ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-8037-2015Tropospheric ozone variability in the tropics from ENSO to MJO and
shorter timescalesZiemkeJ. R.jerald.r.ziemke@nasa.govDouglassA. R.https://orcid.org/0000-0002-5556-9988OmanL. D.StrahanS. E.https://orcid.org/0000-0002-7511-4577DuncanB. N.Morgan State University, Baltimore, Maryland, USANASA Goddard Space Flight Center, Greenbelt, Maryland, USAUniversities Space Research Association, Columbia, Maryland, USAJ. R. Ziemke (jerald.r.ziemke@nasa.gov)22July20151514803780498December20145March201522June20158July2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/8037/2015/acp-15-8037-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/8037/2015/acp-15-8037-2015.pdf
Aura OMI and MLS measurements are combined to produce daily maps of
tropospheric ozone beginning October 2004. We show that El Niño-Southern
Oscillation (ENSO) related inter-annual change in tropospheric ozone in the
tropics is small in relation to combined intra-seasonal/Madden–Julian
Oscillation (MJO) and shorter timescale variability by a factor of ∼ 3–10
(largest in the Atlantic). Outgoing longwave radiation (OLR), taken as a
proxy for convection, suggests that convection is a dominant driver of
large-scale variability of tropospheric ozone in the Pacific from
inter-annual (e.g., ENSO) to weekly periods. We compare tropospheric ozone
and OLR satellite observations with two simulations: (1) the Goddard Earth
Observing System (GEOS) chemistry-climate model (CCM) that uses observed sea
surface temperatures and is otherwise free-running, and (2) the NASA Global
Modeling Initiative (GMI) chemical transport model (CTM) that is driven by
Modern Era Retrospective-Analysis for Research and Applications (MERRA)
analyses. It is shown that the CTM-simulated ozone accurately matches
measurements for timescales from ENSO to intra-seasonal/MJO and even
1–2-week periods. The CCM simulation reproduces ENSO variability but not
shorter timescales. These analyses suggest that a model used to delineate
temporal and/or spatial properties of tropospheric ozone and convection in the
tropics must reproduce both ENSO and non-ENSO variability.
Introduction
The El Niño-Southern Oscillation (ENSO) and its effects on the
atmosphere and ocean have been extensively studied and documented.
Trenberth (1997) provides several key references with an overview description and
historical account of ENSO. The terminology, ENSO, is understood to
consist of El Niño (warmer than average ocean temperatures in the
tropical eastern Pacific – i.e., warm phase) typically followed by La
Niña (cooler than average ocean temperatures in the tropical eastern
Pacific – i.e., cool phase). ENSO events have ∼ 2–7-year
timescales and produce planetary-scale changes in tropical sea surface
temperature (SST), convection, and winds. Peak activity for ENSO occurs
generally centered about Northern Hemisphere autumn to mid-winter months
(e.g., largely October–January).
The effects of El Niño on atmospheric composition, including ozone, have
been studied from both satellite and ground-based measurements (e.g.,
Chandra et al., 1998; Fujiwara et al., 1999; Thompson et al., 2001;
Nasser et al., 2009; Lee et al., 2010; Ziemke et al., 2010; Randel and
Thompson, 2011; Neu et al., 2014), global chemical transport models driven
by specified meteorology (e.g., Valks et al., 2003; Duncan et al., 2003;
Chandra et al., 2009; Murray et al., 2013) and general circulation models
(GCMs) (e.g., Sudo and Takahashi, 2001; Zeng and Pyle, 2005; Doherty et
al., 2006; Randel et al., 2009; Oman et al., 2011; Sekiya and Sudo,
2014). Tropospheric ozone is important as both a greenhouse gas and precursor
of the hydroxyl radical (OH), the primary atmospheric oxidant. Tropospheric
ozone in the tropics is especially sensitive to changes in deep convection
associated with ENSO. An increase (decrease) in dynamical convection from
ENSO events in the tropical Pacific induces a decrease (increase) in
tropospheric column ozone. Although changes in convection are fundamentally
dynamical there are also ENSO-related changes in composition that affect
ozone precursors, such as increases in emissions from biomass burning over
Indonesia due to suppressed rainfall during El Niño. There can also be
long-range transport effects on tropospheric ozone at northern mid-latitudes
related to ENSO including induced trends over long records. Lin et
al. (2014) studied the effects of ENSO/Pacific Decadal Oscillation (PDO) on
tropospheric ozone at Mauna Loa Observatory (19.5∘ N,
156.5∘ W, altitude 3.4 km). By combining 40 years of surface ozone
measurements with a set of chemistry-climate model simulations they found
that the flow of ozone-rich air from Eurasia towards Hawaii during spring
weakened in the 2000s as a result of La Niña-like decadal cooling in the
tropical Pacific. This circulation-driven ozone decrease offsets the ozone
increase that otherwise would have occurred at Mauna Loa in spring due to
rising Asian anthropogenic emissions.
Ziemke et al. (2010) produced a monthly tropospheric ozone ENSO index (OEI) over a
multi-decadal time record (beginning 1979) by differencing
satellite-measured column ozone in the tropics between the eastern and
western Pacific. They noted that the OEI could be used as a diagnostic test
for modeled ozone including tropospheric ozone sensitivity relating to
changes in SSTs. Oman et al. (2011) found excellent agreement between the measured OEI
with the OEI produced by the Goddard Earth Observing System (GEOS)
free-running chemistry-climate model (CCM) with observed SSTs over a 25-year
period. This demonstrated an appropriate response of the CCM meteorology to
the ENSO signature of the imposed SSTs; the fidelity of the ozone response
to the induced circulation and photochemical changes included realistic
horizontal and vertical gradients in tropospheric ozone.
The tropical atmosphere and ocean exhibits intra-seasonal and shorter
timescale variability with periods much shorter than ENSO from days or weeks
to several months. The leading source of intra-seasonal variability is
related to the Madden–Julian Oscillation (MJO) with characteristic
timescales of about 1–2 months (Madden and Julian, 1971, 1994). The MJO is
identified as large-scale circulation cells in equatorial latitudes that
propagate eastward from the Indian Ocean to at least the central Pacific. In
the original discovery of the MJO, Madden and Julian (1971) described the
oscillation as a 40–50-day variation in surface pressure, zonal winds and
temperature at different levels of the troposphere. Madden and
Julian (1994) note that zonal wind anomalies in the upper troposphere
associated with the MJO sometimes traverse the entire circumference of the
Earth. The strongest variability for the MJO occurs around northern
wintertime months when the intensity of ENSO events is largest. The MJO
modulates regional monsoon (in particular the Australian and Indian monsoon)
which impacts particulate matter (e.g., Ragsdale et al., 2013) and surface
ozone (e.g., Barrett, et al., 2012), both of which contribute to poor air
quality in the tropics and/or subtropics. The ocean-atmosphere coupling
associated with the MJO may also affect the duration and onset of ENSO (e.g.,
Hoell et al., 2014, and references therein). The MJO alters stratospheric
circulation including the strength of the Northern Hemisphere polar vortex
and timing of stratospheric sudden warming events (e.g., Garfinkel et al.,
2012, 2014, and references therein) and also modulates tropical Kelvin waves
(Guo et al., 2014). Wheeler and Hendon (2004) quantify the MJO using two
leading derived Empirical Orthogonal Functions (EOFs) of combined
tropospheric zonal winds and Outgoing Longwave Radiation (OLR) in the
tropics. Their method quantifies the MJO into eight separate identifiable
temporal phases beginning from onset extending through the full 1–2 month
cycle.
