The interannual variability of the greenhouse gases methane
(CH4) and tropospheric ozone (O3) is largely driven by natural variations in global emissions and meteorology. The El Niño–Southern
Oscillation (ENSO) is known to influence fire occurrence, wetland emission
and atmospheric circulation, affecting sources and sinks of CH4 and
tropospheric O3, but there are still important uncertainties associated
with the exact mechanism and magnitude of this effect. Here we use a
modelling approach to investigate how fires and meteorology control the
interannual variability of global carbon monoxide (CO), CH4 and O3
concentrations, particularly during large El Niño events. Using a
three-dimensional chemical transport model (TOMCAT) coupled to a
sophisticated aerosol microphysics scheme (GLOMAP) we simulate changes to
CO, hydroxyl radical (OH) and O3 for the period 1997–2014. We then use
an offline radiative transfer model to quantify the climate impact of
changes to atmospheric composition as a result of specific drivers.
During the El Niño event of 1997–1998, there were increased emissions
from biomass burning globally, causing global CO concentrations to increase
by more than 40 %. This resulted in decreased global mass-weighted
tropospheric OH concentrations of up to 9 % and a consequent 4 %
increase in the CH4 atmospheric lifetime. The change in CH4
lifetime led to a 7.5 ppb yr-1 increase in the global mean CH4
growth rate in 1998. Therefore, biomass burning emission of CO could account
for 72 % of the total effect of fire emissions on CH4 growth rate in
1998.
Our simulations indicate that variations in fire emissions and meteorology
associated with El Niño have opposing impacts on tropospheric O3
burden. El Niño-related changes in atmospheric transport and humidity
decrease global tropospheric O3 concentrations leading to a -0.03 W m-2 change in the O3 radiative effect (RE). However, enhanced fire emission of precursors such as nitrogen oxides (NOx) and CO
increase O3 and lead to an O3 RE of 0.03 W m-2. While globally
the two mechanisms nearly cancel out, causing only a small change in global
mean O3 RE, the regional changes are large – up to -0.33 W m-2 with potentially important consequences for atmospheric heating and
dynamics.
Introduction
In terms of radiative forcing, methane (CH4) is the second most
important anthropogenically emitted greenhouse gas after CO2 (Myhre et al., 2013). Concentrations
of CH4 have risen from approximately 722 ppb in 1750 to over 1850 ppb
in 2018, an increase of more than 150 % (Dlugokencky, 2019).
During this time period, CH4 has contributed an estimated radiative
forcing (RF) of 0.48±0.05 W m-2, around 20 % of the total
direct anthropogenic RF from greenhouse gases (Myhre et al., 2013).
Furthermore, CH4 is a precursor of tropospheric ozone (O3), which
is also a greenhouse gas responsible for a RF of 0.4±0.2 W m-2
since the pre-industrial era (Myhre et
al., 2013), as well as a harmful pollutant that damages human health
(Anenberg et al., 2010) and ecosystems (Sitch
et al., 2007). While anthropogenic emissions have driven the long-term
increase in CH4 concentrations, CH4 is also emitted from a range
of natural sources, leading to strong interannual variability (IAV) (Bousquet et al., 2006; Dlugokencky et al., 2011; Nisbet et al., 2016).
Understanding the mechanisms driving IAV is important for accurate
predictions of future CH4 concentrations, especially in the context of anthropogenic emission reductions.
Previous studies indicate that although anthropogenic sources may contribute
to seasonal variations in atmospheric CH4, natural sources are the
primary drivers of IAV (Bousquet et al., 2006; Meng et al., 2015).
Emissions from natural wetlands have been shown to be the dominant process,
with emissions from fires and changes to the atmospheric sink also playing
important roles (Bousquet et al., 2006; Chen and Prinn, 2006; Dlugokencky
et al., 2011; Kirschke et al., 2013; McNorton et al., 2016a, 2018; Corbett et al., 2017). These natural sources are climate sensitive,
so interannual changes to temperature and precipitation affect the amount of
CH4 emitted into the atmosphere, as well as the spatial distribution
(Zhu et al., 2017). A number of studies have found that biomass
burning emissions are largely responsible for the IAV of carbon monoxide
(CO) and also affect O3 concentrations (Granier et al., 2000;
Monks et al., 2012; Voulgarakis et al., 2015); however, Szopa et
al. (2007) suggested that meteorology is a more important driver of IAV for
CO, explaining 50 %–90 % of IAV.
A major driver of climatic IAV is the El Niño–Southern Oscillation
(ENSO) – a mode of climate variability originating in the Pacific Ocean
with alternating warm (El Niño) and cold (La Niña) modes
(McPhaden et al., 2006). Positive-phase El Niño events lead
to warmer and drier conditions in much of the tropics, disrupting global
circulation patterns and leading to widespread changes in fire occurrence,
wetland emissions and atmospheric transport (Feely et al., 1987; Jones et
al., 2001; McPhaden et al., 2006). These influences occur most strongly in
the tropics but have global consequences (Jones et al., 2001). Global CH4 concentrations have been observed to increase
significantly during El Niño events, with an especially strong signal
during the 1997–1998 event when the CH4 growth rate was 12 ppb yr-1, almost triple the 1750–2018 mean annual growth rate (Rigby et
al., 2008; Hodson et al., 2011). Due to the wide-ranging effects of El
Niño and varied sources of CH4, there are multiple factors which
could trigger the increase in CH4 growth rate. Chen and Prinn
(2006) attributed the increase to anomalies in global wetland emissions;
however, Zhu et al. (2017) estimated that although 49 % of the
interannual variation in wetland emissions can be explained by ENSO, wetland
emissions were significantly lower during El Niño, including the
1997–1998 event. Conversely, Schaefer et al. (2018) estimated that ENSO is responsible for up to 35 % of global CH4 variability, but the effect of wetland and biomass burning emission changes are
dwarfed by processes affecting the OH sink. Bousquet et al. (2006) suggested that
the increased CH4 growth rate during the 1997–1998 El Niño was
primarily caused by abnormally large peat fires in Indonesia emitting huge
amounts of CH4 while wetlands emissions remained stable (van der
Werf et al., 2004; Butler et al., 2005; Bousquet et al., 2006).
