ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-13925-2018Seasonal ozone vertical profiles over North America using the
AQMEII3 group of air quality models: model inter-comparison and
stratospheric intrusionsSeasonal ozone vertical profiles over North AmericaAstithaMarinaastitha@engr.uconn.eduhttps://orcid.org/0000-0002-3892-6672KioutsioukisIoannisFissehaGhezae ArayaBianconiRobertoBieserJohanneshttps://orcid.org/0000-0003-2938-3124ChristensenJesper H.https://orcid.org/0000-0002-6741-5839CooperOwen R.GalmariniStefanohttps://orcid.org/0000-0002-0321-152XHogrefeChristianhttps://orcid.org/0000-0003-3280-3513ImUlashttps://orcid.org/0000-0001-5177-5306JohnsonBryanLiuPengNopmongcolUarpornPetropavlovskikhIrinahttps://orcid.org/0000-0001-5352-1369SolazzoEfisiohttps://orcid.org/0000-0002-6333-1101TarasickDavid W.YarwoodGregUniversity of Connecticut, Civil and Environmental Engineering,
Storrs, CT 06269-3037, USAUniversity of Patras, Physics Department, 26504 Rio, GreeceEnviroware srl, via Dante 142, 20863 Concorezzo, ItalyHelmholtz-Zentrum Geesthacht, Institute of Coastal Research,
Geesthacht, GermanyGerman Aerospace Center (DLR), National Aeronautics and Space Center,
Weßling, GermanyAarhus University, Department of Environmental Science,
Frederiksborgvej 399, 4000 Roskilde, DenmarkCooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, CO 80309, USAChemical Sciences Division, NOAA Earth System Research Laboratory,
Boulder, CO 80305, USAEuropean Commission Joint Research Center, Ispra, ItalyEnvironmental Protection Agency Research Triangle Park, Research
Triangle Park, NC, USANRC Fellowship Participant at Environmental Protection Agency
Research Triangle Park, NC, USARamboll, 773 San Marin Dr., Suite 2115, Novato, CA 94945, USAAir Quality Research Division, Environment and Climate Change Canada,
Downsview, Ontario, CanadaMarina Astitha (astitha@engr.uconn.edu)2October20181819139251394530January201823February20184August20189August2018This 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/18/13925/2018/acp-18-13925-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/13925/2018/acp-18-13925-2018.pdf
This study evaluates simulated vertical ozone profiles produced in the
framework of the third phase of the Air Quality Model Evaluation
International Initiative (AQMEII3) against ozonesonde observations in North
America for the year 2010. Four research groups from the United States (US)
and Europe have provided modeled ozone vertical profiles to conduct this
analysis. Because some of the modeling systems differ in their meteorological
drivers, wind speed and temperature are also included in the analysis. In
addition to the seasonal ozone profile evaluation for 2010, we also analyze
chemically inert tracers designed to track the influence of lateral boundary
conditions on simulated ozone profiles within the modeling domain. Finally,
cases of stratospheric ozone intrusions during May–June 2010 are investigated
by analyzing ozonesonde measurements and the corresponding model simulations
at Intercontinental Chemical Transport Experiment Ozonesonde Network Study
(IONS) experiment sites in the western United States. The evaluation of the
seasonal ozone profiles reveals that, at a majority of the stations, ozone
mixing ratios are underestimated in the 1–6 km range. The seasonal change
noted in the errors follows the one seen in the variance of ozone mixing
ratios, with the majority of the models exhibiting less variability than the
observations. The analysis of chemically inert tracers highlights the
importance of lateral boundary conditions up to 250 hPa for the
lower-tropospheric ozone mixing ratios (0–2 km). Finally, for the stratospheric
intrusions, the models are generally able to reproduce the location and
timing of most intrusions but underestimate the magnitude of the maximum
mixing ratios in the 2–6 km range and overestimate ozone up to the first kilometer
possibly due to marine air influences that are not accurately described by
the models. The choice of meteorological driver appears to be a greater
predictor of model skill in this altitude range than the choice of air
quality model.
Introduction
Since its initiation in 2008, the Air Quality Model Evaluation International
Initiative (AQMEII) has brought together scientists from both sides of the
North Atlantic Ocean to perform regional model experiments using common
boundary conditions, emissions and model evaluation frameworks with a
specific focus on regional modeling domains over Europe and North America
(Galmarini and Rao, 2011; Rao et al., 2012; Galmarini et al., 2017). Phase 3
of the AQMEII activities (AQMEII3) focuses on joint modeling experiments
with the second phase of the Task Force on Hemispheric Transport of Air
Pollution (TF-HTAP) to conduct global and regional assessments of
intercontinental transport of air pollutants (Huang et al., 2017; Nopmongcol
et al., 2017) and uncertainties stemming from emissions and boundary
conditions (Huang et al., 2017; Hogrefe et al., 2018). Investigation of the
vertical ozone distribution has occurred during previous phases of the
AQMEII activities (Schere et al., 2012; Solazzo et al., 2013) but with model
simulations that vary in emissions and boundary conditions for different
years. The motivation behind this work is that in AQMEII3 common
anthropogenic emission inventories and lateral chemical boundary conditions
were implemented by all modeling groups, which helps us further investigate
model-to-model variability and performance evaluation.
Regional air quality model evaluation is most commonly performed for
ground-level ozone mixing ratios (Hogrefe et al., 2001; Appel et al., 2007,
2012; Herwehe et al., 2011; Solazzo et al., 2012; Kioutsioukis and Galmarini,
2014; Kioutsioukis et al., 2016; Im et al., 2015, 2018, among others) and less frequently for
free tropospheric ozone distributions in longer, non-episodic time frames (Schere
et al., 2012; Solazzo et al., 2013; Jonson et al., 2010, using HTAP global
modeling systems). This is mainly due to the scarcity of upper-air
measurements as well as the need to investigate the efficacy of emission
reduction policies and attainment demonstration which apply to surface ozone
exceedances. Previous studies related to AQMEII phases 1 and 2 have used
measurements from ozonesonde networks and instrumented commercial airliners
as part of the Measurement of Ozone and Water Vapor by Airbus In-Service Aircraft (MOZAIC) program (Solazzo et
al., 2013; Giordano et al., 2015). Accurate representation of the entire
troposphere in air quality models influences the prediction of air pollutant
vertical distributions, stratosphere–troposphere exchange processes and
ground-level mixing ratios. The AQMEII3 framework is ideal for providing the
platform and collaborations to assess multi-model simulated ozone vertical
profiles from the ground up to the planetary boundary layer (PBL) and evaluate the
models' capability to reproduce ozone mixing ratios aloft as well as to
assess contributions from boundary conditions (inert tracer experiments)
which have important effects on surface and upper-air ozone mixing ratios
(Tarasick et al., 2007; Pendlebury et al., 2017).
This study utilizes modeling results for the North American domain from four
research groups that participated in AQMEII3 to evaluate seasonal ozone
vertical profiles simulated for the year 2010 against ozonesonde
observations. The objectives of this analysis are to (a) evaluate simulated
seasonal ozone vertical profiles with ozonesonde measurements, (b) assess
variations in model performance related to ozone vertical distribution
(model inter-comparison), (c) assess influence of lateral boundary conditions
to ozone profiles within the modeling domain, and (d) investigate cases of
stratospheric ozone intrusion above the western United States during May and June 2010.
Because some of the modeling systems differ in their meteorological
drivers, available wind speed and temperature are also included in the
evaluation. In addition to the ozone profile evaluation for 2010, we analyze
chemically inert tracer modeling experiments that estimated the influence of
lateral boundary conditions to ozone profiles within the modeling domain.
Finally, several cases of stratospheric ozone intrusions are investigated by
analyzing ozonesonde measurements and the corresponding model simulations at
Intercontinental Chemical Transport Experiment Ozonesonde Network Study
(IONS) experiment sites in the western United States (Cooper et al., 2011,
2012). IONS-2010 was a component of the CalNex (Research at the Nexus of Air
Quality and Climate Change) 2010 experiment, which focused on understanding
the effects of air pollutants on air quality across California (Ryerson et
al., 2013). The data and methods of analysis are described in Sect. 2, the
evaluation and model inter-comparison of ozone seasonal profiles are
provided in Sect. 3, the results from the model experiments using chemically
inert tracers are provided in Sect. 4 and the case study of stratospheric
ozone intrusions is discussed in Sect. 5. The summary and conclusions are
presented in Sect. 6.
Specifications of the modeling systems used in this study.
All models use chemical boundary conditions from C-IFS (see notes). The
common North American analysis domain has the following extent:
130 to 59.5∘ W, 23.5 to 58.5∘ N.
