Introduction
Surface ozone (O3) is toxic to both people and plants. Present-day
and recent historical O3 levels reduce carbon sequestration in the
biosphere (Reich and Lassoie, 1984; Guidi et al., 2001; Sitch et al., 2007;
Ainsworth et al., 2012), perturb the terrestrial water cycle (Lombardozzi et
al., 2012, 2015), and cause around $ 25 billion in annual crop
losses (Reich and Amundson, 1985; Van Dingenen et al., 2009; Avnery et al.,
2011; Tai et al., 2014). The basic plant responses to O3 injury are
well established from controlled exposure experiments (e.g., Wittig et al.,
2009; Ainsworth et al., 2005, 2012; Hoshika et al., 2015), but few datasets
are available to quantify O3 fluxes and responses for whole
ecosystems or plant functional types that are represented within regional and
global biosphere and climate models. The eddy covariance method has been
widely used to measure land–atmosphere fluxes of carbon, water, and energy
and evaluate their representation in models (Baldocchi et al., 2001; Bonan et
al., 2011), but few towers measure O3 fluxes (Munger et al., 1996;
Fowler et al., 2001; Keronen et al., 2003; Gerosa et al., 2004; Lamaud et
al., 2009; Fares et al., 2010; Stella et al., 2013; Zona et al., 2014). A
recent review identified just 78 field measurements of O3 fluxes
over vegetation during the last 4 decades, many lasting just a few weeks
(Silva and Heald, 2018). This paper demonstrates a reliable method to
estimate O3 fluxes at 103 eddy covariance flux towers spanning over
2 decades to enable O3 impact studies on ecosystem scales.
The land surface is a terminal sink for atmospheric O3 due to the
reactivity of O3 with unsaturated organic molecules and the modest
solubility of O3 in water. Surface deposition is 20 % of the
total loss in tropospheric O3, making it an important control on
air pollution (Wu et al., 2007; Young et al., 2013; Kavassalis and Murphy,
2017). This O3 deposition flux includes stomatal uptake into
leaves, where O3 can cause internal oxidative damage, and less
harmful non-stomatal deposition to plant cuticles, stems, bark, soil, and
standing water (Fuhrer, 2000; Zhang et al., 2002; Ainsworth et al., 2012).
O3 can also react with biogenic volatile organic compounds,
particularly terpenoid compounds, in the plant canopy air, and this process
is commonly included in non-stomatal deposition (Kurpius and Goldstein,
2003). The deposition flux (mol O3 m-2 s-1) can be
described as
FO3=vdnχ-χ0=vdnχ,
where χ and χ0 are the O3 mole fractions
(mol mol-1) in the atmosphere and at the surface, respectively, n is
the molar density of air (mol m-3), and vd is a deposition
velocity (m s-1) that expresses the net vertical O3 transport
between the height where χ is measured and the surface. FO3 is defined positive for flux towards the ground.
Equation (1) reasonably assumes that χ0=0 because terrestrial
surfaces have abundant organic compounds that react with and destroy
O3. The deposition velocity can be decomposed into resistances
(s m-1) for aerodynamic transport (ra), diffusion in the
quasi-laminar layer (rb), stomatal uptake (rs), and
non-stomatal deposition (rns) (Wesely, 1989):
vd-1=ra+rb+rs-1+rns-1-1.
For stomatal and non-stomatal processes, the rates are often expressed as
conductances (m s-1), which are the inverse of the resistances: gs=rs-1 and gns=rns-1. The
sum of stomatal and non-stomatal conductances is the vegetation canopy
conductance, gc=gs+gns. The stomatal
O3 flux is the portion of FO3 that enters the
stomata, and can be described as
Fs,O3=FO3gsgs+gns-1=vdnχgsgs+gns-1.
To construct the synthetic O3 flux, or SynFlux, we use measurements
of O3 concentration and standard eddy covariance flux measurements
to derive nearly all of the terms in Eqs. (1)–(3) from surface observations,
using some additional information from remote sensing and models. This
enables the estimation of FO3 and Fs,O3, as
described in Sect. 2. Section 3 evaluates the method against observations at
three sites that measure FO3 and examines the importance of
stomatal and non-stomatal deposition. Section 4 uses SynFlux to assess the
spatial patterns of O3 uptake to vegetation and to compare
flux-based metrics of O3 damage with concentration-based metrics.
Finally, we discuss the strengths, limitations, and implications of our
approach in Sect. 5.
Mean stomatal conductance for O3 (gs) during
daytime in the growing season at FLUXNET2015 sites in the United States and
Europe. Symbols of some sites have been moved slightly to reduce overlap and
improve legibility.
Data sources and methods
SynFlux: synthetic O3 flux
The FLUXNET2015 dataset (Pastorello et al., 2017) aggregates measurements of
land–atmosphere fluxes of CO2, H2O, momentum, and heat
at sites around the world
(http://fluxnet.fluxdata.org/data/fluxnet2015-dataset; last access: 24
February 2017). Measurements are made with the eddy covariance method on
towers above vegetation canopies (Baldocchi et al., 2001; Anderson et al.,
1984; Goldstein et al., 2000) with consistent gap filling (Reichstein et al.,
2005; Vuichard and Papale, 2015) and quality control across sites (Pastorello
et al., 2014). Flux and meteorological quantities are reported in half-hour
intervals. We analyze data from all sites in the United States and Europe in
the FLUXNET2015 Tier 1 dataset. This analysis is restricted to the US and
Europe because these regions have dense O3 monitoring networks,
described below. There are 103 sites meeting these criteria, all listed in
Table S1 in the Supplement with references to full site descriptions. Three
of these sites – Blodgett Forest, Harvard Forest, and Hyytiälä
Forest – measure O3 flux with the eddy covariance method, which we
will use in Sect. 3 to evaluate our methods.
