Tropospheric ozone (O3) is the third most important
anthropogenic greenhouse gas. O3 is detrimental to plant productivity,
and it has a significant impact on crop yield. Currently, the Joint UK Land
Environment Simulator (JULES) land surface model includes a representation
of global crops (JULES-crop) but does not have crop-specific O3 damage
parameters and applies default C3 grass O3 parameters for soybean that
underestimate O3 damage. Physiological parameters for O3 damage
in soybean in JULES-crop were calibrated against leaf gas-exchange
measurements from the Soybean Free Air Concentration Enrichment (SoyFACE)
with O3 experiment in Illinois, USA. Other plant parameters were
calibrated using an extensive array of soybean observations such as crop
height and leaf carbon and meteorological data from FLUXNET sites near
Mead, Nebraska, USA. The yield, aboveground carbon, and leaf area index (LAI)
of soybean from the SoyFACE experiment were used to evaluate the newly
calibrated parameters. The result shows good performance for yield, with the
modelled yield being within the spread of the SoyFACE observations. Although
JULES-crop is able to reproduce observed LAI seasonality, its magnitude is
underestimated. The newly calibrated version of JULES will be applied
regionally and globally in future JULES simulations. This study helps to
build a state-of-the-art impact assessment model and contribute to a more
complete understanding of the impacts of climate change on food production.
Introduction
Surface ozone (O3) pollution is one of the major threats to global food
security due to the detrimental effects of ozone exposure on crops
(Ainsworth
et al., 2012; Avnery et al., 2011b; Leung et al., 2020; Long et al., 2005;
Tai et al., 2014; Tai and Val Martin, 2017). In the United States alone, crop
loss due to tropospheric O3 costs more than USD 5 billion annually
(Ainsworth et
al., 2012; Avnery et al., 2011a; Van Dingenen et al., 2009).
Soybean is one of the main staple crops for human consumption; it also
serves as an important source of animal feed. It is a cheap source of
proteins, and therefore soybean products are consumed around the world. The
impact of O3 on soybean physiology and growth has been studied
extensively
(Ainsworth
et al., 2012; Betzelberger et al., 2012; Dermody et al., 2008; Morgan et
al., 2003). Crop yield losses to tropospheric O3 have been quantified
using model projection and experiments. The National Crop Loss Assessment
Network and European Open Top Chamber programmes have established the air
quality guideline, which derived dose–response relationships from comparable
experimental data. These campaigns provided critical information such as the
O3 response relationship and estimated yield loss due to O3 damage
that enabled regional projections of O3 effects on crop yields
(Fuhrer, 2009). However, open top chambers modify
plant response to O3 due to the “chamber effects” which create
microclimates
(Elagöz and
Manning, 2005) and environmental differences between the chamber and open
air micrometeorology in which yield loss is underestimated (Van Dingenen et
al., 2009). Recently the introduction of free-air concentration enrichment
(FACE) technology avoids the artefacts from enclosed chambers, and O3
fumigation was adapted to FACE facilities
(Agathokleous
et al., 2017; Paoletti et al., 2017). The application of FACE experiment on
crops took place in China (Zhu et al., 2011) and the USA, including experiments
with soybean at the SoyFACE facility in Champaign, Illinois (Morgan et
al., 2004; Betzelberger et al., 2010, 2012).
Crops are a significant component of the land surface; e.g. croplands and
pasturelands represent 12 % and 26 % of the global terrestrial land,
respectively (Van den Hoof et al., 2011). Moreover,
the phenology of crops is very different from that of natural vegetation
and is characterised by high growth, turnover rate, and strong seasonality.
It is thus necessary to include a crop-specific parameterisation scheme to
improve simulations of land surface fluxes and regional climate in
agroecosystems (Van den Hoof et al., 2011). The Joint UK Land Environment
Simulator with crops (JULES-crop) is a crop parameterisation
(Osborne et al., 2015) within the land surface
model JULES
(Best
et al., 2011; Clark et al., 2011). Global simulations have been performed
with JULES-crop for rice, wheat, maize, and soybean
(Osborne et al., 2015). These four crop types
contribute more than 70 % of human calorie intake
(Ray et al., 2013). JULES-crop includes routines
representing growth, development, and harvesting of crops driven by the
overlying meteorological inputs. In JULES-crop, four new prognostic
variables have been added: crop development index (DVI), root carbon
(Croot), harvest carbon (Charv), and reserve carbon (Cresv). DVI controls the
duration of the crop growing season in four distinct stages – sowing,
emergence, flowering, and maturity – and it determines when changes in carbon
partitioning occur (Osborne and Hooker, 2011). Croot, Charv, and
Cresv are the carbon pools for roots, harvested organs (e.g. grains of
cereal, fruits, and root), and stem reserves, respectively. Carbon pools for
stem and leaves are determined from the existing prognostic variables,
leaf area index (LAI) and canopy height. In Osborne et al. (2015), global runs
of maize, wheat, soybean, and rice were carried out using JULES-crop. Site
runs were performed at four FLUXNET sites with soybean–maize rotation:
Bondville (US-Bo1), Fermi (US-IB1), and Mead (US-Ne2 and US-Ne3). Simulated
yield was compared against country and global FAO crop yields. Osborne et al. (2015) used generic representations for each of the crops in their
global study. For the plant parameters that are needed outside the crop
model such as leaf nitrogen and leaf respiration parameters, these are set
to those of the C3 or C4 grass functional types. Osborne et al. (2015)
suggested that these parameters could be tuned to be more crop specific to
improve fit to observations. These JULES parameters have been calibrated
against observations for maize, using data from the Mead FLUXNET sites in
Nebraska (Williams et al., 2017). However,
to date, these parameters have not been calibrated to soybean data.
There are many crop models developed by institutions/organisations around
the world. Most are designed for application to an individual field up to
the regional scale and do not include O3 impacts on vegetation.
