)\right],\end{array}$$

where ΔH is the enthalpy of vaporization and is set as 42.0 kJ mol⁻¹ for isoprene (Chung and Seinfeld, 2002; Henze and Seinfeld, 2006) and 72.9 kJ mol⁻¹ for α-pinene. K_sc is the saturation concentrations at the temperature T_sc (295 K) and set as 1.62 m³ µg⁻¹ (0.064 m³ µg⁻¹)and 0.0086 m³ µg⁻¹ (0.0026 m³ µg⁻¹) for the two products formed by oxidation of isoprene (α-pinene), respectively (Presto et al., 2005; Henze and Seinfeld, 2006).

2.2 Schemes describing the effect of O₃ damage to vegetation

2.2.1 The effect of O₃ damage to photosynthesis and stomatal conductance

A semi-mechanistic scheme proposed by Sitch et al. (2007) is applied in this study that simulates the effect of O₃ damage to the photosynthesis rate via the following formula:

$$\begin{array}{}\text{(8)}& {\displaystyle }& {\displaystyle }{A}_{\text{totd}}=F\times A_{\text{tot}},\text{(9)}& {\displaystyle }& {\displaystyle }{g}_{\text{sd}}=F\times g_{\mathrm{s}},\end{array}$$

where A_totd (g_sd) and A_tot (g_s) are the O₃-affected and original total leaf photosynthesis (stomatal conductance), respectively. F is the ratio between affected and original photosynthesis. It depends on the instantaneous leaf uptake of O₃ as follows:

$$\begin{array}{}\text{(10)}& F=\mathrm{1}-a\times max\left[F_{{\mathrm{O}}_{\mathrm{3}}}-F_{{\mathrm{O}}_{\mathrm{3}},\text{crit}},\mathrm{0.0}\right],\end{array}$$

where parameter a represents the O₃-damaging sensitivity dependent on vegetation types with a range from low to high values. $F_{{\mathrm{O}}_{\mathrm{3}},\text{crit}}$ is a critical threshold for damage (Table S1). $F_{{\mathrm{O}}_{\mathrm{3}}}$ is the O₃ uptake rate by the stomata, which is calculated by

$$\begin{array}{}\text{(11)}& F_{{\mathrm{O}}_{\mathrm{3}}}={\displaystyle \frac{\left[{\mathrm{O}}_{\mathrm{3}}\right]}{R_{\mathrm{a}}+\left[\frac{k_{{\mathrm{O}}_{\mathrm{3}}}}{g_{\mathrm{sd}}}\right]}},\end{array}$$

where [O₃] is the surface O₃ concentrations and R_a is the aerodynamic resistance in Eq. (3). $k_{{\mathrm{O}}_{\mathrm{3}}}$ is 1.67, which is the ratio of leaf resistance for O₃ to leaf resistance for water vapor. This scheme has been utilized in many previous studies, which have reported that O₃ reduces GPP by 4 %–8 % on an annual mean basis in the eastern US and by 10 %–20 % during the summer in China (Yue and Unger, 2014; Yue et al., 2017).

2.2.2 The effect of O₃ damage to IPE

To date, there are no mature parameterizations that calculate the contributions of O₃ damage to IPE. Here, we propose two schemes based on observations to quantify the changes in surface O₃ concentrations resulting from O₃ damage to IPE.

The first scheme assumes that O₃ leads to the same percentage of damage to photosynthesis and IPE because IPE are observed to linearly vary with photosynthesis (Yuan et al., 2016). The affected IPE (IPE_d) can be calculated as follows:

$$\begin{array}{}\text{(12)}& {\text{IPE}}_{\mathrm{d}}=F\times \text{IPE},\end{array}$$

where F is calculated by using Eq. (10) and IPE is the original level of IPE. Hereafter, this scheme is termed the “F scheme”.