SynFlux aims to constrain O3 deposition and stomatal uptake as much
as possible from measured water, heat, and momentum fluxes, in contrast to other methods (Finkelstein et
al., 2000; Mills et al. 2011; Schwede et al., 2011; Yue et al., 2014) that
rely more heavily on atmospheric models or parameterizations of stomatal
conductance. From the eddy covariance measurements, we derive the resistance
components of Eq. (2) using methods similar to past studies (Kurpius and
Goldstein, 2003; Gerosa et al., 2005; Fares et al., 2010). The aerodynamic
and quasi-laminar layer resistances (ra and rb,
respectively) are derived from measured wind speed, friction velocity, and
fluxes of sensible and latent heat every half hour using Monin–Obukhov
similarity theory (Foken, 2017). The stomatal conductance for O3 (gs) is derived from the measured water vapor flux and
meteorological data every half hour with the inverted Penman–Monteith
equation (Monteith, 1981; Gerosa et al., 2007). Supplement S1 provides
further details of the resistance and conductance calculations. Some studies
instead calculate gs from gross primary productivity (Lamaud et
al., 2009; El-Madany et al., 2017), but that method is less widely used than
the Penman–Monteith approach adopted here. The Penman–Monteith method of
calculating stomatal conductance has been successfully applied across FLUXNET
sites previously (Medlyn et al.,
2017; Novick et al., 2016; Knauer et al., 2017; Lin et al., 2018). Those studies and others caution that, since
evapotranspiration measurements include evaporation from ground, the stomatal
conductance could be overestimated. While there are methods for quantifying
and removing the evaporative fraction of evapotranspiration from eddy
covariance data (Wang et al., 2014; Zhou et al., 2016; Scott and Biederman,
2017), a more common approach is to restrict analysis to conditions when
transpiration dominates. We follow this second approach, analyzing only
daytime data during the growing season, and use filtering criteria similar to
Knauer et al. (2017). We define daytime as Sun elevation angle above
4∘ and the growing season as days when gross primary productivity
(GPP) exceeds 20 % of the annual maxima in GPP. To avoid complications to
the Penman–Monteith equation from wet canopies, we exclude times when dew
may be present (RH > 80 %), and days with precipitation
(> 5 mm). We also exclude the top and bottom 1 % of gs values, which include many unrealistic outliers (e.g., gs>0.5ms-1). Figure 1 shows the
mean stomatal conductance during the growing season at all sites.
The terms in Eqs. (1)–(3) that cannot be derived from FLUXNET2015
measurements are O3 mole fraction and non-stomatal conductance. The
O3 mole fraction is taken from a gridded dataset of hourly
O3 measurements that spans the contiguous United States and Europe
(Schnell et al., 2014). This dataset has 1∘ spatial resolution, so
some differences from measured O3 abundances at individual sites
are inevitable. Schnell et al. (2014) estimated these errors to be 6–9 ppb
(rms) or about 15 % of summer mean O3 in the US and similar in
Europe. Figure 2 shows that the daytime gridded O3 concentrations
correlate well with observations at three flux tower sites where O3
was measured (R2=0.63–0.87) and have modest negative bias
(5–10 ppb, -12 % to -28 %), consistent with the accuracy reported
by Schnell et al. (2014). We use the Zhang et al. (2003) parameterization of
non-stomatal conductance, which accounts for O3 deposition to leaf
cuticles and ground and was developed from measurements in the eastern United
States. The parameterization requires leaf-area index, which we take from
satellite remote sensing (Claverie et al., 2014, 2016), snow depth, which we
take from MERRA2 reanalysis (GMAO, 2015; Gelaro et al., 2017), and standard
meteorological data provided by FLUXNET2015. Uncertainties in these variables
are described in Sect. 2.4. Performance of the non-stomatal parameterization
is examined in Sect. 3.2.
Description of sites that measure O3 flux and their
daytime growing season conditions*.
Blodgett Forest,
Hyytiälä Forest,
Harvard Forest,
California, USA
Finland
Massachusetts, USA
Latitude, longitude
38.8953, -120.6328
61.8475, 24.2950
42.5378, -72.1715
Plant functional type
Evergreen needleleaf
Evergreen needleleaf
Deciduous broadleaf
Years of data
2001–2007
2007–2012
1993–1999
Days of observations
1281
1098
1281
Canopy height (m)
8
15
24
GPP (µmol m-2 s-1)
9.22±3.55
11.1±5.02
12.4±7.62
ET (mmol m-2 s-1)
3.25±1.23
1.71±0.82
2.95±1.70
PAR (µmol m-2 s-1)
875±149
690±203
876±222
Air temperature (∘C)
19.1±5.36
13.3±5.99
17.65±5.75
VPD (kPa)
1.51±0.61
0.73±0.32
0.90±0.34
O3 (ppb)
55.4±13.4
32.2±8.68
48.8±15.8
Fs,O3 (nmol O3 m-2 s-1)
5.18±2.11
4.35±1.66
7.23±4.87
Precipitation (mm d-1)
0.09±0.49
0.42±0.89
0.28±0.82
* Values are mean ± standard deviation of daily
averages, using daytime observations only. GPP is gross primary productivity.
ET is evapotranspiration. PAR is photosynthetically active radiation. VPD is
vapor pressure deficit. Fs,O3 is observation-derived
stomatal O3 flux.
Figure 3 shows the stomatal O3 flux at each site calculated with
Eq. (3), and then averaged over the growing season. Figure S1 shows the
corresponding total O3 flux (Eq. 1). We refer to these products as
the “synthetic” total O3 flux (FO3syn) and synthetic stomatal O3 flux
(Fs,O3syn). Superscript “syn” distinguishes
these synthetic quantities from the FO3Fs,O3 observed total O3 flux (FO3obs) and observation-derived stomatal
O3 flux (Fs,O3obs), which are only
available at a few sites. Together, we refer to FO3syn and Fs,O3syn as SynFlux. In total, the measurements
required to calculate Fs,O3syn are
O3 mole fraction, sensible and latent heat fluxes, friction
velocity, temperature, pressure, humidity, canopy height, and leaf-area
index. There are 43 sites in the US and 60 sites in Europe within the FLUXNET
Tier 1 database with sufficient measurements to calculate Fs,O3syn.
