ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-15471-2018The potential effects of climate change on air quality across the conterminous US at 2030 under three Representative Concentration PathwaysChanges in O3 and PM2.5 under RCPsNolteChristopher G.nolte.chris@epa.govhttps://orcid.org/0000-0001-5224-9965SperoTanya L.https://orcid.org/0000-0002-1600-0422BowdenJared H.https://orcid.org/0000-0002-1677-4292MallardMegan S.https://orcid.org/0000-0001-6548-8914DolwickPatrick D.Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, Triangle Park, North Carolina, USADepartment of Applied Ecology, North Carolina State University, Raleigh, North Carolina, USAOffice of Air Quality Planning and Standards, US Environmental Protection Agency, Research Triangle Park, Triangle Park, North Carolina, USAChristopher G. Nolte (nolte.chris@epa.gov)29October20181820154711548923May201813June201816October2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/15471/2018/acp-18-15471-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/15471/2018/acp-18-15471-2018.pdf
The potential impacts of climate change on regional ozone (O3) and
fine particulate (PM2.5) air quality in the United States (US) are
investigated by linking global climate simulations with regional-scale
meteorological and chemical transport models. Regional climate at 2000 and
at 2030 under three Representative Concentration Pathways (RCPs) is simulated by
using the Weather Research and Forecasting (WRF) model to downscale 11-year
time slices from the Community Earth System Model (CESM). The downscaled
meteorology is then used with the Community Multiscale Air Quality (CMAQ)
model to simulate air quality during each of these 11-year periods. The
analysis isolates the future air quality differences arising from
climate-driven changes in meteorological parameters and specific natural
emissions sources that are strongly influenced by meteorology. Other factors
that will affect future air quality, such as anthropogenic air pollutant
emissions and chemical boundary conditions, are unchanged across the
simulations. The regional climate fields represent historical daily maximum
and daily minimum temperatures well, with mean biases of less than 2 K for most
regions of the US and most seasons of the year and good representation of
variability. Precipitation in the central and eastern US is well simulated
for the historical period, with seasonal and annual biases generally less
than 25 %, with positive biases exceeding 25 % in the western US throughout
the year and in part of the eastern US during summer. Maximum daily 8 h
ozone (MDA8 O3) is projected to increase during summer and autumn in
the central and eastern US. The increase in summer mean MDA8 O3 is
largest under RCP8.5, exceeding 4 ppb in some locations, with smaller
seasonal mean increases of up to 2 ppb simulated during autumn and changes
during spring generally less than 1 ppb. Increases are magnified at the upper
end of the O3 distribution, particularly where projected increases in
temperature are greater. Annual average PM2.5 concentration changes
range from -1.0 to 1.0 µg m-3. Organic PM2.5
concentrations increase during summer and autumn due to increased biogenic
emissions. Aerosol nitrate decreases during winter, accompanied by lesser
decreases in ammonium and sulfate, due to warmer temperatures causing
increased partitioning to the gas phase. Among meteorological factors
examined to account for modeled changes in pollution, temperature and
isoprene emissions are found to have the largest changes and the greatest
impact on O3 concentrations.
Introduction
In the United States (US), emissions that lead to the formation of ozone (O3)
and atmospheric particulate matter (PM) have declined
significantly in recent decades, resulting in substantial improvements in air
quality and consequent benefits for human health
. As a result of regulatory actions, such as the
Cross-State Air Pollution Rule and the Tier 2 and Tier 3 emissions standards
for motor vehicles, anthropogenic emissions are projected to continue their
downward trend through 2030 , leading to further reductions in
ambient O3 and concentrations of PM particles smaller than 2.5 µm
in diameter (PM2.5).
Because air pollution is highly sensitive to meteorology, climate change has
the potential to affect air quality by modifying temperatures, wind speeds,
mixing heights, humidity, clouds, and precipitation, which all affect
pollutant formation and removal rates
. Studies using global
climate model (GCM) data to drive global or regional chemical transport
models (CTMs) have found that climate change yields meteorological conditions
that are more conducive to forming high O3, exacerbating summertime
O3 over polluted continental regions
.
Modeling studies conducted using mid-21st century climate data project up to
2–8 ppb increases in summer average ozone levels in the US,
depending on climate change scenario and time period
e.g.,. This deterioration of air quality
due to climate change is known as the “climate penalty”
and could potentially offset some of the
improvement in air quality that would otherwise occur due to reductions in
ozone precursor emissions. The strong evidence for the increase in surface
O3 levels due to climate change was cited in support of the finding
that emissions of greenhouse gases (GHGs) endanger human health and welfare
and are therefore subject to regulation in the US under the Clean Air Act
. The net effects of climate change on PM2.5 are more
uncertain. Some studies that have investigated the impacts of climate change
on PM2.5 have found small but statistically significant effects of
0.5–2.0 µg m-3, but with little consistency, even in the sign
of the change .