Another scheme is based on open-top chamber (OTC) observations. Although many experiments have studied the effects of O₃ on IPE, most have applied a limited range of O₃ levels (e.g., 7.3–56.6 ppbv in Hartikainen et al., 2009, or > 100 ppbv in Fares et al., 2010). In reality, surface O₃ concentrations can vary from several parts per billion by volume (e.g., in the polar region during the winter) to over 100 ppbv (e.g., in megacities of China during the summer). To date, only one study (Yuan et al., 2017) has explored the responses of IPE to different levels of O₃ damage for two poplar clones; a linear regression between the percentage damage of IPE (PDI) and the cumulative stomatal uptake of ${\mathrm{O}}_{\mathrm{3}}>\mathrm{1}\mathrm{nmol}{\mathrm{O}}_{\mathrm{3}}{\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$ (POD₁) was derived as follows:

$$\begin{array}{}\text{(13)}& \text{PDI}=\left(-\mathrm{0.0086}\times {\text{POD}}_{\mathrm{1}}+\mathrm{1.0194}\right)\times \mathrm{100}.\end{array}$$

The POD₁ is calculated by the following formula:

$$\begin{array}{}\text{(14)}& {\text{POD}}_{\mathrm{1}}=\underset{\mathrm{1}}{\overset{n}{\int }}\left(F_{{\mathrm{O}}_{\mathrm{3}}}-\mathrm{1}\right)\mathrm{d}t,\end{array}$$

where $F_{{\mathrm{O}}_{\mathrm{3}}}$ is the O₃ uptake rate by stomata (nmol O₃ ${\mathrm{m}}^{-\mathrm{2}}{\mathrm{s}}^{-\mathrm{1}}$), which is the same as that in Eq. (11). dt indicates the time integration step, and n indicates the total number of time steps during the growing season. In this study, the POD₁ accumulated over the growth season is defined as April to October north of 23.5^∘ N (e.g., Tucker et al., 2001; White et al., 2002; Yin et al., 2014; Wang et al., 2019), November to March south of 23.5^∘ S (e.g., Broich et al., 2015; Moore et al., 2016), and 200 d between 23.5^∘ N and 23.5^∘ S because the leaf phenology in tropical evergreen forests is not determined by seasonality (Xiao et al., 2006). Limited by the data availability, we apply the PDI function (Eq. 13) for poplar to all vegetation types as follows:

$$\begin{array}{}\text{(15)}& {\text{IPE}}_{\mathrm{d}}=min(\text{PDI},\mathrm{100}\mathit{\%})\times \text{IPE}.\end{array}$$

Hereafter, this scheme is termed a “linear scheme.” Different from the F scheme, the linear scheme calculates IPE damage using accumulated O₃ instead of instantaneous O₃ concentrations.

2.3 Descriptions for sensitivity experiments

Seven experiments (Table 1) are conducted to explore the feedback of vegetation on surface O₃ concentrations via influencing O₃ dry deposition, IPE, and meteorological fields. The control simulation (CTRL) does not include the effect of O₃ damage to vegetation. Two cases (DRY_high and DRY_low) are established to investigate the feedback via O₃ dry deposition with high or low O₃ damage sensitivities (a in Eq. 10). Then, the effect of O₃ damage to IPE is added by using either F or linear schemes, resulting in four more experiments (TOTAL_F_high, TOTAL_F_low, TOTAL_LINEAR_high, and TOTAL_LINEAR_low). In the CTRL run, the effects of O₃ damage to photosynthesis, stomatal conductance, and IPE (the linear scheme) are calculated offline; such damages are not fed back to affect vegetation growth and dry deposition of O₃. The offline O₃ damage to IPE produced by using the F scheme is calculated in DYR_high and DYR_low.

Table 1 Summary of the seven experiments in ModelE2-YIBs.

For each experiment, 20-year simulations are performed with five initial spin-up years. Outputs of the last 15 years are averaged and analyzed. Regionally, the results in the eastern US (30–45^∘ N, 75–90^∘ W), western Europe (35–60^∘ N, 10^∘ W–20^∘ E), and eastern China (20–40^∘ N, 105–122^∘ E) are compared and discussed.