Gridded and observed daily daytime O3 concentrations at
Blodgett, Harvard, and Hyytiälä forests. Inset numbers provide the
coefficient of determination (R2), mean and median bias, the standard
major axis (SMA) slope, the Thiel–Sen (Sen) slope, and the 68 %
confidence interval of the slopes. The black arrow points towards outliers
that are not shown.
Observed O3 flux
We evaluate SynFlux and its inputs at three sites where O3 flux
measurements are available: Harvard Forest, Massachusetts, United States
(Munger et al., 1996); Blodgett Forest, California, United States (Fares et
al., 2010); and Hyytiälä Forest, Finland (Keronen et al., 2003;
Mammarella et al., 2007; Rannik et al., 2009). These forest sites sample a
range of environmental and ecosystem conditions summarized in Table 1. All
three sites have at least 6 years of half-hourly or hourly flux measurements.
Two sites are evergreen needleleaf forests (Blodgett and Hyytiälä),
while one is a deciduous broadleaf forest containing some scattered stands of
evergreen needleleaf trees (Harvard). Climate also differs across these
sites. Blodgett Forest has a Mediterranean climate with cool, wet winters and
hot, dry summers. Hyytiälä and Harvard forests have cold winters and
wetter summers, with Harvard Forest being the warmer of the two.
Mean synthetic stomatal O3 flux (Fs,O3syn, Sect. 2.1) during the daytime growing
season at FLUXNET2015 sites in the United States and Europe. Symbols of some
sites have been moved slightly to reduce overlap and improve legibility.
Harvard Forest water vapor flux measurements were recalibrated for this work
based on matching water vapor mixing ratio measured by the flux sensor to
levels calculated from ambient relative humidity and air temperature,
resulting in a 30 % increase in evapotranspiration during the 1990s and
no change since 2006. In addition, we remove sub-canopy evaporation from the
measured water vapor flux before the Penman–Monteith calculation. Based on
past measurements at these sites, the sub-canopy fraction of
evapotranspiration is 20 % at Hyytiälä Forest and 10 % at
Harvard Forest in summer (Moore et al., 1996; Launiainen et al., 2005). We
are unable to make this correction at all FLUXNET sites since water vapor
flux is typically measured only above canopy.
At these three sites, observation-derived vd, gns,
and Fs,O3 can be derived from the FO3
measurements with methods that differ slightly from Sect. 2.1. O3
deposition velocity is inferred from measurements of O3
concentration and flux via vd=FO3nχ-1. Resistance or conductance terms ra, rb, and gs are calculated as described in Sect. 2.1,
and then both canopy and non-stomatal conductance are derived from
observations via gc=vd-1-ra-rb-1 and gns=gc-gs, respectively. With those values,
Eq. (3) gives the observation-derived stomatal O3 flux. Synthetic
and observation-derived stomatal O3 fluxes are both calculated with
Eq. (3) and use the same observation-derived gs, ra, and rb but different values of gns,vd, and O3 mole fraction.
Gap filling for friction velocity
The FLUXNET2015 dataset uses gap filling for most flux and meteorological
measurements (Vuichard and Papale, 2015), but not for friction velocity (u∗), which is required to calculate vd and Fs,O3syn. Filling this one variable would
significantly reduce the fraction of missing data in our analysis.
Monin–Obukhov similarity theory predicts that friction velocity will be
proportional to wind speed in the surface layer, for a given roughness length
and stability regime (Foken, 2017). On this basis, we regress the available
friction velocity measurements against wind speed and net radiation (a proxy
for stability) separately for each site and month (a proxy for vegetation
roughness). This gap filling was possible at 91 sites that report net
radiation measurements.
The predicted friction velocities from the regression model are correlated
with available observations (R2 > 0.5) and have minimal
mean bias (±10 %) at 85 out of 91 eligible sites (Fig. S3 in the
Supplement), with most sites (63 out of 91) showing strong correlations (R2 > 0.7). At the remaining six sites with lower regression
model performance (R2 < 0.5) we do not use u∗ gap
filling. The u∗ gap filling increases the number of Fs,O3syn estimates by 1 %–20 %. Time
periods with u∗ gaps have no significant bias in meteorological
conditions (e.g., mean wind speed, radiation, energy fluxes) compared to
periods with u∗ measurements. As a result, the differences in
monthly mean Fs,O3syn with and without gap
filling are small (10 % rms). So, although the u∗ gap filling
is a potential source of uncertainty, the Fs,O3syn estimates are robust. The following
analysis will use the gap-filled data, but our results do not change in any
meaningful way if we use the unfilled data.
Error analysis, averaging, and numerical methods
We quantify the errors in FO3syn, Fs,O3syn, and all other calculated variables
from the measurement uncertainties using standard techniques for propagation
of errors through all equations (see Supplement S2). This method provides the
uncertainty, quantified as the standard deviation, of each variable in each
half-hour interval. The error analysis reveals that Fs,O3syn and other derived quantities have
uncertainties that change from hour to hour by 2 orders of magnitude
(Fig. S2). In addition, many extreme values of Fs,O3syn, gs, and other variables
have very large uncertainties. We retain these outliers in our analysis and
use the error analysis to appropriately reduce their influence on averages
and other statistics, as described below, without discarding data.
The FLUXNET2015 dataset contains error estimates for sensible and latent heat
measurements. We use these reported values in the error analysis. Where
uncertainties in these fluxes are missing, we fill the gaps using a linear
regression of available flux errors against flux values for that site. For
friction velocity, the uncertainty is the prediction error in the linear
model used for gap filling (Sect. 2.3). Based on expert judgment, the
standard deviation of O3 mole fraction is set to 20 %, pressure
to 0.5 hPa, temperature to 0.5 K, relative humidity to 5 %, and canopy
height to the lesser of 15 % or 2 m. For remotely sensed leaf-area
index, the uncertainty is 1.1 m2 m-2 for all vegetation types
(Claverie et al., 2013, 2016). Snow depth uncertainty in MERRA2 is 0.08 m
(Reichle et al., 2017). The Zhang et al. (2003) gns
parameterization has five vegetation-specific parameters and all are assigned
50 % standard deviation. Zero error is assumed for the flux tower height.