Using a chemical transport model (CTM) and measurements from the Aura
Tropospheric Emission Spectrometer (TES), Sun et al. (2014) indicated that the MJO in
tropospheric ozone in tropical latitudes may be locally up to 47 % of
total variability. Their estimate is comparable to the ∼ 5–10 Dobson units (DU; 1 DU = 2.69 × 1020 molecules m-2) MJO
variability (out of ∼ 15–20 DU background ozone) in
troposphere ozone in the tropical Pacific by Ziemke and Chandra (2003). One of the results of
Sun et al. (2014) was that large-scale advection within the CTM explains most of the
simulated changes in ozone relating to the MJO.
In addition to ENSO and intra-seasonal/MJO changes, Dunkerton and Crum (1995)
showed that there is considerable convective variability in the tropics with
shorter timescales of 2–15 days. Dunkerton and Crum (1995) used daily
outgoing longwave radiation (OLR) in the tropics to relate 2–15-day
disturbances with intra-seasonal oscillations/MJO signals and found
distinction between them as well as moderate interaction between them during
convectively active phases of the intra-seasonal oscillations. A long
existing problem with GCM/CCM simulations is difficult in producing a
realistic MJO in the atmosphere (e.g., Lin et al., 2006; Hung et al.,
2013; Del Genio et al., 2015; and references therein). Efforts have
demonstrated that there is a causal link between how well gross moist
stability and vertical advection is treated in models with how well those
models reproduce a variation similar to the MJO (e.g., Benedict, et al.,
2014, and references therein).
The purpose of our study is to characterize the variability of tropical
tropospheric ozone for timescales ranging from ENSO to MJO and shorter time
periods in relation to tropical convection and atmospheric model simulations
of ozone. We compare observed tropospheric ozone with ozone simulated from
two NASA Goddard models of atmospheric composition, one being a CCM forced
by observed monthly SSTs and the other a CTM driven by meteorological
reanalyses. Section 2 discusses data and models for our analysis while Sect. 3 describes the impact of ENSO- vs. non-ENSO-related changes in
tropospheric ozone in relation with convective forcing. Section 4 describes
derivation of a useful tropospheric ozone diagnostic from OMI/MLS while
Sect. 5 shows some of its applications as applied to model ozone and OLR
measurements. Finally, Sect 6 provides a summary.
Data and models
Daily measurements of tropospheric column ozone (TCO) in tropical latitudes
are calculated using the OMI/MLS residual method of Ziemke et al. (2006). This method
subtracts MLS stratospheric column ozone from OMI total column ozone for
near clear-sky scenes (i.e., radiative cloud fractions < 30 %).
Ziemke et al. (2014) evaluated three other OMI/MLS TCO products and concluded that the
Global Modeling and Assimilation Office (GMAO) data assimilation product was
best to use overall when considering all factors including global coverage
and ozone profile information. However, Fig. 12 of Ziemke et al. (2014) showed that the
assimilation product when limited to tropical latitudes had zonal
variability ∼ 10–15 DU in stratospheric column ozone which was
considerably larger than direct satellite measurements that typically have
zonal variability of only a few DU. In addition, this larger zonal
variability in stratospheric column ozone coincided with a reduced zonal
wave-one pattern of TCO with assimilation, also considered inconsistent with
previous TCO measurements. Our main reason for using the product of Ziemke et al. (2006)
for the tropics stems from being independent of MERRA/GEOS-5 analyses
including winds used by both the assimilation and trajectory ozone products.
There are known errors with tropical stratospheric winds in the analyses
caused by spurious transport (Tan et al., 2004). Although Tan et al. (2004) diagnosed an older
assimilation system, comparisons of MLS ozone and N2O with our CTM
simulations using MERRA meteorology show that spurious transport in the
tropical and subtropical lower stratosphere is still a problem. These errors
in stratospheric winds with assimilation produce errors in the derived ozone
profiles including TCO.
The OMI/MLS residual product combines MLS v3.3 ozone profiles with OMI
version 8.5 total ozone measurements. Data quality and description of the
MLS v3.3 ozone profiles are discussed by Livesey et al. (2011). Description
and access to the OMI data may be obtained from the NASA webpage
http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/OMI. Horizontal
gridding for TCO is 1∘ latitude × 1.25∘ longitude.
The OMI/MLS residual ozone product uses WMO NCEP 2 K km-1 lapse-rate
tropopause pressure to separate tropospheric from stratospheric ozone. Our
study also uses OLR daily measurements for 2004–2012 at
2.5∘× 2.5∘ horizontal gridding obtained from the
National Oceanic and Atmospheric Administration (NOAA) webpage
http://www.esrl.noaa.gov/psd/data/gridded/. OLR is the amount of
radiative flux (units W m-2) re-emitted back to space in the
3.55–3.91 µm wavelength band.
The Global Modeling Initiative (GMI) CTM hindcast simulation includes a
chemical mechanism suitable for the troposphere and stratosphere (Duncan et
al., 2008; Strahan et al., 2007). Although the CTM simulation extends from
1979 through 2012 we include a limited period of October 2004 through
December 2012 to coincide with the OMI/MLS measurements. The emissions of
trace gases and aerosol fields used in the CTM simulations are described by
Duncan et al. (2008), however, anthropogenic emissions have been updated
and include year-specific scaling factors (van Donkelaar et al., 2008).
Anthropogenic and biomass global emissions include surface emissions from
industry/fossil fuel, biomass burning, biofuel combustions, and contributions
from aircraft. Biomass burning emissions in the CTM are from van der Werf et
al. (2010) and are extended through year 2010. Observationally-based biomass
burning emissions are used in the CTM through year 2010 with the 2010
emissions repeated for 2011–2012. Most of the global emissions such as
fossil fuel, biofuel, and biomass burning emissions for the CTM represent
monthly means; however, lightning NOx and biogenic emissions (such as
isoprene) are calculated online within the model and can vary daily. More
detailed description of emissions for this simulation is given by Strode et
al. (2015). The CTM meteorological fields are taken from Modern-Era
Retrospective Analysis for Research and Applications (MERRA) (Rienecker et
al., 2011).
