Here we present results from an evaluation of model simulations from the
International Hemispheric Transport of Air Pollution Phase II (HTAPII) and
Chemistry Climate Model Initiative (CCMI) model inter-comparison projects
against a comprehensive series of ground-based, aircraft and satellite
observations of ozone mixing ratios made at various locations across India.
The study focuses on the recent past (observations from 2008 to 2013, models
from 2009–2010) as this is most pertinent to understanding the health
impacts of ozone. To our understanding this is the most comprehensive
evaluation of these models' simulations of ozone across the Indian
subcontinent to date. This study highlights some significant successes and
challenges that the models face in representing the oxidative chemistry of
the region.
The multi-model range in area-weighted surface ozone over the Indian
subcontinent is 37.26–56.11 ppb, whilst the population-weighted range is
41.38–57.5 ppb. When compared against surface observations from the
Modelling Atmospheric Pollution and Networking (MAPAN) network of eight
semi-urban monitoring sites spread across India, we find that the models tend
to simulate higher ozone than that which is observed. However, observations
of NOx and CO tend to be much higher than modelled mixing
ratios, suggesting that the underlying emissions used in the models do not
characterise these regions accurately and/or that the resolution of the
models is not adequate to simulate the photo-chemical environment of these
surface observations. Empirical orthogonal function (EOF) analysis is used in
order to identify the extent to which the models agree with regards to the
spatio-temporal distribution of the tropospheric ozone column, derived using
OMI-MLS observations. We show that whilst the models agree with the spatial
pattern of the first EOF of observed tropospheric ozone column, most of the
models simulate a peak in the first EOF seasonal cycle represented by
principle component 1, which is later than the observed peak. This suggests a
widespread systematic bias in the timing of emissions or some other unknown
seasonal process.
In addition to evaluating modelled ozone mixing ratios, we explore modelled
emissions of NOx, CO, volatile organic compounds (VOCs) and the ozone response to the
emissions. We find a high degree of variation in emissions from
non-anthropogenic sources (e.g. lightning NOx and biomass
burning CO) between models. Total emissions of NOx and CO
over India vary more between different models in the same model intercomparison project (MIP) than the same
model used in different MIPs, making it impossible to diagnose whether
differences in modelled ozone are due to emissions or model processes. We
therefore recommend targeted experiments to pinpoint the exact causes of
discrepancies between modelled and observed ozone and ozone precursors for
this region. To this end, a higher density of long-term monitoring sites
measuring not only ozone but also ozone precursors including speciated VOCs,
located in more rural regions of the Indian subcontinent, would enable
improvements in assessing the biases in models run at the resolution found in
HTAPII and CCMI.
Introduction
The issues of increasing levels of surface ozone (O3) and its impacts
on human health, the biosphere and climate are of major concern globally.
Recent reports from the Health Effects Institute (2017) highlight that ambient ozone
contributes to the global health burden through its impact on premature
deaths and disabilities from chronic obstructive pulmonary disease (COPD).
Nearly 4.5 million people die prematurely each year due to exposure to
outdoor pollution, 254 000 of which are due to ozone exposure and its impact
on chronic lung disease; the remaining majority are attributed to particulate
matter below 2.5 µm in diameter (PM2.5). Around half of these
premature deaths are in China and India Cohen et al. (2017). However, a
recent study using updated risk estimates suggests that previous analyses
have underestimated the long-term health impacts of tropospheric ozone, and
the true global disease burden could be over 1 million premature deaths per
year, 400 000 of which occur in India (Malley et al., 2017). India and its
neighbouring countries, China, Pakistan and Bangladesh, have experienced the
largest increase in seasonal average population-weighted ozone concentrations
over the last 25 years (Health Effects Institute, 2017), with India alone
accounting for 67 % of the global increase in ambient-ozone-attributable
deaths due to COPD between 1990 and 2015.
The ill effects of ozone are not only limited to human health. Ghude et
al. (2008) calculated relative agricultural yield loss using accumulated
ozone exposure exceedances over a threshold of 40 ppb from the analysis of
7 years of data of hourly surface ozone concentrations over India
(1997–2004) during the pre-monsoon season. They estimated yield losses of
22.7 %, 22.5 %, 16.3 % and 5.5 % for wheat, cotton, soya bean
and rice respectively, sufficient to feed about 94 million people and an
economic value of more than a billion USD per year.
Identifying the sources and sinks of tropospheric ozone and its precursors,
and in turn identifying the ways to reduce ambient ozone exposure, remains a
key challenge. Ozone is a secondary pollutant, meaning it is not directly
emitted into the atmosphere. The tropospheric chemistry of ozone and its
precursor species, such as volatile organic compounds (VOCs), carbon monoxide
(CO) and nitrogen oxides (NOx=NO+NO2),
is complex and involves a large number of species that participate in a
cascade of NOx-catalysed chemical reactions that ultimately
oxidise VOCs to H2O and CO2, generating ozone as a by-product
(Jenkin and Clemitshaw, 2002; Monks et al., 2015). India is experiencing a
rapid growth in its industrial and economic sectors with increasing emissions
of pollutants and trace gases associated with this development (Ghude et al.,
2008, 2013). An increasing trend in tropospheric ozone over most parts of
India has been observed in long-term decadal trend analysis (1979–2000)
using satellite-based approaches to determine the tropospheric ozone residual
(TOR), with the strongest trends observed over the Indo-Gangetic Plain (The
IGP region – a region to the north of India, at the foothills of Himalayas)
(Lal et al., 2012).
Meteorological parameters also play an important role in driving tropospheric
ozone chemistry, as has been demonstrated in many studies in the last few
years. Central to the production of ozone is photolysis (photo-dissociation).
The presence of clouds can greatly impact the rates of photolytic reactions
and so act as a limit for ozone production (Voulgarakis et al., 2009). Ozone
also tends to have a positive correlation with temperature and a negative
correlation with relative humidity (Camalier et al., 2007). Increases in
water vapour directly lead to ozone loss through the reaction of excited
oxygen atoms, formed from ozone photolysis, with water, and indirectly
through the wet scavenging of compounds which act as reservoirs and
precursors for ozone (Monks et al., 2015). These meteorological factors are
of particular importance for the Indian subcontinent, where the seasonal
cycle is dominated by the monsoon season, lasting for 4 months from June to
September and characterised by high precipitation rates, cloudy days,
seasonal reversal of prevailing wind directions, and mixing of the clean
marine boundary layer air from the south-west with the continental air.
Ground-based studies on ozone cycles at various sites in India report that
the minimum ozone values observed during the monsoon season are likely
attributed to high relative humidity, low solar radiation, cloudiness
conditions and wet scavenging of ozone precursors. In contrast, the high
temperatures, high solar radiation and low humidity during the pre- and
post-monsoon seasons provide favourable conditions for photochemical production of
O3. During winter, low temperatures, low solar radiation and fog
limits the photochemical O3 production in most parts of India (Beig
et al., 2007; Sinha et al., 2015; Yadav et al., 2016). An exception is the
Mt. Abu site in northern India. Due to the unique meteorology at this high
altitude site, the seasonal variation in surface ozone shows a maximum in
late autumn and winter (Naja et al., 2003).
