# Global historical soybean and wheat yield loss estimates from ozone pollution considering water and temperature as modifying effects

Bernhard Schauberger, Susanne Rolinski, Sibyll Schaphoff, Christoph Müller

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## To cite this version:

Bernhard Schauberger, Susanne Rolinski, Sibyll Schaphoff, Christoph Müller. Global historical soybean and wheat yield loss estimates from ozone pollution considering water and temperature as modifying effects. Agricultural and Forest Meteorology, 2019, 265, pp.1-15. 10.1016/j.agrformet.2018.11.004. hal-04238761

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Submitted on 11 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Originally published as:

Schauberger, B., Rolinski, S., Schaphoff, S., Müller, C. (2019): Global historical soybean and wheat yield loss estimates from ozone pollution considering water and temperature as modifying effects. - Agricultural and Forest Meteorology, 265, 1-15

DOI: 10.1016/j.agrformet.2018.11.004
Global historical soybean and wheat yield loss estimates from ozone
pollution considering water and temperature as modifying effects

Authors

Bernhard Schauberger<sup>1,2*</sup>, Susanne Rolinski<sup>1</sup>, Sibyll Schaphoff<sup>1</sup>, Christoph Müller<sup>1</sup>

* Corresponding author: schauber@pik-potsdam.de, +33 1 69 08 77 24

¹ Potsdam Institute for Climate Impact Research (PIK), Telegrafenberg A31, 14473 Potsdam, Germany

² Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre-Simon Laplace (IPSL), 91191 Gif sur Yvette, France

Abstract

Ozone pollution can severely diminish crop yields. Its damaging effects depend, apart from ozone concentration, on crop, cultivar, water status, temperature and CO₂ concentration.

Previous studies estimating global yield loss from ozone pollution did not consider all of these co-factors and climate change impact studies on crop yields typically ignore ozone pollution.

Here we introduce an ozone damage module for the widely used process-based crop model LPJmL. The implementation describes ozone uptake through stomata, internal detoxification and short- and long-term effects on productivity and phenology, dynamically accounting for all listed co-factors. Using this enhanced model we estimate historical global yield losses from ozone pollution for wheat and soybeans. We divide wheat into “Western” and “Asian” to account for higher ozone sensitivities in Asian types. We apply daily ozone concentrations obtained from six chemistry-transport models provided by the ACCMIP and HTAP2 projects.

Our implementation of ozone damage follows expected dynamics, for example damage amplification under irrigation. The model is able to reproduce results from chamber and field
studies. Historical ozone-induced losses between 2008 and 2010 vary between countries, and we estimate these between 2 and 10% of ozone-free yields for soybeans, between 0 and 27% for Western wheat and 4 and 39% for Asian wheat.

Our study highlights the threat of ozone pollution for global crop production and improves over previous studies by considering co-factors of ozone damage. Uncertainties of our study include the extrapolation from rather few point observations to the globe, possible biases in ozone data, omission of sub-daily fluctuations in ozone concentration or stomatal conductance and the averaging of different cultivars across regions. We suggest performing further field-scale experimental studies of ozone effects on crops, as these are currently rare but would be particularly helpful to evaluate models and to estimate large-scale effects of ozone.

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46 **Keywords:** ozone, wheat, soybean, crop model, global, yield

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48 Highlights

49 • Global yield damages from surface ozone are calculated for wheat and soybeans

50 • Air temperature and plant water status are considered as co-factors

51 • Ozone damage depends on region and cultivar, ranging from 0-39 % of harvest loss

52 • For wheat, Asia suffers from much higher losses than Europe or North America

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# 1. Introduction

High levels of surface ozone ($O_3$) can lower crop yields substantially (Burney and Ramanathan, 2014; Fuhrer, 2009; Ghude et al., 2014; Long et al., 2005; McGrath et al., 2015; Mills et al., 2015). Up to date, pollutants including ozone may even have contributed more to yield changes than climate change (Shindell, 2016). Ozone is a powerful oxidant and the mechanisms how it affects plants have long been researched (Ainsworth et al., 2012; Wilkinson and Davies, 2010; Wilkinson et al., 2012). The gas enters plant leaves via the stomata and swiftly reacts with apoplast components to form reactive oxygen species (ROS). These react further with membranes and cell components and cause damages to enzymes, including photosynthesis proteins. This leads to lower rates of carbon (C) assimilation. To prevent damage, plants tend to lower stomatal conductance in the presence of $O_3$, causing reduced influx of $CO_2$ and thus also lower photosynthesis rates. Senescence is advanced with higher $O_3$ due to accumulating damages, causing a precocious loss of green leaf area. A share of assimilated carbon is respired for repairing ozone-induced damages and to build up anti-oxidant defenses like ascorbate. Additionally, ovary sterility or kernel abortion could ensue from ozone damage, leaving less sink capacity for yield formation. Furthermore, the weather conditions favorable for $O_3$ formation (dry, sunny and warm) may cause stress for plants, while their capacity to cope with stress is diminished due to $O_3$ (Wilkinson et al., 2012). All these effects lead to a lower net assimilation of C on short and long term, eventually resulting in lower yield levels. Wheat and soybean are two global staple crops (FAO, 2016) and deemed sensitive to $O_3$, with possibly increased damage potential of ozone in recent years due to higher stomatal conductance (Feng et al., 2008; McGrath et al., 2015; Osborne et al., 2016; Wilkinson et al., 2012). Ozone is also a greenhouse gas accelerating climate change and thus affecting yields indirectly (Fishman et al., 1979; IPCC, 2013; Sitch et al., 2007).
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81 Ozone formation in the atmosphere is complex (Rai and Agrawal, 2012), largely determined
82 by three limiting factors: solar radiation, temperature and precursor amount and relative
83 proportion (methane, carbon monoxide, Volatile Organic Compounds (VOCs) and NOx
84 compounds). These factors can vary independently (Fuhrer, 2009; McGrath et al., 2015),
85 leading to substantial variance in O3 levels over space and time (Lin et al., 2015; Stevenson et
86 al., 2006). Trends of ozone concentration diverge between regions. While in industrialized
87 countries concentrations increased previously but have stabilized or slightly declined due to
88 stricter enforcement of thresholds, O3 trends are upwards in transition economies like India
89 and China (Gaudel et al., 2018; Rao et al., 2016; The Royal Society, 2008). Quantifying the
90 global impact of O3 on crop yields is thus a pertinent issue.

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92 There are numerous chamber and field studies quantifying the effect of increased O3 on
93 yields, reviewed, for example, by Broberg et al. (2015), Feng et al. (2008), Long et al. (2005),
94 Morgan et al. (2003) or Rai and Agrawal (2012). Modeling studies based on experiments can
95 be divided into three categories: exposure-response functions (ERF) for empirical correlations
96 between yield and ozone exposure, flux-based approaches accounting for ozone uptake
97 instead of exposure, and process-based models simulating physiological effects of O3 on
98 different plant processes. ERFs are readily computed for large geographical areas and
99 produce reliable results under similar conditions as they were trained on (Musselman et al.,
100 2006; Pleijel et al., 2007). Examples of ERF applications comprise Avnery et al. (2011) or
101 Van Dingenen et al. (2009) where the authors calculate ozone damages for soybean, wheat
102 and other crops in 2000, estimating losses between 4 and 16% depending on crop and region.
103 Chuwah et al. (2015) study effects on eleven crops between 2005 and 2050 and derive that at
104 least 2.5% of additional cropland area would be required to compensate for O3-induced
production losses. Avnery et al. (2013) research two pathways to reduce crop damage: climate change mitigation or crop adaptation. Burney and Ramanathan (2014) apply damage functions to estimate wheat and rice loss in India, correlating yield with O₃ precursors rather than O₃ concentrations directly. Tai et al. (2014) study interactive effects between O₃ and temperature changes in 2000 and 2050 for four crops and assess the impact on food security.

