Surface ozone (O3) pollution levels are strongly
correlated with daytime surface temperatures, especially in highly polluted
regions. This correlation is nonlinear and occurs through a variety of
temperature-dependent mechanisms related to O3 precursor emissions,
lifetimes, and reaction rates, making the reproduction of temperature
sensitivities – and the projection of associated human health risks – a
complex problem. Here we explore the summertime O3–temperature
relationship in the United States and Europe using the chemical transport
model GEOS-Chem. We remove the temperature dependence of several mechanisms
most frequently cited as causes of the O3–temperature “climate
penalty”, including PAN decomposition, soil NOx emissions, biogenic volatile organic compound (VOC)
emissions, and dry deposition. We quantify the contribution of each
mechanism to the overall correlation between O3 and temperature both
individually and collectively. Through this analysis we find that the
thermal decomposition of PAN can explain, on average, 20 % of the overall
O3–temperature correlation in the United States. The effect is weaker
in Europe, explaining 9 % of the overall O3–temperature relationship.
The temperature dependence of biogenic emissions contributes 3 % and 9 %
of the total O3–temperature correlation in the United States and Europe
on average, while temperature-dependent deposition (6 % and 1 %) and
soil NOx emissions (10 % and 7 %) also contribute. Even considered
collectively these mechanisms explain less than 46 % of the modeled
O3–temperature correlation in the United States and 36 % in Europe.
We use commonality analysis to demonstrate that covariance with other
meteorological phenomena such as stagnancy and humidity can explain the bulk
of the remainder of the O3–temperature correlation. Thus, we
demonstrate that the statistical correlation between O3 and temperature
alone may greatly overestimate the direct impacts of temperature on O3,
with implications for the interpretation of policy-relevant metrics such as
climate penalty.
Introduction
Tropospheric ozone (O3) negatively influences human health,
agricultural crop yields, and ecosystem integrity
(Monks
et al., 2015; World Health Organization, 2006; Tai et al., 2014; Fuhrer et
al., 2016). As a secondary pollutant, O3 is not directly emitted from
natural or anthropogenic sources, but rather forms as a result of
photochemistry in the presence of precursors including nitrogen oxides
(NOx), carbon monoxide (CO), and volatile organic compounds (VOCs).
While the chemical processes leading to the formation of tropospheric
O3 are well understood, the sensitivity of O3 production to
changes in ambient conditions and precursor concentrations is complex and
nonlinear. Local NOx and VOC emissions are two of the most important
contributors to daytime tropospheric O3 production, but the ratio
between the two can be as important as the overall emission magnitudes
themselves (Sillman, 1999). NOx/VOC emission ratios of
roughly 1:8 produce the highest O3 production rates in simplified box
models (Sillman and He, 2002). Therefore, increases in precursor
emissions might increase, maintain, or even reduce O3 concentrations,
depending on the initial NOx/VOC ratio.
Further contributing to this complexity, O3 formation and transport are
highly sensitive to local meteorological conditions
(Elminir, 2005). Precursor emissions and
concentrations themselves can depend on the weather, for example in the case
of temperature-dependent emission of biogenic VOCs from vegetation
(Guenther et al., 1995). As
a product of photochemical reactions, tropospheric O3 formation also
requires sunlight, and can be sensitive to atmospheric stability, transport,
and mixing conditions. Hot, sunny, stagnant conditions are often associated
with the greatest risk of extreme O3 events, as these days typically
provide the ideal combination of precursor concentrations, photochemical
reactions, and stable conditions for the pollutant to form and persist over
an extended period of time
(Jacob et al.,
1993; Lin et al., 2001).
Because of this sensitivity to climate, increases in continental surface
O3 has been identified as a possible negative side effect of a warming
climate, a relationship commonly referred to as the “ozone climate
penalty”. First coined in 2008 by Wu et
al., the climate penalty quantifies the additional ozone present in a warmer
environment, as well as the additional anthropogenic emissions reductions
necessary to compensate for this enhanced O3 production. Given a 2–5 ppbv increase in O3 expected with 2050 climate projections, Wu et al. (2008)
concluded that an additional 10 % reduction in NOx emissions would be
necessary to mitigate these climate-driven ozone increases, above and beyond
the ongoing reduction in NOx emissions observed across much of the
industrialized northern midlatitudes. This climate penalty is highly
region-specific, depending on both current local conditions as well as the
nature of future changes. In related work,
Bloomer et al. (2009) defined the slope of the
observed daily O3–temperature correlation as the “climate penalty
factor”, and found a decreasing trend in this factor over time as a result
of NOx emission reduction efforts.
While not synonymous, the long-term climate penalty defined by Wu et al. (2018) and
the daily climate penalty factor calculated by Bloomer et al. (2009) can be
understood to be driven by a similar set of temperature-dependent
mechanisms. Previous work has examined this temperature–O3 relationship
and identified several mechanisms most likely to be responsible, in
particular temperature-dependent biogenic VOC emissions and PAN dissociation
rates (Jacob
et al., 1993; Sillman and Samson, 1995; Jacob and Winner, 2009).