In addition to direct emissions of CH4 from fires, it has been proposed
that anomalously large CO emissions during enhanced El Niño fire events
could explain the changes to CH4 growth rate (Butler et al., 2005;
Bousquet et al., 2006). CO is emitted from biomass burning in much larger
quantities than CH4 (∼20× larger) and its
reaction with the hydroxyl radical (OH) is its primary atmospheric sink (Voulgarakis and Field, 2015). Abnormal increases in CO
concentrations may suppress the availability of OH, thereby extending
CH4 lifetime and increasing its growth rate during and following large
fire events (Butler et al., 2005; Manning et al., 2005). The reaction of
CH4 with OH is the largest term in the global CH4 budget,
accounting for ∼90 % of its sink (McNorton et
al., 2016a); therefore, even minor changes to OH caused by the presence of
other compounds or changes to atmospheric transport and photolysis rates
could have a large impact on CH4 growth rate (Dlugokencky et al., 2011). Butler et al. (2005) found that CO emissions suppressed OH concentrations by 2.2 % in 1997–1998, which accounted for 75 % of the observed change in CH4 concentration. Bousquet et al. (2006)
also reported a weakened OH sink during this El Niño event.
Here we use a modelling approach to investigate how El Niño events
affect global CH4, CO and tropospheric O3 concentrations through
changes to fire occurrence and atmospheric transport. Using long-term
simulations spanning multiple El Niño and La Niña events, we
quantify the relative influence of changes to fire emissions and dynamical
transport. We also differentiate between the effect of direct CH4
emissions from fires and the indirect effect via CO emissions and
atmospheric chemistry changes.
Models and simulationsModel description
For this study we use the three-dimensional chemical transport model (TOMCAT) (Chipperfield, 2006) coupled to the GLOMAP global aerosol
microphysics scheme (Mann et al., 2010). The version of
TOMCAT-GLOMAP used here is a further development of that described by
Monks et al. (2017). Cloud fields are now provided from the
European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (Dee et al., 2011), replacing the climatological clouds fields used previously from the
International Satellite Cloud Climatology Project (ISCCP) (Rossow and Schiffer, 1999), leading to improved
representation of photolysis. Other developments include updated emission
inventories, the inclusion of CERN Cosmic Leaving Outdoor Droplets (CLOUD)-based new particle formation and
the introduction of Mårtensson sea spray emissions (Gordon et al., 2017;
Monks et al., 2017). The model is run at 2.8∘×2.8∘ horizontal resolution with 31 vertical levels from the
surface to 10 hPa, driven by 6-hourly ECMWF ERA-Interim reanalyses. The
planetary boundary layer (PBL) scheme is based on Holtslag and Boville (1993) and sea surface temperatures are from ECMWF reanalyses.
ECMWF ERA-Interim reanalyses have been shown to have good skill in capturing
Madden–Julian Oscillation (MJO) events, which in turn impact the onset of
ENSO events (Dee et al., 2011), giving confidence that the model competently simulates El Niño meteorological conditions.
The tropospheric chemistry scheme used is as described in Monks et
al. (2017), with anthropogenic emissions from the Monitoring Atmospheric
Composition and Climate (MACCity) emissions inventories (Lamarque et al., 2010).
Annually varying emission inventories are included for all fire-emitted
gas-species and aerosol emissions, such as black carbon (BC). The Global Fire
Emissions Database (GFED) used by TOMCAT-GLOMAP has been updated to version
4, with CO, nitrogen oxides (NOx) and volatile organic compound (VOC) emissions from fires (Randerson et al., 2017; Reddington et al., 2018). Monthly varying biogenic VOC emissions are from the MEGAN-MACC emissions
inventory for reference year 2000, calculated from the Model of Emissions of
Gases and Aerosols from Nature (MEGAN version 2)
(Sindelarova et al., 2014). The CH4 inventory
was produced by McNorton et al. (2016b), with wetland
emissions derived from the Joint UK Land Environment Simulator (JULES) and
biomass burning emissions from GFEDv4 (Randerson et al., 2017). These are then combined with anthropogenic emissions from EDGAR version 3.2,
paddy field emissions from Yan et al. (2009) and termite, wild
animal, mud volcano, hydrate and ocean emissions from Matthews and
Fung (1987) (McNorton et al., 2016b). The global mean
surface CH4 mixing ratio is scaled in TOMCAT-GLOMAP to a best estimate
based on observed global surface mean concentration (McNorton et al., 2016a; Dlugokencky, 2019).
Radiative transfer model
Radiative effects of O3 changes are calculated using the O3
radiative kernel approach derived by Rap et al. (2015)
using an offline version of the Edwards and Slingo (1996) radiative transfer
model. This considers six bands in the shortwave (SW), nine bands in the
longwave (LW) and uses a delta-Eddington two-stream scattering solver at all
wavelengths (Rap et al., 2015). This version has been
used extensively in conjunction with TOMCAT-GLOMAP for calculating radiative
forcing from simulated distributions of several short-lived climate
pollutants (SLCPs) including BC, O3 and CH4 (Spracklen et al., 2011; Riese et al., 2012; Rap et al., 2013, 2015; Richards et al., 2013).
Simulations
All simulations are performed for 1997–2014 with a 4-year spin-up through
1993–1996. The control run (CTRL) allows all emissions and meteorology to
vary throughout the modelled period. GFED biomass burning emission
inventories began in 1997; therefore, the 1993–1996 spin-up simulation uses
repeating 1999 emissions instead, as the closest year of “average”
emissions, having excluded 1997 and 1998 due to the exceptionally high
emissions in those years (Schultz et al., 2008).
