ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-3055-2017Effects of ozone–vegetation coupling on surface ozone air quality via
biogeochemical and meteorological feedbacksSadiqMehliyarTaiAmos P. K.amostai@cuhk.edu.hkhttps://orcid.org/0000-0001-5189-6263LombardozziDanicaVal MartinMariaGraduate Division of Earth and Atmospheric Sciences, Faculty of
Science, The Chinese University of Hong Kong, Hong Kong SAR, ChinaEarth System Science Programme, Faculty of Science, The Chinese
University of Hong Kong, Hong Kong SAR, ChinaClimate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USADepartment of Chemical and Biological Engineering, University of
Sheffield, Sheffield, UKAmos P. K. Tai (amostai@cuhk.edu.hk)28February20171743055306618July20165September20161February20172February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/3055/2017/acp-17-3055-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/3055/2017/acp-17-3055-2017.pdf
Tropospheric ozone is one of the most hazardous air pollutants as
it harms both human health and plant productivity. Foliage uptake of ozone
via dry deposition damages photosynthesis and causes stomatal closure. These
foliage changes could lead to a cascade of biogeochemical and biogeophysical
effects that not only modulate the carbon cycle, regional hydrometeorology
and climate, but also cause feedbacks onto surface ozone concentration
itself. In this study, we implement a semi-empirical parameterization of
ozone damage on vegetation in the Community Earth System Model to enable
online ozone–vegetation coupling, so that for the first time ecosystem
structure and ozone concentration can coevolve in fully coupled
land–atmosphere simulations. With ozone–vegetation coupling, present-day
surface ozone is simulated to be higher by up to 4–6 ppbv over Europe, North
America and China. Reduced dry deposition velocity following ozone damage
contributes to ∼ 40–100 % of those increases, constituting a
significant positive biogeochemical feedback on ozone air quality. Enhanced
biogenic isoprene emission is found to contribute to most of the remaining
increases, and is driven mainly by higher vegetation temperature that
results from lower transpiration rate. This isoprene-driven pathway
represents an indirect, positive meteorological feedback. The reduction in
both dry deposition and transpiration is mostly associated with reduced
stomatal conductance following ozone damage, whereas the modification of
photosynthesis and further changes in ecosystem productivity are found to
play a smaller role in contributing to the ozone–vegetation feedbacks. Our
results highlight the need to consider two-way ozone–vegetation coupling in
Earth system models to derive a more complete understanding and yield more
reliable future predictions of ozone air quality.
Introduction
Tropospheric ozone is one of the air pollutants of the greatest concern due
to its significant harm to human respiratory health. Increases of ozone
since preindustrial times have been associated with a global annual
burden of 0.7 ± 0.3 million respiratory mortalities (Anenberg et al.,
2010). Decades of observational records have also demonstrated the damaging
effect of surface ozone on vegetation and crop productivity (Ainsworth et
al., 2012). The phytotoxicity of ozone is shown to induce stomatal closure
and reduce primary production, with ramifications for climate through the
modification of surface energy and water fluxes and a decrease in the land
carbon sink (Sitch et al., 2007; Wittig et al., 2007; Lombardozzi et al.,
2015). Meanwhile, vegetation helps reduce ambient ozone concentration
through stomatal deposition (e.g., Kroeger et al., 2014). However, the
effect of such ozone-induced vegetation damage on ozone concentration
itself, which thereby completes the ozone–vegetation feedback loop, has not
been examined before but is potentially significant in modulating
tropospheric ozone. This work uses a fully coupled land–atmosphere model to,
for the first time, quantify the impacts of ozone–vegetation coupling on
surface ozone, and diagnoses the contributions from various feedback
pathways in terrestrial ecosystems.
Tropospheric ozone is mainly produced from the photochemical oxidation of
carbon monoxide (CO), methane (CH4) and non-methane volatile organic
compounds (VOCs) by hydroxyl radical (OH) in the presence of nitrogen oxides
(NOx≡ NO + NO2). Vegetation plays various significant
roles modulating surface ozone concentration. Precursor gases of ozone have
large anthropogenic and natural sources, including vegetation and soil
microbes for CH4 and other VOCs. The most abundant single non-methane
VOC species emitted by vegetation is isoprene (C5H8), which acts
as a major precursor for ozone formation in polluted, high-NOx regions,
but eliminates ozone by direct ozonolysis or by sequestering NOx as
isoprene nitrate in more pristine environments (Fiore et al., 2011). The
major sinks for tropospheric ozone include photolysis in the presence of
water vapor and uptake by vegetation (i.e., dry deposition, mainly through
the leaf stomata). Vegetation, therefore, plays a significant role in
modulating ozone biogeochemically through dry deposition and biogenic VOC
emissions. Meanwhile, transpiration from vegetation can affect ozone by
regulating the overlying hydrometeorological environment. For instance,
transpiration influences near-surface water vapor content, which affects the
chemical loss rate of ozone. Transpiration also controls surface temperature
and mixing depth, which can all influence the formation and dilution of
ozone in the atmospheric boundary layer (Jacob and Winner, 2009).
