Atmos. Chem. Phys., 21, 1–32, 2021
https://doi.org/10.5194/acp-21-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Tropospheric ozone in CMIP6 simulations
Paul T. Griffiths1,2,, Lee T. Murray3,, Guang Zeng4, Youngsub Matthew Shin1, N. Luke Abraham1,2,
Alexander T. Archibald1,2, Makoto Deushi5, Louisa K. Emmons6, Ian E. Galbally7,8, Birgit Hassler9,
Larry W. Horowitz10, James Keeble1,2, Jane Liu11, Omid Moeini12, Vaishali Naik10, Fiona M. O’Connor13,
Naga Oshima5, David Tarasick12, Simone Tilmes6, Steven T. Turnock13, Oliver Wild14, Paul J. Young14,15, and
Prodromos Zanis16
1Centre for Atmospheric Science, Cambridge University, Cambridge, UK
2National Centre for Atmospheric Science, Cambridge University, Cambridge, UK
3 University of Rochester, Rochester, NY, USA
4National Institute of Water and Atmospheric Research, Wellington, New Zealand
5Department of Atmosphere, Ocean, and Earth System Modeling Research, Meteorological Research Institute, Tsukuba,
Japan
6Atmospheric Chemistry Observations and Modeling, National Centre for Atmospheric Research, Boulder, Colorado, USA
7Climate Science Centre, CSIRO Aspendale, Victoria, Australia
8Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, NSW, Australia
9Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
10NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
11Department of Geography, University of Toronto, Toronto, Canada
12Air Quality Research Division, Environment and Climate Change, Toronto, Canada
13Met Office Hadley Centre, Exeter, UK
14Lancaster Environment Centre, Lancaster University, Lancaster, UK
15Centre of Excellence for Environmental Data Science (CEEDS), Lancaster University, Lancaster, UK
16Department of Meteorology and Climatology, Aristotle University of Thessaloniki, Thessaloniki, Greece
These authors contributed equally to this work.
Correspondence: Paul T. Griffiths (paul.griffiths@ncas.ac.uk) and Lee T. Murray (lee.murray@rochester.edu)
Received: 31 December 2019 – Discussion started: 10 February 2020
Revised: 14 December 2020 – Accepted: 20 December 2020 – Published:
Abstract. The evolution of tropospheric ozone from 1850
to 2100 has been studied using data from Phase 6 of the
Coupled Model Intercomparison Project (CMIP6). We eval-
uate long-term changes using coupled atmosphere–ocean
chemistry–climate models, focusing on the CMIP Histor-
ical and ScenarioMIP ssp370 experiments, for which de-
tailed tropospheric-ozone diagnostics were archived. The
model ensemble has been evaluated against a suite of sur-
face, sonde and satellite observations of the past several
decades and found to reproduce well the salient spatial, sea-
sonal and decadal variability and trends. The multi-model
mean tropospheric-ozone burden increases from 247± 36 Tg
in 1850 to a mean value of 356± 31 Tg for the pe-
riod 2005–2014, an increase of 44 %. Modelled present-
day values agree well with previous determinations (AC-
CENT: 336± 27 Tg; Atmospheric Chemistry and Climate
Model Intercomparison Project, ACCMIP: 337± 23 Tg; Tro-
pospheric Ozone Assessment Report, TOAR: 340± 34 Tg).
In the ssp370 experiments, the ozone burden increases to
416± 35 Tg by 2100. The ozone budget has been exam-
ined over the same period using lumped ozone production
(PO3 ) and loss (LO3 ) diagnostics. Both ozone production and
chemical loss terms increase steadily over the period 1850
to 2100, with net chemical production (PO3 -LO3 ) reaching a
maximum around the year 2000. The residual term, which
contains contributions from stratosphere–troposphere trans-
port reaches a minimum around the same time before re-
covering in the 21st century, while dry deposition increases
Published by Copernicus Publications on behalf of the European Geosciences Union.
2 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
steadily over the period 1850–2100. Differences between the
model residual terms are explained in terms of variation in
tropopause height and stratospheric ozone burden.