InstitutionAbbreviationModeling systemsBoundary conditionsHorizontal gridVerticalApproximate heightInert(meteo)spacinglayersat first layertracersU.S. EPAUS3WRF3.4/CMAQ5.0.2NCEP12 km35 layers up to 50 hPa19 mYesHelmholtz-Zentrum Geesthacht (Germany)DE1COSMO-CLM/CMAQ5.0.1NCEP24 km30 layers up to 50 hPa40 mYesRamboll (US)US1WRF3.4/CAMx6.2NCEP12 km26 layers up to 97.5 hPa19 mNoAarhus University (Denmark)DK1WRF/DEHMECMWF16.7 km29 layers up to 100 hPa25 mYes
Notes: C-IFS: ECMWF's Composition Integrated Forecasting System (IFS); US3
and US1 use the WRF model with the ACM2 PBL module (Asymmetric Convective Model
with nonlocal upward mixing and local downward mixing; Pleim, 2007). DK1
uses the MYJ PBL scheme in WRF: Mellor–Yamada–Janjic (Janjic, 1994).
Data and methodsAtmospheric modeling systems
The base case simulations used in this study are performed by all AQMEII3
participants using lateral chemical boundary conditions prepared from global
concentration fields simulated by ECMWF's global chemistry model C-IFS
(Flemming et al., 2015). Table 1 provides an overview of each participating
research group or institution, their modeling systems and the main specifications
of the simulations. A detailed description of the four modeling systems
(US1, US3, DE1 and DK1) is provided in Solazzo et al. (2017). Harmonization
of all model simulations is achieved by specifying a common simulation time
period (January–December 2010), common regional anthropogenic and fire
emission inventories that include emissions for Canada and Mexico (details
on the emission inventories is provided in Pouliot et al., 2015), and common
lateral chemical boundary conditions. The 2008 National Emission Inventory
is used as basis for the 2010 emissions with necessary updates described in
Pouliot et al. (2015). Anthropogenic emissions totals are the same for all
models, but each group uses their own system to spatially disaggregate and
temporally allocate emissions to their gridded domain (for example, DE1 and
DK1 use HTAP emissions while US3 and US1 use the Sparse Matrix Operator
Kernel Emissions, SMOKE; SMOKE emissions were provided on an hourly basis
while HTAP is monthly, so the temporal, vertical and chemical distributions
might be different among models). The simulations differ in the modeling
systems (air quality and meteorology), horizontal and vertical grid spacing,
chemistry modules and deposition schemes as well as biogenic emissions. Each
modeling group was free to use the meteorological model of their choice
based on compatibility with their chemical transport model. More details on
the AQMEII3 modeling experiments are included in the technical note by
Galmarini et al. (2017). All research groups interpolated their results into
the same 0.25×0.25∘ grid spacing before submitting the model outputs
to the common data platform for the analysis (Joint Research Institute's
ENSEMBLE system).
Each modeling group also included three non-reactive tracers in their
simulations but only three of the four models provided 3-D output of the
tracer concentrations (Table 1). These tracers are designed to track the
inflow of ozone from the lateral domain boundaries and are specified as
lateral boundary conditions, with no emissions or chemical
formation/destruction occurring within the modeling domain. All tracers
undergo advection, diffusion, cloud mixing/transport, scavenging and
deposition, but no chemistry. The tracer mixing ratios and their vertical
profiles are used to investigate the sensitivity of ozone to the lateral
boundary conditions. It should be noted that these inert tracers were not
intended to provide a quantitative attribution of ground-level ozone to
ozone boundary conditions. As noted by Baker et al. (2015) and Nopmongcol et
al. (2017), inert tracers would overestimate such contributions due to the
lack of chemical loss terms which are considered in other attribution tools
such as reactive tracers or ozone source apportionment. However, using them
in a relative manner helps identify the sensitivity of modeled ozone mixing
ratios to lateral boundary conditions. The definition of each tracer is as
follows:
BC1: for layers below 750 hPa (∼2.5km), the boundary
conditions for this tracer are set to the same C-IFS ozone mixing ratios
used as ozone boundary conditions for the regional models. For layers above
750 hPa, the boundary conditions for this tracer are set to zero.
BC2: for layers between 750 hPa (∼2.5km) and
250 hPa
(∼10km), the boundary conditions for this tracer are set to
the same C-IFS ozone mixing ratios used as ozone boundary conditions for the
regional models. For layers below 750 and above 250 hPa, the boundary
conditions are set to zero.
BC3: for layers above 250 hPa (∼10km), the boundary
conditions for this tracer are set to the same C-IFS ozone mixing ratios
used as ozone boundary conditions for the regional models. For layers below
250 hPa, the boundary conditions are set to zero.
Geographic maps of ozonesonde monitoring sites for 2010:
(a) North America (seasonal analysis) and (b) western United States (stratospheric
intrusion evaluation).
Names, codes and geographic locations of ozonesonde sites.
Next to the code is a characterization of the site location relative to the
model domain. The elevation at these sites ranges from sea level to
1.6 km
above sea level.
IDCodeNameLongitudeLatitudeNetworkNumber of profiles1STN021/northStony Plain-114.153.54ECCC432STN107/eastWallops Island-75.4737.93NASA-WFF533STN338/northBratt's Lake-104.750.20ECCC494STN418/southHuntsville-86.6434.72NOAA-ESRL515STN445/westTrinidad Head-124.1640.80NOAA-ESRL776STN456/northEgbert-79.7844.23ECCC547STN457/westKelowna-119.449.94ECCC748STN458/eastYarmouth-66.143.87ECCC709STN487/eastNarragansett-71.4241.49NOAA-ESRL2610BOULDER/centralBoulder-105.2540.00NOAA-ESRL4411RY/westPoint Reyes-122.9538.09IONS20103212PS/westPoint Sur-121.8936.30IONS20103613SN/westSan Nicolas Island-119.4933.26IONS20102314JT/westJoshua Tree-116.3934.08IONS20103615SH/westShasta-122.4940.60IONS201033
Notes: NOAA-ESRL: National Oceanic and Atmospheric Administration Earth System Research Laboratory
(data downloaded from https://www.esrl.noaa.gov/, May 2016); NASA-WFF: National Aeronautic and
Space Agency Wallops Flight Facility; ECCC: Environment and Climate Change
Canada; IONS: Intercontinental Chemical Transport Experiment Ozonesonde
Network Study. Data from ECCC and NASA-WFF were downloaded from the WMO
World Ozone and Ultraviolet Data Centre (WOUDC; 10.14287/10000001).
Ozonesonde sites and statistical metrics
Ozonesonde data are obtained from various networks with availability for the
year 2010. A total of 10 sites across North America are selected for seasonal and
annual analyses (Fig. 1a) and five additional sites located in the western
United States (Fig. 1b) are selected for studies of stratosphere–troposphere
exchange (note that the Trinidad Head, TH, site was selected for both types of
analyses and is shown in both Fig. 1a and b). Information on data
networks and station characteristics, including the number of launches
available for analysis, are summarized in Table 2. The modeled and observed
ozone fields were interpolated at the following 18 standard
vertical heights above ground level (m): 0, 100, 250, 500, 750, 1000, 1500,
2000, 3000, 4000, 5000, 6000, 7500, 8500, 10 000, 12 000, 15 000 and 18 000. The
10 sites depicted in Fig. 1a had launches throughout the entire year and
are used to construct seasonal average profiles by averaging over all
available launches in a given season at each vertical height. Seasonal
averages are chosen to evaluate how models capture transport and
photochemistry processes that influence ozone formation (Winter: DJF;
Spring: MAM; Summer: JJA; Fall: SON). The modeled ozone mixing ratios are
sampled in accordance to available ozonesondes; thus, the variability of the
vertical ozone profiles might be underrepresented since the ozonesondes are
not continuous throughout each month (Lin et al., 2015). The evaluation of
ozone vertical profiles is performed for layers up to 8.5 km since there is
less confidence on the tropopause placement for the regional models which
was evident by large errors in ozone mixing ratios above 8.5 km (not shown).
The study by Makar et al. (2010) has shown that, when models predict a
tropopause height above the one implicit in the ozone background conditions
(ozone climatology), then higher ozone mixing ratios will become available
in the upper troposphere (UT), resulting in high model errors. In addition to the
ozonesonde data, wind speed and temperature profiles are used for stations
that included such data in their repositories (wind and temperature profiles
are included in the Supplement).