Supplement Table S1 compares a selection of land surface
models which include crop tiles and have the functions to model climate
impact on crop productivity. JULES-crop is of particular interest because it
is a development of the global land surface component JULES of the Met
Office numerical weather prediction and climate models and contains a
detailed representation of plant physiological processes at sub-diurnal
timescales, including consideration of O3 effects on natural
vegetation, thus making it suitable for this study. JULES-crop has been
accepted into the JULES trunk with the intention to be coupled with the
Hadley Centre Global Environment Model (HadGEM) in the near future. HadGEM
is recognised as one of the best performing climate models with smaller
errors than typical climate models (Gleckler et
al., 2008; Knutti et al., 2013).
The calibration of O3 damage on soybean would allow land surface and
crop models to more realistically and reliably simulate present-day and
future O3 damage and subsequently to quantify its economic impacts.
The objective of this study is to calibrate soybean representation for
JULES-crop, with a particular focus on the response of soybean to O3
exposure.
This paper is organised as follows: Sect. 2 describes the model set-up and
observations used for the JULES calibration. Section 3 compares the results
from the calibrated JULES runs against independent observations. Section 4
assesses the suitability of the model for modelling soybean under O3
damage and discusses ways of future model improvement.
Methods
A flowchart demonstrating the calibration and evaluation procedure is given
in Fig. 1. We first tuned the JULES-crop soybean parameterisation at the
US-Ne2 and US-Ne3 Mead sites, where three years of soybean physiological and
meteorological observations were available, at ambient ozone (Fig. 1,
steps 1–5). The three years are 2004, 2006, and 2008 in which soybeans were
grown in Mead; maize was grown in other years.
Flowchart of tuning the parameters and calibrating the model.
Secondly, to calibrate the JULES ozone damage parameters (Fig. 1, step 6)
we made the assumption that there is a negligible damage to crop yield at
ambient background levels of O3 at both the SoyFACE and Mead sites.
This is consistent with Mills et al. (2007), who reviewed over 700 published
papers and conference proceedings and found that O3 level of AOT40 over
3 months of 5 ppm h reduced soybean yield by less than 5 %. Then we
calibrated specifically the soybean O3 response using leaf gas exchange
measurements from soybean grown under elevated O3 concentrations at
SoyFACE.
Finally, we applied JULES-crop newly calibrated for soybean and its O3
sensitivity at the leaf level and evaluated model performance against
observed yield and leaf area index from SoyFACE, taken for the full range of
rings and cultivars (Fig. 1, step 7).
Calibration of soybean in the absence of ozone damage, using
observations from Mead
We followed the standard tuning procedure performed on maize by Williams et
al. (2017) but applied to soybean (Fig. 1, steps 1–5). Step 1 involves
using Mead observation to tune the parameters needed by all plant functional types (PFTs) in JULES
with the crop model switched off. Step 2 is to evaluate the model
performance of gross primary productivity (GPP) using Mead meteorology and LAI. Step 3 tunes the
parameters needed by crop only. Step 4 evaluates the JULES-crop run
performance with observed carbon pools in leaf, stem, harvest, etc. Step 5
demonstrates the full JULES-crop runs at Mead using Mead meteorology and
compared the model with observed GPP, aboveground carbon, etc. Step 6 tunes
ozone damage using SoyFACE LI-COR measurements. And finally step 7 evaluates
JULES-crop performance using SoyFACE meteorology and compares with observed
yield and LAI. This method is described in detail in the Supplement, and the resulting parameters are given in Tables 1–3. These are
compared to the parameters used in Osborne et al. (2015), which we refer to
as the “Osborne 2015 tuning”. Note that the parameters in Table 3 in the
Osborne 2015 tuning are typical defaults for C3 grass, rather than
soybean-specific.
JULES module switches, in which F (false) means turned off and T
(true) means turned on.
Osborne et al.(2015)This studyDiscussioncan_rad_mod5 (6 was not available)6Recommended option for layered canopy in version 4.6l_irrig_dmdFTIrrigation on demandirr_crop–0l_trait_physFFl_scale_resp_pmFTl_leaf_n_resp_fixF–Bug fix, affectscan_rad_mod=5 butnot can_ rad_mod=6l_prescsowTTSowing dates availableParametersDescriptionCanopy radiation modelNumber 6 is a multi-layer approach for radiation interception following the two-stream approach of Sellers et al. (1992). This approach takes into account leaf angle distribution and zenith angle and differentiates absorption of direct and diffuse radiation. It has a decline of leaf N with canopy height. Additionally includes inhibition of leaf respiration in the light, including sunfleck penetration though the canopy. Division of sunlit and shaded leaves within each canopy level. A modified version of inhibition of leaf respiration in the light. Exponential decline of leaf N with canopy height proportional to LAI, following Beer's law.L_irrid_dmdSwitch controlling the implementation of irrigation demand code.Irr_cropIrrigation season (i.e. season in which crops might be growing on the grid box) lasts the entire year.l_trait_physSwitch for using trait-based physiology. Vcmax is calculated based on parameters nl0 (kgN kgC-1-1) and neff.l_scale_resp_pmSoil moisture stress reduces leaf, root, and stem maintenance respiration.l_leaf_n_resp_fixSwitch for bug fix for leaf nitrogen content used in the calculation of plant maintenance respiration.l_prescsowSowing dates prescribed
Parameter values in JULES-crop that are used to represent soybean.
Asterisk indicates parameter was hardwired.
