ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-4355-2017Influence of 2000–2050 climate change on particulate matter in the United
States: results from a new statistical modelShenLulshen@fas.harvard.eduMickleyLoretta J.MurrayLee T.https://orcid.org/0000-0002-3447-3952School of Engineering and Applied Sciences, Harvard University,
Cambridge, MA 02138, USADepartment of Earth and Environmental Sciences, University of
Rochester, Rochester, NY 14627, USALu Shen (lshen@fas.harvard.edu)30March20171764355436727October20162November201616February20171March2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/17/4355/2017/acp-17-4355-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/4355/2017/acp-17-4355-2017.pdf
We use a statistical model to investigate the effect of 2000–2050 climate
change on fine particulate matter (PM2.5) air quality across the
contiguous United States. By applying observed relationships of PM2.5
and meteorology to the IPCC Coupled Model Intercomparision Project Phase 5
(CMIP5) archives, we bypass some of the uncertainties inherent in
chemistry-climate models. Our approach uses both the relationships between
PM2.5 and local meteorology as well as the synoptic circulation
patterns, defined as the singular value decomposition (SVD) pattern of the
spatial correlations between PM2.5 and meteorological variables in the
surrounding region. Using an ensemble of 19 global climate models
(GCMs)
under the RCP4.5 scenario, we project an increase of
0.4–1.4 µg m-3 in annual mean PM2.5 in the eastern US
and a decrease of 0.3–1.2 µg m-3 in the Intermountain West
by the 2050s, assuming present-day anthropogenic sources of PM2.5. Mean
summertime PM2.5 increases as much as 2–3 µg m-3 in the
eastern United States due to faster oxidation rates and greater mass of
organic aerosols from biogenic emissions. Mean wintertime PM2.5
decreases by 0.3–3 µg m-3 over most regions in the United
States, likely due to the volatilization of ammonium nitrate. Our approach
provides an efficient method to calculate the potential climate penalty on air quality across a range of
models and scenarios. We find that current atmospheric chemistry models may
underestimate or even fail to capture the strongly positive sensitivity of
monthly mean PM2.5 to temperature in the eastern United States in
summer, and they may underestimate future changes in PM2.5 in a warmer
climate. In GEOS-Chem, the underestimate in monthly mean
PM2.5–temperature relationship in the east in summer is likely caused
by overly strong negative sensitivity of monthly mean low cloud fraction to
temperature in the assimilated meteorology (∼-0.04 K-1)
compared to the weak sensitivity implied by satellite observations
(±0.01 K-1). The strong negative dependence of low cloud cover on
temperature in turn causes the modeled rates of sulfate aqueous oxidation to
diminish too rapidly as temperatures rise, leading to the underestimate of
sulfate–temperature slopes, especially in the south. Our work underscores
the importance of evaluating the sensitivity of PM2.5 to its key
controlling meteorological variables in climate-chemistry models on multiple
timescales before they are applied to project future air quality.
Introduction
Fine particulate matter with an aerodynamic diameter less than 2.5 µm
(PM2.5) is an important surface air pollutant of public concern,
particularly in industrialized regions. Exposure to PM2.5 can result in
respiratory and cardiovascular disease, as well as premature mortality
(e.g., Laden et al., 2006; Pellucchi et al., 2009; Brook et al., 2010). In
the United States, recent reductions in anthropogenic emissions have
decreased PM2.5 concentrations by 20 % from 2001 to 2010 (EPA, 2011;
Hu et al., 2014), and this trend is very likely to continue in the future
due to increasingly stringent emission control (Val Martin et al., 2015).
However, a changing climate modifies local meteorological variables,
synoptic circulation, and natural emissions, and thus brings new challenges
to projections of future PM2.5. PM2.5 is comprised of a variety of
individual components, including sulfate, nitrate, ammonium, organic carbon
(OC) and elemental carbon (EC). The response of different PM2.5
components to meteorology is complex (Tai et al., 2010), and model
projections of PM2.5 under the 21st century climate change have so
far shown little consistency (e.g., Racherla and Adams, 2006; Pye et al.,
2009; Val Martin et al., 2015; Day et al., 2015). In this study, we develop
a new statistical model to quantify the effect of 2000 to 2050 climate
change on PM2.5 air quality across the contiguous United States.
The response of PM2.5 to local meteorological variables differs by
component, region and time of year. Analyzing observations from across the
United States, Tai et al. (2010) found that sulfate, OC and elemental
carbon increase with temperature everywhere due to faster oxidation rates,
as well as the association of warmer temperatures with stagnation, reduced
ventilation, and greater biogenic and fire emissions. Tai et al. (2010) also
determined that the correlation of nitrate with temperature is negative in
the southeast but positive in California and the Great Plains due to the
competing effects of temperature on emissions and condensation. These
authors further found that higher relative humidity (RH) increases both
sulfate, by enhancing in-cloud SO2 oxidation, as well as nitrate due to
the RH dependence of ammonium nitrate formation. Conversely, higher RH
decreases OC and EC due to the association of moist air with reduced
wildfires and greater influx of clean marine air (Tai et al., 2010). The
relationship of PM2.5 with clouds and precipitation is complex: as
cloud cover increases, aqueous-phase oxidation of SO2 increases, but
greater precipitation may also scavenge all PM2.5 components (Koch et
al., 2003; Tai et al., 2010). These varied and sometimes competing effects
of meteorology on the different components of PM2.5 make it challenging
to predict PM2.5 variability.
In addition to local meteorology, synoptic circulation patterns also play an
important role in affecting PM2.5 air quality. For example, Thishan
Dharshana et al. (2010) found that synoptic weather systems contribute
30 % of the PM2.5 daily variability in the midwestern United States.
Tai et al. (2012a) found that 20–40 % of the observed PM2.5 daily
variability can be explained by cold frontal passages in the eastern United
States and maritime inflow in the west. However, characterizing the effects of
cold front passages and other synoptic patterns on surface PM2.5 is
challenging. Indices reflective of such patterns – e.g., the polar jet
(Barnes and Fiore, 2013), cyclone frequency (Mickley et al., 2004;
Leibensperger et al., 2008) and the extent of the Bermuda High (Li et al.,
2011; Shen et al., 2015) – may reflect only a fraction of the total
synoptic activity in some regions, and the relationships between these
patterns and PM2.5 are not completely understood.
