ACCMIP-Ozone-RF-v1_22July1740BST_RD

1Tropospheric
2radiative
3Model

ozone changes, attribution to emissions and

forcing in the Atmospheric Chemistry and Climate

Inter-comparison Project (ACCMIP)

4
5D.S. Stevenson1, P.J. Young2,3, V. Naik4, J.-F. Lamarque5, D.T. Shindell6, R. Skeie7,
6S. Dalsoren7, G. Myhre7, T. Berntsen7, G.A. Folberth8, S.T. Rumbold8, W.J.
7Collins8, I.A. MacKenzie1, R.M. Doherty1, G. Zeng9, T. van Noije10, A. Strunk10, D.
8Bergmann11, P. Cameron-Smith11, D. Plummer12, S.A. Strode13, L. Horowitz14, Y.H.
9Lee6, S. Szopa15, K. Sudo16, T. Nagashima17, B. Josse18, I. Cionni19, M. Righi20, V.
10Eyring20, K.W. Bowman21, O. Wild22
11
12[1]{School of GeoSciences, The University of Edinburgh, Edinburgh, United Kingdom}
13[2]{Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder,
14Colorado, USA}
15[3]{Cooperative Institute for Research in Environmental Sciences, University of Colorado,
16Boulder, Colorado, USA}
17[4]{UCAR/NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA}
18[5]{National Center for Atmospheric Research, Boulder, Colorado, USA}
19[6]{NASA Goddard Institute for Space Studies, New York, New York, USA}
20[7]{CICERO, Center for International Climate and Environmental Research-Oslo, Oslo,
21Norway}
22[8]{Met Office Hadley Centre, Exeter, UK}
23[9]{National Institute of Water and Atmospheric Research, Lauder, New Zealand}
24[10]{Royal Netherlands Meteorological Institute, De Bilt, Netherlands}

25[11]{Lawrence Livermore National Laboratory, Livermore, California, USA}
26[12]{Canadian Centre for Climate Modeling and Analysis, Environment Canada, Victoria,
27British Columbia, Canada}
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1


1[13]{NASA Goddard Space Flight Centre, Greenbelt, Maryland, USA}
2[14]{NOAA Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA}
3[15]{Laboratoire des Sciences du Climat et de l’Environment, Gif-sur-Yvette, France}
4[16]{Department of Earth and Environmental Science, Graduate School of Environmental
5Studies, Nagoya University, Nagoya, Japan}
6[17]{National Institute for Environmental Studies, Tsukuba-shi, Ibaraki, Japan}?
7[18]{GAME/CNRM,

Météo-France,

CNRS

--

Centre

National

de

Recherches

8Météorologiques, Toulouse, France}
9[19]{Agenzia Nazionale per le Nuove Tecnologie, l'energia e lo Sviluppo Economico
10Sostenibile (ENEA), Bologna, Italy}
11[20]{Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre,
12Oberpfaffenhofen, Germany}
13[21]{NASA Jet Propulsion Laboratory, Pasadena, California, USA}
14[22]{Lancaster Environment Centre, University of Lancaster, Lancaster, UK}
15
16Correspondence to: D. S. Stevenson ()
17
18Abstract
19Ozone (O3) from seventeen atmospheric chemistry models taking part in the ACCMIP
20(Atmospheric Chemistry and Climate Model Intercomparison Project) has been used to
21calculate tropospheric O3 radiative forcings (RFs). We calculate a value for the 1750 to 2010
22tropospheric O3 RF of 0.40 W m-2. The model range of pre-industrial to present-day changes
23in O3 produces a spread in RFs of ±17%. Three different radiation schemes were used – we
24find differences in RFs between schemes (for the same ozone fields) of about ±10%. Applying
25two different tropopause definitions we find differences in RFs of ±3%. Given additional
26(unquantified) uncertainties associated with emissions, climate-chemistry interactions and
27land-use change, we estimate an overall uncertainty of ±30% for the tropospheric O 3 RF.
28Experiments carried out by a subset of six models find that the tropospheric O 3 RF can be
29attributed to increased emissions of CH4 (46%), NOx (30%), CO (15%) and NMVOCs (9%).
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1Normalising RFs to changes in tropospheric column O 3, we find a global mean normalised RF
2of 0.042 W m-2 DU-1. Future O3 RFs (W m-2) for the Representative Concentration Pathway
3(RCP) scenarios in 2030 (2100) are: RCP2.6: 0.31 (0.16); RCP4.5: 0.38 (0.26); RCP6.0: 0.33

4(0.24); and RCP8.5: 0.42 (0.56). Models show some coherent responses of O 3 to climate
5change: decreases in the tropical lower troposphere, associated with increases in water
6vapour; and increases in the sub-tropical to mid-latitude upper troposphere, associated with
7increases in lightning and stratosphere-to-troposphere transport.
91

Introduction

10Estimates of many aspects of Earth’s past atmospheric composition can be derived from
11analyses of air trapped in bubbles during ice formation (Wolff, 2011). However, the
12greenhouse gas ozone (O3) is too reactive to be preserved in ice. Direct measurements of
13tropospheric ozone concentrations prior to the 1970s are also extremely limited (Volz and
14Kley, 1988; Staehelin et al., 1994), and most early measurements used relatively crude
15techniques, such as Schӧnbein papers, that are subject to contamination from compounds
16other than ozone (Pavelin et al., 1999). Only in the last few decades have observation
17networks and analytical methods developed sufficiently to allow a global picture of ozone’s
18distribution in the troposphere to emerge (Fishman et al., 1990; Logan, 1999; Oltmans et al.,
192006; Thouret et al., 2006). Despite this paucity of early observations, tropospheric ozone is
20thought to have increased substantially since the pre-industrial era; this is largely based on
21model studies. Ozone photochemistry in the troposphere is relatively well understood
22(Crutzen, 1974; Derwent et al., 1996), and anthropogenic (including biomass burning)
23emissions of ozone precursors (methane (CH4,), nitrogen oxides (NOx), carbon monoxide
24(CO), non-methane volatile organic compounds (NMVOCs)) have changed (generally risen)
25dramatically since 1850 (Lamarque et al., 2010). Increasingly sophisticated models of
26atmospheric chemistry, driven by emission estimates, and sometimes coupled to climate
27models, have been used to simulate the rise of ozone since industrialisation (Hough and
28Derwent, 1990; Crutzen and Zimmerman, 1991; Berntsen et al., 1997; Wang and Jacob, 1998;
29Gauss et al., 2006).
30Although the rise of anthropogenic emissions has been the main driver of ozone change,
31several other factors may also have contributed. Natural sources of ozone precursor emissions