It should be noted, however, that most studies of climate change impacts on
PM2.5 have neglected changes in climate-sensitive PM emissions sources.
The studies that have considered changes in these sources have concluded that
warmer temperatures and earlier snowmelt associated with climate change will
lead to increased impacts from wildfires
and dust storms .
Motivated by high positive biases (exceeding 10 ppb) in present-day
O3 obtained in previous work , which were
attributed to positive biases in temperature in the downscaled meteorology,
we developed improved regional climate modeling techniques that were tested
by downscaling coarse reanalysis data
and using the downscaled meteorology to simulate air quality
. In the present study, we apply this downscaling
methodology to GCM data and use the resulting regional climate fields to
drive simulations of air quality across the conterminous US. The near-future
timeframe of 2030 is chosen because of its relevance for air quality policy
and the current planning horizon. Lateral boundary conditions and
anthropogenic emissions are identical for both the historical and future
periods to isolate the meteorological influences of near-term climate change
on regional air quality. The simulated historical regional climate is
evaluated by comparison to reanalysis fields. Changes in regional climate and
air quality at 2030 are presented, and we relate the changes in air quality
to the meteorological drivers for these changes.
Previous studies of the effects of climate change on air quality have
typically considered a single climate scenario , period of a few years or a single season
. This study examines the impact
of climate change on both ozone and PM air quality for the full annual cycle
using 11-year periods with three GHG trajectories. In addition to presenting
changes in seasonal mean quantities, we also focus on distributions and
examine variability across seasonal and diurnal temporal scales.
Modeling approachGlobal climate model
The GCM used in this study is the National Center for Atmospheric
Research-Department of Energy Community Earth System Model (CESM)
. The model has horizontal grid spacing of 0.9∘
latitude × 1.25∘ longitude. Time slices of 11-year periods from
simulations conducted for the fifth phase of the Coupled Model
Intercomparison Project (CMIP5) were selected for
downscaling: 1995–2005 at the end of the CMIP5 historical 20th century
simulation, as well as 2025–2035 from simulations following three
Representative Concentration Pathways RCPs;. The
RCP8.5 scenario assumes “business as usual”, where GHG
concentrations increase substantially over the 21st century, leading to
8.5 W m-2 radiative forcing by 2100. The RCP6.0 scenario
assumes a modest degree of mitigation of GHG emissions,
where total radiative forcing increases before stabilizing at 6.0 W m-2
in 2100. The RCP4.5 scenario has a GHG emissions
peak in the middle of the 21st century followed by a decline, so that total
radiative forcing is 4.5 W m-2 in 2100. Although the RCP
scenarios are named for their radiative forcing at the year 2100, the GHG
emissions paths in each scenario were developed by independent modeling
groups. As a result, a lower RCP scenario may have higher GHG emissions and a
greater increase in global average temperature than a higher RCP scenario for
the 2025–2035 period examined here .
Regional climate model
The CESM data were downscaled with the Weather Research and Forecasting (WRF)
model version 3.4.1 to a domain with 36 km horizontal
grid spacing covering most of North America (199×127 grid points;
Fig. ) and 34 vertical layers extending to a model top at
50 hPa. Archived 6 h fields used for downscaling included 3-D temperature,
specific humidity, horizontal wind components, pressure, and geopotential
height; 2-D surface pressure, skin temperature, 2 m temperature, and 2 m
specific humidity; and monthly average sea surface temperatures, ice
fraction, soil moisture, and soil temperature. To avoid water temperature
discontinuities that arise from applying GCM ocean temperatures to large
lakes , monthly lake temperature data from the land
component of CESM (i.e., the Community Land Model, CLM) were used to set the
temperature of inland water points on the regional domain .
All monthly fields were temporally interpolated to 6 h intervals to avoid
abrupt transitions in the regional climate simulations.
WRF was initialized at 00:00 UTC 1 October 1994 for the historical run and at
00:00 UTC 1 October 2024 for each of the RCP runs, so that each regional
climate simulation included a 3-month spin-up period. Land use classification
was based on the 24-category USGS land cover database. WRF was configured as
in , with spectral nudging of horizontal wind components,
potential temperature, and geopotential applied above the planetary boundary
layer (PBL) using the nudging coefficients from .
WRF and CMAQ modeling domains, with colored areas representing the
National Centers for Environmental Information (NCEI) US climate regions used
for evaluation: (1) Northwest, (2) West, (3) Southwest, (4) Northern Rockies
and plains, (5) Upper Midwest, (6) Ohio Valley, (7) South, (8) Southeast, and
(9) Northeast.