Based on these inputs, the median relative uncertainty in Fs,O3syn is 44 %, but it rises to several
hundred percent for some half-hour intervals. The error analysis shows that
most of the uncertainty in Fs,O3syn derives
from uncertainty in the latent heat flux measurement.
Daily and monthly averages of Fs,O3syn and
other quantities are constructed in stages. We first calculate a mean diurnal
cycle for the day or month by pooling measurements during each hour in a
maximum likelihood estimate, a weighted average that accounts for the
uncertainty in each measurement. The maximum likelihood estimate is
appropriate when combining values from the same distribution, which is
expected to apply for measurements within a particular hour, but not across
hours of the day. We then average across hours with an unweighted mean to
calculate the daily or monthly value. For the daily averages, there are one
to two observations within each hour. For the monthly averages, there are
typically 30 to 60 in each hour of the day. We calculate seasonal averages
with an unweighted mean of monthly values. Uncertainties are propagated
through each stage of these averages, as detailed in Supplement S2. We
compared averages with and without uncertainty weighting. The
uncertainty-weighted averages tend to be smaller and less variable than
unweighted averages because the error propagation identifies when outliers
and large values have greater uncertainty. For example, the monthly values of
gc derived from observations at Harvard Forest are 0.57±0.11 cm s-1 with uncertainty weighting and 0.68±0.17 cm s-1 without. Our discussion focuses on uncertainty-weighted
daily averages of daytime data.
Synthetic and observation-derived daily daytime stomatal
O3 flux. See Sect. 2.1 for a definition of Fs,O3syn and Fig. 2 for an explanation of the
lines and inset text.
Analyses are performed in Python 3.5 with NumPy, Pandas, PySolar, and
Statsmodels (Reda and Andreas, 2005; Van Der Walt et al., 2006; McKinney,
2010; Seabold et al., 2010). We quantify linear relationships between
variables using the coefficient of determination (R2), a parametric
slope estimator (standard major axis or SMA, Warton et al., 2006) and a
non-parametric slope estimator (Thiel–Sen slope, Sen, 1968), which is more
robust against outliers.
Data availability
The SynFlux dataset produced in this work is available at
https://doi.org/10.5281/zenodo.1402054 (last access: 30 August 2018).
The dataset includes synthetic stomatal and total O3 fluxes,
O3 concentrations, O3 deposition velocity, canopy
conductance, stomatal conductance, and all of their propagated uncertainties.
Monthly mean values are provided with and without u∗ gap filling,
for 103 sites totaling 926 site years.
SynFlux evaluation
Evaluation of synthetic fluxes
Figure 4 compares daily daytime averages of synthetic Fs,O3syn to Fs,O3
observation-derived Fs,O3obs. Fs,O3syn and Fs,O3obs are calculated from the same
observation-derived stomatal conductance (gs) and aerodynamic
resistances (ra and rb), but differ in the
O3 mole fraction and non-stomatal conductance (gns)
that they use (see Sects. 2.1 and 2.2). At all three sites, Fs,O3syn is strongly correlated with measured
values (R2=0.83–0.93). The mean and median biases are -16 %
to -21 % and at least 95 % of Fs,O3syn values agree with measurements within
a factor of 2. The majority of Fs,O3syn
values lie near the 1:1 line with Fs,O3obs
and the slopes (0.71 to 0.85) reflect this. The half-hourly or hourly
measured and synthetic fluxes still have some outliers (Fig. S2), but the
error analysis reveals that many of the outlying points have large
uncertainties. For 98 % of points, the differences between Fs,O3syn and Fs,O3obs are less than the 95 % confidence
interval derived from the error analysis (two-sided t-test). Thus, the
errors in Fs,O3syn are consistent with the
propagated uncertainty in the observations. The half-hourly Fs,O3syn values perform similarly well against
observations (Fig. S4), but our analysis focuses on averages. The performance
of daily Fs,O3syn is partially due to resolving
the seasonal cycle. If we subtract the mean seasonal cycle from both
synthetic and observation-derived daily Fs,O3, the residual
correlation is R2=0.5–0.7 (vs. 0.9 with the seasonal cycle
included). This represents the skill of SynFlux at reproducing within-month
and interannual variability. Overall, these results suggest that synthetic Fs,O3syn is a reliable estimate of stomatal
O3 uptake into plants that can be used at flux tower sites without
O3 measurements.
The measurements also enable us to evaluate synthetic total deposition, FO3syn, and synthetic O3 deposition
velocity, vdsyn, although these are less relevant to
ecosystem impacts than stomatal uptake, Fs,O3syn. For daily averages, Fig. S5 shows
that FO3syn bias (-13 % to +65 %), slope (0.3–1.4), and R2 (0.05–0.43) are all worse than
for Fs,O3syn. The daily vdsyn performance is similar (Fig. S6, bias: -26 % to +41 %, slope: 0.3–1.1, R2: 0.16–0.37). Monthly
averages of vdsyn and FO3syn both improve the correlation with
observations (R2 ∼ 0.12–0.54). The reasons for the better
performance of Fs,O3syn compared to FO3syn can be derived from Eq. (3). The canopy
resistance for O3 is normally much greater than the quasi-laminar
layer and aerodynamic resistances, meaning rc≫raandrc≫rb, often by a factor of 3–10.
Therefore, the O3 deposition velocity is approximately vd≈rc-1=gc. Under these conditions,
Eq. (1) simplifies to FO3≈nχ(gs+gns) and Eq. (3) simplifies to Fs,O3≈nχgs. While gs is
calculated from measured H2O fluxes, gns comes from a
parameterization, which inevitably introduces error into gns and
FO3syn. However, Fs,O3syn has little sensitivity to gns regardless of whether stomatal or non-stomatal conductance is
larger. We confirm this insensitivity in tests where the parameterized gns value is doubled at 10 sites. The hourly Fs,O3syn values change only 3 %–8 %.
