1
Tropospheric ozone changes, attribution to emissions and
2
radiative forcing in the Atmospheric Chemistry and Climate
3
Model Inter-comparison Project (ACCMIP)
4
5
D.S. Stevenson1, P.J. Young2,3, V. Naik4, J.-F. Lamarque5, D.T. Shindell6, R.
6
Skeie7, S. Dalsoren7, G. Myhre7, T. Berntsen7, G.A. Folberth8, S.T. Rumbold8,
7
W.J. Collins8, I.A. MacKenzie1, R.M. Doherty1, G. Zeng9, T. van Noije10, A.
8
Strunk10, D. Bergmann11, P. Cameron-Smith11, D. Plummer12, S.A. Strode13, L.
9
Horowitz14, Y.H. Lee6, S. Szopa15, K. Sudo16, T. Nagashima17, B. Josse18, I.
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Cionni19, M. Righi20, V. Eyring20, K.W. Bowman21, O. Wild22
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12
[1]{School of GeoSciences, The University of Edinburgh, Edinburgh, United Kingdom}
13
[2]{Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder,
14
Colorado, USA}
15
[3]{Cooperative Institute for Research in Environmental Sciences, University of Colorado,
16
Boulder, 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,
21
Norway}
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,
27
British Columbia, Canada}
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
5
Studies, Nagoya University, Nagoya, Japan}
6
[17]{National Institute for Environmental Studies, Tsukuba-shi, Ibaraki, Japan}?
7
[18]{GAME/CNRM,
8
Météorologiques, Toulouse, France}
9
[19]{Agenzia Nazionale per le Nuove Tecnologie, l'energia e lo Sviluppo Economico
Météo-France,
CNRS
--
Centre
National
de
Recherches
10
Sostenibile (ENEA), Bologna, Italy}
11
[20]{Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre,
12
Oberpfaffenhofen, Germany}
13
[21]{NASA Jet Propulsion Laboratory, Pasadena, California, USA}
14
[22]{Lancaster Environment Centre, University of Lancaster, Lancaster, UK}
15
16
Correspondence to: D. S. Stevenson (David.S.Stevenson@ed.ac.uk)
17
18
Abstract
19
Ozone (O3) from seventeen atmospheric chemistry models taking part in the ACCMIP
20
(Atmospheric Chemistry and Climate Model Intercomparison Project) has been used to
21
calculate tropospheric O3 radiative forcings (RFs). We calculate a value for the 1750 to 2010
22
tropospheric O3 RF of 0.40 W m-2. The model range of pre-industrial to present-day changes
23
in O3 produces a spread in RFs of ±17%. Three different radiation schemes were used – we
24
find differences in RFs between schemes (for the same ozone fields) of about ±10%.
25
Applying two different tropopause definitions we find differences in RFs of ±3%. Given
26
additional (unquantified) uncertainties associated with emissions, climate-chemistry
27
interactions and land-use change, we estimate an overall uncertainty of ±30% for the
28
tropospheric O3 RF. Experiments carried out by a subset of six models find that the
29
tropospheric O3 RF can be attributed to increased emissions of CH4 (46%), NOx (30%), CO
2
1
(15%) and NMVOCs (9%). Normalising RFs to changes in tropospheric column O3, we find a
2
global mean normalised RF of 0.042 W m-2 DU-1. Future O3 RFs (W m-2) for the
3
Representative Concentration Pathway (RCP) scenarios in 2030 (2100) are: RCP2.6: 0.31
4
(0.16); RCP4.5: 0.38 (0.26); RCP6.0: 0.33 (0.24); and RCP8.5: 0.42 (0.56). Models show
5
some coherent responses of O3 to climate change: decreases in the tropical lower troposphere,
6
associated with increases in water vapour; and increases in the sub-tropical to mid-latitude
7
upper troposphere, associated with increases in lightning and stratosphere-to-troposphere
8
transport.
9
10
1
Introduction
11
Estimates of many aspects of Earth’s past atmospheric composition can be derived from
12
analyses of air trapped in bubbles during ice formation (Wolff, 2011). However, the
13
greenhouse gas ozone (O3) is too reactive to be preserved in ice. Direct measurements of
14
tropospheric ozone concentrations prior to the 1970s are also extremely limited (Volz and
15
Kley, 1988; Staehelin et al., 1994), and most early measurements used relatively crude
16
techniques, such as Schӧnbein papers, that are subject to contamination from compounds
17
other than ozone (Pavelin et al., 1999). Only in the last few decades have observation
18
networks and analytical methods developed sufficiently to allow a global picture of ozone’s
19
distribution in the troposphere to emerge (Fishman et al., 1990; Logan, 1999; Oltmans et al.,
20
2006; Thouret et al., 2006). Despite this paucity of early observations, tropospheric ozone is
21
thought to have increased substantially since the pre-industrial era; this is largely based on
22
model studies. Ozone photochemistry in the troposphere is relatively well understood
23
(Crutzen, 1974; Derwent et al., 1996), and anthropogenic (including biomass burning)
24
emissions of ozone precursors (methane (CH4), nitrogen oxides (NOx), carbon monoxide
25
(CO), non-methane volatile organic compounds (NMVOCs)) have changed (generally risen)
26
dramatically since 1850 (Lamarque et al., 2010). Increasingly sophisticated models of
27
atmospheric chemistry, driven by emission estimates, and sometimes coupled to climate
28
models, have been used to simulate the rise of ozone since industrialisation (Hough and
29
Derwent, 1990; Crutzen and Zimmerman, 1991; Berntsen et al., 1997; Wang and Jacob, 1998;
30
Gauss et al., 2006).
31
Although the rise of anthropogenic emissions has been the main driver of ozone change,
32
several other factors may also have contributed. Natural sources of ozone precursor emissions
3
1
(e.g., wetland CH4, soil and lightning NOx, biogenic VOCs) show significant variability and
2
have probably also changed since 1850, but these changes are highly uncertain (Arneth et al.,
3
2010). Downwards transport of ozone from the stratosphere is also an important source of
4
tropospheric ozone (Stohl et al., 2003; Hsu and Prather, 2009); this source may have been
5
affected by stratospheric ozone depletion, and its magnitude is forecast to increase in the
6
future, via acceleration of the Brewer-Dobson circulation (Hegglin and Shepherd, 2009),
7
although significant changes have not yet been observed (Engel et al., 2009). Ozone’s
8
removal, via chemical, physical and biological processes is also subject to variability and
9
change. Increases in absolute humidity (driven by warming), changes in ozone’s distribution,
10
and changes in HOx (OH+HO2), have all tended to increase chemical destruction of ozone
11
(Stevenson et al., 2006; Isaksen et al., 2009). Dry deposition of ozone at the surface, and to
12
vegetation in particular, has been influenced by land-use change, but also changes in climate
13
and CO2 abundance (Sanderson et al., 2007; Sitch et al., 2007; Fowler et al., 2009; Andersson
14
and Engardt, 2010, Wu et al. 2012). Fluctuations in these natural sources and sinks are driven
15
by climate variability; climate change and land-use change and may also have contributed
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towards long-term trends in ozone (ref needed).
17
Ozone is a radiatively active gas, and interacts with both solar and terrestrial radiation;
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changes in the atmospheric distribution of ozone affect upwards and downwards fluxes of
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radiation. We use the concept of radiative forcing (RF) (e.g. as defined by Forster et al., 2007)
20
to quantify the impacts of ozone changes on Earth’s radiation budget. Specifically in this
21
paper we follow the IPCC approach for the forthcoming 5th assessment? and use
22
stratospherically adjusted RFs at the tropopause. Previous estimates of O3 RF (e.g., Gauss et
23
al., 2006) span the range 0.25-0.65 Wm-2, with a central value of 0.35 Wm-2 (Forster et al.,
24
2007). Skeie et al. (2011) recently estimated a value of 0.44 W m-2, with an uncertainty of
25
±30%, using one of the models we also use in this study. Cionni et al. (2011) calculated O3
26
RFs for the IGAC/SPARC ozone database, and found a value of 0.23 W m-2, using an earlier
27
version of the radiation scheme used here. We show here that an updated version of the
28
radiation scheme with the same ozone field finds an equivalent value of 0.32 W m -2, and this
29
value is considered more accurate. The tropospheric part of the IGAC/SPARC ozone database
30
was constructed from early ACCMIP integrations from two of the seventeen models used
31
here (GISS-E2-R and NCAR-CAM3.5). Consequently, the multi-model mean results
32
presented here are also considered to be a better estimate of atmospheric composition change
33
than the IGAC/SPARC database.