IONS experiments are aimed at measuring tropospheric ozone variability
across North America (Thompson et al., 2007). During the IONS-2010
experiment, ozonesondes were launched almost daily between 10 May and 19 June 2010.
Its main goal was to determine the latitudinal variability of
baseline ozone along the California coast from the surface to the tropopause
(Cooper et al., 2011). A total of 230 ozonesondes were launched at seven
sites, one in southern British Columbia (Kelowna) and six in California.
Figure 1b shows the locations of the six IONS ozonesonde sites in
California. All IONS sites are located in very rural areas far from fresh
emissions. Four of the sites are right on the coast, almost in the water
(TH, RY, PS, SN), and in the lowest few hundred meters of the atmosphere they
represent depleted ozone from the marine boundary layer, while the other two
are inland (Shasta, SH; and Joshua Tree, JT).
The statistical metrics used in the model evaluation and model
inter-comparison are root mean square error (RMSE); Pearson correlation
coefficient (R); 95 % bootstrapping confidence intervals (indicates
significance in differences between models and observations, Efron, 1987);
and the fractional difference indicator (FD) used in the stratospheric
intrusion case study only, defined as follows:
FD(%)=200(mod-obs)/(mod+obs),
where “mod” and “obs” denote the modeled and observed ozone values. If all modeled
values lie within a factor of 2 of the observations then FD is between
-66.7 % and +66.7 %, and if all modeled values lie within a factor of
3 of the observations then FD is between -100 % and +100 %. The
interpretation of the results is made with caution due to the
incommensurability of the comparison of point measurements with grid cell
model values.
Seasonal vertical profiles of ozone mixing ratios for
2010 (a: winter; b: spring; c: summer), for stations located in
the northern part of the domain. The horizontal lines indicate the 95 %
bootstrapped confidence interval for each vertical layer. Note that Stony Plain
does not include model outputs from DE1 as the model domain does not cover
that station.
Seasonal vertical profiles of ozone mixing ratios for
2010 (a: winter; b: spring; c: summer), for stations located in
the eastern part of the domain. The horizontal lines indicate the 95 %
bootstrapped confidence interval for each vertical layer. Note that Narragansett
has limited amount of ozonesondes for all seasons (less than 10 for each
season) and the results should be viewed with caution.
Seasonal vertical profiles of ozone mixing ratios for
2010 (a: winter; b: spring; c: summer), for stations located in
the central (C), south (S) and west (W) part of the domain. Horizontal lines
indicate the 95 % bootstrapped confidence interval for each vertical
layer.
Evaluation and model inter-comparison of ozone seasonal profiles
for 2010
The ozone vertical profiles for each season and station (Figs. 2–4 and
box plots in Figs. S1 and S2 in the Supplement) highlight the variability of
model behavior depending on the specific model configuration as well as the
impact of seasonal cycles that alter emissions, transport and transformation
of ozone. During winter, all models underestimate the mean and variability
of ozone mixing ratios in the 1.5–5 km vertical levels for all stations,
with the exception of Boulder, Narragansett and Huntsville. In most cases,
the 95 % bootstrapping confidence intervals do not overlap between models
and observations in the 1.5 to 5 km height range, indicating that the
differences in the mean are statistically significant. Model behavior near
the surface (0–1 km) varies, with the majority of the models agreeing with
observations. There is a notable tendency for most models to underestimate
the 0–1 km mean ozone mixing ratios for the two easternmost sites (Yarmouth
and Narragansett; Fig. 3). The ozone mixing ratios exhibit larger
variability in the upper layers (5–8.5 km) with the models behaving
differently depending on the site and altitude.
During spring, all models show better performance for the lower layers for
most stations. Variable behavior is shown in the two easternmost sites
(Yarmouth and Narragansett; Fig. 3). In Yarmouth, the observed ozone is
underestimated by all models in the 0.75–6 km range while the models agree
with observations in the lower layers. At Narragansett, a similar
underestimation is noted in the 2–6 km range but the models' behavior varies
in the lower layers. The results for Narragansett must be viewed with
caution due to the limited number of profiles, which varies from 5 to 8 for
each season.
During summer, all models over-predict ozone in the 0–0.5 km layer at the
northern sites of Bratt's Lake and Stony Plain. For the Egbert site, DK1
shows a significant over-prediction in the 0–2 km range. Egbert is located
near the Great Lakes (Fig. 1a, STN456) and the complexity of the geography
might not be resolved adequately. A similar behavior is noted at Wallops
Island where DK1 results stand out from other models in the lowest 0–2 km,
possibly resulting from a different representation of the land–water
interface and resulting mixing heights. However, as noted below, the summer
temperature profiles for DK1 shown in Fig. S2 do not offer conclusive
evidence that the ozone differences can be attributed to differences in
mixing due to grid spacing, and deposition processes simulated by the model
might be another reason for the over-prediction seen in these two sites
(deposition could not be evaluated at the time of this study). All models,
except DK1, overpredict the mean ozone mixing ratios for Narragansett
(eastern part of the domain) at 0–0.25 km and the same behavior is seen in
Yarmouth. At the westernmost site, Trinidad Head, all models overpredict
ozone in the 0–1 km range. Finally, the mean ozone profiles during fall are
generally well represented by all models, with some variations depending on
the site and height, which cannot be generalized. One common pattern for the
eastern and northern sites is the under-prediction of ozone in the
3–6 km
range (the exception is Wallops Island; SON profiles are shown in the
Supplement, Fig. S2).
Seasonal average RMSE of ozone mixing ratio (ppbv) for
each station and model, calculated for two height ranges: LT (lower
troposphere = 0–2 km) and UT (upper troposphere = 2–8.5 km).
By evaluating the error in the seasonal ozone vertical profiles for two
height ranges (lower troposphere, LT, 0–2 km; and upper troposphere,
UT, 2–8.5 km), we observe the expected error magnitude difference between LT
and UT given the increase in the ozone mixing ratios in the upper layers
(Fig. 5). For this analysis, the RMSE is calculated at each of the standard
altitude levels listed in Sect. 2.2 using all available launches in a
given season and then averaged across all standard levels in the LT and UT
ranges. The LT errors are 2–4 ppb higher for the summer compared to other
seasons for most models (the average RMSE for all stations and models during
summer is 12 and 10 ppb for the fall). The lowest LT errors are seen in
winter and spring with an average error of ∼8ppb across all
models and sites. At most sites, the DK1 simulations for LT exhibit a higher
RMSE than other models during summer and fall with RMSE values that range
from 6 to 32 ppb (32 ppb RMSE for the Wallops Island site and 24 ppb for
Huntsville in the fall are the maximum values). Vertical profiles of
temperature and wind speed for DK1 do not show large variations for Wallops
Island during summer (Figs. S3, S4), but for Huntsville the temperature
profile is underestimated consistently for all seasons and layers (Fig. S3).
Wind speed profiles were not available for Huntsville to further examine the
large RMSE values for DK1.
There is a peak in the LT and UT RMSE at Yarmouth during fall associated
with all modeling systems. Since this is the easternmost site in the model
domain, it might indicate that the eastern boundary condition is not
appropriate for the fall or the weather variables exhibit errors that
influence ozone mixing ratios. The temperature profiles are very similar
between all models and observations for Yarmouth (Fig. S3), but the LT wind
speed is underestimated by DE1 and US1 (Fig. S4). The wind and temperature
profiles for US3 in Yarmouth in the fall do not show any significant
variation from the observations to explain the higher RMSE value. In
general, the average RMSE over all stations for the LT increases for all
models in the following order: winter, spring, fall, summer. All models have
similar error magnitudes for the LT, with DK1 being an outlier during summer
and spring when it has noticeably higher RMSE values than the other models.
The seasonal change in the variance of simulated and observed LT ozone
mixing ratios is the same as the change seen in the RMSE values (higher
during summer and fall and lower during spring and winter). All models are
less variable than the observations with the exception of DK1 for summer and
fall.
For the UT, the highest errors in ozone mixing ratio occur during winter and
spring. The average RMSE across all stations and models during spring is
33 ppb:
26 ppb for winter, 22 ppb for summer and 15 ppb for fall. There is a
tendency for all models to produce high UT errors for the Boulder site
during winter and spring and for Huntsville and Trinidad Head for spring.
For Trinidad Head and Huntsville, only DK1 underestimates the observed
temperature for all vertical levels and seasons, whereas it overestimates
the UT temperature profiles for Boulder (Fig. S3). These results do not
provide any insights into the cause of the common high UT errors across all
models but given that they occur in all models despite different
meteorological drivers and model configurations they do suggest that the
lateral boundary conditions are a major factor. In general, the average UT
RMSE over all stations increases for all models in the following order:
fall, summer, winter, spring. The higher UT errors agree with the vertical
profile analysis discussed previously, where large deviations from the
observed ozone profiles are seen at the 1–6 km vertical range. The seasonal
change in the variance of simulated and observed UT ozone mixing ratios is
the same as the change seen in the RMSE values (higher during spring and
winter and lower during summer and fall). All models are less variable than
observations with the exception of DK1 for winter and summer.