1 Introduction
Tropospheric ozone (O3) is an important component of air
pollution and an oxidizing species with adverse effects on
human health (Jerrett et al., 2009; Turner et al., 2015; Mal-
ley et al., 2017) and vegetation (Fowler et al., 2009). It
is also a greenhouse gas (GHG) with a radiative forcing
of 0.4± 0.2 Wm−2 (Stevenson et al., 2013; Myhre et al.,
2013) and plays an important role in controlling the strength
of the terrestrial carbon sink (Sitch et al., 2007). Ozone is
not emitted directly into the troposphere but is produced
there by the photochemical oxidation of carbon monoxide
(CO), methane (CH4) and non-methane volatile organic com-
pounds (NMVOCs) in the presence of nitric oxide (NO) and
nitrogen dioxide (NO2). The tropospheric-ozone burden is
controlled by the balance between chemical production and
loss processes, deposition at the surface and downward trans-
port from the stratosphere.
In addition to its roles as a GHG and air pollutant, ozone is
an oxidant and a precursor for the hydroxyl (OH) radical. OH
(and by implication ozone) controls the lifetime of methane
(Voulgarakis et al., 2013), the second most important anthro-
pogenic GHG after carbon dioxide (Myhre et al., 2013). Oxi-
dant levels mediate the formation of secondary aerosols such
as sulfate and nitrate and play a major role in the aerosol
budget and burden with important consequences for radiative
forcing (Shindell et al., 2009; Karset et al., 2018). Accurate
knowledge of ozone and how ozone has evolved since prein-
dustrial times is therefore critical to our understanding of the
radiative forcing from aerosol and GHGs.
The lifetime of ozone in the troposphere varies consider-
ably with location and season, ranging from a few hours in
polluted urban regions up to a few weeks in the upper tropo-
sphere (Monks et al., 2015) and the global mean tropospheric
lifetime is estimated to be 23.4± 2.2 d (Young et al., 2013).
Ozone has a sufficiently long lifetime in the troposphere to
be transported over long distances, and this transport may
therefore be affected by climate variability and by the asso-
ciated changes in large-scale atmospheric circulation patterns
that occur on interannual to decadal timescales. Emissions of
ozone precursors from natural sources (e.g. lightning, vege-
tation, fires) also respond to natural variability contributing
to large-scale variability in ozone.
Due to the difficulties of measuring tropospheric ozone
on a global scale, the global burden and budget are esti-
mated using global atmospheric chemistry models which in-
clude chemistry–climate models (CCMs), chemistry trans-
port models (CTMs) and chemistry general circulation mod-
els (chemistry GCMs) (Young et al., 2018). While the
tropospheric-ozone burden and distribution during preindus-
trial times is unknown from observations (Tarasick et al.,
2019), the present-day ozone monitoring network can be
used to calculate the tropospheric-ozone burden and evalu-
ate global atmospheric chemistry models. Multiple satellite
products from the Infrared Atmospheric Sounding Interfer-
ometer (IASI) such as IASI-FORLI and IASI-SOFRID cor-
roborated by the Trajectory-mapped Ozonesonde dataset for
the Stratosphere and Troposphere (TOST) indicate an over-
all mean present-day (2010–2014) global tropospheric-ozone
burden of 338± 6 Tg in broad agreement with the current
range of model estimates (Gaudel et al., 2018).
Recently, Young et al. (2018) presented an updated re-
gional evaluation of tropospheric ozone simulated by mod-
els contributing to the Atmospheric Chemistry and Climate
Model Intercomparison Project (ACCMIP) using data from
ozonesonde measurements, a new compilation of long-term
measurements conducted aboard commercial aircraft of in-
ternationally operating airlines (MOZAIC-IAGOS) and a
comprehensive database of global surface ozone measure-
ments that was compiled within the Tropospheric Ozone
Assessment Report (TOAR) framework. This evaluation re-
vealed that the models are biased high in the Northern Hemi-
sphere (NH) and low in the Southern Hemisphere (SH), with
the biases generally persisting throughout the depth of the
troposphere in agreement with previous global model eval-
uation studies (Fiore et al., 2012; Stevenson et al., 2013).
Most CCMs capture the seasonal cycle of surface and free-
tropospheric ozone over most regions reasonably well, giving
confidence in the relative contribution of the seasonal cycle
of emissions and meteorology to the simulated seasonal cy-
cle in ozone. However, there are still model deficiencies in
simulating the seasonality of free-tropospheric ozone in re-
gions such as equatorial America, Japan and northern high
latitudes (Young et al., 2018) and of near-surface ozone over
northern and north-eastern Europe (Katragkou et al., 2015),
reflecting poor simulation of local and regional dynamics
or missing chemical processes, complicated by the uncer-
tainty in ozone precursor emissions. The spatial patterns in
annual mean surface ozone and regional features of free-
tropospheric ozone are generally captured by current global
chemistry models (Tilmes et al., 2016; Hu et al., 2017) in-
cluding the ozone maximum west of southern Africa over
the South Atlantic Ocean (Sauvage et al., 2007), the mid-
Pacific minimum (Ziemke et al., 2010) and the summertime
free-tropospheric ozone maximum over the Eastern Mediter-
ranean (Akritidis et al., 2016; Zanis et al., 2014).