Chemical transport models (CTMs) and chemistry-climate models (CCMs) show no
consistent sign of the future PM2.5 changes under a changing climate
(e.g., Liao et al., 2006; Racherla and Adams, 2006; Tagaris
et al., 2007; Heald et al., 2008; Avise et al., 2009; Pye et al., 2009).
Reviewing earlier studies, Jacob and Winner (2009) and Fiore et al. (2015)
concluded that most of the projected effects of 21st century climate
changes on PM2.5 concentrations are in the range of
±0.1–1 µg m-3, with changes up to
±2 µg m-3 in certain seasons or regions. More recently,
Val Martin et al. (2015) found that 2000–2050 climate change may decrease
the annual mean PM2.5 concentrations by 0–1 µg m-3 in
the eastern United States under the Representative Concentration pathway
(RCP) 4.5 scenario of climate change. Day et al. (2015) determined that
summer mean PM2.5 increases by 21 % in the southeast but decreases
9 % in the Northeast from 2000 to 2050 under the more-greenhouse-gas-intensive A2 scenario. In contrast, Gonzalez-Abraham et al. (2015) identified
a 10–30 % increase in summer mean PM2.5 across the eastern United
States by the 2050s. A key reason for these inconsistencies is the large
variation in the projections of future meteorology from climate models,
regardless of scenario. Due to their high computation expense, CTMs typically
rely on the meteorological fields from a single climate model. However, the
dependence of PM2.5 on meteorological variables such as temperature is
also uncertain, especially over longer timescales (e.g., interannual or
decadal). To our knowledge, the ability of models to reproduce the dependence
of PM2.5 on major meteorological variables over such long timescales has
not yet been evaluated.
An alternative approach to projecting the effect of climate change on
PM2.5 air quality involves the use of statistical models, in which the
observed relationships of PM2.5 and meteorology are applied to future
climate projections from an ensemble of models. Use of an ensemble provides a
mean or median response and uncertainty range and increases confidence in the
sign and magnitude of the response of a particular variable to climate
change. For example, Tai et al. (2012b) first analyzed 1999–2010
observations using principal component analysis of eight different
meteorological variables and found that the interannual variability of PM2.5
is strongly correlated with the average cyclone period T, defined as the
inverse of the median frequency of the dominant meteorological mode, in the
contiguous United States. They then projected 2000 to 2050 changes in
PM2.5 by applying the local PM2.5-to-period sensitivity (i.e.,
Δ (PM2.5)/ΔT) to the future changes in the average cyclone
period T derived from an ensemble of climate model simulations following
the A1B scenario. Results showed only a weak increase of
∼ 0.1 µg m-3 in annual mean PM2.5 in the eastern
United States, and a likely weak decrease in the Pacific Northwest. However,
Tai et al. (2012b) may have underestimated the change in future PM2.5
because only the influence of synoptic patterns was considered and not the
impact from local meteorology. More recently, Lecœur et al. (2014) developed
a statistical algorithm to estimate future PM2.5 concentrations over
Europe based on a weather-type representation. They resampled future daily
PM2.5 concentrations from a pool of chemistry model simulations, based
on the similarity determined by regression-estimated PM2.5 and
large-scale circulations. They found seasonal mean PM2.5 changes between
-1.6 and +1.1 µg m-3 under the RCP4.5 scenario by the 2050s.
In this study, we revisit the conclusions of Tai et al. (2012b). We develop
a new method to characterize the synoptic circulations using the singular
value decomposition (SVD) of the spatial correlations between PM2.5 and
meteorological variables in the surrounding region. The method takes into
account the influence of both local meteorology and the synoptic circulation
patterns to investigate the effect of 2000–2050 climate change PM2.5
air quality across the contiguous United States. We also evaluate different
CTMs and CCMs in terms of the simulated dependence of seasonal mean
PM2.5 on temperature over 1 decade. In Sect. 2, we introduce the
data and models we use. In Sect. 3, the method used to characterize the
synoptic circulation patterns is described. We discuss the projected 2000 to
2050 changes in PM2.5 in Sect. 4. Section 5 evaluates the
capability of different dynamic models in simulating the dependence of
PM2.5 on key meteorological variables.
Data sources and modelsPM2.5 and meteorological data
Surface daily mean PM2.5 concentrations and speciation data from 1999 to
2013 are taken from the US Environmental Protection Agency Air Quality
System (EPA-AQS, http://www.epa.gov/ttn/airs/airsaqs/). We interpolate
the site measurements onto a 2.5∘× 2.5∘
latitude-by-longitude grid, using inverse distance weighting as in Tai et
al. (2010). The meteorological data used in this study for 1999–2013 consist
of temperature, relative humidity, and east–west and north–south wind speed
from the National Centers for Environmental Prediction (NCEP) Reanalysis 1,
mapped in 2.5∘× 2.5∘ grid resolution (Kalnay
et al., 1996). For precipitation, we rely on the NOAA Climate Prediction
Center (CPC) Unified Gauge-Based Analysis of Daily Precipitation product for
1999–2013 (Xie et al., 2007; Chen et al., 2008). These variables have been
previously used to predict PM2.5 (e.g., Tai et al., 2010, 2012a, b;
Lecœur et al., 2014), and their variability is closely linked to that of
synoptic patterns (e.g., Shen et al., 2015; Thishan Dharshana et al., 2010).
These particular variables have also been validated in CMIP5 models (e.g.,
Sheffield et al., 2013).
Satellite-observed cloud fractions for 2004–2012 are from the Clouds and the
Earth's Radiant Energy System (CERES) ISCCP-D2like products (CERES Science
Team, Hampton, VA, USA; NASA Atmospheric Science Data Center, accessed in October
2016 at
http://doi.org/10.5067/Aqua/CERES/ISCCP-D2LIKE-MERG00_L3.003A). This
merged product combines 3-hourly, daytime cloud properties from Terra and
Aqua on the Moderate Resolution Imaging Spectroradiometer (MODIS) and from
the geostationary satellite (GEO), mapped over
1∘× 1∘ grid resolution (Minnis et al., 1995, 2011).