Owing to the complex interplay between emissions, chemistry and the unique
meteorology that impacts the Indian subcontinent, and the limited coverage
of surface observations, three-dimensional numerical models are required to
estimate the health burden of ozone exposure and predict how ozone levels
will respond to future changes in emissions and climate. Three-dimensional
numerical models include meteorology, emissions and complex photo-chemical
mechanisms to simulate ozone concentrations (Keeble et al., 2017; Surendran
et al., 2015). But these models need to be evaluated with as many
observations of as many species that contribute to ozone production and loss
as possible. The ability of a model to accurately predict the present state
of species gives us the confidence to rely on them for future projections as
well as to predict the levels of pollutants in regions where observations are
limited. Many previous studies have evaluated the ability of
chemistry-transport models to simulate levels of ozone and other key species
for tropospheric chemistry over North America and Europe, where dense, long-term and reliable measurements are available (Im et al., 2015; O'Connor et
al., 2014; Tilmes et al., 2015). Owing to the sparsity of in situ data, these
kinds of studies are limited over the Indian subcontinent. Evaluation of
models and their agreement as well as disagreement over this region will
enhance our understanding about the production of ozone and the factors
controlling it. An improvement in our fundamental ability to simulate the
processes which control ozone will ultimately enable the best policy
decisions to mitigate the impacts of ozone on human health and crops in the
region.
In this paper, we have evaluated model simulations from the international
Hemispheric Transport of Air Pollution Phase II (HTAPII) and Chemistry
Climate Model Initiative (CCMI) model inter-comparison projects against a
comprehensive series of ground-based, aircraft and satellite observations of
ozone, NOx and CO across India. To our knowledge, this
represents the most exhaustive evaluation of ozone for these models in this
region and enables us to characterise seasonal biases and errors between the
models. Section 2 describes the models that we have used in these analyses
and the observations we used to evaluate the models against. In Sect. 3 we
present the results of our evaluation, including empirical orthogonal
function (EOF) analysis to identify similarities and differences in the
spatio-temporal distribution of the tropospheric ozone column simulated in
the models and retrieved from the OMI-MLS instruments (Ziemke et al., 2011).
In Sect. 4, we discuss the results and suggest possible future research
needed to understand ozone chemistry over the Indian subcontinent.
MethodologyDatasets for evaluationGround-based observations
The model simulations have been validated against measurements of surface
ozone from eight stations located across India in Delhi, Patiala, Udaipur,
Jabalpur, Pune, Hyderabad, Guwahati and Chennai. Figure 4 shows the
geographical locations of these stations. Details of all the ground-based
stations have been summarised in Table 1. The coordinated measurements of
trace gases and aerosols at these locations of India are carried out under
the Indian Institute of Tropical Meteorology (IITM), Pune, India, and
Ministry of Earth Sciences (MoES) as part of the “Modelling Atmospheric
Pollution and Networking” (MAPAN) programme. The Lodhi Road station in Delhi
is designated as being an urban background site. All other monitoring stations
are designated as semi-urban, indicating that the stations are away from
downtown areas where the influence of local emissions may be very high. However, as we show in Sect. 3, these are far from
pristine measurement locations and appear to be influenced by high levels of
NOx and CO. Observations at these stations were made with
the Air Quality Management System (AQMS). The AQMS is comprised of US
Environmental Protection Agency approved analysers housed inside walkway
shelters and have a sampling height of 3 m above ground level (Beig et al.,
2013).
Details of the locations of in situ ozone monitoring stations used
in this study. All stations are categorised as semi-urban sites. All data
were collected at an hourly resolution throughout the year 2013. For more
details see Sect. 2.1.1.
The measurements of surface O3, NOx and CO were made
continuously at hourly time resolution during the year 2013. Ozone
measurements were conducted using an Ecotech Ozone analyser (model number EC
9810B), which combines the benefits of microprocessor control with
ultraviolet (UV) photometry at 254 nm to accurately measure ozone mixing
ratios in ambient air. The analyzer provides accurate measurements of ozone
in the range of 0–20 ppmv with a detection limit of 0.5 ppbv and has a
linearity error of less than 3 %.
The measurements of NOx were made by using an Ecotech
Nitrogen Oxides Analyzer (model number EC 9841B). This analyzer works on the
chemiluminescence technique for accurate and reliable measurements of NO,
NO2 and NOx mixing ratios. The technical limitations
(artifacts) of the chemiluminescent-based methods have been well reported
(Fuchs et al., 2009; Winer et al., 1974). CO was measured using an Ecotech
model EC 9830 analyzer based on the infrared (IR) photometry. Information
on the maintenance and calibration of these instruments has been reported
before (Chakraborty et al., 2015; Yadav et al., 2014). Monthly mean values
for O3, NOx and CO were calculated from the 24 h
averages of the hourly data. Days with fewer than 15 h of observations were
excluded from the analysis.
CARIBIC observations
The CARIBIC project (Civil Aircraft for the Regular Investigation of the
atmosphere Based on an Instrument Container;
http://www.caribic-atmospheric.com/, last access: 4 April 2018) aims to
investigate the spatial and temporal distribution of a wide-range of
compounds. It is based on the use of a fully automated scientific instrument
package in a 1.5 t container aboard a passenger aircraft which is equipped with
an advanced multi-probe inlet system (Brenninkmeijer et al.,
2007). In the
region of interest, flights operated monthly from April to December 2008
aboard a Lufthansa Airbus A340-600 passenger aircraft flying from Frankfurt
to Chennai. The total number of flights during this period was 16. Usually
one set of flights consisted of four consecutive flights, i.e. two round
trips from Frankfurt to Chennai within 3 days, with the exception of July and
October, when only one round trip was performed. The ascents and descents of
the flights took place during night, with landing times around 23:30 local
time and take off times around 02:00 and 03:40 local time the next morning
(Ojha et al., 2016).
The ozone measurements were made by a dry chemiluminescence (CL) instrument,
which at typical ozone mixing ratios between 10 and 100 ppb and a
measurement frequency of 10 Hz has a precision of 0.3 %–1.0 %. The
absolute ozone concentration is inferred from a UV photometer designed
in-house which operates at 0.25 Hz and reaches an accuracy of 0.5 ppb. The
CL instrument has been discussed in detail by Zahn et al. (2012).
CO is measured with an AeroLaser AL 5002 resonance fluorescence UV instrument
modified for use on board the CARIBIC passenger aircraft. The instrument has
a precision of 1–2 ppbv at an integration time of 1 s and performs an
in-flight calibration every 25 min. Technical details of the CO instrument
can be found in Scharffe et al. (2012).
The CARIBIC observations taken during ascent as well as descent of the flight
have been considered in this study. These observations are averaged into
vertical bins of 25 hPa. For monthly mean vertical profiles, averages of all
the ascending and descending profiles during that month have been considered.
For comparison, monthly mean model-simulated profiles over Chennai are also
averaged into vertical bins of 25 hPa and have been interpolated to the
CARIBIC pressure levels.
