Global O₃-CO Correlations in a Chemistry and Transport Model During July–August: Evaluation with TES Satellite Observations and Sensitivity to Input Meteorological Data and Emissions

Abstract

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O₃-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA’s Aura satellite during boreal summer (July–August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O₃-CO correlations to input meteorological data. Model simulations of radionuclide tracers (²²²Rn, ²¹⁰Pb, and ⁷Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O₃ values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O₃ columns. Despite the fact that three simulations show significantly different global and regional distributions of O₃ and CO concentrations, all simulations show similar patterns of O₃-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O₃-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O₃-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O₃ and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NO_x emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O₃-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O₃-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O₃ is consumed and CO generated during the atmospheric oxidation process of isoprene under low NO_x conditions. We find that lightning NO_x emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NO_x emissions is not directly related to the emission and transport of CO. Our study concludes that O₃-CO correlations may be used effectively to constrain the sources of regional tropospheric O₃ in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

1. Introduction

Ozone (O₃) is an important greenhouse gas in the troposphere and a pollutant at the surface. It is a primary source of the hydroxyl radical (OH), which controls the oxidizing power of the troposphere. Ozone in the troposphere is produced by photochemical oxidation of carbon monoxide (CO), methane, and volatile organic hydrocarbons (VOCs) in the presence of nitrogen oxides (NO_x ≡ NO + NO₂). Its precursors are emitted by human activity (e.g., fossil fuel combustion and industrial processes), biomass burning, vegetation, soils, and lightning. Ozone is also transported down from the stratosphere by the Brewer Dobson circulation. Carbon monoxide is a product of incomplete combustion. Its sources include fossil fuel and biofuel combustion, biomass burning, and chemical production from atmospheric oxidation of methane, isoprene, and other VOCs. Its primary sink is reaction with OH.

Since CO is a precursor of tropospheric O₃ and an excellent tracer for long-range transport of pollution owing to its tropospheric lifetime of a few months, correlations between O₃ and CO are useful indicators of the efficiency of O₃ production and export (e.g., Parrish et al., 1993; Mao and Talbot, 2004). Generally, a positive correlation in summer indicates strong photochemical production of O₃ downwind of polluted regions (Chin et al., 1994; Tsutsumi and Matsueda, 2000). A negative correlation indicates stratospheric influence (Parrish et al., 1998; Hsu et al., 2004), photochemical O₃ destruction (Fishman and Seiler, 1983; Parrish et al., 1998; Mao and Talbot, 2004), or chemical production of CO (Chin et al., 1994; Real et al., 2008). Small correlation coefficients and small linear regression slopes are indications of fresh pollution plumes that have not yet realized their O₃ production potential due to, for example, incomplete photochemical processes (Naja et al., 2003).

Many studies have used surface and/or aircraft observed O₃-CO correlations to understand anthropogenic influences on O₃, especially in the Northern Hemisphere (NH) continental outflow regions such as the northeastern US/northern Atlantic (Fishman and Seiler, 1983; Anderson et al., 1993; Parrish et al., 1993; Chin et al., 1994; Fehsenfeld et al., 1996; Parrish et al., 1998; Li et al., 2002; Honrath et al., 2004; Mao and Talbot, 2004) and the Asian Pacific Rim (Tsutsumi and Matsueda, 2000; Mauzerall et al., 2000). The studies found strong O₃-CO correlations in outflow regions and concluded that the export of pollution from the NH major continents makes a significant contribution to total tropospheric O₃ over the NH during summer.

The observed O₃-CO correlation coefficients and linear regression slopes have been used to evaluate the capability of models of chemistry and transport to produce proper O₃ levels from its precursors for the right reasons (e.g., Chin et al., 1994; Mauzerall et al., 2000; Zhang et al., 2006; Voulgarakis et al., 2011). Shim et al. (2009) examined the Mexico City pollution outflow using O₃, CO, and their correlations from TES as well as aircraft measurements obtained during the Megacity Initiative: Local and Global Research Observations (MILAGRO) / Intercontinental Chemical Transport Experiment (INTEX-B) campaigns. These investigations found that TES data is characterized by smaller O₃-CO correlation coefficients but larger linear regression slopes than in situ observations at 618 hPa partly due to the lack of variability in TES CO. Two previous studies also examined TES global O₃-CO correlations. Zhang et al. (2006) compared mid-tropospheric TES O₃-CO correlations in July 2005 over the eastern United States with those from the GEOS-Chem model and International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) aircraft observations (July 2004), finding that TES can provide good information on global mid-tropospheric O₃-CO correlations. Voulgarakis et al. (2011) expanded the scope of Zhang et al. (2006), evaluating O₃-CO correlations simulated by two independent models against five years of TES observations. They suggested that in addition to O₃ photochemical processes, transport may also play an important role in the O₃-CO correlations. However, Voulgarakis et al. (2011) could not isolate the effect of transport because the two models in their study used different input meteorological data sets as well as different chemical and transport mechanisms.

In this paper, we present O₃ and CO simulations using the Global Modeling Initiative (GMI) chemistry and transport model (CTM) driven by three meteorological data sets. The model can incorporate different inputs and components (e.g., meteorological fields, emission inventories, and chemical mechanisms), allowing us to test the sensitivity of model simulations to input meteorological data sets (e.g., Douglass et al., 1999; Considine et al., 2005; Liu et al., 2016). Model simulations are evaluated using ozonesonde and satellite observations. We then test the model’s capability to reproduce the mid-tropospheric O₃-CO correlations determined from TES measurements. We present the differences in the simulated O₃-CO correlations due to the use of different meteorological input data, and interpret those differences in terms of transport using radionuclide species (radon-222, lead-210, and beryllium-7) as traces of atmospheric transport (see Section 2.1.3). We also investigate the effect of emission types on O₃ and CO concentrations and their correlations in the model.

This paper is organized as follows. Section 2: Descriptions of the GMI model, meteorological data sets, and observational data sets. Section 3: Presentation of the model simulations of radionuclides, O₃ and CO. Section 4: Evaluation of GMI O₃ and CO simulations with observations. Section 5: Evaluation of GMI O₃-CO correlations with satellite observations from TES. Section 6: Analysis of the effects of various emission types on the model simulated O₃-CO correlations. Section 7: Summary and conclusions.

2. Model and Data

2.1. GMI

2.1.1. The CTM

The GMI CTM is a global 3-D composition model that combines both tropospheric and stratospheric chemical mechanisms, including 124 species, 322 chemical reactions, and 81 photolysis reactions (Ziemke et al, 2006; Duncan et al., 2007a; Considine et al., 2008; Allen et al., 2010). The tropospheric mechanism includes a detailed description of tropospheric O₃-NOX-hydrocarbon chemistry (Bey et al., 2001) with recent updates (e.g., see Allen et al., 2010). The stratospheric mechanism is described in Kinnison et al. (2001) and Douglass et al. (2004). The details of the GMI model are described in Duncan et al. (2007a) and Strahan et al. (2007). The basic structure of the model is described in Rotman et al. (2001).

The GMI model uses a flux-form semi-Lagrangian (FFSL) advection scheme (Lin and Rood, 1996) and also includes parameterizations of convection, wet scavenging, dry deposition, and planetary boundary layer mixing. The anthropogenic emission (e.g., fossil fuel emissions) scheme is from Bey et al. (2001), Benkovitz et al. (1996), and Duncan et al. (2007b). We use the anthropogenic emission inputs for 2005 for all simulations in this study. The biogenic emission scheme is calculated online based on Guenther et al. (2006) and biofuel emissions are estimated from the inventory and emission factors of Yevich and Logan (2003). Biomass burning emissions are from Duncan et al. (2003) climatology, where the spatial and seasonal variability are derived from satellite observations of monthly total fire counts. Lightning NO_x emissions are calculated locally in deep convection events with the scheme of Allen et al. (2010) where flash rates are assumed to be proportional to the square of upward convective mass flux but constrained by monthly average climatological flash rates from V2.2 of the Optical Transient Detector and the Lightning Imaging Sensor (OTD/LIS) climatology. GMI uses modules developed at Harvard University to calculate wet scavenging (Mari et al., 2000; Liu et al., 2001) and dry deposition rates (Jacob and Wofsy, 1990).