The CCM is described by Oman et al. (2013). This CCM is forced by monthly
SSTs and specified boundary conditions and fluxes of important greenhouse
gases including carbon dioxide, methane, and nitrous oxide. The CCM uses
observed monthly mean SSTs over the 1960–2012 time record (Rayner et al.,
2003). All global emissions for the CCM including biomass burning and
non-biomass burning/anthropogenic were chosen to closely match emissions for
the CTM. Lightning NOx for the CCM varies daily as with the CTM. We
again refer to Strode et al. (2015, and references therein) for
quantitative details regarding emissions. Both the CCM and CTM tropopause
pressure use the WMO lapse-rate definition.
(a) Variability in deseasonalized OMI/MLS daily
tropospheric column ozone (solid curves), GMI CTM (dotted curves), and OLR
(long dashed curves) for ENSO signal, intra-seasonal oscillation (ISO)
signals, and with ENSO signals removed (non-ENSO). ISO curves involved
band-pass filtering the time series for 25–65-day periods. OLR (units
W m-2) was multiplied by a factor of 0.18 for plotting with ozone. The
plotted variability was calculated using amplitude of 2σ to estimate
peak-to-peak change. The time record is 1 October 2004–31 December 2012 and
all original time series were averaged over 20∘ S–20∘ N.
The ENSO signals were extracted using the linear regression T(t)=β× Nino34(t)+ε(t), where T is original time series, t is day index, β is a derived constant, Nino34(t) is the
Niño 3.4 ENSO index, and ε(t) is the residual that
represents the non-ENSO component of the time series. (b) Same as
(a) except that all of the time series were filtered for extreme
ENSO events whereby Nino34(t) > 1.0 or
Nino34(t) < -1.0.
ENSO vs. non-ENSO timescale changes in tropical tropospheric
ozone
Variability of tropospheric ozone from OMI/MLS and the CTM, shown in Fig. 1
for ENSO, non-ENSO, and intra-seasonal oscillation (ISO) timescale changes,
is derived from calculated standard deviation (see figure caption). Also
plotted in Fig. 1 are corresponding calculations for OLR, scaled by a factor
of 0.18 for plotting with ozone. Figure 1 is comprised of two sets of
calculations. Figure 1a corresponds to variability calculated using original
daily time series while Fig. 1b is the same but with all daily time series
filtered to include only extreme ENSO events. For the Niño 3.4 index,
ENSO events as defined by NOAA occur when Niño 3.4 is either greater
than 0.5 (El Niño) or less than -0.5 (La Niña) for five consecutive
months. Extreme ENSO events in Fig. 1b were subjectively chosen here to
correspond with Niño 3.4 index being either greater than 1.0 or less
than -1.0.
ENSO signals (bottom curves) in Fig. 1 were extracted at each grid point from
original deseasonalized ozone and OLR time series using the linear regression
T(t)=β× Nino34(t)+ε(t), where T is original
gridded time series, t is day index, β is a derived constant,
Nino34(t) is the Niño 3.4 ENSO index, and ε(t) is the
residual (i.e., ε(t) is identically the non-ENSO component of
the time series). A daily Nino34(t) time series was generated from the
NOAA monthly record using linear extrapolation. All line curves in Fig. 1
represent 20∘ S–20∘ N averages as function of longitude.
The ISO variability (middle curves) involved band-pass filtering of the
original time series for 25–65-day periods (see Appendix A and Fig. 1
caption).
In Fig. 1a ENSO contributes a relatively small amount to the total daily
variability of tropospheric ozone in the tropics at all longitudes. Figure
1b shows that this is the case even when only extreme ENSO events are
considered, although for extreme events the ENSO variability increases in
the Pacific relative to shorter timescales. In Fig. 1a and b ENSO-related change in tropospheric ozone and OLR (bottom curves) is generally
smaller than either non-ENSO change (top curves) or ISO timescale changes
(middle curves). The CTM reproduces all of the OMI/MLS tropospheric ozone
zonal patterns for all three timescale scenarios. Most of the non-ENSO
related changes involve the intra-seasonal/MJO and shorter time period
changes. These changes are larger than ENSO by a factor of ∼ 3–4 in the Pacific and a factor
of 10 or more in the Atlantic in both Fig. 1a and b.
The daily ozone dipole index (ODI)
We calculate a quantity that we refer to as the ozone dipole index (ODI).
This differs from the monthly OEI used by Ziemke et al. (2010) in that it
is calculated using daily measurements rather than monthly means and does not
include the final 3-month running average that is applied to the OEI. We use
this ODI as a diagnostic test for evaluating OMI/MLS tropospheric ozone with
other atmospheric parameters, including satellite-measured OLR and similar
troposphere ozone derived from models. The ODI is the deseasonalized
difference of western minus eastern Pacific TCO time series each day over the
Aura record. Deseasonalization of time series is explained in Appendix A. The
ODI calculation involves first averaging TCO from OMI/MLS each day in the
tropics over the broad eastern and western Pacific regions (i.e.,
15∘ S–15∘ N, 110–180∘ W and
15∘ S–15∘ N, 70–140∘ E, respectively) followed
by computing the difference of western minus eastern Pacific. As with the
monthly OEI, this differencing removes measurement offsets or drifts with
time that would be common to both Pacific time series. We also calculate a
daily dipole index time series for National Oceanic and Atmospheric
Administration (NOAA) OLR measurements in the exact same manner as
calculation of the ODI for investigating connections between tropospheric
ozone and convection in the Pacific.
Statistical coherence and phase of coherence are calculated between the
measured ODI and the ODIs derived from the CTM and CCM. These statistics
are also calculated between the measured ODI and the OLR daily dipole
series. Coherence, a normalized statistic with values lying between 0.0 and
1.0, provides evaluation of statistical connection between two time series
as an explicit function of frequency. We refer the reader to Appendix A for
details regarding these calculations.
Comparisons between measured and modeled ODI
In Fig. 2a we compare time series of measured ODI (red curve) and CTM ODI
(dotted blue curve). The two time series appear remarkably similar for
timescales varying from low-frequency ENSO to 1–2-month periods (e.g., MJO)
and even shorter. Figure 2b is the same as Fig. 2a but for the CCM instead
of CTM. The CCM in Fig. 2b reproduces ENSO variability and appears to
produce variability at shorter timescales similar to the CTM; however, the
evaluation of the models requires more than just visual inspection of time
series.