Seasonal Taylor diagrams using normalized standard
deviations for two height ranges: LT (lower troposphere = 0–2 km) and UT
(upper troposphere = 2–8.5 km). Stony Plain (STN021) is excluded because
DE1's domain does not incorporate the site's location.
The statistical evaluation and inter-comparison of modeled ozone profiles
for the lower (0–2 km) and upper troposphere (2–8.5 km) are further explored
with the Taylor diagrams in Fig. 6 for each season and vertical range. For
these Taylor diagrams, observations and model results for each standard
vertical level were averaged over all vertical levels in a given vertical
range (LT or UT) for each launch and the resulting vertical averages for
each launch were then used to compute the metrics depicted in the diagrams.
Thus, the variability metrics (correlation coefficient and normalized
standard deviation) measure the temporal variability across launches in a
given season at a given station. The seasonal LT Taylor charts highlight the
variability in model performance during all seasons. One common feature
throughout all seasons is that most models underestimate the observed
variability at most sites as indicated by standard deviation ratios
(measured by concentric circles around the origin) of less than 1. During
winter (Fig. 6, DJF_LT) very low (and negative) correlations
and high centered RMS differences are evident for the western sites of
Trinidad Head and Kelowna (all models) in the LT. The predictions are
improved for Egbert, where all models have correlations above 0.85 and low
RMSE. In general, LT variations at both sites in the western part of the
domain are not captured well by the four modeling systems during all
seasons.
Spatial variability in LT model performance is still evident in the
statistical metrics for spring (Fig. 6, MAM_LT). LT
correlations are somewhat improved for the summer (with 13 points showing
correlations above 0.6) and further improved in the fall (with most of the
points having correlations above 0.6). It is apparent that no single model
outperforms the others in the station-by-station comparison. When
considering the overall statistics for all stations (Fig. S5), US3, US1 and
DE1 share similar performance for spring, summer and fall. It is interesting
to also note the differences and commonalities between the models: US3 and US1
share common meteorological inputs, while US3 and DE1 are based on the same
air quality model (though a different model version). There is no obvious
attribution of the model performance to these differences and commonalities
when looking at each individual station.
As discussed earlier, the UT ozone mixing ratios are more challenging for
all four modeling systems and this is evident by looking at the
station-based Taylor diagrams (Fig. 6, UT) as well as the station-averaged
diagrams in the Supplement (Fig. S5). As was the case for the
LT, the modeled temporal variability tends to be lower than the observed
temporal variability across all models and sites. Models US1 and US3 have
very similar performance at most stations. During summer and fall, there is
less spread in the model results, with US3, US1 and DE1 performing similarly
for most stations and DK1 having the most distinct behavior compared to the
other three models. For example, DK1 at Wallops Island during summer and
fall has high RMSE values (shown in Fig. 5) and we can see from Fig. 6
(JJA_UT and SON_UT, red triangle) that the
correlation is low and RMSE is high.
Average ozone profiles for winter (DJF): (a) all stations,
(b) northern sites, (c) western sites, (d) eastern sites for spring (all
stations) and summer (all stations). The number of sites is shown in parentheses next to the panel title.
The variability of model performance and the lower correlations during
winter, spring and summer are further explored by analyzing the average
profiles. The average of winter ozone profiles over all stations (Fig. 7a)
shows under-prediction in the 1–6 km height range. This common condition is
also seen for the western, northern and eastern sites separately (Fig. 7b–d).
For the eastern sites, ozone is under-predicted from the surface to 6 km,
while for the western sites all models indicate over-prediction of ozone
in the levels below 250 m. A similar pattern is seen during spring for the
1–6 km height range but less pronounced compared to winter. During the
summer period all models underestimate ozone in the lower vertical range
(0–1 km) with biases that range from 1 to 12 ppb. This explains some of the
high errors seen in the LT for the summer (seen in Fig. 5). To gain insight into
how lateral boundary conditions might have influenced the performance of
three of the modeling systems (DE1, US3, and DK1), the chemically inert
tracer results are discussed in the following section for all seasons and
sites.
Percentages of lateral boundary contributions (BC1, BC2
and BC3) to the total (BCtot) at each specific height range, ozonesonde
site, model and season. LT represents the lower troposphere (0–2 km), MT the
middle troposphere (2–8.5 km) and UTLS the upper troposphere to lower
stratosphere (8.5–18 km). BC1: lateral boundary conditions nonzero only at
the 0–750 mb level; BC2: lateral boundary conditions nonzero only at the
750–250 mb level; BC3: lateral boundary conditions are nonzero only at
the levels above 250 mb.
Influence of lateral boundary conditions to ozone profiles using
chemically inert tracers
Three chemically inert tracers are included with the simulations by all
modeling groups but only three of the modeling systems provided 3-D data of
the tracer mixing ratios (Table 1). We are interested in the relative
contribution of each lateral boundary tracer to the total tracer mixing
ratios and the characteristics of each tracer's vertical profile at the 10
ozonesonde sites. The relative contribution of each tracer (BC1, BC2 and
BC3) is assessed by normalizing each one with the sum of all tracer mixing
ratios (BCtot=BC1+BC2+BC3). This normalization allows us to compare
contributions from each tracer at each site and season (Fig. 8). The
normalized values are assessed for three vertical layers: LT represents the
lower troposphere (0–2 km), MT the middle troposphere (2–8.5 km) and UTLS the
upper troposphere to lower stratosphere (8.5–18 km) following Nopmongcol et
al. (2017). BCtot is calculated for each vertical layer separately. More
specifically, the percentage contribution from each tracer BC1, BC2 and BC3
to the LT, MT and UTLS for each model, station and season is analyzed and
discussed.
The lower-troposphere mixing ratios (LT) are influenced by both BC1 (lateral
boundary set to nonzero below 750 hPa) and BC2 (lateral boundary set to
nonzero between 750 and 250 hPa). The relative contributions of BC1 and BC2
depend on season and station location. For example, during summer, BC2
contribution is stronger for all sites (50 %–85 %) except Trinidad
Head, where BC1 and BC2 have an almost equal contribution. This indicates the
importance of lateral boundary conditions up to 250 hPa for the
lower-troposphere ozone mixing ratios (0–2 km). Looking back at the poor model
performance for the western sites of Trinidad Head and Kelowna for winter
and summer (Fig. 6; DJF_LT and JJA_LT), one
possible explanation and point of further investigation would be the
influence of lateral boundary conditions up to 10 km (250 hPa).
The MT tracer mixing ratios are primarily influenced by the BC2 tracer with
some contribution from BC3. The BC3 contribution to MT is more pronounced
for the DE1 model for all seasons and sites. The US3 model shows a small
contribution to MT from BC1 and BC3, except for Boulder and Huntsville. This
means that the lateral boundary conditions within the vertical range 750–250 hPa
primarily influence the ozone mixing ratios in the MT. The UTLS mixing
ratio is almost exclusively influenced by the BC3 tracer for all seasons,
models and sites.
Since chemistry is not part of the BC experiments, the relative
contributions analyzed here are primarily proxies for the transport and
deposition mechanisms. The seasonality of contributions seen in the LT and
MT layers is, thus, directly related to planetary boundary layer
processes and designates the significance of the influence that lateral
boundary conditions have during each season. An in-depth multi-model
comparison of the inert tracer mixing ratios at the surface is provided by
Liu et al. (2018).
(a) Mean ozone profiles using all available IONS
ozonesondes at each site (10 May–20 June 2010) interpolated at specific
vertical levels. The dotted lines show the mean difference between the
profiles during average and episodic conditions (episodic – average). The
episodic periods taken are 22–29 May and 7–14 June. During intrusions, the
average O3 enhancement is up to 40 ppb in the first 8 km from the
surface (San Nicolas, SN; green dotted line) and reaches 105 ppb at
10 km
altitude (Point Reyes, RY; blue dotted line). Note that JT and SH are inland
sites; all other sites are coastal. (b) Observed (red) and modeled
(blue) ozone percentiles (5th, 50th, 95th) during the May–June IONS campaign
(131 profiles at six sites). Each panel corresponds to a different modeling
system.