To test the impact of El Niño events on atmospheric chemistry, we also
performed three perturbed simulations (Table 1). Where model simulations used
“fixed” parameters in Table 1, the year 2013 emissions or meteorology are
specified as invariant throughout the simulation. This year is chosen as the
ENSO-neutral case, due to it being the least active ENSO year during
1997–2014, with a maximum bimonthly multivariate ENSO index (MEI) magnitude
of -0.4 and the only year without a single MEI value that could be
considered an active El Niño or La Niña (Wolter and Timlin, 1993,
1998). Throughout this study, an El Niño event was taken to be ongoing
if the MEI was greater than +1.0. We perform a factorial analysis based on
perturbed simulations in which we fix global biomass burning emissions
(FIREFIX) or global meteorology (METFIX) to the “ENSO-neutral” case. An
additional perturbed simulation was performed in order to examine the
secondary impact of CO on CH4 via oxidation changes, where only CO
emissions from biomass burning were fixed (COFIX).
Details of TOMCAT model simulations. All simulations are run for 1997–2014.
SimulationMeteorologyCO biomassAll other biomassnameburningburningemissionsemissionsCTRLVaryingVaryingVaryingMETFIXFixedVaryingVaryingFIREFIXVaryingFixedFixedCOFIXVaryingFixedVaryingModel evaluation
We have conducted a comprehensive evaluation of the coupled TOMCAT-GLOMAP
model using aircraft observations and data from ozone-sondes and
satellites. In general, the model is able to capture absolute
concentrations, global distribution and seasonal variations in major species
including O3, CO and CH4. MOPITT satellite retrievals have been
used to evaluate CO at 800 and 500 hPa (Emmons et al., 2004) and are
shown in Figs. S1 and S2 in the Supplement, respectively, along with a description of the
satellite product and the averaging kernels applied to the model output.
TOMCAT performs similarly here, as in Monks et al. (2017),
underestimating CO concentrations in the Northern Hemisphere (NH) while
overestimating peak concentrations in biomass burning regions, with a
maximum difference of ∼75 ppb (Figs. S1 and S2). However,
TOMCAT is able to reproduce seasonal variations in CO and locates peak CO
accurately over East Asia and Central Africa.
Simulated O3 concentrations from TOMCAT were also compared with
satellite observations of lower tropospheric (0–6 km) O3 from the Ozone
Monitoring Instrument (OMI). These data were provided by the Rutherford
Appleton Laboratory (RAL; data version fv0214) using an optimal estimation
retrieval scheme, which resolves O3 in the 0–6 km layer by exploiting
information in the Hartley and Huggins UV bands. The scheme derives from
the one discussed by Miles et al. (2015) for another UV
sounder GOME-2. TOMCAT representation of O3 concentrations between 0 and 6 km in NH winter are slightly improved on the Monks et al. (2017)
version, particularly in tropical and Southern Hemisphere (SH) concentrations (Fig. S3). However, there remains a general low bias in
global O3 of up to 10 Dobson Units (DU) in winter in regions such as
the southern Atlantic Ocean.
TOMCAT O3 has also been evaluated using sonde observations (Figs. 1 and S4) (Tilmes et al., 2012), with the model
generally representing the vertical profiles, seasonal variation and
absolute concentrations of O3 very well, with a normalized mean bias
(NMB) of 1.1 % across all sites at 700–1000 hPa and 2.1 % at 300–700 hPa. The model capably simulates the seasonality of tropospheric O3
(Fig. 1), with a maximum seasonal bias of 6.3 % at 300–700 hPa in
March–May. There is no apparent regional or latitudinal bias, although
simulated concentrations are overestimated in India (Fig. S4). In addition,
the TOMCAT-simulated global tropospheric burden of O3 in 2000 is 342 Tg,
which falls within the range of published values (Table 2).
Comparison of seasonal mean
simulated O3 concentrations (ppb) against mean ozone-sonde observations from Tilmes et al. (2012), for the period 1995–2011. Panels (a–d) show mean concentrations at 700–1000 hPa across all sites, while panels (e–h) show mean concentrations at 300–700 hPa. Values in each panel are seasonal means in (from left to right) December–February (DJF), March–May (MAM), June–August (JJA) and September–November (SON). The red line represents the linear regression. Normalized mean bias (NMB) values between the model and observations are also shown.
Present day (2000) TOMCAT model diagnostics compared to
previous model version from Monks et al. (2017) and other published values.
DiagnosticTOMCATMonks etOtherReference(this study)al. (2017)estimatesO3 burden (Tg)342331337±23Young et al. (2013)Tropospheric OH concentration1.041.080.94–1.06Prinn et al. (2001); Krol and Lelieveld (2003);(×106 molec. cm-3)Bousquet et al. (2005); Wang et al. (2008)CH4 lifetime (years)8.07.99.3±0.9Voulgarakis et al. (2013)
We have also assessed the capability of TOMCAT-GLOMAP to simulate observed
responses to El Niño events. Ziemke et al. (2010)
derived an O3 ENSO index using satellite observations, finding that for
a +1 K change in the Niño 3.4 index, there was a 2.4 DU increase in the
Ozone ENSO Index (OEI). In TOMCAT-GLOMAP, we calculate a 2.8 DU increase per +1 K in the Niño 3.4, indicating a slightly larger but comparable response to El Niño
events. The regional response of tropospheric O3 to El Niño was
evaluated against an analysis using various observations and a
chemistry–climate model in Zhang et al. (2015). That study
observed increased total O3 column in the North Pacific, southern USA,
northeastern Africa and East Asia, with decreases over central Europe and
the North Atlantic. All of these observed responses were present in
TOMCAT-GLOMAP simulations, except with a slight increase in tropospheric ozone column (TOC) in central
Europe and a simulated decrease in Western Europe and the eastern Atlantic (Fig. S5).
