Past studies have suggested that ozone in the troposphere has
increased globally throughout much of the 20th century due to increases in
anthropogenic emissions and transport. We show, by combining satellite
measurements with a chemical transport model, that during the last four
decades tropospheric ozone does indeed indicate increases that are global in
nature, yet still highly regional. Satellite ozone measurements from
Nimbus-7 and Earth Probe Total Ozone Mapping Spectrometer (TOMS) are merged
with ozone measurements from the Aura Ozone Monitoring Instrument/Microwave Limb
Sounder (OMI/MLS) to determine trends in tropospheric ozone for 1979–2016.
Both TOMS (1979–2005) and OMI/MLS (2005–2016) depict large increases in
tropospheric ozone from the Near East to India and East Asia and further
eastward over the Pacific Ocean. The 38-year merged satellite record shows
total net change over this region of about +6 to +7 Dobson units (DU)
(i.e., ∼15 %–20 % of average background ozone), with the
largest increase (∼4 DU) occurring during the 2005–2016 Aura
period. The Global Modeling Initiative (GMI) chemical transport model with
time-varying emissions is used to aid in the interpretation of tropospheric
ozone trends for 1980–2016. The GMI simulation for the combined record also
depicts the greatest increases of +6 to +7 DU over India and East Asia, very
similar to the satellite measurements. In regions of significant increases
in tropospheric column ozone (TCO) the trends are a factor of 2–2.5 larger
for the Aura record when compared to the earlier TOMS record; for India and East Asia the trends in TCO for both GMI and satellite measurements are
∼+3 DU decade-1 or greater during 2005–2016 compared
to about +1.2 to +1.4 DU decade-1 for 1979–2005. The GMI simulation
and satellite data also reveal a tropospheric ozone increases in
∼+4 to +5 DU for the 38-year record over central Africa
and the tropical Atlantic Ocean. Both the GMI simulation and
satellite-measured tropospheric ozone during the latter Aura time period
show increases of ∼+3 DU decade-1 over the N Atlantic
and NE Pacific.
Introduction
Over the last several decades there have been substantial regional changes in
emissions and concentrations of global pollutants, including precursors of
tropospheric ozone, as documented by many studies (e.g., Granier et al., 2011;
Parrish et al., 2013; Young et al., 2013; Cooper et al., 2014; Lee et al.,
2014; Zhang et al., 2016; Heue et al., 2016; Lin et al., 2017). The largest
increases in global pollutants over the last four decades occurred broadly
over a region extending from the Near East to India and East and Southeast Asia. Lin et
al. (2017) used a global chemistry-climate model (CCM) for 1980–2014 to
study the effects of global changes in emissions on surface ozone. They show
that rising increases in emissions, including a tripling of Asian
NOx (NO +NO2) since just 1990, lead to large
increases in surface ozone over India and East Asia and to a lesser extent over
the western US due to long-range transport. Young et al. (2013) combined 15
global chemistry climate models projected to the year 2100 and found significant
inter-model differences; relative to the year 2000, global tropospheric ozone
from the models indicated both increases and decreases up to the year 2030, and
largely decreases by 2100. One conclusion from Young et al. (2013) is that
the models are sensitive to emission and climate changes in different ways;
they mention that this requires a unified approach to ozone budget
specifications and rigorous investigation of the factors driving tropospheric
ozone to attribute changes in tropospheric ozone and inter-model differences
more clearly.
The changes in global emissions since 1980 are described by Zhang et
al. (2016) as an equatorward redistribution over time into developing
countries of India and those of SE Asia. Zhang et al. (2016) used a global
chemical-transport model (CTM) for 1980–2010 to quantify the effects of
these changes in emissions on tropospheric ozone. The model simulations and
OMI/MLS satellite measurements employed by Zhang et al. (2016) indicated
the largest increases in tropospheric ozone extending from the Near East to India
and SE Asia and further eastward over the Pacific Ocean. Zhang et al. (2016)
included IAGOS aircraft ozone profiles that also showed large increases
(i.e., double-digit percent increases) for India, SE Asia, and East Asia
between the 1994–2004 and 2005–2014 time records. The model used by Zhang
et al. (2016) also simulated a net increase in global tropospheric ozone of
about 28 Tg (∼8.9 %) over the 30-year record. The results by Zhang et
al. (2016) appear consistent with the Bulletin of the American Meteorological
Society BAMS State of the Climate Report for the year 2016, which indicates about
a 21.8 Tg increase in OMI/MLS tropospheric ozone when averaged over
60∘ S–60∘ N between October 2004 and December 2016, with
the largest contribution to global trends (about +3 to +4 DU decade-1
for OMI/MLS) originating from the same India and the East and Southeast Asia region. The
increases in tropospheric ozone for OMI/MLS are from a shorter record than
the 30-year record of Zhang et al. (2016) and are not global. (We discuss trends
for a 38-year merged record from combined TOMS and OMI/MLS satellite
measurements later in Sect. 3.3.) The first evidence of increases in
tropospheric ozone over SE Asia from satellite data was shown by Beig and
Singh (2007). Beig and Singh used a version of convective-cloud differential
(CCD) gridded tropospheric ozone for 1979–2005 that was a predecessor to the
current CCD data used for our study (discussed in Sect. 2). The CCD algorithm
is described by Ziemke et al. (1998). The largest increases in tropospheric
ozone reported by Beig and Singh (2007) were up to
7 %–9 % decade-1 and were located in SE Asia.