Vegetation not only affects but is also affected by surface ozone. Stomatal
uptake of ozone by leaves damages internal plant tissues, leading to severe
damage to forest, grassland and agricultural productivity (Ashmore, 2005;
Karnosky et al., 2007; Ainsworth et al., 2012). Elevated ozone since the
industrial revolution is suggested to have reduced light-saturated
photosynthetic rate and stomatal conductance by 11 and 13 %,
respectively (Wittig et al., 2007). Modeling studies have also suggested
that elevated ozone could decrease gross primary production (GPP) by 4–8 %
in the eastern USA and more severely so (11–17 %) in several hot spots
there (Yue and Unger, 2014), and decrease transpiration rate globally by
2–2.4 % (Lombardozzi et al., 2015), with significant implications for
climate. For instance, the ozone-induced reduction in the global land carbon
sink by 2100 is shown to have an indirect radiative forcing of +0.62–1.09 W m-2,
which is comparable to the direct radiative forcing of ozone as
a greenhouse gas (0.89 W m-2) and contributes to more pronounced
warming (Sitch et al., 2007). Changes in stomatal conductance also modify
the land–atmosphere exchange of water and energy and thus regional
hydrometeorology (Bernacchi et al., 2011; Lombardozzi et al., 2015). In view
of the important roles vegetation plays in shaping tropospheric ozone, the
above biogeochemical and biogeophysical effects induced by ozone damage
would affect not only weather and climate but would also constitute important
feedbacks that ultimately affect ozone air quality itself.
In many land surface models, photosynthetic rate and stomatal conductance
are highly coupled through the computation within the Farquhar–Ball-Berry
model (Farquhar et al., 1980; Ball et al., 1987; Bonan et al., 2011). In
global modeling studies on ozone-mediated vegetation changes and climate
(Sitch et al., 2007; Collins et al., 2010; Yue and Unger, 2014), the effects
of ozone damage on photosynthesis and stomata are thus strongly coupled to
each other. Ozone uptake is assumed to directly affect photosynthetic rate,
which in turn affects stomatal conductance via changes in internal CO2
concentration. However, recent studies have suggested that separate
modification of photosynthetic rate and stomatal conductance by cumulative
ozone uptake in the Community Land Model (CLM) leads to better
representation of plant responses to ozone exposure (Lombardozzi et al.,
2012). This decoupling of ozone effects on photosynthesis and stomata is
shown to decrease water use efficiency of affected plants, but leads to an
overall smaller impact of ozone on transpiration and GPP than previously
predicted.
Many climate–chemistry–biosphere modeling studies performed to date have
demonstrated the importance of the coevolution of climate, land cover and
terrestrial ecosystems in air quality simulations and predictions (Wu et
al., 2012; Tai et al., 2013; Pacifico et al., 2015), but they have not taken
into account the potentially strong feedbacks arising from ozone damage on
vegetation. For instance, ozone exposure can reduce stomatal conductance and
thus transpiration rate, which may modify the partition between latent and
sensible heat fluxes and lead to a cascade of meteorological changes: lower
humidity that reduces the chemical loss rate of ozone; a thicker boundary
layer that dilutes all pollutants, but may enhance entrainment, which either
increases or decreases surface ozone depending on the vertical ozone profile
(Super et al., 2015); and higher temperature that enhances ozone mainly
through increased biogenic emissions and higher abundance of NOx (Jacob
and Winner, 2009). These transpiration-mediated pathways can be
characterized as biogeophysical feedbacks, as they are commonly known in the
context of climate change, but here we prefer to call them
hydrometeorological or simply “meteorological feedbacks” to emphasize that
they are effected through ozone-induced changes in the hydrometeorological
variables that ultimately affect ozone. On the other hand, reduced dry
deposition caused by lower stomatal conductance and a possible decline in
leaf area index (LAI) following ozone exposure can potentially increase
ozone. The short-term impact of ozone on foliage-level isoprene emission is
still under debate (Fares et al., 2006; Calfapietra et al., 2007), but as
foliage density (e.g., represented by LAI) declines due to chronic ozone
exposure (Yue and Unger, 2014), isoprene emission would likely decrease in the
long term. These pathways directly involving plant biogeochemistry and
atmospheric chemistry can be collectively termed “biogeochemical
feedbacks”. Figure 1 summarizes the potentially important biogeochemical and
meteorological feedbacks on surface ozone concentration, which are expected
to have ramifications for simulations and future projections of ozone air
quality. Such feedbacks may further alter atmospheric composition (e.g.,
aerosol and oxidant concentrations) and climate at large but remain poorly
characterized in an Earth system modeling framework.