4
1
Because ozone is a secondary pollutant (it is not directly emitted) it is most useful to
2
understand how emissions of its precursors have driven up its concentration. Model
3
experiments carried out by Shindell et al. (2005, 2009) attributed ozone changes to increases
4
in CH4, NOx and CO/NMVOC emissions between the pre-industrial and present-day periods.
5
Furthermore these authors found that CH4 emissions were responsible for most of the O3
6
change. These emissions also influence the oxidising capacity of the atmosphere in general,
7
and affect a range of radiatively active species beyond ozone, including methane and
8
secondary aerosols (Shindell et al., 2009).
9
In the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; see
10
www.giss.nasa.gov/projects/accmip), a considerable number of global models (~17)
11
simulated atmospheric composition between 1850-2100. Lamarque et al. (2012a) give an
12
overview of ACCMIP whilst Lamarque et al. (2012b) present detailed descriptions of the
13
participating models. Shindell et al. (2012) describe total radiative forcings, particularly those
14
from aerosols; Lee et al (2012) further focusses on black carbon aerosol. Young et al. (2012)
15
describes the tropospheric ozone results for the pre-industrial, present-day and future periods,
16
including a range of comparisons with observations; Bowman et al. (2012) focus on
17
comparisons with measurements from TES (Tropospheric Emission Spectrometer). Finally,
18
two papers focus on the historical and future evolution of the oxidising capacity of the
19
atmosphere (Naik et al., 2012; Voulgarakis et al., 2012). In this paper, we estimate
20
tropospheric ozone radiative forcing based on results from global models participating in
21
ACCMIP. In Section 2, the models used and the experiments they performed are described.
22
Results of simulated tropospheric? ozone and resulting radiative forcings are presented in
23
Section 3; these are discussed and conclusions drawn in Section 4. For reasons of space and
24
conciseness, the main text focusses on generalised results (often presented as the multi-model
25
mean); specific results from individual models are predominantly presented in the
26
Supplementary Material.
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28
2
Methods
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2.1
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Results from seventeen different models are analysed here (Table 1). Detailed model
31
descriptions are provided elsewhere (Lamarque et al., 2012b; Huijnen et al., 2010). All are
Models employed
5
1
global atmospheric chemistry models, and most are coupled to climate models which provide
2
the driving meteorological fields online. Climate model output of sea-surface temperatures
3
and sea-ice (SST/SI) from prior CMIP-5 runs typically provide the lower boundary
4
conditions; well-mixed atmospheric greenhouse gas concentrations are also specified. Two
5
models (B and Q) are chemistry-transport models, driven by meteorological analyses – these
6
provide only a single year’s output for each experiment and were run with the same
7
meteorology in each case. Models M and O are chemistry-transport models driven by climate
8
models, but chemical fields are not passed back to the climate model. In all other models the
9
chemical fields are regularly passed to the climate model’s radiation scheme: they are fully
10
coupled chemistry-climate models (CCM). Models G and H are two versions of GISS, but set
11
up in different ways: G has a fully interactive coupled ocean (the only model with this); H
12
uses SST/SI and also includes aerosol chemistry. Models I and J are two versions of
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HadGEM2: I uses a relatively simple tropospheric chemistry scheme, whereas J has a more
14
detailed scheme with several hydrocarbons. Several models (C, D, E, F, N?) include detailed
15
stratospheric chemistry schemes; tropospheric schemes range from simple methane oxidation
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(A, C?) through models with a basic representation of NMVOCs (G, H, I, P?) to those with
17
more detailed hydrocarbon schemes (B, E, F, J, K, L, M, N, O, Q?). In addition, some models
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include interactions between aerosols and gas-phase chemistry (B, F, H, I, J, K, L, N?).
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Models with no stratospheric chemistry handled their upper levels in a variety of different
20
ways. Model B prescribed a stratospheric ozone influx following SYNOZ (McLinden et al.,
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2000). Models I, J, O and P all used the IGAC/SPARC ozone climatology (Cionni et al.,
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2011) to prescribe O3 in the stratosphere. In models I and J, ozone is overwritten in all model
23
levels which are 3 levels (approximately 3-4 km) above the tropopause. Model O used the O3
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fields together with vertical winds, to calculate a vertical O3 flux at 100 hPa, added as a
25
source at these levels in regions of descent. Model P prescribed O3 at pressures below 100
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hPa between 50°S-50°N and pressures below 150 hPa poleward of 50°.Other models did
27
what…?
28
Some models allowed natural emissions of ozone precursors to vary with climate (all except
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B and E for lightning NOx; only D,E, G, and O for isoprene); others fixed these sources
30
(Table 2).
6
1
2.2
Experiments analysed
2
The main experiments analysed here are multi-annual simulations for the 1850s and the
3
2000s. Every model performed these experiments. Table 1 shows the model run length for
4
each experiment: typically 10 years, but in a few cases longer or shorter. Model G ran five 10-
5
year ensemble members. In most cases, driving climate models simulated climates of the
6
1850s and 2000s, typically by specifying decadal-mean SST/SI fields (from prior coupled
7
ocean-atmosphere climate simulations) and setting well-mixed greenhouse gas concentrations
8
at appropriate levels. Models B, J and Q ran with the same climate in the 1850s as in their
9
2000s runs, so only assess how emissions have changed composition; single year experiments
10
are thus not unreasonable in these cases.
11
All models used anthropogenic (including biomass burning) emissions from Lamarque et al.
12
(2010). This harmonisation of all models to the same source of emissions removes a
13
potentially large source of inter-model difference (c.f. Gauss et al., 2006). However, as each
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model did not run exactly the same years to represent the 1850s and 2000s (see Table 1), and
15
models used a range of values for natural emissions (Table 2) there are still differences
16
between models in the magnitude of the applied change in emissions (see Young et al., 2012,
17
Figure 1). These differences are also added to as a result of the different chemistry schemes
18
used in the different models and decisions within each model of how to partition NMVOC
19
emissions between individual species and/or direct CO emissions.
20
Most models ran with prescribed methane concentrations of around 791 ppbv (1850) and
21
1751 ppbv (2000) (Meinshausen et al., 2011). One model (K) ran with methane emissions that
22
varied over the historical period; this model and another (G) ran with methane emissions in
23
the future.
24
The experiment set used in this paper includes additional simulations to those described in
25
Young et al. (2012), Voulgarakis et al. (2012) and Lamarque et al. (2102). Six of the models
26
(Table 1) ran a series of attribution experiments, based on the 2000s simulations. In these,
27
specific drivers of O3 change (anthropogenic emissions of NOx, CO, NMVOCs, and CH4
28
concentrations) were individually reduced to 1850s levels. These experiments are closely
29
related to previous studies with the GISS model (Shindell et al., 2005, 2009), and allow us to
30
attribute CH4 and O3 radiative forcings since the 1850s to these individual drivers.
31
A subset of ten models (Table 1) ran experiments where they fixed emissions at 2000s levels,
32
but applied 1850s climate. These simulations allow us to investigate how climate change has
7
1
contributed to the ozone change since the 1850s. Most of these models ran equivalent
2
experiments for future climates.
3
Finally, most models (Table 1) ran additional historical and future simulations, utilizing
4
harmonized emissions from the ‘Representative Concentration Pathway’ (RCP) scenarios.
5
Ozone fields from these experiments are presented in detail by Young et al. (2012) – here we
6
use future column O3 changes in conjunction with normalised radiative forcings (mW m-2
7
DU-1) (for 1850s-2000s; we assume this normalised forcing does not change significantly in
8
future) to estimate future ozone radiative forcings.
9
2.3
Radiative forcing calculations
10
Ozone fields were inserted into an offline version of the Edwards and Slingo (1996) radiation
11
scheme, updated and described in Walters et al. (2011) (their Section 3.2). The scheme
12
includes gaseous absorption in six bands in the SW and nine bands in the LW. The treatment
13
of O3 absorption is as described in Zhong et al. (2008). The RF calculations use an updated
14
version of the radiation code compared with those presented in Cionni et al (2011), and it is
15
found that these updates make substantial differences in the values. We recommend that the
16
values presented here are used rather than those presented in Cionni et al. (2011).