The cloud optical depths are archived in three wavelength bins (0–3.6,
3.5–23 and 23–380 µm) in both liquid and ice phases. In this
study, we focus on clouds in the lower troposphere below 680 hPa, which have
the greatest implications for surface PM2.5 air quality.
To project the 2000–2050 effect of climate change on PM2.5 air
quality, we use five meteorological variables – surface temperature,
relative humidity, precipitation, and east–west and north–south wind speed
– from an ensemble of 19 climate models participating in the Coupled Model
Intercomparison Project Phase 5 (CMIP5) and following the RCP4.5 scenario
(Taylor et al., 2012). RCP4.5 is an intermediate scenario, in which the
radiative forcing reaches 4.5 W m-2 by 2100, approximately 650 ppm
CO2 concentration, and stabilizes after that (Taylor et al., 2012). The
CMIP5 data are archived at a horizontal resolution of ∼ 200 km, and the details of these models can be found in Table S1.
To remove the effects of long-term trend, we subtract the 5-year moving
average from monthly mean values in both PM2.5 and meteorological
data as in Tai et al. (2012b). The choice of 5 years is arbitrary, but we
find that this choice produces good correlations between surface PM2.5 and
meteorological variables over the relatively short 15-year PM2.5 time
history of observations, thus allowing us to bypass the impact of nonlinear
emission changes. Throughout this study, we use p<0.05 as the
threshold for statistical significance.
Atmospheric chemistry models
We perform a 9-year simulation of PM2.5 in the GEOS-Chem CTM (v9-02,
http://geos-chem.org) with coupled gas-phase and aerosol
chemistry. The model has a horizontal resolution of 2∘× 2.5∘ with 47 pressure levels extending from surface to 0.01 hPa
(∼ 38 in the troposphere), driven by GEOS-5-assimilated
meteorological data for 2004 to 2012 from the NASA Global Modeling and
Assimilation System (GMAO). The aerosol thermodynamical partitioning of
nitrate and ammonium between gas and aerosol phases is calculated by the
ISORROPIA II model (Fountoukis and Nenes, 2007). The scheme to produce
sulfate via aqueous-phase oxidation of SO2 uses liquid water content
and cloud fraction from the assimilated meteorology (Fisher et al., 2011).
Formation of secondary organic aerosol (SOA) follows Pye et al. (2010), with
many subsequent updates to the isoprene oxidation mechanism (Paulot et al.,
2009a, b; Rollins et al., 2009). Biogenic emissions are from the inventory
of Guenther et al. (2012). We follow Hudman et al. (2012) for emissions of nitrogen oxides
(NOx) from soil and Murray et al. (2012) for lightning NOx. US
anthropogenic emissions of PM2.5 precursors are from the EPA 2005
National Emissions Inventory (NEI05). We use monthly biomass burning
emissions from Global Fire Emission Database (GFED; van der Werf et al.,
2010).
GEOS-5 assimilates a large array of observations but calculates cloud
properties using a prognostic algorithm without assimilation. The algorithm
considers both liquid and ice phases of cloud condensate with two types of
cloud types, anvil and large-scale clouds (Reinecker et al., 2008). The basic
moist processes include a convective scheme using the relaxed
Arakawa–Schubert parameterization (Moorthi and Suarez, 1992), a large-scale
cloud condensate scheme (Smith, 1990; Rotstayn, 1997) and cloud destruction
schemes as described in Reinecker et al. (2008). Column cloud fraction in
the lower troposphere is calculated using a random overlap approximation
(Stephens et al., 2004). In Sect. 5, we validate the GEOS-5 cloud fraction in
the lower troposphere against CERES satellite observations.
Finally, we use modeled 1995–2010 PM2.5 surface concentrations and
temperature data from the Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP). For this historical simulation, the ACCMIP
models follow the same time-varying anthropogenic and biomass burning
emissions (Lamarque et al., 2010). Only four ACCMIP models provide archived
total PM2.5 concentrations: NCAR-CAM3.5, GFDL-AM3, MIROC-CHEM and
GISS-E2-R (Table S2). Here we use an updated simulation with the
GISS-ModelE2 model in its atmosphere-only mode, forced using the ACCMIP
emissions (Lamarque et al., 2010), observed daily sea-surface temperatures,
and sea ice from Reynolds et al. (2007), and with winds nudged to the
Modern-Era Retrospective Analysis for Research and Applications (MERRA)
meteorological reanalysis (Rienecker et al., 2011). The rate constants for
oxidation of SO2 and dimethyl sulfide by OH have been updated to those recommended
by Burkholder et al. (2015), consistent with GFDL-AM3 and GEOS-Chem. All
four ACCMIP models are CCMs. The horizontal resolution of these models is
∼ 200 km; more details are described in Lamarque et al. (2013).
Construction of synoptic circulation factors
PM2.5 variability is not only related to local meteorology but also
synoptic circulation. Previous studies have identified many synoptic
patterns that are important for surface air quality in different regions
under certain seasons, such as cyclone frequency (Mickley et al., 2004;
Leibensperger et al., 2008), the position of the polar jet wind in the
Northeast (Barnes and Fiore, 2013; Shen et al., 2015) and the extent of the
Bermuda High west edge in summer in the southeast (Li et al., 2011; Shen et
al., 2015). However, identification and interpretation of the dominant
synoptic patterns for each region and each month would be time consuming and
subject to some uncertainty. Instead, as a first step, we attempt to find a
more general way to characterize the major synoptic patterns that modulate
the PM2.5 variability.