Since Fs,O3syn has little sensitivity to gns or its errors, it can be calculated more accurately than FO3syn, as seen when comparing Figs. 4 and S4.
Despite its larger errors, the means of FO3syn
and vdsyn are within 50 % of the observed value
at two sites and within a factor of 2 at all, which may be useful for some
applications, given the scarcity of prior FO3 measurements
and observation-derived estimates of vd.
Stomatal and non-stomatal deposition
Figure 5 shows the seasonal cycles of observation-derived O3
deposition velocity and its important components at the three study sites
with O3 flux measurements. For low or moderately reactive gases
like O3, canopy resistance is typically greater than aerodynamic or
quasi-laminar layer resistance, so it controls the overall deposition
velocity. At these three sites, deposition velocity is lowest in winter
(0.1–0.2 cm s-1) and highest in summer (0.5–0.6 cm s-1).
Stomatal conductance peaks during warm and wet months, which explains most of
this seasonal variation, except at Blodgett Forest as discussed below.
Traditionally, stomatal conductance was thought to exceed non-stomatal
conductance during the growing season at most vegetated sites (Wesely, 1989;
Zhang et al., 2003), although this has been challenged more recently (Altimir
et al., 2006; Stella et al., 2011a; Wolfe et al., 2011; Plake et al., 2015).
At both Harvard and Hyytiälä forests, the mean stomatal conductance
(0.2–0.6 cm s-1) is 1.5–6 times larger than non-stomatal conductance
(0.08–0.2 cm s-1) during the growing season, so about
60 %–90 % of O3 deposition occurs through stomatal uptake.
At Blodgett, non-stomatal conductance slightly exceeds stomatal conductance
in summer (0.4 vs. 0.3 cm s-1). The fast non-stomatal deposition is
explained by O3 reacting with biogenic terpenoid emissions below
the flux measurement height (Kurpius and Goldstein, 2003; Fares et al.,
2010). As documented in past work, these biogenic emissions depend strongly
on temperature and light and have a large seasonal cycle with maxima in
summer and minima in winter, so stomatal uptake is generally
< 50 % of O3 deposition at Blodgett in the summer but
> 70 % in winter (Kurpuis and Goldstein, 2003; Fares et al.,
2010; Wolfe et al. 2011).
Observed O3 deposition velocity and its in-canopy
components at sites with O3 flux measurements. Lines show the
multi-year mean and multi-year standard deviation calculated from the monthly
averages described in Sect. 2.4. Dashed lines in the stomatal conductance
panel show the stomatal fraction of total canopy conductance (gsgc-1) and dashed lines in the non-stomatal
conductance panel show the parameterized gns value.
A recent analysis of O3 flux measurements at Harvard Forest
suggests that non-stomatal deposition averages 40 % of daytime
O3 deposition during summer months, with a range of
20 %–60 % across years (Clifton et al., 2017). Our analysis of the
same site does not support such a large role for non-stomatal deposition at
this site in summer. For each year, we calculate summer daytime means of gs and gc by averaging the June–September values,
and then calculate the non-stomatal fraction of deposition (1-gs/gc). Averaged across years 1993–2000, we find that
8 % of daytime O3 deposition is non-stomatal during the summer,
with a range of -33 % to 34 % across years. Negative fractions
mean that stomatal conductance is large enough to explain all O3
deposition. A large negative non-stomatal fraction (-33 %) occurs in
only one year (1996) and no other year is less than -11 %, which is
within uncertainty of 0 % (2σ) according to the error
propagation. Despite the small or zero non-stomatal fraction found here, our
results continue to support the large year-to-year variability of this
fraction reported by Clifton et al. (2017). The re-calibrated latent heat
flux measurements are the main reason that our results differ from prior work
and Supplement S3 provides further details. At Hyytiälä Forest, our
results are consistent with prior work that found that the non-stomatal
deposition is 26 % to 44 % of daytime O3 deposition during
the growing season (Rannik et al., 2012). Nevertheless, non-stomatal
deposition equals or exceeds stomatal uptake where there are large terpene
emissions (e.g., Blodgett) and at some other temperate sites that probably
lack large biogenic emissions (Fowler et al., 2001; Cieslik, 2004; Lamaud et
al., 2009; Stella et al., 2011b; El-Madany et al., 2017). We also examined
interannual variation in O3 deposition velocity. We find that the
mean summer daytime vd is 0.40–0.68 cm s-1 at Harvard
Forest, 0.42–0.65 cm s-1 at Blodgett Forest, and
0.43–0.51 cm s-1 at Hyytiälä. This range for Harvard Forest
is somewhat smaller than other recent work (0.5–1.2 cm s-1; Clifton
et al., 2017) because of the uncertainty-weighted averages used here
(Sect. 2.4).
The data here also provide an opportunity to evaluate the parameterization of
gns non-stomatal conductance (Zhang et al., 2003). The
parameterized gns has a similar mean to observation-derived
values in summer at Harvard Forest (0.16 vs. 0.12 cm s-1) and
Hyytiälä (0.15 vs. 0.25 cm s-1). At Blodgett Forest, the
parameterized gns is about half of observation-derived gns in summer, but this is not surprising since the
parameterization does not account for O3 reactions with biogenic
volatile organic compounds (BVOCs), which are known to be important at this
site (Fares et al., 2010). In winter, however, the parameterized gns values at Blodgett Forest are similar to observations (0.10
vs. 0.08 cm s-1). The parameterization is therefore able to roughly
predict mean non-stomatal conductance in the absence of major BVOC emissions.