We calculate coherence and coherence phase as functions of frequency to
establish a statistical connection between measured and simulated ODIs on
varying timescales. The coherence and coherence phase calculated between the
OMI/MLS and CTM ODIs are shown in Fig. 3a where square of coherence is
shown in the top panel with coherence phase on the bottom. Time periods in
days are printed along the horizontal frequency axes for all panels in Fig. 3.
If a simulated ODI exactly matched that obtained from OMI/MLS then the
squared coherence would be 1.0 and the phase shift would be 0.0 over the
entire frequency spectrum. For the CTM in Fig. 3a, statistical significance
of squared coherence exceeds the 99 % level for values greater than 0.684.
The CTM squared coherence exceeds this value for a broad range of timescales
from ENSO (at far left in panel) to the MJO (30–60 days), down to timescales
as short as 7–14 days. The excellent agreement in Fig. 3a over broad
timescales attests to the realism of the input meteorology and computed
photochemistry within the CTM. Figure 3b shows similar calculations for the
CCM. The squared coherence in Fig. 3b (top) is statistically significant for
ENSO but not shorter timescales. In addition the phase between OMI/MLS and
the CCM in Fig. 3b (bottom) is near zero only for very low-frequency ENSO
variability.
(a) Daily ODI (in DU) for OMI/MLS data (solid red curve)
and CTM (dotted blue curve). The beginning labels “O”, “J05”, “A”, and
“J” on the horizontal time axis in (a) and (b) denote
October, January 2005, April, and July, respectively (similar labels for
subsequent years). The monthly-mean Niño 3.4 ENSO index (thick black
curve; units K and multiplied by 3 for plotting) is included for comparison
with the two ODI time series. The ODI time series is derived by subtracting
the eastern Pacific (15∘ S–15∘ N, 110–180∘ W)
from western Pacific (15∘ S–15∘ N, 70–140∘ E)
deseasonalized tropospheric column ozone. The correlation between the two
daily ODI time series printed in the upper right of this figure is 0.857.
(b) Same as (a) but with the CCM (dotted green curve) in
place of CTM. Calculated standard deviations of the ODI time series from
OMI/MLS, CTM, and CCM are 3.7, 3.9, and 2.6, respectively.
Convection activity is inferred using OLR flux measured from NOAA polar
orbiting satellites (e.g., Chelliah and Arkin, 1992; Liebmann and Smith, 1996). Clouds that are high in the
troposphere have cloud-top temperatures colder than cloud tops lying below.
The colder cloud tops coincide with reduced OLR and therefore low OLR
corresponds to deep convection.
This figure plots calculated coherence and phase of coherence
between OMI/MLS ODI and model (i.e., CTM and CCM) ODI as functions of
frequency (periods in days shown). (a) Top panel: coherence-squared
between OMI/MLS ODI and CTM ODI. Included are confidence levels for
coherence-squared of 95 % (i.e., value of 0.393), 99 % (value of
0.536), and 99.9 % (value of 0.684). Bottom panel: phase of coherence in
degrees. Panel (b) is the same as (a) but for the CCM
instead of CTM (see Appendix A for details of these calculations).
This figure plots ODI and OLR dipole time series in (a)
followed by calculated coherence and phase of coherence between OLR and
OMI/MLS ODI in (b). Panel (a) is similar to Fig. 2a except
with calculated OLR dipole series (blue dashed curve) replacing the CTM ODI.
OLR time series values have been divided by 4 for plotting with ozone.
Panel (b) is similar to Fig. 3a except that the calculated coherence
and coherence phase is between the OMI/MLS ODI and OLR dipole series.
Comparison of the OMI/MLS ODI with the OLR dipole series in Fig. 4 indicates
that convection is the main driver of the ODI from ENSO to MJO and shorter
periods. Aside from convection/advection forcing, the variability of
precursors may also affect the variability of tropospheric ozone on different
timescales; however, chemical timescale vs. dynamical timescale must be
considered. As an example, CO is a precursor of tropospheric ozone with an
average lifetime of ∼ 2 months (e.g., Petrenko et al., 2013).
Conversion of CO to ozone will have a relatively long timescale compared to
daily or weekly variability, but not when compared to intra-seasonal to
inter-annual variability. As a test, we repeated our analyses where all
emissions for the CTM were held constant in time (figures not shown). We
found that the variability of CTM ozone such as that shown in Fig. 1a and the
coherence/phase in Fig. 3a were nearly identical for the constant-emissions
simulation. This suggests that the variability of precursors is not important
overall in affecting tropospheric ozone variability on these timescales and
on planetary scales. However, the variability of precursors on regional
scales can be significant. It was shown by Ziemke et al. (2009) using the
CTM and OMI/MLS ozone that biomass burning over Africa, South America, and
Indonesia can generate 10–25 % and even greater increases of
tropospheric ozone in localized regions within or near the burning. The high
coherence calculated between measured ODI and the OLR dipole series from
inter-annual (i.e., ENSO) to shorter timescales suggests that convection has
a dominant influence in forcing large-scale changes in tropospheric ozone in
the tropical Pacific. The behavior of OLR with ozone in Figs. 1 and 4
indicates further that convection in the MERRA analyses is being well
simulated from ENSO down to weekly timescales.
This figure plots calculated spectral amplitudes (in DU) of ODI
derived from OMI (solid red curve), CTM (dotted blue curve), and CCM (dashed
green curve) as functions of frequency (periods in days shown). Spectral
amplitude is defined as the square root of c(ω)2+s(ω)2‾, where c and s denote Fourier cosine and sine
coefficients, ω is frequency and the over-bar denotes application of
a smoothed spectral estimator (see Appendix A for details of these
calculations).
Figure 5 compares calculated spectral amplitudes of the ODI obtained from
OMI/MLS data (red curve), CTM output (blue dotted curve), and CCM output
(green dashed curve). The spectral amplitudes for OMI/MLS and the CTM in
Fig. 5 are everywhere comparable and the variability shown by peaks and
valleys as functions of frequency are closely identical for periods even
shorter than ∼ 30 days. In comparison, the CCM has
considerably smaller amplitudes at all frequencies and the frequency
variability of spectral amplitudes is not consistent with the observations.
The spectral analysis including the coherence/coherence-phase statistics
moves beyond visual inspection of time series to give a quantitative measure
of model performance.
Power spectra for TCO time series averaged over the Indian Ocean just north
of the Equator are shown in Fig. 6 for OMI/MLS ozone and the CTM and CCM
simulated ozone. This tropical region is where the 1–2-month MJO
signal-to-noise in tropical TCO maximizes for both data and the CTM. MJO
variability in Fig. 5 has well-defined peak amplitudes around 45–50-day
period for both the data and the CTM. However the CCM power spectrum does
not show any consistent MJO or shorter timescale variability and essentially
only generates an ENSO variation at very low frequency.