Additionally, the temperature dependence of natural soil NOx emissions
(Yienger and Levy, 1995) and O3 dry
deposition (Wesely, 1989) have been recognized in previous
studies, and could contribute to the overall O3–temperature
correlation. Each of these four mechanisms is included in typical chemical
transport models (CTMs) used to study atmospheric chemistry, making these
models useful tools for estimating the relative contributions of each
mechanism to the overall O3–temperature relationship.
Model description
To investigate the relative importance of each temperature-dependent
mechanism in governing the overall O3–temperature relationship we
explore multiple regional sensitivity cases with the chemical transport
model GEOS-Chem v9-02 (http://www.geos-chem.org, last access: 7 August 2018). GEOS-Chem is driven by
assimilated meteorology from the NASA Global Modeling and Assimilation
Office (GMAO); here we use the GEOS-5 product for 2010–2011. Our simulations
over North America and Europe are performed at the native grid horizontal
resolution of 0.5∘×0.667∘ with 47 vertical levels.
Boundary conditions are provided from a global GEOS-Chem simulation at
2∘×2.5∘ horizontal resolution.
The default tropospheric chemical mechanism in GEOS-Chem v9-02 includes a
description of NOx–hydrocarbon–O3–aerosol chemistry with over 120
species which participate in over 400 kinetic and photolytic reactions
(Mao et al., 2013). To better
capture the temperature dependence of O3 formation as a result of
biogenic emissions, we add monoterpene chemistry to the standard GEOS-Chem
v9-02 gas-phase mechanism following
Fisher
et al. (2016), as in Porter et al. (2017). We use the EPA's NEI2005
emissions inventory for anthropogenic emissions over the United States after
scaling them up to match NEI2011 national totals for the years 2010 and
2011, then reducing NOx emissions following the recommendations of
Travis et al. (2016). European anthropogenic emissions are taken from EMEP
inventories (Auvray and Bey, 2005). To
represent global biomass burning we use the GFED3 inventory
(Mu et al., 2011). NOx
emissions from lightning are treated using a modified parameterization first
developed by Price and Rind (1992) and further constrained by
satellite data (Murray et al.,
2012). Soil NOx emissions and biogenic hydrocarbon emissions are calculated
online following the Hudman et al. (2012) and MEGAN2.1 (Guenther et al.,
2012) schemes. Dry deposition is modeled using the Wesely “resistor in
series” approach (1989). Wet removal includes contributions
from scavenging in convective updrafts, in-cloud rainout, and below-cloud
washout and is described by
Amos
et al. (2012).
GEOS-Chem has been shown to reproduce key spatiotemporal features of surface
and column ozone observations, though biases and uncertainties are also
known
(Zhang
et al., 2011; Hu et al., 2017). In particular, uncertainties in
anthropogenic emission inventories
(Travis
et al., 2016), various drivers of biogenic emissions
(Arneth
et al., 2011; Vinken et al., 2014), and lightning NOx
(Murray, 2016) have been found to play important
roles in the variability of tropospheric ozone and its precursors.
Uncertainties in spatial inputs, including the datasets used to drive
biogenic emissions such as plant functional type and leaf area index
distributions, can also influence the resulting biogenic emissions and ozone
impacts, and changes or updates to these inputs would influence the
magnitude and distribution of the resulting temperature sensitivities
(Guenther et
al., 2006; Arneth et al., 2011). Ongoing advances in the development of
chemical mechanisms relevant to ozone formation and loss
(Mao
et al., 2013; Sherwen et al., 2016) have also underscored the importance of
chemistry. While a full analysis of the sensitivity of the O3–T
relationship to each of these factors is beyond the scope of this work,
uncertainties in these and other modeled parameters and inputs can all
influence both overall ozone production and the temperature
sensitivities examined here.
Ozone–temperature mechanisms in the GEOS-Chem model
The temperature dependence of biogenic VOC emissions (especially
those of isoprene) has been frequently cited as an important mechanism
contributing to the observed O3–temperature correlation
(Wu
et al., 2008; Jacob and Winner, 2009; Doherty et al., 2013; Rasmussen et
al., 2013), but the magnitude of this biogenic contribution to
O3–temperature sensitivity remains uncertain. Additional VOC emissions
on hot days would be expected to increase O3 production in areas high
in NOx, but other areas – especially those with a particularly low
NOx/VOC ratio – might show constant or even reduced O3 levels due
to ozone quenching (Loreto and Velikova, 2001),
leading to an inverse relationship. Biogenic emissions also do not
necessarily vary linearly with temperature. Isoprene emissions, for example,
are observed to plateau and eventually shut down completely at very high
temperatures (Harley et al., 1999). Representative isoprene
and monoterpene emissions response curves are shown in
Fig. 1a, based on the GEOS-Chem implementation of MEGAN2.1. In the United
States, isoprene and monoterpene emissions are highest in the southeast
region, where high temperatures and foliage density provide ideal conditions
in summer months (Fig. 2a and b). Europe is characterized by much lower
emissions of isoprene overall, though monoterpene emitters are relatively
common across the region (Fig. 2a and b).