2.4 Observed ground-level O₃ network and model evaluation

To evaluate simulated O₃ concentrations, three observational networks are utilized as follows: the Air Quality Monitoring Network from the Ministry of Ecology and Environment (AQMN-MEE) in China, Clean Air Status and Trends Network (CASTNET) in the US, and European Monitoring and Evaluation Programme (EMEP) in Europe. The summer concentrations for CASTNET and EMEP are averaged over the year 2010, but those for AQMN-MEE are averaged over 2014 because this network was established in 2013 and started to provide high-quality data beginning in 2014. The simulated O₃ concentrations are interpolated in the observational sites by using a bilinear interpolation method. Normalized mean biases (NMBs) are calculated by using the following equation:

$$\begin{array}{}\text{(16)}& \text{NMB}=\sum _i^n(S_i-O_i)/\sum _i^n{O}_i\times \mathrm{100}\mathit{\%},\end{array}$$

where S_i and O_i are the simulated and observed O₃ concentrations, respectively, and n is the total number of observational sites.

3 Results

3.1 CTRL simulation and model evaluation

Figure 1 shows a comparison of the simulated summer O₃ concentrations to the observations. The model reasonably reproduces spatial patterns, with a correlation coefficient of 0.41. The NMBs between simulations and observations in US and Europe are 11.7 % and 13.2 %, respectively, which are comparable with the simulation performed by CESM (Lamarque et al., 2012; Sadiq et al., 2017). However, the model overestimates O₃ concentrations by 29.3 % with a regression intercept of 32 ppbv, suggesting that simulated O₃ vegetation damage might be overestimated especially over some regions with a low ambient O₃ level. The large overestimate is mainly a result of overestimation in China. However, if we validate maximum daily 8 h average (MDA8) O₃ concentrations, we found that the model shows much lower biases (Fig. S1 in the Supplement). The main reason for the overestimation is that the model predicts high nighttime O₃ concentrations that are not consistent with observations. Since O₃–vegetation interactions usually occur in the daytime, the validation shows that ModelE2-YIBs is good to use for this study. Meanwhile, most of the observational sites in AQMN-MEE are located in urban areas, which might be another reason for the surface O₃ overestimates in China (Yue et al., 2017).

Figure 1 Evaluations of simulated summer (June–August) daily (24 h average) surface O₃ concentrations in the CTRL run. (a–c) Spatial distribution of observed O₃ concentrations (circles) in AQMN-MEE in China, CASTNET in the US, and EMEP in Europe, respectively, and the simulated O₃ concentrations. (d) Scatter plot of O₃ concentrations (ppbv) over observational sites in the three regions. The x and y axes indicate the observed and simulated O₃ concentrations, respectively. The purple line shows the linear regression between the observed and simulated O₃ concentrations. The black dashed line shows the 1 : 1 lines.

To further compare the performance of ModelE2-YIBs with other chemistry–climate models, we select six simulated cases performed by different model members in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Lamarque et al., 2013) and implement the evaluation with the same observational data (Fig. S2 in the Supplement). The correlation coefficient (0.41) and NMB (29.3 %) for ModelE2-YIBs are located in the ranges of 0.36 % to 0.60 % and −16.0 % to 45.1 % by the model ensembles, suggesting that ModelE2-YIBs has comparable performance to other state-of-the-art models. However, most of the current chemistry–climate models lack the interactive vegetation growth module, let alone studying O₃–vegetation interactions. The vegetation variables (e.g. GPP and LAI) in ModelE2-YIBs have been fully evaluated in previous studies (Yue and Unger, 2015), making ModelE2-YIBs a suitable tool for this work.

Figure 2 The summer daily (a) surface O₃ concentrations, (b) O₃ dry deposition velocity, (c) gross primary productivity, and (d) isoprene emissions in the CTRL simulation without O₃ damage to vegetation.

Figure 2 shows the global June–July–August (JJA) surface O₃ concentrations, O₃ dry deposition velocity, GPP, and IPE. Simulated O₃ is high in the eastern US, western Europe, India, and eastern China (Fig. 2a). The spatial pattern of O₃ dry deposition velocity (Fig. 2b) resembles that of the GPP (Fig. 2c) because the O₃ stomatal uptake dominantly contributed to the dry deposition. Both are high in the eastern US, western Europe, Amazon, eastern China, and Indonesia and show a reasonable magnitude consistent with previous modeling studies (Val Martin et al., 2014; Yue and Unger, 2015; Sadiq et al., 2017). The spatial pattern of IPE (Fig. 2d) also resembles that of the GPP (Fig. 2c) except that the IPE in Europe are lower than those in other regions. Such discrepancies are likely attributed to the lower fraction of deciduous broadleaf forest, which provides a high yield of IPE (Potter et al., 2001).