The Tropospheric Ozone Assessment Report (TOAR) provides analyses of trends
in tropospheric ozone calculated from a large array of data sources including
satellite, aircraft, balloon ozonesondes and surface measurements (Gaudel et
al., 2018). Figure 24 of Gaudel et al. (2018) shows calculated linear
trends during the Aura time record for six global data
products, five from satellite and one from trajectory-mapped ozonesondes. The
six products show large divergence in estimated trends, in part due to their
short and differing time records; it was noted that one should be careful
about placing precise numbers on estimated trends in tropospheric column ozone (TCO) from the results.
Figure 25 of Gaudel et al. (2018) combined all six TCO products together
statistically and showed that the largest and most consistent (and positive)
trends between the six products were centered over SE Asia.
Heue et al. (2016) derived a merged 1995–2015 tropical tropospheric ozone
dataset from multiple satellite instruments using a variant of the CCD
approach for the latitude range ±20∘. Their dataset was constructed
by concatenating measurements from several instruments including SCIAMACHY
and GOME (but not including either TOMS or OMI/MLS). Their main findings
included evidence for increases in tropospheric ozone over both India and SE Asia
and the tropical African and Atlantic region; however, their largest detected
positive trends were across tropical Africa and the Atlantic rather than India and SE
Asia. Heue et al. (2016) estimated a mean trend in TCO of about
+0.7 DU decade-1 in the tropics (15∘ S–15∘ N).
Heue et al. (2016) indicated that significant positive trends occurred over
central and southern Africa that maximized during June–August, which
represents the peak burning season for this region; they suggested that the
trends in central–southern Africa are associated with an increase in biomass
burning. Leventidou et al. (2018), using similar (but processed differently)
SCIAMACHY/GOME CCD TCO measurements for 1995–2015, found
∼+3 DU decade-1 trend over southern Africa, but no statistical
change in the tropics (15∘ S–15∘ N).
The purpose of our study is to derive trends in tropospheric ozone for
1979–2016 by combining TOMS (1979–2005) and OMI/MLS (2005–2016)
measurements. A main incentive is to evaluate TCO trends for a longer
satellite record than previous investigations including TOAR, and to identify
and possibly explain the regional trend patterns that emerge from the data.
Areal coverage for calculated trends is all longitudes and latitudes from
30∘ S to 30∘ N for TOMS and 60∘ S–60∘ N
for OMI/MLS. The Global Modeling Initiative (GMI) CTM replay simulation is
included to assess ozone trends during both the TOMS and OMI/MLS time
periods. All satellite ozone products were re-processed from previous
versions to improve data quality for trend calculations. We also provide a
preliminary evaluation of TCO measured from the
Ozone Mapping Profiler Suite (OMPS) nadir-mapper and limb-profiler
instruments beginning in 2012 as a possible future continuation of the OMI/MLS
TCO record. Section 2 discusses the satellite measurements, GMI model,
ozonesonde data, and trend calculations. Section 3 discusses derived trends
in tropospheric ozone including net changes for the combined 38-year record.
Results are summarized in Sect. 4. We also include a Supplement
(Sects. S1–S4) that discusses validation of
OMI/MLS, TOMS, and OMPS TCO, and comparisons of decadal changes or trends
between ozonesonde and OMI/MLS TCO.
Satellite measurements, MERRA-2 GMI model, ozonesondes, and trend
calculationsSatellite measurements
All satellite measurements of TCO used for our study are developed at NASA
Goddard Space Flight Center (Code 614) and updated and upgraded periodically
for the science community. TCO measurements and their validation from
Nimbus-7 (N7) and Earth Probe (EP) TOMS instruments are discussed by Ziemke
et al. (2005, and references therein). TOMS TCO for 1979–2005 is derived
using the CCD algorithm (Ziemke et al., 1998)
which differences clear versus thick cloud measurements of column ozone.
Useful CCD gridded TCO is limited mostly to tropical latitudes due to having
both a large number of deep convective clouds and small zonal variability of
stratospheric column ozone (SCO). Our TOMS CCD dataset originates from a preliminary TOMS CCD gridded
dataset that Beig and Singh (2007) used for evaluating TCO trends, but now
includes a re-processing with extensive flagging of outliers out to latitudes
±30∘. The N7 and EP TOMS instruments have similar
spectral, spatial, and temporal resolution with TCO obtained from both using the
same version 8 algorithm. TOMS TCO is determined by subtracting thick cloud
column ozone measurements (to estimate SCO) from
near clear-sky total column ozone. By differencing SCO and total ozone from
the same instrument, derived TCO is largely self-calibrating over time and
should not be affected by instrument or inter-instrument drifts or offsets.
Standard precision error (i.e., 1σ standard deviation) of TOMS
gridded TCO is estimated to be about 1.7 DU (e.g., Ziemke et al., 1998).
Validation of TOMS TCO is discussed in Sect. S3 of the Supplement. The
validation of TOMS TCO involves comparisons with ozonesondes beginning in
1979.