Tropospheric column ozone (TCO) for the year 2010 is derived using the
TOR method, which is the residual of total
column ozone from the Ozone Measuring Instrument (OMI) and stratospheric column
ozone from the Microwave Limb Sounder (MLS) with the spatial resolution of Aura/MLS
(Ziemke et al., 2011; Schoebert et al. 2007). TOR is an integrative product
which accounts for changes in ozone not only at the surface, where it is most
detrimental to human and crop health, but also in the free troposphere, where it
has a longer lifetime and so is influenced by more sources and has a larger
climate impact (Stevenson et al., 2013).
OMI and MLS are two out of four instruments on board the Aura satellite,
which orbits the Earth in sun-synchronous polar orbit at 705 km altitude and
98.2∘ inclination. OMI is a nadir-viewing instrument which detects
back-scattered solar radiance from Earth at visible (350–500 nm) and UV
(270–314 nm, 306–380 nm) wavelengths to measure total column ozone with a
spatial resolution of 13 km × 24 km. The MLS instrument detects
microwave thermal emissions from the limb of Earth's atmosphere to measure
mesospheric, stratospheric and upper tropospheric temperature, ozone and
other constituents. MLS measurements are taken about 7 min before OMI views
the same location during ascending (daytime) orbital tracks. Details of these
instruments are discussed elsewhere (Waters et al., 2006).
Model description
In this work we aim to evaluate how a range of models perform over the Indian
subcontinent to understand what the level of agreement in ozone modelling is,
in this observationally sparse region. We focus here on
global models as these are increasingly used in assessments of the health
impacts of air pollution (e.g. Malley et al., 2017; Lelieveld et al., 2018).
There is a long history of coordinated model intercomparison projects (MIPs),
with the general aim of coordinating modelling centres to better understand
how the state-of-the-science models compare against each other and
observations. MIPs are generally focused on specific science questions which
define the length of the integrations performed with the models and the
amount of model output requested. MIPs have been the key mechanism to bring
together our understanding of climate change and are increasingly enabling
our understanding of atmospheric composition to be improved.
Description of the eight global chemistry climate models used in
this study. The table also gives global emissions of NOx, CO
and global tropospheric ozone burden simulated by each model.
Global emissions GlobalNOxCOTroposphericModel nameAbbr.MIPInstitutionVersionExperimentResolution lat × long ×References(Tg N(Tg COozone burdenno. of vertical levelsyear-1)year-1)(Tg year-1)HadGEM2-ESHDGMHTAPIIMet Office and Univ. of Cambridge, UKBASE_2009∼ 1.25∘× 1.875∘× 38Collins et al.(2011)37.5978.5379.6GEOSCHEM -ADJOINTGCADHTAPIIUniv. of Colorado, BoulderBASE_2010∼ 2∘× 2.5∘× 47Henze et al. (2007)54.31001.3340.7CHASERCHSRHTAPIINagoya Univ., JAMSTECCHASER-V4 MIROC -ESMBASE_2010∼ 2.76∘× 2.8∘× 32Sudo et al.(2002)49.7915.05318.5MOZART4MOZTHTAPIIIndian Institute ofTropical Meteorology, IndiaMOZART4BASE_2010∼ 1.89∘× 2.5∘× 56Surendran et al.(2015)44.21014.1358.1MRI-ESM1r1MRIECCMIMeteorological Research Institute,Japanr1i1p1,v1*REFC1_2010∼ 2.7∘× 2.8∘× 80Adachi et al.(2013)55.471172.4384.8GEOSCCMGCCMCCMINASA Goddard Spaceflight Centre, USAr1i1p1,v3*REFC1_2010∼ 2∘× 2.5∘× 72Oman et al.(2011);Rienecker et al. (2008)40.841176.2336.9CHASER- MIROC- ESMCHSMCCMINagoya Univ., JAMSTECr1i1p1,v1*REFC1SD _2010∼ 2.76∘× 2.8∘× 57Sudo et al.(2002)43.3908.64326.8UMUKCA- UCAMUKCACCMIUniv. of Cambridge, UKr1i1p1,v1*REFC1_2010∼ 2.5∘× 3.75∘× 60Bednarz et al.(2016); Morgenstern et al. (2017)32.76867.31353.1
* r = realisation number of simulation,
i = initialisation method, p = perturbed physics, v = version of
publication level.
The most recent global MIPs include both the CCMI (Morgenstern et al., 2017) and international HTAPII (Koffi et al., 2016). We opted
to look at data from both of these MIPs but, owing to constraints on time and
data availability, chose to focus on a subset of models. Specifically, we
examine output from simulations from the following eight models:
HadGEM2-ES model (Collins et al., 2011; Jones et al., 2011), hereafter
referred to as HTAPII-HDGM;
GEOS-Chem Adjoint (Henze et al., 2007), hereafter referred to as
HTAPII-GCAD;
CHASER-v4-MIROC-ESM and CHASER-MIROC-ESM (two different configurations of
essentially the same model run for HTAPII and CCMI and referred to as
HTAPII-CHSR and CCMI-CHSM respectively) (Sudo et al., 2002a, b);
MOZART-4 (Divya et al., 2015), hereafter referred to as HTAPII-MOZT;
MRI-ESM1r1 (Yukimoto et al., 2011; Deushi and Shibata 2011), hereafter
referred to as CCMI-MRIE;
GEOSCCM (Oman et al., 2011; Reinecker et al., 2008; Duncan et al., 2007;
Strahan et al., 2007), hereafter referred to as CCMI-GCCM;
UMUKCA-UCAM (Bednarz et al., 2016), hereafter referred to as CCMI-UKCA.
Table 2 outlines the details of the above models, with which the MIPs were
run, and documents our calculations of the tropospheric ozone burden in each
model (using a consistent treatment of a chemical
tropopause defined using a 150 ppb monthly mean ozone iso-surface). These
models span a range of horizontal resolution (the lowest resolution is
CCMI-UKCA at lat 2.5∘× long 3.75∘ and highest
resolution is HTAPII-HDGM at 1.25∘ lat × 1.85∘ long)
and vertical resolution (HTAPII-CHSR/CCMI-CHSM have 32 vertical model levels,
whilst CCMI-MRIE has 80 vertical model levels) and use chemical mechanisms of
differing complexity and scope (e.g. CCMI-UKCA has been designed for
simulations of mainly stratospheric nature whilst HTAPII-CHSR and CCMI-CHSM
use a chemistry scheme much more focused on tropospheric oxidation, with a
larger number of non-methane VOCs). The lowest model level varies from a
minimum of 25 m for CCMI-MRIE to 124 m for HTAPII-GCAD. For further details
of the model set-ups please see the cited references for each model in
Table 2 and the MIP description papers (i.e. for the CCMI models see
Morgenstern et al., 2017). From our analysis of the tropospheric ozone
burden, we see that all models lie within the range of the Atmospheric
Chemistry and Climate Model Intercomparison Project (ACCMIP) models (Young et
al., 2013) and the likely range as recently quantified through satellite
retrievals of the tropospheric column analysed by the IGAC Tropospheric Ozone
Assessment Report (TOAR) (Gaudel et al., 2018).