3.2 Offline O₃ damage to IPE

Figure 3 shows the effect of O₃ damage to IPE during the boreal summer. For different schemes, reductions in IPE show a similar spatial distribution with significant damages in the eastern US, western Europe, and eastern China, where both O₃ concentrations and vegetative cover are high. For the F scheme with high sensitivity, the damage mediated by the IPE can reach as high as 30 % in eastern China and >20 % in the eastern US and western Europe (Fig. 3a). However, the F scheme with low sensitivity predicts low damage of ∼10 % in these regions (Fig. 3b). At a global scale, IPE decrease by 1.2 %–3.2 % because of the O₃ effect. The damage using the linear scheme is generally within the low-to-high range of predictions by using the F schemes. For the linear scheme, IPE in eastern China show the greatest damage of ∼15 %.

Figure 3 Offline O₃ damage (%) to IPE averaged over summer using the F scheme with (a) high or (b) low sensitivities and results obtained by using (c) the linear scheme. The dotted grids show significant damage at the 95 % confidence level. Global land area-weighted percentage changes in IPE are shown in the titles.

Figure 4 shows seasonal variations in the effect of O₃ damage to IPE in eastern China, the eastern US, and western Europe. The magnitude of IPE changes is generally within the range of 10 %–29 %, as summarized by the observational meta-analysis (Feng et al., 2019b). The F scheme is dependent on instantaneous O₃ uptake, which peaks during the summer when both surface O₃ and stomatal conductance are high. In contrast, the linear scheme depends on the accumulated O₃ flux, which increases from zero to high levels during the growth season. As shown, the percentage of O₃ damage to IPE is low during April and May but increases to a similar magnitude to that in the F scheme with high sensitivity during August; it reaches a maximum in October. The differences in the F (instantaneous) and linear (accumulated) schemes cause distinct seasonal variations in the IPE damage, which might cause different feedback to the O₃ concentrations. However, the IPE peaks during summer (Fig. S3 in the Supplement), suggesting that absolute changes in IPE are most significant during this season (Fig. S4 in the Supplement). Meanwhile, since the surface O₃ concentrations and the vegetation growth both peak during boreal summer in the Northern Hemisphere, the O₃–vegetation interactions are supposed to be the strongest in this season. As a result, we focus our analyses on the summer to explore the O₃–vegetation interactions and feedback.

Figure 4 Monthly mean percentage O₃ damage to IPE averaged over (a) eastern China, (b) the eastern US, and (c) western Europe by using the F scheme with high and low sensitivities and the linear scheme, respectively. The dashed lines indicate the range of IPE damage summarized by observational meta-analysis.

3.3 O₃–vegetation feedbacks on surface O₃ concentrations

The effect of O₃ damage to stomatal conductance inhibits dry deposition (Fig. S5 in the Supplement), leading to significant increases in summer surface O₃, particularly in eastern China, Japan, the eastern US, and western Europe (Fig. 5a and b). The positive feedback can be greater than 5 ppbv in eastern China with high sensitivity (Fig. 5a). Smaller changes are predicted for low sensitivity, which shows limited perturbations in the US and Japan (Fig. 5b). Including the effect of O₃ damage to both stomatal conductance and IPE maintains the spatial pattern of O₃ changes but occurs at a lower magnitude (Fig. 5c–f) because these two effects offset each other. With high damage to stomatal conductance, surface O₃ remains increasing in eastern China, Japan, the eastern US, and western Europe even with reduced IPE (Fig. 5c and e). However, with low damage to stomatal conductance, surface O₃ shows limited changes in Europe, China, and Japan when IPE are simultaneously reduced (Fig. 5d and f). Surprisingly, surface O₃ increases over the eastern US in these cases (Fig. 5d and f) compared to the limited changes when IPE remain unperturbed (Fig. 5b).

Figure 5 O₃–vegetation feedback on surface O₃ concentrations during summer. The results shown are changes in surface O₃ resulting from O₃ damage to stomatal conductance alone with (a) high and (b) low sensitivity. In addition to stomatal conductance, O₃ damage to IPE is also included by using the F scheme with (c) high and (d) low sensitivity. In comparison, O₃ damage to IPE is added for the linear scheme in (e) and (f). The dotted grids indicate significant changes at the 95 % confidence level. The three regions in boxes denote eastern China, the eastern US, and western Europe.