Synoptic circulation plays a vital role in controlling PM2.5 air
quality. The correlations of surface PM2.5 with meteorological
variables in the surrounding regions may in fact be stronger than those in
the local regions. For example, Fig. 1a shows the correlations between
May–June–July (MJJ) monthly mean PM2.5 concentrations in one
2.5∘× 2.5∘ grid box in Georgia in the
southeastern United States with MJJ surface air temperatures in grid boxes
across a much larger domain (32.5∘× 17.5∘)
over the 1999–2013 time period. Positive correlations extend across the
whole southeast, suggesting that PM2.5 air quality in Georgia is
affected by regional climate; the strongest correlations are located in
Mississippi, ∼ 500 km west of Georgia. The relationship of
PM2.5 in the Georgia grid box with relative humidity also shows a
regional signature, with negative correlations spanning the southeast to the
Gulf of Mexico (Fig. 1b). Precipitation can scavenge particles, and we
identify negative correlations of the Georgia PM2.5 with regional
precipitation (Fig. 1c). The relationships of Georgia PM2.5 with
east–west wind speed are relatively weak, with negative correlations in the
Midwest and Gulf of Mexico (Fig. 1d). However, the relationships of
PM2.5 in the Georgia grid box with the north–south wind speed show a
strong bimodal structure, with significant negative correlations stretching
over the eastern Atlantic and positive correlations in the south central
United States (Fig. 1e), suggesting anticyclonic circulation. In contrast,
the correlation of this variable with PM2.5 within Georgia is close to
zero, which means the local north–south wind speed does not provide
predictive capability for PM2.5 here. Taken together these results
imply that PM2.5 variability is partly controlled by regional-scale
synoptic patterns, and consideration of only local meteorology will not
suffice in predicting PM2.5.
Example of observed correlations of monthly mean PM2.5 in one
grid box with surrounding meteorology in the southeastern United States from
1999–2013. Panels show correlations of May–June–July monthly PM2.5
concentrations from EPA-AQS observations in the
2.5∘× 2.5∘ grid box centered at 82.5∘ W,
32.5∘ N (black circle), with different meteorological variables from
NCEP Reanalysis1, including (a) surface air temperature,
(b) relative humidity, (c) total precipitation,
(d) east–west wind speed and (e) north–south wind speed.
Grid boxes with significant correlation with p<0.05 level are
stippled. All data are detrended by subtracting the 5-year moving average
from the monthly values.
We construct the synoptic circulation factors driving PM2.5 across the
eastern United States through the use of SVDs of the spatial correlations
between PM2.5 in each grid box and meteorological variables in the
surrounding region. This SVD method effectively compresses the information
from several meteorological variables in a multidimensional matrix into a
set of scalars that represent the oscillation of the PM2.5-related
synoptic patterns. For each grid box, the process proceeds as below. First,
we calculate the correlations of monthly mean PM2.5 in the grid box
with five meteorological variables (temperature, relative humidity,
precipitation, and north–south and west–east wind speed) within a
∼ 1000 km radius of the grid box on a 2.5∘× 2.5∘ horizontal grid. This step yields a 13 × 9 × 5 (longitude × latitude × variable) matrix
that we call A. Second, we align the dimension of longitude–latitude into
one column and resize matrix A into a 117 × 5 two-dimensional matrix
F. The SVDs of F can be written as
F=ULVT,
where L is a diagonal matrix with non-negative numbers on the diagonal. Each
column of V represents the variable weights and each column of U represents
the spatial weights of the corresponding SVD mode. For example, Fig. 2a–b
shows the spatial and variable weights of the first SVD (SVD1) mode for
PM2.5 in the same grid box in Georgia as in Fig. 1, where SVD1
explains 32 % of the total variance. The spatial weights show a bimodal
structure with negative anomalies over the eastern Atlantic and positive
anomalies over the Great Plains and Midwest (Fig. 2a), in a pattern
similar to that in Fig. 1e. The corresponding variable weights in Fig. 2b reveal the importance of the north–south wind speed in this mode,
suggesting that SVD1 is characterized by dynamic, synoptic-scale
meteorology. In the second SVD (SVD2) mode, the spatial weights (Fig. 2c)
show positive anomalies in the southeastern United States, and this corresponds
to the positive temperature anomalies in Fig. 1a as well as negative
relative humidity and precipitation anomalies in Fig. 1b–c. The
meteorological composition of the variable weights shows that temperature,
relative humidity and precipitation dominate (Fig. 2d), suggesting that
SVD2 reflects a regional-scale thermal effect. The magnitudes of SVD1 and
SVD2 oscillate over time, contributing to PM2.5 variability in the
Georgia grid box. We repeat this exercise for each grid box across the United
States.
The magnitude of each PM2.5-related mode in a new meteorological field
can be calculated as follows. For each grid box, we first construct a matrix
M, consisting of
the monthly mean values of each meteorological variable across the
surrounding region. We scale the time series of each variable in each grid
box to achieve zero mean and unit standard deviation across the time frame.
The magnitude of each SVD mode for every month t is then calculated using
the inverse process of SVD, which can be written as
Sk=UkTMtVk,
where Uk refers to the kth column in the spatial weights matrix U,
Vk to the kth column in the variable weights matrix V and Sk is a
scalar depicting the magnitude of the kth SVD mode of the new
meteorological field for that month. This inverse SVD transforms a large
matrix into a few scalars, and these scalars reflect the variability of
synoptic patterns that are closely related to PM2.5 air quality.
(a, c) Spatial and (b, d) variable weights of the
(a, b) first and (c, d) second singular value decomposition
(SVD) modes describing the spatial correlations of May–June–July PM2.5
anomalies in one grid box in the southeast from 1999 to 2013 and five different
meteorological variables: temperature (T), relative humidity (RH),
precipitation (precip), and east–west and north–south wind speed (EW wind and
NS wind). The explained variance by each SVD mode is shown inset. See Sect. 3
for more details.
We first construct a multiple linear regression model to correlate observed
monthly mean 1999–2013 PM2.5 concentrations and five local
meteorological variables (surface temperature, relative humidity,
precipitation, and east–west wind and north–south wind) and the two most
important synoptic factors in each grid box, diagnosed using SVD. The model is
of the form
Y=∑k=15αkXk+∑n=12βnSn+b,
where Y is three continuous monthly mean PM2.5 concentrations for
1999–2013 with a total number of 45 values in the time series. For example,
for July PM2.5, we train the model using June, July and August values
for each year over the 15 years. X is a scalar consisting of the five local
meteorological variables, S represents the two synoptic circulation factors
constructed using SVD, α and β are the corresponding
coefficients, and b is the intercept. We test this model in two steps. In the
first step, we use only the local meteorological variables – i.e., we set
all βs to zeros. In the second step, we use both local meteorology and
synoptic patterns. In order to avoid overfitting, we use leave-one-out
cross validation to determine the best variable combinations for each
grid box. Each time we reserve one observation in the time series as the test
set and use the remaining ones as the training set, and we repeat this
process until all observations have been predicted. Throughout this study,
we predict monthly PM2.5 concentrations using this regression model,
but projected changes of PM2.5 in the future climate will be
displayed as seasonal and annual means.