17
The offline code was set up so that all input fields except ozone remained fixed (at present-
18
day values) – thus differences between two runs of the radiation code with different ozone
19
yield the changes in fluxes of radiation due to ozone change. Monthly mean ozone fields were
20
interpolated from each model to a common resolution: 5° longitude by 5° latitude, and 64
21
hybrid vertical levels. The vertical levels were chosen to be compatible with the base
22
climatological fields (temperature, humidity, cloud fields) which were taken from a present-
23
day simulation of the HadAM3 model (Pope et al., 2000; Tian and Chipperfield, 2005).
24
Values for cloud particle effective radii were taken from the GRAPE dataset (Sayer et al.,
25
2011).
26
To calculate an ozone radiative forcing, the code is applied as follows. A base calculation of
27
radiation fluxes is performed, using multi-annually averaged monthly ozone data from the
28
1850s, for each column of model atmosphere. The radiation calculation is then repeated,
29
keeping everything the same, but using a different ozone field (e.g. from the 2000s). The
30
change in net radiation at the tropopause between these two calculations gives the
31
instantaneous radiative forcing.
8
1
2
By changing the ozone field, heating rates in the stratosphere will have changed. If such a
3
change were to happen in the real atmosphere, stratospheric temperatures would respond
4
quickly (days to months) – much more quickly than the surface-troposphere system, which
5
will adjust on multiannual timescales. A better estimate of the long-term forcing on the
6
surface climate takes into account this short-term response of stratospheric temperatures
7
(Forster et al., 2007). Stratospheric temperature adjustment was achieved by first calculating
8
stratospheric heating rates for the base atmosphere. The stratosphere was assumed to be in
9
thermal equilibrium, with dynamical heating exactly balancing the radiative heating.
10
Furthermore, the dynamics were assumed to remain constant following a perturbation to
11
ozone. Hence to maintain equilibrium, radiative heating rates must also remain unchanged. To
12
achieve this, stratospheric temperatures were iteratively adjusted in the perturbed case, until
13
stratospheric radiative heating rates returned to their base values. This procedure is called the
14
fixed dynamical heating approximation (Ramanathan and Dickinson, 1979). Here we report
15
annual mean forcings at the tropopause, after stratospheric temperature adjustment.
16
We compare the calculations from the Edwards-Slingo radiation scheme to results from
17
similar schemes used in the Norwegian Met Office in Oslo and at the National Center from
18
Atmospheric research (NCAR) in the USA. The Oslo radiative transfer calculations are
19
performed with a broad band longwave scheme (Myhre and Stordal, 1997) and a model using
20
the discrete ordinate method (Stamnes et al., 1988) for the shortwave calculations (see further
21
description in Myhre et al. (2011)). In the radiative transfer calculations meteorological data
22
from ECMWF are used and stratospheric temperature adjustment is included. Ozone RFs
23
were also calculated offline using the NCAR Community Climate System Model 4 radiative
24
transfer model and allowing for stratospheric temperatures to adjust (Conley et al., 2012). We
25
compute the net longwave and shortwave all-sky flux at the tropopause (based on a
26
climatology of tropopause pressure from the NCAR/NCEP reanalyses) using the same
27
conditions for all parameters except for the ozone distribution.
28
9
1
3
Results
2
3.1
3
3.1.1 Core ACCMIP experiments
4
3.1.1.1 Ozone distributions and changes
5
Figure 1 shows the multi-model mean (MMM) annual zonal mean (AZM) ozone for the
6
1850s and 2000s. All models are included in the MMM, with equal weighting. In the
7
supplementary material, Figure S1 shows AZM (ppb) for the 1850s and 2000s for all of the
8
individual 17 models. Figure 2 shows maps of MMM tropospheric column ozone (DU) for
9
the 1850s and 2000s. Figure S2 is the equivalent for all 17 models. In these figures, we use
10
the same monthly zonal mean climatological tropopause (hitherto referred to as MASKZMT)
11
for all models, based on the 2 PVU definition applied to NCEP/NCAR reanalysis data (Cionni
12
et al., 2011). We also calculate O3 changes and radiative forcing results using a different
13
tropopause definition (1850s O3 = 150 ppbv; hitherto referred to as MASK150) to test how
14
sensitive O3 and RF results are to this choice. Table 3 compares global mean tropospheric
15
column O3 changes using both definitions for all models. Evaluation of simulated present-day
16
ozone fields against a variety of observational data sets can be found elsewhere (Young et al.,
17
2012). Present-day ozone distributions of AZM and tropospheric columns are similar to those
18
presented in Stevenson et al. (2006) from the ACCENT PhotoComp model intercomparison.
19
Figure 3 shows the multi-model mean change in AZM ozone (ppb) and the change in
20
tropospheric column ozone (DU) for MASKZMT between 2000 and 1850. Figure S3 is the
21
equivalent for all 17 models. Ozone generally increases throughout the troposphere, most
22
strongly in the Northern Hemisphere sub-tropical upper troposphere (Figure 3). This mainly
23
reflects the industrialised latitudes where emissions are concentrated, and the fact that the
24
ozone lifetime is longer in the upper troposphere. Decreases in ozone are seen in the high
25
latitudes of the Southern Hemisphere (SH), to varying degrees in many models (Figures 3,
26
S3a). This reflects downwards transport of ozone depleted air from the stratosphere, and is
27
especially 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).
Pre-industrial (1850s) and present-day (2000s) simulations
10
1
3.1.1.2 Ozone radiative forcings
2
Figure 4 shows maps of the multi-model annual mean radiative forcing (mW m-2) in the SW,
3
LW and total derived using MASKZMT. Figure S4a shows the total RFs for all 17 models;
4
Figure S4b shows the equivalent plot for O3 from the IGAC/SPARC database (Cionni et al.,
5
2011). The LW RF peaks in regions where the largest ozone changes coincide with hot
6
surface temperatures and cold tropopause temperatures (e.g. over the Sahara and Middle East;
7
Figure 4). The D??SW RF peaks where large ozone changes coincide with high underlying
8
albedos (either refective surfaces, such as deserts or ice, or low cloud). SW and LW RFs are
9
lower over high altitude regions (e.g. Tibet, Rockies, Greenland) as there is simply less air
10
mass (and hence column ozone, see Figure 2). Figure 5 shows the normalised total RF (mW
11
m-2 DU-1) for MASKZMT. Figure S5 is equivalent for all 17 models. Normalized RFs peak in
12
the tropics, where the troposphere is thicker, and where the temperature difference between
13
the surface and tropopause is largest. Normalized RFs are highest in clear-sky tropical
14
regions, and peak over NW Australia. Similar distributions for normalized RFs have been
15
found previously (e.g., Gauss et al., 2003, their Figure 7).
16
In order to estimate the uncertainty associated with these RFs, we tested how the following
17
processes and choices influenced results: (i) choice of radiation scheme; (ii) choice of
18
tropopause definition; (iii) stratospheric adjustment; and (iv) treatment of clouds.
19
Tropopause 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
21
definition (MASK150). This method was also used to define the troposphere in most of the
22
other ACCMIP papers (e.g., Young et al., 2012; Naik et al., 2012), and has been widely used
23
in earlier studies (e.g., Stevenson et al., 2006). Using MASK150, we find that the global mean
24
1850s-2000s column O3 changes are larger by 0.1-1.1 DU (1-12%) compared to MASKZMT.
25
Net O3 RFs are larger by 5-41 mW m-2 (1-10%) with MASK150 (Table 3).