Possible pathways of ozone–vegetation coupling and feedbacks. The
sign on each arrow indicates the sign of correlation or effect of one
variable with or on another variable; the product of all signs along a given
pathway indicates the overall sign of feedback. Orange arrows indicate
biogeochemical feedbacks (i.e., via modulating atmospheric chemistry
directly); purple arrows indicate meteorological feedbacks (i.e., via
modifying the hydrometeorological environment). We focus only on processes
that directly affect ozone; meteorological feedbacks on photosynthesis and
stomatal conductance are included in the model but not emphasized in this
figure.
In this study, we adopt and implement a semi-empirical scheme for
ozone-induced vegetation damage (Lombardozzi et al., 2015) into a coupled
land–atmosphere model with fully interactive atmospheric chemistry and
biogeochemical cycles, and examine the resulting impacts on present-day
simulations of tropospheric ozone air quality with respect to observations.
We perform sensitivity simulations to quantify the relative importance of
different biogeochemical and meteorological feedback pathways, elucidate the
larger sources of uncertainties, and make specific suggestions regarding
Earth system model development.
MethodsModel description
This study investigates the impacts of ozone–vegetation coupling on ozone
concentrations using the Community Earth System Model (CESM), which includes
several different model components representing the atmosphere, land, ocean,
and sea ice to be run independently or in various coupled configurations
(Oleson et al., 2010; Lamarque et al., 2012; Neale et al., 2013). We employ
CESM version 1.2.2 with fully interactive atmosphere and land components,
but with prescribed ocean and sea ice consistent with the scenarios of
concern. For the atmosphere component, we use the Community Atmosphere Model
version 4 (CAM4) (Neale et al., 2013) fully coupled with an atmospheric
chemistry scheme (i.e., CAM-Chem) that contains full tropospheric
O3–NOx–CO–VOC–aerosol chemistry based on the MOZART-4 chemical
transport model (CTM) (Emmons et al., 2010; Lamarque et al., 2012). This
version of CAM-Chem simulates the concentrations of 56 atmospheric chemical
species at a horizontal resolution of 1.9∘× 2.5∘
latitude–longitude and 26 vertical layers for the atmosphere
up to around 40 km.
For the land component, we use the Community Land Model version 4 (CLM4)
(Oleson et al., 2010) with active carbon–nitrogen biogeochemistry (CLM4CN),
which contains prognostic treatment of terrestrial carbon and nitrogen
cycles (Lawrence et al., 2011). In CLM4, the Model of Emissions of Gases and
Aerosols from Nature (MEGAN) version 2.1 is used to compute biogenic
emissions online as functions of changing LAI, vegetation temperature, soil
moisture and other environmental conditions (Guenther et al., 2012). For dry
deposition of gases and aerosols we use the resistance-in-series scheme in
CLM4 as described in Lamarque et al. (2012) with a further update of
optimized coupling of stomatal resistance to LAI (Val Martin et al., 2014).
Evapotranspiration is calculated based on the Monin–Obukhov similarity
theory and the diffusive flux-resistance model with dependence on
vegetation, ground and surface temperature, specific humidity, and an
ensemble of resistances that are functions of meteorological and land
surface conditions (Oleson et al., 2010; Lawrence et al., 2011; Bonan et
al., 2011). Evapotranspiration is partitioned into transpiration, ground
evaporation and canopy evaporation, with updates from Lawrence et al. (2011),
and is linked to photosynthesis via the computation of stomatal
resistance, as described below.
Photosynthesis–stomatal conductance model and ozone damage
parameterization
The Farquhar–Ball-Berry model is used in CLM4CN to compute leaf-level
photosynthetic rate and stomatal conductance under different environmental
conditions (Farquhar et al., 1980; Ball et al., 1987). Leaf photosynthetic
rate, A (µmol CO2 m-2 s-1),
is calculated as
A=min(Wc,Wj,We),
where Wc is the Ribulose-1,5-bisphosphate carboxylase
(RuBisCO)-limited rate of carboxylation, Wj is the
light-limited rate, and We is the export-limited rate.
Photosynthesis and stomatal conductance (gs) are related by
gs=1rs=mAcseseiPatm+b,
where gs is the leaf stomatal conductance; rs is
the leaf stomatal resistance (s m2µmol-1); m is
the slope of the conductance–photosynthesis relationship with values ranging
from 5 to 9; cs is the CO2 partial pressure at leaf
surface (Pa); es is the vapor pressure at leaf surface (Pa);
ei is the saturation vapor pressure inside the leaf (Pa);
Patm is the atmospheric pressure (Pa); and b is the
minimum stomatal conductance when A= 0, and is set to give a
maximum stomatal resistance of 20 000 s m-1 in CLM4 (Oleson et al.,
2010).