Representative temperature-dependent mechanism responses within GEOS-Chem for biogenic emissions (a), soil NOx emissions (b), PAN lifetime (c), and stomatal resistance (d).
Summer mean values (JJA 2010–2011) for modeled isoprene and monoterpene emissions (a, b), soil NOx emissions (c), surface PAN mixing ratios (d), O3 deposition velocity (e), and NOx/VOC sensitivity as represented by the surface H2O2/HNO3 ratio (f).
While NOx levels in the lower troposphere are dominated by
anthropogenic sources throughout the year, natural processes can also play
an important role (Zhang et al., 2003). Of the commonly
recognized biogenic sources of NOx, emissions of NO as a result of
microbial activity in the soil have the clearest and most widely observed
temperature relationship (Williams et al.,
1992). Building upon the work of Hudman et al. (2012) and others, GEOS-Chem includes an exponential temperature-dependent
factor for soil NOx emissions, with plateaus at 30 ∘C (Fig. 1b), along with additional factors to account for vegetation type, soil
moisture, fertilizer treatment, and canopy losses. This scheme has been
shown to produce NO2 levels in broad agreement with satellite
observations in terms of spatial and temporal variability, though a
systematic underprediction in model results suggests that modeled soil
emissions may need to be further increased overall
(Vinken et al.,
2014). Modeled summer NOx emissions vary greatly by location, peaking
in the American Midwest and southern European countries
(Fig. 2c).
As a so-called NOx reservoir species, PAN (CH3COO2NO2)
serves as an important means of nitrogen transport and is one of the primary
chemical links between O3 and daytime temperature. A product of
reactions between non-methane VOCs and NOx, PAN has an atmospheric
lifetime that is typically longer than its ozone-producing precursors.
However, due to the temperature dependence of its primary sink – thermal
decomposition – this lifetime varies significantly based on meteorological
conditions, with warmer temperatures favoring PAN decomposition and thus
local NOx production
(Fig. 1c and Fischer et al., 2014). This temperature sensitivity has
been identified as a dominant reason for the O3–temperature
relationship in past measurement and modeling studies
(Beine
et al., 1997; Dawson et al., 2007; Jacob and Winner, 2009). PAN
concentrations tend to correlate with NOx emissions, and therefore
modeled concentrations peak in the eastern United States as well as central
Europe (Fig. 2d), where anthropogenic emissions are highest.
Depositional loss to vegetation and other surfaces is a key sink of O3
and other pollutants. Traditional models of dry deposition processes use a
“resistor-in-series” approach, in which barriers to O3 deposition
through various pathways are parameterized and represented as an electrical
circuit (Wesely, 1989). This model has had some success in
reproducing observed patterns of O3 deposition velocities, though large
uncertainties remain due to the scarcity of long-term measurements
(Silva and Heald, 2018). In the Wesely
resistance scheme, surface temperature influences deposition rates in two
ways: through a stomatal resistance term that is very high at two extremes
(typically freezing temperatures and around 40 ∘C) and
reaches a minimum at some ideal temperature (Fig. 1d), and an
exponentially decreasing nonstomatal term designed to reduce deposition over
frozen (or nearly frozen) surfaces. In typical summer environments across
the United States and Europe, only the stomatal term is relevant in
practice, linking extremely high temperatures with increased stomatal
resistance, thereby increasing local O3 levels on very hot days. While
observations of O3 dry deposition velocities relative to meteorological
drivers show mixed results (Clifton
et al., 2017), in principle large-scale increases in stomatal resistance as
a result of changes in temperature could lead to increases in O3
concentrations. Summer O3 deposition velocities across the United
States and Europe as simulated by GEOS-Chem tend to range from 0.2 to 0.5 cm s-1, depending on local surface type and climatology (Fig. 2e).
Methodology
To represent our control case we use a 2-year base scenario (BASE) for
2010–2011 in which temperature-dependent processes within GEOS-Chem are
unchanged. We then sequentially remove the temperature dependence from the
four key O3–T mechanisms discussed in Sect. 2.1 to explore the impact
that each has on the overall O3–T relationship over a 3-month summertime period (JJA), with an additional month for spinup (Table 1). Finally,
we run nested regional simulations for each year over the United States and
Europe, again discarding the first month of each run to focus on the three
summer months (JJA). To isolate the impact of temperature dependence on
biogenic emissions (BIO case), dry deposition (DEP case), and soil NOx
emissions (SOIL case), we generate a set of hourly temperatures representing
the mean summer (JJA) value at each nested grid cell. To do so, we generate
mean hourly temperatures for each modeled grid cell by averaging each hour
(0 through 23) across the 3 modeled months. This averaged diurnal cycle is
then substituted into each examined mechanism in turn, resulting in a
repeating temperature profile being applied to calculations related to the
modified mechanism. Through this procedure, diurnal patterns are preserved
while day-to-day temperature variability for that mechanism is removed,
preventing it from directly influencing the overall daily
O3–temperature correlation. In the PAN case, the default GEOS-Chem
chemical mechanism is modified to remove temperature dependence from PAN
dissociation by assuming a local constant temperature of 15 ∘C everywhere for that particular reaction.