The main chemical reactions contributing to tropospheric-
ozone production are reactions between NO and hydroper-
oxyl (HO2) and other peroxyl radicals that are intermedi-
ate products of volatile organic compound (VOC) degrada-
tion. Ozone chemical production occurs throughout the tro-
posphere, particularly near the surface close to emissions and
also in the upper troposphere via lightning-produced NOx .
Deposition of ozone occurs at the surface via reactive chem-
ical loss to surfaces. In the free troposphere, ozone loss by
Atmos. Chem. Phys., 21, 1–32, 2021 https://doi.org/10.5194/acp-21-1-2021
P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 3
photolysis to produce O1D, and the subsequent reaction of
O1D with H2O, and by chemical destruction involving reac-
tion with hydroxy and hydroperoxyl radicals are important
(Ayers et al., 1992).
The ozone source and sink terms vary between models
due to differing approaches in representing the processes in-
volved and also due to differences in how these budget terms
are defined (Stevenson et al., 2006; Young et al., 2013, 2018).
Key issues include the representation of NMVOC chemistry
which affects chemical production and loss terms, surface
loss processes, and stratospheric influences. The definition of
the tropopause will also influence the diagnosed burden and
any influx from the stratosphere. The Tropospheric Ozone
Assessment Report reviewed the ozone budget terms using
results from models that took part in ACCENT and AC-
CMIP model intercomparisons and from recent single model
studies (Young et al., 2018). They reported budget terms for
the nominal year 2000, calculating a multi-ensemble mean
global tropospheric-ozone burden of 340± 34 Tg, chemical
production of 4937± 656 Tg O3 per year, chemical loss of
4442± 570 Tg per year and deposition loss of 996± 203 Tg
per year, leaving a residual term of 535± 161 Tg per year,
which is assumed to represent the net stratospheric influx
(Archibald et al., 2020a).
During the 21st century, changes in climate, stratospheric
ozone-depleting substances (ODSs) and emissions of ozone
precursor species are expected to be the major factors gov-
erning the amount of ozone and its distribution in the strato-
sphere, the free troposphere and at the surface (Fiore et al.,
2015; Revell et al., 2015). Changes in ozone precursor emis-
sions have the largest effect on future tropospheric-ozone
concentrations, and precursor emission scenarios described
by shared socioeconomic pathways (SSPs) and representa-
tive concentration pathways (RCPs) show reductions that
would drive a decrease in ozone. A strong sensitivity to emis-
sion scenarios is supported by previous and recent model re-
sults that reveal a net decrease in the global tropospheric bur-
den of ozone in 2100 compared to that in 2000 for all RCPs
except RCP8.5, which shows an increase due to much larger
methane concentrations than the other pathways (Stevenson
et al., 2006; Naik et al., 2013; Banerjee et al., 2016; Sekiya
and Sudo, 2014; Meul et al., 2018; Revell et al., 2015; Young
et al., 2013).