Parameterization for the impact of ozone exposure on photosynthesis and
stomatal conductance follows the work of Lombardozzi et al. (2015), who
tested the sensitivity of global ecosystem productivity and hydrometeorology
to ozone damage on vegetation using satellite phenology (i.e., prescribed
LAI, canopy height, etc.) and present-day ozone concentrations. The scheme
uses two sets of ozone impact factors, one for modifying photosynthetic rate
and another for stomatal conductance independently. These factors account
for different plant groups, and are calculated based on the cumulative
uptake of ozone (CUO) under different levels of chronic ozone exposure
(Lombardozzi et al., 2013). CUO (mmol m-2) integrates ozone flux into
leaves over the growing season as
CUO=10-6∑[O3]kO3rs+raΔt,
where [O3] is the instantaneous surface ozone concentration (nmol m-3)
computed from CAM-Chem at a given model time step Δt (Δt= 1800 s here); kO3=1.67
is the ratio of leaf resistance to ozone to leaf resistance to water,
rs is the stomatal resistance (s m-1), and
ra is the boundary layer and aerodynamic resistance between
leaf surface and reference level (s m-1) (Sitch et al., 2007). Ozone
uptake is only cumulated over time steps during the growing season when
vegetation is most vulnerable to air pollution episodes; growing season is
defined as the period in which total leaf area index (TLAI) > 0.5
(Lombardozzi et al., 2012). Ozone uptake only cumulates when the ozone flux
is above an instantaneous critical threshold, 0.8 nmol O3 m-2 s-1,
to account for ozone detoxification by vegetation at
lower ozone levels (Lombardozzi et al., 2015). Three different plant groups
are accounted for: evergreen, deciduous, and crops or grasses. We also include
a leaf-turnover ozone decay rate for evergreen plants so that accumulated
ozone damage does not accrue beyond the average foliar lifetime. The
ozone impact factors have empirical linear relationships with CUO
such that
FpO3=ap×CUO+bp,FcO3=ac×CUO+bc,
where FpO3 is the ozone damage factor multiplied
to the photosynthesis rate (A), and ap and
bp are slope and intercept, respectively, from empirical and
experimental studies (listed in Table 1); FcO3
is the ozone damage factor multiplied with stomatal conductance
(gs), and ac and
bc are the corresponding slope and intercept (Table 1). The ozone damage is applied to the optimal photosynthesis and stomatal
conductance values, which are calculated iteratively at first without ozone
damage, to allow the damage to be applied independently.
Slopes (per mmol m-2) and intercepts (unitless) used to
calculate ozone impact factors in Eqs. (4) and (5), following Lombardozzi et al. (2015).
Incorporating the ozone–vegetation parameterization above into CLM4CN and
coupling it with CAM-Chem, we allow, for the first time, ecosystem structure
(e.g., in terms of LAI and canopy height) to evolve in response to ozone
exposure but at the same time allow ozone concentration to evolve in
response to such ecosystem changes. Therefore, previously discussed
feedbacks are mostly included. We conduct four sets of fully coupled
land–atmosphere simulations: (1) a control case without ozone damage on
vegetation ([CTR]); (2) a simulation with both photosynthetic rate and stomatal
conductance modified by ozone impact factors (independently) ([PHT + COND]),
following the approach of Lombardozzi et al. (2015); (3) a simulation where we
apply the ozone impact factor to photosynthetic rate only ([PHT]), but
stomatal conductance is calculated using the intact, optimal photosynthetic
rate; and (4) simulation where we apply the ozone impact factor to stomatal
conductance only ([COND]), but photosynthetic rate is calculated using the
intact stomatal conductance. Simulations [PHT] and [COND], when compared
with [PHT + COND], allow us to quantify the relative contribution from each
pathway. To determine the relative contribution of those pathways involving
biogenic emissions toward the overall ozone–vegetation feedback, we conduct
an additional set of sensitivity simulations with prescribed isoprene
emission and MEGAN turned off: a control case with no MEGAN
(CTR_nM), and a simulation with modified photosynthesis and
stomatal conductance but with no MEGAN ([PHT + COND_nM]). To
determine the relative contribution of pathways involving dry deposition vs.
transpiration, we compare simulated results with that of Val Martin et al. (2014) who have used the similar CAM-Chem–CLM framework but without
ozone–vegetation coupling to test the sensitivity of ozone to perturbations
in dry deposition velocity.
All simulations are conducted for 20 years using year 2000 initial
conditions and the corresponding land cover data (e.g., land cover and land
use types, satellite LAI, etc.). The first 5 years of outputs are treated
as spin-up and thus discarded in the analysis. We observe that the annual
averages of key aboveground ecosystem parameters such as LAI and ozone
concentration come into a relatively steady state after 5 years. We focus on
changes in the 15-year northern summertime (June, July and August: JJA) averages for most of the
variables in the rest of this paper because this is the period when the
growing season of the majority of global vegetation overlaps most
significantly with the high-ozone season, especially in the northern
midlatitudes.