Figure 3a shows the cross-validated skills expressed in the coefficients of
determination (R2) between observed and predicted 1999–2013 monthly mean
PM2.5 concentrations using only local meteorology. We find that R2
averages 34 % across the United States, with the largest R2 located
in the Midwest, Northeast and northwest. This spatial pattern of R2 is
consistent with the pattern in Tai et al. (2010), who regressed daily
PM2.5 concentrations onto only local meteorological variables. By
including synoptic circulation factors in the model, the average R2 of
the regression model increases over most regions, with an average R2
across the United States of 43 % and R2 values greater than 50 %
over a broad region that includes the upper Midwest, Ohio, parts of the
Northeast and areas as far south as Tennessee (Fig. 3b). This result
demonstrates that inclusion of synoptic circulation factors can
significantly improve the regression model. We also find that the
cross-validated values of R2, calculated from both local meteorology and
patterns of synoptic circulation and averaged across the United States, are
35 % in spring, 44 % in summer, 42 % in autumn and 43 % in winter
(Fig. S1 in the Supplement). To check the multi-colinearity among predictors in this model,
we calculate the variance inflation factors (VIFs) for all variables in each
grid box and each month. Results in Fig. S2 show that about 98.9 % of the
VIFs are less than 5, well below the threshold of 10 that defines
significant multi-colinearity (Kutner et al., 2004).
Cross-validated coefficients of determination (R2) between
observed and predicted 1999–2013 monthly PM2.5 across the United
States, calculated with (a) local meteorological variables and
(b) both local meteorology and patterns of synoptic circulation.
Spatially averaged coefficients of determination are shown inset.
Impact of 2000–2050 climate changes on PM2.5 from statistical
inference
To estimate the impacts of climate change on future PM2.5 concentrations from 2000–2019 to 2050–2069, we apply the regression model
including both local and synoptic meteorology to the CMIP5 meteorological
projections. We calculate mean surface PM2.5 in both timeframes and
then the resulting change. We assume that anthropogenic emissions of
PM2.5 sources remain at mean 1999–2013 levels during the 2050–2059
timeframe. An ensemble of 19 CMIP5 models in the RCP4.5 scenario is used
here, and we calculate the PM2.5 change for each model separately.
Computing the average PM2.5 change across the ensemble improves
confidence in our predictions of the climate impact on PM2.5.
Future climate change by the 2050s leads to significant warming across North
America but has minimal effects on precipitation and circulation patterns
across the continent. Figure S3 shows the seasonal changes in temperature,
relative humidity, precipitation and surface wind field for June–July–August
(JJA) across the United States, averaged across the CMIP5 ensemble. Mean
temperature increases by 2–2.5 K over much of the north in this timeframe,
and 1.5–2 K over the southeast. Relative humidity decreases by up to 0.03
over most regions across the United States, but the models show no
consistent sign in the future change in precipitation in the summer. The
flux of maritime air into the southern United States increases due to increased
land–ocean thermal contrast. In winter (Fig. S4), mean temperature
increases by 3 K in the north, while relative humidity decreases across the
Intermountain West and the Northeast, similar to the pattern in summer.
Precipitation shows a slight increase of 0.1 mm d-1 in the north, and
the surface circulation pattern shows little change over the United States
(Fig. S4).
Figure 4a–d shows the response of the seasonal mean PM2.5 concentrations to 2050s climate change across the United States, shown as
the average of all projections from the CMIP5 models. PM2.5 increases
by ∼ 2–3 µg m-3 in summer in the eastern United
States (Fig. 4b), likely due to faster oxidation rates and more abundant
organic aerosol (OA) in the warmer climate of the 2050s (e.g., Tai et al.,
2010; Kelly et al., 2012; Gonzalez-Abraham et al., 2015). This can be also
inferred from the positive sensitivity of sulfate and OA to temperatures
from observations, which will be discussed in more detail in Sect. 5. We
also find an increase of ∼ 0.8–1.5 µg m-3 in the
summer over the Intermountain West, partly driven by enhanced biomass
burning in a warmer climate (e.g., Yue et al., 2013, 2015). In winter,
future PM2.5 decreases by 0.3–3 µg m-3 across much of the
United States (Fig. 4d), likely driven by greater volatilization of
ammonium nitrate at warmer temperatures (Dawson et al., 2007, 2009). In
spring and autumn, PM2.5 increases in the eastern United States by
∼ 0.5 µg m-3. Annual mean PM2.5 increases as
much as 1.4 µg m-3 in the eastern United States but decreases by up
to 1 µg m-3 in the Intermountain West (Fig. 4e).
Effects of climate change from 2000–2019 to 2050–2069 on
(a–d) seasonal and (e) annual mean PM2.5
concentrations, calculated with observed relationships of PM2.5 and
meteorology and with meteorology projected by an ensemble of 19 CMIP5 models.
The panels show the mean change in surface PM2.5, averaged across the
projections. White areas refer to the regions with no PM2.5 observations
or where fewer than 14 models yield the same sign of change.
To evaluate the uncertainty of projected PM2.5 concentrations, we
analyze the range of these projections among the 19 CMIP5 models as well as
the interannual time series of regional projections from 2000 to 2069. Even
though many models have multiple simulations, when we calculate the effects
of climate change on PM2.5 concentrations, we only use the simulated
meteorology from the first ensemble run for each model. In general, these
models agree on the sign of the change of PM2.5 across the east by
the 2050s, but the magnitude of the change varies among models (Fig. S5). To more
rigorously characterize this uncertainty, we calculate the 90th and 10th
percentile changes in PM2.5 concentrations as calculated from the 19
CMIP5 models (Figs. S6–S7). In the summertime, the 90th percentile changes
of PM2.5 can be greater than 3 µg m-3 across most of the
eastern United States (Fig. S6b), but the 10th percentile changes are only
0.5–1.5 µg m-3 (Fig. S7b). These discrepancies underscore
the importance of using an ensemble of climate models to project future
PM2.5 concentrations. Such an approach allows us to identify robust
results across models, quantify uncertainty and diagnose model outliers. We
also examine the 2000–2069 time series of projected PM2.5 concentrations
as annual, summertime and wintertime means, averaged over eight different
US regions
(Figs. S8–S11). The spread in PM2.5 trends is
one measure of the uncertainty in our projections, arising in part from
differences in model sensitivity to changing greenhouse gases and in part
from internal variability of the climate system (e.g., Deser et al.,
2014). Averaging results across the CMIP5 ensemble reveals a
robust response of PM2.5 to increasing greenhouse gases, at least in
some regions, giving us confidence in our approach.