Case study: stratospheric intrusions during May–June 2010
Stratosphere to troposphere transport is an important process that affects
tropospheric ozone (Stohl et al., 2003; Akritidis et al., 2016; Langford et
al., 2018). This analysis addresses the ability of different air quality
modeling systems to represent the relevant dynamical processes during
springtime stratospheric intrusions above the western United States, capitalizing on
the AQMEII3 simulations for 2010 and ozonesondes from the IONS campaign
(Cooper et al., 2011, 2012). For average conditions, the upper-tropospheric
ozone mixing ratios decrease from north to south for a given altitude (Liu
et al., 2013). The IONS measurement data demonstrate a gradient of
∼40ppb at 8 km a.s.l. between the northernmost and
southernmost coastal sites during the study period (Fig. 9a). Factors
contributing to the gradient include stronger influence from a lower
tropopause and more frequent stratospheric intrusions at higher latitudes,
as well as greater influence from low-ozone tropical air masses at lower
latitudes (Cooper et al., 2011). Below 4 km there is little latitudinal
difference in the average ozone profiles. Only Joshua Tree (Fig. 1b),
downwind of the Los Angeles Basin, exhibits a departure from the mean
profile with enhanced mixing ratios (Fig. 9a).
A comparison of the distribution of modeled versus observed ozone profiles
(5th, 50th and 95th percentiles using 131 profiles at six IONS
sites; Fig. 9b) reveals that the median ozone mixing ratio increases with
altitude in the first 1000 m, as deposition reduces ozone mixing ratios near
the ground (e.g., Chevalier et al., 2007). In addition, the coastal sites
(four out of six) represent depleted ozone from the marine boundary layer,
which can also be seen in the mean ozone profiles for each station in Fig. 9a;
the four coastal sites have almost identical ozone mixing ratios between
0 and 250 m. The models might not be able to capture the influence of marine
air due to the horizontal grid spacing and how each model treats
subgrid-scale processes (i.e., for a grid cell that includes both land and sea
surface). The effect of surface processes on ozone is also evident by the
strong gradient in the first 2 km of the troposphere, ranging between 10 and
20 ppbkm-1 at all sites. The observed and modeled median profiles are
in close agreement mostly above 250 m (Fig. 9b). All models show a similar
general structure, with overestimation of the median in the first kilometer and
with few exceptions above 6 km. Another common feature to all models is the
smaller range between the 5th and 95th percentiles compared to the
observed spread at all levels, with the only exception being DK1 in the
first 2 km. The positive bias in the PBL during summer at North American
stations was also found for the simulations performed as part of AQMEII
Phase 1 (e.g., Solazzo et al., 2013), although it should be noted that those
simulations were performed with a different suite of models for a different
year, were driven by different boundary conditions and were not evaluated
at the IONS locations. In the first kilometer, the overestimations are likely
due to inaccuracies in PBL processes such as marine air influence,
emissions, photochemistry and deposition. Given the proximity of the
IONS sites to the regional domain boundaries, the analysis of the inert
boundary tracers in Sect. 4, and the comparison of global and regional
model simulations at Trinidad Head presented in Hogrefe et al. (2018), the
errors above 6 km are likely caused by errors in the representation of
tropopause dynamics in the models that affected the downward mixing of
higher stratospheric ozone mixing ratios.
Indicative fields of total ozone column (TOC, a, c) and
potential vorticity (IPV, b, d) at the 330 K isentropic surface during
28 May 2010 (a, b) and 10 June 2010 (c, d). Source: Era-Interim.
Ozone profiles (observed: diamond; modeled: colored
lines) and relative humidity (dashed line in %; shares the same scale
with ozone in the x axis) at each IONS site during the 28 May and 8–9 June
intrusion. The stratospheric intrusion is denoted by the sudden drop in
relative humidity that is accompanied by an increase in ozone mixing ratios
from the ozonesondes.
The identification of stratospheric intrusions is typically quantified using
tracers of stratospheric origin in numerical models. On this basis, seven
stratospheric O3 intrusions occurred in the western United States during the
IONS2010 campaign in May–June 2010 (Cooper et al., 2011; Lin et al., 2012a,
b). The four strongest intrusions occurred on 22–24 May, 27–29 May,
7–8 June and 9–14 June (Lin et al., 2012a, b). Enhanced ozone mixing ratios in
combination with very low relative humidity (RH) provides a qualitative
proxy for dry air of possible stratospheric origin. High isentropic
potential vorticity (IPV) in the troposphere and high total ozone column
(TOC) are other indicators of stratospheric air and tropopause folding.
Figure 10 displays both IPV at 330 K and TOC fields over the western
United States during 28 May and 10 June, when the strongest stratospheric intrusions
occurred (source: ERA-Interim; Dee et al., 2011). Both fields demonstrate
higher-than-normal values over the region during the examined periods. This
result is also supported from the soundings at the six IONS sites (Fig. S6).
Dry air masses with enhanced O3 are recorded at various levels, in
spatial agreement with areas of enhanced TOC and IPV (Fig. 10). The periods of 28 May and
8–9 June 2010 are selected as the most representative of strong
stratospheric intrusions, and the vertical ozone profiles for all models and
stations are depicted in Fig. 11. On 28 May, the soundings show high ozone
values (above 100–150 ppb) for the northern sites (TH, RY and SH) in the
6–10 km range and for the southern sites (PS, SN and JT) in the 2–5 km range;
these high ozone values coincide with a strong drop in RH. The high ozone
mixing ratios are not captured by any model, except at Trinidad Head
and Shasta (SH). Similar performance is seen in the 9 June vertical
profiles, where the models capture the vertical gradient of the ozone mixing
ratios but not the high values seen in the northern sites, RY and PS (all
vertical profiles are included in the Supplement, Fig. S6).
Meteorological fields are expected to influence the ozone production and
distribution between the troposphere and stratosphere although the influence
exerted to the ozone vertical profiles from meteorological fields is
inherently nonlinear and thus difficult to link directly. A tropopause fold
is typically identified by the presence of a very dry stable layer in the
free atmosphere at potential temperatures around 310–320 K, which corresponds
to the frontal zone beneath the polar jet stream (Vaughan et al., 1994). As
an example, very dry layers were observed at 22, 27 and 28 May over around 8 km a.g.l. at RY (Fig. S6). At the same days, the range of potential
temperatures 310–320 K was typically found at those heights (Fig. S7). Both
facts combined explain the origin of the high ozone levels recorded on the
22nd and 27th. The stratospheric intrusion was also simulated by all models
(Fig. S7), with varying intensity though. Models US1 and US3, which share
the same meteorological driver, better represent the vertical extent of the
ozone penetration. The isentropic isolines for US1 and US3 are in better
agreement with the observed ridges in potential temperature during
stratospheric intrusions.
Fractional difference (%) between observed and
simulated ozone profiles. Results are presented aggregated from all
soundings (a, c) and at each site separately (b, d). (a) and (b) use all
profiles (10 May–20 June 2010). (c) and (d) present results during
episodic conditions (22–29 May, 7–14 June). (FD is calculated for each
individual profile and then averaged.)
We also calculated the aggregated fractional difference indicator
across all stations (here aggregation denotes that FD is calculated for each
individual profile and then averaged). The general model errors found
earlier, such as the tendency for all models to overestimate mixing ratios
in the first kilometer, are also evident in the FD plot (Fig. 12). Moreover,
the tendency of some models to depart from the average error profile is also
reproduced, such as the underestimation of DE1 between 1 and 2 km and the
overestimations of DK1 in the 5–7 km layer. When calculating the FD at each
site, it is found that the overestimation in the first kilometer occurs at all
sites and has a latitudinal gradient across the coastal sites with larger
values towards the south, which relates to the impact of the marine boundary
layer. Above 5 km, the bias also has a latitudinal gradient starting with
negative values in the north (TH) and progressively becoming positive moving
southwards. During episodic conditions, significant overestimations and
underestimations are evident above 9 km at some sites (e.g., RY and PS in
panel d). Those high FD values of both signs are found at the sites
exhibiting stratospheric intrusion signals in Fig. S6 (e.g., RY at 27 May,
PS at 11 June), indicating that the stratosphere–troposphere exchange in the
regional model and/or the C-IFS model providing boundary conditions may not
be fully captured during these episodes. The performance of the modeling
systems appears to be more closely linked to the meteorological driver
rather than the actual air quality model. The two simulations using CMAQ
(US3 and DE1) do not produce similar results at any of the sites, although
they share the same BCs and emissions. In contrast, the CMAQ and CAMx
simulations (US3 and US1 respectively) which share common meteorological
fields, and thus the same PBL scheme (but use a different vertical
resolution as noted by Liu et al., 2018), have rather similar results.