Chemical transport model
The chemical transport model used was the Community Multiscale Air Quality
(CMAQ) model (https://www.epa.gov/cmaq, last access: 25 October 2018) version 5.0.2
. The model was configured with
the multipollutant version of the Carbon Bond 2005 gas phase chemical
mechanism (cb05tump) and the AERO6 aerosol module
. CMAQ simulations were conducted over a 36 km
domain covering the conterminous US (148×110 grid cells;
Fig. ). The Meteorology-Chemistry Interface Processor (MCIP)
version 4.1.3 was used to prepare meteorological fields for
CMAQ using the same vertical layering as in WRF. Reported pollutant
concentrations are taken from the lowest model layer, which has a depth of
about 38 m. Each 11-year CMAQ simulation was run continuously following a
10-day spin-up period.
Numerous studies using regional CTMs that have considered both changing
climate and changing emissions on future air pollutant concentrations have
found that changes in emissions dominate . Modeled pollutant
concentrations are highly sensitive to lateral chemical boundary conditions
e.g.,, and different assumptions
regarding changes in long-range transport have been shown to have a
significant impact on future pollutant levels
. Several
previous studies have also highlighted the importance of rising levels of
methane for ozone chemistry . To
isolate the effects of climate change on air quality, only the meteorological
conditions and the meteorologically dependent emissions that are modeled
within CMAQ were modified between the historical and future CMAQ simulations.
All other input variables, including anthropogenic emissions, chemical
lateral boundary conditions, and land use and land cover classifications,
were unchanged across the air quality modeling scenarios.
Anthropogenic air pollutant emissions for each year of both the historical
and future periods were modeled using the 2030 emissions projection that was
used as the reference case for the Tier 3 motor vehicle standards rulemaking
analyses . This projection assumed the
implementation of previously adopted air quality policies, with the result
that anthropogenic NOx, SO2, and volatile organic
compound (VOC) emissions are 54 %, 69 %, and 25 % lower, respectively, than in the
2011 National Emissions Inventory (Table S1 in the Supplement). Biogenic VOC emissions were
modeled using the Biogenic Emission Inventory System (BEIS)
and thus responded to climate-driven meteorological
changes. Monthly and diurnal temporal profiles were applied to other
emissions source sectors, including wildfires, but did not vary across years.
Emissions of NOx due to lightning were not modeled. Chemical lateral
boundary conditions derived from an independent simulation of the year 2011
by the GEOS-Chem global chemical transport model
were used for each year of the historical and the RCP simulations.
Evaluation for historical period
The CMAQ modeling system has been extensively evaluated for simulation of
historical (“retrospective”) air quality
. It is challenging, however, to
evaluate air quality simulated using meteorology downscaled from a global
climate model . Because climate models are run
without assimilating weather observations, the weather conditions simulated
by downscaling a GCM for a particular historical day cannot be expected to
correspond to the hourly meteorology that occurred on that day. For the same
reason, it is inappropriate to evaluate air pollutant concentrations
simulated using downscaled meteorology against hourly or daily historical
measurements. Instead, regional climate and air quality should be evaluated
at seasonal and monthly temporal scales. As a further complication, to
account for interannual meteorological variability it is necessary to run the
model for periods of several years or even decades, but anthropogenic
emissions of pollutants such as NOx, VOC, and SO2 can exhibit
significant trends that confound the analysis of the impact of using
downscaled meteorology. An evaluation of 2000–2010 ozone and PM2.5 air
quality simulated using historical emissions and meteorology downscaled from
a coarse-scale historical reanalysis showed a performance comparable
to that obtained in typical air quality modeling applications . This
demonstrates that the downscaling procedure does not introduce substantial
bias into the modeled air quality, providing confidence in the method's use
for future air quality projections.
Because this study uses projected 2030 emissions in all simulations,
including for the historical period, modeled air quality is not compared to
observations. Instead the evaluation of the historical period is focused on
monthly and seasonal means and selected percentiles of regional temperature
and precipitation, two meteorological fields that strongly affect air
quality. Temperature and precipitation from the historical period
are evaluated by comparing against the Climate Forecast System Reanalysis
CFSR; and the North American Regional Reanalysis
NARR;, respectively. CFSR is a global reanalysis with
hourly 2 m temperature at 0.31∘ resolution, enabling evaluation of
daily maximum and daily minimum temperatures. NARR is used to evaluate
precipitation because it has been shown to represent precipitation well over
the conterminous US , while CFSR precipitation is
positively biased . Regional analysis is performed using
US climate regions defined by the National Centers for Environmental Information
(Fig. ).