26
We additionally calculated instantaneous tropospheric O3 RFs with the E-S scheme, both
27
leaving clouds as before, and also for clear-skies. We found very similar results for the
28
influence of stratospheric adjustment and clouds for all models; results are summarised in
29
Table 4. Stratospheric temperature adjustment changes the SW, LW and net RFs,
30
respectively, by: 0, -24 ± 1, and -20 ± 1% for MASKZMT; and 0, -26 ± 1, and -22 ± 1% for
31
MASK150. Inclusion of clouds affects RFs by: 20 ± 4, -16 ± 1, and -12 ± 1% for
32
MASKZMT; and 21 ± 5, -16 ± 1, and -12 ± 1% for MASK150. Quoted uncertainties are
11
1
standard deviations across all the models. Model B sits close to the mean values. The Oslo
2
radiation scheme repeated these calculations for just model B, and found (results in the same
3
format as above) that stratospheric adjustment changes RFs by: 0, -21, and -17%; whilst
4
clouds change RFs by: 35, -30, -22%. Thus stratospheric adjustment has a slightly smaller
5
effect in the Oslo scheme, whereas clouds have a stronger influence.
6
Comparing the clear-sky instantaneous results between the E-S and Oslo schemes for
7
MASK150 (Table 5) indicates that the Oslo LW RFs are 6% lower than E-S, but that the SW
8
RFs are 13% higher. Since these differences are in opposite directions, the difference between
9
schemes for the net RF is smaller (Oslo is 4% less than E-S).
10
Comparing stratospherically adjusted RFs between these two schemes (Table 5) (for
11
MASKZMT) shows that the SW RF is 25% larger in the Oslo scheme, but the LW RF is 16%
12
smaller, and the net RF is 8% lower. A similar result is found when comparing the E-S and
13
NCAR schemes (Table 5): the NCAR scheme has 17% larger values for SW RF, 16% lower
14
LW RF values, and net RFs that are 10% lower. These comparisons between radiation
15
schemes are used to infer levels of uncertainty associated with radiation calculations (see
16
Section 4).
17
3.1.2 Attribution experiments
18
A 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,
20
NMVOCs, and CH4 concentrations) were individually reduced to 1850s levels. In all these
21
experiments, the driving meteorology was identical to the base 2000s case, thus differences
22
between simulations completely isolate the influence of the specific component that is
23
changed. For the CH4 case, its concentrations are reduced to 1850s levels (791 ppb), and are
24
fixed at this level. In the other experiments, CH4 is fixed at present-day levels (1751 ppb), and
25
emissions of everything else? are reduced to their 1850s levels. It must be noted that fixing
26
CH4 has important consequences for how these experiments are interpreted, and this set-up
27
differs from previous approaches, where methane emissions were used, and methane
28
concentrations were allowed to respond (Shindell et al., 2005, 2009).
29
Differences in ozone fields between attribution experiments and the year 2000s base case
30
suggests that the largest component of the 1850s-2000s ozone change comes from NOx
31
emissions, the next largest from changes in CH4, and relatively small contributions from
12
1
changes in CO and NMVOC emissions (e.g. Figures S6 and S7 show ozone changes and
2
radiative forcings for model B). However, by fixing methane concentrations, these runs are
3
some way out of equilibrium for methane (and hence ozone). Making the assumption that the
4
base 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
6
experiments (τatt) and calculate equilibrium methane concentrations ([CH4]eq) for each
7
experiment. We apply the method described in West et al. (2007) and Fiore et al. (2009),
8
using the following equation:
[CH4]eq = [CH4]base (τatt/τbase)f
9
(1),
10
where f is the methane adjustment factor, that accounts for the effect of CH4 concentrations
11
on its own lifetime, which we take to be 1.35 (Prather et al., 2012). Methane lifetimes are for
12
the whole atmosphere; we use diagnosed tropospheric lifetimes (with respect to OH) (Naik et
13
al., 2012), and adjust to include losses in the stratosphere (120 yr lifetime) and soils (160 yr
14
lifetime) (Table 7). Differences between these equilibrium methane concentrations and the
15
observed year 2000s value were used to calculate a methane radiative forcing associated with
16
attribution experiments #2-5 (Ramaswamy et al., 2001). This methane adjustment will also
17
generate a further ozone change and radiative forcing – these have been estimated by
18
assuming a linear scaling of the ozone RF
19
experiment.
20
For example, for model B, the NOx removal/reduction? experiment (#3) yields a methane
21
lifetime 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
23
emissions. If this experiment had been carried out with methane free to adjust, methane would
24
have responded by increasing from 1751 ppbv to an equilibrium level of 2581 ppbv (Table 7),
25
generating a radiative forcing of 276 mW m-2. Thus the CH4 radiative forcing associated with
26
NOx emission increases from the 1850s up to the 2000s is -276 mW m-2. The associated extra
27
ozone forcing, found by scaling model B’s ozone response to methane, is -132 mW m-2.
28
Adding this to the ozone forcing found directly from the NOx attribution experiment (193
29
mW m-2) yields a net ozone forcing of 61 mW m-2 for model B (Table 8).
30
We further extend our analysis to include the impacts of CH4, CO and NMVOC emissions on
31
CO2 concentrations (see supplementary material). We have not attempted to include further
32
effects here such as impacts of CH4 on stratospheric H2O, or impacts of changes in oxidants
to what? found in each model‘s methane
13
1
on secondary aerosol. We summarise the average results of all the models that ran the
2
attribution experiments in Table 9.
3
Based on this analysis, the tropospheric ozone RF can be attributed to emissions increases as
4
follows: 52% (CH4), 21% (NOx), 17% (CO) and 10% (NMVOC). The sum of the
5
contributions to O3 RF diagnosed from the individual experiments is 364 mW m-2 (Table 8),
6
compared 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.
8
However, 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
10
deficiencies in our analysis.
11
The 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
13
equilibrium methane concentrations (equation (1)) is only valid for small perturbations to the
14
methane lifetime (West et al., 2007); the method is probably reasonable for the CO and
15
NMVOC experiments, and possibly for the CH4 experiments, but is likely to produce poor
16
results for the large perturbations from the NOx experiments. If we make the crude
17
assumption that all of the error is in the [CH4]eq values from the NOx experiments, and force
18
the sum of the indirect effects on CH4 to be zero, then the mean indirect RF for CH4 from
19
NOx must be -217 mW m-2 rather than the value in Table 9 of -327 mW m-2; the inferred O3
20
RF associated with the CH4 change will also reduce proportionately (linearly), increasing the
21
net O3 RF associated with NOx emissions from 76 to 125 mW m-2. Applying these crude
22
corrections, we find the relative contributions to the O3 RF are now: 46% (CH4), 30% (NOx),
23
15% (CO) and 9% (NMVOC). These proportions may be more accurate than those without
24
any corrections applied.
25
3.1.3 Experiments that isolate the climate change component
26
Most models performed the core 1850s and 2000s experiments with driving climates
27
appropriate for these decades (Table 1). In addition, ten models carried out sensitivity
28
experiments with 2000s emissions, but driven by 1850s climate. By comparing runs with the
29
same emissions, but different climates, we can diagnose the impact of climate change on O3.
30
Figure 6 shows the impact of climate change from 1850s to 2000s on AZM ozone and
31
tropospheric column O3, for the ten models.
14
1
Question to all modellers who ran Em2000Cl1850: do you also change stratospheric ozone in
2
these runs? Or put another way, does your Em2000Cl1850 stratosphere look more like a
3
2000s stratosphere, or an 1850s stratosphere?
4
Modelled ozone shows a range of responses to climate change (Figure 6). The largest overall
5
response is seen in models G and H (the two GISS versions), where climate change is the
6
main driver of the SH decreases in O3 seen in these models (Figure S3). In this context, it is
7
interesting to note that for the other model with large decreases in SH ozone (M), climate
8
change does not seem to be the driver (so I guess it must be stratospheric ozone depletion?).
9
Some models show increases in tropical mid- to upper tropospheric ozone, with these
10
increases centred over the continents (G, H, and to a lesser extent O, F and L). All these
11
models (except F) show small increases (x-y %) in lightning NOx emissions (Table 2);
12
however, other models that also show increases in lightning do not show obvious increases in
13
tropical ozone (A, M, N and P). Most of the models show decreases in ozone, particularly in
14
the tropical lower troposphere, as would be expected due to increases in water vapour and
15
hence ozone destruction. Several models also show indications of small (but extensive)
16
increases in the stratospheric source of ozone, e.g. in the sub-tropical jet region (A, F, I, L,
17
and P). Similar features have been seen in some future simulations under climate change
18
scenarios (e.g., Zeng and Pyle, 2003; Kawase et al., 2011). On average over the troposphere,
19
the net impact of climate change on ozone is a small decrease in tropospheric ozone.