Aircraft observations
We compare annual mean simulated gas-phase species for 1999 against a
climatological dataset of aircraft observations from 16 campaigns conducted
with a broad spatial and temporal range from 1992 to 2001 (Emmons et
al., 2010). While the comparison of observational data from intermittent
aircraft campaigns does not offer a perfect comparison with the model
simulated long-term mean concentrations, it allows evaluation of broad
characteristics of a number of species over vertical profiles in many global
regions. Figure 2 shows the comparison of simulated annual mean global
concentrations of CO, CH4 and Peroxyacetyl nitrate (PAN), with aircraft observations at 0–2, 2–6 and 6–10 km. We have also calculated the normalized mean bias
between the model and observations (Fig. S6). Full details of the aircraft
measurement campaigns used can be found in Table S1 in the Supplement.
Global mean volume mixing ratios of CO (ppb), CH4 (ppb) and PAN (ppt) from TOMCAT for the period 1993–2001 at 0–2 km (left panels), 2–6 km (middle panels) and 6–10 km (right
panels). The filled circles show mean values from aircraft observation
campaigns that took place between 1992 and 2001 (Table S1) (Emmons et al., 2010).
The model captures broad characteristics of spatial distribution for all
species, simulating higher concentrations in polluted urban or biomass
burning regions, with lower concentrations over ocean and in the SH. CO
concentrations decrease with altitude but the largest values still occur
around urban areas and burning regions, which can be seen in both model and
aircraft concentrations. Consistent with the comparison with MOPITT
satellite retrievals (Figs. S1 and S2), the model underestimates CO
concentrations particularly near the surface, with a NMB of -11.1 %,
-9.93 % and -0.25 % at 0–2, 2–6 and 6–10 km, respectively.
Absolute concentrations of CH4 in TOMCAT simulations match aircraft
data very well, although given the global mean surface concentration scaling,
we expect the magnitude of CH4 to be well simulated. The latitudinal
and vertical distributions are also well captured, giving confidence in the
model transport and OH simulation. Aircraft observations show CH4 also
decreases with altitude and the hemispheric disparity becomes more
pronounced, with higher concentrations in the NH. For PAN concentrations,
the simulated spatial distribution is broadly well captured, as is the
increased concentration with altitude. There is a general low bias in
absolute concentrations near the surface (NMB =-12.3 %), with a better
comparison at 2–6 km (NMB =1.68 %) and overestimation at 6–10 km
(NMB =18.17 %).
OH evaluation
Due to its very short lifetime, it is challenging to evaluate
model-simulated OH over representative spatial and temporal scales. Here we
follow the evaluation methodology recommended by Lawrence et al. (2001)
of dividing tropospheric OH into 12 subdomains, from the surface to a
climatologically derived tropopause. This method was also used to evaluate a
previous version of TOMCAT(version 1.76) by Monks et al. (2017),
allowing direct comparison. The evaluation is performed for the year 2000.
Figure 3 shows our simulated OH compared to Monks et al. (2017),
the ACCMIP model mean (Naik et al., 2013) and the Spivakovsky et al. (2000) OH dataset estimated from methyl chloroform observations.
Annual zonal mean hydroxyl radical (OH) concentrations
(×106 molec. cm-3) divided into 12 subdomains, as recommended by Lawrence et al. (2001). The simulated OH from this study is compared to a dataset estimated from methyl chloroform observations (Spivakovsky et al., 2000) and the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) multi-model mean (Naik et
al., 2013). Results from a previous version of TOMCAT from Monks et al. (2017) are also shown. A climatological tropopause, indicated by the
smooth black line near the top of each panel, has been used to remove
stratospheric OH.
The models and observationally constrained distribution broadly agree in
terms of the latitudinal spread of OH concentrations with a minimum in the
SH and a maximum in the tropics; however, there is disagreement over the
exact altitude of the maximum OH concentrations. In both versions of TOMCAT
the highest concentration is between the surface and 750 hPa, while ACCMIP
and Spivakovsky et al. (2000) find peak OH in the upper and mid-level
troposphere, respectively. The updated cloud fields used in the current
TOMCAT-GLOMAP version have slightly increased OH concentrations in the
mid-level and upper domains compared to Monks et al. (2017) but
concentrations remain significantly higher in the NH and surface domains
than in other studies. In addition, our simulated NH:SH ratio of 1.48 in the
current TOMCAT version remains substantially higher than in the ACCMIP
models (1.28±0.1), indicating that TOMCAT photolysis rates and OH
production in the NH are larger.
The total global tropospheric average OH in this version of TOMCAT is
1.04×106 molec. cm-3, a decrease from Monks et al. (2017) and within the range of other published values (Table 2). This
is primarily due an updated treatment of clouds, in which climatological
cloud fields have been replaced with cloud fraction from ECMWF reanalyses
data, affecting photolysis rates. The tropospheric O3 burden of 342 Tg has increased relative to Monks et al. (2017) (331 Tg) and is within the range found in Wild (2007) (335±10 Tg) and ACCMIP
models (337±23 Tg) (Young et al., 2013). Due to the simplified treatment of CH4, the scaling applied and
its relatively long atmospheric lifetime, the total atmospheric lifetime
cannot be determined from TOMCAT simulations. Instead a chemical lifetime
due to reaction with OH is calculated from CH4 and OH burdens,
disregarding stratospheric sinks and soil sinks (Fuglestvedt et al., 1999;
Berntsen et al., 2005; Voulgarakis et al., 2013). The lifetime diagnosed
from TOMCAT is 8.0 years, compared to the multi-model mean and range of 9.3±0.9 years from Voulgarakis et al. (2013). The shorter lifetime in TOMCAT is due to the overestimation of OH at
the surface, particularly in the NH where CH4 concentrations are
highest due to anthropogenic emissions.