But these ERF-based assessments are agnostic about the underlying mechanisms how O₃ reduces yields. Additionally, interactions between O₃ and other environmental factors like CO₂ or water stress are usually not considered. Approaches that account for actual fluxes to the leaves, rather than outside concentrations, are thus necessary to complement experimental studies (Ainsworth et al., 2012; Franz et al., 2017). These could support adaptation or plant breeding for more O₃-resistant cultivars.

A hybrid between process-based and empirical models is the DO₃SE flux-based model by Emberson et al. (2000). Stomatal conductance is described in dependence of limiting factors including water stress, light, temperature and ozone. The resulting stomatal ozone flux can be distinct from ozone concentrations. The DO₃SE model has recently been applied in a novel combination of two model types: first, ozone flux to plants is calculated considering temperature, vapor pressure deficit, radiation and soil moisture, and, second, the ensuing damage to yields is then derived by a linear equation with this flux as exogenous variable (Mills et al., 2018a; Mills et al., 2018b). Another semi-empirical damage function is derived by Reilly et al. (2007) who calculate economic effects of interacting CO₂, O₃ and climate change on crop yields. But they only consider a generic C₃ crop on monthly time step.

Few process-based crop models including ozone stress have been designed. Fuhrer (2009) developed a model for O₃ damage with explicit stomatal conductance and detoxification, but remained on a conceptual basis. Sitch et al. (2007) assessed indirect effects of O₃ on climate change, but considered only generic vegetation and crops. Finally, Ewert and Porter (2000)
provide a detailed study of CO₂ and O₃ interactive effects on wheat yields. They consider short-term (reduced photosynthesis) and long-term (advanced senescence) damages of O₃, but do not include water stress effects or possible costs of cell repair measures. Emberson et al. (2018) provide an excellent overview of current modelling efforts and sketch next steps in capturing ozone effects on crops (see Discussion for details on how this study follows their recommendations).

In this study, we extend the global vegetation and crop model LPJmL (Bondeau et al., 2007; Schaphoff et al., 2018) towards ozone effects on crops. We model the effect of historical O₃ concentrations on global wheat and soybean yields. We explicitly consider interaction effects of O₃ with temperature, water stress, phenology and CO₂ at the physiological level in plants. We separately analyze Western (i.e. European and North American) and Asian wheat varieties to account for differences in their ozone responses (Emberson et al., 2009; Feng et al., 2012). This is, to our knowledge, the first study analyzing ozone-induced yield losses at the global scale with an integral consideration of modulating co-factors within a crop model.
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## 2. Materials and methods

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### 2.1 Crop model and crops

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We consider two staple crops, wheat and soybean, which together cover 22% of the global harvested area (Portmann et al., 2010). Since Asian and European/North American (“Western”) wheat varieties are known to react differently to ozone (Emberson et al., 2009; Feng et al., 2012) we separately consider these two types (though the reasons for this difference are currently not known). Asian wheat is assumed as dominant in all countries east of 40° Eastern longitude (plus Turkey). For soybean this distinction is not made since differences are currently unclear (Emberson et al., 2009). A choice between spring and winter wheat is computed internally by LPJmL, depending on climatic suitability with a preference for winter wheat: spring wheat is only sown when winters are too cold or long, that is mostly in high Northern latitudes (Bondeau et al., 2007).

For the global runs, we applied a limited irrigation scheme: only water that is actually available for irrigation (after extraction of water necessary for household, industry and livestock) can be applied (Schaphoff et al., 2018). This is important particularly in South Asia where irrigation share is high, but available water limits effective crop irrigation (Elliott et al., 2014; Zampieri et al., 2018).

## 2.2 Modeling ozone effects

The complex interaction of ozone with crops is simplified to three steps in our model (Figure 1, Table 1). First, $O_3$ outside the leaf ($O_{3,out}$) is taken up via the stomata, leading to an inner-stomatal concentration $O_{3,in}$. Stomatal conductance for $O_3$ is derived from the conductance for water vapor by using a factor of 0.58 (dividing by 1.6, the difference in diffusion coefficients of CO$_2$ and water vapor, (Haxeltine and Prentice, 1996a) and by 1.075 to account for differences between CO$_2$ and $O_3$ (Ewert and Porter, 2000)). The concentration of $O_3$ in cells is virtually zero (Ewert and Porter, 2000; Plöchl et al., 2000), as other oxidizing agents (reactive oxygen species, ROS) are quickly formed. We do not resolve these intermediate reactions for
the sake of model simplicity, but subsume from now on all ozone-derived ROS as Oₓ. Second, Oₓ,in is lowered by a detoxification process in cells and cell walls (Castagna and Ranieri, 2009; Plöchl et al., 2000) that dynamically reduces Oₓ,in to a potentially harmful concentration Oₓ,harm. This process is split into two parts: an amount of Oₓ,in is scavenged at no additional cost to the plant owed to a basal, un-targeted rate of antioxidants production (Blokhina et al., 2003; Jaleel et al., 2009), while the remaining fraction requires energy to be detoxified and thus increases the amount of carbon lost to maintenance respiration (Dizengremel et al., 2008; Ewert and Porter, 2000; Franz et al., 2017; Fuhrer et al., 1997). Franz et al. (2017) state that about half of external O₃ is taken up and detoxified via non-stomatal pathways (Kollist et al., 2000; Tuzet et al., 2011; Yin and Struik, 2009) and that this O₃ destruction pathway is important when assessing risks for plants. We do not directly account for these non-stomatal effects, but consider them as captured by the stomata-dependent detoxification. Though this may be oversimplified, in particular since the non-stomatal disposal of ozone depends on weather conditions or leaf age, we argue that the additional parameters necessary for a dynamic detoxification aside the stomata, i.e. when both stomatal and non-stomatal pathways are explicitly considered, are intractable with few experimental measurements only (note that an explicit calibration is not performed by Franz et al. (2017)). Moreover, we assume the core dynamics of detoxification, i.e. a fractional disposal of ozone before damage accrues, as sufficiently captured with a single pathway in our model, containing both basal (passive) and respiration-driven (active) detoxification. We do also not explicitly account for damage recovery, which is particularly relevant for younger leaves (Ewert and Porter, 2000), but subsume this effect in the detoxification process. We assume this as valid surrogate since damage repair requires energy as well. Since LPJmL employs a big-leaf approach, the distinction between younger and older leaves is currently also not possible. Third, the inner harmful concentration Oₓ,harm induces damages: gross photosynthesis is reduced (short-term
damage) and senescence starts earlier (long-term), thereby shortening the time to acquire biomass and yield. The day of senescence onset is earlier for both wheat and soybeans, but the total growing time until maturity is not altered by ozone. Maturity is not advanced due to contrasting evidences or dose-dependent effects with low study coverage from the literature (Feng et al., 2008; Feng et al., 2011; Finnan et al., 1998; Pleijel et al., 1997; Zhu et al., 2011).

The rate of senescence, i.e. the speed of decay, is not increased in our model, also due to ambiguous literature results (Dermody et al., 2006; Finnan et al., 1998; Pleijel et al., 1997).

These three steps are added to the existing photosynthesis model (Table 1). Potential damages of O₃ to stomatal functioning (Hoshika et al., 2015; Mills et al., 2009), which would affect O₃ uptake in the long term, are not considered due to data scarcity for crops. Similarly, the direct sensing of O₃ by stomata, with subsequent closure independent of photosynthesis (Lombardozzi et al., 2012), is not simulated due to data scarcity. CO₂ fertilization of crops is considered in LPJmL (Bondeau et al., 2007; Haxeltine and Prentice, 1996a; Haxeltine and Prentice, 1996b) and affects the response of crops to O₃ via its effect on stomatal conductance: higher concentrations of CO₂ result in lower stomatal conductance, which in turn allows less O₃ to enter the leaves. The two molecules therefore act as antagonists: the more there is of one, the less the other will diffuse into leaves since both can lower stomatal conductance.