20
Many of these climate change induced changes in O3 can be seen more strongly in results
21
from associated experiments that fixed emissions at 2000s levels but simulated climate of the
22
2030s and 2100s (see Figure S8 and discussion in Young et al., 2012).
23
3.2
24
Several models ran timeslice simulations that covered intervening decades between the 1850s
25
and 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
27
tropospheric O3 from these simulations can be found in Young et al. (2012). Here, we use
28
changes in tropospheric column O3, and convolve these together with individual model‘s
29
normalised O3 RFs (Figure 5/S5), to estimate the total tropospheric RF for each timeslice
30
experiment relative to the 1850s (Figure 7). A subset of these results (for the 1980s, 2000s,
31
2030s and 2100s) are also presented in Table 10. We use spatially resolved annual O3 column
Other timeslice simulations
15
1
changes and normalised O3 RFs. We don’t take into account seasonal variations in either
2
column changes or normalised RFs. This indirect method of calculating RFs also assumes that
3
the normalised O3 RF for 1850s-2000s doesn’t change with time – i.e. that the shape of the
4
change in ozone vertical profile doesn’t change with time. We consider that these
5
approximations introduce only small errors in the estimates of O3 RF presented in Figure 7
6
and Table 10.
7
Table 10 also shows mean values for selected time periods, constructed in three different
8
ways: (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.
11
Comparing the mean values calculated by these different methods shows that there is little
12
influence on the overall results (the maximum devation is 0.024 Wm-2, or ~10%, for RCP6.0
13
in 2100) of the variable model coverage of different timeslices.
14
4
15
With the MASKZMT tropopause, we find a mean value for the tropospheric ozone radiative
16
forcing (1850s-2000s) of 356 mW m-2, with a standard deviation across 17 models of ±58
17
mW m-2 (±16%) (Table 10). The median model has a value of 367 mW m-2, and the full range
18
spans 211-429 mW m-2. The model at the low end of this range (model M) is an isolated
19
outlier – 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
21
the two sets of calculations differ by 6% for the MMM; this suggests that tropopause
22
definition introduces an uncertainty of at least ±3%.
23
These values were calculated by the Edwards and Slingo (1996) (E-S) radiation scheme. We
24
find that the E-S radiation scheme gives net, stratospherically adjusted O3 RFs that are 8%
25
and 10% higher than comparable schemes from Oslo and NCAR (Table 5). Taking the mean
26
of our values for the two different tropopauses with the E-S scheme, and adjusting for the
27
Oslo and NCAR schemes producing slightly lower values, our best estimate of tropospheric
28
O3 RF between the 1850s and 2000s is 344 mW m-2.
29
Based on a comparison of instantaneous clear sky SW O3 RFs between the E-S and Oslo
30
schemes (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
32
uncertainty of at least ±7% (Table 4). The influence of stratospheric adjustment also varies
Discussion and Conclusions
16
1
between the two schemes, adding uncertainty of about ±3%. Based on a quadratic sum of
2
these, 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
4
due to tropopause definition (±3%), we estimate an overall uncertainty of ±20% from these
5
factors.
6
Further sources of uncertainty in the O3 RF stem from uncertainties in emissions (natural and
7
anthropogenic), and changes in climate and stratospheric ozone. Most models predict
8
relatively small impacts on tropospheric O3 via climate change to date (Figure 6), but these
9
impacts may increase in future (Figure S8). Uncertainties associated with these factors, and
10
emissions in particular, are probably similar or larger than the ±20% estimated above. We
11
therefore estimate an overall uncertainty of ±30% on our central estimate (Skeie et al. (2011)
12
estimate the same value for uncertainty). Given the magnitude of the uncertainty, we quote
13
values to two significant figures, giving our best estimate and uncertainty of 340 ± 100 mW
14
m-2. It should be noted that this value is for 1850s to 2000s (which we take to be 1855 to
15
2005). Skeie et al (2011) calculated tropospheric O3 increases between 1750 and 1850 of 1.0
16
DU (using model B), suggesting an extra 42 mW m-2 should be added to give the RF from
17
1750 to 2005. Similarly, they calculate an increase from 2005 to 2010 of 0.25 DU, which
18
would add a further 11 mW m-2. Hence for 1750-2010 our best estimate of tropospheric O3
19
RF is 400 ± 120 mW m-2.
20
It is well understood that increases in CH4, NOx, CO and NMVOCs have driven up
21
tropospheric O3, however only one model has previously explored the relative contributions
22
of these different O3 precursors (Shindell et al., 2005, 2009). Applying six different models
23
here, we estimate that CH4, NOx, CO and NMVOCs are respectively responsible for 46%,
24
30%, 15% and 9% of the 1850s-2000s O3 RF. There remains some uncertainty over the exact
25
values for these fractions, but all models show the same relative rankings; an important source
26
of uncertainty stems from extrapolating results from the experiments to yield equilibrium
27
methane concentrations. These contributions compare to values of 51% (CH4), 15% (NOx),
28
and 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 1750-
30
2010, the O3 RF of 400 mW m-2 has been derived from emissions of CH4, NOx, CO and
31
NMVOC as follows: 180, 120, 60, and 40 mW m-2. These emissions also influence the CH4
32
RF by affecting the CH4 lifetime, and we find respective values of: 140, -220, 60 and 20 mW
17
1
m-2. All these emissions except NOx also oxidise to form CO2 (Table 9 and Supplementary
2
Material). There are additional RF impacts of these emissions via secondary aerosol
3
formation and stratospheric water vapour that have not been estimated here (see Shindell et
4
al., 2009). Based on their impacts on CO2, CH4 and O3, we estimate emissions based RFs for
5
CH4, NOx, CO and NMVOC of: 760, -100, 210 and 90 mW m-2, respectively.
6
Normalizing the O3 RF by the change in column O3 (Figure 5), we find a global mean value
7
of 42 mW m-2 DU-1 (using MASKZMT). This is similar to values from earlier studies: 42
8
mW m-2 DU-1 (Ramaswamy et al., 2001; their Table 6.3 - a mean value from 11 studies); 36
9
mW m-2 DU-1 (Gauss et al., 2003; their Table 3 - a mean value from 11 models simulating
10
2000 to 2100 changes). Normalised forcings vary slightly between models (Figure S5),
11
reflecting differing ozone changes at different latitudes and heights (Figure S3).
12
Using the normalized forcing from each model, together with the simulated tropospheric
13
column ozone change, we have calculated total tropospheric ozone RFs for each model for
14
each available timeslice (Figure 7). Although different subsets of models ran the timeslices,
15
we find this has only a small influence on calculated multi-model mean values (Table 10).
16
Making the harmonization to our best estimate of 1850s-2000s O3 RF, we estimate MMM O3
17
RFs of 80, 290 and 340 mW m-2 for the 1930s, 1980s and 2000s, respectively. For the RCP2.6
18
scenario, we find MMM values of 310 and 160 mW m-2 for the 2030s and 2100s; for RCP4.5:
19
380 and 260 mW m-2; for RCP6.0: 330 and 240 mW m-2; and for RCP8.5: 420 and 560 mW
20
m-2. All these have similar uncertainties to our pre-industrial to present-day estimate of at
21
least ±30%. Uncertainties are arguably smaller for the future scenarios, as they are for exactly
22
prescribed emissions; however, other sources of uncertainty increase, in particular the effects
23
of climate change, land-use change and changing stratospheric ozone on tropospheric ozone.
24
Over the 1850s-2000s, climate change has had relatively small influences on tropospheric
25
ozone in most models (Figure 6), but is more important in some (e.g., models G and H). In the
26
future, models suggest these changes will generally increase, with models displaying some
27
coherent responses (Figure S8). All models suggest O3 in the tropical lower troposphere will
28
reduce, mainly due to warmer temperatures and higher water vapour concentrations. Most
29
models indicate that O3 will increase in the sub-tropical to mid-latitude upper troposphere,
30
due to a combination of both increase lightning NOx production, but also an increase of
31
stratosphere-to-troposphere transport, as suggested by some earlier studies.
32
Rather abrupt end - mention land –use and strat O3 perhaps. THE END?