From the eight models described above, we focus our analysis on monthly and
daily mean mixing ratios of ozone, NOx and CO, and monthly
mean surface emissions of CO, NOx and lightning-derived
NOx. We focus on output from the models appropriate for the
year 2010 and limit the main analysis to the domain of 56–105∘
longitude and 5–38∘ latitude, which covers the entire Indian
subcontinent.
In spite of simulating the same period of time, CCMI and HTAPII use different
base emission inventories as part of their protocol. Surface CO and
NOx emissions, which over the Indian subcontinent are
dominated by anthropogenic sources, should generally be consistent within
MIPs, which we largely see but explore in more detail below. Lightning is an
important source of NOx to the remote atmosphere. It is an
emission term that tends to be not possible to specify in the MIPs, and hence
it reflects an area of emissions that models should differ in. We assess this
in more detail below.
Shows the high variability in NOx and CO emissions
(anthropogenic + natural) between the two MIPs over the domain considered
in this study. CCMI models show larger variability for NOx
emissions and HTAPII models shows larger variability for CO emissions.
Annual vertical profiles of lightning NOx
emissions over the domain considered in this study (inset: global emissions
of lightning NOx in Tg (N) year-1 as simulated by each
model).
Description of emissions from model simulations
The annual total NOx and CO emissions for all models over
the domain are shown in Figs. 1, S2 and S3. Briefly, there is large
variability in input emissions of NOx and CO for the
different models and MIPs (Figs. S2 and S3). The intra-MIP variability is
greater than the inter-MIP variability for NOx; i.e. there is more
variability within a MIP for NOx emissions than between them
(see Fig. 1). However, the converse is true for CO where the CCMI emissions
tend to be higher than those used in the HTAPII MIP. For individual MIPs,
every modelling group was required to use the same anthropogenic emissions
data. Disparities in emissions may be due to the use of different natural and
biomass burning sources.
Lightning is the largest contributor to upper tropospheric
NOx and it is a source of largest uncertainty. Global
emissions of lightning NOx (LNOx) as
simulated by the models show a variance of 7.56 Tg (N) year-1 (annual
global emission of LNOx as simulated by each model is given
in Fig. 2). The vertical profiles of LNOx emissions are very
different in each model over the domain considered in this study (Fig. 2).
Parameterisation of LNOx is highly dependent on the vertical
and horizontal resolution of the models. CCMI-UKCA and HTAPII-HDGM models
show similar vertical profiles as they have a similar internal configuration.
The difference in the convection parameterisation in these models leads to a
difference in the magnitudes of LNOx emissions. CCMI-MRIE
clearly stands out, giving the highest values of LNOx emissions
globally as well as over the Indian subcontinent.
Results
Here we evaluate four model simulations each from HTAPII and CCMI, with a set
of ground-based, satellite and airborne observations of O3; ground-based and airborne observations of CO; and ground-based observations of
NOx.
The spatial patterns of annual mean surface ozone (ppb) as simulated
by the lowest level in each model, highlighting the regions where the models
show maxima and minima over the Indian subcontinent.
Annual mean model-simulated surface ozone
Figure 3 shows the spatial patterns of annual mean surface ozone mixing
ratios from the model simulations described in Table 2 and the multi-model
mean (MMM), shown in the lower right-hand panel. Ozone mixing ratios from the
lowest model level are considered as surface ozone in this study. There is
general agreement in the spatial characteristic of annual mean surface ozone
across the models, except for HTAPII-HDGM (it shows different maxima and
minima as compared to the other models). The range in area-weighted surface
annual mean ozone is 22.9–35.3 ppb, with HTAPII-CHSR at the lower and
HTAPII-MOZT at the upper end of the range, and the MMM value is 29.3 ppb. We
also investigated the population-weighted surface annual average statistics
using population data from NCAR climate and global dynamics (Gao, 2017; Jones
and O'Neill, 2016). These data have a range of 28.5–38.85 ppb, with
HTAPII-CHSR at the lower end and CCMI-UKCA at the upper end and a MMM of
33.0 ppb.
The MMM shows that the highest values of surface ozone are over the Tibetan
plateau and northern part of India and the lowest values over the southern
peninsula. However, whilst the models broadly agree on the regions of higher
and lower ozone, there is significant intermodal variability in the magnitude of
ozone concentration. Variations in models can be attributed to the different
chemical schemes, physical parameterisations, grid resolution and
non-anthropogenic emissions used in the models. CCMI-UKCA shows the highest
values of surface mean annual average ozone compared to the other models.
This may be attributed to the fact that CCMI-UKCA was designed for
stratospheric chemistry and hence contains only a limited set of tropospheric
chemistry reactions and no isoprene chemistry (more details in Sect. 2.2).
Relative standard deviation of surface ozone from the eight models.
The plot also shows the location of ground-based observational MAPAN stations
considered in this study.
The standard deviation of the multi-model ensemble is shown in Fig. 4. The
standard deviation of the multi-model mean can be used as an indicator of the
level of agreement between the models. Here we show that there is a
reasonably low level of agreement between the models, with an average of
23 % standard deviation in the mean. This is slightly worse than the
level of agreement between the ACCMIP models over the same region shown in
Young et al. (2013) (<20 % standard deviation in the mean) and could
reflect the fact that here we compare simulations from two different MIPs
which make use of different emissions. However, we find the difference
between the emissions within models of a particular MIP is as large as those
between MIPs (Figs. 1, S2 and S3). Figure 4 highlights that models differ
most in the northern and eastern part of India and standard deviation is the
least in the central part of India. For the more well studied regions such as
North America and Europe, Young et al. (2013) show that global model multi-model analyses have similar if not slightly larger variability than over the
Indian subcontinent. Young et al. (2013) show that the variability in the
south-eastern USA is very high, > 30 %, across the ACCMIP models, which
is likely linked to the impacts of different biogenic emissions (not
specified in MIP protocols) and chemistry over this isoprene-rich area.
Comparison between models and ground-based surface observationsOzone
Comparison of model-simulated monthly mean surface ozone with the monthly
mean of hourly observations from the eight ground-based monitoring stations
listed in Table 2 is shown in Fig. 5. In contrast to locations in Europe and
North America, but in agreement with previous observational analysis of
surface ozone over India (Beig et al., 2007; Jain et al., 2005; Lal et al.,
2012), our observational data highlight a double peak structure in the
seasonal cycle of surface ozone. Cloudiness and wet scavenging of ozone
precursors during the monsoon period (June–September) limit the
photochemical production of ozone, resulting in lower values of ozone during
these months. Due to favourable meteorological conditions during pre-
(April–May) and post- (October–November) monsoon seasons, such as strong
solar radiation, high temperature and low humidity, photochemical production
of ozone is enhanced during these months. Emissions from biomass burning also
contribute to ozone production during the post-monsoon season at sites such
as Delhi and Patiala. The seasonal variability in the models is captured
fairly well at all stations, except at Chennai. Figure 5 includes the MMM and
standard deviation (dark dashed blue and light blue envelope), which can be
compared with the mean and standard deviation of the observations (solid
black line and grey envelope). In seven out of eight cases, the ozone mixing ratio
is higher in the MMM than in the observations (except at Jabalpur, where MMM is
within 1σ deviation). The overestimation by the models is due to the
overestimation in production and/or the underestimation of loss of ozone.