Seasonal averages of daily mean 2 m temperatures simulated by downscaling
CESM with WRF are compared against CFSR fields horizontally interpolated to
the WRF grid in Fig. . The seasonal and spatial patterns of 2 m
temperatures are generally well represented by WRF, though in areas of
complex terrain in the western US there are positive and negative biases
exceeding 4 K (Fig. ). Daily minimum temperatures are within
±2 K of CFSR for every region and season except during summer (JJA) in
the Northwest and West regions (see Fig. for the region designations), which have warm biases of 2.7 and 3.4 K,
respectively. Daily maximum temperatures are also generally well simulated,
with absolute biases only exceeding 2 K for the Southwest during spring (MAM),
the Upper Midwest during spring, summer, and autumn (SON), and for the
Northeast during spring and autumn (Fig. ). Though these
regionally averaged temperature biases are somewhat larger than those typically
obtained in retrospective meteorological modeling for air quality
applications, they are comparable to biases reported in dynamically
downscaled meteorology utilizing nudging
e.g.,. We note that these biases in
the downscaled regional climate fields are largely attributable to the
driving CESM fields rather than to errors within WRF (see Supplement).
Seasonally averaged biases in daily mean 2 m temperature compared
to CFSR (K) and precipitation relative to NARR (%), simulated by downscaling
CESM with WRF for 1995–2005.
Monthly box plots of daily maximum 2 m temperature simulated by
downscaling CESM with WRF for the historical 1995–2005 period compared
against CFSR for each of the US climate regions shown in
Fig. . Boxes range from the 25th to 75th percentiles with
the dark line denoting the median, and whiskers extend to 5th and
95th percentiles. Seasonal biases (K) are shown at
bottom.
Distributions of the daily maximum 2 m temperatures simulated by WRF for each
region and month over the historical 1995–2005 period in comparison to CFSR
are shown in Fig. ; regional distributions of daily minimum
2 m temperatures are provided in the Supplement. The downscaled simulations using WRF
reasonably capture the regional variation in the annual cycle of median
values as well as the width of the interquartile range (IQR). Narrower
distributions are simulated during summer than winter, in agreement with the
pattern in CFSR, but WRF accentuates this difference in some regions, with
excessively narrow daily maximum temperature distributions simulated in the
Upper Midwest, Northeast, Ohio Valley, South, and Southeast. For maximum
temperatures, the WRF simulations of the Northwest and the Northern Rockies
regions have the best overall agreement with CFSR. Though maximum
temperatures are negatively biased most of the year in the Southwest and
West, the magnitude of the IQR is well represented in those regions. During
the summer, the IQR of daily maximum temperatures in WRF is much lower than
in CFSR in several regions, including the Upper Midwest, South, Ohio Valley,
and Southeast. In the regions and months with the largest biases, the
distribution is shifted by nearly a quartile. The worst performance is in
August in the Upper Midwest, in which the 25th and 50th percentile daily
maximum temperatures simulated by WRF exceed the median and 75th percentile
CFSR values, respectively.
Projected changes in maximum daily 8 h average (MDA8) O3
mixing ratios (ppb) from 1995–2005 to 2025–2035 under RCP4.5, RCP6.0, and
RCP8.5 (in rows). Columns show projected changes for spring, summer, and
autumn seasonal means, as well as fourth-highest annual values (“HI4”). Dark
pixels indicate where differences are significant according to Student's t test
(p<0.05).
The spatial and seasonal distributions of precipitation across the
conterminous US in WRF are broadly consistent with NARR
(Fig. ). WRF generally has a wet bias relative to NARR, except
for the South, Upper Midwest, and Ohio Valley regions. Regional biases
relative to NARR are given in Table . Precipitation is
reasonably well simulated in the central and eastern US, with most seasonal
and annual biases 25 % or less. In the western US, however, WRF
precipitation is positively biased relative to NARR throughout the year,
particularly in the Southwest during winter and spring and in the West and
Northwest regions during summer. A less severe positive bias in precipitation
also exists during the summer in the eastern US north of Florida.
Changes at 2030 under RCPs
Potential changes in seasonal mean air pollutant concentrations are presented
under the three RCPs for 2025–2035 relative to 1995–2005. Next, the
meteorological drivers influencing the changes in air quality are examined.
Seasonally and regionally averaged biases in accumulated precipitation (%)
in comparison to NARR for 1995–2005.
Changes in seasonal mean maximum daily 8 h average (MDA8) O3 levels
for spring, summer, and autumn are shown in Fig. ; plots
showing absolute magnitudes are provided in the Supplement. The general locations of
the seasonal changes are consistent across the three RCP scenarios, although
the magnitudes are less pronounced under RCP4.5 and RCP6.0, as expected.
Statistically significant increases of 1–5 ppb are simulated during summer
under RCP8.5 across most of the northern and eastern US, with regional
average increases of at least 2 ppb across the Northern Rockies, Upper
Midwest, and Ohio Valley (Table ). Summer decreases of up to
1.5 ppb are projected in the South and Southeast regions, particularly along
the Texas Gulf Coast. The summer decrease is widespread under RCP4.5 and
RCP6.0, averaging 0.4–0.5 ppb across the South and Southeast regions.