32(e.g., wetland CH4, soil and lightning NOx, biogenic VOCs) show significant variability and

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1have probably also changed since 1850, but these changes are highly uncertain (Arneth et al.,
22010). Downwards transport of ozone from the stratosphere is also an important source of
3tropospheric ozone (Stohl et al., 2003; Hsu and Prather, 2009); this source may have been
4affected by stratospheric ozone depletion, and its magnitude is forecast to increasechange in
5the future, via acceleration of the Brewer-Dobson circulation (Hegglin and Shepherd, 2009),
6although significant changes have not yet been observed (Engel et al., 2009). Ozone’s
7removal, via chemical, physical and biological processes is also subject to variability and
8change. Increases in absolute humidity (driven by warming), changes in ozone’s distribution,
9and changes in HOx (OH+HO2), have all tended to increase chemical destruction of ozone
10(Stevenson et al., 2006; Isaksen et al., 2009). Dry deposition of ozone at the surface, and to
11vegetation in particular, has been influenced by land-use change, but also changes in climate
12and CO2 abundance (Sanderson et al., 2007; Sitch et al., 2007; Fowler et al., 2009; Andersson
13and Engardt, 2010, Wu et al. 2012). Fluctuations in these natural sources and sinks are driven
14by climate variability; climate change and land-use change and may also have contributed
15towards long-term trends in ozone (ref needed).
16Ozone is a radiatively active gas, and interacts with both solar and terrestrial radiation;
17changes in the atmospheric distribution of ozone affect upwards and downwards fluxes of
18radiation. We use the concept of radiative forcing (RF) (e.g., as defined by Forster et al.,
192007) to quantify the impacts of ozone changes on Earth’s radiation budget.; Sspecifically in
20this paper we follow the IPCC approach for the forthcoming 5 th assessment? and use
21stratospherically adjusted RFs at the tropopause. Previous estimates of O 3 RF (e.g., Gauss et
22al., 2006) span the range 0.25-0.65 Wm-2, with a central value of 0.35 Wm-2 (Forster et al.,
232007). Skeie et al. (2011) recently estimated a value of 0.44 W m -2, with an uncertainty of

24±30%, using one of the models we also use in this study. Cionni et al. (2011) calculated O 3
25RFs for the IGAC/SPARC ozone database, and found a value of 0.23 W m -2, using an earlier
26version of the radiation scheme used here. We show here that an updated version of the
27radiation scheme with the same ozone field finds an equivalent value of 0.32 W m -2, and this
28value is considered more accurate. The tropospheric part of the IGAC/SPARC ozone database
29was constructed from early ACCMIP integrations from two of the seventeen models used here
30(GISS-E2-R and NCAR-CAM3.5). Consequently, the multi-model mean results presented
31here are also considered to be a better estimate of atmospheric composition change than the
32IGAC/SPARC database.

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1Because ozone is a secondary pollutant (it is not directly emitted) it is most useful to
2understand how emissions of its precursors have driven up its concentration. Model
3experiments carried out by Shindell et al. (2005, 2009) attributed ozone changes to pre4industrial to present-day increases in CH4, NOx and CO/NMVOC emissions between the pre5industrial and present-day periods.; Furthermore these authors found that CH4 emissions were
6responsible for most of the O3 change. These emissions also influence the oxidising capacity
7of the atmosphere in general, and affect a range of radiatively active species beyond ozone,
8including methane and secondary aerosols (Shindell et al., 2009).
9In In this paper, we present results from global models participating in tthe Atmospheric
10Chemistry

and

Climate

Model

Intercomparison

Project

(ACCMIP;

see

11www.giss.nasa.gov/projects/accmip), . Within ACCMIP, multiplea considerable number of
12global models (~17) simulated atmospheric composition between 1850-2100. Lamarque et al.
13(2012a) give an overview of ACCMIP whilst Lamarque et al. (2012b) present detailed
14descriptions of the participating models. Shindell et al. (2012) describe total radiative
15forcings, particularly those from aerosols; Lee et al (2012) further focusses on black carbon
16aerosol. Young et al. (2012) describes the tropospheric ozone results for the pre-industrial,
17present-day and future periodsin detail, including a range of comparisons with observations;
18Bowman et al. (2012) focus on comparisons with measurements from TES (Tropospheric
19Emission Spectrometer). Finally, two papers focus on the historical and future evolution of the
20oxidising capacity of the atmosphere (Naik et al., 2012; Voulgarakis et al., 2012). In this
21paper, we estimate tropospheric ozone radiative forcing based on results from global models
22participating in ACCMIP. In Section 2, the models used and the experiments they performed
23are described. Results of simulated tropospheric? ozone and resulting radiative forcings are
24presented in Section 3; these are discussed and conclusions drawn in Section 4. For reasons of
25space and conciseness, the main text focusses on generalised results (often presented as the
26multi-model mean); specific results from individual models are predominantly presented in
27the extensive Supplementary Material.
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22.1