After calculating an optimized stomatal conductance that accounts for temperature, water, ozone and CO₂ concentration, the final daily C assimilation, C allocation and evapotranspiration are derived. All other steps in LPJmL are as described in Bondeau et al. (2007).
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Reaction scheme of ozone as modeled in LPJmL. The size of $O_{3/x}$ boxes reflects their relative concentrations. Numbers refer to the three steps as described in text and Table 1. Symbols are $O_{3,out}$: outer ozone concentration, $O_{x,in}$: inner ozone/oxidative agent concentration (accounting for the fact that $O_3$ concentration inside cells is virtually zero), $O_{x,harm}$: harmful inner concentration, nil: non-oxidative reaction products. The dashed arrow from photosynthesis to stomata indicates a feedback, lowering stomatal aperture as a consequence of reduced photosynthesis.</figcaption></figure>

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<table><caption>**Table 1:** Relevant equations in LPJmL to account for ozone stress. Units are omitted for clarity; see SI Table S2 for all symbols and units.</caption><thead><tr><th>Step</th><th>Process</th><th>Affected variable</th><th>Relevant code lines</th><th>Explanation</th></tr></thead><tbody><tr><td rowspan="2">1</td><td rowspan="2">Uptake</td><td>Stomatal conductance</td><td>λ = min(λ(water; CO<sub>2</sub>), λ(O<sub>3</sub>; CO<sub>2</sub>))</td><td>Lambda (λ) is the relation between inner and outer [CO<sub>2</sub>] with a maximum of 0.8. Water-limited and O<sub>3</sub>-limited lambda are separately optimized, then the minimum of these two is taken to represent stomatal conductance<sup>1</sup>.</td></tr><tr><td>Inner [O<sub>3</sub>]</td><td>O<sub>x,in</sub> = O<sub>3,out</sub> * λ / λ<sub>max</sub> * gc<sub>max</sub></td><td>Relation between inner and outer O<sub>3</sub> depends on stomatal conductance, represented by λ over λ<sub>max</sub> (0.8) times the maximum conductance under no stress.</td></tr><tr><td rowspan="3">2</td><td rowspan="3">Detoxification</td><td>Basic scavenging</td><td>O<sub>x,inR</sub> = max(0, O<sub>x,in</sub> - b<sub>SPT</sub>)</td><td>A certain amount of O<sub>3</sub> (or other ROS) is not harmful for the plant and is scavenged without additional energy costs (as detoxifying agents are assumed as basally present).</td></tr><tr><td>Respiration</td><td>rd = b<sub>C3</sub> * V<sub>max</sub> * (1 + r<sub>PFT</sub> * O<sub>x,inR</sub>)</td><td>The remaining O<sub>x</sub> (i.e. O<sub>3</sub> or other ROS) increases cell respiration, for repairing and scavenging.</td></tr><tr><td>Harmful [O<sub>3</sub>]</td><td>O<sub>x,harm</sub> = O<sub>x,inR</sub> * (1 - d<sub>PFT</sub>)<br>O<sub>x,harm.cum</sub> = O<sub>x,harm.cum</sub> + O<sub>x,harm</sub></td><td>O<sub>x</sub> is reduced by a percentage to the remaining harmful concentration. The cumulative harmful concentration O<sub>x,harm.cum</sub> is calculated (set to 0 at sowing).</td></tr><tr><td rowspan="2">3</td><td rowspan="2">Damage</td><td>Reduction of photosynthesis</td><td>jc = c<sub>C3</sub> * V<sub>max</sub> * max(0, 1 - j<sub>PFT</sub> * O<sub>x,harm</sub>)</td><td>Rubisco-limited photosynthesis *jc* is reduced by O<sub>x</sub>. See also comments on V<sub>max</sub> in the discussion.</td></tr><tr><td>Senescence onset</td><td>Senescence starts when<br>PHU<sub>sum</sub> / PHU<sub>max</sub> > fracsen; with<br>fracsen = 0.7 * max(0, 1 - s<sub>PFT</sub> * O<sub>x,harm.cum</sub>)</td><td>Advancing of senescence is realized by lowering the phenological heat unit (PHU) threshold necessary to reach senescence (fracsen). The value of 0.7 (equal for wheat and soybeans) is the fraction of maximum attainable heat units (PHU<sub>max</sub>) necessary for senescence onset under no ozone.</td></tr></tbody></table>

<footnote n="1">¹ The interaction between water stress and O₃ would best be represented by a hyperplane; but evaluation data is too sparse for an interactive response.</footnote>
248 **2.3 Parameter calibration**

249 The ozone module requires five crop-specific parameters: $bs_{PFT}$ (in mmol/m²/day) describing
250 the basal scavenging without energy cost, $d_{PFT}$ (between 0 and 1, unitless) describing the
251 fraction of $O_{x,in}$ that is detoxified at the cost of higher respiration, $r_{PFT}$ (mmol⁻¹m⁻² day)
252 describing the respiration increase for this detoxification, $j_{PFT}$ (mmol⁻¹m⁻² day) describing the
253 Rubisco (i.e. ribulose 1,5-bisphosphate carboxylase/oxygenase)-limited photosynthesis
254 reduction due to ozone and $s_{PFT}$ (mmol⁻¹m⁻²) describing the advance in senescence. No
255 literature values were available for these five parameters, but they result as a compromise
256 between our global focus and the harmonization of differently available measured variables
257 across studies. Parameters were subjected to a calibration aiming to reproduce experimental
258 studies. After a first round of calibration, where all five parameters were free, it became
259 obvious that there are pairwise inverse correlations that require some parameters to be kept
260 constant. We decided to fix $bs_{PFT}$ and $r_{PFT}$, to which the model is either not very sensitive
261 ($r_{PFT}$, Figure 4) or there is a reference value that can be derived from the literature ($bs_{PFT}$). We
262 fixed $bs_{PFT}$ at 0.16 mmol m⁻² day⁻¹, corresponding to a threshold of non-damaging $O_3$
263 concentration of 40 ppbv (as in the AOT40 exposure metric often used in ERF studies, e.g.
264 Avnery et al. (2011) or Fuhrer et al. (1997)) at a maximum stomatal conductance of 6 mm
265 sec⁻¹ (equal to 0.162 mol m⁻² sec⁻¹ of conductance to $O_3$ at 25°C and 1000 hPa pressure) for 8
266 hours (SI Table S3). The fixed value of $r_{PFT}$ was determined by a linear regression using
267 experiments (SI Table S1). Other crop-specific parameters like base temperatures or
268 allocation constraints were not calibrated since these are based on literature values (Bondeau
269 et al., 2007). No scaling from leaf to plant was used, i.e. the whole plant was treated as one
270 big leaf.

271 Calibration was performed by traversing a full three-dimensional cube with 20 values for each
272 of the three parameters to be calibrated ($d_{PFT}$, $j_{PFT}$, $s_{PFT}$), resulting in a total of 20³ = 8,000
model runs per crop. Values were iterated, ranging from 0.05 to 5.0 times of the starting value (jPFT, sPFT) derived from linear regressions using experimental evidence, or between 0 and 100% (dPFT). The weighted average root mean square error (RMSE) was used as target function, calculated for all pairs of observed and simulated variables. Growing-season averages (for model and experiment) were used if the measurement time point or the phenological phase was not clearly indicated in the studies. Weights for calibration variables were: 2 for the O3 flux to leaves, 2 for relative stomatal conductance, 1 for Asat as percentage of control, 1 for relative yield loss and 0.1 for respiration. Stomatal conductance and ozone uptake were weighed highest since these are the decisive processes that allow an upscaling from experimental to global level. Reduction in Asat and relative yield loss are of second weighting since these are used as a mixture of result (from ozone uptake) and independent observation. Respiration was weighed least since experimental values were rare (see comments in SI Table S1). Resulting RMSE values were often similar (difference only in the third or fourth decimal place) such that of the 100 simulations with the lowest RMSE values the parameter set with the lowest reduction factors was chosen. This is justified by lower observed damages in reality than in experiments (Morgan et al., 2003). The LAImax management parameter was adapted for each experiment before calibration such that the control yield level was correctly simulated.