Table 2 Summary of the O₃–vegetation feedbacks on summertime (June–August) mean surface O₃ concentrations ([O₃]), surface air temperature (T), surface relative humidity (RH), and isoprene emissions (IPE) in different sensitivity experiments. The values are calculated as the online differences between sensitivity and CTRL experiments. At each region, the minimum and maximum changes are shown as uncertainties.

Figure 6 summarizes the changes in surface O₃ over sensitive regions. Without IPE feedback, the effect of O₃ damage to stomatal conductance leads to changes in regionally averaged surface O₃ by +2.1 ppbv (+1.2 ppbv) in eastern China, +1.8 ppbv (−0.3 ppbv) in the eastern US, and +1.3 ppbv (+1.0 ppbv) in western Europe for high (low) damage sensitivity (Table 2). Changes in eastern China are the greatest compared to those of the other two regions, mainly because of the high O₃ level (Fig. 1a) and sensitive tree species (the high a and low $F_{{\mathrm{O}}_{\mathrm{3}},\text{crit}}$ for deciduous broadleaf forest; Table S1). Surface O₃ is predicted to decrease in the eastern US with the low damage sensitivity, though such a change is not significant over most grids (Fig. 5b). The inclusion of the effect of O₃ damage for both stomatal conductance and IPE slightly weakens the O₃ feedback, leading to changes in O₃ concentrations of +1.5 ppbv (+0.02 ppbv) with the F scheme and +2.0 ppbv (−0.3 ppbv) with the linear scheme in eastern China for high (low) sensitivity. The regional maximum O₃ changes can reach 7.4 ppbv (4.6 ppbv) in eastern China. Further, the effect of O₃ damage to IPE weakens the positive feedback in western Europe by approximately 1–2 ppbv. The average O₃ changes in the eastern US due to high (low) O₃ damage are +1.4 ppbv (+1.6 ppbv) with the F scheme and +1.8 ppbv (+1.1 ppbv) with the linear scheme when IPE feedback is included.

Figure 6 Box plots of summer O₃ changes in three sensitive regions among different sensitivity experiments. The error bars show the ranges of O₃ changes in individual grids over the selected regions. Asterisks indicate the mean O₃ changes averaged over the selected regions.

Although damage to stomatal conductance and IPE exert opposite effects, surface O₃ in general increases after including both processes (Fig. 6), suggesting that dry deposition inhibition plays the dominant role. For the same high O₃ damage sensitivity to stomatal conductance, changes in surface O₃ remain similar over eastern China and the eastern US between the F and linear schemes in terms of the responses of the IPE (Table 2). However, responses in western Europe are weaker for the linear scheme (Fig. 5e) compared to that of the F scheme (Fig. 5c), though the former predicts lower reductions in IPE (Fig. 3). Nevertheless, inclusion of IPE reductions helps increase surface O₃ over the eastern US (Fig. 5d and f vs. Fig. 5b), which is unexpected, since the reduction in IPE is supposed to decrease O₃ concentrations. These changes are speculated to be indirectly related to O₃–vegetation feedback to meteorology and would be further examined in the next section.

3.4 Effects of O₃–vegetation interactions on meteorology and vegetation

Figures 7 and 8 show the changes in surface air temperature and relative humidity (RH) between different sensitivity experiments and the CTRL simulation, respectively. When considering the effect of O₃ damage on stomatal conductance alone, eastern China becomes warmer (Fig. 7a and b) and drier (Fig. 8a and b), favoring O₃ chemical production and increasing surface O₃ concentrations (Jacob and Winner, 2009). The damaged stomatal conductance weakens leaf-level transpiration and thus reduces the latent heat flux at the surface (Fig. S6 in the Supplement), leading to a higher temperature and lower RH. The effect of O₃ damage is weaker in the eastern US and western Europe because of the lower O₃ concentrations, resulting in insignificant changes in temperature and RH over these regions.

Figure 7 Same as Fig. 5 but for changes in surface air temperature.

Figure 8 Same as Fig. 5 but for changes in relative humidity.