Methods
Models employed

3Results from seventeen different models are analysed here (Table 1). Detailed model
4descriptions are provided elsewhere (Lamarque et al., 2012b; Huijnen et al., 2010). All are
5global atmospheric chemistry models, and most are coupled to climate models which provide
6the driving meteorological fields online. Climate model output of sea-surface temperatures
7and sea-ice (SST/SI) from prior CMIP-5 runs typically provide the lower boundary
8conditions; well-mixed atmospheric greenhouse gas concentrations are also specified. Two
9models (B and Q) are chemistry-transport models, driven by meteorological analyses – these
10provide only a single year’s output for each experiment and were run with the same
11meteorology in each case. Models M and O are chemistry-transport models driven by climate
12models, but chemical fields are not passed back to the climate model. In all other models the
13chemical fields are regularly passed to the climate model’s radiation scheme: they are fully
14coupled chemistry-climate models (CCM). Models G and H are two versions of GISS, but set
15up in different ways: G has a fully interactive coupled ocean (the only model with this); H
16uses SST/SI and also includes aerosol chemistry. Models I and J are two versions of
17HadGEM2: I uses a relatively simple tropospheric chemistry scheme, whereas J has a more
18detailed scheme with several hydrocarbons. Several models (C, D, E, F, N?) include detailed
19stratospheric chemistry schemes; tropospheric schemes range from simple methane oxidation
20(A, C?) through models with a basic representation of NMVOCs (G, H, I, P?) to those with
21more detailed hydrocarbon schemes (B, E, F, J, K, L, M, N, O, Q?). In addition, some models
22include interactions between aerosols and gas-phase chemistry (B, F, H, I, J, K, L, N?).
23Models with no stratospheric chemistry handled their upper levels in a variety of different
24ways. Model B prescribed a stratospheric ozone influx following SYNOZ (McLinden et al.,

252000). Models I, J, O and P all used the IGAC/SPARC ozone climatology (Cionni et al.,
262011) to prescribe O3 in the stratosphere. In models I and J, ozone is overwritten in all model
27levels which are 3 levels (approximately 3-4 km) above the tropopause. Model O used the O 3
28fields together with vertical winds, to calculate a vertical O 3 flux at 100 hPa, added as a
29source at these levels in regions of descent. Model P prescribed O 3 at pressures below 100 hPa
30between 50°S-50°N and pressures below 150 hPa poleward of 50°.Other models did what…?

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1Some models allowed natural emissions of ozone precursors to vary with climate (all except
2B and E for lightning NOx; only D,E, G, and O for isoprene); others fixed these sources (Table
32).
42.2

Experiments analysed

5The main experiments analysed here are multi-annual simulations for the 1850s and the
62000s. Every model performed these experiments. Table 1 shows the model run length for
7each experiment: typically 10 years, but in a few cases longer or shorter. Model G ran five 108year ensemble members. In most cases, driving climate models simulated climates of the
91850s and 2000s, typically by specifying decadal-mean SST/SI fields (from prior coupled
10ocean-atmosphere climate simulations) and setting well-mixed greenhouse gas concentrations
11at appropriate levels. Models B, J and Q ran with the same climate in the 1850s as in their
122000s runs, so only assess how emissions have changed composition; single year experiments
13are thus not unreasonable in these cases.
14All models used anthropogenic (including biomass burning) emissions from Lamarque et al.
15(2010). This harmonisation of all models to the same source of emissions removes a
16potentially large source of inter-model difference (c.f. Gauss et al., 2006). However, as each

17model did not run exactly the same years to represent the 1850s and 2000s (see Table 1), and
18models used a range of values for natural emissions (Table 2) there are still some differences
19between models in the magnitude of the applied change in emissions (see Young et al., 2012,
20Figure 1). These differences are also added to as a result of the by different chemistry
21schemes used in the different models and decisions within each model of how to partition
22NMVOC emissions between individual species and/or direct CO emissions.
23Most models ran with prescribed methane concentrations of around 791 ppbv (1850) and
241751 ppbv (2000) (Meinshausen et al., 2011). One model (K) ran with methane emissions that
25varied overfor the historical period; this model and another (G) ran with methane emissions in
26the future.
27The experiment set used in this paper includes additional simulations to those described in
28Young et al. (2012), Voulgarakis et al. (2012) and Lamarque et al. (2102). Six of the models
29(Table 1) ran a series of attribution experiments, based on the 2000s simulations. In these,
30specific drivers of O3 change (anthropogenic emissions of NOx, CO, NMVOCs, and CH4
31concentrations) were individually reduced to 1850s levels. These experiments are closely
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1related to previous studies with the GISS model (Shindell et al., 2005, 2009), and allow us to
2attribute CH4 and O3 radiative forcings since the 1850s to these individual drivers.
3A subset of ten models (Table 1) ran experiments where they fixed emissions at 2000s levels,
4but applied an 1850s climate. These simulations allow us to investigate how climate change
5has contributed to the ozone change since the 1850s. Most of these models ran equivalent
6experiments for future climates.
7Finally, most models (Table 1) ran additional historical and future simulations, utilizing
8harmonized emissions from the ‘Representative Concentration Pathway’ (RCP) scenarios.
9Ozone fields from these experiments are presented in detail by Young et al. (2012) – here we
10use future column O3 changes in conjunction with normalised radiative forcings (mW m -2 DU111) (for 1850s-2000s; we assume this normalised forcing does non’t change significantly in