We also include OMI/MLS TCO (Ziemke et al., 2006) for
January 2005–December 2016 and latitude range
60∘ S–60∘ N. TCO is determined by subtracting MLS SCO from
OMI total column ozone each day at each grid point. Tropopause pressure used
to determine SCO invoked the WMO 2 K km-1 lapse-rate definition from NCEP re-analyses. For consistency these
same lapse-rate tropopause pressure fields were used to derive TCO for
ozonesondes, OMPS, and the GMI model (discussed below). OMI total column
ozone is retrieved using the OMTO3 v8.5 algorithm that includes co-located UV
cloud pressures from OMI (Vasilkov et al., 2008) and several other
improvements from version 8. The OMI total ozone and cloud data including
discussion of data quality are available from
https://ozonewatch.gsfc.nasa.gov/data/omi/ (last access: 7 March 2019). The MLS data used to obtain SCO were derived from their v4.2 ozone
profiles (https://mls.jpl.nasa.gov/data/datadocs.php/, last access: 7 March 2019). We estimate 1σ precision for the OMI/MLS
monthly-mean gridded TCO product to be about 1.3 DU. The additional
Supplement discusses both validation and adjustments made to OMI/MLS TCO. It
can be shown that OMI/MLS TCO derived from this residual technique is nearly
identical to the TCO from OMI CCD measurements for the same time period,
albeit with the CCD data limited mostly to tropical or subtropical latitudes
(e.g., Ziemke and Chandra, 2012).
Tropospheric ozone for January 2012 through 2016 is also determined from the
OMPS nadir-mapper and limb-profiler instruments on board the National
Polar-orbiting Operational Environmental Satellite System (NPP) spacecraft.
The OMPS tropospheric ozone is evaluated for possibly continuing the OMI/MLS
data record. TCO is determined by subtracting OMPS v2.5 limb-profiler SCO
from OMPS v2.3 nadir-mapper total column ozone. SCO is determined from the
limb-profiler measurements using the same tropopause pressure fields as for
MLS SCO. With both OMPS instruments on board the same NPP satellite, the time
difference between the limb and nadir measurements is about 7 min (similar
to Aura MLS and OMI instruments). The OMPS data including evaluation of data
quality are available from
https://ozonewatch.gsfc.nasa.gov/data/omps/ (last access: 7 March 2019). Section S2 of the Supplement discusses the derived OMPS TCO. A
main conclusion regarding this preliminary version of OMPS TCO is that these
measurements will be useful for extending the OMI/MLS record of TCO.
All satellite-derived TCO represents monthly means under mostly clear-sky
conditions with radiative cloud fractions <40 %. This cloud threshold
reduces the number of total column ozone pixels by ∼20 %. The cloud
filtering was applied to reduce precision error in satellite-measured TCO due
to errors in assumed climatological below-cloud ozone for thick cloud scenes.
These errors in tropospheric ozone are largely random in nature on a
pixel-by-pixel basis and do not affect calculated trend magnitudes whether or
not such measurements are removed from the analyses. Satellite-derived TCO
was gridded to 5∘×5∘ bins centered on longitudes
-177.5, -172.5, …, 177.5∘, and latitudes -27.5, -22.5,
…, 27.5∘ for TOMS and latitudes -57.5, -52.5, …,
57.5∘ for OMI/MLS (and also OMPS). This bin size for all
measurements was chosen for consistency because the original bin size for the
CCD measurements for 1979–2005 is 5∘×5∘.
MERRA-2 GMI model
The Modern-Era Retrospective analysis for Research and Applications (MERRA-2)
GMI simulation is produced with the Goddard Earth Observing System (GEOS)
modeling framework (Molod et al., 2015), using winds, temperature, and
pressure from the MERRA-2 reanalysis (Gelaro et al., 2017). The
configuration for this study is a dynamically constrained replay (Orbe et
al., 2017) coupled to the Global Modeling Initiative's (GMI) stratospheric
and tropospheric chemical mechanism (Duncan et al., 2007; Oman et al.,
2013; Nielsen et al., 2017). The GMI mechanism includes a detailed
description of ozone–NOx–hydrocarbon chemistry and has over
100 species and approximately 400 chemical reactions. The simulation was run
at ∼0.5∘ horizontal resolution at c180 on the cubed sphere, and
output on the same 0.625∘ longitude × 0.5∘ latitude
grid as MERRA-2 from 1980–2016.
The MERRA-2 GMI simulation includes emissions of NO, CO, and other
nonmethane hydrocarbons from fossil fuel and biofuel sources, biomass
burning, and biogenic sources. There are also NO emissions from lightning and
soil. Fossil fuel and biofuel sources are prescribed from the
Measuring Atmospheric Composition and Climate megaCity – zoom for the
environment (MACCity) inventory (Granier et al., 2011), which interpolates
to each year from the decadal Atmospheric Chemistry and Climate Model
Intercomparison Project (ACCMIP) emissions (Lamarque et al., 2010) and
applies a seasonal scaling factor. The MACCity inventory ends in 2010, and so for
later years we use fossil fuel and biofuel emissions from the Representative
Concentration Pathways 8.5 (RCP8.5) scenario. Time-dependent biomass burning
emissions for 1997 onwards come from the Global Fire Emissions Dataset (GFED)
version 4s (Giglio et al., 2013). Biomass burning emissions for prior years
have interannual variability from regional scaling factors based on the TOMS
aerosol index (Duncan et al., 2003) imposed on a climatology derived from
GFED-4s, similar to the approach used in Strode et al. (2015). Emissions of
isoprene and other biogenic compounds are calculated online using the Model
of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et
al., 1999, 2000) and thus respond to MERRA-2 GMI meteorology. NO emissions
from soil, parameterized based on Yienger and Levy (1995), also respond to
the MERRA-2 meteorology. Lightning NO production is prescribed monthly based
on the scheme of Allen et al. (2010) using a detrended cumulative mass
flux in the midtroposphere from MERRA-2, constrained seasonally with the
OTDLIS v2.3 lightning climatology (Cecil et al., 2014). A global mean
scaling factor is applied to the detrended cumulative mass flux so that the
annual average global mean lightning NOx production is
6.5 Tg N yr-1 for each year of simulation. Methane is specified as a
latitude- and time-dependent surface boundary condition. In addition to
chemical loss, dry deposition provides a major sink for tropospheric ozone.