Several studies have previously evaluated the GMI CTM simulations of tropospheric O₃ and CO. Ziemke et al. (2006) compared the tropospheric ozone columns (TOC) in an earlier version of the GMI CTM, which was driven by the fvGCM meteorological fields (details in Section 2.1.2), with those determined from Ozone Monitoring Instrument/Microwave Limb Sounder (OMI/MLS) measurements from the NASA Aura satellite. The comparison showed similarities with respect to zonal and seasonal variations of TOC, but the model overestimated TOC over northern Africa by as much as 10 DU, likely due to desert dust effects while underestimating TOC over the western Pacific warm pool by up to 10 DU. Chandra et al. (2009) evaluated GMI TOC when driven by the GEOS4 meteorological fields (see Section 2.1.2) with OMI/MLS TOC and found the model overestimated TOC by 5-10 DU for the latitude band 30°N - 35°N all the year and over east China in winter and spring when stratosphere-troposphere exchange (STE) is greatest. Duncan et al. (2008) showed that the annual average surface O₃ concentrations in the GMI/GEOS4 simulation had a high bias of about 11%, with higher biases in summer when photochemical production is the dominant source of O₃. Considine et al. (2008) examined the ability of GMI/fvGCM (4° × 5°) to represent the observed near-tropopause O₃ distributions and found that annual mean O₃ concentrations were biased high by 45% at the model thermal tropopause likely due to insufficient vertical resolution near the tropopause (~ 1.1 km) and/or too high vertical diffusivity.

For CO, Duncan et al. (2007a) compared GMI/fvGCM simulated tropospheric CO concentrations with NOAA Global Monitoring Division (GMD) surface observations. They showed that the model was biased low at most sites in local winter/spring likely due to overestimation of OH in the simulation when the CO burden is typically at an annual maximum. Schoeberl et al. (2006) showed that GMI/fvGCM was able to reproduce the upper troposphere/lower stratosphere (UT/LS) CO tape recorder caused by seasonal changes in biomass burning, as identified with the MLS data.

2.1.2. fvGCM, GEOS4, and MERRA Meteorological Data Sets

We drive the GMI CTM with three meteorological datasets from: the free-running NASA Global Modeling and Assimilation Office (GMAO) finite-volume General Circulation Model (fvGCM) for 1995, the Goddard Earth Observing System Data Assimilation System Version 4 (GEOS4-DAS) for 2005, and the Modern-Era Retrospective Analysis for Research and Applications (MERRA) for 2005. Note that the fvGCM is the general circulation model in the assimilation system used to generate GEOS4-DAS (Bloom et al., 2005). The native vertical coordinate of fvGCM and GEOS4-DAS models is a generalized hybrid sigma-pressure coordinate system with 55 vertical layers and a smooth transition between sigma in the troposphere (pressure > 176 hPa) and pure pressure in the stratosphere. MERRA is a NASA atmospheric reanalysis data set from a new version of GEOS-DAS Version 5 (GEOS-5.2.0). GEOS-5 is a system of models integrated using the Earth System Modeling Framework (ESMF). Compared to GEOS-4, GEOS-5 adopts an analysis system developed jointly with the National Centers for Environmental Prediction (NCEP) and a different set of physics packages for the atmospheric GCM. MERRA has 72 vertical levels with a lid at 0.01 hPa. The native horizontal resolution of all meteorological data sets is 1° × 1.25°. To improve computational efficiency, we drive GMI CTM with the meteorological data sets at a degraded resolution (2° latitude by 2.5° longitude). The number of vertical levels, top pressure, near-tropopause resolution, and bottom layer depth in the three meteorological data sets are listed in Table 1.

Table 1.

Characteristics of meteorological data sets used to drive the GMI CTM

Data Set	# of levels	Top Pressure (hPa)	Vertical Coordinate	Interface Pressure (hPa)a	Near-Tropopause resolution (km)	Bottom Layer Depth (hPa, m)	Update Period (h)
fvGCM	42(55b)	0.9(0.01b)	σ-P	200	~1.0	~15 hPa ~130 m	3
GEOS4	42(55b)	0.9(0.01b)	σ-P	200	~1.0	~15 hPa ~130 m	3
MERRA	72	0.01	σ-P	~177	~1.0	~15 hPa ~130 m	3

^aThe hybrid vertical coordinate consists of sigma (σ) levels below the interface pressure and constant pressure (P) levels above.

^bThe total number of vertical levels and top level pressure in the original meteorological data sets.

The different convective parameterizations used to generate the meteorological data sets alters the characteristics of convective transport of chemical species. Both fvGCM and GEOS4 use the deep convection scheme of Zhang and McFarlane (1995) and the shallow convection scheme of Hack (1994) whereas MERRA uses a modified version of the Relaxed Arakawa-Schubert scheme for convection (Moorthi and Suarez, 1992). Figure 1 shows the latitude-height cross sections of zonal mean convective mass fluxes averaged over July – August in the three meteorological data sets. fvGCM shows the strongest shallow convection in the Southern Hemisphere (SH) mid- and low-latitudes among the models. While deep convection is seen in the tropics, the NH mid-latitude convection in the fvGCM reaches higher altitudes, compared to the other two data sets. MERRA is characterized by the weakest shallow convection in both hemispheres. MERRA has the strongest tropical convection in the lower and middle troposphere (LT/MT), but its tropical convection is not as deep as in the others. Shallow convection in fvGCM and GEOS4 extends to higher latitudes compared to MERRA.

Figure 1.

Latitude-height cross sections of zonal mean convective mass fluxes averaged over July-August in the fvGCM (1995), GEOS4 (2005), and MERRA (2005) meteorological data sets.

2.1.3. Radionuclide Tracers

We conduct GMI model simulations of radionuclides (²²²Rn, ²¹⁰Pb, and ⁷Be) to examine the relative effects of convection, stratospheric influence, and large-scale subsidence on the transport of trace species and their sensitivity to input meteorological data sets. ²²²Rn has a half-life of 3.8 days and is emitted primarily from continental crust. It is useful as a tracer of convective transport in global models (e.g., Jacob et al., 1997). ²¹⁰Pb, a decay daughter of ²²²Rn, has a radioactive half-life of 22.3 years, and ⁷Be, which is produced by cosmic ray spallation reactions in the stratosphere and UT, has a radioactive half-life of 53.3 days. Because ²¹⁰Pb and ⁷Be attach to submicron aerosols after production and are therefore scavenged by precipitation or deposited to the surface, they have been used as a pair to test wet deposition schemes in global models (e.g., Liu et al., 2001). ⁷Be is also used as a tracer for STE (Dibb et al., 1994; Liu et al., 2001; Liu et al., 2016). The ratio ⁷Be/²¹⁰Pb is useful as an indicator of vertical transport because the ratio is insensitive to precipitation scavenging (Koch et al., 1996).

2.2. Data Sets

2.2.1. Ozonesonde O₃

We use climatological ozone profiles from 23 ozonesonde stations averaged over July – August from 1985 to 2000, originally constructed by Considine et al. (2008) based on Logan (1999) and Thompson et al. (2003). The number of soundings at each station is adequate for defining monthly means used to evaluate the accuracy of the model results (Considine et al., 2008; Liu et al., 2016).