12future) to estimate future ozone radiative forcings.
132.3

Radiative forcing calculations

14Ozone fields were inserted into an offline version of the Edwards and Slingo (1996) radiation
15scheme, updated and described in Walters et al. (2011) (their Section 3.2). The scheme
16includes gaseous absorption in six bands in the SW and nine bands in the LW. The treatment
17of O3 absorption is as described in Zhong et al. (2008). The RF calculations use an updated
18version of the radiation code compared with those presented in Cionni et al (2011), and it is
19found that these updates make substantial differences in the values. We recommend that the
20values presented here are used rather than those presented in Cionni et al. (2011).
21The offline code was set up so that all input fields except ozone remained fixed (at present22day values) – thus differences between two runs of the radiation code with different ozone
23yield the changes in fluxes of radiation due to ozone change. Monthly mean ozone fields were
24interpolated from each model to a common resolution: 5° longitude by 5° latitude, and 64
25hybrid vertical levels. The vertical levels were chosen to be compatible with the base
26climatological fields (temperature, humidity, cloud fields) which were taken from a present27day simulation of the HadAM3 model (Pope et al., 2000; Tian and Chipperfield, 2005).
28Values for cloud particle effective radii were taken from the GRAPE dataset (Sayer et al.,
292011).
30To calculate an ozone radiative forcing, the code is applied as follows. A base calculation of
31radiation fluxes is performed, using multi-annually averaged monthly ozone data from the
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11850s, for each column of model atmosphere. The radiation calculation is then repeated,
2keeping everything the same, but using a different ozone field (e.g., from the 2000s). The
3change in net radiation at the tropopause between these two calculations gives the
4instantaneous radiative forcing.

5
6By changing the ozone field, heating rates in the stratosphere will have changed. If such a
7change were to happen in the real atmosphere, stratospheric temperatures would respond
8quickly (days to months) – much more quickly than the surface-troposphere system, which
9will adjust on multiannual timescales. A better estimate of the long-term forcing on the
10surface climate takes into account this short-term response of stratospheric temperatures
11(Forster et al., 2007). Stratospheric temperature adjustment was achieved by first calculating
12stratospheric heating rates for the base atmosphere. The stratosphere was assumed to be in
13thermal equilibrium, with dynamical heating exactly balancing the radiative heating.
14Furthermore, the dynamics were assumed to remain constant following a perturbation to
15ozone. Hence to maintain equilibrium, radiative heating rates must also remain unchanged. To
16achieve this, stratospheric temperatures were iteratively adjusted in the perturbed case, until
17stratospheric radiative heating rates returned to their base values. This procedure is called the
18fixed dynamical heating approximation (Ramanathan and Dickinson, 1979). Here we report
19annual mean forcings at the tropopause, after stratospheric temperature adjustment.
20We make some compareisons theof calculations fromwith the Edwards-Slingo radiation
21scheme to results from similar schemes used in the Norwegian Met Office in from Oslo and at
22the National Center from Atmospheric research (NCAR) in the USA. The Oslo radiative
23transfer calculations are performed with a broad band longwave scheme (Myhre and Stordal,
241997) and a model using the discrete ordinate method (Stamnes et al., 1988) for the shortwave
25calculations (see further description in Myhre et al. (2011)). In the radiative transfer
26calculations meteorological data from ECMWF are used and stratospheric temperature
27adjustment is included. Ozone RFs were also calculated offline using the NCAR Community
28Climate System Model 4 radiative transfer model and allowing for stratospheric temperatures
29to adjust (Conley et al., 2012). We compute the net longwave and shortwave all-sky flux at
30the tropopause (based on a climatology of tropopause pressure from the NCAR/NCEP
31reanalyses) using the same conditions for all parameters except for the ozone distribution.
32
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13
23.1

Results
Pre-industrial (1850s) and present-day (2000s) simulations

33.1.1 Core ACCMIP experiments
43.1.1.1 Ozone distributions and changes
5Figure 1 shows the multi-model mean (MMM) annual zonal mean (AZM) ozone for the
61850s and 2000s. All models are included in the MMM, with equal weighting. In the
7supplementary material, Figure S1 shows AZM (ppb) for the 1850s and 2000s for all of the
8individual 17 models. Figure 2 shows maps of MMM tropospheric column ozone (DU) for
9the 1850s and 2000s. Figure S2 is the equivalent for all 17 models. In these figures, we use
10the same monthly zonal mean climatological tropopause (hitherto referred to as MASKZMT)
11for all models, based on the 2 PVU definition applied to NCEP/NCAR reanalysis data (Cionni
12et al., 2011). We also calculate O3 changes and radiative forcing results using a different
13tropopause definition (1850s O3 = 150 ppbv; hitherto referred to as MASK150) to test how
14sensitive O3 and RF results are to this choice. Table 3 compares global mean tropospheric
15column O3 changes using both definitions for all models. Evaluation of simulated present-day
16ozone fields against a variety of observational data sets can be found elsewhere (Young et al.,
172012). Present-day ozone distributions of AZM and tropospheric columns are similar to those
18presented in Stevenson et al. (2006) from the ACCENT PhotoComp model intercomparison.
19Figure 3 shows the multi-model mean change in AZMzonal annual mean ozone (ppb) and the
20change in tropospheric column ozone (DU) for MASKZMT between 2000 and 1850. Figure
21S3 is the equivalent for all 17 models. Ozone generally increases throughout the troposphere,
22most strongly in the Northern Hemisphere sub-tropical upper troposphere (Figure 3). This
23mainly reflects the industrialised latitudes where emissions are concentrated, and the fact that

24the ozone lifetime is longer in the upper troposphere. Decreases in ozone are seen in the high
25latitudes of the Southern Hemisphere (SH), to varying degrees in many models (Figures 3,
26S3a). This reflects downwards transport of ozone depleted air from the stratosphere, and is
27especially pronounced in models M, G and H. This effect is strong enough in several models
28(especially M, G, H) to produce decreases in column ozone in high SH latitudes (Figure S3b).