The Web of Science® (http://apps.webofknowledge.com) was searched in spring 2016 for experimental studies that described ozone effects on wheat or soybean and reported one or more physiological observations useful for calibration (yield loss, O3 uptake, light-saturated photosynthesis, stomatal conductance, respiration and/or growing season length). Six different studies containing 16 experiments were considered for Western wheat, seven studies with 12 experiments for Asian wheat and four studies with 11 experiments for soybeans (SI Table
S1). The experimental conditions described in these studies, including in particular O3 and CO2 concentrations, water provision and temperature, were provided as input for LPJmL.

Output variables for comparison were extracted from the manuscripts, involving the use of the software Engauge Digitizer (Mitchell et al., 2018).

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An out-of-sample calibration was performed to evaluate the reliability of the calibration
process. Every study, each containing several measurements, was omitted from calibration in
turn and the best parameters identified for the reduced experiment set. Simulation results for
the experiments from the omitted study were then calculated with the out-of-sample calibrated
parameters. The omission of studies corresponds to a 14-25% cross validation since each
study contains at least two experiments (SI Table 1).

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## 2.4 Ozone data

The ideal ozone data set for this exercise would contain observed daily, global surface O3 concentrations over several years at 0.5° spatial resolution. But such a data set does not exist.

Therefore we used an ensemble of daily global surface ozone concentrations, derived from chemical transport models participating in the ACCMIP model inter-comparison (Lamarque et al., 2013) and HTAP2 atmospheric pollution (Stjern et al., 2016) projects. Six models are included in the ensemble, all providing hourly or daily ozone concentrations: GEOSCCM, GFDL-AM3, MIROC-CHEM (all from ACCMIP), CHASER_re1, EMEP_rv48, GEOS-Chem (all from HTAP2). For either of the two experiment sets, an ensemble median was calculated pixel-wise, resulting in a total of eight global daily ozone input fields (six models plus two ensembles). Common available years for all models are 2008 to 2010. The usage of model ensembles is motivated by a better agreement of ensemble than single-model values with observed data (Fiore et al., 2009).
323 Hourly data were aggregated to daily data by averaging concentrations between 8 am and 4 pm. The daily values (aggregates or directly provided by the model) were downscaled from 324 model resolution to 0.5° spatial resolution with a double-conservative remapping that 325 conserves spatial gradients. A comparison of both ACCMIP and HTAP2 ensembles to 326 observed values is shown SI Figure S2. Crop loss estimates were calculated with a reference 327 of zero ozone and additionally with a scenario when all anthropogenic emissions (except 328 methane) were reduced by 20%. Ozone data for the latter were provided by the HTAP2 329 “GLOALL” simulations (for 2010 only), for the three HTAP2 models included here. 330

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333 **2.5 Climate and land-use data**

334 Temperature, precipitation, shortwave and longwave solar radiation are taken from the 335 WFDEI data set (Weedon et al., 2014). These data have often been used by climate change 336 impact models (Warszawski et al., 2014), and in particular in the Agricultural Model 337 Intercomparison and Improvement Project's (AgMIP) global gridded crop model inter- 338 comparison, GGCMI (Elliott et al., 2015).

339 Crop-specific land-use and irrigation fractions are extracted from the MIRCA2000 data set on 340 0.5° spatial resolution, representative of the global crop distribution around the year 2000 341 (Portmann et al., 2010). These fractions are held fixed to limit potential co-variation of O3 342 damages and land-use shifts. 343

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344 **2.6 Model evaluation**

345 Four levels of model evaluation were applied. First, the model was tested against 346 experimental observations. These studies were also used to calibrate model parameters, once 347 with the full data set and once as an out-of-sample calibration. Second, sensitivity runs were
performed where either input variables or model parameters were varied. These runs were not compared to observations, but gave insights whether the inner mechanics of the model were reasonably adherent to the modelled processes. Third, simulated national yield losses were compared to previous studies by McGrath et al. (2015), Ghude et al. (2014) and Burney and Ramanathan (2014). Fourth, the global historical loss estimates produced by LPJmL were compared to previous estimates using Exposure Response Functions (ERFs), which linearly relate ozone exposure above a threshold to crop losses.

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3. Results

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3.1 Parameter calibration

The calibration procedure leads to crop-specific parameter sets (Table 2). Asian and Western wheat parameters differ in detoxified percentage, photosynthesis decrease and respiration increase; all of them show larger effects of O₃ on Asian wheat. Calibration plots for each crop (Figure 2) show the agreement between experimentally observed and simulated values for ozone uptake, light-saturated (i.e. Rubisco-limited) photosynthesis Aₐₘ, stomatal conductance for water and yield loss. For each of the variables different counts of observations are available. Out-of-sample calibration shows that the results are robust towards omission of several experiments (SI Figure S3). Stomatal conductance and ensuing ozone uptake show variation around the 1:1 line, but no systematic bias, and largely match with observations in dynamics and magnitude. Note that ozone uptake is not measured for any experiment with Asian wheat. Relative yield loss is estimated rather conservatively for all three crops – there is no simulation below the 1:1 line.

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<table><caption>**Table 2:** Calibrated values for the five O₃ parameters</caption><thead><tr><td>Parameter</td><td>Western wheat</td><td>Asian wheat</td><td>Soybeans</td><td>Comment</td></tr></thead><tbody><tr><td>b<sub>S PFT</sub></td><td>0.1600</td><td>0.1600</td><td>0.1600</td><td>Fixed value</td></tr><tr><td>d<sub>PFT</sub></td><td>0.7395</td><td>0.6353</td><td>0.7395</td><td></td></tr><tr><td>r<sub>PFT</sub></td><td>0.1000</td><td>0.1729</td><td>0.9470</td><td>Fixed value</td></tr><tr><td>j<sub>PFT</sub></td><td>0.0100</td><td>2.6874</td><td>0.568</td><td></td></tr><tr><td>S<sub>PFT</sub></td><td>0.0844</td><td>0.0896</td><td>0.0323</td><td></td></tr></tbody></table>

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<figure><img src="image_4.png" /><figcaption>(a) Western wheat</figcaption></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>(b) Asian wheat</figcaption></figure>

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<figure><img src="image_7.png" /></figure>

**Figure 2:** Calibration results for (a) Western wheat, (b) Asian wheat, (c) soybeans. Subpanels show experimentally observed values on the x-axis and simulated values on the y-axis. Different colors denote different experiments; detailed descriptions are listed in SI Table S1. There can be several measurements of one variable within one experiment. Ideally all points would lie on the 1:1 line shown for comparison. Numbers in brackets in sub-captions denote calibration weights (see Methods).

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384 **3.2 Sensitivity towards weather or parameter variation**

385 Sensitivity of the model towards input variation is displayed in Figure 3 and SI Figure S4,
386 similar to the tests conducted by Ewert and Porter (2000). Response dynamics of three key
387 variables (light-saturated photosynthesis $A_{sat}$, stomatal conductance and ozone uptake) are
388 evaluated under different growth conditions for one example day during late leaf expansion or
389 early senescence. Variations include ozone concentration, exposure time, CO₂ concentration
390 and precipitation. Temperature and illumination were simulated as optimal and repeated each
391 day. All crops convey similar dynamics (SI Figure S4). Since phenological development, in
392 particular when the LAI changes with development, influences photosynthetic performance,
393 analogous plots without senescence advancing can be found in SI Figure S5. This exercise
394 with ozone-unaffected senescence onset conveys similar patterns as the original analysis
395 (except ozone uptake monotonically increasing with exposure time).