2.2.2. Satellite Tropospheric Ozone Column (TOC)

Three TOC products are used in this study: Total Ozone Mapping Spectrometer (TOMS) – Solar Backscatter Ultraviolet (SBUV), OMI-MLS, and directly retrieved TOC from TES. The TOMS-SBUV TOC and OMI-MLS TOC are determined using the tropospheric ozone residual (TOR) method, which involves subtracting measurements of SBUV and MLS stratospheric column ozone (SCO) from TOMS and OMI total column ozone, respectively (Fishman et al., 2003; Ziemke et al., 2006). The TES TOCs are integrated from directly retrieved volume mixing ratios. We did not consider different instrument sensitivities because integrating retrievals significantly reduces the error due to averaging over pressure ranges larger than TES vertical resolution (Osterman et al., 2008; Zhang et al., 2012). Tropopause pressure is taken from the GEOS4 meteorological data (2° x 2.5°). A description of TES retrievals is given in Section 2.2.3.

2.2.3. TES O₃ and CO.

The TES instrument on EOS-Aura routinely provides observations of tropospheric O₃ and CO across the globe (Beer et al., 2001; Beer, 2006). The Aura satellite is on a polar sun-synchronous orbit with equator crossing at 01:45 (descending) and 13:45 (ascending) local time. TES is a Fourier transform infrared emission spectrometer with high spectral resolution (0.1 cm⁻¹) and wide spectral range (650 – 3050 cm⁻¹) (Beer et al., 2001). The nadir footprint of TES is 5 × 8 km. TES observations consist of two modes: global survey and special observations (Beer et al., 2001). We use TES level 2, version 4 global survey nadir observations (http://eosweb.larc.nasa.gov/) and only O₃ and CO retrievals with the “Master” quality flag are used in this analysis. The retrievals of O₃ have 1 – 1.5 degrees of freedom (DOF) in the profile at mid-latitudes in summer, with peak sensitivities near 700 hPa and 300-400 hPa, respectively (Parrington et al., 2008). TES CO profiles generally have 1 – 1.5 DOFs in the troposphere (Luo et al., 2007a,b). Detailed descriptions of the TES instrument and the O₃ and CO retrieval algorithms are described in Beer et al. (2001, 2006), Worden et al. (2004), and Bowman et al. (2002, 2006).

In this study, we use O₃ and CO retrievals at 618 hPa level, where TES has good sensitivity for both O₃ and CO centered in the MT, and exclude latitudes > 60° where TES measurements are less reliable due to low brightness temperatures (Zhang et al., 2006). Due to limitation of TES vertical resolution (1 – 1.5 DOFs in the troposphere for both O₃ and CO), TES averaging kernels are applied to the simulations to take into account the different sensitivities of the instruments. TES uses MOZART model output binned by month and in blocks of 10° latitude by 60° longitude as a priori profiles (Worden et al., 2004). Validation of TES O₃ against ozonesondes showed that TES ozone typically has a high bias of about 10% in the UT (Worden et al., 2007) or 3-10 ppbv in the MT/UT (Nassar et al., 2008). TES CO has a negative bias (<10%) compared to aircraft measurements in the NH mid-latitude LT/MT during the INTEX-B mission (spring 2006) (Luo et al., 2007a).

3. Model Simulations of Radionuclides, O₃, and CO

3.1. GMI Simulations of Radionuclides

Figure 2 shows the zonal mean concentrations of ²²²Rn and stratospheric fraction (%) of tropospheric ⁷Be concentrations averaged over July – August as a function of latitude and pressure, as simulated by the GMI CTM driven with meteorological output from fvGCM (1995), GEOS4 (2005), and MERRA (2005), respectively. Differences in zonal mean ²²²Rn concentrations at SH mid-latitudes among the three simulations are small despite much stronger shallow convection in fvGCM (Figure 1). This reflects the fact that most convection at SH mid-latitudes occurs over the ocean. However, GMI/fvGCM ²²²Rn concentrations in the UT at NH subtropics and mid-latitudes are ~ 20 – 70 % higher than those in other simulations due to the deeper convection in fvGCM (Figure 1). In the tropical UT/MT, GMI/MERRA produced the lowest ²²²Rn concentrations, consistent with the lower cutoff of convection in GMI/MERRA, Associated with this is the largest stratospheric influence in the tropical UT/MT in GMI/MERRA among the three meteorological data sets (Figure 2, right column). Previously, Liu et al. (2010) and Zhang et al. (2011) used GEOS-Chem simulations of CO and ²²²Rn (driven by GEOS4 DAS and GEOS5 DAS meteorological data) to show that the tropical convection in GEOS4 is deeper than in GEOS5. Because the MERRA reanalysis utilizes the same GCM as the GEOS5 DAS, it also utilizes the Relaxed Arakawa-Schubert (RAS) convection.

Figure 2.

Zonal mean ²²²Rn concentrations (mBq SCM⁻¹, left) and stratospheric fraction (%) of zonal mean tropospheric ⁷Be concentrations (right) in the GMI model simulations as a function of latitude and pressure. The model is driven by the fvGCM (1995), GEOS4 (2005), and MERRA (2005) meteorological fields. Values are July – August average.

The stratospheric contribution to the lower-tropospheric ⁷Be concentrations in GMI/fvGCM peaks near 30 - 75°N (20 – 25%), in contrast to the GMI/GEOS4 and GMI/MERRA simulations (Figure 2, right column). The GMI/GEOS4 and GMI/MERRA simulations show a similar pattern of stratospheric influence on the troposphere with maximum contributions from the stratosphere near 0 – 40°S and 0 – 50°S (20 - 30%), respectively, in the LT. However, GMI/GEOS4 suggests more stratospheric influence than GMI/MERRA in the MT near 30°S (30 - 35%) and near 30 – 45°N (~ 25%). The stratospheric impacts on the tropical MT/UT are weakest in GMI/fvGCM and strongest in GMI/MERRA. At NH mid-latitudes, stratospheric influences on the LT are largest and most extensive in GMI/fvGCM and smallest in GMI/MERRA. These differences in stratospheric influence that characterize these meteorological data sets will be used to interpret GMI O₃ and CO simulations driven by these meteorological fields (Sections 3.2 and 4).

3.2. GMI Simulations of O₃ and CO

Figure 3 shows the zonal mean mixing ratios of O₃ and CO as a function of latitude and pressure during July – August, as simulated by the GMI CTM driven with the three meteorological data sets. The latitudinal distributions of O₃ from all simulations show lowest O₃ concentrations near the surface at high latitudes and in the tropical LT. Relatively low O₃ in the tropical free troposphere results from transport of ozone-poor air from the LT to UT/MT via deep convection. High O₃ concentrations are seen in the (subtropical) descending branches of the Hadley circulation partly due to the influence of STE. Compared with GMI GEOS4 and GMI/MERRA, GMI/fvGCM simulates higher O₃ in the NH mid-latitude MT and lower O₃ in the SH LT/MT. This is likely due to higher STE in the NH and weaker STE in the SH, respectively, as suggested by the higher (lower) fraction of stratospheric ⁷Be seen in the GMI/fvGCM simulation compared to the other two simulations (Figure 2). On the other hand, GMI/MERRA simulates the highest O₃ in the tropical MT/UT as a result of “stronger but shallower” deep convection in the tropics (Figure 1). All simulations show the largest CO concentrations in the tropical LT/MT and NH mid-latitude boundary layer. The former reflects convective lifting of tropical biomass burning CO emissions and the latter anthropogenic CO emissions, respectively. Among the three simulations, GMI/fvGCM simulates the lowest CO concentrations in the tropical MT/UT as well as both hemispheres. In the NH MT/UT in GMI/fvGCM, the low CO concentrations result from high OH concentrations associated with high O₃ concentrations due to higher STE, which will be discussed in Section 4.1. In the SH GMI/fvGCM, the low CO concentrations are due to high OH concentrations as a result of too low NO_x emissions from lightning (see Section 4.1). Tropical MT/UT CO concentrations in GMI/MERRA are not as high as those in GMI/GEOS4, again reflecting the “shallower” tropical deep convection in MERRA.

Figure 3.

Zonal mean O₃ and CO mixing ratios (ppbv) in the GMI model simulations as a function of latitude and pressure. The model is driven by the fvGCM (1995), GEOS4 (2005), and MERRA (2005) meteorological fields. Values are July – August average.