Summary of GEOS-Chem cases.
CaseModifications from default GEOS-ChemBASEReduced United States NOx, added monoterpene chemistryBIOBASE, plus normalized temperature for biogenic VOC emissionsSOILBASE, plus normalized temperature for soil NOx emissionsDEPBASE, plus normalized temperature for dry depositionPANBASE, plus removed temperature dependence for PAN thermal decompositionALLBASE, plus all changes from BIO, SOIL, DEP, and PAN cases
To confirm that our four chosen mechanisms (biogenic VOC emissions, soil
NOx emissions, PAN dissociation, and dry deposition) are in fact
collectively responsible for most of the direct connection between
temperature and O3 within GEOS-Chem, we perform an additional set of
sensitivity tests over each of our regional domains. In one modified case we
uniformly increase all temperatures by 1 ∘C, resulting in
widespread increases in average surface O3 levels (Fig. 3a). In a
second modified case we again increase temperature by 1 ∘C,
but decouple temperature from the four chosen mechanisms using original mean
hourly temperatures as described above. In the decoupled case, surface
O3 shows negligible differences in mean surface O3 (Fig. 3b
and c), indicating that the four decoupled mechanisms dominate the directly
modeled O3–T relationship, with the residual O3 changes likely
resulting from temperature-dependent chemical kinetics for species other
than PAN.
Increase in O3 with a 1 ∘C increase in temperature in the BASE case (a) and with fixed temperature mechanisms in the ALL case (b). Distribution of changes for each shown in boxplots (c).
For observational comparison, we use data from the EPA's Air Quality System
(AQS) network of monitoring sites (US Environmental
Protection Agency, 2016), as well as the AirBase air quality database maintained
by the European Environment Agency (EEA).
Results and discussion
The simulated O3–temperature relationship in GEOS-Chem for the two
modeled summers, as represented by the slope of a gridded O3–T ordinary
least-squares (OLS) regression, is fairly consistent with AQS and AirBase
observations, lending confidence to the use of modeled sensitivity
comparisons to examine the significance of underlying mechanisms (Fig. 4a). In both the United States and Europe, spatial patterns and overall mean
values of the O3–T correlation are fairly well represented, though the
full range of sensitivities is not reproduced in the model output (root-mean-square error of 0.84 and 0.79, mean bias of 0.02 and -0.13 ppbv O3∘C-1
for the United States and Europe respectively). In spite of the relatively
strong agreement between modeled and observed O3–T correlations, we
highlight a number of shortcomings in the modeled representation of this
relationship which may explain the remaining discrepancies between the model
and observations. For one, the anthropogenic emission inventories used in
GEOS-Chem are independent of daily temperatures, while in reality there are
connections between meteorological variability and emissions from human
activities such as transportation and energy production. In addition, the
grid cell size in GEOS-Chem is incapable of capturing the full diversity of
subgrid meteorological phenomena, many of which may be important at the
surface station level. Local temperature and O3 fluctuations may vary
significantly from those of the gridded average. These issues, among others,
may contribute to some of the differences seen in the comparison between
observed and modeled sensitivities. In particular, the magnitude of both
high and low extremes tends to be underestimated in gridded output from
GEOS-Chem, resulting in a tighter distribution of modeled output and skewed
slope of modeled vs. observed values, especially in Europe (Fig. 4c).
However, in spite of the notable differences between modeled and observed
O3–T relationships at the tails of the distributions, a relatively
small overall bias is apparent across station types in both urban and remote
regions (Fig. 4b). Here, the more remote stations associated with the
National Park Service (NPS) are separated from the rest of the AQS dataset
for comparison over the United States, while AirBase stations in Europe are
split by station area category (urban/suburban and rural). In each category,
nearest-neighbor grid cells effectively capture the center of the observed
distribution, even though extremes are not fully represented, particularly
at rural European stations.
Regression slopes of summer (JJA) daily maximum 8 h average O3 vs. daily maximum temperature for GEOS-Chem and station observations in the United States and Europe. Station data points are overlaid on gridded model output in panel (a). Distributions for observed (black) and modeled (red) O3–T slopes are shown in panel (b), further separated by station category: US stations are separated between the more remote NPS stations and the remaining stations of the AQS network, while European stations are split by AirBase area category into urban/suburban and rural station types. Scatterplots in panel (c) show modeled values vs. observed, with green points used to mark NPS stations in the US and rural area stations in Europe. The remaining AQS stations, as well as those AirBase stations categorized as urban or suburban, are shown as black points.