All three panels show calculated power spectra (in units of
DU2) of daily tropospheric column ozone time series averaged over a
broad region of the tropical Indian Ocean (0–10∘ N,
70–80∘ E) where the MJO signal is statistically significant well
above 95 % for OMI/MLS and the CTM. The top, middle, and bottom panels
are for OMI/MLS data, CTM output, and CCM output, respectively. The power
spectra are plotted vs. frequency with periods in days shown. A power
spectrum is defined by [c(ω)2+s(ω)2‾]/2 where
c and s denote derived Fourier cosine and sine coefficients, ω is
circular frequency and the over-bar denotes application of a smoothed
spectral estimator. Estimated background noise is denoted “BG” with
95 % confidence level shown in each panel (see Appendix A for details
of these calculations).
Summary
We have studied the variability of tropospheric ozone in the tropics from
ENSO to intra-seasonal/MJO and weekly timescales using satellite
measurements and two simulation models. Aura OMI and MLS satellite
measurements are combined to derive daily maps of tropospheric ozone for
October 2004 through 2012. Daily OLR from NOAA for the same time record are
included to relate tropospheric ozone variability to changes in convection.
The two models that we use are (1) the free-running GEOS Chemistry-Climate
Model (CCM) and (2) the Global Modeling Initiative (GMI) chemistry-transport
model (CTM) driven by Modern-Era Retrospective Analysis for Research and
Applications (MERRA) meteorological analyses.
Non-ENSO timescale changes in measured tropospheric ozone and convection in
the tropics are found to be larger than ENSO-related changes by a factor of
about 3–4 in the Pacific and up to a factor of ∼ 10 in the
Atlantic. The non-ENSO variability in tropospheric ozone and convection is
comprised mostly of intra-seasonal/MJO to 1–2-week timescale changes. Time
series analysis including coherence calculations with OLR satellite data
suggests that large-scale variability of tropospheric ozone in the Pacific
from ENSO to weekly timescales is driven largely by convection.
We developed a tropospheric ozone dipole index (ODI) from OMI/MLS
measurements by differencing western minus eastern Pacific tropospheric
column ozone time series. The ODI is demonstrated to be a useful diagnostic
for testing model ozone variability from ENSO down to weekly timescales. The
ODI is derived similarly to the monthly-mean Ozone ENSO Index (OEI) of Ziemke et al. (2010), but instead using daily measurements. The ODI was compared with ODI
calculated from both the CTM and CCM. It is shown that the ODI obtained from
the CTM is highly coherent with the measured ODI for timescales varying from
ENSO to 1–2-month MJO and even shorter weekly time periods. The remarkable
coherent behavior between the CTM ODI and measured ODI attests to the
accuracy of the MERRA analyses and also that the CTM largely combines the
effects of dynamics and photochemistry correctly over this broad range of
timescales.
Our analyses show that the Goddard CTM reproduces ozone observations
exceptionally well over timescales from ENSO down to weekly periods whereas
the Goddard CCM reproduces only ENSO variability. The inability of the CCM
to generate shorter timescales such as an MJO is a known problem with
GCMs/CCMs. Using daily instead of monthly SSTs would likely not improve
performance of the CCM in light of previous studies. Del Genio et al. (2015) suggest that
for these models to generate an MJO they need to have
cloud/moisture-radiative interactions and convection-moisture sensitivity.
Understanding the differences in ozone variability between the CCM and CTM
can help quantify possible missing or inaccurate feedback processes as
future work. An important result we find is that using a model to quantify
temporal and spatial properties of tropospheric ozone in the tropics
requires that the model properly simulate the non-ENSO variability which
includes the MJO and shorter periods.
Estimated precision errors for OMI/MLS TCO including calculated ODI
Estimated root-mean-square (RMS) precision errors for OMI/MLS
1∘× 1.25∘ daily gridded TCO are given by Kar et
al. (2010). Precision values in the extra tropics were shown to be up to
∼ 10 DU or greater while in tropical latitudes values were smaller at
∼ 5 DU. Figure A1 shows daily time series of eastern and western
Pacific OMI/MLS TCO used to calculate the ODI (the two Pacific regions are
defined in the figure caption). The ODI follows by taking the western minus
eastern Pacific TCO each day followed by deseasonalizing this difference time
series (deseasonalization is discussed in Sect. A2 below). The time series
in Fig. A1 appear generally of opposite signature with evidence of some
temporal phase shifts for intra-seasonal and shorter timescales. An El
Niño (La Niña) condition coincides when these two time series have
largest (smallest) separation on inter-annual timescale.
RMS precision errors for the time series in Fig. A1 were obtained by taking
local daily RMS uncertainties at 1∘× 1.25∘ resolution and adjusting these numbers by the spatial averaging invoked. By
taking an upper bound of 10 DU for this number and dividing it by N
(N is the total number of the grid points included in the spatial averaging)
we get an estimate of time series precision. (This precision estimate
represents standard error of the mean.) Dividing by N assumes that
tropospheric ozone measurements detected by OMI are stochastically
independent. For either the western or eastern Pacific region encompassing a
domain of 30∘ latitude × 70∘ longitude there are a total
of 1680 grid points. Largely because of applied cloud filtering (i.e., cloud
fractions < 30 %) the actual average number of grid points is
about 680 (i.e., N=680). This yields 10/680=0.38 DU as an
estimated precision for either eastern or western Pacific time series in
Fig. A1. An estimate of precision for the daily ODI is then 0.382+0.382=0.54 DU assuming stochastic independence between
the two regions.
Spectral analysis
Koopmans (1974) details calculation of coherence and its phase using Fourier
spectral analysis with smoothed spectral estimators. All daily ozone time
series in our study were deseasonalized prior to any Fourier analysis.
Deseasonalization was accomplished by first applying a low-pass filter (with
half-amplitude filter response at 60-day period and zero phase shift at all
periods) to original daily time series; this was followed by averaging
similar days over consecutive years to obtain a 365-day pseudo-climatology
for the annual cycle. This estimated annual climatology was then subtracted
from original daily time series for each consecutive year. Potential leakage
of nearby Fourier cosine and sine coefficients was reduced by applying a
tapered cosine window to deseasonalized time series with 25 % cosine
tapering at each end (e.g., Harris, 1978). For all derived spectra including
cross-spectra for coherence we applied a Daniell seven-point smoothed spectral
estimator. Resulting critical coherence at 95, 99, and 99.9 %
confidence levels is 0.627, 0.732, and 0.827, respectively.