4. Evaluation of GMI O₃ and CO Simulations with Observations

In this section, we evaluate GMI O₃ and CO simulations driven by the fvGCM, GEOS4, and MERRA meteorological data sets with ozonesonde O₃ vertical profiles, satellite TOC, and TES O₃ and CO retrievals.

4.1. Ozone Vertical Profiles and Tropospheric Ozone Column

Figure 4 compares GMI simulated tropospheric O₃ profiles with ozonesonde observations averaged over July-August for a range of latitudes. These results are typical of other stations at similar latitudes. GMI/fvGCM overestimates O₃ in the NH high-/mid-latitude UT/MT. This may be due to excessive STE given the relatively high fractions of stratospheric NH ⁷Be seen in Figure 2. The overestimate may also be partly attributed to strong convective mass fluxes in the NH mid-latitude that lift more O₃ and/or its precursors from the surface (Figures 1 and 2). Figure 4 also shows that GMI/fvGCM underestimates O₃ in the SH. Since stratospheric ⁷Be fractions are relatively low in this simulation, the O₃ underestimate may be due to overly weak STE (cf. Figure 2). Low emissions of lightning NO_x, an important precursor of tropospheric O₃ could also play a role. Lightning NO_x emissions between 10°S and 70°S in GMI/fvGCM during July-August are similar to those in GMI/GEOS4 and GMI/MERRA, but the emissions during May-June are a factor of ~ 2.5 lower than those in GMI/GEOS4 and GMI/MERRA (Table 2). Since O₃ has a lifetime of weeks to months in the UT/MT, a low-O₃ bias during May-June will lead to lower O₃ during July-August in GMI/fvGCM. GMI/GEOS4 simulates O₃ in both hemispheres reasonably well but underestimates O₃ in the tropical UT/MT, as seen at Paramaribo and Nairobi in Figure 4. GMI/MERRA underestimates O₃ in the NH high-latitude UT likely due to weak STE compared to GMI/GEOS4 as suggested by ⁷Be tracer simulations (Figure 2, right column), while it overestimates O₃ with a high bias in the SH subtropics because of a combination of excessive influences from lightning NO_x emissions in May (Table 2) and STE (or subsidence from LIT) (Figure 2). In addition, the shallower tropical convection (Figure 1) accompanied by larger STE contribution in the southern tropical MT/UT (Figure 2) results in less clean air being lifted from the LT to MT/UT.

Figure 4.

Comparisons of GMI simulated tropospheric ozone profiles (color lines) with ozonesonde observations (black line) for a range of latitudes. The model is driven by the fvGCM, GEOS4, and MERRA meteorological fields. Values are July–August average.

Table 2.

Global lightning NO_x emissions (Tg N/mon) during May – August in GMI CTM driven by three meteorological data sets (fvGCM, GEOS4, and MERRA)

	May	June	July	August
fvGCM	0.57 (0.03a)	0.65 (0.02)	0.80 (0.03)	0.78 (0.05)
GEOS4	0.64 (0.05)	0.73 (0.07)	0.82 (0.03)	0.72 (0.05)
MERRA	0.49 (0.07)	0.69 (0.07)	0.81 (0.03)	0.78(0.04)

^aValues in parenthesis denote lightning NO_x emissions between 10°S and 70°S.

Figure 5 shows GMI simulated zonal mean TOCs averaged over July – August in comparison with TORs determined from TOMS/SBUV (Fishman et al., 2003), OMI/MLS (Ziemke et al., 2006), and TOCs directly retrieved from TES measurements. The World Meteorological Organization (WMO) definition of thermal tropopause is used to calculate the model TOC, following Liu et al. (2016). The latitudinal distribution of TOCs shows a trough in the tropics and polar regions, and a peak at mid-latitudes in both the models and the observations. The TORs determined from TOMS/SBUV and OMI/MLS agree well with each other in the NH, but those from OMI/MLS are lower at ~10°N and higher at south of 50°S. The TOCs determined from TES are more similar to the OMI/MLS TORs, but biased high in the northern subtropics, and biased low at south of 40°S. A comparison of Figure 5 with Figure 4 indicates that the TOCs from three model simulations coincide with the above results from model evaluations with ozonesonde O₃ profiles. For example, both evaluations suggest negative biases in the SH and positive biases in the NH high-/mid-latitudes in GMI/fvGCM, and positive biases in the southern subtropics in GMI/MERRA.

Figure 5.

GMI simulated zonal mean tropospheric ozone columns (TOCs) compared with tropospheric ozone residuals (TORs) determined from TOMS/SBUV (July–August 1979-2005 multi-year average) (Fishman et al., 2003), OMI/MLS (July–August 2005 average) (Ziemke et al., 2006), and TOCs determined from TES retrievals (July–August 2005 average).

4.2. O₃ and CO Concentrations at 618 hPa

Figure 6 shows the July-August mean concentrations of O₃ and CO at 618 hPa in the GMI simulations. Figure 7 shows the corresponding global distributions of ²²²Rn concentrations, stratospheric fractions (%) of mean tropospheric ⁷Be concentrations, and ratios ⁷Be/²¹⁰Pb. All simulations show highest O₃ concentrations at NH mid-latitudes and lowest O₃ concentrations in the tropical western Pacific. They also simulate a narrow band of relatively high O₃ concentrations in the southern tropics and subtropics. GMI/fvGCM simulates highest O₃ concentrations at NH mid/high latitudes (Figure 6, left panel) likely due to STE, as indicated by a large fraction of ⁷Be transported down from the stratosphere (Figure 7, middle top panel). By contrast, it simulates the lowest O₃ concentrations in the southern tropics and subtropics, especially over southern Africa and South Atlantic Ocean. In this region, GMI/MERRA simulates the highest O₃ concentrations attributed to high lightning NO_x emissions (Table 2), large STE (Figure 7, middle bottom panel), and biomass burning emissions lifted by shallow but strong convection (Section 3; Figure 7, left bottom panel). Thompson et al. (1996) previously suggested that O₃ maximum observed in southern Africa and the adjacent Atlantic during September - October 1992 is caused by the coincidence of O₃ precursors from biomass burning with long residence time, and deep convection with additional lightning NO_x and biogenic sources. As we will show in Section 5, the emission types contributing to the O₃ enhancements over this region in July – August mainly include lightning NO_x and, to a lesser extent, biomass burning.

Figure 6.

July-August mean mixing ratios of O₃ and CO (ppbv) at 618 hPa as simulated by GMI CTM driven by three meteorological datasets (1995 for fvGCM, 2005 for GEOS4 and MERRA).

Figure 7.

Mean ²²²Rn concentrations (mBq SCM⁻¹) (left column), stratospheric fraction (%) of tropospheric ⁷Be concentrations (middle column), and ratios of ⁷Be to ²¹⁰Pb volume mixing ratios (right column) at 618 hPa in the GMI model driven by the fvGCM (1995), GEOS4 (2005), and MERRA (2005) meteorological data sets for the period of July - August.

All simulations show a similar pattern of CO concentrations at 618 hPa, e.g., CO enhancements due to biomass burning emissions lifted by convection (e.g., South America, Africa, Indonesia, and Alaska) and anthropogenic emissions (e.g., East Asia, South Asia, and eastern North America) (Figure 6, right column). This pattern also reflects the geographic distribution of these emissions. GMI/fvGCM simulates lowest CO concentrations at 618 hPa in most of the polluted regions due to stronger STE of O₃, as discussed in Section 4.1. GMI/GEOS4 simulates slightly lower CO concentrations in East and South Asia, North America and their outflow regions, and Indonesia than GMI/MERRA does. GMI/GEOS4 also simulates lower CO concentrations over subtropical South American and African westerly outflow regions.