Osborne etal. (2015)This studyDiscussionTbBase temperature (K)278.15278.15Kept at Osborne et al. (2015) valueToOptimum temperature (K)313.15313.15Kept at Osborne et al. (2015) valueTmMaximum temp (K)300.15300.15Kept at Osborne et al. (2015) valuePsenSensitivity of development rate to photoperiod(h-1)0.00.0Kept at Osborne et al. (2015) valuePcritCritical photoperiod (hours)––Not used when Psen=0rdirCoefficient determining relative growth of roots vertically and horizontally0.00.0Kept at Osborne et al. (2015) valueαrootCoefficient of partitioning to root20.019.8Supplement Sect. 1.4.1αstemCoefficient of partitioning to stem18.518.5Supplement Sect. 1.4.1αleafCoefficient of partitioning to leaf19.519.2Supplement Sect. 1.4.1βrootCoefficient of partitioning to root-16.5-15.47Supplement Sect.1.4.1βstemCoefficient of partitioning to stem-14.5-13.195Supplement Sect. 1.4.1βleafCoefficient of partitioning to leaf-15.0-14.287Supplement Sect. 1.4.1γCoefficient of specific leaf area (m2 kg-1)25.924.0Supplement Sect. 1.4.3δCoefficient of specific leaf area (m2 kg-1)-0.14510.15Supplement Sect. 1.4.3τRemobilisation factor, fraction of stem growth partitioned to RESERVEC0.180.26Supplement Sect. 1.4.3fC,rootCarbon fraction for dry root0.50.47Supplement Sect. 1.4.4fC,stemCarbon fraction for dry stem0.50.49Supplement Sect. 1.4.4fC,leafCarbon fraction for dry leaf0.50.46Supplement Sect. 1.4.4fC,harvCarbon fraction for harvest0.50.53Supplement Sect. 1.4.4κAllometric coefficient relating STEMC to CANHT1.61.9Supplement Sect. 1.4.2λAllometric coefficient relating STEMC to CANHT0.40.47Supplement Sect. 1.4.2μAllometric coefficient for calculation of senescence0.05∗5.0Supplement Sect. 1.4.2νAllometric coefficient for calculation of senescence0.0∗6.0Supplement Sect. 1.4.2DVIsenDVI at which leaf senescence begins1.5∗1.25Supplement Sect. 1.5CinitCarbon in crop at emergence in kgC m-2.0.01∗3.5E-3 (Mead), 7.0E-3 (SoyFACE)Supplement Sect. 1.4.5DVIinitDVI at which the crop carbon is set to initial carbon0.0∗0.2Supplement Sect. 1.4.5TmortSoil temperature (second level) at which to kill crop if DVI>1t_bse_io∗263.15Sect. 2.3fyieldFraction of the harvest carbon pool converted to yield carbon1.0∗0.74Sect. 2.3
JULES plant functional type parameters extended to represent
soybean.
Osborne et al. (2015)This studyDiscussionc3c3_io11Soybean is a C3 plant.drrootd_ft_io0.50.5Not important in irrigated runs, so could not be tuned using US-Ne2 data. Kept at Osborne et al. (2015) valuedqcritdq_crit_io0.10.1Kept at Osborne et al. (2015) valuefdfd_io0.0150.008Supplement Sect. 1.4.6f0f0_io0.90.9Kept at Osborne et al. (2015) valueneffneff_io8.0 × 10-412.0 × 10-4Table 1nl(0)nl0_io0.0730.1Table 1Tlowtlow_io0.00.0Kept at Osborne et al. (2015) valueTupptupp_io36.036.0Kept at Osborne et al. (2015) valueknkn_io0.78–Default for C3 grass for can_rad_mod 5.knlknl_io–0.2Default for C3 grass for can_rad_mod 6.Q10,leafq10_leaf_io2.02.0Kept at Osborne et al. (2015) valueμrlnr_nl_io1.00.390Supplement Figs. S1–S3μslns_nl_io1.00.51Supplement Figs. S1–S3rgr_grow_io0.250.32Supplement Sect. 1.4.6orient_io00Kept at Osborne et al. (2015) valueαalpha_io0.120.12Kept at Osborne et al. (2015) valueωPARomega_io0.150.15Kept at Osborne et al. (2015) valueαPARalpar_io0.10.1Kept at Osborne et al. (2015) valuefsmc_mod_io00Not important in irrigated runs, so could not be tuned using US-Ne2 data. Kept at Osborne et al. (2015) value.fsmc_p0_io0.00.5FAO document 56 (Allen and Pereira, 2006)acan_struct_a_io1.01.0Kept at Osborne et al. (2015) valueCalibration of JULES ozone damage parametersOzone effects on vegetation (exposure response)
Many studies have shown that the impacts of O3 are closely related to
accumulated exposure above a threshold concentration rather than the mean
growing season concentration
(Gerosa et al., 2012; Mills et al., 2007). An index of
accumulated exposure above a threshold concentration of x ppb (AOTx) has
thus been developed as a measure of assessing O3 pollution effects on
vegetation. AOTx is calculated as the summed product of the concentration
above the threshold concentration and time (T), with values expressed in ppb h or ppm h (Mills et al., 2007).
The O3 exposure index AOT40 (accumulated O3 exposure over a
threshold of 40 parts per billion; Eq. 1) has been widely used by crop
impact models in the forestry and agriculture industry and was used at
SoyFACE.
AOT40=∫maxO3-40ppb,0.0dt
The metric ensures only O3 concentrations above 40 ppb are included.
The integral is taken over daytime hours between 07:00 to 19:00 LT (UTC-6). AOT40 does
not account for the actual uptake of O3 by plants and how this varies
with ontogenetic (life span of the plant) and climatic factors such as
temperature, irradiance, vapour pressure deficit, and/or soil moisture
(Ashmore, 2005; Fuhrer et al., 1997).
There is a drawback of the cumulative O3 exposure indices (Pleijel et al., 2000), which assume an instantaneously fixed threshold flux below which
there is no effect of O3, which may not be realistic. Also in nature,
the threshold value is unlikely to be constant (Ashmore,
2005) since the capacity of detoxification of O3 varies with climate
and plant species. To improve these indices, the Stockholm Environment
Institute developed the Deposition of Ozone for Stomatal Exchange model
(DO3SE)
(Emberson
et al., 2007; ICP Vegetation, 2017). DO3SE was developed to estimate
the risk of O3 damage to European vegetation and is capable of
providing O3 flux estimation by evaluating the soil water deficits and
their influence on stomatal conductance which affect plant O3 uptake.
Phyto-toxic O3 dose (POD) above a stomatal threshold over a growing
season (the accumulated stomatal flux above threshold Y) PODY can
differentiate species sensitivity to rising background concentration, while
AOT40 can only incorporate the effect of rising global background O3
above the threshold 40ppb. This difference means the AOT40 metric is less
sensitive to O3 peaks, and stomatal flux-based metrics (e.g. PODY and
DO3SE) perform better on O3 damage estimation in general
(Büker
et al., 2012; Dentener et al., 2010; Pleijel et al.,
2007).
Description of ozone response scheme in JULES
The current O3 scheme in JULES uses a dose-response approach to model
O3 damage (Sitch et al., 2007; Clark et al., 2011). It uses the O3
concentration in the atmosphere to modify net photosynthesis Ap by an
O3 uptake factor F:
A=ApF,
where F represents the fractional reduction of plant production:
F=1-aUO>FO3crit.