Given that the mean values and spatial distribution of regional O3–T
sensitivities are generally consistent with observations, we analyze the
mechanisms contributing to modeled sensitivities by decoupling them from
temperature variability individually and simultaneously. Removing
temperature dependence from the four chosen mechanisms has noticeable
impacts on correlations between temperature and O3 in the simulated
cases, with regional differences apparent in each case. For each of the four
cases examined, the strength of the O3–temperature dependence (measured
via the coefficient of determination R2) was examined through linear
regression and compared to that seen in the BASE case. When subtracted from
the BASE values, the resulting difference in R2 can be understood as
the contribution of that particular mechanism to the overall modeled
sensitivity (Fig. 5).
Impact of temperature dependence of biogenic emissions, O3 dry deposition, soil NOx emissions, PAN lifetime, and all mechanisms at once. Plotted values show the difference between O3–temperature correlation in the BASE case and that of the modified case in which dependence on daily temperature variability is removed.
Temperature-dependent biogenic VOC emissions have a positive impact on
O3–temperature correlation through most of the United States,
especially around urban centers, but have a negative impact across much of
the southeast. This is consistent with expectations based on NOx/VOC
ratios (Fig. 2f), in which NOx-rich regions experience a boost in
O3 production when rising temperatures lead to additional VOC
emissions. Much of the southeast region of the United States, however, is
already saturated in VOCs (primarily isoprene), and thus additional
emissions on hot days reduce O3 production efficiency, or even act as
an O3 sink. The heavily forested northern regions of Europe are
likewise less influenced (or even negatively influenced) by the temperature
dependence of biogenic emissions, while the high-NOx regions of central
and southern Europe show strong positive contributions. Changes in R2
reach up to 0.14 and 0.21 in the United States and Europe, respectively,
representing on average 3 % and 9 % of the overall regional O3–T
correlation (Fig. 5).
The impact of temperature dependence in dry deposition is distributed
roughly congruently with leaf area index (LAI) coverage across the United States, contributing
up to 0.14 to the O3–TR2 but only 0.02 on average. Little effect
is seen in the heavily forested regions of Northern California and the
Pacific Northwest, but since deposition is a removal effect and O3
levels are relatively low in those regions to begin with, changes in
deposition rates could be expected to have minimal impact on the overall
O3–temperature relationship there. Relative contributions of deposition
on a local basis, however, can represent over one-quarter of the overall
O3–T correlation in some US locations. The overall impact of
temperature-dependent dry deposition is even less pronounced in Europe,
reaching up to 0.08, but averaging less than 0.01 across the region.
Temperature-dependent soil NOx emissions contribute around 0.04 to the
coefficient of determination in both regions, representing 10 % of the
total R2 value in the United States and 7 % in Europe. Notably, the
impact of temperature dependence in soil emissions does not match up
directly with the overall magnitude of those emissions themselves (Fig. 2c), indicating that this fluctuation represents a relatively minor and
diffuse effect. Areas characterized by lower NOx/VOC ratios due in part
to low NOx emissions (Fig. 2f) are also more likely to exhibit
stronger sensitivity to temperature-driven soil NOx variability.
The temperature dependence of PAN decomposition is a strong contributor to
the O3–temperature relationship in both the United States and Europe,
particularly in the American Midwest, where the positive impact of this
mechanism reaches 0.32. Impacts are also visible across most of the eastern
United States, as well as the Southern California and Central Valley regions,
and the O3–TR2 increases by 0.07 on average in the US (almost
20 % of the total mean). PAN temperature sensitivity is also a strong
contributor to the O3–T relationship in Europe by up to 0.14 (9 % of
total mean R2). Of the examined model mechanisms in the United States,
PAN lifetime is the strongest overall contributor to the correlation between
O3 and temperature, though it places a close second to biogenic
emissions in Europe.
While each modeled mechanism contributes to the overall O3–T
relationship in the United States and Europe, none of them come close to
completely explaining the BASE case correlation between O3 and T. Even
when all temperature-dependent mechanisms are removed from the model (the
ALL case), most regions still show O3–temperature sensitivities of
50 % or more of their original BASE values as measured by R2. While
there are uncertainties associated with comparing statistical sensitivities
across these simulated cases, it seems clear that the O3–temperature
relationship cannot be fully (or even mostly) explained by these four
mechanisms within GEOS-Chem (Fig. 5).
Beyond the directly temperature-dependent emission and loss mechanisms
examined within GEOS-Chem, many other meteorological effects can influence
surface O3 levels, and correlations between these phenomena and
temperature could show up as part of the observed O3–temperature
correlation. For example, strong winds can act as a removal mechanism for
locally produced O3. If strong winds are also correlated to cooler
temperatures, this would show up as a positive correlation between O3
and temperature, despite the lack of any explicit temperature-dependent
mechanism. While decoupling other meteorological processes from temperature
in the manner demonstrated above can be highly problematic, even within a
model, statistical methodologies such as commonality analysis allow for some
degree of attribution of observed predictive power between temperature and
the other meteorological drivers (Seibold and McPhee,
1979). Derived from the analysis of linear regression output, commonality
analysis involves the calculation of R2 values for all possible
permutations of predictor variables included in the analysis. These R2
values are then compared, allowing for the calculation of explained
variability that is uniquely provided by one variable or another, along with
explained variability that is shared between two or more of the covariates.