18
1
2
Acknowledgements
3
ACCMIP is organized under the auspices of Atmospheric Chemistry and Climate (AC&C), a
4
project of International Global Atmospheric Chemistry (IGAC) and Stratospheric Processes
5
And 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
7
Atmospheric Data Centre (BADC), which is part of the NERC National Centre for
8
Atmospheric Science (NCAS), for collecting and archiving the ACCMIP data.
9
GAF, STR and WJC were supported by the Joint DECC and Defra Integrated Climate
10
Programme (GA01101) and the Defra SSNIP air quality contract AQ 0902.
11
GZ acknowledges NIWA HPCF facility and funding from New Zealand Ministry of Science
12
and Innovation.
13
The 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
15
operated by the University Corporation for Atmospheric Research under sponsorship of the
16
National Science Foundation.
17
The work of DB and PC was funded by the U.S. Dept. of Energy (BER), performed under the
18
auspices of LLNL under Contract DE-AC52-07NA27344, and used the supercomputing
19
resources of NERSC under contract No. DE-AC02-05CH11231.
20
VN and LWH acknowledge efforts of GFDL's Global Atmospheric Model Development
21
Team in the development of the GFDL-AM3 and Modeling Services Group for assistance
22
with data processing.
23
The GEOSCCM work was supported by the NASA Modeling, Analysis and Prediction
24
program, with computing resources provided by NASA's High-End Computing Program
25
through the NASA Advanced Supercomputing Division.
26
The MIROC-CHEM calculations were perfomed on the NIES supercomputer system (NEC
27
SX-8R), and supported by the Environment Research and Technology Development Fund (S-
28
7) of the Ministry of the Environment, Japan.
29
The STOC-HadAM3 work was supported by cross council grant NE/I008063/1, and used
30
facilities provided by the UK's national high-performance computing service, HECToR,
19
1
through Computational Modelling Services (CMS), part of the NERC National Centre for
2
Atmospheric Science (NCAS).
3
The LMDz-OR-INCA simulations were done using computing ressources provided by the
4
CCRT/GENCI computer center of the CEA.
5
The CICERO-OsloCTM2 simulations were done within the projects SLAC (Short Lived
6
Atmospheric Components) and EarthClim funded by the Norwegian Research Council.
7
The MOCAGE simulations were supported by Météo-France and CNRS. Supercomputing
8
time was provided by Météo-France/DSI supercomputing center.
9
DTS and YHL acknowledge support from the NASA MAP and ACMAP programs.
10
DP would like to thank the Canadian Foundation for Climate and Atmospheric Sciences for
11
their long-running support of CMAM development.
12
20
1
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26
1
Experiments (as used in this paper)
Model
1850sa
2000sb
Attribc
ΔClimd
Futuree
A. CESM-CAM-superfast
10
10
-
10
YnYY
1 (2006)
1 (2006)
1
-
YYnY
C. CMAM
10 (1860s)
10 (2010s)
-
-
nYnY
D. EMAC
10
10
-
-
nYnY
E. GEOSCCM
10 (1870s)
14 (1996-)
-
-
nnnn
F. GFDL-AM3
10 (1860s)
10
-
10
YYYY
G. GISS-E2-Rf
10 (x5)
10 (x5)
-
40
YYYY
10
10
-
10
nnnn
I. HadGEM2
10 (1860s)
10
-
10
YYnY
J. HadGEM2-ExtTC
10 (2000s)
10
10
-
nnnn
K. LMDzORINCA
10
5 (1996-)
-
-
YYYY
L. MIROC-CHEM
11 (1850-)
11 (2000-)
-
5 (1850-)
YnYY
M. MOCAGE
4 (1850-)
4 (2000-)
-
4 (1850-)
YnYY
N. NCAR-CAM3.5
8 (1852-)
8 (2002-)
8
8 (1852-)
YYYY
O. STOC-HadAM3
10
10
10
10
YnnY
P. UM-CAM
10
10 (1996-)
10
10
YYnY
1 (2000)
1 (2000)
1
-
nnnn
B. CICERO-OsloCTM2
H. GISS-E2-R-TOMAS
Q. TM5
2
Table 1: Models and experiment run lengths (in years).
3
4
a
5
6
b
7
c
8
d
9
10
e
11
12
f
Where models did not run 1850-1859 or 1851-1860, the climate model decade ran is
indicated. Where other than 10 years were ran, the starting year is shown.
Where models did not run 2000-2009 or 2001-2010, the climate model years ran are
indicated. Where other than 10 years were ran, the starting year is shown.
Details of the attribution experiments are given in Section 3.1.2
Details of the climate experiments are given in Section 3.1.3
The code shown corresponds to the four future scenarios (RCP2.6, RCP4.5, RCP6.0 and
RCP8.0, in order). “Y“ indicates that the scenario was run, “n“ indicates that it was not.
Model G ran five ensembles of the 1850s and 2000s experiments, and an average of the five
ensembles is used.
13
14
27
1
Model
Lightning NOx
TgN/yr
Isoprene
Tg/yr
Soil NOx?
TgN/yr
1850s
2000s
1850s
2000s
1850s
2000s
A. CESM-CAMsuperfast
3.8
4.2
Constant PD
B. CICERO-OsloCTM2
5.0
5.0
449
449
8.0
8.0
C. CMAM
4.5
3.8
250
Tg/a CO
250
Tg/a CO
8.7
8.7
D. EMAC
5.3
5.7
Simulated by
model
Simulated by
model
E. GEOSCCM
5.0
5.0
Climate
sensitive
Climate
sensitive
F. GFDL-AM3
4.5
4.4
Constant PD
Constant PI
G. GISS-E2-R
7.5
7.7
Climate
sensitive
Constant PD
H. GISS-E2-R-TOMAS
7.5?
7.7?
I. HadGEM2
1.2
1.2
-
-
Constant
J. HadGEM2-ExtTC
6.4
6.4
579
460
5.6
K. LMDzORINCA
?
?
Constant PD
Constant PD
L. MIROC-CHEM
9.3
9.7
Constant PD
Constant PD
M. MOCAGE
5.0
5.2
568
568
N. NCAR-CAM3.5
3.7
4.1
483
483
O. STOC-HadAM3
6.9
7.2
Climate
sensitive
606
Constant PD
5.6
4.5
4.5
7.0
7.0
571
P. UM-CAM
Q. TM5
2
4.9
5.1
390
390
?
?
524
524
Table 2: Natural emissions (lightning NOx, biogenic isoprene, soil NOx) in 1850s and 2000s.
3
4
28
1
Tropospheric column
O3 change (2000s1850s) (DU)
Model
Tropospheric O3
radiative forcing
(mW m-2)
MASKZMT MASK150 MASKZMT MASK150
A. CESM-CAM-superfast
9.4
10.0
428
446
B. CICERO-OsloCTM2
8.7
9.3
383
401
C. CMAM
7.2
7.6
315
322
D. EMAC
9.8
10.8
429
460
E. GEOSCCM
8.0
8.7
364
387
F. GFDL-AM3
9.7
10.3
406
423
G. GISS-E2-R
7.9
8.3
286
314
H. GISS-E2-R-TOMAS
8.4
8.7
305
333
I. HadGEM2
7.2
7.3
301
xxx
J. HadGEM2-ExtTC
8.2
8.4
315
not run yet
K. LMDzORINCA
7.9
8.2
344
351
L. MIROC-CHEM
8.4
9.2
376
402
M. MOCAGE
4.7
4.8
210
219
N. NCAR-CAM3.5
9.3
10.2
406
433
O. STOC-HadAM3
9.4
10.5
396
437
P. UM-CAM
8.5
8.7
371
376
Q. TM5
9.3
10.0
399
422
2
Table 3: Changes in tropospheric column O3 (DU) and radiative forcing (mW m-2) for two
3
different tropopause definitions (MASKZMT and MASK150).
4
5
29
1
Radiation
scheme
Models
Tropopause
mask
Influence of stratospheric
Influence of clouds
adjustment (%)
(%)
SW
LW
net
SW
LW
net
E-S
all
MASKZMT
0
-24 ± 1
-20 ± 1
20 ± 4
-16 ± 1
-12 ± 1
E-S
all
MASK150
0
-26 ± 1
-22 ± 1
21 ± 5
-16 ± 1
-12 ± 1
E-S
B
MASKZMT
0
-25
-21
21
-17
-12
E-S
B
MASK150
0
-27
-22
22
-16
-12
Oslo
B
MASK150
-
-
-
35
-30
-22
Oslo
B
MASKOsloa
0
-21
-17
-
-
-
2
Table 4: Influence of stratospheric adjustment and clouds (% change in O3 RFs when
3
included) in the Edwards-Slingo (E-S) and Oslo schemes. First two rows are for all models:
4
values are means and standard deviations. Lower rows are just for model B.