Simulated ozone with and without ozone–vegetation coupling
Figure 2 shows the 15-year mean summertime surface ozone concentration from
the [PHT + COND] simulation. The corresponding CUO used to affect vegetation is shown in Supplement Fig. S1. Simulated
ozone is generally higher in the northern midlatitudes than elsewhere, and
is the highest over the Mediterranean where solar radiation is particularly
strong. CUO also has high values in Europe, but the overall distribution
does not exactly follow that of surface ozone concentration because CUO also
depends on the length of the growing season and stomatal conductance. CUO
ranges between 20 and 70 mmol m-2 over regions with both high summertime
ozone and high productivity. The simulated CUO is comparable in both
magnitude and spatial distribution with Lombardozzi et al. (2015), who used
prescribed meteorology, ozone and vegetation phenology with no active
carbon–nitrogen cycle or atmospheric coupling, as opposed to this study.
This suggests that online ozone–vegetation coupling, which can modify ozone
concentration substantially depending on the region, leads to a similar
pattern of ozone uptake by vegetation to the case using prescribed ozone due
to the compensation between higher (lower) concentration and higher (lower)
stomatal resistance, as reflected in Eq. (3). During the growing season, CUO
is used to calculate the ozone impact factors that modify photosynthetic
rate and stomatal conductance according to Eqs. (4) and (5) and parameter
values listed in Table 1.
Mean summertime (JJA) surface ozone concentration from the
[PHT + COND] case, where ozone uptake simultaneously modifies both
photosynthetic rate and stomatal conductance. Results are averaged over the
last 15 years of simulations.
Figure 3 shows the differences in surface ozone concentration in different
simulations from the control case (corresponding relative changes shown in
Fig. S2). Implementing ozone–vegetation coupling that includes
simultaneous modification of photosynthetic rate and stomatal conductance by
ozone exposure (the [PHT + COND] case) increases mean surface ozone
globally, and significant increases by up to 4–6 ppbv are found over China,
North America and Europe (Fig. 3a). Ozone exposure is thus found to
constitute a positive feedback loop via vegetation that ultimately enhances
surface ozone levels when ozone–vegetation coupling is accounted for.
The simulated increases in ozone levels due to ozone–vegetation coupling are
significant when compared with the possible impacts of 2000–2050 climate and
land cover changes on surface ozone, which are in the range of +1–10 ppbv
(Jacob and Winner, 2009; Tai et al., 2013; Val Martin et al., 2015). This
coupling effect is smaller than the potential ozone changes driven by
anthropogenic emissions (up to +30 ppbv), but it more likely reflects
compensation among various pathways (e.g., Ganzeveld et al., 2010). These
simulated increases, however, slightly worsen the performance of CAM-Chem in
reproducing ozone concentrations against observations, as seen in Fig. 4,
which shows the model–observation comparison for the control case (standard
CAM-Chem-CLM with dry deposition improvement of Val Martin et al., 2014) and
the [PHT + COND] case. The high biases in CESM-simulated summertime
surface ozone concentrations in North America and Europe are a commonly
acknowledged issue with CAM-Chem (Lamarque et al., 2012) and other global and
regional models (Lapina et al., 2014; Parrish et al., 2014). Uncertain
emissions, coarse resolution (Lamarque et al., 2012), misrepresentation of
dry deposition process and overestimation of stomatal resistance (Val Martin
et al., 2014) are all likely factors contributing to these high biases.
Inclusion of ozone–vegetation coupling in the model further increases the
normalized mean biases of the modeled results against three sets of
observational data: the Clean Air Status and Trends Network (CASTNET)
(1999–2001), Air Quality System (AQS) (1999–2001) and European Monitoring
and Evaluation Programme (EMEP) (1999–2001), from 18 to 22 %, 31 to
35 % and 14 to 22 %, respectively. Although there remains
considerable uncertainty in the parameterization of ozone–vegetation
coupling and in ozone simulations by Earth system models, we show that
including ozone damage in a coupled climate-chemistry–biosphere framework
can have a potentially significant impact on surface ozone simulations.
Changes in summertime surface ozone concentrations in different
simulations: (a) the case where both photosynthetic rate and
stomatal conductance are modified by ozone uptake; (b) modified
photosynthetic rate only; and (c) modified stomatal conductance
only, all relative to the control case (CTR). Stippling with dots indicates
significant changes at 90 % confidence from Student's t test.
Scatter plots of simulated summertime ozone concentration in
(a) the control case (CTR) and (b) the case where both
photosynthesis and conductance are modified by ozone uptake ([PHT + COND]),
vs. observed average values from the Clean Air Status and Trends Network
(CASTNET) (1999–2001), Air Quality System (AQS) (1999–2001) and European
Monitoring and Evaluation Programme (EMEP) (1999–2001). Normalized mean
biases (NMB) are also shown.
Attribution to different biogeochemical and meteorological feedback
pathways
Figure 3b and c show the differences in ozone for the cases where
ozone damages stomatal conductance alone and photosynthesis alone,
respectively, noting that each of them is calculated using the undamaged,
intact values of the other variable. Comparison of Fig. 3a with b–c
shows that the modification of stomatal conductance by ozone uptake
contributes more dominantly to the overall effect of ozone–vegetation
coupling (Fig. 3a). This suggests that, among the various feedback pathways
that may influence surface ozone (Fig. 1), those triggered by changes in
stomatal conductance are generally more important than those associated with
photosynthesis or the associated changes in ecosystem production and
structure including LAI, at least in the modeling framework of this study.