Top curve is daily tropospheric column ozone in Dobson units from
OMI/MLS averaged over the western Pacific (15∘ S–15∘ N,
70–140∘ E). Bottom curve is daily tropospheric column ozone in
Dobson units from OMI/MLS averaged over the eastern Pacific
(15∘ S–15∘ N, 110–180∘ W). The bottom curve for
eastern Pacific ozone was displaced by -10 DU for plotting.
Top: OMI/MLS tropospheric column ozone (in Dobson Units) for the
ENSO regression fit (thick curve) and non-ENSO components (thin curve). Also
shown is the estimated annual cycle (dotted curve) which is offset from its
average value by -20 DU for plotting. The chosen region for these time
series is 10–20∘ S, 115–120∘ E and coincides with largest
ENSO variability. Included in the panel are RMS values for the ENSO,
non-ENSO, and annual cycle time series. ENSO signals were extracted from the
deseasonalized time series using the linear regression T(t)=β× Nino34(t)+ε(t), where T is deseasonalized time
series, t is day index, β is a derived constant, Nino34(t) is
the Niño 3.4 ENSO index, and ε(t) is the residual. Bottom:
same as top panel except for the GMI CTM instead of OMI/MLS. The average
annual cycle value for OMI/MLS TCO (GMI TCO) is 26.0 (31.2) DU; annual cycle
minimum for OMI/MLS and GMI occurs in March–April with maximum in
October–November. Correlation between the GMI and OMI/MLS non-ENSO time
series is 0.703.
Power spectra with estimated 1–2-month signal-to-noise were calculated in the
tropics for OMI/MLS and CTM TCO similar to Ziemke et at. (2007). Figure 6
in the main text shows power spectra with estimated signal-to-noise for both
background and 1–2 month signal for the Indian Ocean region where the MJO
signal for both OMI/MLS and CTM TCO is largest. In Fig. 6, an estimated
background noise power spectrum (i.e., denoted BG) for each time series
was estimated using a first-order autoregressive model T(t)=α×T(t-1)+N(t), where α is a derived constant, t is the day index,
and N(t) is normally distributed random noise with mean of zero. For power
spectra using the seven-point estimator the 95 % critical signal-to-noise
ratio level is 1.69.
ENSO vs. non-ENSO variability
The top panel in Fig. A2 shows OMI/MLS time series for the ENSO component
(thick curve), non-ENSO component (thin curve), and annual cycle (dotted
curve) in the tropical western Pacific. The bottom panel in Fig. A2 is the
same as the top panel but instead for the CTM. The selected region for these
time series is 10–20∘ S, 115–120∘ E which coincides with largest ENSO variability for both
OMI/MLS and CTM TCO. The ENSO variability was extracted using linear
regression (see figure caption). Figure A2 shows that the CTM closely tracks
OMI/MLS measurements for the non-ENSO components. ENSO variability for both
the CTM and OMI/MLS is smaller than non-ENSO (comprised mostly of MJO and
shorter timescales).
Acknowledgements
The authors thank the Aura MLS and OMI instrument and algorithm teams for
the extensive satellite measurements used in this study. We thank the NOAA
Earth System Research Laboratory (ESRL) for producing the OLR daily data
product and the modeling teams involving the NASA CCM and CTM at Goddard
Space Flight Center. We also thank the two anonymous reviewers whose helpful
comments have improved our paper and also Sarah Strode and Steve Steenrod
for discussions regarding the models. OMI is a Dutch–Finnish contribution to
the Aura mission. Funding for this research was provided in part by NASA
NN10ZDA001N-AURA.
Edited by: J. West
ReferencesBarrett, B. S., Fitzmaurice, S. J., and Pritchard, S. R.: Intraseasonal
variability of surface ozone in Santiago, Chile: Modulation by phase of the
Madden–Julian Oscillation (MJO), Atmos. Env., 57, 55–62,
10.1016/j.atmosenv.2012.04.040, 2012.Benedict, J. J., Maloney, E. D., Sobel, A. H., and Frierson, D. M. W.: Gross
moist stability and MJO simulations skill in three full-physics GMSs, J. Geophys. Res., 71,
3327–3349, 10.1175/JAS-D-13-0240.1, 2014.Chandra, S., Ziemke, J. R., Min, W., and Read, W. G.: Effects of 1997–1998 El
Niño on tropospheric ozone and water vapor, Geophys. Res. Lett., 25, 3867–3870,
10.1029/98GL02695, 1998.Chandra, S., Ziemke, J. R., Duncan, B. N., Diehl, T. L., Livesey, N. J., and
Froidevaux, L.: Effects of the 2006 El Niño on tropospheric ozone and
carbon monoxide: implications for dynamics and biomass burning, Atmos. Chem.
Phys., 9, 4239–4249, 10.5194/acp-9-4239-2009, 2009.Chelliah, M. and Arkin, P.: Large-scale interannual variability of monthly
Outgoing Longwave Radiaion anomalies over the global tropics, J. Climate,
5, 371–389,
10.1175/1520-0442(1992)005<0371:LSIVOM>2.0.CO;2, 1992.Del Genio, A. D., Wu, J., Wolf, A. B., Chen, Y., Yao, M.-S., and Kim, D.:
Constraints on cumulus parameterization from simulations of observed MJO
events, J. Climate, in press, 10.1175/JCLI-D-14-00832.1, 2015.Doherty, R. M., Stevenson, D. S., Johnson, C. E., Collins, W. J., and
Sanderson, M. G.: Tropospheric ozone and El Niño–Southern Oscillation:
Influence of atmospheric dynamics, biomass burning emissions, and future
climate change, J. Geophys. Res., 111, D19304, 10.1029/2005JD006849,
2006.Duncan, B. N., Bey, I., Chin, M., Mickley, L. J., Fairlie, T. D., Martin, R.
V., and Matsueda, H.: Indonesian wildfires of 1997: Impact on tropospheric
chemistry, J. Geophys., Res., 108, 4458, 10.1029/2002JD003195, 2003.Duncan, B. N., West, J. J., Yoshida, Y., Fiore, A. M., and Ziemke, J. R.: The
influence of European pollution on ozone in the Near East and northern
Africa, Atmos. Chem. Phys., 8, 2267–2283, 10.5194/acp-8-2267-2008, 2008.Dunkerton, T. J. and Crum, F. X.: Eastward propagating ∼ 2 to 15-day
equatorial convection and its relation to the tropical intraseasonal
oscillation, J. Geophys. Res., 100, 25781–25790, 10.1029/95JD02678,
1995.Fujiwara, M., Kita, K., Kawakami, S., Ogawa, T., Komala, N., Saraspriya, S.,
and Suripto, A.: Tropospheric ozone enhancements during the Indonesian forest
fire events in 1994 and in 1997 as revealed by ground-based observations,
Geophys. Res. Lett., 26, 2417–2420, 10.1029/1999GL900117, 1999.Garfinkel, C. I., Feldstein, S. B., Waugh, D. W., Yoo, C., and Lee, S.:
Observed connection between stratospheric sudden warmings and the
Madden–Julian Oscillation, Geophys. Res. Lett., 39, L18807,
10.1029/2012GL053144, 2012.Garfinkel, C. I., Benedict, J. J., and Maloney, E. D.: Impact of the MJO on
the boreal winter extra-tropical circulation, Geophys. Res. Lett., 41,
6055–6062, 10.1002/2014GL061094, 2014.Guo, Y. J., Jiang, X. A., and Waliser, D. E.: Modulation of the convectively
coupled Kelvin waves over South America and the tropical Atlantic Ocean in
association with the Madden–Julian Oscillation, J. Atmos. Sci., 71,
1371–1388, 10.1175/JAS-D-13-0215.1, 2014.