Nevertheless, the parameterization reproduces almost none of the daily
variability of gns at any site (R2 < 0.1,
Fig. S7). This corroborates the recent field assessment that non-stomatal
conductance is a weak point of most current dry deposition algorithms (Wu et
al., 2018). We attempted, unsuccessfully, to use BVOC emissions from the
MEGAN biogenic emission model (Guenther et al., 2012) to improve the gns parameterization, but the correlations between compounds that
react fastest with O3 (monoterpenes and sesquiterpenes) and the
observation-derived daily mean gns were poor (R2≤0.15). On that basis, FO3syn may also
underestimate total O3 deposition at other sites with high
monoterpene and sesquiterpene emissions, such as warm-weather pine forests,
but Fs,O3syn should retain its quality
everywhere.
SynFlux applications
Spatial patterns of synthetic fluxes
Across the 43 sites in the US shown in Fig. 3, mean Fs,O3syn during the growing season ranges from
0.5 to 11.0 nmol O3 m-2 s-1 with an average of
4.4 nmol O3 m-2 s-1. The highest Fs,O3syn generally occurs in the Midwest
(5–9 nmol O3 m-2 s-1 in Wisconsin, Michigan,
Nebraska, and Ohio) due to its moderate O3 concentrations (Fig. S6)
and moisture levels, which promotes stomatal conductance (Fig. 1). The
western US has higher average O3 concentrations, but generally
lower moisture and stomatal conductance, especially the southwestern US, so Fs,O3syn (0–4 nmol
O3 m-2 s-1) is mostly lower than the Midwest. Land
cover, land management, and plant types can drive large differences in Fs,O3syn between nearby sites, even when
O3 concentrations and meteorology are similar. For example, three
Nebraska sites are all crop fields and O3 concentrations are nearly
identical, but two irrigated fields have higher stomatal conductance and
higher Fs,O3syn than the nearby rainfed field
(6.2 vs. 4.8 nmol O3 m-2 s-1). Two sites in central
California have high gs and Fs,O3syn compared to surrounding sites due to
irrigation and naturally wet soil in the California Delta. A combination of
topography and climate is also an important factor in California: forest
sites in the Sierra Nevada have lower gs and Fs,O3syn than the lowland crops and wetland
grasses. In Oregon, an evergreen needleleaf site regrowing after a fire has
higher gs and Fs,O3syn than two
older forest stands nearby. The differences between nine Wisconsin forest
sites, however, are mostly due to different years of data at each site
combined with interannual variability in Fs,O3syn; fluxes at these sites are similar in
overlapping years.
Mean O3 SynFlux, deposition velocity, and its conductance
components during daytime in the growing season, grouped by plant functional
type (PFT)a.
PFTb
Sites
Site
gs
gns
gc
vd
FO3syn
Fs,O3syn
CUO
CUO3
years
CRO
18
148
0.42±0.17
0.28±0.09
0.68±0.18
0.53±0.12
7.66±1.96
4.77±1.52
24.8±12.4
14.9±9.3
ENF
25
254
0.37±0.10
0.25±0.06
0.60±0.11
0.54±0.10
7.37±1.33
4.61±1.16
20.0±5.69
11.9±6.30
EBF
3
31
0.21±0.02
0.15±0.02
0.36±0.03
0.33±0.03
5.02±0.65
2.90±0.28
12.1±0.81
5.12±0.45
DBF
16
158
0.41±0.14
0.20±0.09
0.60±0.18
0.53±0.15
7.87±2.28
5.37±1.69
28.6±13.8
15.7±6.66
MF
5
83
0.44±0.17
0.19±0.01
0.62±0.15
0.56±0.14
7.82±1.91
5.53±2.15
24.9±10.5
15.9±8.90
WSA
2
25
0.10±0.02
0.31±0.06
0.39±0.04
0.36±0.04
6.14±0.20
1.47±0.31
6.46±1.43
2.54±1.72
OSH
4
14
0.19±0.07
0.29±0.10
0.47±0.10
0.41±0.09
5.69±1.33
2.23±0.87
8.60±3.27
2.27±1.54
CSH
2
15
0.27±0.11
0.29±0.01
0.57±0.09
0.49±0.05
6.78±0.95
3.34±1.24
14.3±5.30
7.62±5.49
GRA
18
136
0.40±0.30
0.24±0.11
0.64±0.26
0.47±0.15
7.04±7.04
4.12±2.45
18.3±10.7
9.90±6.98
WETc
10
53
0.48±0.16
0.27±0.09
0.74±0.21
0.58±0.14
8.80±2.74
5.77±2.08
25.1±9.65
19.4±15.6
a Values are the mean ± standard deviation
across sites within each PFT. Units are cm s-1 for gs,
gns, gc, and vd; nmol O3 m-2 s-1 for FO3syn and
Fs,O3syn; and mmol O3 m-2 for
CUO and CUO3. b CRO: crop, ENF: evergreen needleleaf forest, EBF:
evergreen broadleaf forest, DBF: deciduous broadleaf forest, MF: mixed
forest, WSA: woody savanna, OSH: open shrubland, CSH: closed shrubland, GRA:
grassland, WET: wetland. c Fluxes may be overestimated at wetland
sites due to evaporation of surface water affecting the calculation of gs, but any errors are likely modest because the gs
values here are reasonable (Drake et al., 2013).
Variability across the 60 sites in Europe is controlled by similar factors.
Stomatal uptake ranges from 1.4 to 9.6 nmol
O3 m-2 s-1, with an average of 4.7 nmol
O3 m-2 s-1 (Fig. 3). The Mediterranean region has high
O3 concentrations (Fig. S8) but generally low stomatal conductance
due to the dry climate (Fig. 1). Within this region, vegetation type explains
broad patterns. Shrub sites in Spain, France, and Sardinia have very low gs (∼0.15 cm s-1), so Fs,O3syn is low (1–3 nmol
O3 m-2 s-1), while most of the sites in mainland Italy
are broadleaf and evergreen forests that have slightly greater gs (∼0.2–0.4 cm s-1) and Fs,O3syn
(3–6 nmol O3 m-2 s-1), despite similar climate and
O3. In central and northern Europe, temperate climate promotes
higher stomatal conductance, while O3 concentrations remain modest
throughout the growing season. The largest Fs,O3syn is 9.8 nmol
O3 m-2 s-1 at a deciduous broadleaf forest in
Switzerland, while nearby evergreen forests, cereal crops, and grasslands all
have lower fluxes (6–8 nmol O3 m-2 s-1). While
Finland has a generally low Fs,O3syn of
2–5 nmol O3 m-2 s-1, the high end of this range is
similar to rural sites in Germany, illustrating that O3 can impact
remote ecosystems with high stomatal conductance, even where O3
concentrations are low.