The projected impact of climate change on MDA8 O3 is lower during
the spring and autumn seasons than in summer under all three RCPs. For the
spring, small increases of 0.5–1.0 ppb are simulated over parts of the Ohio
Valley, South, and Upper Midwest regions under RCP4.5 and RCP8.5, which
generally are not statistically significant. Statistically significant
decreases of 0.5–1.0 ppb are simulated along the Southeast coast in RCP6.0.
During the autumn, significant increases of 1–2 ppb are simulated across a
broad area of the central US including most of the South, Ohio Valley, and
Upper Midwest regions under both RCP8.5 and RCP4.5, but no significant change
is evident under RCP6.0.
The preceding analysis focused on changes in seasonal mean MDA8 O3.
Because compliance with the US National Ambient Air Quality Standard (NAAQS)
for O3 is assessed using the annual fourth-highest MDA8
O3 (“HI4”), changes in HI4 averaged across the 11-year periods
are also shown in Fig. and Table . Under
RCP8.5, regional average increases in HI4 exceeding 3 ppb are simulated for
the Upper Midwest, Ohio Valley, and Northeast, with increases exceeding 5 ppb
over large areas within those regions as well as parts of the Southwest and
West. Under RCP4.5 and RCP6.0, regional average HI4 increases by 1.0–1.7 ppb in
the Upper Midwest, Ohio Valley, and Northeast, exceeding 3 ppb through large
parts of those regions. The modeled increases in HI4 under all three RCPs
examined in the Upper Midwest, Ohio Valley, and Northeast regions, which are
highly populated areas of the US, have potentially significant implications
for human health and NAAQS compliance.
Some previous observational and modeling studies
have found that extreme
O3 values have a greater sensitivity to temperature than do mean
values. Projected changes across the O3 distribution are examined
using seasonal percentiles that are calculated for each grid cell, then
averaged across regions and years for the historical and future climate
periods (Fig. ). In summer in the Northeast, Ohio Valley,
and Southeast, the change in O3 under each of the RCPs is projected
to be greater at the upper end of the distribution. In the South and
Southeast, there is a projected decrease of 0.5–1.0 ppb at the lower end of
the distribution under all three RCPs. A gradual increase is projected in the
slope through the 90th percentile, with more pronounced increases at the
upper tail. By contrast, the projected change in O3 is
comparatively uniform across the distribution in the Northern Rockies region,
while there is little change at any part of the distribution in the
Northwest, West, and Southwest regions. During autumn under RCP8.5, increases
ranging from 1 ppb at the low end of the distribution to 2 ppb at the high
end are projected in the Upper Midwest and Ohio Valley regions, while changes
under RCP4.5 and RCP6.0, as well as changes during spring under each of the
RCPs, are less than 1 ppb throughout the distribution for each region (Supplement).
To investigate changes over the entire annual cycle, regional monthly
box plots of MDA8 O3, simulated for the historical period and the
RCP8.5 simulation, are compared in Fig. . Analogous
comparisons with the RCP4.5 and RCP6.0 runs are included in the Supplement.
Consistent with Fig. , the largest changes in median
values are projected in the Upper Midwest, Northern Rockies, Northeast, and
Ohio Valley regions during the summer. Though some of the highest extreme
MDA8 O3 values are simulated in the West and Southwest, changes in
those regions are comparatively small. While most previous studies of the
effect of climate change on O3 pollution have emphasized the
summer, when O3 concentrations are highest, a few investigators
have reported increases during spring and autumn, suggesting a lengthening of
the ozone season .
have projected a reversal of the O3 seasonal cycle in the
northeastern US by the end of the 21st century, with increased methane
levels and decreased NOx levels combining to produce a wintertime
maximum in surface O3. Here we find that median, 75th, and
98th percentile MDA8 O3 values increase in nearly every region of the US
during October, November, and December under the RCPs, but do not show a
consistent response during the months January through May.
Projected changes in percentiles of summer average MDA8 O3
mixing ratios (ppb) simulated by CMAQ under RCP4.5, RCP6.0, and RCP8.5 within
each of the US climate regions shown in Fig. .
Monthly box plots of MDA8 O3 simulated for the historical
1995-2005 period and 2025–2035 under RCP8.5 for each of the US climate
regions shown in Fig. . Boxes range from the 25th to
75th percentiles with the dark line denoting the median, and whiskers
extend to 2nd and 98th percentiles.
Projected changes in annual mean concentrations
(µg m-3) of total PM2.5 and principal PM2.5
components from 1995–2005 to 2025–2035 under RCP4.5, RCP6.0, and RCP8.5.