To evaluate GMI O₃ and CO simulations with satellite observations, we use TES retrievals at 618 hPa where TES has good sensitivity for both O₃ and CO in the MT (Zhang et al., 2006). GMI model output was sampled along the TES orbit track at the observation time and then interpolated onto the 67 vertical pressure levels of TES retrievals. Since the model output was saved every 3 hours, the temporal offset with TES is up to 1.5 hours. To compare the model output with the TES retrieved profiles, TES averaging kernels and a priori were applied to the model output. Both the model output and TES data were gridded onto grids of 10° latitude by 10° longitude by averaging all values within each grid box. Figures 8–9 show the mean concentrations of O₃ and CO at 618 hPa observed by TES during July - August 2005 and corresponding GMI CTM results.

Figure 8.

Mean mixing ratios of O₃ at 618 hPa observed by TES during July- August 2005 and corresponding GMI CTM results with 3-hourly output sampled along the TES orbit tracks. TES averaging kernels and a priori were applied to the model output. Results are averaged into 10°×10° grid cells.

Figure 9.

Mean mixing ratios of CO at 618 hPa observed by TES during July- August 2005 and corresponding GMI CTM results with 3-hourly output sampled along the TES orbit tracks. TES averaging kernels and a priori were applied to the model output. Results are averaged into 10°×10° grid cells.

TES observed enhanced O₃ concentrations over the Middle East, northern Africa, southern Africa, North America, and East Asia (Figure 8). Increased levels of O₃ were also observed in continental outflow regions, especially the northwestern Pacific, North Atlantic, tropical south Atlantic, and southern subtropical Indian Ocean. All simulations capture the spatial distributions of O₃ well but underestimate the enhancements over southern Africa and adjacent oceans. GMI/fvGCM simulates reasonably well the TES-observed O₃ enhancements at NH mid/high latitudes but slightly underestimates the low O₃ concentrations in the tropical western Pacific and Indian Ocean. GMI/GEOS4 and GMI/MERRA simulations show lower O₃ concentrations at NH mid/high latitudes compared to TES observations. However, considering that TES O₃ has a positive bias of 3-10 ppbv in the MT (Nassar et al., 2008), GMI/fvGCM may very well overestimate O₃ at NH mid-latitudes while GMI/GEOS4 and GMI/MERRA simulations are closer to reality. This conclusion is consistent with that from the comparison of GMI simulations with ozonesonde observations (Figure 4).

Enhanced CO concentrations were observed by TES over Africa, South America, North America, and Eurasia (Figure 9). All simulations underestimated CO concentrations in most of those CO hot spots in the NH. GMI/GEOS4 captured fairly well high CO concentrations over biomass burning regions in South America and Africa. However, considering TES CO biases, i.e., a negative bias at NH mid-latitudes and a positive bias in the tropics (Luo et al., 2007a; Lopez et al., 2008), all simulations significantly underestimate CO enhancements at NH mid-latitudes but simulate better CO enhancements over the tropical biomass burning regions. This is consistent with a previous study by Shindell et al. (2006) who found multi-model underestimate of NH extratropical CO likely due to current inventories underestimating fossil fuel emissions in East Asia and biomass burning emissions in south-central Africa.

5. O₃ and CO Relationships

In this section, we examine O₃ and CO relationships at 618hPa in GMI CTM. We interpret GMI simulated O₃-CO correlations and their slopes in the context of emissions, photochemical transformation, and transport (e.g., convection, STE, and large-scale subsidence), using model meteorological data and radionuclide simulations. We then evaluate them with those derived from TES satellite observations.

5.1. GMI O₃-CO Correlations

Figure 10 shows the O₃-CO correlation coefficients (R) and linear regression slopes (dO₃/dCO) at 618 hPa for July – August, as calculated using the reduced major axis method with 3-hourly output from the GMI/fvGCM, GMI/GEOS4, and GMI/MERRA simulations. We discuss the common features in the correlation patterns in all simulations, followed by their discrepancies. All simulations show strong positive O₃-CO correlations and large dO₃/dCO enhancement ratios in the NH major continental outflow regions, e.g., Atlantic Seaboard, northern Atlantic, and northern Pacific, consistent with previous modeling studies (Zhang et al., 2006; Voulgarakis et al., 2011) and in situ observations (e.g., Anderson et al. 1993; Chin et al., 1994; Jaffe et al., 1996; Parrish et al., 1998; Tsutsumi and Matsueda, 2000; Mao and Talbot, 2004). Our simulations also suggest a much larger area with high correlations that extends from the NW to NE Pacific. We found that strong positive correlation regions are not co-located with maximum O₃ and CO concentrations in all simulations. Instead, they are located between most polluted and clean areas, reflecting the intrusion of high O₃ (and CO) air from mid-latitudes and low O₃ (and CO) air from the tropics. Fishman and Seiler (1983) and Mauzerall et al. (2000) previously suggested that strong positive O₃-CO correlations in low CO regions may be caused by the depletion of both O₃ and CO in tropical air.

Figure 10.

O₃-CO correlation coefficients (R) and linear regression slopes (dO₃/dCO) at 618 hPa in the GMI model driven by the fvGCM (1995), GEOS4 (2005), and MERRA (2005) meteorological fields. Results are calculated in 2°×2.5° grid cells using 3-hourly model output and the reduced major axis method. White areas denote absolute values of O₃-CO correlation coefficients less than 0.2.

All simulations show positive O₃-CO correlations in the SH marine regions, but GMI/MERRA simulates much stronger negative correlations over the equatorial Atlantic. The latter reflects the stronger convection in the LT/MT in MERRA, which will be discussed below. Positive O₃-CO correlations were previously observed over the tropical South Atlantic during the TRACE-A aircraft mission (September–October, 1992) (e.g., Collins et al., 1996). Collins et al. concluded that the O₃-CO correlations over the tropical South Atlantic are more affected by in situ photochemical production from aged biomass burning plumes (positive O₃-CO correlation) than transport from the stratosphere (negative O₃-CO and O₃ - dew point correlations).

Strong positive O₃-CO correlations are present in all simulations at 618 hPa over Indonesia (Figure 10), reflecting convective transport of biomass burning CO (Figure 6) and photochemical production of O₃ from its precursors. The dO₃/dCO enhancement ratios over Indonesia are not as large as those over the NH mid-latitude continental outflow regions due to the fact that biomass burning emits NO_x less efficiently than fossil fuel does.

Positive O₃-CO correlations over the westerly African biomass burning outflow region (southern Indian Ocean, ~ 45°S) are seen in all simulations (Figure 10). The positive O₃-CO correlations over both the NH mid-latitude continental outflow regions and the westerly African biomass burning outflow regions mainly reflect O₃ and CO signatures from different sources: 1) anthropogenic emissions of CO and other O₃ precursors in the former and biomass burning emissions in the latter (Figure 6), and 2) significant influences from the stratosphere and subsidence from UT/LS (Figure 7, middle and right columns, respectively). In the case of 1), the dO₃/dCO slopes in the westerly African biomass burning outflow are smaller than those in the NH mid-latitude continental outflow, again reflecting the lower efficiency of biomass burning NO_x emissions than that of fossil fuel NO_x emissions. In the case of 2), mixing of stratospheric air (high O₃) with polluted air masses (high CO) has previously been found associated with positive O₃-CO correlations downwind from outflow regions (Cooper et al., 2002; Kim et al., 2013).

Strong negative O₃-CO correlations are seen in all simulations over the northern tropical eastern Pacific, Caribbean, northern tropical Atlantic, and equatorial Africa. These negative correlations are primarily a result of convective transport of low-O₃ air masses impacted by biogenic emissions. As will be discussed in Section 6, significant decreases in O₃ and increases in CO occur near the above regions due to atmospheric oxidation of biogenic VOCs (e.g., isoprene) over tropical America and Africa. In addition, our results show weak positive (in GMI/fvGCM and GMI/GEOS4) or strong negative (in GMI/MERRA) O₃-CO correlations over much of the southern tropics and subtropics, especially near the biomass burning outflow regions. Negative O₃-CO correlations in the southern tropics during July-August were previously reported by Fishman and Seiler (1983). Based on aircraft measurements, they concluded that O₃ destruction in the southern tropical LT, where the major CO sources (biomass burning emissions) are located, may lead to strong negative correlations (see their Figure 3).