It assumes that O3 suppresses the potential net leaf photosynthesis in
proportion to the O3 flux through stomata above a specified critical
threshold (Clark et al., 2011).
UO>FO3crit is the instantaneous leaf uptake of O3
over a plant functional type specific threshold (FO3crit) (nmol m-2 s-1), and the plant type specific parameter a is the
fractional reduction of photosynthesis with O3 uptake by leaves (Clark
et al., 2011; Sitch et al., 2007).
UO>FO3crit=maxFO3-FO3crit,0.0
From Eqs. (3) and (4), F depends on the O3 uptake rate by stomata
(FO3) over a critical (plant functional type specific) threshold for
damage. It uses an analogy of Ohm's law, the O3 flux through stomata,
FO3 (nmol O3 m-2 s-1), which is given by
FO3=O3Ra+κO3gl,
where [O3] is the molar concentration of O3 at reference level
(nmol m-3) and Ra is the combined aerodynamic and boundary layer
resistance between leaf surface and reference level (s m-1). gl is
the leaf conductance for H2O (m s-1), and κO3=1.67 is the ratio of leaf resistance for O3 to leaf resistance for
water vapour (Sitch et al., 2007). The uptake flux is dependent on the
stomatal conductance, which is reliant on the photosynthetic rate in JULES.
Given that gl and photosynthetic rate are linearly related, gl is given by
gl=gpF,
where gp is the leaf conductance in the absence of O3 effects. The
set of Eqs. (3, 5, 6) produces a quadratic relationship as a function of
F that can be solved analytically (Sitch et
al., 2007).
Fractional reduction of photosynthesis with the instantaneous uptake of
O3 by leaves (mmol m-2) determines the sensitivity of soybean to
O3, and the PFT-specific O3 critical level (FO3crit)
determines the threshold O3 flux above which would cause damage to
photosynthesis
(Oliver et al.,
2018; Sitch et al., 2007). The higher the sensitivity of plants to O3
the lower photosynthesis the plant has at a given constant critical
threshold. Sitch et al. (2007) configured plant functional types with two
different O3 sensitivities (fractional reduction of photosynthesis by
O3, F, Eqs. 2, 3), where a=1.40 is high sensitivity, and a=0.25 is lower
sensitivity for C3 grass (Sitch, 2007), using monthly average
O3 data and calibration to yield observations.
Calibrating the ozone effects on crop leaf photosynthesis in JULES
using SoyFACE
The SoyFACE experiment in Illinois allows controlled CO2 or O3
enrichment across large plots within a soybean field without an enclosure.
SoyFACE O3 fumigation typically began after the emergence of soybean,
and the plots were fumigated with O3 for 8–9 h daily except when
leaves were wet. In 2009 and 2010, soybeans were exposed to nine different
concentrations of O3 ranging from the ambient level to a target level
of 200 ppb (Supplement Fig. S2). The fumigation ended when soybean was
mature.
Plant damage from O3 is cumulative, and the target concentration for the
experiment was not always met (e.g. when wind speeds are low, during rain,
or when O3 generators or analysers are down). Therefore, the 8 h
mean and the AOT40 index (accumulated ozone exposure above the threshold of
40 ppb) were used for the analysis in SoyFACE instead of using the target
O3 concentration. The planting dates were 6 June 2009 (day 159) and
27 May 2010 (day 157). Fumigation began on 29 June 2009 (day 179–260)
and 6 June 2010 (day 167–271), and harvest occurred on 20 October 2009
(day 293) and 20 September 2010 (day 273). O3 concentrations measured
at SoyFACE fluctuated greatly, as they were strongly influenced by weather
conditions, especially by wind speed. The magnitude of O3 concentration
fluctuations in the high targeted concentration was greater than the low
concentration (Supplement Fig. S2). On some days of the year when the
fumigation was off, very low O3 concentrations were recorded for all
target rings.
To calibrate the O3 parameters for soybean in JULES-crop, we used
midday photosynthetic gas-exchange measurements from Betzelberger et al. (2012). These were taken at four stages during the growing season, from
seven soybean cultivars growing at nine different O3 concentrations, using
open gas exchange systems (LI-6400 and LI-6400-40). These observations were
used in conjunction with the daytime 8 h mean O3 concentration
measurements and the parameters calibrated at the Mead site to drive the
Leaf Simulator computer package, which reproduces the calculation of leaf
photosynthesis within JULES. We then tuned the O3 parameterisation of
fractional reduction of photosynthesis by O3 (sensitivity) and
threshold of O3 flux (nmol m-2 s-1) to match the modelled leaf
photosynthesis rate to the observed rate (Fig. 2). The tuned parameters
are shown in Table 4.
Summary of ozone parameter configurations employed in JULES-crop
for the default Osborne et al. (2015) value and the tuned as calibrated to
SoyFACE leaf gas-exchange measurements (note that these have been calibrated
to daytime 8 h concentrations and therefore will be different to
parameters calibrated to monthly 24 h means).
JULES ozone damageparametersFractional reduction of photosynthesis by O3 (sensitivity) (mmol-1 m2) (dfp_dcuo_io)Threshold of ozone flux (nmol m-2 s-1)(fl_ o3_ ct_ io)Tuned value0.515.0Osborne et al. (2015): high sensitivity5.01.4Osborne et al. (2015): low sensitivity5.00.25
Net leaf CO2 assimilation rate for calibrated JULES,
simulated using the Leaf Simulator (black crosses) and observations from
Betzelberger et al. (2012) (grey circles). X axis is the daytime 8 h
mean O3 concentration (ppb).