396 Light-saturated photosynthesis ($A_{sat}$) increases with higher CO₂, but high O₃ concentrations
397 dampen this increase (Figure 3; SI Figure S4). The relative loss in $A_{sat}$ in reference to O₃-free
398 conditions is, however, levelling off with higher CO₂. Stomatal conductance is reduced by a
399 higher load of either CO₂ or O₃. At high O₃ and low CO₂ concentrations simulated stomata
400 are completely closed. The influence of O₃ diminishes with higher CO₂ concentrations.
401 Closely connected to stomatal conductance is O₃ uptake, which decreases with higher CO₂ but
402 increases with higher O₃.

403 Scarcity of water leads to a less important role of O₃, evidenced by smaller differences
404 between $A_{sat}$, conductance and O₃ uptake at low water levels. Both relative and absolute
405 photosynthetic damages increase with water provision. For high O₃ loads stomatal
406 conductance levels off: it does not increase with more water to avoid excess O₃ uptake.

407 Longer O₃ exposure times lead to higher O₃ uptake and thus lower $A_{sat}$ and stomatal conductance. Stomatal conductance, $A_{sat}$ and O₃ uptake turn down to zero at high levels of O₃.
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The day of senescence onset slightly advances with higher management intensity (SI Figure 410 S6). Simulated plant growth (LAI) and development (as relative phenology) are influenced by 411 atmospheric ozone (SI Figure S7).

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<figure><img src="image_8.png" /><figcaption>(a) Varying CO₂ concentrations, at 8 hours of exposure and 6mm daily precipitation</figcaption></figure>

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<figure><img src="image_9.png" /><figcaption>(b) Varying precipitation levels, at 8 hours of exposure and CO₂ at 340 ppm</figcaption></figure>

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<figure><img src="image_10.png" /></figure>

**Figure 3:** Sensitivity of crop responses against varying inputs of (a) CO₂ concentration, (b) water supply and (c) ozone exposure times for Asian wheat (for other crops see SI Figure S4). Data are taken as one-day snapshots at mid growing season (81 days after sowing, which is in the leaf expansion phase for ozone doses up to 50 ppb, but already in senescent phase for the others – see also SI Figure S7). The response of Aₘₐt, stomatal conductance and O₃ uptake is shown. Different colors denote different ozone concentrations.

Model sensitivity towards varying ozone parameters, at constant weather and O₃ conditions, is shown in Figure 4. The five ozone-related parameters were varied between +/- 90% of their calibrated or fixed values. Simulated yields react most to changes in the detoxified fraction (dₚ₉₅) of O₃: the more is detoxified, the higher are yield values. The second largest sensitivity is found towards senescence advancing (sₚ₉₅); then follows the basal scavenging (bsₚ₉₅). The Rubisco-limited photosynthesis reduction factor (jₚ₉₅) shows impact only for Asian wheat, while the respiration increase factor (rₚ₉₅) shows an influence only for soybeans. Sensitivities with parameters fixed at low, rather than mean, reduction factors are shown in SI Figure S8. These show distinct responses and orders of parameters, but underlining the choice of dₚ₉₅,
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$s_{PFT}$ and $j_{PFT}$ as calibration parameters. Basal scavenging would exert measurable influence on yields, but was fixed as this is the only parameter that can be tied to literature values (see Methods).

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<figure><img src="image_11.png" /></figure>

**Figure 4:** Sensitivity of simulated yields against perturbed crop parameters. Each of the five parameters was varied from -90% to +90% of its calibrated value (Table 2, except that $d_{PFT}$ was limited to a maximum of 100%); the other four parameters were held at their calibrated values. Black lines indicate mean yield values across all parameter values; grey dots indicate yields when all parameters are held at their calibrated/fixed values. Constant optimal temperature, precipitation and illumination were used (details see SI Figure S8).

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440 **3.3 Historical yield losses**

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442 Historical global yield loss due to ozone pollution between 2008 and 2010 was calculated
443 with LPJmL. Mean ozone levels during summer, based on the ACCMIP model ensemble, are
444 displayed in Figure 5. These range from 7 ppbv in Amazonia and Papua-New Guinea up to
445 more than 60 ppbv in the Middle East. Eastern US and Europe, in particular Italy, are stricken
446 by high O₃ levels (50-60 ppbv) in the summer season. This suggests that major crop
447 producing regions, which are mostly in the Northern Hemisphere, are substantially affected
448 by O₃ pollution.

449 Maps of relative losses, separately for rainfed and irrigated yields, referring to a hypothetical
450 scenario with zero surface ozone are shown for Western wheat (Figure 6), Asian wheat
451 (Figure 7) and soybeans (Figure 8). Both wheat types are simulated globally for comparison.
452 The (unrealistic) scenario with zero surface ozone was chosen for ease of comparison with
453 two previous global studies, Van Dingenen et al. (2009) and Avnery et al. (2011),
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455 Irrigated yields (with irrigation limited by available water) show more pronounced relative
456 losses than rainfed yields for all crops. Western wheat losses range from 0 to 25% for rainfed
457 yields, with high losses in Central Europe, followed by the Eastern US (Figure 6). Irrigated
458 Western wheat yield losses range between 5 and 35%, with low spatial variation within main
459 producing areas. Globally, land-use weighted wheat losses were estimated at 5.4% for rainfed
460 and 15.1% for irrigated yields, if all planted wheat was of Western type.

461 Asian wheat losses range between 0 and 30% for rainfed and 10-50% for irrigated crops
462 (Figure 7). The highest losses occur in India, Pakistan and China where ozone load is high
463 and a substantial fraction of crops is irrigated. Globally, land-use weighted wheat losses were
464 estimated at 9.8% for rainfed and 25.6% for irrigated yields, if all planted wheat was of Asian
465 type.

466 Relative yield losses for soybeans range between 0 and 13% for rainfed crops, where the highest reductions are observed in the Balkans and Eastern US (Figure 8). Reductions for irrigated soybeans are up to 15% in several regions, in particular in Northern China, Iran and the US Midwest. Globally, land-use weighted soybean losses are estimated at 3.8% for rainfed and 8.0% for irrigated yields.

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472 Nationally aggregated yield losses due to ozone pollution are shown in Figure 9. Only the top producers (cumulatively accounting for at least 90% of global production between 2000 and 2011, split between Asian and Western wheat) for each crop are considered. Uncertainties in the estimation due to different O₃ model inputs are shown by black (ACCMIP) and blue (HTAP2) lines. Mean losses for Western wheat range from 1-3% in Argentina, Australia or Canada to up to 23% in France and 27% in Egypt. For Asian wheat mean losses range from 4-6% in Iran and Turkey up to 39% in India. Soybeans show mean losses between 2% in Argentina and 10% in China. The uncertainty range due to O₃ concentrations is around 11% of the mean loss, averaged over all crops, models and countries. Differences between countries are usually due to different levels of O₃ pollution, water limitation or management intensities, which affect canopy conductance, and different matches of crop growing season and peak ozone load. Since an atmospheric ozone concentration of zero is not realistic, a similar loss assessment was performed where all anthropogenic emissions (except methane) were reduced by 20% in 2010, relative to observed levels. Loss ranges are much lower in this case (SI Figure S9).