Harris, F.: On the use of windows for harmonic analysis with the discrete
Fourier transform, Proc. Inst. Electr. Eng., 66, 51–83, 1978.Hoell, A., Barlow, M., Wheeler, M. C., and Funk, C.: Disruptions of El
Niño-Southern Oscillation teleconnections by the Madden–Julian
Oscillation, Geophys. Res. Lett, 41, 998–1004, 10.1002/2013GL058648,
2014.Hung, M. P., Lin, J.-L., Wang, W., Kim, D., Shinoda, T., and Weaver, S. J.:
MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate
Models, J. Climate, 26, 6185–6214, 10.1175/JCLI-D-12-00541.1, 2013.Kar, J., Fishman, J., Creilson, J. K., Richter, A., Ziemke, J., and Chandra,
S.: Are there urban signatures in the tropospheric ozone column products
derived from satellite measurements?, Atmos. Chem. Phys., 10, 5213–5222,
10.5194/acp-10-5213-2010, 2010.
Koopmans, L. H.: The Spectral Analysis of Time Series, 366 pp., Academic
Press, New York, USA, 1974.Lee, S., Shelow, D. M., Thompson, A. M., and Miller, S. K.: QBO and ENSO
variability in temperature and ozone from SHADOZ, 1998–2005, J. Geophys.
Res., 115, D18105, 10.1029/2009JD013320, 2010.
Liebmann, B. and Smith, C. A.: Description of a complete (interpolated)
outgoing longwave radiation dataset, B. Am. Meterol. Soc., 77, 1275–1277,
1996.Lin, J.-L., Kiladis, G. N., Mapes, B. E., Weickmann, K. M., Sperber, K. R.,
Lin, W., Wheeler, M. C., Schubert, S. D., Del Genio, A., Donner, L. J.,
Emori, S., Gueremy, J.-F., Hourdin, F., Rasch, P. J., Roeckner, E., and
Scinocca, J. F.: Tropical Intra-seasonal Variability in 14 IPCC AR4 Climate
Models. Part I: Convective Signals, J. Climate, 19, 2665–2690,
10.1175/JCLI3735.1, 2006.Lin, M., Horowitz, L. W., Oltmans, S. J., Fiore, A. M., and Fan, S.:
Tropospheric ozone trends at Manna Loa Observatory tied to decadal climate
variability, Nat. Geosci., 7, 136–143, 10.1038/NGEO2066, 2014.Livesey, N. J., Read, W. G., Froidevaux, L., Lambert, A., Manney, G. L.,
Pumphrey, H. C., Santee, M. L., Schwartz, M. J., Wang, S., Cofield, R. E.,
Cuddy, D. T., Fuller, R. A., Jarnot, R. F., Jiang, J. H., Knosp, B. W., Stek,
P. C., Wagner, P. A., and Wu, D. L.: EOS MLS Version 3.3 Level 2 data quality
and description document, Tech. rep., Jet Propulsion Laboratory, available
at: http://mls.jpl.nasa.gov/ (last access: 20 July 2015), 2011.Madden, R. A. and Julian, P. R.: Description of the 40–50 day oscillation in
the zonal wind in the tropical Pacific, J. Atmos. Sci., 28, 702–708,
10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2, 1971.Madden, R. A. and Julian, P. R.: Observations of the 40–50 day tropical
oscillation – a review, Mon. Weather Rev., 122, 814–837,
10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2, 1994.Murray, L. T., Logan, J. A., and Jacob, D. J.: Interannual variability in
tropical tropospheric ozone and OH: The role of lightning, J. Geophys. Res.,
118, 11468–11480, 10.1002/jgrd.50857, 2013.Nasser, R., Logan, J. A., Megratskaia, I. A., Murray, L. T., Zhang, L., and
Jones, D. B. A.: Analysis of tropical tropospheric ozone, carbon monoxide,
and water vapor during the 2006 El Niño using TES observations and the
GEOS-Chem model, J. Geophys. Res., 114, D17304, 10.1029/2009JD011760,
2009.Neu, J. L., Flury, T., Manney, G. L., Santee, M. L., and Livesey, N. J.:
Tropospheric ozone variations governed by changes in stratospheric
circulation, Nat. Geosci., 7, 340–344, 10.1038/NGEO2138, 2014.Oman, L. D., Ziemke, J. R., Douglass, A. R., Waugh, D. W., Lang, C.,
Rodriguez, J. M., and Nielsen, J. E.: The response of tropical tropospheric
ozone to ENSO, Geophys. Res. Lett., 38, L13706, 10.1029/2011GL047865,
2011.Oman, L. D., Douglass, A. R., Ziemke, J. R., Rodriguez, J. M., Waugh, D. W.,
and Nielsen, J. E.: The ozone response to ENSO in Aura satellite measurements
and a chemistry climate model, J. Geophys. Res., 118, 965976,
10.1029/2012JD018546, 2013.Petrenko, V. V., Martinerie, P., Novelli, P., Etheridge, D. M., Levin, I.,
Wang, Z., Blunier, T., Chappellaz, J., Kaiser, J., Lang, P., Steele, L. P.,
Hammer, S., Mak, J., Langenfelds, R. L., Schwander, J., Severinghaus, J. P.,
Witrant, E., Petron, G., Battle, M. O., Forster, G., Sturges, W. T.,
Lamarque, J.-F., Steffen, K., and White, J. W. C.: A 60 yr record of
atmospheric carbon monoxide reconstructed from Greenland firn air, Atmos.