Model configuration for the JULES-crop SoyFACE runs
The meteorological forcing data measured at Champaign, Illinois, in 2009
were used to drive the JULES-crop model. The
downward longwave radiation and diffuse radiation data from NOAA at
Bondville site (SURFRAD) were used as SoyFACE does not have these variables
available. The driving data were repeatedly applied (recycled 25 times) to
spin up the model from an arbitrary starting point with soil temperature
initially set to 278 K and soil moisture to 75 % of saturation. A single
crop type was modelled – soybean – using a single plant tile. Observed
CO2 (NOAA) and 8 h mean observed O3 concentrations from the
SoyFACE rings (averaged over a month) were used as the driving data of the
model since natural O3 is produced around 8 h in daytime, and it is
a typical temporal resolution for O3 fumigation. The soil ancillary
parameters used in SoyFACE were extracted from the global dataset of soil
ancillary from the HadGEM2-ES model (a coupled Earth system model that was
used by the Met Office Hadley Centre for the CMIP5). Observed ambient
O3 was used as the control. The new parameters for soybean were used,
which we calibrated to observations from the Mead FLUXNET sites as described
in the Supplement. The exception is the initial carbon: since
the row spacing at the SoyFACE facility is half that used at the Mead
sites, we doubled the initial carbon for SoyFACE compared to Mead. The
resulting model yield, above ground carbon, and LAI were compared to the
SoyFACE observations.
Results and discussion
Results from JULES runs with crop model and ozone damage turned on are
shown in Figs. 3 and 4. Figure 3 shows the evaluation of the soybean
aboveground biomass carbon for different O3 exposure levels (AOT40)
using the O3 damage parameters in Table 4. The model aboveground carbon
(solid lines) is compared to the line fitted in Betzelberger et al. (2012) to
their aboveground carbon observations. The run with the newly-calibrated
parameters overestimated the carbon at ambient ozone levels. One
contributing factor could be that water stress is underestimated in the new
configuration, since it was not possible to evaluate the response to soil
water availability using the Mead site data, so we instead derived a value
for fsmc_p0 (parameterised in the calculation of the threshold
for water stress; see Table 3) from the literature. We tested the sensitivity to
this choice by re-running this configuration with fsmc_p0=0,
which represents water-stressed conditions, and this caused a 12 %
reduction in aboveground carbon (plots shown in Supplement). In addition,
the representation of the soil properties in the JULES SoyFACE run could be
improved by calibration to site measurements. In contrast, the Osborne
2015 tuning intersects the line fitted to observed aboveground carbon at
zero ozone concentration (partially because of higher water stress) but
then shows a sharp decrease from zero to ambient levels, which is not
realistic. Note that no observations were taken for below-ambient ozone
concentrations at SoyFACE, so this section of the fitted line is an
extrapolation. The slope of the aboveground carbon response to increasing
ozone concentrations is similar for all three runs and compares very well
to the Betzelberger et al. (2012) fitted line.
Aboveground carbon biomass of soybean at harvest stage for
calibrated Joint UK Land Environment Simulator with Crop module turned on
(JULES-crop) using the Mead soybean tuning (red), Osborne et al. (2015)
standard parameters with Sitch et al. (2007) low ozone sensitivity (blue),
high ozone sensitivity (green), and observation from SoyFACE from
Betzelberger et al. (2012).
The yield-O3 response curve in Fig. 4 shows that new parameterisation
slightly overestimates yield in the ambient SoyFACE ring, compared to the
spread of SoyFACE yield observations from Betzelberger et al. (2012). The
Osborne 2015 tuning with high ozone sensitivity is within the spread of
measured yield in ambient conditions, but note that the modelled yield has
decreased sharply from zero ozone concentration to ambient levels, which is
undesirable. The magnitude of the gradient of yield against AOT40 for all
three model configurations is within the spread of the observations.
However, the slope is underestimated for the new, calibrated run and
overestimated for the Osborne 2015 tuning, especially for the range from
ambient to 40 ppm h. Recall that ozone concentration modifies net leaf
CO2 assimilation rate in JULES and that the model parameters governing
this process (Fo3crit, a) are calibrated directly to net leaf CO2 assimilation
rate observations from SoyFACE in our new configuration (Sect. 2).
Reductions in the modelled net leaf CO2 assimilation rate lead to the
reductions in model aboveground biomass, yield, and LAI, which we show in this
section. However, Betzelberger et al. (2012) also reported additional impacts
of ozone damage, such as changes in leaf absorptance and specific leaf mass,
that are not represented in JULES, and therefore our tuning does not account
for them. In contrast, the values of Fo3crit and a in the high and low sensitivity
versions of the Osborne 2015 tuning simulations (Table 4) were calibrated
in Sitch et al. (2007) to yield observations. Therefore, they can be seen as
“effective” parameters in these configurations, since they incorporate the
effect of the ozone damage processes that are not explicitly represented in
JULES.
Black dashed line is the line of best fit from SoyFACE observation,
and the blue and green lines with crosses are the modelled output for each
ozone concentration using the Osborne et al. (2015) tuning with Sitch et al. (2007) low and high sensitivity, respectively. The red line and crosses are
the tuned parameters with Mead FLUXNET observation and SoyFACE ozone damage
according to Table 4.
Note that we plot AOT40 on the x axis for illustrative purposes only, to be
comparable with results presented in Betzelberger et al. (2012) – AOT40 was not
used in the JULES run. An alternative would be to plot ring number or ring
target concentration. Ideally, we would plot the x axis with the metric
phytotoxic ozone dose (POD) for JULES and observed data, which account for the
dosage of O3 that get into the stomata of soybean, but this is beyond the
scope of the present study.
Time series of leaf area index (LAI) responses on different target
ozone concentration at SoyFACE. Black line is observed LAI from Betzelberger
et al. (2012), and the other lines are JULES-crop LAI with different
tunings. Blue: calibrated JULES-crop using Mead observations. Green: Osborne
2015 tuning with low sensitivity. Red: Osborne 2015 tuning with high
sensitivity to ozone.
Figure 5 compares the model and observed LAI at SoyFACE for different
O3 concentrations. JULES was able to reproduce LAI seasonality;
however, it underestimated the amplitude. The maximum LAI for calibrated
JULES peaked around day 240 in September, and observations peaked at DoY
220–230. The peak LAI in the model runs was less than half
the observed LAI in all cases. While the Mead model runs also showed a
slight underestimation of peak LAI compared to observation (Supplement), the majority of the underestimation of the modelled SoyFACE LAI
is due to a difference between the observed relationships between peak LAI
and yield at the Mead and SoyFACE sites. At both sites, observed maximum
yield increases with observed peak LAI. However, for similar observed
yields, the observed SoyFACE yield tends to be higher than the observed Mead
LAI. Given that our calibration is based on Mead observations, it is
therefore not surprising that our model runs at SoyFACE underestimate peak
LAI compared to the SoyFACE observations.