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488 Yield losses estimated with LPJmL were compared to two previous global assessments based on exposure-response functions. In Van Dingenen et al. (2009) and Avnery et al. (2011) the
authors each compile a global ozone field for the year 2000, using a chemical model, and estimate yield losses with previously published ERFs for wheat and soybeans based on two different ozone damage indices. A comparison of the loss estimates is provided in Table 3. For LPJmL the mean across all eight ozone inputs is supplied while for the ERF studies the mean from both indices is shown. Losses for soybeans are estimated consistently lower by LPJmL, with Latin America as the only exception. Loss estimates for wheat deviate from the ERF studies, too, but not in a consistent direction.

<table><caption>**Table 3:** Comparison of relative yield losses between previous estimates and LPJmL. Exact regional definitions can be found in the two ERF studies. Western and Asian wheat parameters for LPJmL are denoted with [W] and [A].</caption><thead><tr><th>Crop</th><th>Region (Country)</th><th>Loss by ERF: Van Dingenen et al. (2009)</th><th>Loss by ERF: Avnery et al. (2011)</th><th>Loss by LPJmL</th></tr></thead><tbody><tr><td rowspan="7">Soybeans</td><td>North America</td><td>12.4%</td><td>14.4%</td><td>10.2%</td></tr><tr><td>Latin America</td><td>n.a.</td><td>3.3%</td><td>3.8%</td></tr><tr><td>Europe</td><td>23.9%</td><td>25.6%</td><td>10.7%</td></tr><tr><td>Africa & Middle East</td><td>n.a.</td><td>5.9%</td><td>1.7%</td></tr><tr><td>China / East Asia</td><td>16.1%</td><td>22.8%</td><td>6.2%</td></tr><tr><td>India / South Asia</td><td>11.9%</td><td>8.2%</td><td>5.3%</td></tr><tr><td>Oceania</td><td>n.a.</td><td>1.9%</td><td>1.9%</td></tr><tr><td rowspan="8">Wheat</td><td>North America [W]</td><td>4.3%</td><td>6.8%</td><td>3.9%</td></tr><tr><td>Latin America [W]</td><td>n.a.</td><td>3.7%</td><td>1.4%</td></tr><tr><td>Europe [W]</td><td>4.4%</td><td>7.7%</td><td>14.2%</td></tr><tr><td>Africa, Mid-East [W, A]</td><td>n.a.</td><td>13.0%</td><td>6.1%</td></tr><tr><td>China / East Asia [A]</td><td>14.4%</td><td>9.8%</td><td>34.2%</td></tr><tr><td>India / South Asia [A]</td><td>20.4%</td><td>17.4%</td><td>36.7%</td></tr><tr><td>Northern Asia [A]</td><td>n.a.</td><td>6.9%</td><td>6.4%</td></tr><tr><td>Oceania [W]</td><td>n.a.</td><td>0.5%</td><td>0.0%</td></tr></tbody></table>

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<figure><img src="image_12.png" /><figcaption>**Figure 5:** Means of daily ozone levels in ppbv, averaged between 2008 and 2010 during the summer growing season, derived from the ensemble of ACCMIP models. Growing season is defined as April to August on the Northern and December to April on the Southern Hemisphere.</figcaption></figure>

<figure><img src="image_13.png" /></figure>
<figure><img src="image_14.png" /><figcaption>**Figure 6:** Historical Western wheat yield losses as fraction of unharmed yields at zero $O_3$. White areas have no cropping area in MIRCA2000. Panels show (a) rainfed and (b) irrigated yields. The ACCMIP model ensemble was used as $O_3$ input.</figcaption></figure>

<figure><img src="image_15.png" /></figure>
<figure><img src="image_16.png" /><figcaption>(b) Irrigated</figcaption></figure>

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**Figure 7:** As Figure 6, but for Asian wheat (same color scale).

<figure><img src="image_17.png" /><figcaption>(a) Rainfed</figcaption></figure>

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<figure><img src="image_18.png" /><figcaption>**Figure 8:** As Figure 6, but for soybeans.</figcaption></figure>

<figure><img src="image_19.png" /></figure>
<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

**Figure 9:** Nationally aggregated yield losses due to ozone, relative to zero pollution, for the main producers of each crop: (a) Western wheat, (b) Asian wheat, (c) soybeans. Red bars show yield losses as land-use weighted averages over all grid cells with at least 0.05% land-use share of the respective crop, as the mean of eight different global O₃ input fields (see Methods). Black lines show loss ranges among ACCMIP models, while blue lines show loss ranges among HTAP2 models. Countries are ordered by total production. Yield losses with
respect to a 20% reduction of emissions (instead of zero ozone) are shown in SI Figure S9.

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# 4. Discussion

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We have implemented a novel ozone damage module into the widely used global crop model LPJmL. Parameters were calibrated to experiments and with these we have estimated global historical yield losses. Losses range from virtually 0% up to 40% and agree with previous yield loss estimates in several cases. This study is the first to consider water stress, temperature, management intensity and CO₂ concentration as co-variates of ozone effects at the global level within a crop model. To account for distinct sensitivities to O₃, Western and Asian wheat were simulated separately.

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## 4.1 Model design

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Simulating ozone damages with a process-based model is more complex than regressions between yield and accumulated pollutant exposure as in ERFs. But mechanistic descriptions offer several advantages: capturing non-linear effects on sub-seasonal scale, accounting for variation in ozone response due to variable water supply or temperature and including the antagonistic role of O₃ and CO₂. Our equation design is based on a diverse literature background and was tailored for seamless integration into the LPJmL framework. Yet there are several caveats concerning the formulation. First, the earlier onset of senescence, but an unaffected rate of senescence or maturity day may contradict with some experimental observations (though there is no consensus; see Methods). But a reducing effect of O₃ on active LAI is visible with this approach (SI Figure S7a), which we deem sufficient for our aims in this study. Second, previous approaches implemented a decrease in photosynthesis from ozone by reducing Vₘₐₓ, the CO₂-limited maximum rate of carboxylation, as observed by experiments (Farage and Long, 1995). We chose to reduce the Rubisco-limited photosynthesis rate *jc* instead of Vₘₐₓ, as observed in Betzelberger et al. (2012), since
548 respiration in LPJmL is linearly dependent on $V_{max}$ (Haxeltine and Prentice, 1996a) and would
549 therefore decrease with higher $O_3$ load. However, a decrease in respiration has not been
550 observed in experiments – respiration increases with $O_3$ (Feng et al., 2008). Third, LPJmL
551 utilizes a big-leaf approach to scale from molecular processes to ecosystem level. This
552 simplification neglects differential effects of $O_3$ on young and old leaves or other plant parts
553 (Ewert and Porter, 2000), but is deemed necessary for the global scale. Fourth, a direct impact
554 of $O_3$ on stomata apart from the coupling via photosynthesis effects (Lombardozzi et al.,
555 2012) is not considered since no data are available for crops. Fifth, the amount of ozone that
556 is scavenged without any effect on the plant may change over time or in stressful conditions
557 when ROS defense mechanisms may also be required under, for example, drought stress
558 (Ewert and Porter, 2000; McGrath et al., 2015). This is not considered due to lacking data on
559 crops. Sixth, damage repair is not explicitly considered but subsumed with detoxification for
560 the sake of model simplicity. This is inaccurate, though, since repair capacities diminish with
561 leaf age (Ewert and Porter, 2000). Seventh, the daily time step may be too coarse to capture
562 non-linear impacts of sub-daily ozone peaks. We account for this uncertainty by calibrating
563 model parameters for daily time step simulations. Overall, we developed an ozone damage
564 module of intermediate complexity that does not capture sub-daily, leaf-specific effects but
565 runs swiftly, globally and accounts for co-variates like temperature and water. The level of
566 detail for capturing ozone effects on crops suggested by Emberson et al. (2018) is thus only
567 partly fulfilled. Of their recommendations we do, specifically, consider daily variations in
568 stomatal conductance, timing of ozone peaks during the growing season, influences of
569 management (dynamic growing seasons, spring/winter cultivar choice and irrigation) and
570 interaction with climate and $CO_2$. But our model does not include leaf-specific dynamics
571 (stomatal conductance and repair capacity dependent on leaf age), differential effects during
572 particularly sensitive times during the growing season like anthesis, sub-daily variations in
ozone and stomatal conductance or a variable harvest index (see also the seven points above).
Note, though, that it is recognized also by Emberson et al. (2018) that the necessary
experimental data set to calibrate a model of such depth are yet to be identified. Therefore we
consider our ozone module as a first step towards more advanced modelling of physiological
effects. Recently, new model types have been proposed accounting for the phytotoxic ozone
dose (POD) i.e. the effectively damaging amount of O₃ entering the plant, which consider
stomatal conductance and phenology in a simplified way (Mills et al., 2018a; Mills et al.,
2018b) and are thus preferable over purely exposure-based damage functions (Mills et al.,
2011). Yet our module, integrated in a larger vegetation model, in principle allows for an
integral study of advanced research questions for the following reasons. The simulation of
ozone damage is directly coupled with plant growth which is, in turn, dependent on
phenology, dynamic soil water content, allocation of photosynthesates to plant compartments
and available nutrients. The use of daily climate information allows for non-linear responses
over the growing season, for example when vernalization requirements for winter wheat have
not been met. Furthermore, an interaction with other vegetation (e.g. weeds) is, in principle,
possible. A particular example for the advantage of an integrated crop model is to consider the
modulating effect of actually available irrigation water on ozone damages, as demonstrated in
this study.