All GMI simulations show strong negative O₃-CO correlations over the Asian continent including the Middle East (Figure 10). Over Southwest China (e.g., Sichuan Basin), monsoonal convective lifting of air masses with high-CO and low-O₃ leads to negative O₃-CO correlations. For most of other regions, high O₃ and low CO associated with stratospherically influenced air (Figure 7, middle column) result in negative O₃-CO correlations with large (negative) dO₃/dCO ratios. As will be discussed in Section 6, lightning NO_x emissions also contribute to these negative correlations over the Asian continent. Our simulations over the Tibetan Plateau are consistent with the study of Wang et al., (2006), who inferred negative O₃-CO correlations from in situ measurements at Mount Waliguan located at the northeastern edge of the Tibetan Plateau during summer due to downward transport from the UT/LS.

While the O₃-CO correlations in the three simulations show similarities, they also show differences. The global O₃-CO correlation patterns in GMI/fvGCM and GMI/GEOS4 are more similar, presumably because fvGCM is the GCM in the GEOS4 assimilation and they use the same convection scheme. Even so, significantly different O₃-CO correlation coefficients between GMI/fvGCM and GMI/GEOS4 are seen in northern Africa, where the former simulates strong negative but the latter shows weak positive correlations. As indicated by radionuclide tracers (²¹⁰Pb and ⁷Be), fvGCM has relatively stronger large-scale subsidence over northern Africa at 618 hPa than GEOS4, resulting in strong correlations with a large negative slope. In addition, the O₃-CO correlations in GMI/MERRA are strongly negative over northern South America, tropical western South Atlantic Ocean, Indian Ocean, and tropical western Pacific Ocean. By contrast, the correlations in these regions in GMI/fvGCM and GMI/GEOS4 are either weak or positive. The convection in fvGCM is much weaker than in GEOS4 or MERRA except at SH mid-latitudes and over Tibetan Plateau (not shown). MERRA has the strongest convection in Central America, tropical western Pacific Ocean, tropical eastern Pacific Ocean, tropical western Atlantic Ocean, tropical eastern Indian Ocean, and Bay of Bengal. These differences of convective mass fluxes result in broader regions with negative O₃-CO correlations in the tropics in GMI/MERRA than those in GMI/fvGCM and GMI/GEOS4. Kim et al. (2013) also simulated different O₃-CO correlations in some tropical regions with GEOS-Chem driven by GEOS4 and GEOS5 meteorological data sets because of the model transport error associated with deep convection.

5.2. Evaluation of GMI O₃-CO Correlations with TES Observations

Figures 11 and 12 show the O₃-CO correlation coefficients (R) and linear regression slopes (dO₃/dCO), respectively, at 618 hPa as determined by TES observations for July - August 2005, and corresponding GMI CTM results with 3-hourly output sampled along the TES orbit tracks. Values are calculated in 10°x10° grid cells. The regions of > 60°S and > 60°N are excluded in this study because O₃ and CO concentrations over these regions are low (Figure 6) and absolute covariances of O₃ and CO over these regions are also low (not shown). Therefore, as suggested by Voulgarakis et al. (2011), discrepancies in these regions are not scientifically important in terms of the O₃-CO correlation. Since only two months of TES O₃ and CO observations were used, the correlation patterns are somewhat patchy and correlations are weak (|R| < 0.2) over more than half of the globe. Using TES data for July – August over 5 years improves the consistency of the correlation patterns (Figure 13), as discussed later. TES-observed O₃ and CO concentrations show highest correlations (R up to 0.6) with large slopes over the western Pacific, and relatively high correlations (R = 0.2-0.4) with relatively large slopes over North America, the Middle East, northern South America, central and southern Africa, as well as continental outflow regions, e.g., northwestern Pacific Ocean, western Indian Ocean, subtropical South Atlantic Ocean, tropical eastern Pacific, and northwestern Atlantic (Figures 11 and 12). Negative correlations were observed over the Tibetan Plateau (R < −0.6), northern Africa, and SH mid-latitudes (R < −0.4) (Figure 11). Global TES O₃-CO correlation patterns and magnitudes are similar to those reported by Zhang et al. (2006) and Voulgarakis et al. (2011). The slope patterns (Figure 12) follow the correlation ones (Figure 11), suggesting that the slopes of the regression lines are useful indicators of the correlation strength.

Figure 11.

O₃-CO correlation coefficients (R) at 618 hPa as determined by O₃ and CO observations from TES during July - August 2005, and corresponding GMI CTM results with 3-hourly output sampled along the TES orbit tracks. TES averaging kernels, spectral errors, and a priori are applied. Results are calculated in 10°×l0° grid cells. White areas denote absolute values of O₃-CO correlation coefficients less than 0.2.

Figure 12.

dO₃-dCO correlation coefficients (R) at 618 hPa as determined by dO₃ and dCO observations from TES during July - August 2005, and corresponding GMI CTM results with 3-hourly output sampled along the TES orbit tracks. TES averaging kernels, spectral errors, and a priori are applied. Results are calculated in 10°×l0° grid cells. White areas denote absolute values of dO₃-dCO correlation coefficients less than 0.2.

Figure 13.

O₃-CO correlation coefficients (R) and linear regression slopes (dO₃/dCO) at 618 hPa as determined by O₃ and CO observations from TES during July–August, 2005 – 2009. Results are calculated in 4°×5° grid cells. White areas denote absolute values of O₃-CO correlation coefficients less than 0.2.

The GMI simulated O₃-CO correlation coefficients and linear regression slopes (dO₃/dCO) calculated from each of the three model outputs sampled along the TES orbit tracks show similar global patterns but overall weaker correlations and smaller slopes (Figures 11 and 12) than non-sampled raw model results (Figure 10) due to spatiotemporal sampling and application of TES averaging kernels. All simulations capture the TES-observed positive O₃-CO correlations in various regions. On the other hand, all simulations indicate strong negative correlations over the Tibetan Plateau and tropical convective regions where TES misses such correlations or only shows much weaker negative correlations in much narrower areas.

To get a more statistically robust view of TES O₃-CO correlations, we conduct a similar analysis using multi-year observations. Figure 13 shows the O₃-CO correlation coefficients (R) and linear regression slopes (dO₃/dCO) at 618 hPa as determined by TES O₃ and CO retrievals for July - August over 5 years (2005 – 2009). Values are calculated in 4°x5° grid cells. The global distributions provide more details and are consistent with the coarse patterns for July – August 2005 shown in Figures 11 and 12. The negative correlations over the Tibetan Plateau and northern Africa are more apparent than those using the TES data only for July – August 2005 (Figure 11). Overall our results of multi-year (2005 – 2009) O₃-CO correlation coefficients at 618 hPa for July – August are similar to those inferred from the mean mid-tropospheric (400 – 800 hPa) TES O₃ and CO concentrations averaged over July – August 2005 – 2008 (Voulgarakis et al., 2011).

6. Sensitivity of O₃-CO Correlations to Emissions

In order to understand how O₃-CO correlation patterns are driven by emissions, we examine the sensitivity of O₃-CO correlations to emission types in the GMI model driven by the MERRA meteorological fields, which represent the state-of-the-art of GEOS-DAS at the time of this study. Figures 14 – 17 show the mean changes in O₃ and CO concentrations (ppbv) and their correlation coefficients, as well as the areas where correlation signs change relative to the standard simulation at 618 hPa when each emission type (fossil fuel, biomass burning, biogenic, and lightning NO_x emissions) is excluded in the model for July – August 2005. Figure 18 shows the O₃-CO correlation coefficients (R) at 618 hPa in the standard simulation and when each emission type is excluded. Results are calculated using 3-hourly model output. These figures provide the context for discussions in this section.