This is also supported by sensitivity simulations performed under the same
modeling framework but without ozone damage, in which a 50 % increase
in LAI decreases summertime surface ozone by on average 3 ppb, which is
relatively small in comparison with the changes following optimization of
stomatal resistance (Val Martin et al., 2014). Indeed, the effect of
modifying stomatal conductance alone ([COND]; Fig. 3b) is slightly larger
than the case of [PHT + COND] (Fig. 3a), where the additional effect of
modifying photosynthesis together with stomatal conductance would slightly
offset the overall positive feedback on ozone. It is noteworthy that this
additional effect is, however, not consistent with the effect of modifying
photosynthesis alone ([PHT]; Fig. 3c), reflecting nonlinear interactions
between photosynthesis and stomatal conductance.
Figure 5 shows the differences in dry deposition velocity, transpiration
rate and biogenic isoprene emission between the [PHT + COND] and [CTR]
simulations (relative changes shown in Fig. S3). Over China,
Europe and North America, ozone dry deposition velocity is lower (by up to
∼ 20 %) in [PHT + COND]. In these same regions but
especially in the eastern USA, southern Europe and southern China, isoprene
emission is significantly higher (by up to ∼ 50 %). In
addition, in similar regions but especially in central North America, the
transpiration rate is reduced by ozone exposure (by up to ∼ 20 %),
which would reduce boundary-layer humidity, increase surface
temperature, enhance dry convection and thicken the boundary layer. In view
of Fig. 1, all of these pathways may add to or offset each other, leading to
the overall ozone changes seen in Fig. 3a. The sensitivity simulations and
comparison with Val Martin et al. (2014), which examined the sensitivity of
simulated ozone to differences in dry deposition schemes under essentially
the same modeling framework, allow us to quantify more precisely which of
these pathways are more important, as we discuss next.
Changes in (a) dry deposition velocity,
(b) transpiration rate and (c) isoprene emission in the
[PHT + COND] case, where both photosynthetic rate and stomatal conductance
are modified by ozone uptake, relative to the control case (CTR).
Changes in surface ozone concentration in (a) the case
where both photosynthesis and stomatal conductance are modified by ozone
uptake, but with prescribed isoprene emission from the original control case
(CTR) by turning off MEGAN (stippling with dots indicates significant changes
at 90 % confidence from Student's t test); and
(b) theoretical changes calculated by multiplying our simulated dry
deposition changes with the change in ozone concentration per unit change in
dry deposition from Val Martin et al. (2014), which did not include ozone
damage on vegetation.
Changes in (a) stomatal resistance, (b) surface
temperature, (c) latent heat flux, (d) gross primary
production (GPP), (e) effective leaf area index (ELAI) and
(f) photosynthetic rate in the [PHT + COND] case, where both photosynthetic rate
and stomatal conductance are modified by ozone uptake, relative to the
control case (CTR).
Figure 6a shows the changes in surface ozone in the
[PHT + COND_nM] minus CTR_nM simulations,
where we use prescribed biogenic emissions from the original control case
(CTR) to drive ozone chemistry so that we essentially shut down any feedback
pathways involving biogenic emissions. A comparison between Figs. 6a and
3a shows that the changes in biogenic VOC emissions account for
∼ 0–60 % of the ozone increases over Europe, North America
and China, while dry deposition and/or transpiration-driven meteorological
changes (excluding the temperature effect on isoprene emission) account for
remaining ∼ 40–100 %. We further show in Fig. 6b the
theoretical changes in surface ozone, by multiplying the dry deposition
changes in Fig. 5a by the change in ozone concentration per unit change in
dry deposition velocity from the study of Val Martin et al. (2014), which
provided an approximate sensitivity of simulated ozone to perturbed dry
deposition velocity only to separate this impact from that due to
hydrometeorological changes associated with changing stomatal conductance,
e.g., changes in mixing depth. We find that the ozone changes in Fig. 6a
and b are similar in magnitude, suggesting that globally most of the
non-isoprene-driven differences in ozone are driven by dry deposition.
Notable exceptions include the US Midwest and southeastern Europe, where
higher mixing depth following reduced transpiration might have partly offset
the ozone positive feedback, whereas in western Europe the lower chemical
loss rate following reduced transpired water might have further enhanced the
positive feedback.
The simulated general reduction in dry deposition velocity and transpiration
rate (Fig. 5a and b) is mostly due to increased stomatal resistance (Fig. 7a),
i.e., reduced stomatal conductance, a direct response to CUO. The reduced dry deposition velocity represents a positive
biogeochemical feedback on ozone (orange arrows in Fig. 1). The simulated
increase in biogenic isoprene emission (Fig. 5c) is found to be mostly
driven by higher surface (thus vegetation) temperature (Fig. 7b) that
results from lower transpiration rate and latent heat flux (Fig. 7c).