## 4.2 Parameter calibration and sensitivity

The high model sensitivity towards the detoxified fraction is due to its influence on all
downstream processes (respiration, photosynthesis, senescence). Basal scavenging is an
additive reduction and therefore does not show as large an influence as the respiration-driven
detoxification *d*<sub>PFT</sub>. Precocious senescence displays substantial influence on yields since it
deprives the plant of radiation interception twice: less new leaves are formed and the existing
ones are less active. The sensitivity towards parameters depends on crop and absolute
parameter value. Therefore the parameters can only be interpreted as a complete set since they are dependent on each other and the rather few experimental observations do not allow for an unambiguous quantification of mechanisms. This suggests that possibly a more simple reduction of net photosynthesis by a combined factor (including respiration changes) would also be sufficient, as long as further experimental constraints are not available. Calibration is more reliable for experiments that measure several considered target variables simultaneously (e.g. experiments 4 and 5 for soybeans) than for experiments with only few observed variables.

Though dynamics of stomatal conductance, ozone uptake and photosynthesis response are captured by the model, there is a bias towards lower yield losses in the model than observed in experiments. This bias is small for Western wheat and concerns only some experiments for Asian wheat, but is more substantial for soybeans. The three experiments with the highest deviation (numbers 1, 2, and 11; Figure 2) show, however, unusually high yield losses within the experiments for a given O₃ concentration (SI Figure 10). Therefore the deviance of LPJmL from these observations is not considered problematic. The agreement between previous and our national soybean loss estimates indicates that this underestimation in experiments is not present under large-scale conditions, for which LPJmL is designed.

The usage of a global model with parameters that have been calibrated with point-based experiments may entail uncertainties. This concerns management, weather or unobserved influences on yields that are not resolved at larger scales. We aimed to limit these uncertainties by using different types of experiments with varying locations and conditions. Experimental results should also be treated with caution since among them there is uncertainty in the magnitude of ozone effects (Bernacchi et al., 2006). Therefore it is reasonable to allow some error in the reproduction of experimental observations as long as
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these are unbiased and the dynamic range – large response differences from largely different conditions – is captured.

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The association of predominant wheat type (Western or Asian) with country in our study is arbitrary and does not account for differences within each group. Additionally, except the ozone factors, all other crop parameters – which are derived from literature and intended to cover a broad range of wheat-type cereals like barley or rye (Bondeau et al., 2007) – are kept constant, which may not reflect physiological reality. But the different values for ozone factors after calibration, with higher penalties for Asian wheat, and the agreement with previous studies of coherently larger losses in Asia support the geographical split into two types. Yet hypotheses about physiological reasons for the different response, e.g. a particularly sensitive photosynthesis in Asian types, cannot be deduced in the light of the current uncertainties with a global model.

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## 4.3 Sensitivity towards input data

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Simulated responses to different climate conditions agree with expectations. The antagonistic roles of O₃ and CO₂ (Bernacchi et al., 2006; Ewert and Porter, 2000) and the protective role of water deficit against O₃ damage (at the price of generally lower yields) are captured. Higher ozone loads or longer exposure also lead to more damage, as expected.

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Absolute and relative yield losses depend on management intensity. Management including fertilizer, cultivar choice or pest control is reflected only by the parameterized maximum leaf area index (LAI<sub>max</sub>) in LPJmL (Fader et al., 2010), since the utilized version does not contain explicit nitrogen cycles or pest dynamics. Therefore the correct adjustment of management in the model is of salient importance (SI Figure S1). Experimental evidence also suggests that a

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scaling of losses with better management is reasonable, in particular when leading to higher stomatal conductance (Biswas et al., 2008). LAI<sub>max</sub> was separately scaled for each country and crop between 1 and 7 such that national average yield levels between 2008 and 2010 (the time frame with available ozone data) match between LPJmL simulations and FAO reported yields (Fader et al., 2010). The calibration considers reported ozone values to avoid implicit ozone damage in the national management settings when atmospheric concentrations are altered in an experiment. Calibration at national level does not account for within-country differences in production intensity, as is the case e.g. in France or the US. We argue, however, that comparisons between nations are nonetheless possible, in particular when considering that the required time series of sub-national yield data are not everywhere available (Ray et al., 2012). An earlier onset of senescence with higher LAI<sub>max</sub> (SI Figure S6) can be expected, since better plant growth allows ozone damage to accumulate faster.

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## 4.4 Reliability of ozone input data

We compared monthly ozone data, derived from hourly or daily ozone concentrations from six models, to the observational data set provided by Sofen et al. (2016); see SI Figure S2. Both ACCMIP and HTAP2 ensembles tend to overestimate low monthly ozone pollution, in particular in northern latitudes. Reasons for this bias are discussed in Fiore et al. (2009), Schnell et al. (2015), Guo et al. (2018) and Solazzo et al. (2017). A local-mean-based bias correction was attempted for our study, but did not alter results much (data not shown). The existing uncertainties in ozone modeling require more sophisticated methods for correction, which we did not aim for in this study. A study on pollution-related mortality (Fang et al., 2013) used ozone inputs with a similar bias as our ensemble. Therefore we used the uncorrected single models and ensembles, assuming to cover uncertainties regarding ozone input by this approach.
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674 Ozone input was assumed as static in our study, i.e. daily concentrations are not modified by
675 uptake or dry deposition. An atmospheric coupling between transpiration, vapor pressure
676 deficit and uptake of CO2 or O3 would be necessary to capture the full dynamics of this
677 complex process. This is currently not included in any crop model and requires interaction
678 between biosphere and atmosphere models. Thus we assume static ozone fields as sufficient
679 to assess national yield losses due to ozone.