5
E-S all MASK150 results to be confirmed
6
a
Results using the Oslo model tropopause.
7
8
30
1
Clear-sky, instantaneous
O3 RF (mW m-2)
Cloudy-sky, stratospherically
adjusted O3 RF (mW m-2)
Tropopause
mask
SW
LW
net
SW
LW
net
E-S (B)
MASKZMT
62
491
552
75
309
384
E-S (B)
MASK150
64
521
585
78
322
401
Oslo (B)
MASK150
72
488
560
-
-
-
Oslo (B)
MASKOsloa
-
-
-
94
259
353
E-S (11)
MASKZMT
58 ± 9
437 ± 87
495 ± 96
70 ± 13
277 ± 51
347 ± 64
E-S (11)
MASK150
58 ± 9
463 ± 93
521 ± 101
71 ± 12
291 ± 56
362 ± 68
NCAR(11)
MASK150
-
-
-
83 ± 16
243 ± 88
326 ± 100
E-S (all)
MASKZMT
60 ± 9
452 ± 82
512 ± 90
72 ± 12
286 ± 49
358 ± 60
E-S (all)
MASK150
61 ± 8
483 ± 89
543 ± 96
74 ± 12
303 ± 54
377 ± 65
2
Table 5: Comparison of total tropospheric ?O3 RFs from different radiation schemes for both
3
the clear-sky, instantaneous case, and cloudy-sky, stratospherically adjusted case. Results are
4
shown for model B alone (to allow direct comparison of E-S and Oslo schemes), 11 models
5
(ACEFGIKLMNP; to allow direct comparison of E-S and NCAR schemes), and all models
6
(for context).
7
E-S MASK150 results to be confirmed
8
a
Results using the Oslo model tropopause.
9
10
31
1
Attribution experiment
Climate
[CH4]
Anthropogenic Emissions
NOx
CO
NMVOC
1850s
1850s
1850s
1850s
2000s
2000s
2000s
2000s
2000s
#2 Em2000CH41850
2000s
1850s
2000s
2000s
2000s
#3 Em2000NOx1850
2000s
2000s
1850s
2000s
2000s
#4 Em2000CO1850
2000s
2000s
2000s
1850s
2000s
#5 Em2000NMVOC1850
2000s
2000s
2000s
2000s
1850s
#0 Em1850CH41850
1
2000s
#1 Em2000CH420002
2
Table 6: Attribution experiments.
3
1
Experiment Em1850CH41850 is the same as the core 1850s experiment for models B, J, Q.
4
2
Experiment Em2000CH42000 is the same as the core 2000s experiment for all models.
5
6
32
1
#0
1850s
#1
2000s
#2
1850CH4
#3
1850NOx
#4
1850CO
#5
1850NMVO
Model
τ
[CH4]
τ
[CH4]
τ
[CH4]eq
τ
[CH4]eq
τ
[CH4]eq
τ
[CH4
B
8.06
791
8.70
1751
7.31
1384
11.60
2581
8.14
1599
8.61
172
J
9.02
791
9.29
1751
7.80
1384
12.02
2479
8.70
1603
9.29
175
N
9.26
791
8.11
1751
6.62
1330
12.06
2989
7.49
1571
7.82
166
O
8.47
791
8.06
1751
6.76
1382
10.84
2612
7.68
1641
7.99
173
P
12.29
791
11.61
1751
9.99
1428
16.38
2786
10.75
1577
11.09
164
Q
8.55
791
8.66
1751
7.13
1349
13.15
3080
8.01
1577
8.16
161
2
Table 7: Methane lifetimes (yr, for the whole atmosphere), from attribution experiments, and
3
corresponding equilibrium methane concentrations (ppbv), calculated using equation (1), for
4
experiments #2-5. For experiments #0 and #1, we show observed imposed methane values.
5
6
33
1
#2. 1850CH4
Model
B
J
N
O
P
Q
Mean
BJNOPQ
2
3
4
5
6
7
8
9
10
11
#3. 1850NOx
CH4 RF
#4. 1850CO
O3 RF
CH4 RF
O3 RF
153
418
193
38
37
59
132
-132
24
4
212
550
61
103
418
178
29
29
39
132
-78
16
0
142
550
100
168
418
253
48
15
74
155
-217
31
15
242
573
36
153
418
205
36
42
59
133
-137
18
3
212
551
68
85
418
246
35
38
29
113
-92
15
9
114
531
154
155
418
252
45
6
65
147
-215
28
21
220
565
37
136
418
221
39
28
54
135
-145
22
9
190
553
76
-276
-245
-395
-286
-337
-420
-327
O3 RF
62
45
79
54
50
73
61
CH4 RF
#5. 1850NMVOC
58
56
68
41
66
66
59
O3 RF
41
29
30
45
47
27
37
CH4 RF
10
0
32
7
39
50
23
Table 8: Total tropospheric ozone and methane radiative forcings (mW m-2) for each model
and attribution experiments #2-5 relative to experiment #1 (year 2000s). For methane
radiative forcings, an equilibrium [CH4] is calculated based on the diagnosed perturbation to
the CH4 lifetime (Table 4); the RF is then calculated from the difference between the
prescribed and equilibrium methane concentrations. For the methane experiment, also shown
is the methane RF due to the prescribed (observed) 1850s-2000s change (upper box: 418 mW
m-2; the same in each case) and the net CH4 RF (lower box). For ozone radiative forcings,
three numbers are given: the uppermost is the RF from the calculated O3 field (e.g., Figure
S7); the middle value is the inferred O3 RF associated with the methane adjustment to
equilibrium; the lower number is the net O3 RF.
12
34
1
Radiative forcing (mW m-2) via:
Emission
CO2
CH4
O3
CO2+CH4+O3
CH4
18
553 (418 + 135)a
190
761
-327 (-217)b
76 (125)b
-251 (-92)b
NOx
CO
87
59
61
207
NMVOC
33
23
37
93
Total:
138
308 (418)b
364 (413)b
810 (969)b
2
Table 9: Emission-based RFs (via changes in CO2, CH4 and tropospheric O3) for emitted
3
CH4, NOx, CO, NMVOC, based on the mean response of the 6 models that conducted the
4
attribution experiments. (cf. IPCC-AR4 Table 2.13).
5
a
6
2
) and the component from the change in CH4 lifetime.
7
b
The values in brackets apply a crude correction to the NOx components to force the CH 4 RF
8
to match the observed value (see text for details).
The CH4 RF is shown as the direct RF (due to the increase in CH4 concentration: 418 mW m-
9
10
35
1
RCP2.6
Model
RCP4.5
RCP6.0
RCP8.5
1980s 2000s 2030s 2100s 2030s 2100s 2030s 2100s 2030s 2100s
A
364
428
329
150
-
-
364
229
521
733
B
307
384
345
164
406
262
-
-
456
527
C
262
315
-
-
341
202
-
-
385
490
D
337
429
-
-
439
308
-
-
-
680
E
312
365
-
-
-
-
-
-
-
-
F
365
407
367
195
443
328
439
335
513
775
G
275
297
293
184
330
235
325
301
420
567
H
271
311
-
-
-
-
-
-
-
-
I
227
302
-
150
-
270
-
-
-
570
J
-
316
-
-
-
-
-
-
-
-
K
279
345
280
123
355
237
307
227
391
484
L
302
377
323
177
-
-
367
260
441
527
M
190
211
191
106
-
-
222
157
309
430
N
337
406
338
156
402
261
353
230
449
587
O
336
396
339
154
-
-
-
-
468
560
P
296
371
367
253
427
356
-
-
474
637
Q
-
399
-
-
-
-
-
-
-
-
Mean±SD
(all)
297
±49
356
±58
317
±52
165
±39
393
±45
273
±49
340
±66
249
±58
439
±61
582
±100
Mean±SD
(FGKN)
314
±44
364
±53
320
±40
165
±32
382
±50
265
±43
356
±58
273
±53
443
±52
603
±123
Mean±SD
305
±51
362
±65
317
±52
166
±40
-
-
-
-
444
±62
583
±107
(ABFGKLMNOP)
2
Table 10: Tropospheric O3 RFs (mW m-2) relative to the 1850s (for MASKZMT). (Also see
3
Figure 7.)