Chem. Phys., 13, 7567–7585, 10.5194/acp-13-7567-2013, 2013.Ragsdale, K. M., Barret, B. S., and Testino, A. P.: Variability of
particulate matter (PM10) in Santiago, Chile by phase of the Madden–Julian
Oscillation (MJO), Atmos. Env., 81, 304–310,
10.1016/j.atmosenv.2013.09.011, 2013.Randel, W. J. and Thompson, A. M.: Inter-annual variability and trends in
tropical ozone derived from SAGE II satellite data and SHADOZ ozonesondes,
J. Geophys. Res., 116, D07303, 10.1029/2010JD015195, 2011.Randel, W. J., Garcia, R. R., Calvo, N., and Marsh, D.: ENSO influence on
zonal mean temperature and ozone in the tropical lower stratosphere,
Geophys. Res. Lett., 36, L15822, 10.1029/2009GL039343, 2009.Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res., 108, 4407, 10.1029/2002JD002670,
2003.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA – NASA's Modern-Era Retrospective Analysis for
Research and Applications, J. Climate, 24, 3624–3648,
10.1175/JCLI-D-11-00015.1, 2011.Sekiya, T. and Sudo, K.: Roles of transport and chemistry processes in global
ozone change on interannual and multidecadal time scales, J. Geophys.
Res.-Atmos., 119, 4903–4921, 10.1002/2013JD020838, 2014.
Strahan, S. E., Duncan, B. N., and Hoor, P.: Observationally derived
transport diagnostics for the lowermost stratosphere and their application to
the GMI chemistry and transport model, Atmos. Chem. Phys., 7, 2435–2445,
doi:10.5194/acp-7-2435-2007, 2007.
Strode, S. A., Rodriguez, J. M., Logan, J. A., Cooper, O. R., Witte, J. C.,
Lamsal, L. N., Damon, M., Steenrod, S. D., and Strahan, S. E.: Trends and
Variability in Regional Surface Ozone over the United States, J. Geophys.
Res., in review, 2015.Sudo, K. and Takahashi, M.: Simulation of tropospheric ozone changes during
1997–1998 El Niño: Meteorological impact on tropospheric photochemistry,
Geophys. Res. Lett., 28, 4091–4094, 10.1029/2001GL013335, 2001.Sun, W., Hess, P., and Tian, B.: The response of the equatorial tropospheric
ozone to the Madden–Julian Oscillation in TES satellite observations and
CAM-chem model simulation, Atmos. Chem. Phys., 14, 11775–11790,
10.5194/acp-14-11775-2014, 2014.Tan, W. W., Geller, M. A., Pawson, S., and da Silva, A.: A case study of
excessive subtropical transport in the stratosphere of a data assimilation
system, J. Geophys. Res., 109, D11102, 10.1029/2003JD004057, 2004.Thompson, A. M., Witte, J. C., Hudson, R. D., Guo, H., Herman, J. R., and
Fujiwara, M.: Tropical tropospheric ozone and biomass burning, Science,
291, 5511, 2128–2132, 10.1126/science.291.5511.2128, 2001.Trenberth, K. E.: The definition of El Niño, B. Am. Meteorol. Soc., 78,
2771–2777,
10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2,
1997.Valks, P. J. M., Koelemeijer, R. B. A., van Weele, M., van Velthoven, P.,
Fortuin, J. P. F., and Kelder, H.: Variability in tropical tropospheric
ozone: Analysis with Global Ozone Monitoring Experiment observations and a
global model, J. Geophys., Res., 108, 4328, 10.1029/2002JD002894, 2003.van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M.,
Kasibhatla, P. S., Morton, D. C., DeFries, R. S., Jin, Y., and van Leeuwen,
T. T.: Global fire emissions and the contribution of deforestation, savanna,
forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10,
11707–11735, 10.5194/acp-10-11707-2010, 2010.van Donkelaar, A., Martin, R. V., Leaitch, W. R., Macdonald, A. M., Walker,
T. W., Streets, D. G., Zhang, Q., Dunlea, E. J., Jimenez, J. L., Dibb, J. E.,
Huey, L. G., Weber, R., and Andreae, M. O.: Analysis of aircraft and
satellite measurements from the Intercontinental Chemical Transport
Experiment (INTEX-B) to quantify long-range transport of East Asian sulfur to
Canada, Atmos. Chem. Phys., 8, 2999–3014, 10.5194/acp-8-2999-2008, 2008.Wheeler, M. C. and Hendon, H. H.: An All-Season Real-Time Multivariate MJO
Index: Development of an Index for Monitoring and Prediction, Mon. Weather
Rev., 132, 1917–1932,
10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2, 2004.Zeng, G. and Pyle, J. A.: Influence of El Niño Southern Oscillation on
stratosphere/troposphere exchange and the global tropospheric ozone budget,
Geophys. Res. Lett., 32, L01814, 10.1029/2004GL021353, 2005.Ziemke, J. R. and Chandra, S.: A Madden–Julian Oscillation in tropospheric
ozone, Geophys. Res. Lett., 30, 2182, 10.1029/2003GL018523, 2003.Ziemke, J. R., Chandra, S., Duncan, B. N., Froidevaux, L., Bhartia, P. K.,
Levelt, P. F., and Waters, J. W.: Tropospheric ozone determined from Aura OMI
and MLS: Evaluation of measurements and comparison with the Global Modeling
Initiative's Chemical Transport Model, J. Geophys. Res., 111, D19303,
10.1029/2006JD007089, 2006.Ziemke, J. R., Chandra, S., Schoeberl, M. R., Froidevaux, L., Read, W. G.,
Levelt, P. F., and Bhartia, P. K.: Intra-seasonal variability in tropospheric
ozone and water vapor in the tropics, Geophys. Res. Lett., 34, L17804,
10.1029/2007GL030965, 2007.
Ziemke, J. R., Chandra, S., Duncan, B. N., Schoeberl, M. R., Damon, M. R.,
Torres, O., and Bhartia, P. K.: Recent biomass burning events in the tropics
and elevated concentrations of tropospheric ozone, Geophys. Res. Lett., 36,
L15819, 10.1029/2009GL039303, 2009.Ziemke, J. R., Chandra, S., Oman, L. D., and Bhartia, P. K.: A new ENSO index
derived from satellite measurements of column ozone, Atmos. Chem. Phys., 10,
3711–3721, 10.5194/acp-10-3711-2010, 2010.Ziemke, J. R., Olsen, M. A., Witte, J. C., Douglass, A. R., Strahan, S. E.,
Wargan, K., Liu, X., Schoeberl, M. R., Yang, K., Kaplan, T. B., Pawson, S.,
Duncan, B. N., Newman, P. A., Bhartia, P. K., and Heney, M. K.: Assessment
and applications of NASA ozone data products derived from Aura OMI/MLS
satellite measurements in context of the GMI Chemical Transport Model, J.
Geophys. Res.-Atmos., 119, 5671–5699, 10.1002/2013JD020914, 2014.