GMI uses a resistance-in-series method (Wang et al., 1998; Wesely and
Hicks, 1977) for dry deposition and thus depends on factors including land
surface type and leaf area index. Ozone-depleting substances are specified
using the A12014 scenario from WMO (2014).
TCO is derived from the GMI simulation by integrating the generated ozone
profiles from the surface up to tropopause pressure. GMI TCO (discussed
below) was also averaged monthly and re-gridded from original
0.5∘ latitude ×0.625∘ longitude resolution to
this same 5∘×5∘ gridding. Where we refer to GMI in
this paper it is equivalent to MERRA-2 GMI.
Ozonesondes
We include balloon-launched ozonesonde measurements for comparisons and
validation of the OMI/MLS TCO. The ozonesonde database extends from
2004 to 2016 and includes measurements from Southern Hemisphere ADditional
OZonesondes (SHADOZ) (Thompson et al., 2017; Witte et al., 2017), World Ozone
and Ultraviolet Data Center (WOUDC) (https://woudc.org/, last access: 7 March 2019), and Network for the Detection of Atmospheric
Composition Change (NDACC) (http://www.ndsc.ncep.noaa.gov/, last access: 7 March 2019). The ozonesondes provide daily ozone profile concentrations as a
function of altitude from several dozen global station sites. The ozone
profiles are integrated vertically each day to derive tropospheric column
measurements. Most of the sonde ozone profile measurements during the Aura
record that we used are derived from electrochemical concentration cell (ECC)
instruments. Non-ECC instruments include Brewer-Mast (for the entire Aura
record at Hohenpeißenberg) and carbon iodide (up through November 2009 at
Sapporo and Tateno, up through October 2008 at Naha, and through March 2005
at Kagoshima). Section S1 of the Supplement discusses the ozonesonde analyses
and includes evaluation of potential offset and/or drift in OMI/MLS data. The
ensuing corrections made to the OMI/MLS TCO were small. The corrections
included a +2 DU offset adjustment (via ozonesonde comparisons) and a
-1.0 DU decade-1 drift adjustment (via OMI row anomaly analysis).
Trend calculations
For the short 15-month overlapping time period of
October 2004–December 2005 between TOMS and OMI/MLS, mean offset differences
in TCO were found to be regionally varying with values up to 5 DU or
greater, which hampers any useful effort for deriving trends from their concatenated
datasets. Offsets of several DU between TOMS and OMI total ozone have been
well documented (e.g., Witte et al., 2018, and references therein).
Therefore, we have calculated trends independently for the TOMS (1979–2005)
and OMI/MLS (2005–2016) datasets. Total net change in TCO (in DU) at each
grid point for the 38-year record was determined by adding together the net
changes (i.e., trend in DU month-1× number of months) for the
TOMS and OMI/MLS records. The year 2017 and later months were not included in our
analyses because the MERRA-2 GMI simulation ended at December 2016 and also that
the global ozonesonde measurements used for validating the OMI/MLS TCO
extended only into mid-2016.
(a) Trends in OMI/MLS TCO (in DU decade-1) for
2005–2016. Asterisks denote grid points where trends are statistically
significant at the 2σ level. (b) Same as (a) except
for MERRA-2 GMI TCO.
Multivariate linear regression (MLR) (Ziemke et al., 1997, and references
therein) was applied to estimate trends in TCO. The regression includes
components for the seasonal cycle, linear trend, and ENSO (e.g., NINO3.4
index) from TCO(x,t)=A(x,t)+B(x,t)⋅tC(x,t)⋅NINO3.4(t)+ε(x,t), where x is the grid point and t is
the month. The term ε(x,t) represents residual error. We applied two
approaches regarding NINO3.4(t) in the MLR model. One approach was to
detrend NINO3.4(t) prior to the regression analysis and the other was
not to detrend this proxy. A main reason for possibly wanting to detrend
NINO3.4(t) is that TCO variability is not truly linear with NINO3.4(t)
variability over any timescale, including decadal, which may potentially
influence linear trend calculations in the MLR method. We opted not to
include detrending of NINO3.4(t) after finding little or no difference
between either approach for both OMI/MLS and TOMS records. The seasonal
coefficient A in the MLR equation above includes a constant plus annual and
semi-annual harmonics while coefficients B and C each include a
constant. Since our study does not evaluate the seasonality of trends, we
constrained the number of regression constants for trend B to only one,
which tends to improve overall statistical trend uncertainties when compared
to using several regression seasonal constants for B. Trend magnitudes
exceeding the calculated 2σ value uncertainty for B are deemed
statistically significant. Calculated 2σ uncertainties for trends
included an autoregressive-1 adjustment as presented in Weatherhead et
al. (1998). Trends were calculated similarly for GMI TCO and NO emissions
using this MLR approach.
Trends in MERRA-2 GMI NO emissions (units mg m -2 yr-1)
for 2005–2016.