We also compare our results to those from recent studies using
chemistry-climate models. Among the seven recent studies reviewed in Fiore
et al. (2015), only two of them projected a significant increase in
PM2.5 concentrations in summer over the eastern United States. Kelly
et al. (2012) estimated an increase of 0.5–1.0 µg m-3 in
summertime PM2.5 over much of the east from 2000 to 2050, mainly
resulting from rapid increases in SOA from biogenic emissions.
Gonzalez-Abraham et al. (2015) found that the effect of 2000–2050 climate
change alone without changes in biogenic emissions can increase PM2.5 concentrations by up to 1.0 µg m-3 in the eastern United States,
a combined effect of increasing sulfate and ammonium as well as decreasing
nitrate. Consideration of the changes in biogenic emissions drives up this
increase to 0.5–3 µg m-3.
To diagnose which meteorological variable plays the greatest role in these
PM2.5 changes, we perform a series of tests with the regression model.
For each test, we keep one variable in the 2050–2069 calculation the same as
for the 2000–2019 timeframe and calculate the resulting changes in
PM2.5. We find that the changes of PM2.5 almost vanish if we
hold surface temperatures for 2050–2069 at their 2000–2019 values (Fig. S12), suggesting that temperature drives most of the PM2.5 changes in
the future climate.
Our study shows much larger regional effects of 2000–2050 climate change on
annual mean PM2.5 compared to Tai et al. (2012b). An increase of only
∼ 0.1 µg m-3 was predicted by Tai et al. (2012b) in the
eastern United States, an order of magnitude smaller than what we find. We
trace the reason for this discrepancy to the choice of predictors in the two
studies. Tai et al. (2012b) identified the dominant meteorological modes
driving daily PM2.5 variability in 4∘× 5∘
grid cells across the United States and calculated the local sensitivity of
PM2.5 to synoptic period T for that mode. Using the simulated changes
in T from a set of climate models, they then projected future PM2.5 in
each grid cell. Tai et al. (2012b) further found a strong correlation (r=-0.63) between T and the maximum eddy growth rate, a quantity that
reflects the meridional temperature gradient. This finding implies that
trends in T only represent the changes in the meridional temperature
gradient but do not take into account the effects of homogeneous warming
across the mid- and high latitudes. Partly to remedy this bias, we have
included both local meteorology and synoptic circulation patterns in our
regression model, leading to a much higher response of PM2.5 to climate
change.
The slopes of detrended, monthly mean PM2.5 versus temperature
for summer months (June–July–August) in (a) observations and
(b–f) different chemistry models. The timeframes shown in the panel
are as follows: (a) 2004–2012, (b) 2002–2009,
(c) 2001–2010, (d) 1995–2005, (e) 2000–2010 and
(f) 2004–2012. Results in panels (b–e) are taken from
ACCMIP (Lamarque et al., 2010) and use an updated GISS simulation
(d) relative to their ACCMIP contributions (see text for more
details). The dashed contour line in some panels denotes a slope of
+1 µg m-3 K-1. White areas indicate either missing
data or grid boxes where the slope is not significant at the 0.05 level.
One weakness of this study is that when estimating the sensitivity of
PM2.5 to meteorological variables, we do not consider the impact of
changing anthropogenic emissions on this sensitivity. Figure S13 compares
the slopes of monthly mean PM2.5 and its components with temperature
for two time periods: 1999–2006 summers with high anthropogenic emissions
and 1997–2013 summers with low anthropogenic emissions. Using the monthly
data, we find that the changes of sensitivity of PM2.5 to temperature
vary across different locations and species. As the anthropogenic emissions
decrease, the slopes of PM2.5 and temperature decrease over the Great
Plains and Midwest, but increase slightly in the southern Atlantic states.
Sulfate exhibits decreased sensitivity across the eastern United States, and
OA shows no significant pattern of change. Reasons for such inconsistencies
may be related to the shorter time periods and therefore less-robust
sensitivity. In this study, we have thus chosen not to investigate the
influence of changing emissions on the sensitivity of PM2.5 to
climate change using this statistical model.
Evaluation of PM2.5 sensitivity to surface temperature in chemistry
models
A key question is why previous model studies show no consistent sign
in the change of future PM2.5 relative to the present (Jacob and
Winner, 2009). Such discrepancies no doubt arise in part because of
differences in model projections of future climate or in model speciation of
PM2.5. In this section we investigate whether differences in model
representation of the sensitivity of PM2.5 to meteorological
variability may also contribute to uncertainty in projections of future
PM2.5. As we point out above, few or no models have undergone
evaluation of their capability in simulating this sensitivity over
relatively long timescales (e.g., the interannual variability over a
decade). Our tests with the regression model show that temperature is the
most important driver of changing PM2.5 in a changing climate, making
it the primary candidate for evaluation in these models. We focus on summer
(JJA) because our predictions point to an increase in PM2.5
of 2–3 µg m-3 in the eastern United States by the 2050s at that
time of year, values much greater than previous predictions.
This section consists of two parts. First, we test the capability of four
ACCMIP models and GEOS-Chem in capturing the observed relationship between
JJA monthly mean PM2.5 and temperature. We find that no model simulates
this relationship well. Second, using GEOS-Chem as a test bed, we investigate
the reasons of this failure in this particular model.
Figure 5 shows the distributions of the slopes of monthly PM2.5 and
temperature over the United States in observations and in different
chemistry models for summer months in the present day. All PM2.5 and
temperature values have been detrended, as described above, so that the
slopes reflect only the PM2.5 response to the interannual variability
in temperature. For both the observations and the model results, the
sensitivities of PM2.5 to temperature shown here encapsulate the
response of PM2.5 to all variables associated with temperature,
including cloud cover, relative humidity and boundary layer height. The
observations display positive slopes over the whole United States, with
slopes in the east greater than 1 µg m-3 K-1 (Fig. 5a).