Dark pixels indicate where differences are significant according to Student's t test
(p<0.05).
Particulate matter
Projected changes in annual mean concentrations of total PM2.5 and its
largest components under the three RCPs are shown in Fig. ,
while absolute quantities for the historical period and relative changes are
provided in the Supplement. Statistically significant PM2.5 decreases of up to
0.7 µg m-3 (5 %–10 %) are simulated in the Northern Rockies and
Ohio Valley regions under RCP8.5 and RCP4.5, while increases of up to
1.0 µg m-3 occur in the Southeast under RCP8.5 and RCP6.0. Most of
the decreases in PM2.5 are due to decreases in nitrate (NO3-)
of up to 0.4 µg m-3 (40 %). The decreases in NO3- are
accompanied by lesser decreases in ammonium (NH4+) and sulfate
(SO42-). Increases in PM2.5 in the Southeast are largely
attributable to organic matter (OM), which increases up to 0.5 µg m-3 (10 %–20 %).
Seasonally averaged changes in NO3- and OM are shown in
Figs. –; the patterns of seasonal changes
in SO42- and NH4+ (Supplement) are similar to the changes in
NO3-. The decreases in annual average NO3- levels under
RCP8.5 and RCP4.5 (Fig. ) are driven by decreases during
winter and spring (Fig. ). The decrease is strongest during
winter under RCP8.5, when average NO3- concentrations decrease by
0.3–0.9 µg m-3 over most of the eastern US. By contrast,
the increases in OM primarily occur during summer and autumn
(Fig. ). During summer under RCP8.5, projected changes to OM
are most pronounced in the Southeast and Ohio Valley regions, where there are
projected increases of 0.2–0.8 µg m-3. There are less
pronounced increases of 0.1–0.3 µg m-3 in those regions under
RCP4.5 and RCP6.0.
Changes in seasonal mean concentrations (µg m-3) of
PM2.5 nitrate under three RCP scenarios. Dark pixels indicate where
differences are significant according to Student's t test (p<0.05).
Changes in seasonal mean concentrations (µg m-3) of
PM2.5 organic matter under three RCP scenarios. Dark pixels indicate
where differences are significant according to Student's t test (p<0.05).
Projected changes in seasonal averages of daily maximum 2 m
temperature (K) from 1995–2005 to 2025–2035 under RCP4.5, RCP6.0, and
RCP8.5. Dark pixels indicate where differences are significant according to Student's t test (p<0.05).
Relative changes projected in seasonal accumulated
precipitation (%) from 1995–2005 to 2025–2035 under RCP4.5, RCP6.0, and
RCP8.5. Dark pixels indicate where differences are significant according to Student's t test (p<0.05).
Projected changes (%) in summer and autumn averages of biogenic
isoprene emissions (a) and cloud fraction (b)
between 1995–2005 and 2025–2035 under RCP4.5, RCP6.0, and
RCP8.5.
Meteorological influences on projected changes in air quality
Because the anthropogenic emissions and chemical lateral boundary conditions
are the same in all CMAQ simulations, all projected changes in air quality
are due to differences in meteorology downscaled from the climate scenarios.
To gain insight into the parameters most strongly influencing the changes in
air quality, correlation coefficients were calculated between monthly mean
changes in several meteorological variables and changes in pollutant
concentrations, focusing on the species and seasons where the impacts of
climate change were greatest. Variables examined included daily mean,
maximum, and minimum 2 m temperatures; daily mean and daily maximum PBL
heights; precipitation; cloud cover; 10 m wind speeds; number of days with
stagnant meteorological conditions ; and biogenic
isoprene emissions. The variables with the strongest correlations to changes
in O3 were daily maximum 2 m temperature, isoprene emissions, and
cloud cover, while temperature, isoprene, and stagnation had the strongest
correlations with NO3- and OM (Supplement).
Projected changes in seasonally averaged daily maximum 2 m temperatures are
shown in Fig. . As expected, the temperature increase is
greatest under RCP8.5. In RCP8.5, daily maximum temperatures increase by
0.5 K across most of the conterminous US in all seasons, by more than 2 K in
the South, Upper Midwest, and Ohio Valley regions during winter, and by more
than 3 K in much of the Upper Midwest and Ohio Valley regions during summer.
Under RCP4.5, daily maximum temperatures increase by 0.5–3.0 K in most of
the conterminous US throughout the year, with the largest and most
widespread increase projected during spring. By contrast, the changes in
daily maximum temperatures under RCP6.0 are less pronounced, with summertime
increases of 1–3 K over most of the US but little change in the eastern US
during autumn, and even slight cooling of 0.5–1.0 K projected in parts
of the Southeast and Ohio Valley regions during winter. Across the
conterminous US, annual average daily maximum temperatures increase by 1.2 K
under RCP4.5, 0.7 K under RCP6.0, and 1.7 K under RCP8.5.