Results and discussionImpact of meteorology and fire emissions on trace gas interannual
variability
First we examine the mechanisms controlling interannual variability of
simulated tropospheric CO, O3 and mean OH. We use the difference
between the control (CTRL) and the perturbed simulations with fixed fires
(FIREFIX) and fixed meteorology (METFIX) to determine the driving cause of
IAV. Of particular interest is the effect of the 1997–1998 El Niño event
(henceforth referred to as 1997 El Niño) and how the prevailing
mechanisms controlling IAV change during such events. To define El Niño
events, we use the bimonthly multivariate ENSO index, which is calculated
from six observed variables and standardized to accurately monitor ENSO
occurrence (Wolter and Timlin, 1998, 2011).
Previous studies examining the dominant factor controlling global CO IAV
have found contrasting results. Szopa et al. (2007) suggested
that meteorology was the main driver, accounting for 50 %–90 % of IAV in the
tropics. Conversely, a study by Monks et al. (2012)
considered CO IAV in the Arctic, finding that biomass burning was the
dominant driver with a strong correlation to El Niño. Voulgarakis et al. (2015) also suggested that biomass burning was
the more important driver of IAV with only a small effect from meteorology.
Some of these differences in results can be explained by Szopa
et al. (2007) considering only surface CO, rather than the whole troposphere
as in Voulgarakis et al. (2015). Here we also consider the whole of
tropospheric CO and our results are in line with those from Voulgarakis et al. (2015). We find the dominant source of IAV
across the entire period is emissions from biomass burning – indicated by
the large difference between simulations CTRL and FIREFIX (Fig. 4a), with a
small effect from meteorological changes (CTRL – METFIX). This effect was
largest during the 1997 El Niño, where an increase in fire events
increased CO concentrations by more than 40 %. Smaller increases of
5.8 % and 7.6 % occur during less extreme El Niño events of
2002/03 and 2006, respectively, with only a 1.8 % increase during the
2009/10 El Niño, indicating that El Niño only significantly
impacts CO concentrations when there is an associated increase in global
fire events.
Time series of simulated differences (%) between the
control and the fixed meteorology (CTRL – METFIX, blue line) and fixed fire
emissions (CTRL – FIREFIX, purple line) simulations for the global
tropospheric burden of (a) CO, (b) OH and (c)O3. The ENSO bimonthly mean multivariate index is plotted with the dashed red line using the right-hand y axis in each panel.
Expanding on the work of Voulgarakis et al. (2015), we analysed
IAV using the coefficient of variation (CV), calculated as the multi-year
standard deviation normalized by the mean (Fig. 5). The global annual mean
CO IAV over the whole period is 11.0 % for the whole troposphere and
14.3 % for surface concentrations. This is in very good agreement with
Voulgarakis et al. (2015), who calculated 10 % IAV; in fact, the
comparison is even better when we consider the same time period (2005–2009)
with our corresponding IAV estimate at 9.7 %. The slightly lower estimate
here may be a result of the fixed-year biogenic volatile organic compound (BVOC) emissions, removing the effect
of the IAV of biogenic emissions on CO IAV. BVOC oxidation is estimated to
contribute 15 % of the total source of CO (Duncan
et al., 2007); however, the IAV of BVOC emissions has been found to be
relatively small, ∼2–4 % (Naik et
al., 2004; Lathière et al., 2005). Despite good global comparison with Voulgarakis et al. (2015), there are regional differences; CO
IAV from TOMCAT is much larger in high-latitude boreal regions. This is
likely due to the difference in the period studied, meaning this study includes
additional extreme events including unusually large Russia boreal wildfires
in 2010 and 2012 (Gorchakov et al., 2014; Kozlov et al., 2014).
Infrequent and extreme events such as these significantly increase IAV.
The calculated interannual variability (coefficient of
variation) of CO over the period 1997–2014 for September–October (a, c, e) and March–April (b, d, f) from (a, b) the control simulation
(CTRL), (c, d) fixed meteorology (METFIX) and (e, f) fixed fire emissions (FIREFIX).
CO IAV is significantly greater in September–October, with peaks in known
fire regions such as tropical South America, Africa, Southeast Asia and
boreal forests. This indicates a strong contribution of fire emissions to
IAV, especially from Indonesia (Fig. 4a), as also suggested by previous
studies (Monks et al., 2012; Huang et al., 2014; Voulgarakis et al., 2015). In the FIREFIX simulation IAV is ∼55 % of the CTRL
value, showing a large reduction in variability when interannual variability
in fire emissions is removed. The IAV in March–April is significantly
smaller than September–October as this period is outside the primary fire
season for South America and Eurasia, although hotspots remain in Southeast
Asia and Africa where fires commonly occur in March–April (van der Werf et al., 2017). Meteorology
and atmospheric transport changes are most important in Africa in
September–October and Indonesia in March–April (Fig. 5c, d). Fire emissions
occur in these regions but the meteorological effects are important sources
of IAV. This is in good agreement with Voulgarakis et al. (2015),
who found that with fixed biomass burning emissions, high IAV remained
over Africa during December–January, and Huang et al. (2014), who
found CO over Central Africa correlated more closely with ice water content
than CO emissions due to increased convective transport. However, the
overall effect of meteorology on global IAV found here is much smaller than
the 50 %–90 % suggested by Szopa et al. (2007): when we
consider only surface CO over the same period, fixing meteorology decreases
the mean CO IAV by just 5 %.
The IAV of OH and O3 have more complex contributions from fire
emissions and meteorology (Fig. 4b, c). For both species, meteorology is the
dominant cause of variability for the majority of the period, indicated by,
on average, greater deviation from CTRL in METFIX simulation than FIREFIX,
including during El Niño events outside of the 1997 El Niño, such as
in 2006. Our results compare well to Inness et al. (2015), who also found that changes to tropospheric O3 during El
Niño were driven by a combination of emissions and atmospheric dynamics.