This could be attributed to a combination of factors. The principal factor is
most likely a mismatch in the representativeness of the observational sites
for comparison with the coarse-resolution models. At coarse resolution, the
models cannot capture fine-scale processes, such as the impact of nearby
sources of pollution (e.g. NOx emissions) on the
observations of ozone. Ozone production is highly non-linear in terms of the
precursor emissions VOCs and NOx (Monks et al., 2015).
Figure 5 also highlights differences between the models. There is
considerable inter-model variation in simulating the seasonal variation in
surface ozone, as we discuss in more detail below.
Comparison between ground-based observations, model-simulated data and the ensemble mean of monthly mean surface ozone
over the eight MAPAN stations.
To evaluate the performance of models at each station we compare the
normalised mean biases (NMBs) and Pearson correlation coefficient (R). These
were calculated using the following equations:
1NMB=ΣModel-ΣObsΣObs,2R=Modeli-Model‾×(Obs[i]Obs‾)σ(Model)×σ(Obs)‾,
where σ is the standard deviation.
Scatter plot of Pearson correlation coefficient (R) values against
normalised mean bias (NMB), highlighting the models performance in surface
ozone at each station.
Figure 6 shows the relationship between R and NMB for each of the models we
have studied, as well as the multi-model mean, at each of the surface site
locations. As is evident from Fig. 5, all models show a positive NMB at all
stations. All models have low biases and high R values (except for CCMI-UKCA)
at Jabalpur and Pune. Models show high biases at Guwahati and Chennai, and
low R values at Chennai and Udaipur. Observations at Chennai peak in April
and October, i.e. during pre- and post-summer monsoon seasons. Models show poor
correlation with the seasonal cycle of ozone at Chennai. To some extent this
might be affected by the model's ability to simulate summer monsoons (from the
south-west) and winter monsoons (from the north-east) that affect Chennai. It
would be worth comparing model simulations with ozone observational data at
Mumbai on the west coast of India, which receives rainfall only during the
summer, to understand the role of the monsoon near these coastal sites, and we
suggest further analysis assessing the performance of the models at the
coastal impacted locations specifically. Overall, the performance of the
models across all the sites is inconsistent. There is no one model that
performs systematically well at all stations. Conversely, the models perform
differently at each station in terms of their R value and NMB. Unlike in
previous studies (e.g. Young et al., 2013) the MMM also does not outperform
the individual models in Fig. 5. CCMI-UKCA acts as an outlier at five out of
eight sites. The impact of the underlying emission biases can be seen by comparing
the results between HTAPII-CHSR and CCMI-CHSM in this study. These are in
effect the same model (see Sect. 2.2) but include different emissions data as
part of the different MIP protocols. Figure 6 shows that these two
simulations result in large differences at only one of the eight sites
investigated (Chennai), whereas the difference between different models in
the same MIP is typically much larger. This implies that the differences
between the simulations are more down to the differences in model set-up,
representation of chemical and physical processes, and non-anthropogenic
emission sources in the models than the differences in anthropogenic
emissions between the two MIPs.
In order to better understand the causes of biases between the model and
observations shown in Figs. 5 and 6, 24 h average model and observation data
have been analysed to determine probability density functions (PDFs) as shown
in Fig. 7 for a subset of the sites considered (Delhi, Pune, Guwahati and
Chennai). The PDFs for the observations show a multimodal distribution (with
2–3 modes most common) with the highest peak at lower ozone values. This
pattern is typical of situations in which nearby sources of NOx
titrate ozone, through the following reaction:
O3+NO→NO2+O2.
The observed PDFs are typically low in Guwahati and Chennai, whereas Delhi
and Pune show several days where high levels of ozone are seen, especially in
Pune where daily average ozone can be as high as 97 ppb.
Probability density functions (PDFs) for in situ observations and
model-simulated 24 h average surface ozone at Delhi, Pune, Guwahati and
Chennai.
The PDFs for the model simulations also show a multimodal distribution but the
nature of their distributions is very different from the observed
distribution. Moreover, the differences between the simulation PDFs is larger
than the differences between the multi-model mean and the observations.
Again, this highlights the large variability among models in their simulation
of ozone in these regions. The most obvious feature from Fig. 7 is that the
models overestimate the PDFs at the four sites and significantly overestimate
the tails of the ozone distributions. As well as overestimating the ozone
concentration at the modes, in most models the highest peak is at the second
mode with higher ozone values, in contrast to the observations where the
highest peak is usually at lower ozone concentrations. The amplitude and
shift in the PDF peaks compared to observations is greatest at Guwahati and
Chennai. This may be due to the inability of the models to adequately
simulate NOx titration at these sites, which occurs at a
finer scale than can be resolved by the coarse model grids. Studies have
shown that the model's ability to simulate surface ozone is very sensitive to
horizontal resolution and high-resolution models generally perform better when
compared to observations (Stock et al., 2014).
Ozone precursors
When compared with the set of available surface ozone observations we have
used, the current state-of-the-art global chemistry models overestimate
surface ozone in India. There is a large amount of variability among the
models, much larger over India than in previous model inter-comparisons over
the northern and southern hemispheres (Young et al., 2013). In order to better
understand the variation in ozone, we have also compared the model
simulations of NOx and carbon monoxide at the eight sites
that form part of the MAPAN network. Similar to Fig. 5, Fig. 8 shows the
seasonal variation in surface NOx in the models and
observations. The observations of NOx (black line with grey
envelope) vary from location to location. High values are observed during
autumn–winter due to the transport of pollutants from polluted regions, such
as the IGP region, through north-easterly winds. During the winter months
NOx emissions are trapped closer to the surface due to low
boundary layer heights, caused by frequent temperature inversions, while in
summer months south-westerly winds bring in clean marine air to almost the
entire Indian region and there is greater mixing with free tropospheric air,
causing dilution of pollutants in general (Beig et al., 2007; Jain et al.,
2005; Lal et al., 2012). Figure 8 shows that in Pune, Guwahati and Jabalpur,
the highest observed monthly average NOx is seen in the
winter months. In Delhi and Patiala, the pre- and post-monsoon season (when
biomass burning is high) are when NOx levels are at their
highest levels, with lower levels of NOx in the monsoon
months. Figure 8 highlights that there is a large range of
NOx values in the observations, with Delhi having the
largest monthly average levels of NOx of up to 180 ppb
(November) and Chennai having the lowest levels of NOx
(8 ppb, November).
Comparison between ground-based observations and model simulations
of monthly mean surface NOx over the eight MAPAN stations.
A different scale has been used for Delhi and Patiala.
Comparing the observations and the MMM highlights that on average the
simulations underestimate levels of NOx at these eight locations
across India. An exception is for HTAPII-HDGM at Patiala, where the model
simulation overestimates the levels of NOx present. The
monthly average NOx in the model simulations at all sites is
dominated by NO2 whereas in observations at Delhi (the only site for
which separate measurements of NO and NO2 are available), NO
dominates monthly average NOx (see Sect. S4 in the
Supplement). This discrepancy could be attributed to the coarse resolution of
the models, meaning high NOx emissions are diluted over
a larger volume of air. Hence, models underestimate ozone titration due to high
levels of NO near emission sources, which results in the overestimation of
surface ozone and a photo-stationary state with greater proportion of
NOx as NO2.