Trends in tropospheric ozoneThe Aura record (2005–2016)
OMI/MLS TCO trends for 60∘ S–60∘ N are shown in Fig. 1a
with asterisks denoting regions that are statistically significant at
2σ level. Positive trends lie in the tropics and extratropics in
both hemispheres, with the largest trends (shown in red) of
∼+3 DU decade-1 or greater extending from India to East and Southeast Asia
and further eastward over the Pacific Ocean. There are also statistically
significant increases in ozone in the north Atlantic extending eastward over
central Africa.
(a) Deseasonalized TCO for OMI/MLS (red, dashed curve) and
the MERRA-2 GMI model (blue, solid curve) for SE Asia. Included are MLR
regression fits for linear trends and calculated 2σ values (both in
DU decade-1). Shown at the bottom is the correlation r between the two
time series after removing their linear trends. (b) Same
as (a), but for equatorial Africa. (c) Same, but for the NE
Pacific. (d) Same, but for the north Atlantic.
Trends for GMI TCO (Fig. 1b) have features similar to trends for OMI/MLS TCO.
Large positive trends for GMI also extend from Saudi Arabia and India to
Southeast and East Asia and further eastward over the Pacific Ocean. Changes for both
OMI/MLS and GMI TCO over this region are ∼+3 DU decade-1. GMI TCO
also indicates evidence of positive trends over Africa and in the north
Atlantic, although these trends are generally weak compared to India and East Asia. For the north Atlantic region the positive trends for GMI are also not
in the same location as the positive trends for OMI/MLS. There are other
differences between GMI and OMI/MLS trends in Fig. 1 such as in the Southern Hemisphere (SH), where
GMI does not indicate statistically significant positive trends as the
satellite observations do. Anet et al. (2017) examined surface ozone data
from El Tololo, Chile (30∘ S, 71∘ W), and found a small
positive trend of ∼+0.7 ppbv decade-1 for the period 1995–2010.
Their analyses indicated that the positive increase at the site was driven
mainly by stratospheric intrusions and not photochemical production from
anthropogenic and biogenic precursors. The results from Anet et al. (2017)
suggest that the positive trends in SH OMI/MLS TCO in Fig. 1a (primarily over
ocean) may be real; however, one cannot make any conclusion based on only
ground-level measurements and from only one station. Lu et
al. (2018)
detected positive trends in ozone throughout the SH
since 1990 from a large number of surface, ozonesonde, and satellite
measurements; they also included the GEOS-Chem CTM that showed similar
increases throughout the SH. Lu et al. (2019) suggested that the increases in
tropospheric ozone in the SH are linked to a broadening of the Hadley
association. Their analyses indicate that broadening of the Hadley
circulation is associated with changes in meridional transport, which
coincides with a greater influx of ozone from the stratosphere and larger
tropospheric ozone production due to stronger uplifting of tropical ozone
precursors into the upper troposphere. We have calculated ozonesonde column
ozone trends for the 2005–2016 Aura record to compare with the GMI and
OMI/MLS TCO trends in Fig. 1. (Section S4 of the Supplement discusses these
trend comparisons.) Figure S10 in Sect. S4 indicates that it is not possible
from the ozonesondes to conclude anything definitive regarding trends,
particularly in the SH extratropics where the ozonesondes are relatively
scarce over the short Aura time record.
Trends for NO emissions for 2005–2016 from the GMI simulation are shown in
Fig. 2, again with positive (negative) trends shown in red (blue). The largest
increases in tropospheric NO emissions in Fig. 2 are located over India and
East and Southeast Asia while greatest decreases originate over the eastern US, Europe,
and Japan. We note that although there are large increases in NO emissions
over eastern China for 2005–2016 depicted in Fig. 2, observations show
NO2 concentrations decreased over this region after the year 2012
(e.g., Krotkov et al., 2016). This recent downturn is not included in the GMI
emissions, likely contributing to the overestimate of the ozone trend over
eastern China in the GMI simulation. Overall, however, the ability of the GMI
simulation to capture the positive trends above and downwind of regions with
large NOx emission increases suggests that the
NOx emission trends are driving the trends in TCO over India
and East Asia.
Figure 1 shows that the regions of large decrease in NO emissions such as the
eastern US and Europe in Fig. 2 do not coincide with similar decreases in TCO
for either GMI or OMI/MLS. Both GMI and OMI/MLS TCO instead show essentially
zero or slightly positive trends for these regions, despite the fact that the
GMI simulation indicates significant negative trends in tropospheric column
NO2 over the eastern US and Europe. This contrasts with the
situation at the surface, in which simulations with GMI chemistry indicate
decreases in surface ozone over the eastern US in response to
NOx reductions (Strode et al., 2015).
Figure 3 shows comparisons between OMI/MLS and GMI deseasonalized TCO time
series and their calculated linear trends for (a) SE Asia, (b) equatorial
Africa, (c) NE Pacific, and (d) north Atlantic. Included in each panel are
MLR regression fits for linear trends and their calculated 2σ
uncertainties (both in DU decade-1). Not only are trends for GMI and
OMI/MLS comparable and statistically significant in Fig. 3 in each panel, but
their month-to-month variations in their detrended time series have
relatively large cross-correlations varying from +0.64 to +0.70. Several
inter-annual features are common with both MERRA-2 GMI and OMI/MLS TCO time
series in Fig. 3 such as large reductions (exceeding -5 DU) during spring
2008 over the NE Pacific and spring 2010 in the north Atlantic.
(a) Trends (DU decade-1) calculated for TOMS CCD TCO
measurements for the years 1979–2005. Asterisks denote grid points where trends
are statistically significant at the 2σ level. (b) Similar
to (a), but for MERRA-2 GMI TCO and for 1980–2005.