Therefore, this feedback loop involving biogenic emissions is indeed an
indirect, meteorological feedback that is also initiated by stomatal and
transpiration changes (purple arrows in Fig. 1). Relative changes in
variables shown in Fig. 7 are included in Fig. S4.
By including immediate ozone–vegetation coupling, we find a larger decline
in transpiration rate (6.4 % globally) than in the offline, uncoupled land
model results (2.0–2.4 %) estimated by Lombardozzi et al. (2015). On the
other hand, although reduced photosynthesis and the resulting long-term
changes in GPP and LAI (Fig. 7d–e) play a smaller role than reduced stomatal
conductance in shaping simulated ozone (Fig. 3b–c), the impacts are not
negligible (up to 3 ppb), especially as these changes are also nonlinearly
coupled to stomatal changes. Photosynthetic rate decreases by up to 20 %,
directly due to the ozone effect (Fig. 7f), which is quite similar both in
magnitude and spatial pattern to the results of Lombardozzi et al. (2015),
but the corresponding GPP and LAI changes are relatively small
(∼ 5 % over regions concerned, except for Southeast Asia,
where the highest ozone-induced LAI reduction is simulated and leads to
isoprene emission decrease despite higher surface temperature). Grid-level
GPP and LAI in certain areas increase despite reduced leaf-level
photosynthetic rate, likely reflecting more carbon allocation to leaves to
compensate for the reduced photosynthetic rate and relaxation of resource
limitation, as nutrients and water become less limiting upon lower
photosynthetic and evaporative demands, as well as favorable
hydrometeorological changes following ozone exposure (enhanced soil moisture
and precipitation as shown in Fig. S5). These LAI increases induced by ozone
are not represented in Fig. 1 because they more likely reflect the fully
coupled effect of changing hydrometeorology, instead of the direct effect of
ozone on LAI as is typically observed in experimental studies (Ainsworth et
al., 2012).
Conclusions and discussion
Tropospheric ozone is one of the most hazardous air pollutants due to its
harmful effects on human health and damage to forest and agricultural
productivity. Stomatal uptake of ozone by leaves reduces both photosynthetic
rate and stomatal conductance. These vegetation changes can induce a cascade
of biogeochemical and biogeophysical (or meteorological) effects (Fig. 1)
that ultimately modulate climate, carbon cycle and also feedback onto ozone
air quality itself. The direct, biogeochemical feedback pathways include
reduced ozone dry deposition and biogenic VOC emissions. The indirect,
meteorological feedback pathways are facilitated by transpiration-driven
changes in the meteorological environment that influence ozone formation and
removal. A few land surface modeling studies have estimated the direct
effects of ozone on ecosystem production and land–atmosphere water exchange
(Yue and Unger, 2014; Lombardozzi et al., 2015), and predicted a possible
positive radiative forcing from the ozone-induced decline in the land carbon
sink (Sitch et al., 2007).
In this study, we implement a semi-empirical parameterization of ozone
damage on vegetation (Lombardozzi et al., 2015) into the CESM
(CAM4-Chem–CLM4CN) modeling framework to enable online ozone–vegetation
coupling so that vegetation variables can evolve in response to ozone
exposure, and at the same time simulated ozone concentration can respond to
ecosystem changes. Our scheme modifies leaf-level photosynthesis and
stomatal conductance separately via the ozone impact factors, which are
assumed to have empirical linear relationships with CUO and account for different plant groups. Sensitivity simulations are
conducted to determine the relative importance of different feedback
pathways.
With ozone–vegetation coupling, surface ozone is simulated to be higher by
up to 4–6 ppbv over Europe, North America and China. This coupling effect is
significant in view of the 2000–2050 effects of climate and land cover
changes on surface ozone (+1–10 ppbv) as found in previous work (Jacob and
Winner, 2009; Ganzeveld et al., 2010; Tai et al., 2013), and should be
considered in future air quality projection studies. Reduced dry deposition
velocity following the modification contributes to ∼ 40–100 % and enhanced biogenic isoprene emission contributes to
∼ 0–60 % of the higher ozone concentrations. The dry
deposition-driven ozone increases (by up to 4 ppbv) arise mostly from
reduced stomatal conductance, and are consistent with the sensitivity of
ozone to perturbations in dry deposition velocity found by Val Martin et
al. (2014). This pathway constitutes a significant positive biogeochemical
feedback on surface ozone. The other major feedback associated with enhanced
isoprene emission is mostly driven by higher vegetation temperature that
results from lower transpiration rate. This pathway constitutes an indirect,
positive meteorological feedback on surface ozone. Depending on the region,
transpiration-driven meteorological changes such as lower humidity and
deeper mixing depth may also influence surface ozone. Transpiration rate is
simulated to decrease by 6.4 % globally, which is a larger change compared
with the decrease estimated by Lombardozzi et al. (2015), who used
prescribed instead of synchronously simulated atmospheric forcings. This
also suggests an augmented effect on transpiration due to changes in carbon
allocation and foliage density arising from ozone–vegetation coupling.