This is also in agreement with Doherty et al. (2006),
where a strong correlation was found between ENSO meteorology and global
O3 burden, albeit with a lag period of several months. Various
meteorological variables are known to affect OH and O3 variability,
including humidity, clouds and temperature (Stevenson et al., 2005;
Holmes et al., 2013; Nicely et al., 2018). OH variability is particularly
sensitive to changes in lightning NOx production which decreases during
El Niño conditions (Turner et al., 2018). Murray et al. (2014) also examined factors affecting
OH variability since the last glacial maximum, finding tropospheric water
vapour, overhead stratospheric O3 and lightning NOx to be key
controlling factors. Furthermore, circulation changes during El Niño
events have been linked to lower stratospheric O3 variability (Zhang
et al., 2015; Manatsa and Mukwada, 2017), which in turn influences
tropospheric OH and O3 concentrations (Holmes et al., 2013; Murray
et al., 2014). Despite the importance of meteorological drivers, we find
that fire emissions are the dominant cause of variation in both OH and
O3 during the 1997 El Niño, increasing global tropospheric O3
burden by up to ∼7 % and decreasing tropospheric OH by up
to ∼6 %. This result is supported by several other studies,
which have found that during large fire events such as that caused by the
1997 El Niño, fire emissions substantially decrease tropospheric OH and
increase tropospheric O3 (Hauglustaine et al., 1999; Sudo and
Takahashi, 2001; Holmes et al., 2013). Our results indicate that while
meteorology is generally the most important driver of IAV in global
tropospheric OH and O3, fire emissions can also play a key role and
become the dominant driver when there are particularly large fire emissions
related to El Niño.
Figure 6 shows the IAV of O3, supporting the analysis of Fig. 4 that
also suggests meteorology is the dominant process in controlling IAV.
METFIX-simulated IAV differs substantially from the CTRL, with much lower
IAV in September–October (33 % decrease) and in March–April (42 % decrease) when
meteorology is repeated. However, in the METFIX run there remain peaks in
variability in close proximity to regions with large biomass burning
emissions, demonstrating the significant contribution from fire emissions.
In the FIREFIX simulation the distribution of IAV is broadly similar to the
CTRL simulation and only shows a small change in global mean CV, indicating
that fire emissions have less control on O3 IAV. These results are
again comparable to Voulgarakis et al. (2015) as the distribution
of O3 IAV in both CTRL and FIREFIX simulations is similar, despite slightly larger values of variation due to differing time period.
The calculated interannual variability (coefficient of
variation) of ozone over the period 1997–2014 for September–October (a, c, e) and March–April (a, c, e) from (a, b) the control simulation
(CTRL), (c, d) fixed meteorology (METFIX) and (e, f) fixed fire emissions
(FIREFIX).
Indirect effect of CO on oxidation and lifetime of CH4
The COFIX sensitivity experiment was conducted to determine the indirect
influence of CO emissions on CH4 variability through changes in
tropospheric OH concentrations. Figure 7a shows the difference in COFIX
monthly mean OH concentrations from the control experiment, compared to that
from the METFIX and FIREFIX simulations. When CO emissions from biomass
burning are fixed, OH concentrations are consistently higher than in the
CTRL simulation. This indicates that high CO emissions decrease global mean
tropospheric OH. The greatest impact is during the 1997 El Niño, where CO
emissions were abnormally large, suppressing mass weighted global monthly
mean OH concentrations by up to 9 %. The mean effect on OH over the 1997
El Niño of -3.6 % is comparable to that simulated by Butler et al. (2005), who also found an increase in CO
resulted in a change in OH of -2.2 %. Duncan et al. (2003) found a similar magnitude response in OH to the Indonesian wildfires
in 1997 of between -2.1 % and -6.8 %. The suppression of OH
concentrations due to CO emission is also simulated to a lesser degree in
the 2003 and 2006 El Niño events but is absent in the 2010 El Niño as
this event had little impact on global fire occurrence
(Randerson et al., 2017). The effect of fixing only CO from
fires is greater than the effect of fixing all fire emissions due to
co-emitted species such as NOx, which act to increase OH
concentrations.
Time series of (a) the change (%) in mass-weighted
tropospheric OH, (b) change (%) in CH4 lifetime
and (c) resultant change (ppb) in annual CH4 growth
rate calculated using an offline box model. The ENSO bimonthly mean
multivariate index is plotted with the dashed red line using the right-hand
y axis in (a).
As OH is also the primary sink of CH4 (∼90 %) (McNorton et al., 2016a), another effect of the decrease in OH
due to CO emissions is to weaken the sink of CH4, increasing its
atmospheric lifetime. The magnitude of this can be seen in Fig. 7b; the
COFIX simulation indicates that CO emissions from fires extended CH4
atmospheric lifetime by more than 4 % during the 1997 El Niño. Fixing
all fire emissions also enhances CH4 lifetime by around 2 %.
Increasing the lifetime of a species increases its concentration in
steady-state equilibrium. Due to the scaling applied to CH4 in TOMCAT
we are unable to directly calculate the response in CH4 growth rate
from TOMCAT, as simulated global mean surface CH4 concentrations are
nudged to the observed value. Therefore, to determine the impact of the
change to OH on CH4 concentrations, we used a simple global box model.
This box model is similar to that described in McNorton et al. (2016a), which was found to compare well with other global and 12-box
CH4 models (Rigby et al., 2013; McNorton et al., 2016a). In this
case, the box model used monthly mean tropospheric OH concentrations and
CH4 emissions for each simulation, while assuming constant temperature
to calculate the effect of changing OH on global mean surface CH4. A
fixed temperature was used as varying the temperature has been found to have a
relatively small impact on derived CH4 concentrations (McNorton et al., 2016a). The impact of fire emissions on the
CH4 growth rate is greatest in 1998, where all emissions from fires
increased global CH4 by 10.5 ppb (Fig. 7c). Analysis of the COFIX
simulation demonstrates that up to 7.5 ppb (72 %) of that change could
have been caused by the release of CO alone and its role as a sink for OH.