Conclusions
We analyze four annual air quality model simulations for North America
performed under AQMEII3 to evaluate seasonal ozone vertical profiles for the
year 2010 against ozonesonde observations. The objectives of this analysis
are to (a) evaluate simulated seasonal ozone vertical profiles with
ozonesonde measurements, (b) assess variations in model performance related
to ozone vertical distribution (model inter-comparison), (c) assess the
influence of lateral boundary conditions on ozone profiles within the
modeling domain, and (d) investigate cases of stratospheric ozone intrusions
in the western United States during May–June 2010.
The evaluation of the seasonal ozone profiles reveals that, at a majority of
the stations, ozone mixing ratios are underestimated in the 1–6 km range.
Model performance as measured by RMSE is better during winter and spring for
the lower troposphere (LT, 0–2 km) and during summer and fall for the upper
troposphere (UT; 2–8.5 km). In general, the average RMSE over all stations
for the LT increases for all models in the following order: winter, spring,
fall, summer. Average RMSE for all stations and models during summer is 12 ppb, 10 ppb
for the fall, and 8 ppb for winter and spring. Average RMSE for
all stations for the UT during spring is 33 ppb: 26 ppb for
winter, 22 ppb for summer and 15 ppb for fall. There is a tendency for all models to agree
on high UT errors for the Boulder site during winter and spring and for
Huntsville and Trinidad Head during spring. For both LT and UT, the same
seasonal change noted in the RMSE is seen in the variance of ozone mixing
ratios for both observations and model results, with the majority of the
models exhibiting less variability than the observations. Even though the
modeling systems differ in horizontal grid spacing, meteorological drivers
and atmospheric vertical layers, it was not possible to connect model
performance to these variations. The results show that the meteorological
driver is more impactful compared to the air quality model, without
specifically indicating that one driver is more skillful that the others.
The chemically inert tracers provide a relative assessment of influences of
the lateral boundary conditions on ozone profiles. The results indicate that
the lower-troposphere mixing ratios (LT) are influenced by both BC1 (lateral
boundary set to nonzero below 750 hPa) and BC2 (lateral boundary set to
nonzero between 750 and 250 hPa). The relative contributions of BC1 and BC2
depend on season and station location, with the BC2 contribution being
stronger in the summer for all sites (50 %–85 %) compared to BC1. This
highlights the importance of lateral boundary conditions up to 250 hPa for
lower-tropospheric ozone mixing ratios (0–2 km). The middle troposphere
mixing ratios are primarily influenced by the BC2 tracer with some
contribution from BC3 (lateral boundary set to nonzero above 250 hPa). The
upper-troposphere–lower-stratosphere mixing ratios (UTLS) are almost
exclusively influenced by the BC3 tracer for all seasons, models and sites.
For the stratospheric intrusion case study, the comparison of the four
modeling systems against O3 soundings in California during May–June 2010
revealed that the models can reproduce the location and timing of most
intrusions but underestimate the magnitude of the maximum mixing ratios in
the 2–6 km range. There is a general tendency of the models to overestimate
ozone mixing ratios in the 1 km layer adjacent to the surface and above 5 km.
The former is possibly related to inaccuracies in surface and/or PBL
processes while the latter points to potential errors in boundary conditions
and/or the representation of the exchange between the upper troposphere and
the lower stratosphere in the regional models. The differences between the
four modeling systems are mostly evident above 6 km and the choice of
meteorological driver appears to be a greater predictor of model skill in
this altitude range than the choice of air quality model.
The modeling and observational data generated for the AQMEII Phase 3 are
accessible through the ENSEMBLE data platform
(http://ensemble.jrc.ec.europa.eu, last access: September 2017) upon contact with the managing
organizations. The Joint Research Center Ispra's Institute for Environment and
Sustainability provided its ENSEMBLE system for model output harmonization
and analyses and evaluation.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-13925-2018-supplement.
MA and IK designed the analysis and wrote the manuscript with contributions from
all co-authors; GAF and IK conducted the data analysis; JB, JC, UI, UN, GY, CH and PL were responsible
for the numerical simulations; RB, ES and SG
provided access to the ENSEMBLES system and data retrieval; ORC, IP, BJ and DWT are responsible for the
ozonesonde data used in the study.
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 U.S. Environmental
Protection Agency.
This article is part of the special issue “Global and regional assessment of
intercontinental transport of air pollution: results from HTAP, AQMEII and MICS”. It is not associated with a conference.
Acknowledgements
We gratefully acknowledge the contribution of all research groups and
organizations that provided the datasets used in this study: U.S. EPA (North
American emissions processing and gridded meteorology); ECMWF MACC (chemical
boundary conditions); the WMO World Ozone and Ultraviolet Data Centre
(WOUDC) and its data-contributing agencies provided North American
ozonesonde profiles; additional ozonesonde profiles were downloaded from
NOAA's Earth System Research Laboratory, Global Monitoring Division
(https://www.esrl.noaa.gov/, last access: September 2017).
Edited by: Martin Dameris
Reviewed by: three anonymous referees
ReferencesAkritidis, D., Pozzer, A., Zanis, P., Tyrlis, E., Škerlak, B., Sprenger, M.,
and Lelieveld, J.: On the role of tropopause folds in summertime tropospheric
ozone over the eastern Mediterranean and the Middle East, Atmos. Chem. Phys., 16, 14025–14039, 10.5194/acp-16-14025-2016, 2016.
Appel, K. W., Gilliland, A. B., Sarwar, G., and Gilliam, R. C.: Evaluation
of the Community Multiscale Air Quality (CMAQ) model version 4.5:
Sensitivities impacting model performance; Part I – ozone, Atmos. Environ.,
41, 9603–9615, 2007.
Appel, W., Chemel, C., Roselle, S. J., Francis, X. V., Hu, R.-M., Sokhi, R.
S., Rao, S. T., and Galmarini, S.: Examination of the Community Multiscale
Air Quality (CMAQ) model performance for North America and Europe for the
AQMEII project, Atmos. Environ., 53, 142–155, 2012.
Baker, K., Emery, C., Dolwick, P., and Yarwood, G.: Photochemical grid model
estimates of lateral boundary contributions to ozone and particulate matter
across the continental United States, Atmos. Environ., 123,
49–62, 2015.Chevalier, A., Gheusi, F., Delmas, R., Ordóñez, C., Sarrat, C., Zbinden, R.,
Thouret, V., Athier, G., and Cousin, J.-M.: Influence of altitude on ozone
levels and variability in the lower troposphere: a ground-based study for
western Europe over the period 2001–2004, Atmos. Chem. Phys., 7, 4311–4326, 10.5194/acp-7-4311-2007, 2007.Cooper, O. R., Oltmans, S. J., Johnson, B. J., Brioude, J., Angevine, W.,
Trainer,
M., Parrish, D. D., Ryerson, T. R., Pollack, I., Cullis, P. D., Ives, M. A., Tarasick, D. W., Al-Saadi, J., and Stajner, I.: Measurement of western U.S. baseline ozone from the
surface to the tropopause and assessment of downwind impact regions, J.
Geophys. Res., 116, D00V03, 10.1029/2011JD016095, 2011.Cooper, O. R., Gao, R.-S., Tarasick, D., Leblanc, T., and Sweeney, C.: Long-term
ozone trends at rural ozone monitoring sites across the United States,
1990–2010, J. Geophys. Res., 117, D22307, 10.1029/2012JD018261, 2012.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P.,
Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M.,
Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C.,
Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteorol. Soc., 137, 553–597, 10.1002/qj.828, 2011.
Efron, B.: Better bootstrap confidence intervals, J. Amer. Stat. Assoc., 82,
171–185, 1987.Flemming, J., Huijnen, V., Arteta, J., Bechtold, P., Beljaars, A., Blechschmidt, A.-M.,
Diamantakis, M., Engelen, R. J., Gaudel, A., Inness, A., Jones, L., Josse, B.,
Katragkou, E., Marecal, V., Peuch, V.-H., Richter, A., Schultz, M. G., Stein, O.,
and Tsikerdekis, A.: Tropospheric chemistry in the Integrated Forecasting
System of ECMWF, Geosci. Model Dev., 8, 975–1003, 10.5194/gmd-8-975-2015, 2015.Galmarini, S. and Rao, S. T.: The AQMEII two-continent Regional Air Quality
Model evaluation study: Fueling ideas with unprecedented data, Atmos.