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13.1.1.2 Ozone radiative forcings
2Figure 4 shows maps of the multi-model annual mean radiative forcing (mW m -2) in the SW,
3LW and total derived using, for MASKZMT. Figure S4a shows the total RFs for all 17
4models; Figure S4b shows the equivalent plot for O 3 from the IGAC/SPARC database (Cionni
5et al., 2011). The LW RF peaks in regions where the largest ozone changes coincide with hot
6surface temperatures and cold tropopause temperatures (e.g. over the Sahara and Middle East ;
7Figure 4). The D??SW RF peaks where large ozone changes coincide with high underlying
8albedos (either refective surfaces, such as deserts or ice, or low cloud). SW and LW RFs are
9lowerreduced over high altitude regions (e.g. Tibet, Rockies, Greenland) as there is simply
10less air mass (and hence column ozone, see Figure 2). Figure 5 shows the normalised total RF
11(mW m-2 DU-1) for MASKZMT. Figure S5 is equivalent for all 17 models. Normalized RFs
12peak in the tropics, where the troposphere is thicker, and where the temperature difference
13between the surface and tropopause is largest. Normalized RFs are highest in clear-sky
14tropical regions, and peak over NW Australia. Similar distributions for normalized RFs have
15been found previously (e.g., Gauss et al., 2003, their Figure 7).
16In order to estimate the uncertainty associated with these RFs, we tested how the following
17processes and choices influenced results: (i) choice of radiation scheme; (ii) choice of
18tropopause definition; (iii) stratospheric adjustment; and (iv) treatment of clouds.
19Tropopause definitions are problematic (e.g., Prather et al., 2011). The Edwards-Slingo

20(hitherto E-S) scheme was additionally run for all models using the different tropopause
21definition (MASK150). This method was also used to define the troposphere in most of the
22other ACCMIP papers (e.g., Young et al., 2012; Naik et al., 2012), and has been widely used
23in earlier studies (e.g., Stevenson et al., 2006). Using MASK150, we find that the global mean
241850s-2000s column O3 changes are larger by 0.1-1.1 DU (1-12%) compared to MASKZMT.
25Net O3 RFs are larger by 5-41 mW m-2 (1-10%) with MASK150 (Table 3).
26We additionally calculated instantaneous tropospheric O 3 RFs with the E-S scheme, both
27leaving clouds as before, and also for clear-skies. We found very similar results for the
28influence of stratospheric adjustment and clouds for all models; results are summarised in
29Table 4. Stratospheric temperature adjustment changes the SW, LW and net RFs, respectively,
30by: 0, -24 ± 1, and -20 ± 1% for MASKZMT; and 0, -26 ± 1, and -22 ± 1% for MASK150.
31Inclusion of clouds affects RFs by: 20 ± 4, -16 ± 1, and -12 ± 1% for MASKZMT; and 21 ± 5,
32-16 ± 1, and -12 ± 1% for MASK150. Quoted uncertainties are standard deviations across all
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1the models. Model B sits close to the mean values. The Oslo radiation scheme repeated these
2calculations for just model B, and found (results in the same format as above) that
3stratospheric adjustment changes RFs by: 0, -21, and -17%; whilst clouds change RFs by: 35,
4-30, -22%. Thus stratospheric adjustment has a slightly smaller effect in the Oslo scheme,
5whereas clouds have a stronger influence.
6Comparing the clear-sky instantaneous results between the E-S and Oslo schemes for
7MASK150 (Table 5) indicates that the Oslo LW RFs are 6% lower than E-S, but that the SW
8RFs are 13% higher. Since these differences are in opposite directions, the difference between
9schemes for the net RF is smaller (Oslo is 4% less than E-S).
10Comparing stratospherically adjusted RFs between these two schemes (Table 5) (for
11MASKZMT) shows that the SW RF is 25% larger in the Oslo scheme, but the LW RF is 16%
12smaller, and the net RF is 8% lower. A similar result is found when comparing the E-S and

13NCAR schemes (Table 5): the NCAR scheme has 17% larger values for SW RF, 16% lower
14LW RF values, and net RFs that are 10% lower. These comparisons between radiation
15schemes are used to infer levels of uncertainty associated with radiation calculations (see
16Section 4).
173.1.2 Attribution experiments
18A subset of six models ran a series of attribution experiments, based on the 2000s simulations
19(Tables 1 and 6). Specific drivers of O3 change (anthropogenic emissions of NOx, CO,
20NMVOCs, and CH4 concentrations) were individually reduced to 1850s levels. In all these
21experiments, the driving meteorology was identical to the base 2000s case, thus differences
22between simulations completely isolate the influence of the specific component that is
23changed. For the CH4 case, its concentrations are reduced to 1850s levels (791 ppb), and are
24fixed at this level. In the other experiments, CH 4 is fixed at present-day levels (1751 ppb), and
25emissions of everything else? are reduced to their 1850s levels. It must be noted that fixing
26CH4 has important consequences for how these experiments are interpreted, and this set-up
27differs from previous approaches, where methane emissions were used, and methane
28concentrations were allowed to respond (Shindell et al., 2005, 2009).
29Differences in ozone fields between attribution experiments and the year 2000s base case
30suggests that the largest component of the 1850s-2000s ozone change comes from NOx
31emissions, the next largest from changes in CH 4, and relatively small contributions from

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1changes in CO and NMVOC emissions (e.g., Figures S6 and S7 show ozone changes and
2radiative forcings for model B). However, by fixing methane concentrations, these runs are
3some way out of equilibrium for methane (and hence ozone). Making the assumption that the
4base 1850s and 2000s runs are in equilibrium for methane (with methane concentration
5[CH4]base and lifetime τbase), we take diagnosed methane lifetimes from the attribution

6experiments (τatt) and calculate equilibrium methane concentrations ([CH4]eq) for each
7experiment. We apply the method described in West et al. (2007) and Fiore et al. (2009), using
8the following equation:
9