The effect on growth rate in the FIREFIX simulation is larger than in the COFIX
despite a greater effect on CH4 lifetime from the COFIX, due to
directly emitted CH4 varying with El Niño conditions in the COFIX
simulation and not in FIREFIX. The influence of CO emissions on CH4
growth rate calculated here is smaller than in Butler et al. (2005), despite a much larger effect on tropospheric OH. The radiative effect
of the change to CH4 from CO emitted from biomass burning alone in 1998
is 0.004 W m-2, calculated using updated expressions from
Etminan et al. (2016).
Limiting factors of O3 production
In this section we examine trends and the impact of El Niño on the
production of tropospheric O3. El Niño is known to have a large
effect on tropospheric O3 precursors such as CO and NOx;
therefore, examining O3 production regimes during El Niño can provide
insights into the main mechanism responsible for the observed changes in
tropospheric O3. The ratio between formaldehyde (HCHO) and nitrogen
dioxide (NO2) concentrations can be used to indicate the limiting
factor for tropospheric O3 production (Duncan et al., 2010). Ratios smaller than 1 indicate that removing VOCs will decrease
tropospheric O3 formation (i.e. a VOC-limited regime), while ratios
larger than 2 indicate that removing NOx will reduce O3 (i.e. a
NOx-limited regime). Ratios of 1–2 indicate that both NOx and VOC
reductions could decrease O3 (i.e. a “both-limited” regime). Here we apply this methodology to determine the changes to this ratio from 1997 to 2014
and dependence of O3 formation during the 1997 El Niño event. We
compare the early period mean (1999–2003) to the end period mean (2010–2014)
to determine whether significant changes have occurred over the 18-year
period and compared mean El Niño conditions to both.
Mean ratio of simulated tropospheric column HCHO to NO2 amounts for (a) the beginning of model period (1999–2003), (b) the end of model period (2010–2014) and (c) during all El Niño events. Panels (d, e) show the difference during El Niño from the 5-year mean values in panels (a, b), respectively.
In general, the SH and tropical regions have very high ratios, meaning they
are strongly NOx-limited (Fig. 8). The NH is also predominantly
NOx-limited, although less robustly, and polluted regions tend to be
either VOC-limited or both-limited regimes. The ratio is largely constant
across the modelled period; however, there are some significant shifts, such
as in India, which was once solely NOx-limited, becoming increasing
VOC-limited due to increased NOx pollution (Hilboll
et al., 2017). This shift in the spatial distribution of O3 precursor
emissions to lower latitudes leads to increased tropospheric O3
production proportional to total emissions (Zhang et al., 2016).
During El Niño there are large changes, increasing the ratio and
therefore the NOx limitation by more than 40 % in the tropical Pacific.
Significant changes to the ratio were also found in biomass burning regions
of South America and Southeast Asia. This is due to the increase in NOx
emissions in larger fire seasons associated with El Niño. However, these
regions are already very heavily NOx-limited due to high VOC emissions
in forest regions, meaning that although the shift in HCHO/NO2 ratio
during El Niño is large, it is not substantial enough to alter the
limiting factor for formation of tropospheric O3 from one regime to
another. Over India, El Niño conditions inhibit the trend towards a
both-limited regime, as the NOx-limited regime continues to dominate throughout.
Impact on tropospheric ozone and radiative effects
The 1997 El Niño significantly altered the vertical distribution of
O3 in the troposphere, increasing O3 concentrations in the NH
while decreasing in the SH and tropics with an overall decrease in
tropospheric O3 of -0.82 % compared to the 1997–2014 mean (Fig. 9a).
In the CTRL simulation there is decreased O3 in the tropical upper
troposphere, possibly related to increased convection over the eastern
Pacific (Oman et al., 2013; Neu et al., 2014). We also simulate large
increases in the midlatitude upper troposphere of both hemispheres in the
CTRL and FIREFIX simulations but not in METFIX, implying that this is
produced by El Niño-associated meteorological processes which promote
intrusion of stratospheric air into the troposphere. These positive
anomalies were also observed in Oman et al. (2013) and
Zeng and Pyle (2005), attributed to El Niño influence on
circulation patterns and enhanced stratosphere–troposphere exchange.
Latitude–pressure cross sections of the percentage
difference in O3 concentrations during the 1997 El
Niño event compared to 1997–2014 period mean for the TOMCAT simulations:
(a) CTRL, (b) METFIX and (c) FIREFIX simulations.
In general, the METFIX run simulates higher O3 concentrations in the NH
than the period mean and lower concentrations in the SH (Fig. 9b). This
hemispherical shift is also present in the CTRL and FIREFIX simulations but
with greater negative O3 anomalies in the SH. The simulated NH
increases in the CTRL simulation correspond to other studies of the 1997 El
Niño (Koumoutsaris et al., 2008), while Oman et al. (2013) similarly reported negative O3
anomalies in the SH during El Niño. Large increases in tropospheric
O3 in the western Pacific, Indian Ocean and Europe contribute to the
increase in O3 in the NH, despite decreased O3 in the eastern
Pacific (Chandra et al., 1998; Koumoutsaris et al., 2008; Oman et al., 2011).
Tropospheric O3 radiative effects (W m-2) from the TOMCAT simulations: (a) control (CTRL), (b) fixed meteorology and fire emissions (BOTHFIX), (c) fixed meteorology only (METFIX) and (d) fixed fire emissions only (FIREFIX). Panels (e–g) show percentage differences between the control and the three perturbed simulations.
There is an overall increase in O3 (∼2 %) when
meteorology was fixed to an ENSO-neutral year (i.e. 2013), meaning that
meteorology during the 1997 El Niño caused a decrease in tropospheric
O3 concentrations despite large increases in O3 in regions of the
upper troposphere due to stratospheric intrusion. During the 1997 El
Niño we find a 0.4 % increase in global tropospheric humidity compared
to the period mean. This is likely partly responsible for the general
decrease in O3 due to meteorology, as increased humidity enhances
O3 loss (Stevenson et al., 2000; Isaksen et al., 2009; Kawase et
al., 2011). Changes to transport and distribution of O3 will also
impact how efficiently tropospheric O3 is produced and lost.