For the purposes of this study, “unique” refers to that portion of a
variable's correlation with the response variable (ozone) that is not shared
with any other predictor, while “shared” refers to the portion of the
correlation that could be attributed to multiple predictors. A more detailed
explanation of the equations involved, as well as examples of their
application, can be found in Seibold and McPhee (1979).
To quantify the contributions of meteorological variables to the modeled
O3–temperature correlation, we apply commonality analysis to all
gridded output. Through this methodology we are able to decompose all
gridded surface O3–temperature R2 values into unique and shared
contributions among each of the five variables examined, which are summarized
in Table 2: maximum daily temperature (T), humidity (HUM, represented by dew
point temperature), mean wind speed (WSPD), wind direction (WDIR), change in
mean surface pressure (ΔP), and planetary boundary layer (PBL) height. The unique correlations for each of these variables are shown in
Fig. 6, along with the portion of their correlation shared with any other
variables (in the case of T) or shared with daily maximum temperature (in
the case of the other five meteorological variables).
Unique and shared O3 correlation among meteorological variables in the BASE case. Unique contributions represent predictive power provided by one meteorological covariate alone, while shared correlation could be attributed to one or more other covariates.
Meteorological variables examined.
VariableDescriptionTMaximum daily temperatureHUMMean daily vapor pressure (humidity)WDIRNormalized U and V wind vectorsWSPDMean daily wind speedΔPChange in daily mean surface pressurePBLMaximum daily planetary boundary layer height
Each unique component represents the portion of explained variability
that could be explained solely by one meteorological variable among the six,
meaning that the R2 value would be expected to drop by that amount if
the predictor were removed from the linear fitting equation. Shared
components can be understood as overlap between predictor variable
contributions, meaning that the actual mechanism responsible for the
correlation might reasonably be attributed to any of the involved
predictors. While this methodology is imperfect, especially given the
assumption that not all relevant meteorological processes are represented by
these six predictors, it does provide additional insight into how and where the
O3–temperature correlation might be at least partially explained by
correlation with other meteorological phenomena.
As shown, temperature has the strongest and most widespread unique
correlation with O3 variability of any of the six meteorological
variables included in both the United States and Europe. However, even this
strong unique contribution is significantly less than the magnitude of the
shared component, meaning that collectively the remaining five predictors could
potentially explain the majority of the predictive power that temperature
offers alone. The overall predictive power for each meteorological variable,
along with the respective shared and unique components, can be further
visualized through their mean values across all grid cells. Figure 7 shows
region-averaged attribution of shared and unique correlation through stacked
columns: each individual column height shows the total correlation (in the
form of R2) between ozone and a single meteorological variable, while
individually shaded sections differentiate unique and shared components. In
each meteorological column that particular variable's unique contribution is
at the bottom, and shared contributions are grouped where possible into
clusters of two or three total variables for clarity. To best represent the
unique contribution of temperature, commonality analysis presented here is
performed on the ALL case, with all four chosen temperature-dependent
mechanisms decoupled. The difference in total correlation between ALL and
BASE cases (which are driven by identical meteorology) is then added into
the unique temperature contribution, as this gap can be fully attributed to
temperature dependence. Therefore, performing commonality analysis on the
BASE case alone would underpredict the unique temperature contribution
since some percentage of variability driven specifically by
temperature-dependent mechanisms could also correlate with other
meteorological variables. Combining commonality analysis along with the
results of the BASE-ALL comparison makes full use of the attribution
information contained in each since any lost correlation with temperature-dependent mechanisms turned off can be attributed directly to temperature
alone, better constraining the commonality analysis itself. Through this
analysis it is apparent that over half of the O3–temperature
relationship in the United States and Europe (shown by the leftmost bar in
each panel) can be explained through correlation with one or more
meteorological covariates, especially wind direction, humidity, and
planetary boundary layer height. Europe shows an even stronger overall
correlation between temperature and O3, and much of that increase
appears to be related to a stronger influence from wind and humidity.
Unique and shared contributions to O3 correlation for each of six different meteorological variables in the BASE case. Column heights represent overall predictive power for each variable, while individual colors indicate predictive power unique to that variable (bottom color in each column) or shared by one or more other meteorological variables.
We note that these unique and shared designations are heavily dependent on
predictor variable choice and would certainly vary when calculated using a
different set of meteorological predictor variables. Uniqueness in these
figures should, therefore, be taken as an upper limit estimate, as the
inclusion of additional meteorological covariates could demonstrate
commonality with temperature where this six-variable set does not.