The positive slopes are driven by faster oxidation rates and increased biogenic
emissions, as well as the stagnation frequently concurrent with higher
temperatures. The models, however, either underestimate the positive slopes
or even yield negative slopes in some regions, with no consistent spatial
patterns in these discrepancies. For example, CAM3.5 shows significant
positive slopes in Texas, the Midwest and the Northeast (Fig. 5b). GFDL-AM3
displays a bimodal structure, with positive slopes in the Northeast but
negative slopes in the south (Fig. 5c). The GISS-ModelE2 shows slight
positive slopes over parts of the east (Fig. 5d). The slopes in MIROC-CHEM
are nearly flat, indicating little sensitivity of the monthly mean
PM2.5 concentrations to temperature variability (Fig. 5e).
GEOS-Chem shows positive slopes over much of the eastern United States, but
the magnitudes are much less than those observed (Fig. 5f). In a more
recent study, Westervelt et al. (2016) used a multivariate linear model to
check the dependence of PM2.5 on meteorology in the GFDL Coupled Model
(CM3) and identified a positive PM2.5–temperature sensitivity in the
east in CM3 when all monthly data across the year were considered. For
summer, however, Westervelt et al. (2016) found a mix of positive and
negative sensitivities across the 21st century, depending on the scenario.
Sulfate concentrations declined strongly by the 2090s in all future model
scenarios, contrary to what our results imply. Our results suggest that
these chemistry models may underestimate the impact of future climate change
on US PM2.5 air quality.
The slopes of detrended (a–b) monthly mean PM2.5 and
(c–j) different PM2.5 components with surface air temperature
for 2004–2012 summer months. The left column shows slopes from AQS observations,
and the right column shows results from GEOS-Chem. Organic aerosol (OA) in
(e) is inferred from the measured organic carbon (OC) component using an
OA / OC mass ratio of 1.8 (Canagaratna et al., 2015). Panels (a)
and (b) are the same as Fig. 5a and f. White areas indicate either
missing data or grid boxes where the slope is not significant at the 0.05
level.
Using GEOS-Chem, we further explore the sensitivity of monthly mean
PM2.5 to temperature in the summertime. We regress the simulated
monthly mean concentrations of key PM2.5 components – sulfate,
ammonium, OA and nitrate – onto temperature over the
2004–2012 summers. In the observations, the positive slopes in
sulfate–temperature and OA-temperature clearly drive the positive
PM2.5–temperature slopes (Fig. 6a, c and e). In GEOS-Chem, the
OA–temperature slopes match those in the observations (Fig. 6e–f), but the
modeled sulfate–temperature slopes exhibit negative values in the south
(Fig. 6d), contrary to observations (Fig. 6c). For other PM2.5 species, the slopes with temperature are relatively weak, with minimal
contributions to the total PM2.5–temperature slopes in both
observations and GEOS-Chem (Fig. 6g–j). The observed ammonium–temperature
slopes are weakly positive over the east, but are positive in the Northeast
and negative in the southeast in GEOS-Chem, in a spatial pattern similar to
that of modeled sulfate–temperature (Fig. 6g–h). The nitrate–temperature
slopes are negligible in AQS observations but weakly negative over the east
in GEOS-Chem (Fig. 6i–j). For both ammonium and nitrate, GEOS-Chem
underestimates the dependence on temperature, indicating that the model
likely has difficulty in simulating the competition between increased
emission and faster evaporation at higher temperatures. In any event, Fig. 6 makes clear that the underestimate of PM2.5–temperature slopes in
GEOS-Chem is mainly caused by the underestimate in sulfate–temperature
slopes.
We next search for the reasons of the underestimate in sulfate–temperature
slopes in GEOS-Chem. Three important pathways for sulfate oxidation chemistry
exist: gas-phase oxidation by OH and aqueous-phase oxidation by either
H2O2 or O3 (Jacob, 1999). Total sulfate production rate is
much greater in the eastern United States due to abundant anthropogenic
emissions there. The relative importance of these three pathways varies by
region: in summer, aqueous-phase oxidation by H2O2 is most
important in the east, while gas-phase oxidation by OH dominates in the west.
We calculate the monthly total sulfate production rates
(kg month-1 grid-1) in each pathway and then regress them onto
the monthly temperature in summer. As demonstrated by Fig. 7a, as temperature
increases, OH oxidation rates in GEOS-Chem vary little. In contrast, modeled
H2O2 oxidation rates decrease rapidly with temperature in the south
and increase significantly in the Northeast (Fig. 7b), displaying a similar
spatial pattern as the sulfate–temperature slopes in Fig. 6d. Modeled O3
oxidation rates also decrease with temperature in the south (Fig. 7c), but
with slopes much smaller than those of the H2O2 oxidation rates.
Given that atmospheric SO2, H2O2 and O3 concentrations
all increase with temperature in GEOS-Chem (not shown), our results suggest
that the relationship of cloud fraction and temperature may not be well
parameterized in GEOS-5, the earth system model that provides the
meteorology driving GEOS-Chem. In GEOS-5, cloud fraction is not assimilated
from observations but is calculated online as a prognostic variable (Rienecker
et al., 2008).
Slopes of monthly mean sulfate production with surface air
temperature for 2004–2012 summer months, as calculated by GEOS-Chem. The
panels show slopes from three different production pathways: (a)
gas-phase oxidation by OH and aqueous-phase oxidation by (b)
H2O2 and (c) O3. See Sect. 5 for more details. White
areas indicate either missing data or grid boxes where the slope is not
significant at the 0.05 level.
As a check on our hypothesis, we compare the sensitivity of cloud fraction
to temperature in GEOS-5 with that in the ISCCP-D2like D2 product from CERES
satellite observations. We focus on cloud fraction in the lower troposphere
(> 680 hPa), as surface sulfate PM2.5 is likely most
responsive to oxidation in this part of the atmosphere. Because no reliable
observations of nighttime cloud fraction exist, we focus on daytime
measurements. On average, increased cloud fraction is associated with cooler
surface air temperatures, but the relationship between cloud fraction and
temperature can also have a strong seasonal cycle and vary by region
(Groisman et al., 2000; Sun et al., 2000). Figure 8 shows the slopes of
monthly mean cloud fraction (> 680 hPa) and surface temperature
in summer from 2004 to 2012 over the southeast in daytime. The satellite
observations yield relatively weak slopes (±0.01 K-1), but
GEOS-5 displays strongly negative slopes (∼-0.04 K-1).