A contributing factor to the different relationship between observed peak
LAI and observed yield at SoyFACE compared to Mead could be the different
methods used to measure LAI at the Mead sites (which this parameter set was
tuned against) and at SoyFACE. At Mead, destructive measurements were taken,
whereas at SoyFACE, LAI was measured indirectly, using radiation attenuation
through the canopy.
Another plausible contributing factor for the different relationship between
observed peak LAI and observed yield at SoyFACE compared to Mead is the row
density of the soybean. The SoyFACE row spacing was half that of Mead, so as
described above we set the initial carbon to twice that observed at Mead.
The denser planting allowed soybean at SoyFACE to reach higher LAI earlier
in the growing season. If this also resulted in thinner leaves at the
beginning of the season than with the Mead row spacing, then this could
explain the difference in the peak LAI to yield relationship between the two
sites. Ricaurte et al. (2016) showed that higher sowing density would
increase phyllochron in a linear relationship, which results in a higher LAI
measured, which is consistent with our study. JULES also does not account for
leaf age on leaf assimilation rate – in reality a lower leaf assimilation is
observed in the late season associated with leaf ageing, and it is plausible
that this could also be affected by row spacing.
Figure 5 also demonstrates that model LAI responds more to ozone
concentrations than the observed LAI. One contributing factor is the
observed decrease in specific leaf area at SoyFACE in increased ozone
(Betzelberger et al., 2012). As mentioned above, this process is not captured
by JULES. This issue is particularly pronounced in the Osborne 2015 tuning
runs, where the modelled LAI in the ring with target 200 ppb is roughly a
third of the peak LAI in the ambient ring.
Conclusions
Climate change and air pollution are a great threat to food production.
JULES-crop has been developed to represent crops in the land surface model
and allows us to estimate the future climate and air pollution impact on
crops. The O3 impact on crops could be quantified with an improved
parameterisation to the existing O3 damage scheme for C3 plants. The
default soybean biochemical and respiratory parameters in JULES were based
on C3 grass parameters. Characteristics of soybean are more similar to a
shrub than grass; therefore, parameter calibration is needed to improve the
performance of soybean in JULES-crop.
In this paper, the parameters needed to describe soybean in JULES-crop were
first revised against observations from the Mead FLUXNET sites to ensure
that the crop biochemical and respiratory parameters explicitly represented
soybean. Comparison with observations from these sites showed that GPP and LAI
were well represented for irrigated soybean at Mead. The O3 damage
parameterisation was subsequently calibrated against leaf gas exchange
observations from the Soybean Free Air Concentration Enrichment (SoyFACE)
facility for the O3 damage, by tuning the sensitivity and critical
threshold of O3 damage. On the whole, JULES-crop reproduces the
observed negative correlation between yield and O3 exposure. It also
reproduced the negative impacts of ozone on LAI and the seasonality of
phenology, although the simulated LAI was underestimated at SoyFACE. This
method of calibrating soybean could be replicated for other crops once data
become available and would contribute to more accurate parameters for crop
models. The calibration will be applied to a regional and transient run and
eventually the newly calibrated JULES-crop for soybean and its sensitivity
to O3 damage, coupled within an Earth system model.
Code availability
This study uses JULES version 5.0 releases. The code and configuration for
the SoyFACE runs can be downloaded via the Met Office Science Repository
Service (MOSRS) at https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk
(JULES Collaboration, 2018) (registration required) and are
freely available subject to accepting the terms of the software licence. The Leaf
Simulator can be downloaded from https://code.metoffice.gov.uk/trac/utils (Williams et al.,
2018) (login required).
Data availability
Unless otherwise noted, all site observations discussed in this paper were
obtained from the site information pages of the AmeriFlux website hosted by the
Oak Ridge National Laboratory (http://fluxnet.fluxdata.org/, AmeriFlux collaboration, 2018)
or by personal communication with the Mead site research technologist. The
longwave radiation, diffuse radiation, and air pressure from Bondville,
Illinois, site can be obtained by the SURFRAD (surface radiation) network from
ftp://aftp.cmdl.noaa.gov/data/radiation/surfrad/Bondville_IL/ (NOAA, 2018). The SoyFACE data used for the run are available on MOSRS at
https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/driving_data (Ainsoworth, 2017a),
https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/bin/SoyFACE_gas_exchange_data_2009.csv (Ainsoworth, 2017b), and
https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/ancil_data (Ainsoworth, 2017c).
Accessing the MOSRS requires registration, but once you access the
system, there is no information about who is downloading or viewing which
pages.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-6201-2020-supplement.
Author contributions
FL led the study design, data analysis and writing. KW contributed substantially to the Mead calibration, data analysis, study design and writing. SS contributed to the writing and study design. APKT contributed to the writing. AW and JG contributed to the study design. EAA contributed the SoyFACE data. TA and DS contributed the Mead data.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Felix Leung gratefully acknowledges financial support from the
NERC CASE Studentship with Met Office (NE/J017337/1), “Impact of tropospheric O3
on crop production under future climate and atmospheric CO2
concentrations, and their interactions within the Earth System”. Karina
Williams gratefully acknowledges financial support from the European
Commission under grant agreements 308291 (EUPORIAS) and 603864 (HELIX). We
acknowledge the following AmeriFlux sites for their data records: US-Ne1,
US-Ne1, and US-Ne3. In addition, funding for AmeriFlux data resources and core
site data was provided by the US Department of Energy's Office of Science.
I would also like to acknowledge Gerd Folberth and Eddy Robertson for
helping me with the technical part of JULES.
Financial support
This research has been supported by the NERC CASE Studentship with Met Office (grant no. NE/J017337/1) and the European
Commission (EUPORIAS (grant no. 308291) and HELIX (grant no. 603864)).
Review statement
This paper was edited by Jatin Kala and reviewed by two anonymous referees.