Comparison between ground-based observations and model simulations
of monthly mean surface CO over the eight MAPAN stations. A different scale has
been used for Delhi.
Figure 9 shows a comparison of the observed and model-simulated CO at the
different sites across India we focus on here. At the majority of the other
sites considered, there is a clear seasonal cycle in CO, with peaks in the
winter months and minimum values during the summer and monsoon period. As
with NOx (Fig. 8), Delhi is the region with the highest observed
values of carbon monoxide, and the highest levels of CO occur in the pre- and
post-monsoon period (consistent with the periods of highest agricultural
burning). The variations in observed carbon monoxide are caused by a
combination of factors, including changes in the strength of direct emissions
of CO (Fig. S5) as well as the contribution of secondary sources such as
the oxidation of VOCs (Grant et al., 2010), variations in the boundary layer
height and changes in local wind patterns (Ahammed et al., 2006).
The model simulations capture the seasonal variability in monthly mean CO
well (R values > 0.4 for all models) at most locations; the exception
is in Hyderabad, where all models generally show a negative correlation with
the observations, and at Jabalpur, where correlation is poor (see Sect. S5).
Interestingly, the model simulations at Jabalpur and Hyderabad show the lowest
correlations with the observations in spite of having the lowest biases. This
could point towards some important processes which the models are struggling
to simulate, but further work would be needed to clarify this. The site with
the best correlation is Udaipur, where the MMM correlation coefficient is
0.96. Models are in agreement with the observed CO at all sites but highly
underestimate the observed values at Delhi and Patiala. As with
NOx, an exception is HTAPII-HDGM, which tends to
overestimate CO at Patiala, but with good correlation (R value of 0.63),
picking up the peaks in pre- and post-monsoon CO associated with burning.
OMI/MLS-determined and model-simulated annual average tropospheric
ozone column (AATOC) in Dobson units (DU) over the Indian subcontinent.
Values in the bottom left corner indicate the mean AATOC in the domain.
Comparison between models and satellite dataAnnual average tropospheric ozone column (AATOC)
Figure 10 shows the annual average tropospheric ozone column (AATOC)
retrieved by the OMI/MLS for the year 2010 on board the AURA satellite and
the model simulations. The OMI/MLS AATOC shows the highest values (45–60 Dobson
units) over the IGP and the central and north-western regions of India. These high
values are not uncommon globally (Gaudel et al., 2018). High levels of AATOC
are associated with high anthropogenic activities and large-scale biomass
burning. The IGP and the regions of India mentioned above are examples of
regions affected by these sources. Lower values of AATOC are observed over
the maritime regions and a minimum is observed over the Tibetan plateau. The
seasonal cycle of tropospheric ozone column (TOC) peaks in May–June and is fairly widespread over India.
The onset of the monsoon leads to lower levels of TOC across the region on
the whole. Hence, differences in emissions are not the only factor that leads
to differences in the observed AATOC values; regional variations in
meteorological conditions are also an important factor that controls AATOC
(David and Nair, 2013).
Percentage biases in model-simulated AATOC with respect to the OMI-determined AATOC.
In order to evaluate the model simulations and observations we first compare
the mean total ozone column (MTOC), defined as the spatial mean of AATOC over
the study domain. Over the entire region we focus on (56 to 105∘
longitude and 5 to 38∘ latitude), the MTOC from OMI/MLS is 30.1 DU.
Models overestimate the MTOC over this region (see Fig. 10), with MTOC values
for models ranging from 35 to 42 DU. HTAPII-HDGM shows the highest bias
(∼ 40 %) and HTAPII-CHSR, HTAPII-MOZT and CCMI-GCCM show the lowest
bias (∼ 16 %). It is worth noting that the AATOC values are not the
highest for CCMI-UKCA, even though the annual average surface ozone values
are the highest for CCMI-UKCA as compared to the other models.
The differences between the OMI/MLS observations and the model simulations
are further highlighted in Fig. 11, where the percentage biases in AATOC are
shown. The model simulations, in general, show similar spatial patterns in
AATOC to OMI/MLS, but all models overestimate the total TOC values over the
domain. The total TOC values for HTAPII-CHSR and CCMI-CHSM are somewhat
different and show different bias patterns in spite of having same chemistry
schemes and being based on the same model. However, the differences between
different models in the same MIP (i.e. between HTAPII-CHSR and the other
HTAPII models, or between CCMI-CHSM and the other CCMI models) are typically
larger, both in terms of the average total TOC over the domain and the
spatial distribution. Thus, there is greater inter-model variation due to
model set-up (either differences in model chemistry schemes, dynamics or
non-anthropogenic emission sources) than due to differences in anthropogenic
emissions prescribed by the two MIPs.
Empirical orthogonal function analysis
Several previous studies have focused on harmonic or spectral analysis of
time series of ozone in both observations and models (Bowdalo et al., 2016;
Derwent et al., 2013; Parrish et al., 2014; Solazzo et al., 2017). A key goal
of the studies and types of analysis above is to determine the causes of
biases between models and observations to enable improvements in modelling of
ozone. Typically spectral analysis allows the complex time series present in
an ozone dataset to be decomposed into a set of spectral features. Studies
have applied these methods to many parts of the world such as Europe, North
America and Australia (e.g. Derwent et al., 2013; Young et al., 2013; Bowdalo
et al., 2016), but to date no study has applied spectral analysis on global
model and observed ozone across India.
In this study, we have used EOF analysis on
the OMI/MLS-observed and the model-simulated TOC from HTAPII and CCMI. EOF
analysis reduces the dimensionality of the input spatial variables (i.e.
ozone column, which is f(lat, long, time)) to find new sets of variables
that capture most of the observed variance from the original data through a
linear combination of the original variables. Principle components (PCs)
represent the sign and overall amplitude of the EOF as a function of time.
EOF analysis is commonly used in the climate science community (Nair et al.,
2014) but has been less widely used in the ozone modelling community. EOF
analysis is analogous to Fourier transform (FT) analysis but performs better
than FT when the signal differs from the pure sinusoidal waveform (Cepeda and
Colome, 2014).
EOF analysis was applied to both the OMI/MLS and modelled TOC across a domain
of 56 to 105∘ longitude and 5 to 38∘ latitude, which covers
the entire Indian subcontinent. Figure 12a depicts the spatial patterns of
EOF1, which explains the maximum variance in tropospheric ozone over the
domain. EOF1 has a loading for each variable; in this case the variables are
the grid points, they have correlation structures both in space and time. The
amplitudes of the EOF1 spatial patterns have a time series as shown by PC1 in
Fig. 12b.
(a) Dominant spatial pattern (i.e. EOF1), which explains
the maximum variance in the tropospheric ozone column. (b) Time
series of the amplitude of EOF1 (PC1) with the values of accounted variance
by the EOF1 in the legend for each model.
The spatial patterns depicted by EOF1 (Fig. 12a) for models are similar to
the spatial pattern for the OMI/MLS observations: they show higher values in
the north-western part of domain and lower in the southern part and over the
ocean. However, the magnitudes of the loading are different between each of
the models and between the MMM and the observations.