This result suggests that cloud fraction in GEOS-5 is too sensitive to
temperature, which in turn makes aqueous-phase oxidation rates decrease too
rapidly as temperature increases in the south and leads to negative
sulfate–temperature slopes.
With regard to the ACCMIP results, understanding the failure of these models
to capture the observed slopes of monthly mean total PM2.5 and
temperature is beyond the scope of this paper. Key diagnostics, such as the
production rates of sulfate through different oxidation pathways, are not
available.
Daytime slopes of monthly mean cloud fractions in the lower
troposphere (> 680 hPa) versus surface air temperature over land
for June–July–August from 2004 to 2012 in (a) the merged
ISCCP-D2like products from CERES and (b) GEOS-5 meteorology. White
areas indicate that the slope is not significant at the 0.05 level.
Discussion and conclusions
In this study, we use a statistical model to investigate the effect of
2000–2050 climate change on fine particulate matter (PM2.5) air quality
across the contiguous United States. To our knowledge, this study represents
the first time that the influences of both local meteorology and synoptic
circulations are considered in projecting future changes in PM2.5 air
quality. We have developed a new method to characterize PM2.5-related
circulation patterns, using singular value decomposition (SVD) of the
spatial correlations between PM2.5 and meteorological variables
across the surrounding region (∼ 1000 km). Our regression
model uses both of these synoptic-scale relationships and relationships of
PM2.5 with local meteorology. Use of SVD increases the explained
variability in 1999–2013 monthly PM2.5 across the United States from
34 %, when only local meteorology is considered, to 43 %.
To estimate the impacts of climate change on future PM2.5 concentrations from 2000–2019 to 2050–2069, we apply our regression model
to the CMIP5 future meteorological projections from an ensemble of 19 GCMs
under the RCP4.5 scenario. The average change in PM2.5 across models
provides a robust estimate of the climate impact on US PM2.5, and the
spread of projected changes allows us to determine the statistical
significance of the average. Assuming that anthropogenic emissions remain at
present-day levels, we project an increase of ∼ 0.4–1.4 µg m-3 in annual mean PM2.5 in the eastern US and a decrease of
0.3–1.2 µg m-3 in the Intermountain West. Mean summer PM2.5
increases as much as 2–3 µg m-3 in the eastern United States
due to faster oxidation and greater biogenic emissions. Mean winter
PM2.5 decreases by 0.3–3 µg m-3 over most regions in the United
States, probably due to the volatilization of ammonium nitrate.
Previous model simulations show no consistent sign of the future PM2.5
changes under a warmer climate (Jacob and Winner, 2009; Fiore et al., 2015),
and the magnitudes of these changes are much smaller than this study. We
examine the ability of four different atmospheric chemistry models to
simulate the observed relationship between PM2.5 and temperature.
Results show that these models underestimate or even fail to capture the
observed positive relationship between monthly mean PM2.5 and
temperature in the eastern United States in summer, implying that they may also
underestimate future changes in PM2.5 under a warmer climate regime.
By comparing with in situ observations, we find that the discrepancies of
monthly mean PM2.5–temperature slopes in GEOS-Chem are mainly caused by
the underestimate of sulfate–temperature slopes, which in turn appears
related to deficiencies in the parameterization of cloud processes in
GEOS-5, the earth system model that provides assimilated meteorology for
GEOS-Chem. The 2004–2012 slopes of monthly mean cloud fraction
(> 680 hPa) and surface temperature are relatively weak (±0.01 K-1)
in satellite observations but strongly negative (∼-0.04 K-1) in GEOS-5 over the southeast in daytime. This result suggests that
cloud fraction, a prognostic variable in GEOS-5, is too sensitive to
temperature and that the rate of aqueous-phase H2O2 oxidation in
GEOS-Chem decreases too rapidly with increasing temperature. This hypothesis
would explain the negative sulfate–temperature slopes in GEOS-Chem in the
south, in contrast to the positive slopes in observations. Other chemistry
models may have similar problems in cloud fraction or other variables
important to PM2.5 production or loss.
CTMs and CCMs are frequently applied to predict future air quality. Our work
underscores the importance of evaluating the skill of such models to
simulate long-term relationships between PM2.5 and temperature and
perhaps other variables. Without such evaluations, the credibility of future
model projections of PM2.5 is not clear. Drawbacks of this study
include its assumption of constant anthropogenic emissions and its
dependence on a relative short history (∼ 15 years) of
PM2.5 observations. We also do not explicitly consider the role of
interannual variability in the climate system and how that might influence
our results (Deser et al., 2013). Within these limitations, this study
provides an up-to-date, observationally based prediction of future
PM2.5 with relevance for air quality management. It also demonstrates
the utility of a computationally efficient model whose projections of the
climate penalty on air quality can be readily compared to those from more
traditional dynamic models.
All datasets used in this study are publically accessible at 10.7910/DVN/MHN3NY (Shen et al., 2017).
The Supplement related to this article is available online at doi:10.5194/acp-17-4355-2017-supplement.
L. Shen and L. Mickley designed the experiments. L. Shen developed the model
code and performed most experiments. L. Murray performed the GISS-ModelE2
simulations. L. Shen prepared the manuscript with contributions from all
co-authors.
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the guidance in cloud fraction analysis from Hongyu Liu in
National Institute of Aerospace at the NASA Langley Research Center. This work
was supported by the National Aeronautics and Space Administration (NASA Air
Quality Applied Sciences Team and NASA-MAP NNX13AO08G), the National
Institute of Environmental Health Sciences (NIH R21ES022585) and the
Environmental Protection Agency (EPA-83575501-0). This publication was
developed under Assistance Agreement 83575501-0 awarded by the US
Environmental Protection Agency. It has not been formally reviewed by the EPA.
The views expressed in this document are solely those of the authors and do
not necessarily reflect those of the EPA. The EPA does not endorse any
products or commercial services mentioned in this publication.
Edited by: Q. Zhang
Reviewed by: two anonymous referees
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