ReferencesAgathokleous, E., Vanderstock, A., Kita, K., and Koike, T.: Stem and crown
growth of Japanese larch and its hybrid F1 grown in two soils and exposed to
two free-air O3 regimes, Environ. Sci. Pollut. Res., 24, 6634–6647,
10.1007/s11356-017-8401-2, 2017.Ainsoworth, E. A.: Meteorology of SoyFACE site, the SoyFACE data used for the run are available on MOSRS at https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/driving_data, last access: 5 September 2017a.Ainsworth, E. A.: Gas exchange data of SoyFACE, available at: https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/bin/SoyFACE_gas_exchange_data_2009.csv, last access: 5 September 2017b.Ainsworth, E. A: Soil data of SoyFACE, available at: https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk/ancil_data, last access: 5 September 2017c.Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J., and Emberson, L.
D.: The effects of tropospheric ozone on net primary productivity and
implications for climate change, Annu. Rev. Plant Biol., 63, 637–661,
10.1146/annurev-arplant-042110-103829, 2012.Allen, R. G. and Pereira, L. S.: Crop Evapotranspiration, guidelines for
computing crop water requirements, available at:
https://www.kimberly.uidaho.edu/water/fao56/fao56.pdf (last access: 28 September 2018), 2006.AmeriFlux collaboration: AmeriFlux Site Information, available at: http://fluxnet.fluxdata.org/ (last access: 11 November 2018), 2018.Ashmore, M. R.: Assessing the future global impacts of ozone on vegetation,
Plant Cell Environ., 28, 949–964, 10.1111/j.1365-3040.2005.01341.x,
2005.Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield
reductions due to surface ozone exposure: 1. Year 2000 crop production
losses and economic damage, Atmos. Environ., 45, 2284–2296,
10.1016/j.atmosenv.2010.11.045, 2011a.Avnery, S., Mauzerall, D. L., Liu, J., and Horowitz, L. W.: Global crop yield
reductions due to surface ozone exposure: 2. Year 2030 potential crop
production losses and economic damage under two scenarios of O3 pollution,
Atmos. Environ., 45, 2297–2309, 10.1016/j.atmosenv.2011.01.002,
2011b.Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, 10.5194/gmd-4-677-2011, 2011.Betzelberger, A. M., Gillespie, K. M., Mcgrath, J. M., Koester, R. P., Nelson, R. L., and Ainsworth, E. A.: Effects of chronic elevated ozone concentration on antioxidant capacity, photosynthesis and seed yield of 10 soybean cultivars, Plant. Cell Environ., 33, 1569–1581, 10.1111/j.1365-3040.2010.02165.x, 2010.Betzelberger, A. M., Yendrek, C. R., Sun, J., Leisner, C. P., Nelson, R. L.,
Ort, D. R., and Ainsworth, E. A.: Ozone exposure response for US soybean
cultivars: linear reductions in photosynthetic potential, biomass, and
yield, Plant Physiol., 160, 1827–39, 10.1104/pp.112.205591, 2012.Büker, P., Morrissey, T., Briolat, A., Falk, R., Simpson, D., Tuovinen, J.-P., Alonso, R., Barth, S., Baumgarten, M., Grulke, N., Karlsson, P. E., King, J., Lagergren, F., Matyssek, R., Nunn, A., Ogaya, R., Peñuelas, J., Rhea, L., Schaub, M., Uddling, J., Werner, W., and Emberson, L. D.: DO3SE modelling of soil moisture to determine ozone flux to forest trees, Atmos. Chem. Phys., 12, 5537–5562, 10.5194/acp-12-5537-2012, 2012.Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, 10.5194/gmd-4-701-2011, 2011.Dentener, F., Keating, T., and Akimoto, H.: Hemispheric Transport of 2010
Part A: Ozone and Particulate Matter, Air Pollut. Stud., available at:
https://www.unece.org/fileadmin/DAM/env/lrtap/Publications/11-22136-Part-D_01.pdf (last access: 18 March 2013), 2010.Dermody, O., Long, S. P., McConnaughay, K., and
DeLucia, E. H.: How do
elevated CO2 and O3 affect the interception and utilization of radiation
by a soybean canopy?, Glob. Change Biol., 14, 556–564,
10.1111/j.1365-2486.2007.01502.x, 2008.Elagöz, V. and Manning, W. J.: Responses of sensitive and tolerant bush
beans (Phaseolus vulgaris L.) to ozone in open-top chambers are influenced
by phenotypic differences, morphological characteristics, and the chamber
environment, Environ. Pollut., 136, 371–383,
10.1016/j.envpol.2005.01.021, 2005.Emberson, L. D., Büker, P., and Ashmore, M. R.: Assessing the risk caused
by ground level ozone to European forest trees: A case study in pine, beech
and oak across different climate regions, Environ. Pollut., 147,
454–466, 10.1016/j.envpol.2006.10.026, 2007.Fuhrer, J.: Ozone risk for crops and pastures in present and future
climates., Naturwissenschaften, 96, 173–94,
10.1007/s00114-008-0468-7, 2009.
Fuhrer, J., Skärby, L., and Ashmore, M. R.: Critical levels for ozone
effects on vegetation in Europe, Environ. Pollut., 97, 91–106, 1997.Gerosa, G., Finco, A., Marzuoli, R., Ferretti, M., and Gottardini, E.: Errors
in ozone risk assessment using standard conditions for converting ozone
concentrations obtained by passive samplers in mountain regions, J. Environ.