The TOMS record (1979–2005)
Trends for TOMS (1979–2005) and GMI (1980–2005) TCO are shown in Fig. 4. As
with both OMI/MLS and GMI TCO for the Aura period 2005–2016 in Fig. 1,
largest positive trends in Fig. 4 are also located over the Near East to East
Asia and extend further eastward over the Pacific Ocean. Calculated trends
for this region are ∼+1.2 to +1.4 DU decade-1 for both TOMS and
GMI, which are considerably smaller than during the Aura record. An important
conclusion is that both the model and measurements in Figs. 1 and 4 suggest
that the trends in tropospheric ozone over this region are markedly larger
during the Aura period compared to the earlier TOMS period, by a factor of
about 2–2.5.
Trends in MERRA-2 GMI NO emissions (units mg m -2 yr-1)
for 1980–2005. This figure is similar to Fig. 2, except for having an
earlier 1980–2005 time record.
As with OMI/MLS and GMI TCO trends in Fig. 1 there are discrepancies between
the TOMS and model TCO trends in Fig. 4. For TOMS TCO in Fig. 4 there are
regions of negative trends (in blue) of as much as -0.6 DU decade-1 over
ocean in both hemispheres that are not explainable. Trends for GMI in Fig. 4
are instead largely positive within these regions and actually positive
throughout much of the SH when compared with TOMS. This suggests that the
TOMS trends may be biased slightly low overall, provided that the simulation
is closer to truth.
(a) Deseasonalized TCO for TOMS (red, dashed curve) and the
MERRA-2 GMI model (blue, solid curve) for Brazil. Included are their MLR
linear trends and calculated 2σ values (both in DU decade-1)
averaged over the specified region. Shown also is the cross-correlation r
between the two time series after removing their linear trends.
(b) Same as (a), but for Indonesia. (c) Same as
(a), but for SE Asia. (d) Same as (a), but for
tropical Atlantic/Africa.
The trends for GMI TCO are positive over Brazil whereas OMI/MLS TCO shows
only a hint of positive trends. It is likely that there will be smaller
trends for TOMS because most ozone produced from biomass burning over Brazil
lies in the lower troposphere, and also that TOMS has reduced the ability to
detect ozone in the lower troposphere. The GMI simulation shows that of the
∼+1.4 DU decade-1 TCO trend over Brazil in Fig. 4, about
+0.9 DU decade-1 of this trend comes from ozone in the lower
troposphere below 500 hPa. With a known retrieval efficiency of
50 %–60 % below 500 hPa (and essentially 100 % above 500 hPa) for
TOMS over Brazil, the model suggests that TOMS should detect a trend of about
+0.5 DU decade-1 below 500 hPa. Therefore TOMS would then have a
trend in TCO of about +0.9 DU decade-1, which is comparable to the
∼+0.8 DU decade-1 measured for TOMS in Fig. 4.
Trends in NO emissions during 1980–2005 for the GMI simulation are shown in
Fig. 5. Figure 5 is similar to Fig. 2 except for an earlier time period
coinciding with the TOMS record. The largest increases in tropospheric NO
emissions in Fig. 5 are located over India and East and Southeast Asia, as noted earlier
for Fig. 2. Negative trends over the eastern US are much less pronounced
(nearly nonexistent) in Fig. 5 during the TOMS record compared to the
negative trends for the region in Fig. 2.
In Fig. 6 we show some examples of time series of TCO for TOMS and MERRA-2
GMI in regions where both records exhibit statistically significant positive
trends. The positive correlations between TOMS and model TCO in Fig. 6 are
generally small compared to the correlations between OMI/MLS and model TCO in
Fig. 3. The only large correlation in Fig. 6 is over Indonesia and is due to
the intense El Niño of 1997–1998 that caused record increases in TCO in
October 1997 in the region due to record levels of biomass burning (e.g.,
Chandra et al., 2003). The cross-correlations in the other panels in Fig. 6
are small; these smaller correlations indicate the noisy nature of TOMS
measurements compared to OMI/MLS and also possibly larger uncertainties
present in meteorological winds, temperatures, and emissions during these
earlier TOMS years for the GMI simulation. Changes in the observing system
increases transport uncertainties for MERRA-2; these transport uncertainties
increase the further back we go in time with MERRA-2, in particular the TOMS
record. The recent Aura period for MERRA-2 has both more observations and
higher vertical resolution than during the TOMS record. Stauffer et
al. (2019) suggests that there is less impact of the changing observing
system using the “Replay” technique compared to traditional CTMs. Wargan et
al. (2018) discusses changes in the observing system for MERRA-2 for
1998–2016, including changes in the input-assimilated radiances.
(a) Net changes in TOMS and OMI/MLS TCO calculated for
their combined time records (1979–2016). The net changes for TCO are shown
in the color bar in both DU and metric tons of ozone per km2
(1 DU ≡0.0214 t km-2 for ozone). Asterisks denote
grid points where net changes are statistically significant at the 2σ
noise level. (b) Similar to (a), but for GMI TCO and years
1980–2016. Net change for GMI TCO is determined similar to the satellite
measurements by adding together the net changes for the two records (i.e.,
for GMI, the 1980–2005 and 2005–2016 periods).
A main result from Figs. 4 and 6 is that the positive trends for both TOMS
and MERRA-2 GMI TCO are substantially larger, by a factor of about 2 or more,
during the OMI/MLS record compared to the TOMS record. The GMI simulation
suggests that larger trends during the Aura record are the manifestation of
an escalation of anthropogenic emissions and transport.