Environ., 45, 2464, 10.1016/j.atmosenv.2011.03.025, 2011.Galmarini, S., Koffi, B., Solazzo, E., Keating, T., Hogrefe, C., Schulz, M., Benedictow, A.,
Griesfeller, J. J., Janssens-Maenhout, G., Carmichael, G., Fu, J., and Dentener, F.:
Technical note: Coordination and harmonization of the multi-scale, multi-model
activities HTAP2, AQMEII3, and MICS-Asia3: simulations, emission inventories,
boundary conditions, and model output formats, Atmos. Chem. Phys., 17, 1543–1555, 10.5194/acp-17-1543-2017, 2017.
Giordano, L., Brunner, D., Flemming, J., Hogrefe, C., Im, U., Bianconi, R.,
Badia, A., Balzarini, A., Baro, R., Chemel, C., Curci, G., Forkel, R.,
Jimenez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote,
C., Kuenen, J. J. P., Makar, P. A., Manders- Groot, A., Neal, L., Perez, J.
L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder,W., Sokhi,
R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya,
K., Žabkar, R., Zhang, Y., and Galmarini, S.: Assessment of the MACC
reanalysis and its influence as chemical boundary conditions for regional
air quality modelling in AQMEII-2, Atmos. Environ., 115, 371–388, 2015.
Herwehe, J. A., Otte, T. L., Mathur, R., and Trivikrama Rao, S.: Diagnostic
analysis of ozone concentrations simulated by two regional-scale air quality
models, Atmos. Environ., 45,
5957–5969, 2011.
Hogrefe, C., Rao, S. T., Kasibhatla, P., Hao, W., Sistla, G., Mathur, R., and
McHenry, J.: Evaluating the performance of regional-scale photochemical modeling
systems: Part II – ozone predictions, Atmos. Environ., 35,
4175–4188, 2001.Hogrefe, C., Liu, P., Pouliot, G., Mathur, R., Roselle, S., Flemming, J., Lin, M.,
and Park, R. J.: Impacts of different characterizations of large-scale
background on simulated regional-scale ozone over the continental
United States, Atmos. Chem. Phys., 18, 3839–3864, 10.5194/acp-18-3839-2018,
2018.Huang, M., Carmichael, G. R., Pierce, R. B., Jo, D. S., Park, R. J., Flemming, J.,
Emmons, L. K., Bowman, K. W., Henze, D. K., Davila, Y., Sudo, K., Jonson, J. E.,
Tronstad Lund, M., Janssens-Maenhout, G., Dentener, F. J., Keating, T. J., Oetjen, H.,
and Payne, V. H.: Impact of intercontinental pollution transport on North American
ozone air pollution: an HTAP phase 2 multi-model study, Atmos. Chem. Phys., 17, 5721–5750, 10.5194/acp-17-5721-2017, 2017.
Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini,
A., Baro, R., Belassio, R., Brunner, D., Chemel, C., Curci, G., Flemming,
J., Forkel, R., Giordano, L., Jimenez-Guerrero, P., Hirtl, M., Hodzic, A.,
Honzak, L., Jorba, O., Knote, C., Kuenen, J. J. P., Makar, P. A.,
Manders-Groot, A., Neal, L., Perez, J. L., Piravano, G., Pouliot, G., San
Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Torian, A.,
Werhahn, K., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Zhang, J.,
Hogrefe, C., and Galmarini, S.: Evaluation of operational online-coupled
regional air quality models over Europe and North America in the context of
AQMEII phase 2. Part I: Ozone, Atmos. Environ., 115, 404–420, 2015.Im, U., Christensen, J. H., Geels, C., Hansen, K. M., Brandt, J., Solazzo, E.,
Alyuz, U., Balzarini, A., Baro, R., Bellasio, R., Bianconi, R., Bieser, J.,
Colette, A., Curci, G., Farrow, A., Flemming, J., Fraser, A., Jimenez-Guerrero, P.,
Kitwiroon, N., Liu, P., Nopmongcol, U., Palacios-Peña, L., Pirovano, G., Pozzoli, L.,
Prank, M., Rose, R., Sokhi, R., Tuccella, P., Unal, A., Vivanco, M. G., Yarwood, G.,
Hogrefe, C., and Galmarini, S.: Influence of anthropogenic emissions and boundary conditions
on multi-model simulations of major air pollutants over Europe and North America in the
framework of AQMEII3, Atmos. Chem. Phys., 18, 8929–8952, 10.5194/acp-18-8929-2018,
2018.
Janjic, Z. I.: The Step-Mountain Eta Coordinate Model: Further Developments
of the Convection, Viscous Sublayer, and Turbulence Closure Schemes, Mon.
Weather Rev., 122, 927–945, 1994.Jonson, J. E., Stohl, A., Fiore, A. M., Hess, P., Szopa, S., Wild, O., Zeng, G.,
Dentener, F. J., Lupu, A., Schultz, M. G., Duncan, B. N., Sudo, K., Wind, P.,
Schulz, M., Marmer, E., Cuvelier, C., Keating, T., Zuber, A., Valdebenito, A.,
Dorokhov, V., De Backer, H., Davies, J., Chen, G. H., Johnson, B., Tarasick, D. W.,
Stübi, R., Newchurch, M. J., von der Gathen, P., Steinbrecht, W., and Claude, H.:
A multi-model analysis of vertical ozone profiles, Atmos. Chem. Phys., 10, 5759–5783, 10.5194/acp-10-5759-2010, 2010.Kioutsioukis, I. and Galmarini, S.: De praeceptis ferendis:
good practice in multi-model ensembles, Atmos. Chem. Phys., 14, 11791–11815, 10.5194/acp-14-11791-2014, 2014.Kioutsioukis, I., Im, U., Solazzo, E., Bianconi, R., Badia, A., Balzarini, A., Baró, R.,
Bellasio, R., Brunner, D., Chemel, C., Curci, G., van der Gon, H. D., Flemming, J.,
Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Jorba, O.,
Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., San Jose, R.,
Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Tuccella, P., Werhahn, J., Wolke, R.,
Hogrefe, C., and Galmarini, S.: Insights into the deterministic skill of air
quality ensembles from the analysis of AQMEII data, Atmos. Chem. Phys., 16, 15629–15652, 10.5194/acp-16-15629-2016, 2016.Langford, A. O., Alvarez, R. J., Brioude, J., Evan, S., Iraci, L. T., Kirgis, G.,
Kuang, S., Leblanc, T., Newchurch, M. J., Pierce, R. B., Senff, C. J., and Yates, E. L.:
Coordinated profiling of stratospheric intrusions and transported
pollution by the Tropospheric Ozone Lidar Network (TOLNet) and NASA Alpha
Jet experiment (AJAX): Observations and comparison to HYSPLIT, RAQMS, and
FLEXPART, Atmos. Environ., 174, 1–14,
10.1016/j.atmosenv.2017.11.031,
2018.Lin, M., Fiore, A. M., Cooper, O. R., Horowitz, L. W., Langford, A. O., Levy
II, H., Johnson, B. J., Naik, V., Oltmans, S. J., and Senff, C. J.:
Springtime high surface ozone events over the western United States:
Quantifying the role of stratospheric intrusions, J. Geophys. Res., 117,
D00V22, 10.1029/2012JD018151, 2012a.Lin, M. Y., Fiore, M., Horowitz, L. W., Cooper, O. R., Naik, V., Holloway,
J., Johnson, B. J., Middlebrook, A. M., Oltmans, S., J., Pollack, I. B.,
Ryerson, T. B., Warner, J. X., Wiedinmyer, C., Wilson, J., and Wyman, B.:
Transport of Asian ozone pollution into surface air over the western United
States in spring, J. Geophys. Res.-Atmos., 117, D00V07, 10.1029/2011JD016961, 2012b.Lin, M., Horowitz, L. W., Cooper, O. R., Tarasick, D., Conley, S., Iraci, L. T.,
Johnson,
B., Leblanc, T., Petropavlovskikh, I., and Yates, E. L.: Revisiting the
evidence of increasing springtime ozone mixing ratios in the free
troposphere over western North America, Geophys. Res. Lett., 42, 8719–8728,
10.1002/2015GL065311, 2015.Liu, G., Liu, J., Tarasick, D. W., Fioletov, V. E., Jin, J. J., Moeini, O., Liu, X.,
Sioris, C. E., and Osman, M.: A global tropospheric ozone climatology from
trajectory-mapped ozone soundings, Atmos. Chem. Phys., 13, 10659–10675, 10.5194/acp-13-10659-2013, 2013.Liu, P., Hogrefe, C., Im, U., Christensen, J. H., Bieser, J., Nopmongcol, U., Yarwood, G.,
Mathur, R., Rosselle, S., and Spero, T.: Multi-Model Comparison in the Impact of Lateral
Boundary Conditions on Simulated Surface Ozone across the United States Using Chemically
Inert Tracers, Atmos. Chem. Phys. Discuss., 10.5194/acp-2018-106, in review, 2018.Makar, P. A., Gong, W., Mooney, C., Zhang, J., Davignon, D., Samaali, M., Moran, M. D.,
He, H., Tarasick, D. W., Sills, D., and Chen, J.: Dynamic adjustment of climatological
ozone boundary conditions for air-quality forecasts, Atmos. Chem. Phys., 10, 8997–9015, 10.5194/acp-10-8997-2010, 2010.Nopmongcol, U., Liu, Z., Stoeckenius, T., and Yarwood, G.: Modeling intercontinental
transport of ozone in North America with CAMx for the Air Quality Model Evaluation
International Initiative (AQMEII) Phase 3, Atmos. Chem. Phys., 17, 9931–9943, 10.5194/acp-17-9931-2017, 2017.Pendlebury, D., Gravel, S., Moran, M. D., and Lupu, A.: Impact of chemical lateral
boundary conditions in a regional air quality forecast model on surface
ozone predictions during stratospheric intrusions, Atmos. Environ., 174, 148–170, 10.1016/j.atmosenv.2017.10.052,
2017.