The spatial patterns of the mean changes in winter and spring daily maximum
temperatures in the RCPs (Fig. ) correspond to the changes
in NO3- concentrations (Fig. ), and monthly
variations in NO3- and maximum temperatures are strongly negatively
correlated (Supplement). Aerosol NO3- increases in the portions of the
Southeast and the Ohio Valley regions where wintertime daily maximum
temperatures decrease slightly under RCP6.0. The patterns of changes in
aerosol NH4+ concentrations (Supplement) largely mirror the changes in
NO3-. There is little decrease in aerosol NO3- during
summer because nitrate exists almost totally in the gas phase during that season.
While anthropogenic emissions are unchanged across these simulations,
biogenic emissions of VOCs are modeled within CMAQ and respond to changes in
meteorology. Isoprene emissions depend on temperature as well as
photosynthetically active radiation, which is attenuated in the presence of
clouds. Modeled emissions of biogenic isoprene increase across all future
scenarios, due to both warmer temperatures and decreased cloudiness
(Fig. ). Modeled average annual isoprene emissions over the
conterminous US increase by 11 %, 8 %, and 19 %, under RCP4.5, RCP6.0, and
RCP8.5, respectively. The increased emissions of isoprene and other biogenic
VOCs in the heavily forested Southeast region not only enhance production of
O3 (Fig. ), but also account for most of the
increases in OM concentrations (Fig. ).
Scavenging of soluble aerosols by precipitation is an important removal
process for atmospheric particulate matter. Shown in Fig. are
percent changes in seasonal precipitation for each of the RCP scenarios. The
decrease in summer and autumn precipitation in the South, Southeast, and Ohio
Valley regions under all three climate scenarios may be contributing to
increases of OM in those regions. However, comparing the changes in seasonal
precipitation to changes in PM2.5 indicates that changes in aerosol
scavenging of soluble aerosols are not strongly affecting average PM2.5
concentrations in these simulations. In particular, precipitation decreases
strongly in the central US during the winter under all three RCPs, but
wintertime PM2.5 concentrations decrease in that region under RCP4.5 and
RCP8.5, and are largely unchanged under RCP6.0.
The increment in MDA8 O3 per degree of warming projected during
summer and autumn varies regionally (Fig. ), but there is some
consistency in spatial patterns between the RCPs. During summer under RCP4.5
and RCP8.5, ΔO3/ΔT ranges from 0.5 to 1.0 ppb K-1
over much of the Northern Rockies, Upper Midwest, Ohio Valley, and
Northeast regions, and 0.5–1.0 ppb K-1 in the South,
Southeast, Ohio Valley, and Upper Midwest regions during autumn. The
temperature change projected under RCP6.0 during autumn
(Fig. ) is near zero, which explains the extreme values for
ΔO3/ΔT. By contrast, the proportionality between
projected O3 and daily maximum temperature is negative in much of
the South and Southeast, particularly under RCP4.5 and RCP6.0. This negative
relationship between daily maximum temperature and MDA8 O3 is
consistent with observation-based sensitivities reported for the Southeast
during summer .
Conclusions
This study investigated the impacts of climate change on regional ozone and
PM2.5 air quality across the conterminous US. Global CESM simulations were dynamically downscaled to 36 km
horizontal grid spacing with WRF, and these meteorological fields were used
by CMAQ to simulate air quality. The climate scenarios represent the year 2030
under RCP4.5, RCP6.0, and RCP8.5, and differences were analyzed relative
to a historical period representing the year 2000. Comparison of simulated
temperature and precipitation to reanalysis data showed that the CESM–WRF
modeling system performed well for temperature, with absolute biases of less
than 2 K for most regions of the US and most seasons of the year. WRF also
showed reasonable skill at representing the variability in daily maximum and
minimum temperatures throughout the conterminous US. Seasonal and annual
precipitation biases in the central and eastern US were generally less than
25 %, but precipitation was positively biased in the western US throughout
the year and in most of the eastern US during summer.
For the air quality simulations, anthropogenic emissions and boundary
conditions were unchanged between the historical and future periods to
isolate the meteorological effects of climate change on air quality from
non-meteorological factors. Results indicated increases in seasonal mean MDA8
O3 during summer in the Northern Rockies, Upper Midwest, Ohio
Valley, and Northeast regions under all scenarios. The increase was largest
under RCP8.5, exceeding 4 ppb in parts of the Northern Rockies and Upper
Midwest regions. Smaller increases of up to 2 ppb were simulated during
autumn, while changes during spring were generally less than 1 ppb. Increases
were magnified at the upper end of the O3 distribution in the Upper
Midwest, Ohio Valley, Northeast, and Southeast regions. PM2.5
concentration changes varied by scenario and by season, with annual average
changes of up to ±1.0µg m-3. Decreases in PM2.5
were principally due to reductions in aerosol NO3- during winter and
spring, accompanied by lesser decreases in NH4+ and
SO42-, due to warmer temperatures causing increased gas-phase
partitioning. Increases in secondary organic aerosol occurred during summer
and autumn due to increased biogenic emissions.