Table 2 quantifies SynFlux, O3 deposition velocity, and
conductance for each plant functional type. Wetlands, crops, and forests have
the highest average Fs,O3syn, which is about
2 times higher than woody savanna or shrublands, the vegetation types with
the lowest Fs,O3syn. At wetland sites, gs andFs,O3syn could be
overestimated due to evaporation of surface water (Sect. 2.1), but any error
is likely modest because our estimates of stomatal conductance at these sites
(0.48±0.16 cm s-1; Table 2) are reasonable for wetland
vegetation (up to 1 cm s-1; Drake et al., 2013). The vegetation types
rank in the same order for stomatal conductance, again showing stomata as the
main control on O3 uptake into vegetation. Stomatal uptake exceeds
non-stomatal uptake for all plant functional types except woody savanna and
shrubland. O3 deposition velocities reported in Table 2 fall within
the ranges of past literature, as reviewed by Silva and Heald (2017).
However, while Silva and Heald found that the mean deposition velocity was
greater over deciduous forests than coniferous forests, crops, or grass, we
do not. Rather, we find that variability between sites within each of these
categories is large, having a standard deviation of about 30 % of the
multi-site mean.
Metrics for O3 damage to plants
Since O3 injures plants mainly by internal oxidative damage after
entering the leaves through stomata, the most physiological predictor of
plant injuries is the cumulative uptake of O3 (CUO, Reich, 1987;
Fuhrer, 2000; Karlsson et al., 2004; Cieslik, 2004; Matyssek et al., 2007).
CUO is defined as the cumulative stomatal O3 flux exceeding a
threshold flux Y that can be detoxified by the plant, integrated over a
period of time:
CUOY=∑iH(Fs,O3,i-Y)(Fs,O3,i-Y)Δti.
Here, H(x) is the Heaviside step function and Δti is
the time elapsed during measurement of Fs,O3,i. The sum is
carried out over time i in the growing season, which we define based on
GPP (Sect. 2.1). The detoxification threshold varies across vegetation types,
even among related species (Karlsson et al., 2004; Büker et al., 2015),
and thresholds for specific FLUXNET sites are generally unknown. As a
compromise, we calculate CUO, with Y=0, and also CUO3, with Y=3 nmol
O3 m-2 s-1, which has been suggested as a reasonable
generic threshold (Mills et al., 2011). CUO is always greater than CUO3, but
the sites with high CUO tend to also have high CUO3, so their spatial
patterns are similar (Fig. S8).
While CUO is a physiological dose, concentration-based metrics remain common
for assessing ozone impacts because they are easier to measure.
Concentration-based metrics quantify O3 in ambient air irrespective of
whether that O3 enters leaves. These metrics follow the general form
M=∑iwχiχi-χcΔti,
where w(χ) is a weighting function applied to the O3 mole
fraction χ, and χc is a constant. Like CUO, the sum is
usually over time i during the growing season. Three of the most common
concentration-based O3 metrics are the mean O3
concentration, the accumulated concentration over a threshold of 40 ppb
(AOT40; UNECE, 2004), and the sigmoidal-weighted index (W126; Lefohn and
Runeckles, 1987). For mean, wχ=∑Δti-1 and χc=0. For AOT40, w(χ)=H(χ-χc) and χc= 40 ppb. For W126, wχ=1+4403exp-126ppb-1χ-1 and χc=0. Both AOT40 and
W126 use only daytime (08:00–20:00) measurements and W126 also takes the
maximum value over all 3-month periods during the growing season. The
weighting functions for AOT40 and W126 give little or no weight to
O3 concentrations below 40 ppb. In addition, W126 gives increasing
weight to concentrations up to about 110 ppb and full weight for higher
concentrations based on the understanding that exposure to high O3
concentrations is more injurious than moderate or low concentrations. Other
concentration-based metrics (e.g., SUM60) use other thresholds or weighting
functions, but many are strongly correlated with AOT40 or W126 or otherwise
qualitatively similar (Paoletti et al., 2007).
Comparison of cumulative uptake of O3 (CUO) to
concentration-based metrics of O3 exposure during the daytime growing
season at 103 sites: mean O3 concentration (a), AOT40 (center), and
W126 (b). There is one value (dot) per site per year. Colors show mean
vapor pressure deficit during the growing season.
The spatial patterns of AOT40 and W126 closely resemble that of mean
O3 concentration in the US and Europe despite their different
weighting functions (Fig. S9). AOT40 and W126 are well correlated with each
other across sites (R2=0.87) and with mean O3 mole fraction
(R2=0.76 and R2=0.52 for mean O3 vs. AOT40 and
W126, respectively) despite their different weighting functions. As a result,
all of these concentration-based metrics have similar spatial patterns in the
US and Europe. The CUO and CUO3 spatial patterns, however, are similar to Fs,O3syn and distinct from the
concentration-based metrics. This illustrates that locations with high AOT40
or W126, like the southwestern US or Mediterranean Europe, can have low CUO.
Even though concentration-based metrics do not measure the physiological
O3 dose to plants, they can be useful if the metric is proportional
to the flux-based dose and injuries. Indeed, many controlled experiments and
observational studies have documented correlations between both AOT40 and
W126 and either uptake or plant injuries (e.g., Fuhrer et al., 1997; Cieslik,
2004; Musselman et al., 2006; Matyssek et al., 2010). However, many of these
studies were carried out at a single site or under conditions where stomatal
conductance was relatively steady while O3 concentrations varied,
for example by maintaining well-watered soil. When stomatal conductance
varies widely, such as between arid and humid climates or seasons,
concentration-based metrics may not correlate with stomatal O3 flux
(Mills et al., 2011).