[CH4]eq = [CH4]base (τatt/τbase)f

(1),

10where f is the methane adjustment factor, that accounts for the effect of CH4 concentrations on
11its own lifetime, which we take to be 1.35 (Prather et al., 2012). Methane lifetimes are for the
12whole atmosphere; we use diagnosed tropospheric lifetimes (with respect to OH) (Naik et al.,
132012), and adjust to include losses in the stratosphere (120 yr lifetime) and soils (160 yr
14lifetime) (Table 7). Differences between these equilibrium methane concentrations and the
15observed year 2000s value were used to calculate a methane radiative forcing associated with
16attribution experiments #2-5 (Ramaswamy et al., 2001). This methane adjustment will also
17generate a further ozone change and radiative forcing – these have been estimated by
18assuming a linear scaling of the ozone RF

to what? found in each model‘s methane

19experiment.
20For example, for model B, the NOx removal/reduction? experiment (#3) yields a methane
21lifetime of 11.60 yrs, compared to the base year 2000s experiment (#1) value of 8.70 yrs
22(Table 7). The longer lifetime reflects lower levels of OH due to the removal of NOx
23emissions. If this experiment had been carried out with methane free to adjust, methane would
24have responded by increasing from 1751 ppbv to an equilibrium level of 2581 ppbv (Table 7),
25generating a radiative forcing of 276 mW m-2. Thus the CH4 radiative forcing associated with
26NOx emission increases from the 1850s up to the 2000s is -276 mW m -2. The associated extra
27ozone forcing, found by scaling model B’s ozone response to methane, is -132 mW m -2.

28Adding this to the ozone forcing found directly from the NOx attribution experiment (193
29mW m-2) yields a net ozone forcing of 61 mW m-2 for model B (Table 8).
30We further extend our analysis to include the impacts of CH 4, CO and NMVOC emissions on
31CO2 concentrations (see supplementary material). We have not attempted to include further
32effects here such as(e.g., impacts of CH4 on stratospheric H2O, or impacts of changes in
1

13


1oxidants on secondary aerosol). We summarise the average results of all the models that ran
2the attribution experiments in Table 9.
3Based on this analysis, the tropospheric ozone RF can be attributed to emissions increases as
4follows: 52% (CH4), 21% (NOx), 17% (CO) and 10% (NMVOC). The sum of the
5contributions to O3 RF diagnosed from the individual experiments is 364 mW m -2 ,(Table 8),
6compared to a mean result for the 1850s-2000s experiments by the same models of 385 mW
7(mean to Table 3?- would be helpful to add a mean to this table if so?, or 355 in Fig 4?) m-2.
8However, the sum of the indirect effects on methane must sum to zero, but actually sum to
9-110 mW m-2, dominated by the large negative contribution from NOx. This indicates some
10deficiencies in our analysis.
11The mean changes in methane lifetime across all 6 models in the attribution experiments
12(Table 7) are: -16% (CH4), +40% (NOx), -7% (CO) and -3% (NMVOC). The estimation of
13equilibrium methane concentrations (equation (1)) is only valid for small perturbations to the
14methane lifetime (West et al., 2007); the method is probably reasonable for the CO and
15NMVOC experiments, and possibly for the CH 4 experiments, but is likely to produce poor
16results for the large perturbations from the NOx experiments. If we make the crude assumption
17that all of the error is in the [CH4]eq values from the NOx experiments, and force the sum of
18the indirect effects on CH4 to be zero, then the mean indirect RF for CH 4 from NOx must be
19-217 mW m-2 rather than the value in Table 9 of -327 mW m-2; the inferred O3 RF associated
20with the CH4 change will also reduce proportionately (linearly), increasing the net O3 RF

21associated with NOx emissions from 76 to 125 mW m-2. Applying these crude corrections, we
22find the relative contributions to the O 3 RF are now: 46% (CH4), 30% (NOx), 15% (CO) and
239% (NMVOC). These proportions may be more accurate than those without any corrections
24applied.
253.1.3 Experiments that isolate the climate change component
26Most models performed the core 1850s and 2000s experiments with driving climates
27appropriate for these decades (Table 1). In addition, ten models carried out sensitivity
28experiments with 2000s emissions, but driven by 1850s climate. By comparing runs with the
29same emissions, but different climates, we can diagnose the impact of climate change on O 3.
30Figure 6 shows the impact of climate change from 1850s to 2000s on annual zonal mean
31AZM ozone and tropospheric column O3, for the ten models.

1

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1Question to all modellers who ran Em2000Cl1850: do you also change stratospheric ozone in
2these runs? Or put another way, does your Em2000Cl1850 stratosphere look more like a
32000s stratosphere, or an 1850s stratosphere?
4Modelled ozone shows a range of responses to climate change (Figure 6). The largest overall
5response is seen in models G and H (the two GISS versions), where climate change is the
6main driver of the SH decreases in O 3 seen in these models (Figure S3). In this context, it is
7interesting to note that for the other model with large decreases in SH ozone (M), climate
8change does not seem to be the driver (so I guess it must be stratospheric ozone depletion?).
9Some models show increases in tropical mid- to upper tropospheric ozone, with these
10increases centred over the continents (G, H, and to a lesser extent O, F and L). All these
11models (except F) show (small) increases (x-y %) in lightning NOx emissions (Table 2);
12however, other models that also show increases in lightning do not show obvious increases in
13tropical ozone (A, M, N and P). Most of the models show decreases in ozone, particularly in

14the tropical lower troposphere, as would be expected due to increases in water vapour and
15hence ozone destruction. Several models also show indications of small (but extensive)
16increases in the stratospheric source of ozone, e.g. in the sub-tropical jet region (A, F, I, L,
17and P). Similar features have been seen in some future simulations under climate change
18scenarios (e.g., Zeng and Pyle, 2003; Kawase et al., 2011). On average over the troposphere,
19the net impact of climate change on ozone is a small decrease in tropospheric ozone.
20Many of these climate change induced changes in O 3 can be seen more strongly in results
21from associated experiments that fixed emissions at 2000s levels but simulated climate of the
222030s and 2100s (see Figure S8 and discussion in Young et al., 2012).
233.2