The merged record (1979–2016)
The net increases in tropospheric ozone over India and East and Southeast Asia for the
merged 38-year record are sizable. Total changes in GMI and
satellite-measured TCO for the merged record are shown in Fig. 7 where
contour values were determined by adding changes from the individual TOMS and
OMI/MLS records together. There are two regions of greatest increase in TCO
in Fig. 7 for both GMI and the satellite measurements, one coinciding with
the Near East to East Asia (increases of ∼+6 to +7 DU, or about
15 %–20 % average background ozone) and the other being tropical
Africa and the Atlantic (increases of ∼+4 to +5 DU, with about 10 %–15 %
average background ozone). There is also an area of negative net change in
the SH lying between Australia and the maritime continent in Fig. 7 for both
GMI and measurements (shown in blue); these negative variations over the SH
Indian Ocean appear small and are not statistically significant.
The color bar in Fig. 7 also provides conversion from DU to tropospheric
ozone mass surface density in units of metric tons per square kilometer. This
conversion was included primarily to compare our results with the model
simulation of Zhang et al. (2016). The large TCO trends over India and
East and Southeast Asia in Fig. 7 are about +0.13 to +0.15 t km-2
for both GMI and the satellite data. These numbers are comparable to
increases of ∼+0.11 t km-2 for this region as modeled
by Zhang et al. (2016) for the years 1980–2010.
Using the 38-year net changes from the two independent regression analyses of
TOMS+OMI/MLS TCO we can estimate the mass of ozone in the bands
0–30∘ N and 0–30∘ S for the years 1979 and 2016 that
model simulations can compare with. Based on 2016 OMI/MLS TCO fields and
extrapolated backwards linearly in time, mean area-weighted 0–30∘ N
ozone masses are about 75.1 Tg for the year 1979 and 83.1 Tg for the year 2016,
yielding about a 8.0±4.6 (2σ) Tg net increase. For
0–30∘ S, the mean numbers are 73.7 Tg for 1979 and 78.2 Tg for
2016, yielding about a 4.4±4.2 (2σ) Tg net increase. Changes in the tropospheric ozone mass in the 0–30∘ N band
increased by about 10.1 %, and about 5.8 % for 0–30∘ S from
1979 to 2016 from the satellite measurements.
Figure 8 shows TCO time series from the merged satellite measurements for
1979–2016 centered over the two regions of largest increase in Fig. 7 (i.e.,
eastern Asia and equatorial Africa). In Fig. 8a and b TOMS is the solid red
curve and OMI/MLS is the dotted blue curve. For plotting purposes, offsets
were applied to the TOMS data in both panels using 2005 overlap measurements
(see figure and caption). The last 5 years in both panels in Fig. 8 show
that current OMPS TCO (solid black curves) with several years of overlap with
OMI/MLS TCO will be useful to continue the OMI/MLS record which has already
extended past 13 years.
(a) Merged time series of TOMS/OMI/MLS/OMPS TCO for
1979–2016 over East Asia centered at 22.5∘ N and 112.5∘ E
(5∘×5∘ region). The solid red curve is TOMS TCO and
dashed blue curve is OMI/MLS. OMPS TCO (solid black curve) is also
over-plotted with OMI/MLS TCO starting with 2012 for comparison. A constant
adjustment of about -5 DU (using year 2005 coincident overlap data) was
applied to the TOMS measurements for plotting with OMI/MLS. Both OMI/MLS and
OMPS TCO also included offsets of +2 and -2 DU following comparisons
with ozonesonde measurements (see Supplement). The indicated total increase
of 6.2 DU was estimated using a regression best-fit line (black line shown)
to the TOMS/OMI/MLS merged time series and agrees well with the 6–7 DU net
increase for this region in Fig. 7. (b) Similar to (a)
except for central Africa centered at 2.5∘ S, 22.5∘ E and a
TOMS offset of +3 DU. The line-fit increase is slightly smaller than the
4–5 DU in Fig. 7. The estimated mean increases in both panels include
calculated 2σ uncertainties.
Studies suggest that ozone in the lower stratosphere in both hemispheres has
been decreasing over the last 1–2 decades despite the decrease in global CFC
concentrations following the 1987 Montreal Protocol. Ball et al. (2018)
evaluated global ozone trends for 1985–2016 by combining models with
measurements from several satellite instruments. A conjecture as stated by
Ball et al. (2018) is that while ozone in the upper stratosphere above
∼10 hPa appears to be recovering, ozone in the lower stratosphere
appears to be decreasing, which models do not seem to replicate despite the
decrease in CFCs. A main point of Ball et al. (2018) is that total ozone has
not changed because the ongoing stratospheric ozone decrease is opposed by
tropospheric ozone increase. A global decrease in lower stratospheric ozone
of about 2 DU below 32 hPa was detected by Ball et al. (2018) and it
appeared to be compensated largely by opposite increases in tropospheric
ozone. In their study they included OMI/MLS TCO for 2005–2016 (i.e., their
Figs. 4 and S13) and measured a trend in 60∘ S–60∘ N TCO
of about +1.7 DU decade-1, which mostly cancels out the negative trend
in stratospheric ozone. Wargan et al. (2018) in a related paper evaluated
MERRA-2 assimilated ozone for 1998–2016 using an idealized atmospheric
tracer also driven from MERRA-2 meteorological fields. Similar to Ball et
al. (2018), Wargan et al. (2018) also found a net decrease in ozone in the
lower stratosphere (i.e., within a 10 km layer above the tropopause) in both
hemispheres; their trend values were about -1.2 DU decade-1 in the SH
and about -1.7 DU decade-1 in the NH. Wargan et al. (2018) found
evidence that these negative trends over the last two decades have been
driven by enhanced isentropic transport of ozone between the tropical and
extratropical lower stratosphere.
