2.1 Model 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 $\mathrm{2.8}{}^{\circ }\times \mathrm{2.8}{}^{\circ }$ 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 (NO_x) 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).

2.2 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).

2.3 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).

Table 1Details of TOMCAT model simulations. All simulations are run for 1997–2014.

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3.1 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.

Figure 2Global 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).

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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 $=-\mathrm{12.3}$ %), with a better comparison at 2–6 km (NMB =1.68 %) and overestimation at 6–10 km (NMB =18.17 %).

3.2 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.

Figure 3Annual 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.

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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.

4.1 Impact 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.

Figure 4Time 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.

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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.

Figure 5The 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).

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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 NO_x 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 NO_x 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.

Figure 6The 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).

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4.2 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 NO_x, which act to increase OH concentrations.

Figure 7Time 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).

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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).

4.3 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 NO_x; 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 NO_x will reduce O₃ (i.e. a NO_x-limited regime). Ratios of 1–2 indicate that both NO_x 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.

Figure 8Mean 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.

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In general, the SH and tropical regions have very high ratios, meaning they are strongly NO_x-limited (Fig. 8). The NH is also predominantly NO_x-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 NO_x-limited, becoming increasing VOC-limited due to increased NO_x 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 NO_x 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 NO_x emissions in larger fire seasons associated with El Niño. However, these regions are already very heavily NO_x-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 NO_x-limited regime continues to dominate throughout.

4.4 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.

Figure 9Latitude–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.

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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).

Figure 10Tropospheric 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.

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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 NO_x, 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 ($\sim -\mathrm{0.36}$ 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.