Furthermore, commonality shared between meteorological variables does not
imply causation by any one of the members – it only indicates shared
statistical predictive power and the possibility of alternative
O3-producing mechanisms. However, there are a number of possible
mechanisms that could explain some of the predictive power demonstrated by
non-temperature variables. Wind speed and direction have perhaps the most
straightforward meteorological relationship to O3, and they represent
transport of the pollutant either to or away from its source location. Wind
speed is generally inversely correlated to high temperatures, and stable
conditions are also favorable for the buildup and retention of high O3
concentrations. Depending on local topography and pressure patterns, wind
direction can also correlate strongly with changes in temperature, shifting
the final destination of polluted air masses from one location to another.
Previous work described a relatively small role for these advective
mechanisms (Camalier et al., 2007), but the results
here suggest that, after temperature-specific mechanisms have been accounted
for, wind speed and direction together account for a larger fraction of
explained O3 variance than previously suggested. Humidity can influence
O3 formation in a number of ways, both directly and indirectly. Water
vapor itself participates in competing O3-related effects: water
molecules act as O3 sinks by reacting with O(1D) atoms to produce
OH, preventing the excited oxygen from re-generating O3. However, in
polluted conditions the OH can then act as an O3 precursor itself,
potentially increasing production through reactions with CO and VOCs. These
competing effects may explain the relatively weak unique contribution of
humidity on average, though the high shared fraction (especially in Europe)
suggests that other indirect impacts may be involved, such as correlation
with cloud cover or fog. Mixing depth has shown mixed results as a predictor
for ozone in past studies as well (Jacob and Winner, 2009),
as the impact of PBL variability depends strongly on location and local
conditions. Areas with low surface O3 can show positive correlations
with mixing layer height due to the entrainment of higher concentrations
from aloft, while polluted regimes can show strongly negative correlations
due to the higher concentrations of trapped precursors on low-PBL days.
Total contribution of modeled mechanisms to the O3–temperature correlation in GEOS-Chem (a, b), possible contribution of the other included meteorological variables (c, d), and mean value for each category by region as a fraction of the total O3–temperature correlation (e, f).
Distribution of O3–T sensitivities as measured by the slope
of OLS regression (above) and mean surface O3 differences from a flat 1 ∘C temperature perturbation (below). Regression values are
shown for all modeled drivers (BASE case, black) and the portion of those
slopes attributable to temperature-dependent mechanisms (BASE - ALL, blue).
Although the specific mechanisms through which the non-temperature
meteorological variables impact O3 are not identified through this statistical
methodology, it is apparent that the majority of modeled O3–temperature
correlation left unexplained with the decoupling of temperature-dependent
mechanisms (T–O3R2 in the ALL case, Fig. 5) can itself be
explained in principle through covariance with other meteorological
variables, indicating that this covariance could explain the residual
correlation left over when temperature-dependent mechanisms are turned off
within GEOS-Chem. While the difference in O3–T correlation between the
BASE and ALL cases shows that these temperature-dependent mechanisms do
indeed strongly influence the O3–temperature correlation across a large
portion of the northern United States and southern Europe (Fig. 8a, b),
the remaining correlation makes up the larger overall fraction. Shared
explanatory power, as indicated by the shared contribution of temperature in
the ALL case (Fig. 8c, d), indicates that covariance with one or more
additional meteorological variables could explain most of the remaining
O3–T correlation (Fig. 8e). In this panel, red areas of each
column represent the fraction of BASE O3–T correlation that is lost
through the decoupling of temperature-dependent mechanisms, blue areas show
the shared fraction of remaining temperature dependence in the ALL case, and
the gray region represents remaining O3 variability that is uniquely
explained by temperature but unaffected by the four described mechanisms. This
remaining correlation could be the result of imperfectly chosen
meteorological variables, residual temperature dependence within the model
from chemistry or other mechanisms, or other fluctuations in emissions or
other inputs that happen to covary with temperature.
While day-to-day O3–temperature variability is a useful and commonly
examined metric for estimating future changes in air quality under a warming
climate, it presents challenges with respect to the extrapolation of daily
variability into long-term trends. For example, areas that exhibit little
day-to-day variability in summer temperature over the study period may
appear to be insensitive to climate change, even though the low
O3–temperature correlation is simply a result of short-term
climatological stability. The temperature perturbation cases described
previously and shown in Fig. 3a provide some additional information on how
the daily sensitivities examined here compare to larger, long-term shifts.
Figure 9a, b show the distribution of O3–T sensitivities,
both as a whole (black fill) and considering only the four key mechanisms
previously examined (blue fill). These distributions can then be compared to
the distribution of O3 changes apparent with a simple temperature
perturbation (panels c and d), which intrinsically includes no other
meteorological covariance. While the day-to-day correlation between O3
and temperature from all modeled drivers (Fig. 9a, b, black fill)
predicts increases in O3 of around 1.4 ppb for a 1 ∘C
increase in temperature, roughly half of that is attributable to the
examined mechanisms alone. This portion attributed to mechanisms alone is
consistent with the mean change in O3 observed from a 1 ∘C increase in temperature (0.58 ppb in the US and 0.47 ppb
in Europe). Together, the consistency of these two outcomes
indicates that projections of O3 concentrations under future climate
scenarios will be dependent on an accurate representation of temperature-dependent meteorology and dynamics and that models relying on temperature-dependent emissions and chemical mechanisms alone may underpredict the
strength of O3–T sensitivities by over 60 %.