The effect of O₃ damage to IPE has limited impacts on RH (as shown in Fig. 8c and e vs. 8a and Fig. 8d and f vs. 8b) but significantly increases surface air temperature in the eastern US (as shown in Fig. 7c and e vs. 7a and Fig. 7d and f vs. 7b). The temperature in western Europe also slightly increases when IPE reductions are included, particularly when utilizing the F scheme with high sensitivity (Fig. 7c). Isoprene is among the most important precursors for the formation of SOAs (Claeys et al., 2004), which are able to reduce surface air temperature by light extinction (Charlson et al., 1992). As a result, the O₃-induced reduction of IPE decreases SOA loading and weakens the “cooling effect” of aerosols, leading to a higher temperature at the surface. The positive changes in shortwave radiative forcing following SOA reduction are the strongest in the eastern US when considering the effect of O₃ damage to IPE, particularly for the F schemes with high sensitivity (Fig. 9). Such warming explains why the reduced IPE help increase the surface O₃ in the eastern US (Fig. 6). However, aerosols in regions with high anthropogenic emissions (such as eastern China) are more dominated by inorganic components (Sun et al., 2006; Yang et al., 2011); thus, the changes in SOAs are less important. As a result, the feedback of O₃-induced IPE reductions on temperature is not significant in eastern China compared to that of other regions.

Figure 9 Effects of O₃-induced IPE reductions on SOA shortwave radiative forcing at the surface during the boreal summer. The impacts of O₃ damage to IPE are isolated by determining the differences in the experiments for (a) high and (b) low sensitivities by using the F schemes or (c, d) the linear scheme. Dotted grids indicate significant changes at the 95 % confidence level.

In addition to the direct damage (Fig. 3), IPE are indirectly affected by perturbations in the LAI and meteorology. Figure S5 shows that the LAI decreases in three polluted regions (eastern China, the eastern US, and western Europe) because of the O₃-mediated inhibition of photosynthesis, although the magnitude is typically within 5 %. Moderate changes in the LAI by O₃ have also been reported in previous studies (Yue and Unger, 2015; Sadiq et al., 2017), suggesting that LAI feedback is too low to effectively influence IPE and the consequent surface O₃. Furthermore, the warming effects resulting from the O₃-induced inhibition on stomatal conductance (Fig. 7) and the changes in the LAI (Fig. S7 in the Supplement) cause limited changes in IPE (Fig. S8 in the Supplement), suggesting that O₃–vegetation feedback does not significantly change IPE. In comparison, Sadiq et al. (2017) reported a strong positive feedback (3–5 times greater than our results) on IPE caused by increased temperature from reduced transpiration when the effect of O₃ damage to stomatal conductance is considered. However, Sadiq et al. (2017) might have overestimated temperature feedback because their parameterizations of O₃ damage to plants employ constant intercepts for some PFTs, which results in sustained damage even at low O₃ concentrations.

4 Conclusions and discussion

In this study, we explore the effect of O₃–vegetation feedback on surface O₃ concentrations by considering the effects of O₃ damage on photosynthesis, stomatal conductance, and IPE in a fully coupled global chemistry–carbon–climate model. Three regions with high O₃ levels and dense vegetation cover, including eastern China, the eastern US, and western Europe, are examined during the summer. Results are summarized in Table 2. The positive feedback increases O₃ concentrations on average by +2.1 ppbv (+1.2 ppbv) in eastern China, +1.8 ppbv (−0.3 ppbv) in the eastern US, and +1.3 ppbv (+1.0 ppbv) in western Europe for high (low) O₃ damage to stomatal conductance and the consequent inhibition of dry deposition. Additionally, the effect of O₃ damage to stomatal conductance increases the surface temperature and decreases the RH by weakening transpiration, which favors O₃ chemical production and increases surface O₃ concentrations. Including the effect of O₃ damage to IPE slightly weakens the positive feedback in eastern China and western Europe but increases O₃ concentrations especially with low O₃ damaging sensitivity in the eastern US. The increased temperatures following reduced SOA concentrations are speculated to be a possible cause for this result. Our results show that O₃–vegetation interactions increase surface O₃ by reducing dry deposition (from inhibition of stomatal conductance) and increasing chemical formation (from surface warming by weakening transpiration and SOA radiative forcing). However, changes in precursor IPE as well as the LAI have limited impacts on surface O₃.

