IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group I: The Physical Science Basis

7.4.4 Global Tropospheric Ozone Present-Day Budgets of Ozone and its Precursors

Tropospheric ozone is (after CO2 and CH4) the third most important contributor to greenhouse radiative forcing. Trends over the 20th century are discussed in Chapter 2. Ozone is produced in the troposphere by photochemical oxidation of CO, CH4 and non-methane VOCs (NMVOCs) in the presence of NOx. Stratosphere-troposphere exchange (STE) is another source of ozone to the troposphere. Loss of tropospheric ozone takes place through chemical reactions and dry deposition. Understanding of tropospheric ozone and its relationship to sources requires three-dimensional tropospheric chemistry models that describe the complex nonlinear chemistry involved and its coupling to transport.

The past decade has seen considerable development in global models of tropospheric ozone, and the current generation of models can reproduce most climatological features of ozone observations. The TAR reported global tropospheric ozone budgets from 11 models in the 1996 to 2000 literature. Table 7.9 presents an update to the post-2000 literature, including a recent intercomparison of 25 models (Stevenson et al., 2006). Models concur that chemical production and loss are the principal terms in the global budget. Although STE is only a minor term in the global budget, it delivers ozone to the upper troposphere where its lifetime is particularly long (about one month, limited by transport to the lower troposphere) and where it is of most importance from a radiative forcing perspective.

The post-2000 model budgets in Table 7.9 show major differences relative to the older generation TAR models: on average a 34% weaker STE, a 35% stronger chemical production, a 10% larger tropospheric ozone burden, a 16% higher deposition velocity and a 10% shorter chemical lifetime. It is now well established that many of the older studies overestimated STE, as observational constraints in the lower stratosphere impose an STE ozone flux of 540 ± 140 Tg yr–1 (Gettelman et al., 1997; Olsen et al., 2001). Overestimation of the STE flux appears to be most serious in models using assimilated meteorological data, due to the effect of assimilation on vertical motions (Douglass et al., 2003; Schoeberl et al., 2003; Tan et al., 2004; Van Noije et al., 2004). The newer models correct for this effect by using dynamic flux boundary conditions in the tropopause region (McLinden et al., 2000) or by relaxing model results to observed climatology (Horowitz et al., 2003). Such corrections, although matching the global STE flux constraints, may still induce errors in the location of the transport (Hudman et al., 2004) with implications for the degree of stratospheric influence on tropospheric concentrations (Fusco and Logan, 2003).

Table 7.9. Global budgets of tropospheric ozone (Tg yr–1) for the present-day atmospherea.

Reference Modelb Stratosphere-Troposphere Exchange  Chemical Productionc Chemical Lossc Dry Deposition Burden (Tg) Lifetimed (days) 
TARe 11 models 770 ± 400 3420 ± 770 3470 ± 520 770 ± 180 300 ± 30 24 ± 2 
Lelieveld and Dentener (2000) TM3 570 3310 3170 710 350 33 
Bey et al. (2001) GEOS-Chem 470 4900 4300 1070 320 22 
Sudo et al. (2002b) CHASER 593 4895 4498 990 322 25 
Horowitz et al. (2003) MOZART-2 340 5260 4750 860 360 23 
Von Kuhlmann et al. (2003) MATCH-MPIC 540 4560 4290 820 290 21 
Shindell et al. (2003) GISS 417 NRf NR 1470 349 NR 
Hauglustaine et al. (2004) LMDz-INCA 523 4486 3918 1090 296 28 
Park et al. (2004) UMD-CTM 480 NR NR 1290 340 NR 
Rotman et al. (2004) IMPACT 660 NR NR 830 NR NR 
Wong et al. (2004) SUNY/UiO GCCM 600 NR NR 1100 376 NR 
Stevenson et al. (2004) STOCHEM 395 4980 4420 950 273 19 
Wild et al. (2004) FRSGC/UCI 520 4090 3850 760 283 22 
Folberth et al. (2006) LMDz-INCA 715 4436 3890 1261 303 28 
Stevenson et al. (2006) 25 models 520 ± 200 5060 ± 570 4560 ± 720 1010 ± 220 340 ± 40 22 ± 2 


a From global model simulations describing the atmosphere of the last decade of the 20th century.

b TM3: Royal Netherlands Meteorological Institute (KNMI) chemistry transport model; GEOS-Chem: atmospheric composition model driven by observations from the Goddard Earth Observing System; CHASER: Chemical AGCM for Study of Atmospheric Environment and Radiative Forcing; MOZART-2: Model for (tropospheric) Ozone and Related Tracers; MATCH-MPIC: Model of Atmospheric Transport and Chemistry – Max Planck Institute for Chemistry; GISS: Goddard Institute for Space Studies chemical transport model; LMDz-INCA: Laboratoire de Météorologie Dynamique GCM-Interactive Chemistry and Aerosols model; UMD-CTM: University of Maryland Chemical Transport Model; IMPACT: Integrated Massively Parallel Atmospheric Chemistry Transport model; SUNY/UiO GCCM: State University of New York/University of Oslo Global Tropospheric Climate-Chemistry Model; STOCHEM: Hadley Centre global atmospheric chemistry model; FRSGC/UCI: Frontier Research System for Global Change/University of California at Irvine chemical transport model.