Differences in model skill compared to surface station observations as a function of overall O3–temperature correlation (a, b) and the relative importance of modeled temperature-dependent mechanisms (c, d).
Model behavior can be further analyzed through comparison to surface station
observations, which reveals a significant difference (P<0.001) in
model skill (as measured by modeled vs. observed daily mean O3) when
grouping stations by overall O3–T correlation as well as by the
relative importance of modeled mechanisms (Fig. 10). Matching observations
from the EPA's AQS network in the United States and the EEA's AirBase
dataset for Europe with nearest-neighbor grid cells from GEOS-Chem output
shows that model skill tends to be higher in regions characterized with
above-average O3–temperature correlation (BASE case O3–temperature
R2>0.42). While this does not imply that temperature-dependent processes are all modeled correctly, it does at least suggest that
temperature-based drivers tend to be better captured by the model than other
influences on O3 variability. However, splitting observed stations
based on the relative importance of internally modeled mechanisms shows that
more work may need to be done on these implementations. Grid cells in which
the modeled O3–temperature relationship was dominated by temperature-dependent mechanisms (greater than 50 % of the O3–temperature
correlation lost when temperature dependence was removed in the ALL case)
showed much less overall predictive power when compared to the corresponding
surface observations (Fig. 10c, d).
Conclusions
A changing climate implies changes in the physical and chemical regimes
governing the emission, formation, and transport of pollutants such as
tropospheric O3. Previous work has identified increasing temperatures
in particular as a driver of elevated surface O3 concentrations,
mitigating the effectiveness of ongoing emissions reductions in the United
States and Europe. This means that, under a warming climate, polluted
regions would need to cut emissions even further to achieve the same
improvement in air quality, adding economic and human health costs to the
bottom line of climate change adaptation. Understanding the mechanisms
driving the observed relationship between O3 and temperature is
important for guiding improvements in model performance, as well as for
better understanding the effects of future changes.
We show here that while temperature-dependent mechanisms such as biogenic
emissions and PAN dissociation are often cited as key contributors to the
observed O3–temperature relationship, model simulations maintain strong
O3–temperature correlations even when these mechanisms are completely
decoupled from temperature variability. Analysis of other meteorological
variables suggests that meteorological covariance with temperature may
explain a large proportion of O3–temperature correlation – over 40 %
in the United States and nearly 60 % in Europe. The relative importance of
covarying atmospheric dynamics indicates that simulations investigating
temperature perturbations alone will underestimate overall O3 impacts
by a factor of 2 or more, unless temperature-driven changes in other
meteorological patterns are also included and accurately represented.
Furthermore, comparison with station observations shows that modeled daily
O3 values are less skillful in areas where the O3–temperature
correlation is dominated by modeled temperature-dependent mechanisms rather
than meteorology, indicating that improved representation of these
mechanisms in particular may improve overall model skill with respect to
O3 modeling and forecasting.
These results highlight the complexity of pollution projections under
changing emissions and climatological conditions, as well as with the
attribution of those changes to any individual driver or metric. While
surface temperatures can be easily linked to O3 variability
statistically, it is apparent that the robustness of this relationship
depends on how consistently coupled those temperature changes continue to
be, not only with temperature-dependent physical and chemical drivers of
O3 formation, but also with the covarying meteorological patterns that
appear to be just as influential. These relationships are further confounded
by ongoing changes in anthropogenic emissions, making it especially
important to understand the ways in which policy-driven emissions reductions
may improve – or fail to improve – air quality within a changing climate.
Ongoing investigations into the importance of these mechanisms, emissions,
and atmospheric dynamics will guide future model development, improving
forecast skill and better informing policy decision-making.
Data availability
GEOS-Chem code is available at http://acmg.seas.harvard.edu/geos/ (last access: 1 March 2018). Data from the EPA's AQS network of surface air quality observations are available at https://www.epa.gov/aqs (last access: 9 August 2016), while AirBase data maintained by the EEA are available at https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-7 (last access: 31 July 2015).
Author contributions
Development of the ideas and concepts behind this work was performed by both
authors. Model execution, data analysis, and paper preparation were
performed by WCP with feedback and advice from CLH.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the EPA STAR program (RD-83522801) and a core
center grant from the National Institute of Environmental Health Sciences,
National Institutes of Health (P30-ES002109). Although the research
described in this article has been funded in part by the US EPA through
grant/cooperative agreement, it has not been subjected to the agency's
required peer and policy review and therefore does not necessarily reflect
the views of the agency and no official endorsement should be inferred. The
authors acknowledge Brian J. Reich for useful discussions.
Financial support
This research has been supported by EPA STAR (grant no. RD-83522801) and the NIH (grant no. P30-ES002109).
Review statement
This paper was edited by Federico Fierli and reviewed by two anonymous referees.
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