The amplitude of EOF1 (Fig. 12b) has negative values in winter and positive
values during the summer monsoon seasons. There is a discernible difference
in the phase of PC1, with most of the models peaking in July–August, but the
observations peaking in June. The annual-cycle-like structure of PC1 shows a
strong correlation with the movement of the intertropical convergence zone
(ITCZ) over India, which heads southward during winter and
northward during summer. Physically these spatial patterns thus represent
surface pressure changing with the movement of ITCZ. Precipitation also
migrates with ITCZ over India. Hence maximum variance in tropospheric ozone
is explained by the monsoon over South Asia (i.e. EOF1 reflects the monsoon).
It is worth noting that the maximum variance in tropospheric ozone column
explained by EOF1 in observations is ∼ 60 % whereas in models it is
greater than 70 %. The maximum variance in tropospheric ozone column
explained by EOF1 in CCMI-UKCA is ∼ 55 %, which is less than that
of the observations. The differences in the EOF1 spatial pattern, the
amplitudes of EOF1 (as given by the PC1) and the percentage of maximum
variance explained indicate that each model is capturing monsoons differently
both in space and in time.
In spite of reasonable agreement between the models and observations for EOF1
and PC1, the comparison for EOF2 and PC2 is poor (Fig. S2a and b). There is
no agreement between the spatial pattern of EOF2 and the amplitude of EOF2
(PC2) among the models and OMI-MLS. Whilst this EOF analysis has provided a
novel approach to comparing and contrasting the modelled and observed
tropospheric ozone column distributions, it does not give a clear
understanding about the underlying reasons for the discrepancies in the
models, as with many of the previous studies (Bowdalo et al., 2016; Derwent
et al., 2013; Parrish et al., 2014; Solazzo et al., 2017).
Comparison with the IAGOS-CARIBIC observations
We now focus on the comparison of the model data to vertical profiles of
carbon monoxide and ozone measured on board a commercial airliner as part of
the IAGOS-CARIBIC programme (Brenninkmeijer et al., 2007). The observations
from IAGOS-CARIBIC are important as they provide a connection between the
surface and satellite observations discussed above, but they are
statistically less powerful owing to small samples sizes.
Comparison of ozone and carbon monoxide profiles from model
simulations, 2009–2010, with the CARIBIC observations in Chennai for
pre-monsoon (April–May), monsoon (June–September) and post-monsoon
(October–December) seasons, 2008. Model simulations have been vertically
interpolated along the CARIBIC flight pressure levels. The mean of the data
collected during the aircraft descent and ascent is shown here.
Figure 13 shows the seasonal mean vertical profiles of ozone and carbon
monoxide from the IAGOS-CARIBIC aircraft observations from Lufthansa flights
LH758 and LH759, which connect between Frankfurt, Germany, and Chennai,
India, compared with model output over Chennai. In total we have combined the
results from over 16 flights during April to December 2008. We have converted
the IAGOS-CARIBIC data into pseudo-climatological data, by averaging over
25 hPa vertical bins as explained in Sect. 2.1.2, to enable a comparison of
the models pre-monsoon, post-monsoon and during the monsoon. The black lines in Fig. 13
denote the average observed vertical profile, with the grey envelope
reflecting the standard deviation in these average observations. Model data
refer to the average monthly mean model profiles over Chennai airport that
coincide with aircraft and are also interpolated to 25 hPa vertical pressure bins.
During the pre-monsoon season (April–May), high values of ozone and CO are
observed in the lower troposphere (LT) (p>500 hPa) as compared to the
upper troposphere (UT) (p<500 hPa). Generally speaking, models
underestimate the ozone and CO values in the LT and perform fairly well in
the UT. Given the fact that these are very limited observational data, any
specific emission events (for example wild fires) that occurred during the
observing period are unlikely to be reproduced by the models (Ojha et al.,
2016). The levels of model-simulated CO in the pre-monsoon LT generally show
higher biases as compared to the ozone levels. HTAPII-MOZT simulates the
pre-monsoon LT carbon monoxide levels in good agreement with the
observations but highly overestimates the UT values and generally
overestimates the CO mixing ratios in the post- and monsoon periods.
CCMI-UKCA highly underestimates the CO profiles, especially in the UT.
HTAPII-HDGM performs well in the LT for ozone profiles during the pre-monsoon
season.
Chennai experienced a strong pollution event on 15 July 2008 (Ojha et al.,
2016), hence high values of ozone are observed between 900 and 850 hPa
during the monsoon season (June–September). Since the model ozone values are
monthly mean values, models do not capture this strong pollution event. Aside
from this event, models capture the ozone and CO profiles well during the
monsoon season; the MMM bias is ∼ 11 % for ozone and
∼-5 % for CO and the correlation coefficient is ∼ 0.29 for
ozone and ∼ 0.7 for CO. HTAPII-HDGM and CCMI-UKCA tend to overestimate
the ozone profile in the UT whilst HTAPII-MOZT overestimates and CCMI-UKCA
underestimates the CO profiles in the monsoon season.
There are large discrepancies between the models and IAGOS-CARIBIC
observations in the LT during the post-monsoon season. Models overestimate
the ozone and carbon monoxide profiles by a factor of 1.5 and 1.7,
respectively, in the LT during the post-monsoon season (October–December).
However, the models agree much better with the observed ozone and carbon
monoxide profiles in the UT during this season. HTAPII-MOZT overestimates the
carbon monoxide profile in the UT. The majority of the other models tend to
have fairly high levels of carbon monoxide “trapped” within the boundary
layer during the post-monsoon period. There is little evidence for this
trapping in the IAGOS-CARIBIC observations, but more evidence for pollutant
(CO) build-up in this season can be seen in the surface data analysed in
Sect. 3.2.2.
The comparison of the HTAPII and CCMI models to these aircraft data has
been useful in basic evaluation of the vertical profiles of
these key pollutants. However, the limited number of observed vertical
profiles of these pollutants restrict detailed evaluation of models over
this region. Moreover, targeted aircraft-based studies would be illuminating,
especially with comprehensive chemical and aerosol measurements to enable
improvements in modelling in this region.
Ozone as a function of VOC and NOx emissions
Finally, in order to evaluate how the models are simulating ozone at the
surface, we extend the analysis of surface ozone shown in Fig. 3 to contrast
the model-simulated surface ozone against the model input VOC and
NOx emissions following Squire et al. (2015) by creating
ozone isopleth plots. Figure 14 shows the isopleths of surface ozone
concentrations as a function of NOx and VOC emissions for a
subset of models (HTAPII-GCAD, HTAPII-CHSR, CCMI-GEOSCCM and CCMI-CHSM) over
the entire domain of study. These models were chosen as they include
(i) essentially the same model run for the two different MIPs (HTAPII-CHSR
and CCMI-CHSM) and (ii) different model runs for the same MIPs (HTAPII-CHSR and
HTAPII-GCAD, CCMI-CHSM and CCMI-GEOSCCM), and (iii) these were some of the only
models that output total VOC emissions, which are better indicators for ozone
chemistry than carbon monoxide (Monks et al., 2015). The monthly mean surface
ozone data over the study region from these simulations were combined with
the monthly mean surface emissions of VOCs and NOx to
generate the plots in Fig. 14. The dots in each panel indicate the locations
(in VOC and NOx space) that the model ozone data samples. As
can be seen, there is wide variation in the VOC–NOx space
sampled by the models due to differences in their input emissions, as
discussed in Sect. 2.2.