4
5
36
1
Figures
2
3
4
Figure 1: Multi-model mean annual zonal mean ozone (ppb), for the 1850s and 2000s.
5
Vertical grid is hybrid – all models have been interpolated to a common grid. Figure S1
6
(supplementary material) shows equivalent plots for all models.
7
37
1
2
3
4
Figure 2: Multi-model mean annual mean tropospheric column O3 (DU), for the 1850s and
5
2000s (for MASKZMT). Area-weighted global mean values are in brackets. Figure S2
6
(supplementary material) shows equivalent plots for all models.
7
38
1
2
3
4
Figure 3: Multi-model mean changes (2000s-1850s) in: (a) annual zonal mean ozone (ppbv)
5
(masked at tropopause); (b) tropospheric column ozone (DU). Tropopause is a climatological
6
monthly zonal mean (MASKZMT), as used in Cionni et al. (2011). Figure S3 (supplementary
7
material) shows equivalent plots for all models.
8
39
1
2
Figure 4: Multi-model mean annual mean tropospheric O3 total, shortwave (SW; solar
3
wavelengths), and longwave (LW; infrared wavelengths) radiative forcings (mW m-2),
4
between the 1850s and 2000s (for MASKZMT). Area-weighted global mean values are in
5
brackets. Figure S4 (supplementary material) shows equivalent plots for all models.
6
40
1
2
Figure 5: Multi-model mean normalised total tropospheric? ozone radiative forcing (mWm-
3
2
4
(supplementary material) shows equivalent plots for all models.
/DU) (for MASKZMT). Area-weighted global mean value is in brackets. Figure S5
5
41
1
2
Figure 6: Response of ozone to climate change (2000s emissions; 2000s climate – 1850s
3
climate) in ten models. Left hand column are annual zonal mean changes (ppb); right hand
4
column are annual tropospheric column ozone changes (DU; global mean value in brackets;
5
for MASKZMT).
42
1
2
Figure 7: Total tropospheric ozone radiative forcing (mW m-2) relative to 1850s, for all
3
available models and timeslice experiments (1910s, 1930s, 1950s, 1970s, 1980s, 1990s,
4
2000s; then for the four future RCP scenarios 2010s, 2030s, 2050s and 2100s). All models ran
5
the 2000s and 1850s; for other timeslices, varying subsets of models are available. This plot
6
for MASKZMT.
7
43
1
Supplementary material (to be tidied up)
2
3
4
Radiative forcing of increased CO2 due to emissions of CH4, CO and NMVOCs
from fossil sources (from Terje Berntsen)
5
6
Emissions of CH4, CO and NMVOCs from fossil sources leads to increased levels of CO2 in
7
the atmosphere. Assessing the emission-based impact on radiative forcing these CO2
8
contributions need be attributed to the emissions of the source gases. Here we have used the
9
anthropogenic emissions from Lamarque et al. (2010) since 1850 for CO and NMVOCs,
10
while emissions methane were taken from the emission data recommended for CMIP5 use
11
(http://www.iiasa.ac.at/web-apps/tnt/RcpDb/dsd?Action=htmlpage&page=download)
12
Emissions from the sector Power plants, Energy Conversion, Extraction and distribution is
13
regarded as fossil, while the other sectors are assumed to be non-fossil.
.
14
700
600
CH4 Fossil (Tg/yr)
500
400
CO (Tg(CO)/yr)
VOC (Tg(VOC)/yr)
300
200
100
0
1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
15
16
Fig. 1 Historic emissions of CH4, CO and NMVOCs (all in Tg/yr) from fossil/anthropogenic
17
sources.
18
19
The oxidation of CH4, CO and NMVOCs in the atmosphere leads to an atmospheric source of
20
CO2. For methane the lifetime in the atmosphere is long enough to allow for a non-neglible
21
fraction of the historical emissions to be left in the atmosphere. Using a lifetime for methane
22
of 9.6 years (IPCC, 2001) we find that 12% of the methane has not yet been oxidized to CO2.
44
1
2
From the emissions and the atmospheric lifetime of CH4 we calculate the corresponding
3
atmospheric source of CO2 as a function of time. For the NMVOCs we have assumed an
4
average carbon content of 80% by mass. We then calculate the resulting development in the
5
CO2 concentrations using the impulse response function for CO2 (IRF1) given in IPCC
6
(2007). The change in the mixing ratio of CO2 (XCO2(t)) is given by
7
8
9
10
The contribution to atmospheric CO2 is given in figure 2a.
11
12
The radiative forcing due to the CO2 from the fossil/anthropogenic emissions of methane,
13
CO and NMVOCs are calculated by the simple parameterization given in IPCC (2001)
14
15
16
17
The calculations are done with X0 = 278 ppm. The radiative forcings are shown in figure 2b.
18
In 2010 the RFs are 18, 87 and 33 mWm-2 for emissions of methane, CO and NMVOC
19
respectively.
20
21
1
45
5.00
0.10
4.50
0.09
0.08
4.00
CO2-CH4 (ppm)
3.50
CH4 RF (Wm-2)
0.07
CO RF (Wm-2)
0.06
NMVOC RF (Wm-2)
CO2-CO (ppm)
3.00
CO2-NMVOC (ppm)
2.50
0.05
2.00
0.04
0.03
1.50
0.02
1.00
0.01
0.50
0.00
1840
1860
1880
1900
1920
1940
1960
1980
2000
2020
0.00
1840
1860
1880
1900
1920
1940
1960
1980
2000
2020
1
Figure 2. Contribution from fossil/anthropogenic emissions of CH4, CO and NMVOCs to
2
atmospheric CO2 and radiative forcing.
3
4
5
46
1
Figures
2
3
Figure S1: Annual zonal mean ozone (ppb) for the 1850s and 2000s. For scale see Figure 1.
47
1
2
Figure S1 (continued)
48
1
2
Figure S2: Annual mean tropospheric column O3 (DU), for the 1850s and 2000s, using the
3
MASKZMT. For scale see Figure 2.
49
1
2
Figure S2 (continued)
50
1
2
Figure S3: Changes (2000s-1850s) in annual zonal mean O3 (ppbv) and tropospheric column
3
O3 (both masked using MASKZMT). See Figure 3 for scales.
51
1
2
Figure S3 (continued)
52
1
2
Figure S4a: Total tropospheric O3 RFs for all models, masked using MASKZMT.
53
1
2
Figure S4b: Annual mean tropospheric O3 total, SW, and LW RFs for the IGAC/SPARC
3
ozone dataset, as used in Cionni et al (2011). Compare to multi-model mean in Figure 4; also
4
compare to Cionni et al.’s Figure 15a, which shows the total RF calculated with an earlier
5
version of the E-S radiation code, and finds a total RF 27% lower.
6
54
1
2
3
Figure S5: Normalised total tropospheric? RFs for MASKZMT. See Figure 5 for scale.
55
1
2
Figure S6: An example of one model’s results (model B) for the attribution experiments. Left
3
hand side shows contributions to changes in zonal annual mean ozone, right hand side shows
4
contributions to change in annual mean tropospheric ozone column. Referring to the
5
experiment numbers in Table 3, rows from the top show: experiment #1-#0 (all components);
6
#1-#2 (CH4); #1-#3 (NOx); #1-#4 (CO); #1-#5 (NMVOC).
7
56
1
2
Figure S7: Radiative forcings for model B, from the attribution experiments. Left-hand plot
3
shows total 2000s-1850s (#1-#0); middle shows the CH4 component (#1-#2); and the right-
4
hand plot shows the NOx component (#1-#3). The CO and NMVOC components are
5
significantly less (38 and 37 mWm-2, respectively) and are not shown.
6
57
1
2
3
Figure S8: As Figure 6, but showing the impact of climate change on O3 from 1850s to the
4
(a) 2030s; (b) 2100s.
58
1
2
Figure S8 continued
59