Figure 14.

Sensitivity of O₃, CO, and their correlations to fossil fuel (FF) emissions during July – August 2005. The plots show the mean differences in (a) O₃, (b) CO mixing ratios (ppbv), and (c) O₃-CO correlation coefficients (R) at 618 hPa between the standard GMI/MERRA simulation and a simulation where fossil fuel emissions are suppressed (NoFF) in the model. Also shown in (d) are the areas with changed correlation signs. Results are calculated using 3-hourly model output.

Figure 17.

Sensitivity of O₃, CO, and their correlations to fossil fuel (FF) emissions during July – August 2005. The plots show the mean differences in (a) O₃, (b) CO mixing ratios (ppbv), and (c) O₃-CO correlation coefficients (R) at 618 hPa between the standard GMI/MERRA simulation and a simulation where fossil fuel emissions are suppressed (NoFF) in the model. Also shown in (d) are the areas with changed correlation signs. Results are calculated using 3-hourly model output.

Figure 18.

GMI/MERRA-simulated O₃-CO correlations (R) at 618 hPa (a) in the standard simulation and (b) - (e) when fossil fuel (FF), biomass burning (BB), biogenic (BG), and lightning NO_x (LNO_x) emissions are individually suppressed (NoFF, NoBB, NoBG, and NoLNO_x, respectively) in the model during July-August 2005. Results are calculated using 3-hourly model output. White areas denote absolute values of O₃-CO correlation coefficients less than 0.2.

Fossil fuel emissions substantially increase O₃ (by ~5-20 ppbv) and CO (by ~10-30 ppbv) in the NH, notably over the Asian and North American continental outflow regions (Figure 14ab). Fossil fuel emissions lead to strengthened O₃-CO correlations with correlation signs changing from negative to positive over the Asian and North American outflow regions (Figures 14cd and 18b). Such effects are also seen over Europe, the Arabian Sea, the northern Bay of Bengal, and the northeastern Pacific (Figure 14cd). Fossil fuel emissions result in stronger negative O₃-CO correlations over part of the Asian continent (Figure 14c). This is especially true over the Tibetan Plateau where low-level convergence transports air masses with low-O₃ and high-CO to the middle troposphere.

Biomass burning emissions increase O₃ (by ~2-10 ppbv) and CO (by > 25ppbv) in the easterly outflow in the tropical South America and Central Africa, in the westerly outflow in the southern subtropics, and over Indonesia (Figure 15ab). They are responsible for the positive correlations in the SH mid- and high- latitudes (Figures 15cd and 18c). Without biomass burning emissions, O₃-CO correlations over the westerly outflow in the southern subtropics and most of the SH mid- and high- latitudes would be negative or very weak (Figures 15c and 18c). By contrast, biomass burning emissions degrade an already strong correlation from fossil fuel emissions in the NH (e.g., over part of the tropical western Pacific, Bay of Bengal, NH subtropical Atlantic, and especially NH high latitudes, Figure 15d). In the tropics, biomass burning emissions strengthen the positive correlations in Indonesia and weaken the negative correlations over the tropical South American outflow region. In the two models of Voulgarakis et al. (2011), biomass burning emissions have the largest impact on the O₃-CO correlations in the tropics, especially downwind of Central Africa and South America where biomass burning emissions changed the correlation sign from negative to positive. Our results show no apparent changes in the O₃-CO correlation signs (negative) in these downwind regions. This may reflect the differences in biomass burning emissions and/or chemical mechanisms used in the two studies.

Figure 15.

Sensitivity of O₃, CO, and their correlations to fossil fuel (FF) emissions during July – August 2005. The plots show the mean differences in (a) O₃, (b) CO mixing ratios (ppbv), and (c) O₃-CO correlation coefficients (R) at 618 hPa between the standard GMI/MERRA simulation and a simulation where fossil fuel emissions are suppressed (NoFF) in the model. Also shown in (d) are the areas with changed correlation signs. Results are calculated using 3-hourly model output.

Biogenic emissions increase O₃ concentrations at 618 hPa by ~ 2-6 ppbv in the NH subtropics and mid-latitudes, but decrease O₃ concentrations by up to ~10 ppbv in tropical South America, tropical Africa, and Indonesia (Figure 16a). The latter mainly reflects the fact that O₃ is consumed during the atmospheric oxidation process of isoprene under low NO_x conditions (Fan and Zhang, 2004; Seinfeld and Pandis, 1998). Biogenic emissions have large positive impacts on CO concentrations in the easterly and westerly outflow regions of South America and Africa, in the North American outflow, over Southwest China and Indonesia, as well as in the SH background (Figure 16b). The O₃-CO correlations in the model show smaller sensitivity to biogenic emissions relative to other emission types (Figures 16c and 18d). Nevertheless, biogenic emissions lead to strong negative O₃-CO correlations over the tropical eastern Pacific Ocean due to reduced O₃ and enhanced CO concentrations associated with these emissions (Figures 16cd and 18d). Such effects are also seen over central Africa, easterly South American outflow, westerly South American outflow, Indonesia, and subtropical western Pacific.

Figure 16.

Sensitivity of O₃, CO, and their correlations to fossil fuel (FF) emissions during July – August 2005. The plots show the mean differences in (a) O₃, (b) CO mixing ratios (ppbv), and (c) O₃-CO correlation coefficients (R) at 618 hPa between the standard GMI/MERRA simulation and a simulation where fossil fuel emissions are suppressed (NoFF) in the model. Also shown in (d) are the areas with changed correlation signs. Results are calculated using 3-hourly model output.

Lightning NO_x emissions increase O₃ concentrations at 618 hPa by up to ~15-25 ppbv at NH subtropics and mid-latitudes, and by up to ~15-30 ppbv at SH tropics and subtropics (Figure 17a). Such increases are relatively larger in those regions with subsiding air from the UT (cf., Figure 7, right bottom panel), where the largest effect of lightning NO_x emissions occurs. The resulting increase in OH concentrations leads to a general decrease in CO concentrations with maximum effects in the tropics and SH subtropics (Figure 17ab). Consequently, lightning NO_x emissions weaken both the positive O₃-CO correlations at mid- and high- latitudes and the negative correlations in the tropics (Figures 17cd and 18e). They alter the correlation signs from positive to negative in various areas where the correlations are generally weak (Figure 17d). Our results are in contrast with those of Voulgarakis et al. (2011) who showed that lightning NO_x emissions appeared to increase the O₃-CO correlations (400-800 hPa) in various regions (e.g., tropical eastern Pacific, NH continental outflow regions). These may partly reflect the differences in the altitude and strength of lightning NO_x emissions.

7. Summary and Conclusions

We have examined the capability of the Global Modeling Initiative (GMI) chemistry and transport model (CTM) to reproduce the global mid-tropospheric O₃- CO correlations from the TES instrument onboard the NASA Aura satellite during boreal summer (July – August). The model was driven by three meteorological data sets (fvGCM for 1995, GEOS4 for 2005, MERRA for 2005), allowing us to examine the sensitivity of model O₃-CO correlations to input meteorological data. Model simulations of radionuclide tracers (²²²Rn, ²¹⁰Pb, and ⁷Be) were used to illustrate the differences in convection, stratospheric influence, and large-scale subsidence among three meteorological data sets. To understand how various emissions drive global O₃-CO correlation patterns, we also investigated the sensitivity of GMI/MERRA model-calculated O₃ and CO concentrations and their correlations to emission types.