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681 **4.5 Comparison to previous loss estimates**

682 Our loss estimates for rainfed soybeans largely agree in magnitude with previous results. The
683 LPJmL-based loss estimate for total US soybeans is 10.4% (range is 8.5 to 12.6%). For only
684 rainfed yields this figure is 10.1% (8.2-12.3%), and for only irrigated yields 13.1% (11.7-
685 15.6%). The value estimated by LPJmL is therefore double the value of 5.5% for rainfed US
686 soybeans provided by McGrath et al. (2015), possibly due to an overestimation of ozone
687 concentration in the US (SI Figure S2). Indian soybean yield loss estimates by LPJmL are
688 5.3% (3.9-8.4%), corresponding in magnitude with the 2.7% (+/- 1.9%) estimated by Ghude
689 et al. (2014). For Indian wheat, there is agreement in the range of losses between LPJmL with
690 39.4% (32.0-50.9%) and the study by Burney and Ramanathan (2014), who estimate 40%
691 (20-60%, depending on the state). Note that Burney and Ramanathan (2014) consider black
692 carbon and ozone together and do not feed concentrations but rather precursor emissions into
693 their equations. Therefore estimates are not directly comparable. Loss calculations in Ghude
694 et al. (2014), however, are eight-fold lower with 5.0% (+/- 1.2%). In that study, even the
695 region most affected by ozone is estimated to suffer from only 17% yield loss. A possible
696 reason for differences is water stress: LPJmL explicitly simulates the interaction of water and
697 ozone, while most statistical studies use a linear relationship between ozone and yields under
all circumstances. A precipitation control is included in McGrath et al. (2015) and Burney and Ramanathan (2014), but not Ghude et al. (2014). This may explain the difference for wheat, which is mostly irrigated in India, and the better match for soybeans, which are dominantly rainfed in India. Therefore we conclude that the consideration of water availability is of importance when assessing ozone losses. There may even be an economic trade-off for irrigation when ozone load is high (Mills et al., 2018a; Mills et al., 2018b): more irrigation also leads to more ozone damage, such that the benefit of irrigation may just be leveled by the costs of ozone damage. This relationship has to be studied in more detail, though.

Our loss estimates agree only limitedly (Table 3) with the global studies by Avnery et al. (2011) and Van Dingenen et al. (2009). Possible reasons for differences are divergences in ozone concentration due to different chemistry models, the consideration of different years, no distinction between Asian and Western wheat types and, above all, the lack of water levels in their ozone response. This may lead to overestimation of losses in water-stressed regions but to an underestimation in well-watered regions. Other putative causes for differences include a possible mismatch between real and assumed growing seasons (for all three studies), a wrong adjustment of management settings with LAI<sub>max</sub> in LPJmL or temperature effects on crops not considered in the ERF studies. Overall we assume loss assessments by LPJmL as a supplement to the previous estimates, not as a replacement. Although LPJmL integrates co-factors like temperature or water provision including its limited physical availability, the new approach also brings new uncertainties (see above) such that superiority of LPJmL over ERFs is not warranted. But for future assessments, where interaction between CO<sub>2</sub> and O<sub>3</sub> may play a larger role than today, or when soil dynamics or atmospheric couplings are of interest, LPJmL is better equipped for such simulations.
723 **4.6 Historical loss estimations**

724 The estimation of historical yield losses due to ozone pollution suggests that ozone is a major
725 yield-reducing factor in several regions on the globe. This agrees with expectations founded
726 on experimental findings and observed ozone pollution. Reduction on the field is usually less
727 than in chambers due to protective effects of water status (Fuhrer, 1995), sub-daily timing
728 (Heath et al., 2009), though not reflected in LPJmL, and a possible shift between growing
729 season and peak ozone load, depending on crop and region (Van Dingenen et al., 2009).

730

731 LPJmL estimates relative yield losses from irrigated yields as consistently higher than from
732 rainfed yields. This is due to a higher stomatal conductance allowing more ozone to penetrate.
733 Several experiments have shown this relative protective effect of water deficit (Bou Jaoudé et
734 al., 2008; Fuhrer, 1995; Khan and Soja, 2003). In McGrath et al. (2015), however, the authors
735 find the opposite for rainfed soybeans in the US: losses are higher under dry conditions. They
736 argue for a decoupling of stomatal conductance and water status by impaired abscisic acid
737 (ABA) signaling. This eventually allows more ozone to enter under drought than under
738 unstressed conditions, which aggravates losses. Another possible explanation for their finding
739 could be that water-stressed plants have a limited capacity to detoxify ozone as antioxidant
740 compounds are also necessary to combat drought consequences. Since neither model is
741 currently able to resolve these processes, more detailed models and experimental studies are
742 necessary to identify causes for differences.

743

744 Asian countries are simulated as particularly susceptible to losses for two reasons: higher
745 pollution and higher sensitivity of crops (Emberson et al., 2009). The high loss estimation of
746 39% for wheat in India can be explained by combining these two factors and an almost
747 exclusively irrigated cultivation, which allows for stomata to stay open and for more ozone to
enter. Yet this may be an overestimation given the sensitivity of the model towards management intensity and the uncertainty of ozone input data with very few observations in this region. Additionally, our simulations may still assume too much available water for irrigation since environmental flows are not considered here (Jagermeyr et al., 2017). Rather high reductions of around 20% for wheat in France, Germany and UK are unexpected, but may be reasonable in the light of substantial ozone pollution, limited water stress and high management intensity. Another explaining factor is the slight overestimation of ozone pollution in Western Europe by the ozone model ensemble (SI Figure S2). Finally, despite already high loss fractions and the considerations above, it is possible that LPJmL loss estimates are rather conservative given the deviation to lower losses when compared to experiments (Figure 2). Yield losses in similar ranges (averaged over country classes) have recently been provided by Mills et al. (2018a) and Mills et al. (2018b), indicating that substantial crop damage by ozone is plausible also in developed nations.

In our assessment a baseline of zero O₃ was used for comparison. This is unrealistic in practice since background biogenic emission of precursors, as well as contributions from the stratosphere, can hardly be mitigated. A more realistic estimate of *avoidable* ozone damage is therefore provided by the 20% emission reduction (corresponding to a globally averaged 3.3% reduction in ozone concentration) experiments in the HTAP2 simulations (SI Figure S9). These calculations still point to several percent of unnecessary yield sacrifices.

Further model developments could comprise the inclusion of C₄ crops like maize, the combination of O₃ effects with other pollutants like SO₂ or NO₂ (Rai et al., 2007) or other stressors similar to Mills et al. (2018b) or the usage of different cultivars and sensitivities.

Further questions that can be answered are adaptation options (e.g. shifting growing season, using different cultivars, ozone-sensitive water management) or assessment of future losses
773 due to O₃. A coupled modelling between atmosphere, chemistry and biosphere would
774 additionally allow for assessing the effects of O₃ mitigation more realistically.

775

776 **4.7 Conclusion**

777 Our implementation of a process-based global model to estimate historical yield losses from
778 ozone has confirmed previous findings: major crop producers suffer from substantial
779 production damage due to ozone pollution. Our research has emphasized that damaging
780 effects are dependent on co-factors, in particular water status, which should be considered
781 when establishing O₃ pollution thresholds. We consequently consider the inclusion of O₃
782 effects on crops as relevant for climate change impact studies, as climate change can alter
783 water cycles, temperatures and ozone pollution. This would lead to modified yield
784 expectations, with modifications possibly in a similar range as current uncertainties of crop
785 projections (Rosenzweig et al., 2014). As a corollary of our assessment we propose that more
786 surface ozone observation stations in particular in Asia, Africa and Latin America are
787 established, as these regions are currently data scarce (Sofen et al., 2016) but suffer from
788 ozone damage to crops.

789

790

791

792
793 Acknowledgements

794 We thank F. Wechsung, S. Szopa, D. Hauglustaine, J. Lathiere, Y. Balkanski and P. Ciais for
795 helpful discussions and T. Bendixen for data provision. BS acknowledges funding from the
796 German National Academic Foundation. SR and CM acknowledge financial support from the
797 MACMIT project (01LN1317A) funded through the German Federal Ministry of Education
798 and Research (BMBF). Author contributions: BS initiated, designed and performed the study
799 and wrote the manuscript with contributions from all co-authors.

800

801 The authors declare to have no competing financial interests.

802

803
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1042