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the
Atmospheric Boundary Layer, Part II: application and evaluation in a
mesoscale meteorological model, J. Appl. Meteorol. Clim., 46, 1396–1409,
2007.Pouliot, G., Denier van der Gon, H. A. C., Kuenen, J., Zhang, J., Moran, M. D., and Makar, P. A.: Analysis of the emission inventories and model-ready
emission datasets of Europe and North America for phase 2 of the AQMEII
project, Atmos. Environ., 115, 345–360, 10.1016/j.atmosenv.2014.10.061, 2015.
Rao, S. T., Mathur, R., Hogrefe, C., Keating, T., Dentener, F., and Galmarini,
S.:
Path forward, EM, Air and Waste Management Association's Magazine for Environmental Managers, 2012.Ryerson, T. B., Andrews, A. E., Angevine, W. M., Bates, T. S., Brock, C. A., Cairns, B.,
Cohen, R. C., Cooper, O. R., de Gouw, J. A., Fehsenfeld, F. C., Ferrare, R. A., Fischer, M. L.,
Flagan, R. C., Goldstein, A. H., Hair, J. W., Hardesty, R. M., Hostetler, C. A., Jimenez, J. L.,
Langford, A. O., McCauley, E., McKeen, S. A., Molina, L. T., Nenes, A., Oltmans, S. J.,
Parrish, D. D., Pederson, J. R., Pierce, R. B., Prather, K., Quinn, P. K., Seinfeld, J. H., Senff, C. J.,
Sorooshian, A., Stutz, J., Surratt, J. D., Trainer, M., Volkamer, R., Williams, E. J., and Wofsy, S. C.:
The 2010 California Research at the Nexus of Air Quality and Climate
Change (CalNex) field study, J. Geophys. Res., 118, 5830–5866, 10.1002/jgrd.50331,
2013.Schere, K., Flemming, J., Vautard, R., Chemel, C., Colette, A., Hogrefe, C.,
Bessagnet, B., Meleux, F., Mathur, R., Roselle, S., Hu, R., Sokhi, R. S., Rao, S. T.,
and Galmarini,
S.: Trace gas/aerosol boundary concentrations and their impacts on
continental-scale AQMEII modeling domains, Atmos. Environ.,
53, 38–50, 10.1016/j.atmosenv.2011.09.043, 2012.Solazzo, E., Bianconi, R., Vautard, R., Wyat Appel, K., Moran, M. D., Hogrefe, C., Bessagnet, B.,
Brandt, J., Christensen, J. H., Chemelk, C., Coll, I., Denier van der Gon, H., Ferreira, J.,
Forkel, R., Francis, X. V., Grell, G., Grossi, P., Hansen, A. B., Jeričević, A., Kraljeviv́c, L.,
Miranda, A. I., Nopmongcol, U., Pirovanof, G., Prank, M., Riccio, A., Sartelet, K. N., Schaap, M.,
Silver, J. D., Sokhi, R. S., Vira, J., Werhahn, J., Wolke, R., Yarwood, G., Zhang, J., Rao, S. T., and Galmarini, S.: Model evaluation and ensemble modelling of
surface-level ozone in Europe and North America in the context of AQMEII,
Atmos. Environ., 53, 60–74, 2012.
Solazzo, E., Bianconi, R., Pirovano, G., Moran, M. D., Vautard, R., Hogrefe, C.,
Appel, K. W., Matthias, V., Grossi, P., Bessagnet, B., Brandt, J., Chemel, C.,
Christensen, J. H., Forkel, R., Francis, X. V., Hansen, A. B., McKeen, S.,
Nopmongcol, U., Prank, M., Sartelet, K. N., Segers, A., Silver, J. D.,
Yarwood, G., Werhahn, J., Zhang, J., Rao, S. T., and Galmarini, S.: Evaluating
the capability of regional-scale air quality models to capture the vertical
distribution of pollutants, Geosci. Model Dev., 6, 791–818, 10.5194/gmd-6-791-2013, 2013.Solazzo, E., Bianconi, R., Hogrefe, C., Curci, G., Tuccella, P., Alyuz, U., Balzarini, A.,
Baró, R., Bellasio, R., Bieser, J., Brandt, J., Christensen, J. H., Colette, A.,
Francis, X., Fraser, A., Vivanco, M. G., Jiménez-Guerrero, P., Im, U.,
Manders, A., Nopmongcol, U., Kitwiroon, N., Pirovano, G., Pozzoli, L., Prank, M.,
Sokhi, R. S., Unal, A., Yarwood, G., and Galmarini, S.: Evaluation and error
apportionment of an ensemble of atmospheric chemistry transport modeling
systems: multivariable temporal and spatial breakdown, Atmos. Chem. Phys., 17, 3001–3054, 10.5194/acp-17-3001-2017, 2017.Stohl, A., Bonasoni, P., Cristofanelli, P., Collins, W., Feichter, J., Frank, A.,
Forster, C., Gerasopoulos, E., Gaggeler, H., James, P., Kentarchos, T., Kromp-Kolb, H.,
Kruger, B., Land, C., Meloen, J., Papayannis, A., Priller, A., Seibert, P., Sprenger, M.,
Roelofs, G. J., Scheel, H. E., Schnabel, C., Siegmund, P., Tobler, L., Trickl, T., Wernli, H., Wirth, V.,
Zanis, P., and Zerefos, C.: Stratosphere-troposphere exchange: A review, and what we
have learned from STACCATO, J. Geophys. Res., 108, 8516,
10.1029/2002JD002490, 2003.Tarasick, D. W., Moran, M. D., Thompson,
A. M., Carey-Smith,
T., Rochon,
Y., Bouchet,
V. S., Gong,
W., Makar,
P. A., Stroud,
C., Ménard,
S., Crevier,
L.-P., Cousineau,
S., Pudykiewicz,
J. A., Kallaur,
A., Moffet,
R., Ménard,
R., Robichaud,
A., Cooper,
O. R., Oltmans,
S. J., Witte,
J. C., Forbes,
G., Johnson,
B. J., Merrill,
J., Moody,
J. L., Morris,
G., Newchurch,
M. J., Schmidlin,
F. J., and Joseph,
E.: Comparison of Canadian air quality forecast models
with tropospheric ozone profile measurements above midlatitude North America
during the IONS/ICARTT campaign: Evidence for stratospheric input, J.
Geophys. Res., 112, D12S22, 10.1029/2006JD007782, 2007.Thompson, A. M., Stone, J. B., Witte,
J. C., Miller,
S. K., Oltmans,
S. J., Kucsera,
T. L., Ross,
K. L., Pickering,
K. E., Merrill,
J. T., Forbes,
G., Tarasick,
D. W., Joseph,
E., Schmidlin,
F. J., McMillan,
W. W., Warner,
J., Hintsa,
E. J., and Johnson,
J. E.: Intercontinental Chemical Transport Experiment
Ozonesonde Network Study (IONS) 2004: 1. Summertime upper troposphere/lower
stratosphere ozone over northeastern North America, J. Geophys. Res., 112,
D12S12, 10.1029/2006JD007441, 2007.Vaughan, G., Price, J. D., and Howells, A.: Transport into the
troposphere in a tropopause fold, Q. J. Roy. Meteorol. Soc., 120, 1085–1103,
10.1002/qj.49712051814, 1994.