We evaluated GMI-simulated tropospheric O₃ vertical profiles and tropospheric O₃ columns (TOCs) with those from ozonesonde and satellite observations, respectively. Among the three GMI simulations, GMI/GEOS4-simulated O₃ concentrations are in best agreement with the observations. GMI/fvGCM and GMI/GEOS4 simulate lower O₃ concentrations in the tropical MT/UT better than GMI/MERRA does, due to stronger tropical deep convection in the former meteorological data sets. GMI/fvGCM overestimates O₃ concentrations in the NH high-/mid-latitude MT/UT due to excessive STE and convective transport of O₃ and /or its precursors from the surface, as suggested by too fast downward transport of ⁷Be from the stratosphere and strong convective lifting of ²²²Rn at NH mid-latitudes, respectively. GMI/fvGCM simulates lower O₃ in the SH MT/UT because of too weak influences from the stratosphere and too low lightning NO_x emissions. GMI/MERRA underestimates O₃ in the NH high-latitude UT due to weak STE, and overestimates O₃ in the SH subtropics due to tropical deep convection being too shallow, which results in less low-O₃ air transported from LT to MT/UT, as well as excessive NO_x emissions from lightning. The latitudinal distribution of model biases in TOCs relative to satellite observations is consistent with the results from model evaluations with ozonesonde O₃ profiles.

We evaluated GMI simulated O₃ and CO concentrations with TES observations at 618 hPa where TES has most sensitivity. TES observed O₃ enhancements over the NH mid-latitudes (including continental outflow regions), the Middle East, and the subtropical southern Africa and Atlantic. All simulations well capture the global spatial distribution of O₃ at 618 hPa, but appear to underestimate TES O₃ observations over southern Africa and its outflow region. GMI/fvGCM simulates the highest O₃ concentrations at NH mid-/high-latitudes, especially the Asian continent due to strong STE whereas it simulates the lowest O₃ concentrations in the southern tropics and subtropics due to weak STE and low lightning NO_x emissions. GMI/MERRA simulates the highest O₃ concentrations in the southern subtropics, especially southern Africa due to high lightning NO_x emissions and, to a lesser extent, strong convection. GMI/fvGCM underestimates the O₃ minimum in the tropical western Pacific and eastern Indian Ocean. Considering the positive bias in TES O₃ at NH mid-latitudes, GMI/fvGCM appears to overestimate O₃ over the East Asian outflow region due to too fast STE whereas GMI/GEOS4 and GMI/MERRA simulate O₃ enhancements reasonably well in East Asia and its downwind regions. TES observed CO enhancements over North America, Europe, East Asia, tropical South America, and tropical Africa. All simulations capture those enhancements over the latter three regions. However, GMI/fvGCM underestimates the observed CO enhancements at 618 hPa over the Middle East, East Asia, and North America (due to excessive STE fluxes of O₃) and in the SH (due to weaker sinks for OH). Taking account of TES CO biases (i.e., a positive bias in the tropics and a negative bias at NH mid-latitudes), GMI/GEOS4 and GMI/MERRA simulations reproduce well the observed CO enhancements over biomass and biogenic outflow regions (e.g., southern Africa, South America), but underestimate those over anthropogenically polluted regions (e.g., East Asia, North America, and corresponding outflow areas).

The three GMI simulations all show strong positive O₃-CO correlations at 618 hPa over the NH mid-latitude continental outflow regions and the SH biomass burning outflow regions, as shown by TES observations. Generally, positive O₃-CO correlations are simulated in downwind of polluted regions due to photochemical production of O₃ from its precursors. However, owing to significant influences from the stratosphere and subsidence from UT/LS over these regions, mixing of stratospheric air with polluted (anthropogenic or biomass burning) air masses is associated with strong positive O₃-CO correlations with large dO₃/dCO enhancement ratios. Strong positive O₃-CO correlations are also simulated over the Indonesian biomass burning region where deep convection occurs, but the dO₃/dCO enhancement ratios are smaller than those in the NH mid-latitude continental outflow regions. The latter reflects the lower efficiency of NO_x emissions from biomass burning. Strong negative O₃-CO correlations over northern and central Africa, tropical Atlantic, and tropical eastern and western Pacific in all simulations result from convective transport of biomass burning air masses with low-O₃, and consumption of O₃ along with production of CO due to oxidation of biogenic hydrocarbons (e.g., isoprene under low NO_x conditions). The simulated negative O₃-CO correlations over the Asian continent, including the Middle East, are partly attributed to stratospheric influence and/or subsidence from UT/LS. High-O₃ and low-CO associated with stratospherically influenced air lead to strong negative correlations with large dO₃/dCO ratios. On the other hand, over Southwest China, monsoonal convective lifting of air masses with high-CO and low-O₃ results in negative O₃-CO correlations. By contrast, TES O₃ and CO concentrations at 618 hPa either miss such negative correlations (i.e., tropical convective regions) or only show weak negative correlations over much narrower areas (i.e., the Tibetan Plateau and northern Africa).

TES-observed O₃ and CO concentrations at 618 hPa show highest positive correlations with large regression slopes over the western Pacific, and relatively high correlations over North America, the Middle East, northern South America, central and southern Africa, and continental outflow regions. Negative correlations are observed in parts of the Asian continent (Tibetan Plateau), northern Africa, and SH mid-latitudes. All model output sampled along the TES orbit track capture the observed positive O₃-CO correlations over the NH mid-latitude continental outflow regions, southern Africa, western Indian Ocean, subtropical South Atlantic, northern South America, and tropical eastern Pacific . While all simulations show strong negative correlations over the Tibetan Plateau, northern Africa, northern subtropical eastern Pacific, and Caribbean, TES O₃ and CO concentrations at 618 hPa only show weak negative correlations over much narrower areas (i.e., the Tibetan Plateau and northern Africa).

We performed sensitivity simulations with GMI/MERRA to investigate the effect of individual emission types on model-calculated O₃-CO correlations at 618 hPa. Results show that fossil fuel emissions increase global O₃ and CO concentrations and are responsible for the strong positive correlations over the NH continental outflow regions. Both biomass burning and biogenic emissions significantly increase global CO concentrations. Biomass burning emissions increase O₃ concentrations in the easterly outflow in the tropical South America and Central Africa, in the westerly outflow in the southern subtropics, and over Indonesia. Biogenic emissions increase O₃ concentrations in the NH subtropics and mid-latitudes, but decrease O₃ concentrations in tropical South America, tropical Africa, and Indonesia. The decreases mainly reflect the fact that O₃ is consumed during the atmospheric oxidation process of isoprene under low NO_x conditions. Biomass burning emissions are responsible for the positive correlations in the SH mid- and high- latitudes and negative correlations over part of the tropical western Pacific, Bay of Bengal, NH subtropical Atlantic, and NH high latitudes. Biogenic emissions have relatively smaller impact on the correlations than other emissions do, but are largely responsible for the negative O₃-CO correlations over the tropical eastern Pacific. Lightning NO_x emissions lead to large increases in O₃ concentrations at NH subtropics and mid-latitudes, and at SH tropics and subtropics, especially in the regions of subsidence. We find that lightning NO_x emissions weaken both positive O₃-CO correlations at mid- and high-latitudes and negative correlations in the tropics, and change weak positive correlations to negative in various areas. This result contrasts with that of previous studies.

This study demonstrates the utility of O₃-CO correlations to constrain the sources of tropospheric O₃ in global 3-D models. Our model simulations driven by three input meteorological data sets show significantly different global and regional distributions of O₃ and CO concentrations during boreal summer. For instance, GMI/fvGCM simulations show higher O₃ concentrations in the NH and lower CO concentrations than other simulations. Despite such differences, all simulations show similar patterns of O₃-CO correlations on a global scale. The regional features of the correlations, however, are often different due to the discrepancies in various meteorological processes (e.g., convection, STE, subsidence). In particular, GMI/MERRA simulates broader areas of strong negative O₃-CO correlations at 618 hPa in the tropics than GMI/fvGCM and GMI/GEOS4 do due to stronger tropical convection in the LT/MT. In this sense, O₃-CO correlations can be used to constrain better the sources of regional tropospheric O₃ in global models, especially for convective regions than O₃ and CO observations individually. Future work will examine the driving factors for O₃-CO correlations in other seasons.