The increases in measured TCO from TOMS and OMI/MLS as indicated in Figs. 1,
3, 4 and 6–8 can have implications for evaluating global ozone trends,
particularly for trends in total column ozone and assessment of the recovery
of stratospheric ozone. One should be careful using total ozone to infer
stratosphere ozone recovery if trends in TCO are not accounted for. The
increases in TCO of +6 to +7 DU in Figs. 7–8 for India and eastern Asia
represent a sizeable change even for total column ozone.
Summary
Studies suggest that ozone in the troposphere has increased globally
throughout much of the 20th century due largely to increases in
anthropogenic emissions. We provide evidence from combined satellite
measurements and a chemical transport model that tropospheric ozone over the
last four decades does indeed indicate increases that are global in nature,
yet highly regional due to the combined effects of regional pollution and
transport.
We have obtained tropospheric ozone trends for 1979–2016 by merging TOMS
(1979–2005) and Aura OMI/MLS (2005–2016) satellite measurements. We
included the MERRA-2 GMI CTM simulation to evaluate and possibly explain the
global trend patterns found for both TOMS and OMI/MLS TCO. Trends were
calculated independently for TOMS and OMI/MLS records using a linear
regression model. Net changes in both measured and modeled TCO for the entire
merged record were estimated by adding net changes for the TOMS and OMI/MLS
time periods together.
A persistent trend pattern emerges with TCO for the GMI simulation and
satellite measurements for both the TOMS and OMI/MLS records. The GMI model,
and also measurements from TOMS and OMI/MLS all independently show large
(positive) trends in TCO in the NH extending from the Near East to India and
East and Southeast Asia, and further eastward over the Pacific Ocean. An important
finding is that the trends in TCO for both the GMI model and satellite
measurements for this region are smaller during the earlier part of the
merged record; that is, the trends for both GMI and satellite measurements
increase from about +1.2 to +1.4 DU decade-1 (1979–2005) to about
+3 DU decade-1 or greater (2005–2016). Analysis of the NO emissions
input to the GMI simulation indicates that the measured trends in
tropospheric ozone in this region, including the escalation of increased
trends during the latter Aura period, are consistent with increases in
pollution in the region.
For the long merged record there are again strong similarities between the
GMI simulation and satellite measurements of TCO. Net changes in tropospheric
ozone for India and East and Southeast Asia for 1979–2016 are about +6 to +7 DU,
or about 0.13–0.15 t km-2 for both the GMI and satellite
TCO. These are pronounced increases in TCO representing ∼15 %–20 %
average TCO background amounts. Both the GMI simulation and satellite
measurements show that of these +6 to +7 DU increases over this broad
area, about half or a slight majority of the change (i.e., ∼+4 DU) occurs
during the Aura time record of 2005–2016. The GMI simulation and satellite
measurements also depict a secondary maximum of TCO increase for 1979–2016
over the tropical Atlantic and African region of about +4 to +5 DU
(∼10 %–15 % average background ozone).
Data availability
Data used for this paper are publically accessible and can be found at http://acdb-ext.gsfc.nasa.gov/Data_services/ (last access: 7 March 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-3257-2019-supplement.
Author contributions
JRZ contributed as lead author to the
production of the satellite tropospheric ozone measurements and analysis.
LDO, SAS, ARD, and MAO were responsible for the production and analyses involving
the MERRA-2 GMI simulation. RDM, PKB, GJL, DPH, NAK, SMF, LKH, GRJ, CJS, MTD
and SLT all contributed to the paper by their involvement in the development
of the satellite total ozone products including their long-term calibration
necessary for ozone trend evaluation. LF contributed by his involvement in
the MLS product development and use in this study. JCW and AMT provided key
contributions to the paper regarding ozonesonde data and analysis.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We thank the NASA Goddard Space Flight Center Ozone Processing Team for the
TOMS and OMI total ozone measurements and the Jet Propulsion Laboratory MLS
team for MLS v4.2 ozone. OMI is a Dutch–Finnish contribution to the Aura
mission. We thank WOUDC and the NDACC for providing extensive ozonesonde
measurements that we used for the comparisons and validation of satellite
tropospheric ozone. We also thank the NASA MAP program for supporting the
MERRA-2 GMI simulation and the NASA Center for Climate Simulation (NCCS) for
providing high-performance computing resources. Special thanks go to Ryan
Stauffer for important discussions regarding the ozonesonde measurements and
the MERRA-2 GMI simulation. More information on the MERRA-2 GMI simulation
and access is available at
https://acd-ext.gsfc.nasa.gov/Projects/GEOSCCM/MERRA2GMI/ (last access: 7 March 2019). Tropospheric ozone data used in this study are
available from the NASA Goddard Space Flight Center at
http://acdb-ext.gsfc.nasa.gov/Data_services/cloud_slice/ (last access: 7 March 2019) and links from the Aura Validation Data Center
(https://avdc.gsfc.nasa.gov/, last access: 7 March 2019). Funding for this research was provided in part by NASA
NNH14ZDA001N-DSCOVR.Edited by: Michel Van
Roozendael Reviewed by: two anonymous referees
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