Figure 6 shows that all of the concentration-based metrics are poorly
correlated with CUO across the sites (AOT40: R2=0.05, W126: R2=0.03, mean O3: R2=0.04). Humidity helps explain some of the
scatter in Fig. 6. The sites with high concentration-based metrics and low
CUO have high vapor pressure deficit (VPD) and low stomatal conductance, and
are mostly in the western US and Mediterranean Europe. Restricting the
analysis to humid sites (VPD < 1.5 kPa) does not improve the
correlation (R2≈0.05) and at the arid sites
(VPD > 1.6 kPa) the concentration-based metrics are modestly
anti-correlated with CUO (AOT40: R2=0.19, W126: R2=0.05, mean
O3: R2=0.37). This result reinforces that
concentration-based metrics can misrepresent CUO and plant injuries (Mills et
al., 2011).
From the CUO values in Table 2, we can estimate the range of O3
impacts on biomass production at the FLUXNET sites. Although species vary in
their sensitivity to O3 (Lombardozzi et al., 2013), several studies
suggest that the biomass production of broadleaf and needleleaf trees
decreases by 0.2 % to 1 % per mmol O3 m-2 of CUO
(Karlsson et al., 2004; Wittig et al., 2007; Hoshika et al., 2015). Combining
the mean CUO for each plant functional type (Table 2) with these
sensitivities, our work implies that O3 reduces the biomass
production at these FLUXNET sites by 6 %–29 % for deciduous
broadleaf forests and 4 %–20 % for needleleaf forests. The range
represents the spread of reported dose–response sensitivities within each
plant type, meaning the least and most O3-sensitive species.
Several broadleaf crops are more sensitive to O3, with biomass
reductions of 1.3 %–1.6 % per mmol O3 m-2 of CUO3
(Mills et al., 2011). That sensitivity implies a 20 %–24 % drop in
biomass production at FLUXNET crop sites. Some studies have quantified
O3 dose–response relationships with other thresholds Y=1.6 to
6 nmol O3 m-2 s-1 (e.g., Karlsson et al., 2007;
Pleijel et al., 2004, 2014), but the sensitivities have a similar magnitude.
Fares et al. (2013) also demonstrated 12 %–19 % reduction in gross
primary production due to O3 at some of the same crop and forest
FLUXNET sites. Using prognostic models of O3 concentrations and
stomatal uptake, several past studies have also suggested that O3
reduces biomass production and CO2 sequestration by
4 %–20 % in the US and Europe (Sitch et al., 2007; Wittig et al.,
2007; Mills et al., 2011; Yue et al., 2014, 2016; Lombardozzi et al., 2015).
Our results support this range of impacts, although some FLUXNET sites and
species likely experience greater O3 injury, but here the CUO is
highly constrained from observations and therefore avoids the additional
uncertainties of atmosphere–biosphere models.
Conclusions
We have demonstrated a method to estimate O3 fluxes and stomatal
O3 uptake at eddy covariance flux towers wherever regional
O3 monitors exist. The method, called SynFlux, derives stomatal
conductance and O3 deposition velocity from standard eddy
covariance measurements and combines them with gridded O3
concentrations from air quality monitoring networks. We apply this method to
the FLUXNET2015 dataset and derive synthetic flux estimates at 43 sites in
the United States and 60 sites in Europe, totaling 926 site years of
observations. O3 deposition measurements have previously only been
sporadically available for a few sites around the world, so this work
dramatically increases the flux data available for understanding O3
impacts on vegetation and for evaluating air quality and climate models.
Three sites with long-term O3 flux measurements provide an
independent test of SynFlux. These comparisons show that daily averages of
synthetic stomatal Fs,O3syn correlate well
with Fs,O3 observation-derived Fs,O3obs (R2=0.83–0.93) and have a
mean bias under 22 % at all sites. At all three sites 95 % of the
synthetic Fs,O3syn values differ from
measurements by a factor of 2 or less. The differences between Fs,O3syn and Fs,O3obs are also consistent with propagated
uncertainty in the underlying measurements. Synthetic total deposition, FO3syn, is sensitive to errors in the
parameterized non-stomatal conductance, but mean values are still with a
factor of 2 of observations. The errors in this dataset are modest compared
with differences between observations and regional and global atmospheric
chemistry models that are frequently a factor of 2 or more (Zhang et al.,
2003; Hardacre et al., 2015; Clifton et al., 2017; Silva and Heald, 2017),
illustrating the utility of this dataset for evaluating models and
O3 impacts.
Across flux tower sites in the US and Europe, Fs,O3syn ranges from 0.5 to 11.0 nmol
O3 m-2 s-1 during the summer growing season. The
spatial pattern of Fs,O3syn is mainly
controlled by stomatal conductance rather than O3 concentration.
Patterns of stomatal conductance and Fs,O3syn
in turn are explained by climate, especially atmospheric and soil moisture,
vegetation types, and land management, such as irrigation. O3
concentration-based metrics (AOT40, W126, mean O3) have been widely
used to evaluate O3 damages to plants because they are easier and
cheaper to measure than the cumulative uptake of O3 (CUO) into
leaves. However, these metrics have very little correlation with CUO (R2≤0.05) across FLUXNET sites. Using dose–response relationships
between CUO and biomass reduction, we estimate that O3 reduces
biomass production and carbon uptake by 4 %–29 %, depending on the
site and plant type. Unlike most past estimates, which have used prognostic
models of O3 uptake, our assessment of biomass reduction is based
on O3 fluxes that are tightly constrained by observations. To
promote further applications in ecosystem monitoring and modeling, the
SynFlux dataset is publicly available as monthly averages of Fs,O3syn, FO3syn,
O3 deposition velocity, stomatal conductance, and related
variables.