Monit., 14, 1703, 10.1039/c2em10965d, 2012.Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res.-Atmos., 113, D06104, 10.1029/2007JD008972,
2008.ICP Vegetation: Mapping Critical Levels for Vegetation, Chapter III, Manual
on Methodologies and Criteria for Modelling and Mapping Critical Loads and
Levels and Air Pollution Effects, Risks and Trends, Conv. Long-range
Transbound, Air Pollut., Umweltbundesamt, Suhl, Germany, 66 pp., available at: https://www.umweltbundesamt.de/sites/default/files/medien/4292/dokumente/ch3-mapman-2017-10.pdf (last access: 10 September 2017), 2017.JULES Collaboration: ULES collaboration: JULES land-surface model,
available at: https://code.metoffice.gov.uk/trac/roses-u/browser/a/r/8/6/6/trunk (last access:
11 November 2019), 2018.Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation
CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199,
10.1002/grl.50256, 2013.Leung, F., Pang, J. Y. S., Tai, A. P. K., Lam, T., Tao, D. K. C., and Sharps,
K.: Evidence of Ozone-Induced Visible Foliar Injury in Hong Kong Using
Phaseolus Vulgaris as a Bioindicator, Atmosphere-Basel, 11, 266,
10.3390/atmos11030266, 2020.Long, S. P., Ainsworth, E. A., Leakey, A. D. B., and Morgan, P. B.: Global food
insecurity, treatment of major food crops with elevated carbon dioxide or
ozone under large-scale fully open-air conditions suggests recent models may
have overestimated future yields, Philos. T. Roy. Soc. B, 360, 2011–20, 10.1098/rstb.2005.1749, 2005.Mills, G., Buse, A., Gimeno, B., Bermejo, V., Holland, M., Emberson, L.,
and Pleijel, H.: A synthesis of AOT40-based response functions and critical
levels of ozone for agricultural and horticultural crops, Atmos. Environ.,
41, 2630–2643, 10.1016/j.atmosenv.2006.11.016, 2007.Morgan, P. B., Ainsworth, E. A., and Long, S. P.: How does elevated ozone
impact soybean? A meta-analysis of photosynthesis, growth and yield, Plant
Cell Environ., 26, 1317–1328, 10.1046/j.0016-8025.2003.01056.x,
2003.Morgan, P. B., Bernacchi, C. J., Ort, D. R., and Long, S. P.: An in vivo analysis of the effect of season-long open-air elevation of ozone to anticipated 2050 levels on photosynthesis in soybean, Plant Physiol., 135, 2348–2357, 10.1104/pp.104.043968, 2004.NOAA: Surface Radiation of Bondville (SURFRAD), available at: ftp://aftp.cmdl.noaa.gov/data/radiation/surfrad/Bondville_IL/, last access: 11 November 2018.Oliver, R. J., Mercado, L. M., Sitch, S., Simpson, D., Medlyn, B. E., Lin, Y.-S., and Folberth, G. A.: Large but decreasing effect of ozone on the European carbon sink, Biogeosciences, 15, 4245–4269, 10.5194/bg-15-4245-2018, 2018.
Osborne, T. and Hooker, J.: JULES-crop technical documentation Crop
parameterisation, University of Reading, Reading, Berkshire, UK, 1–49, 2011.Osborne, T., Gornall, J., Hooker, J., Williams, K., Wiltshire, A., Betts, R., and Wheeler, T.: JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator, Geosci. Model Dev., 8, 1139–1155, 10.5194/gmd-8-1139-2015, 2015.Paoletti, E., Materassi, A., Fasano, G., Hoshika, Y., Carriero, G., Silaghi,
D., and Badea, O.: A new-generation 3D ozone FACE (Free Air Controlled
Exposure), Sci. Total Environ., 575, 1407–1414,
10.1016/j.scitotenv.2016.09.217, 2017.Pleijel, H., Danielsson, H., Emberson, L., Ashmore, M. R., and Mills, G.:
Ozone risk assessment for agricultural crops in Europe: Further development
of stomatal flux and flux-response relationships for European wheat and
potato, Atmos. Environ., 41, 3022–3040,
10.1016/j.atmosenv.2006.12.002, 2007.Ray, D. K., Mueller, N. D., West, P. C., Foley, J. A.:
Yield Trends Are Insufficient to Double Global Crop Production
by 2050, PLoS One, 8, e66428,
10.1371/journal.pone.0066428, 2013.
Ricaurte, J., Clavijo Michelangeli, J. A., Sinclair, T. R., Rao, I. M. andBeebe, S. E.: Sowing Density Effect on Common Bean Leaf Area Development, Crop Sci., 56, 2713–2721, 10.2135/cropsci2016.01.0056, 2016.
Sitch, S.: Carbon sinks threatened by increasing ozone, Nat. Publ. Gr.,
7, 2335–2340, 2007.Sitch, S., Cox, P. M., Collins, W. J., and Huntingford, C.: Indirect radiative
forcing of climate change through ozone effects on the land-carbon sink,
Nature, 448, 791–794, 10.1038/nature06059, 2007.Tai, A. P. K. and Martin, M. V.: Impacts of ozone air pollution and
temperature extremes on crop yields: Spatial variability, adaptation and
implications for future food security, Atmos. Environ., 169, 11–21,
10.1016/J.ATMOSENV.2017.09.002, 2017.Tai, A. P. K., Martin, M. V., and Heald, C. L.: Threat to future global food
security from climate change and ozone air pollution, Nat. Clim. Change,
4, 817–821, 10.1038/NCLIMATE2317, 2014.Van den Hoof, C., Hanert, E., and Vidale, P. L.: Simulating dynamic crop growth
with an adapted land surface model – JULES-SUCROS: Model development and
validation, Agric. For. Meteorol., 151, 137–153,
10.1016/j.agrformet.2010.09.011, 2011.Van Dingenen, R., Dentener, F. J., Raes, F., Krol, M. C., Emberson, L.,
and Cofala, J.: The global impact of ozone on agricultural crop yields under
current and future air quality legislation, Atmos. Environ., 43,
604–618, 10.1016/j.atmosenv.2008.10.033, 2009.Williams, K., Gornall, J., Harper, A., Wiltshire, A., Hemming, D., Quaife, T., Arkebauer, T., and Scoby, D.: Evaluation of JULES-crop performance against site observations of irrigated maize from Mead, Nebraska, Geosci. Model Dev., 10, 1291–1320, 10.5194/gmd-10-1291-2017, 2017.Williams, K., Hemming, D., Harper, A. B., and Mercado, L. M.: Leaf simulator, available at: https://code.metoffice.gov.uk/trac/utils (last access: 5 November 2019),
2018.Zhu, X., Feng, Z., Sun, T., Liu, X., Tang, H., Zhu, J., Guo, W., and Kobayashi, K.: Effects of elevated ozone concentration on yield of four Chinese cultivars of winter wheat under fully open-air field conditions, Glob. Chang. Biol., 17, 2697–2706, 10.1111/j.1365-2486.2011.02400.x, 2011.