The similarities between the tropospheric O3 distribution in the CTRL
and FIREFIX simulations show that fire emissions have a relatively small
impact on the global distribution of O3 but do affect absolute values,
as concentrations in the FIREFIX run are significantly lower in the tropics.
This is likely because of the removal of large emissions of O3
precursors in that latitude band when fire emissions are fixed to a non-El
Niño year, as several studies have found that enhanced fires in 1997 El
Niño increased tropospheric O3 in the region (Chandra et al., 1998; Thompson et al., 2001; Doherty et al., 2006; Oman et al., 2013).
Figure 10 shows the tropospheric O3 radiative effect (RE) during the
1997 El Niño in each TOMCAT simulation, calculated using the
Rap et al. (2015) tropospheric O3 radiative
kernel. Consistent with the relative changes in O3 concentration, fire
emissions and meteorology have contrasting effects on O3 RE. When
isolated, these effects are opposite and almost equal: fire emissions
increase O3 RE by 0.031 W m-2, while meteorology decreases by
-0.030 W m-2. We performed an additional simulation to determine the
effect of these factors occurring simultaneously (BOTHFIX) and found the
increasing effect from fire emissions to be dominant over the decreasing
effect from meteorology, leading to an overall increase in global mean
O3 RE of 0.015 W m-2. The effect of fire emissions occurs almost
entirely over Indonesia and the eastern Indian Ocean where the large influx
of NOx, CO and CH4 from fire emissions during the 1997 El Niño causes large regional increases in tropospheric O3. This increase, also observed in Chandra et al. (1998), causes a
regional RE of up to 0.17 W m-2. Meteorology has more varied impacts
during El Niño, causing large decreases in O3 RE over the central
Pacific Ocean (∼-0.36 W m-2) but also increases in the
midlatitudes of the Pacific Ocean (∼0.33 W m-2).
Globally the mean change to O3 RE is small, around 0.015 W m-2, but
large regional changes have the potential to significantly alter atmospheric
heating and dynamics.
Summary and conclusions
Global model simulations using annually invariant meteorology and fire
emissions were performed for the period 1997–2014 in order to determine
their relative impacts on the IAV of O3 and CH4, particularly
during El Niño events. The TOMCAT-GLOMAP model used has been updated from
that described by Monks et al. (2017), with improved cloud and
photolysis representation and the introduction of Mårtensson sea spray
emissions (Gordon et al., 2017). Model simulations were evaluated for a
number of chemical species (O3, CH4, NOx, CO), with
observations from aircraft, satellites and ozone-sondes. In general, the
model shows a good agreement with observed values, although with some
regional biases. Differences between the model and observations may be due to a
number of factors, such as the relatively coarse model resolution,
uncertainties in the model emission inventories and errors in observations.
However, good overall agreement of model simulations with different
observations, including the ability of the model to simulate the observed
atmospheric responses to El Niño events (i.e. OEI change of 2.8 DU
compared to 2.4 DU in Ziemke et al., 2010), provides
confidence in model performance and results.
We find that the IAV of global CO concentrations is large and is primarily
controlled by fire emissions over the modelled period. Exceptionally large
CO emissions linked to El Niño in 1997 led to a decrease in OH
concentrations of ∼9 %, which subsequently increased
CH4 lifetime by ∼4 %. Using a box model we quantify
the isolated impact of this change in atmospheric chemistry on global
CH4 growth rate to be 7.75 ppb, ∼75 % of the total
effect of fires. This effect, combined with concurrent direct CH4
emission from fires, explains the observed changes to CH4 growth rate
during the 1997 El Niño.
Variability of oxidants O3 and OH is far more dependent on meteorology
than fire emissions, except during very large El Niño events, such as in
1997 and 1998, when fires become dominant in terms of total tropospheric
burden, although meteorology still controls distribution. The change to
tropospheric O3 concentrations during El Niño has increased O3
RE by 0.17 W m-2 over Southeast Asia and decreased by 0.36 W m-2
over the central Pacific. The global mean O3 RE change due to 1997 El
Niño meteorology and fires is an increase of 0.015 W m-2, as
emissions of O3 precursors from fires causes increased O3. El
Niño also causes significant shifts in the ratio of HCHO:NOx – an
indicator of O3 production regime – but most significantly in the
tropics, which are heavily NOx-limited, so this change does not cause a
regime shift.
This work has shown that El Niño events significantly affect the
variability of two important drivers of anthropogenic climate change.
Further research into how El Niño events, with their associated effect
on fire emissions, are likely to change in a warming climate is required to
understand how these links between ENSO, CH4 and O3 may influence
future climate change mitigation attempts.
Data availability
All data can be accessed upon request to the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-8669-2019-supplement.
Author contributions
MJR, AR and SRA conceptualized the study and planned the experiments. MPC,
KJP, HG, WF and MJR developed and evaluated the version of TOMCAT-GLOMAP
used here. BJK, BLL and RS provided the satellite retrievals for the O3 comparison, which was conducted by RJP. MPC and JM provided assistance and
advice for the CH4 box model. MJR performed the TOMCAT model runs,
SOCRATES and box model calculations. MJR analysed the results with help from
AR and SRA. MJR compiled results and prepared the manuscript. All co-authors
contributed to the final version with comments.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was undertaken using the
ARCHER UK National Supercomputing Service (http://www.archer.ac.uk) and
ARC3, part of the High Performance Computing facilities at the University of
Leeds, UK.
Financial support
This research has been supported by the Leeds York NERC DTP (grant no. NE/L002574/1).
Review statement
This paper was edited by Tim Butler and reviewed by two anonymous referees.
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