Ratio of projected changes in seasonal MDA8 O3 to changes
in seasonal daily maximum 2 m temperature (ppb K-1) for summer and
autumn between 1995–2005 and 2025–2035 under RCP4.5, RCP6.0, and
RCP8.5.
Observational evidence and modeling studies
have argued that the O3 climate penalty
(ppb K-1) is lower at reduced levels of NOx emissions. It
is important to recognize that the results presented here use a projected
2030 emission inventory with continued implementation of NOx
emissions controls. The increase in O3 resulting from a given
climate scenario would be expected to be greater if NOx emissions are
higher than projected here, particularly in NOx-limited regions such
as the eastern US.
The physical and chemical processes that influence air pollutant
concentrations are complex, and there are numerous aspects that may
potentially vary due to climate change. Quantities examined to account for
the modeled changes in pollution included temperature, precipitation, PBL
height, wind speed, cloud cover, isoprene emissions, and the number of days
with stagnant weather conditions. Temperature and isoprene emissions were
found to have the greatest changes under all scenarios, especially in summer,
and the greatest subsequent impact on O3 and PM2.5 concentrations.
There are a number of important limitations of the present study. To isolate
the effect of climate change on air quality, we kept anthropogenic emissions
constant across all modeled years. However, electric sector emissions
increase during peak temperature events due to increased demand for air
conditioning, and emissions from electric generating units used to provide
power during peak periods are less strictly regulated . The
increased emissions associated with increased electricity demand during heat
waves is not represented in our analysis, potentially underestimating the
impact on upper percentile and annual fourth-highest O3 levels.
Although biogenic emissions of VOCs were estimated using the downscaled
meteorology, our modeling did not consider changes to prevalence and
distribution of species of vegetation, or the potential leaf-scale inhibition
of biogenic isoprene emissions due to elevated atmospheric CO2
concentrations . Other natural emissions sources
potentially affected by climate change, including wildfires and windblown
dust, were neglected in this work. We did not model changes in lightning
NOx formation rates or changes in stratosphere–troposphere exchange
of O3. We also did not consider changes in drivers of global
baseline air pollution, including atmospheric methane levels, foreign
emissions scenarios, and long-range transport to the US. Finally, there is
substantial interannual variability in air quality due to year-to-year
changes in meteorology. Though we conducted four sets of 11-year continuous
simulations to account for interannual variability to the extent that our
computational resources made practicable, 11-year simulations are
insufficient to represent the full range of natural variability in the
earth's climate system .
The effects of climate change on O3 and PM2.5 obtained in this
study are in the range of those reported in similar studies focused on air
quality in 2050 and references therein. However, to our
knowledge this study represents the most comprehensive analysis of the
potential changes in US regional-scale air quality due to climate change
conducted to date, in that it encompassed three future climate scenarios for
periods exceeding a decade in duration and considered changes in both
O3 and PM2.5. The significant and widespread increases in
model-projected MDA8 O3 associated with specific future climate
scenarios, including in some densely populated areas, have potentially
important implications for ongoing efforts to reduce exposure to ozone and
protect human health.
CMAQ model code is available via . Model output
associated with this paper will be available via the EPA Environmental Dataset
Gateway at https://edg.epa.gov.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-15471-2018-supplement.
CGN and TLS designed the study in consultation with JHB,
MSM, and PDD. CGN conducted the analysis and wrote the paper. All authors helped
shape the analysis and the content of the paper by commenting on prior versions.
The authors declare that they have no conflict of interest.
The views expressed in this article are those of the authors and
do not necessarily represent the views or policies of the US Environmental
Protection Agency.
Acknowledgements
CESM (CCSM4) global climate model data corresponding to the 20th century,
RCP8.5, RCP6.0, and RCP4.5 “MOAR” simulations were downloaded from the
Earth System Grid via the University Corporation for Atmospheric Research
website at http://www.cesm.ucar.edu/experiments/cesm1.0/ (last access: 25 October 2018). The authors
thank Lara Reynolds (CSRA) and Daiwen Kang (EPA) for assistance conducting
the WRF and CMAQ simulations analyzed here, and Chris Weaver, Darrell Winner,
Barron Henderson, and Marcus Sarofim (EPA) for providing critical reviews of
a draft of this paper.
Edited by: Kostas Tsigaridis
Reviewed by: four anonymous referees
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