c Chemical production and loss rates are calculated for the odd oxygen family, usually defined as Ox = ozone + O + NO2 + 2NO3 + 3 dinitrogen pentoxide (N2O5) + pernitric acid (HNO4) + peroxyacylnitrates (and sometimes nitric acid; HNO3), to avoid accounting for rapid cycling of ozone with short-lived species that have little implication for its budget. Chemical production is mainly contributed by reactions of NO with peroxy radicals, while chemical loss is mainly contributed by the oxygen radical in the 1D excited state (O(1D)) plus water (H2O) reaction and by the reactions of ozone with the hydroperoxyl radical (HO2), OH, and alkenes.

d Calculated as the ratio of the burden to the sum of chemical and deposition losses.

e Means and standard deviations for 11 global model budgets from the 1996 to 2000 literature reported in the TAR. The mean budget does not balance exactly because only nine chemical transport models reported their chemical production and loss statistics.

f Not reported.

The faster chemical production and loss of ozone in the current generation of models could reflect improved treatment of NMVOC sources and chemistry (Houweling et al., 1998), ultraviolet (UV) actinic fluxes (Bey et al., 2001) and deep convection (Horowitz et al., 2003), as well as higher NOx emissions (Stevenson et al., 2006). Subtracting ozone chemical production and loss terms in Table 7.9 indicates that the current generation of models has net production of ozone in the troposphere, while the TAR models had net loss, reflecting the decrease in STE. Net production is not a useful quantity in analysing the ozone budget because (1) it represents only a small residual between production and loss and (2) it reflects a balance between STE and dry deposition, both of which are usually parametrized in models.

Detailed budgets of ozone precursors were presented in the TAR. The most important precursors are CH4 and NOx (Wang et al., 1998; Grenfell et al., 2003; Dentener et al., 2005). Methane is in general not simulated explicitly in ozone models and is instead constrained from observations. Nitrogen oxides are explicitly simulated and proper representation of sources and chemistry is critical for the ozone simulation. The lightning source is particularly uncertain (Nesbitt et al., 2000; Tie et al., 2002), yet is of great importance because of the high production efficiency of ozone in the tropical upper troposphere. The range of the global lightning NOx source presently used in models (3–7 TgN yr–1) is adjusted to match atmospheric observations of ozone and NOx, although large model uncertainties in deep convection and lightning vertical distributions detract from the strength of this constraint. Process-based models tend to predict higher lightning emissions (5–20 TgN yr–1; Price et al., 1997).

Other significant precursors for tropospheric ozone are CO and NMVOCs, the most important of which is biogenic isoprene. Satellite measurements of CO from the Measurements of Pollution in the Troposphere (MOPITT) instrument launched in 1999 (Edwards et al., 2004) have provided important new constraints for CO emissions, pointing in particular to an underestimate of Asian sources in current inventories (Kasibhatla et al., 2002; Arellano et al., 2004; Heald et al., 2004; Petron et al., 2004), as confirmed also by aircraft observations of Asian outflow (Palmer et al., 2003a; Allen et al., 2004). Satellite measurements of formaldehyde columns from the GOME instrument (Chance et al., 2000) have been used to place independent constraints on isoprene emissions and indicate values generally consistent with current inventories, although with significant regional discrepancies (Palmer et al., 2003b; Shim et al., 2005).

A few recent studies have examined the effect of aerosols on global tropospheric ozone involving both heterogeneous chemistry and perturbations to actinic fluxes. Jacob (2000) reviewed the heterogeneous chemistry involved. Hydrolysis of dinitrogen pentoxide (N2O5) in aerosols is a well-known sink for NOx, but other processes involving reactive uptake of the hydroperoxyl radical (HO2), NO2 and ozone itself could also be significant. Martin et al. (2003b) find that including these processes along with effects of aerosols on UV radiation in a global Chemical Transport Model (CTM) reduced ozone production rates by 6% globally, with larger effects over aerosol source regions.

Although the current generation of tropospheric ozone models is generally successful in describing the principal features of the present-day global ozone distribution, there is much less confidence in the ability to reproduce the changes in ozone associated with perturbations of emissions or climate. There are major discrepancies with observed long-term trends in ozone concentrations over the 20th century (Hauglustaine and Brasseur, 2001; Mickley et al., 2001; Shindell and Favulegi, 2002; Shindell et al., 2003; Lamarque et al., 2005c), including after 1970 when the reliability of observed ozone trends is high (Fusco and Logan, 2003). Resolving these discrepancies is needed to establish confidence in the models.