Isopleths of ozone concentration in parts per billion (ppb)
as a function
of NOx and VOC emissions over the domain. The dots in each
panel indicate the locations (in VOC and NOx space) that the
model ozone data samples.
Unlike the ozone isopleths shown in Squire et al. (2015), which focused on
grid boxes dominated by isoprene chemistry, the isopleths here generally show
a double peak structure, with high ozone at both high and low
NOx and VOC emissions (i.e. the bottom left and top right of
each panel). This suggests that this analysis is not connecting in situ
produced O3 to the underlying emissions of VOCs and
NOx and shows effects of pollutants from other regions as
well. HTAPII-CHSR and CCMI-CHSM have the same chemistry scheme but the
different inputs used cause the ozone to respond differently to the
NOx and VOC emissions. The HTAPII models (HTAPII-CHSR and
HTAPII-GCAD) have the same anthropogenic emission inputs but the difference
in the chemistry scheme used causes the ozone to respond differently to the
NOx and VOC emissions. On similar lines, the CCMI models
(CCMI-CHSM and CCMI-GEOSCCM) also give different isopleth patterns.
Conclusions
In this study, we have systematically assessed differences and similarities
in the modelled ozone from eight different models, contributing to the HTAPII
and CCMI model inter-comparison projects, over the Indian subcontinent.
Large inter-model variability is observed in the model-simulated annual
average surface. Tropospheric O3 and ozone precursors from these
models have been evaluated against a set of ground-based, aircraft and
satellite observations over India. Comparison between the model-simulated and
ground-based observations of surface ozone show some similarities between the
seasonal cycle, except at Chennai. However, models overestimate the ozone
mixing ratios at all locations, with CCMI-UKCA giving the highest values of
annual average surface ozone.
While a detailed evaluation of why CCMI-UKCA simulates the highest levels of
annual mean surface ozone is beyond the scope of this study, we note that
further work should be performed to understand the reasons behind this
behaviour. Simulations similar to those in Prather et al. (2018) would
potentially help shed light on the role of the chemical scheme as a source of
bias in the model.
Models underestimate NOx mixing ratios, except for HTAPII-HDGM
at Patiala. NO2 dominates NOx in the models. Models
tend to underestimate CO only at Delhi and Patiala and perform well at the
other ground-based stations. It is important to note that the sites
considered in this study are categorised as semi-urban and are therefore
influenced by local emissions, which are not well represented in global
models. Models with coarse-resolution grids are unable to capture the short timescale processes taking place at the local scale and result in the
underestimation of surface carbon monoxide and NOx and the
overestimation of ozone, as we have shown in Figs. 3–7. In order to better
evaluate global model simulations of surface ozone, we would suggest the need
for a network of rural stations measuring ozone and ozone precursors (i.e.
NOx, CO, VOCs), covering different geographical and chemical
environments across India.
Model simulations of total TOC show similar spatial patterns compared to the
OMI data over the study domain, but they overestimate the total TOC values,
with biases ranging from 16 % to 40 %. EOF analysis highlights that
more than 70 % of the ozone variation in models is dependent on a single
phenomenon, i.e. EOF1.
Comparison with the CARIBIC ozone and CO profiles indicate that models
perform fairly well in the upper troposphere as compared to the lower
troposphere. The sparse observations of CO and O3 profiles limit the
evaluation of model ozone and CO profiles over this region. It is clear from
the ozone isopleths that different inputs and chemistry schemes used in these
models cause the ozone to respond differently to VOCs and
NOx emissions. Large variation in lightning
NOx emissions is one of the major reasons for the differences
in the total NOx emissions. Further investigation to support
this study including the details of chemistry schemes and the simulations of
VOC, HO2 needs to be evaluated within each model. For future
chemistry-climate model intercomparisons, we recommend the inclusion of
simulations with standardisation of non-anthropogenic emission sources as
well as anthropogenic sources in order to be able to diagnose the impact of
model chemistry only on tropospheric ozone.
Data availability
The CCMI data used here are held at the Centre for
Environmental Data Analysis (CEDA;
http://data.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/, last access:
1 November 2018). For instructions on access to the archive see
http://blogs.reading.ac.uk/ccmi/ badc-data-access (IGAC/SPARC
Chemistry-Climate Model Initiative, 2018). The HTAPII model data can be
downloaded upon request from the AeroCom database
(http://www.htap.org/, last access: 1 November 2018.) (TF HTAP, 2018).
The SAFAR observational data can be provided upon request to the SAFAR team
(safar@tropmet.res.in).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6437-2019-supplement.
Author contributions
ZH performed all analysis and led the study, which ATA
designed. GB provided the MAPAN data and expert advice on their use. SAN, ZH
and ATA led the writing of the paper and all co-authors contributed to
reviewing the paper and improving the study. GAF, KS, NLA, SG and DKH all
provided data from their global model runs to be analysed and advice on their
interpretation.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The authors acknowledge the Isaac Newton Trust for funding. We thank the
System for Air Quality and Weather Forecasting and Research (SAFAR) project
and the Modelling Atmospheric Pollution and Networking (MAPAN) project,
Indian Institute of Tropical Meteorology, Pune, India, for the ground-based
measurements used in this study. Gerd A. Folberth was supported by the Joint
UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and the
European Union's Horizon 2020 Research and Innovation Programme under grant
agreement no. 641816 (CRESCENDO). GEOSCCM is supported by the NASA MAP
program, and the high-performance computing resources were provided by the
NASA Centre for Climate Simulation (NCCS). We thank
Stacey M. Frith for helping us access
the GEOSCCM output. We also thank Makoto Deushi, Meteorological Research
Institute (MRI), Japan, for helping us access the MRI-ESM1r1 simulations and
Yanko Davila, Department of Mechanical Engineering, University of Colorado,
Boulder, CO, USA, for the GEOSCHEM-ADJOINT simulations for the present study.
Daven K. Henze recognises support from NASA HAQAST.
UMUKCA-UCAM model integrations were performed using the ARCHER UK National
Supercomputing Service and MONSooN system, a collaborative facility supplied
under the Joint Weather and Climate Research Programme, which is a strategic
partnership between the UK Met Office and the Natural Environment Research
Council. We thank CARIBIC partners as well as Lufthansa, especially Lufthansa
Technik, for support. We acknowledge the AURA MLS and OMI instruments and
algorithm teams for the satellite measurements used in this study. Finally,
we would like to thank Narendra Ojha and the second anonymous reviewer for
taking the time to review the paper and offer helpful feedback and comments.
This research has been supported by the NERC Atmospheric Pollution and Human Health in an Indian Megacity programme (grant no. NE/P016383/1).
Review statement
This paper was edited by Tim Butler and reviewed by Narendra
Ojha and one anonymous referee.
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