Sadiq et al. (2017) also showed positive O₃–vegetation feedback on the surface O₃ in a global model. Compared to their results, we find an ultimate positive feedback with a similar magnitude of surface O₃ concentrations but different spatial pattern. The strongest feedback is in eastern China rather than western Europe, which is more reasonable, as the O₃ level in China is much higher than that in Europe (Lu et al., 2018). In addition, the effect of O₃–vegetation feedback on temperature is lower in our study. The fixed decoupled scheme in Sadiq et al. (2017) may have overestimated the effect of O₃ damage to stomatal conductance, leading to stronger feedback on O₃ concentrations and temperature. Furthermore, the mechanisms of O₃ effects on IPE are different. Sadiq et al. (2017) showed increased IPE because of the warming feedback. However, such warming is not significant in our study (Fig. S8 in the Supplement). Instead, we include the direct effect of O₃ damage to IPE based on observations. Although the simulations show limited impacts of reduced IPE on surface O₃, the simultaneously reduced SOAs contribute to increased surface O₃ by weakening shortwave radiative forcing and increasing temperature in the eastern US.

Our results are subject to uncertainties in modeled O₃ and damaging schemes. ModelE2-YIBs overestimates summer O₃, particularly in China (Fig. 1), which may exacerbate the damage to stomatal conductance and the consequent feedback. The O₃ damage parameterization by Sitch et al. (2007) is a semiphysical scheme that couples photosynthesis and stomatal conductance. However, some observational studies have showed that the sluggish stomatal responses under chronic O₃ exposure lead to stomata losing function and decoupling from photosynthesis (Paoletti and Grulke, 2005; Gregg et al., 2006). The decoupled parameterization proposed by Lombardozzi et al. (2012) has been applied to estimate the effect of O₃ damage to photosynthesis and stomatal conductance (Lombardozzi et al., 2015; Sadiq et al., 2017; Zhou et al., 2018). Nevertheless, we apply the parameterization by Sitch et al. (2007) because the damage is reasonably associated with the ambient O₃ level, and the scheme has been extensively evaluated against available observations (Yue et al., 2017; Yue and Unger, 2018). Fixed damage for low (even zero) O₃ included in some PFTs in the decoupled scheme may result in overestimation of O₃–vegetation feedback in the global model.

To our knowledge, this is the first time that the effect of O₃ damage to IPE is included in a fully coupled global chemistry–carbon–climate model. Both the F and linear schemes can simulate reasonable reductions in IPE compared to global meta-analysis, although with large uncertainties. The reduced IPE, as precursors, have insignificant effects on surface O₃ concentrations in eastern China (Figs. 5 and 6), likely because of high anthropogenic emissions that undermine the feedback of IPE changes to surface O₃. However, the reduced IPE weaken SOA radiative forcing and increase surface temperature in the eastern US, where biogenic SOAs provide important contributions to total aerosols (Fine et al., 2008; Goldstein et al., 2009). These results suggest that IPE feedback to the surface O₃ is quite uncertain and dependent on ambient precursors (anthropogenic vs. biogenic) and oxidizing capacity (NO_x-saturated vs. NO_x-limited).

Variations in meteorological parameters may also influence O₃–vegetation feedback. Plant stomata tend to close under drought stress to prevent water loss. As a result, dry climate may weaken O₃–vegetation feedback through regulation of stomatal conductance (Lin et al., 2019). The effects of drought cannot be evaluated using ModelE2-YIBs, which simulates climatology with small interannual variability. In the future, a chemical transport model (CTM) coupled with a dynamic vegetation model (such as GC-YIBs developed by Lei et al., 2020) will be used to examine drought impacts by using observation-based meteorological forcings.

Despite these uncertainties, our analyses highlight the importance of O₃–vegetation interactions in surface O₃ concentrations. The feedback should be considered in regional and global air quality models for more realistic simulations. Furthermore, the effect of positive feedback on surface O₃ may potentially aggravate O₃ pollution in the future with increased ambient O₃ under a warming climate (Lei et al., 2012; Doherty et al., 2013).