Modification of photosynthesis and further long-term changes in ecosystem
productivity and structure, including LAI changes, are found to play a
smaller role in contributing to the ozone–vegetation feedbacks than direct
stomatal changes, but are not insignificant (up to +3 ppbv). The simulated
changes in LAI (less than 5 %) in this study are similar in magnitude to
that by Yue and Unger (2015), who included an active carbon cycle though using
Yale Interactive terrestrial Biosphere (YIBs) model with a different
ozone–vegetation parameterization. However, prognostic treatment of the
carbon cycle and LAI calculation in CLM4CN are still known to be
problematic, with large uncertainties and biases in the estimation of global
carbon fluxes (Sun et al., 2012), arising from incomplete model
parameterization and from uncertainty in photosynthetic parameters (Bonan et
al., 2011). It is not surprising that changes in GPP as simulated here do
not replicate the results of Lombardozzi et al. (2015), in which vegetation
phenology is prescribed and the carbon and nitrogen cycles are not active
(CLM4.5SP). Implementing ozone damage on vegetation in a model with more
sophisticated and realistic representation of prognostic carbon–nitrogen
cycle is highly warranted, so that the possible effects of ozone-induced
long-term ecosystem changes can be examined more fully.
Large variability in the responses of different plants to ozone leads to
considerable uncertainties in any global-scale studies (Lombardozzi et al.,
2013). In some cases, such large variability in plant responses across different studies weakens the correlation between phytotoxic responses and CUO.
Such correlation is usually more evident in individual studies, and in the
parameterization schemes based on them (Sitch et al., 2007; Yue and Unger,
2015). The parameterization developed by Lombardozzi et al. (2013), based on
the most comprehensive database available for photosynthetic and stomatal
responses to CUO to date, is deemed more appropriate for the global scale of
this study and the plant functional types represented in the model, despite
the weaker correlation between plant responses and CUO as shown by the
compilation of data across studies. The damage is applied after CUO reaches
a certain threshold, so the calculation of CUO is still crucial to the
application of the damage functions. The model results could possibly be
improved with more detailed plant-type-specific ozone damage
parameterization, including better estimates of plant vulnerability to ozone
that will help refine the ozone uptake thresholds (Lombardozzi et al.,
2015). An important caveat of this study is the consideration of only three
plant groups to generalize the responses of global vegetation to ozone
exposure, because data are largely unavailable for other plant groups.
Another potential caveat is the uncertainty and lack of cross-validation in
hydrometeorological simulations with respect to the ozone phytotoxicity
scheme we newly implement, as we only focus on vegetation and atmospheric
chemical changes in this study. Although most simulated vegetation variables
are consistent with previous work, the changes in simulated vegetation
temperature from ozone–vegetation coupling are not small (by up to
+2 ∘C) (Fig. 7b) and they result in quite substantial changes in
isoprene emission, suggesting the need for further tuning of
hydrometeorological processes in the model. Also, MEGAN does not consider the
direct, immediate biochemical connection between photosynthesis and biogenic
emissions, by which ozone damage to photosynthesis may directly reduce
isoprene emission and partially offset the significant temperature-induced
increase in isoprene emission as shown in Fig. 5c (Tiwari et al., 2016).
Whereas the various environmental activity factors used in MEGAN to adjust
baseline emissions may have implicitly encapsulated the biochemical
connection with photosynthesis, further incorporating such a connection into
ozone–vegetation modeling warrants more in-depth investigation. In general,
we have the highest confidence in the quantification of the biogeochemical
pathway via stomata-driven deposition changes, which is straightforward and
accounts for the majority of the ozone–vegetation feedbacks. On the other
hand, the hydrometeorological feedbacks introduce strong nonlinearity in the
interactions between atmospheric chemistry, soil moisture and vegetation, which is more difficult to isolate. Parameterizing the ozone–vegetation
coupling in a standalone chemical transport model with prescribed
meteorology could be particularly helpful, to more confidently separate
the effects of biogeochemical vs. meteorological feedbacks. This
knowledge will be important in projecting the impacts of future climate and
land cover changes on ozone air quality and climate feedbacks in the coming
decades.
Data availability
Model output data used for analysis and plotting, as well as processed
observational data from CASTNET, AQS and EMEP, can be made available in RData
format by contacting the corresponding author (Amos P. K. Tai:
amostai@cuhk.edu.hk).
The Supplement related to this article is available online at doi:10.5194/acp-17-3055-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the Early Career Scheme (project number:
24300614) of the Research Grants Council of Hong Kong, and the associated
Direct Grant for Research (project ID: 4441337, 3132767) from The Chinese
University of Hong Kong (CUHK), given to the principal investigator, Amos P. K. Tai.
We also thank the Information Technology Services Centre (ITSC) at
CUHK for their devotion in providing the necessary computational services
for this work.
Edited by: L. Ganzeveld
Reviewed by: two anonymous referees
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