Other timeslice simulations

24Several models ran timeslice simulations that covered intervening decades between the 1850s
25and 2000s, and also for the four future Representative Concentration Pathway scenarios
26(RCP2.6, RCP 4.5, RCP6.0 and RCP8.5) (Table 1). Details of changes in surface and
27tropospheric O3 from these simulations can be found in Young et al. (2012). Here, we use
28changes in tropospheric column O3, and convolve these together with individual model‘s
29normalised O3 RFs (Figure 5/S5), to estimate the total tropospheric RF for each timeslice
30experiment relative to the 1850s (Figure 7). A subset of these results (for the 1980s, 2000s,
312030s and 2100s) are also presented in Table 10. We use spatially resolved annual O3 column

1

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1changes and normalised O3 RFs. We don’t take into account seasonal variations in either
2column changes or normalised RFs. This indirect method of calculating RFs also assumes that
3the normalised O3 RF for 1850s-2000s doesn’t change with time – i.e. that the shape of the

4change in ozone vertical profile doesn’t change with time. We consider that these
5approximations introduce only small errors in the estimates of O 3 RF presented in Figure 7
6and Table 10.
7Table 10 also shows mean values for selected time periods, constructed in three different
8ways: (i) using all available models for a given timeslice; (ii) just using the four models
9(FGKN) that ran all of the timeslices in Table 10; (iii) using a subset of ten models
10(ABFGKLMNOP) that ran all the timeslices except those for RCP4.5 and RCP6.0.
11Comparing the mean values calculated by these different methods shows that there is little
12influence on the overall results (the maximum devation is 0.024 Wm -2, or ~10%, for RCP6.0
13in 2100) of the variable model coverage of different timeslices.
144

Discussion and Conclusions

15With the MASKZMT tropopause, we find a mean value for the tropospheric ozone radiative
16forcing (1850s-2000s) of 356 mW m-2, with a standard deviation across 17 models of ±58 mW
17m-2 (±16%) (Table 10). The median model has a value of 367 mW m -2, and the full range
18spans 211-429 mW m-2. The model at the low end of this range (model M) is an isolated
19outlier – the next lowest value is 297 mW m -2 (Table 10). Using an alternate tropopause
20(MASK150), we find slightly higher values: 377 ± 65 mW m-2 (±17%) (Table 3). Values from
21the two sets of calculations differ by 6% for the MMM; this suggests that tropopause
22definition introduces an uncertainty of at least ±3%.
23These values were calculated by the Edwards and Slingo (1996) (E-S) radiation scheme. We
24find that the E-S radiation scheme gives net, stratospherically adjusted O 3 RFs that are 8% and
2510% higher than comparable schemes from Oslo and NCAR (Table 5). Taking the mean of
26our values for the two different tropopauses with the E-S scheme, and adjusting for the Oslo
27and NCAR schemes producing slightly lower values, our best estimate of tropospheric O 3 RF
28between the 1850s and 2000s is 344 mW m-2.
29Based on a comparison of instantaneous clear sky SW O 3 RFs between the E-S and Oslo
30schemes (Table 5), we estimate radiative transfer schemes introduce uncertainty of about

31±6%. Clouds influence O3 RFs to different degrees in the E-S and Oslo schemes, and add
32uncertainty of at least ±7% (Table 4). The influence of stratospheric adjustment also varies
1

16


1between the two schemes, adding uncertainty of about ±3%. Based on a quadratic sum of
2these, we estimate the overall uncertainty associated with the radiation scheme of about
3±10%. Combining these uncertainties with the model range (±17%; Table 10) and difference
4due to tropopause definition (±3%), we estimate an overall uncertainty of ±20% from these
5factors.
6Further sources of uncertainty in the O 3 RF stem from uncertainties in emissions (natural and
7anthropogenic), and changes in climate and stratospheric ozone. Most models predict
8relatively small impacts on tropospheric O 3 via climate change to date (Figure 6), but these
9impacts may increase in future (Figure S8). Uncertainties associated with these factors, and
10emissions in particular, are probably similar or larger than the ±20% estimated above. We
11therefore estimate an overall uncertainty of ±30% on our central estimate (Skeie et al. (2011)
12estimate the same value for uncertainty). Given the magnitude of the uncertainty, we quote
13values to two significant figures, giving our best estimate and uncertainty of 340 ± 100 mW
14m-2. It should be noted that this value is for 1850s to 2000s (which we take to be 1855 to
152005). Skeie et al (2011) calculated tropospheric O 3 increases between 1750 and 1850 of 1.0
16DU (using model B), suggesting an extra 42 mW m -2 should be added to give the RF from
171750 to 2005. Similarly, they calculate an increase from 2005 to 2010 of 0.25 DU, which
18would add a further 11 mW m -2. Hence for 1750-2010 our best estimate of tropospheric O 3 RF
19is 400 ± 120 mW m-2.
20It is well understood that increases in CH4, NOx, CO and NMVOCs have driven up
21tropospheric O3, however only one model has previously explored the relative contributions
22of these different O3 precursors (Shindell et al., 2005, 2009). Applying six different models
23here, we estimate that CH4, NOx, CO and NMVOCs are respectively responsible for 46%,

2430%, 15% and 9% of the 1850s-2000s O3 RF. There remains some uncertainty over the exact
25values for these fractions, but all models show the same relative rankings; an important source
26of uncertainty stems from extrapolating results from the experiments to yield equilibrium
27methane concentrations. These contributions compare to values of 51% (CH 4), 15% (NOx),
28and 33 % (CO and NMVOC combined) from Shindell et al (2005), as reported in IPCC-AR4
29(Forster et al., 2007, Table 2.13). Using our calculated fractions, this suggests that for 1750302010, the O3 RF of 400 mW m-2 has been derived from emissions of CH 4, NOx, CO and
31NMVOC as follows: 180, 120, 60, and 40 mW m-2. These emissions also influence the CH4
32RF by affecting the CH4 lifetime, and we find respective values of: 140, -220, 60 and 20 mW

1

17


1m-2. All these emissions except NOx also oxidise to form CO 2 (Table 9 and Supplementary
2Material). There are additional RF impacts of these emissions via secondary aerosol formation
3and stratospheric water vapour that have not been estimated here (see Shindell et al., 2009).
4Based on their impacts on CO2, CH4 and O3, we estimate emissions based RFs for CH4, NOx,
5CO and NMVOC of: 760, -100, 210 and 90 mW m-2, respectively.
6Normalizing the O3 RF by the change in column O3 (Figure 5), we find a global mean value of
742 mW m-2 DU-1 (using MASKZMT). This is similar to values from earlier studies: 42 mW m82 DU-1 (Ramaswamy et al., 2001; their Table 6.3 - a mean value from 11 studies); 36 mW m -2
9DU-1 (Gauss et al., 2003; their Table 3 - a mean value from 11 models simulating 2000 to
102100 changes). Normalised forcings vary slightly between models (Figure S5), reflecting
11differing ozone changes at different latitudes and heights (Figure S3).
12Using the normalized forcing from each model, together with the simulated tropospheric
13column ozone change, we have calculated total tropospheric ozone RFs for each model for
14each available timeslice (Figure 7). Although different subsets of models ran the timeslices,
15we find this has only a small influence on calculated multi-model mean values (Table 10).
16Making the harmonization to our best estimate of 1850s-2000s O 3 RF, we estimate MMM O3
17RFs of 80, 290 and 340 mW m-2 for the 1930s, 1980s and 2000s, respectively. For the RCP2.6

18scenario, we find MMM values of 310 and 160 mW m -2 for the 2030s and 2100s; for RCP4.5:
19380 and 260 mW m-2; for RCP6.0: 330 and 240 mW m-2; and for RCP8.5: 420 and 560 mW
20m-2. All these have similar uncertainties to our pre-industrial to present-day estimate of at least
21±30%. Uncertainties are arguably smaller for the future scenarios, as they are for exactly
22prescribed emissions; however, other sources of uncertainty increase, in particular the effects
23of climate change, land-use change and changing stratospheric ozone on tropospheric ozone.
24Over the 1850s-2000s, climate change has had relatively small influences on tropospheric
25ozone in most models (Figure 6), but is more important in some (e.g., models G and H). In the
26future, models suggest these changes will generally increase, with models displaying some
27coherent responses (Figure S8). All models suggest O 3 in the tropical lower troposphere will
28reduce, mainly due to warmer temperatures and higher water vapour concentrations. Most
29models indicate that O3 will increase in the sub-tropical to mid-latitude upper troposphere, due
30to a combination of both increase lightning NOx production, but also an increase of
31stratosphere-to-troposphere transport, as suggested by some earlier studies.
32Rather abrupt end - mention land –use and strat O3 perhaps. THE END?
1

18


1
2Acknowledgements
3ACCMIP is organized under the auspices of Atmospheric Chemistry and Climate (AC&C), a
4project of International Global Atmospheric Chemistry (IGAC) and Stratospheric Processes
5And their Role in Climate (SPARC) under the International Geosphere-Biosphere Project
6(IGBP) and World Climate Research Program (WCRP). The authors are grateful to the British
7Atmospheric Data Centre (BADC), which is part of the NERC National Centre for
8Atmospheric Science (NCAS), for collecting and archiving the ACCMIP data.
9GAF, STR and WJC were supported by the Joint DECC and Defra Integrated Climate
10Programme (GA01101) and the Defra SSNIP air quality contract AQ 0902.

11GZ acknowledges NIWA HPCF facility and funding from New Zealand Ministry of Science
12and Innovation.
13The CESM project is supported by the National Science Foundation and the Office of Science
14(BER) of the U. S. Department of Energy. The National Center for Atmospheric Research is
15operated by the University Corporation for Atmospheric Research under sponsorship of the
16National Science Foundation.
17The work of DB and PC was funded by the U.S. Dept. of Energy (BER), performed under the
18auspices of LLNL under Contract DE-AC52-07NA27344, and used the supercomputing
19resources of NERSC under contract No. DE-AC02-05CH11231.
20VN and LWH acknowledge efforts of GFDL's Global Atmospheric Model Development Team
21in the development of the GFDL-AM3 and Modeling Services Group for assistance with data
22processing.
23The GEOSCCM work was supported by the NASA Modeling, Analysis and Prediction
24program, with computing resources provided by NASA's High-End Computing Program
25through the NASA Advanced Supercomputing Division.
26The MIROC-CHEM calculations were perfomed on the NIES supercomputer system (NEC
27SX-8R), and supported by the Environment Research and Technology Development Fund (S287) of the Ministry of the Environment, Japan.
29The STOC-HadAM3 work was supported by cross council grant NE/I008063/1, and used
30facilities provided by the UK's national high-performance computing service, HECToR,
1

19


1through Computational Modelling Services (CMS), part of the NERC National Centre for
2Atmospheric Science (NCAS).
3The LMDz-OR-INCA simulations were done using computing ressources provided by the
4CCRT/GENCI computer center of the CEA.
5The CICERO-OsloCTM2 simulations were done within the projects SLAC (Short Lived
6Atmospheric Components) and EarthClim funded by the Norwegian Research Council.

7The MOCAGE simulations were supported by Météo-France and CNRS. Supercomputing
8time was provided by Météo-France/DSI supercomputing center.
9DTS and YHL acknowledge support from the NASA MAP and ACMAP programs.
10DP would like to thank the Canadian Foundation for Climate and Atmospheric Sciences for
11their long-running support of CMAM development.

1

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