The future evolution of methane concentrations and the
emission of ozone precursors, such as biogenic volatile or-
ganic compounds (BVOCs), are a major source of uncer-
tainty among the scenarios, but there are also other sources
of uncertainty related to GHG-induced climate change. Fu-
ture changes in the net influx of ozone from the stratosphere
to the troposphere are linked to changes in the stratospheric
Brewer–Dobson circulation (BDC) and the amount of ozone
in the lowermost stratosphere, which are strongly influenced
in a changing climate by changes in ODSs and long-lived
GHGs. Future decreases in ODSs will lead to an ozone in-
crease throughout the atmosphere with the largest percent-
age changes in the upper stratosphere and in the high-latitude
lower stratosphere (with a particularly large impact on the
SH). However, changes in GHGs will lead to a more com-
plex pattern of ozone changes, with increases in ozone in the
upper stratosphere (from GHG-induced cooling slowing the
rate of gas-phase ozone loss) and an increase in net strato-
spheric influx due to a possible strengthening of the BDC,
with ODS decreases counteracting such a strengthening of
the BDC due to GHG increases (Morgenstern et al., 2018;
Polvani et al., 2018, 2019). Lu et al. report that the increases
in tropospheric ozone in the SH are the result of circulation
changes (Lu et al., 2019). For the coming decades, future net
changes in the BDC depend on the climate change scenario
and compliance with the Montreal Protocol. The BDC ac-
celeration in response to increased GHG forcing is a robust
finding across a range of atmospheric models with varying
representations of the stratosphere (Butchart, 2014; Oberlän-
der-Hayn et al., 2016), although there are still uncertainties
in the magnitude (Morgenstern et al., 2018) and attribution of
the strengthening. The substantial weakening effect of ODS
decreases on the BDC has only recently been established
(Morgenstern et al., 2018; Polvani et al., 2018, 2019). Baner-
jee et al. (2016) reported that a strengthened BDC under
the RCP8.5 scenario has the strongest effect on tropospheric
ozone in the tropics and subtropics, while stratospheric ozone
recovery from declining long-lived ODSs has a larger role
in the midlatitudes and extratropics. Meul et al. (2018) sug-
gested that the global annual mean influx of stratospheric
ozone into the troposphere will increase by 53 % between the
years 2000 and 2100 under the RCP8.5 greenhouse gas sce-
nario and that this will be smaller for the moderate RCP6.0
scenario, but the relative change in the contribution of ozone
of stratospheric origin in the troposphere is of comparable
magnitude in both scenarios.
While all studies agree that changes to net stratosphere-
to-troposphere transport will tend to increase future tro-
pospheric ozone, the relative importance of stratosphere–
troposphere exchange (STE) of ozone versus in situ net
chemical production for future tropospheric-ozone trends re-
mains uncertain. A study using new simulations from mul-
tiple CCMs finds considerable disagreement among models
regarding past and future responses to drivers of tropospheric
ozone even when the same scenario is considered, with much
of the model spread likely due to the uncertainty in impacts
on ozone in the tropopause region driving inter-model varia-
tions in STE trends (Morgenstern et al., 2018). In addition
to these stratospheric influences, further uncertainty arises
from inter-model differences in tropospheric chemistry and
physics (such as photolysis, convection and the boundary-
layer scheme).
In this study, we examine the evolution of tropospheric
ozone and describe the changes to the budget using the com-
mon model diagnostics of ozone production, loss and dry de-
position to the surface. Our study focuses on transient simu-
lations that were performed for CMIP6. The simulations run
https://doi.org/10.5194/acp-21-1-2021 Atmos. Chem. Phys., 21, 1–32, 2021
4 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
from preindustrial times to the present day (i.e. the CMIP
“Historical” simulations of the CMIP6) and from the present
day to the end of the 21st century (i.e. “ssp370” of the future
ScenarioMIP simulations) (Eyring et al., 2016). Five models
including interactive stratospheric chemistry are selected for
this analysis, which differs from previous multi-model stud-
ies (e.g. Stevenson et al., 2006; Young et al., 2013). CMIP6
builds on the approach of the Chemistry Climate Model In-
tercomparison (CCMI) project using long transient simula-
tions but adds more diagnostics and a new, more complete
set of emission data and the most up-to-date and complete
or complex set of interactive models. It draws on an im-
proved set of observational constraints via TOAR to pro-
vide a comprehensive set of evaluation of the models’ per-
formance against well-established metrics (Sect. 3) for recent
decades and the evolution of the tropospheric-ozone burden
and budget over the full period of the experiments of 1850 to
2100 (Sect. 4).
This paper forms part of a set of papers in support of In-
tergovernmental Panel on Climate Change (IPCC) Sixth As-
sessment Report. Other papers published or under discussion
at the time of writing feature an analysis of chemistry and
feedbacks (Thornhill et al., 2020), stratospheric ozone (Kee-
ble et al., 2020), ozone radiative forcing (Skeie et al., 2020;
Morgenstern et al., 2020), air pollution and particulate mat-
ter (Turnock et al., 2020; Allen et al., 2020), and oxidizing
capacity (Stevenson et al., 2020).
2 Models, simulations and configuration details
2.1 Model descriptions
We describe here salient features of the five models used for
this study. Supplement Table S1 summarizes the common
structural aspects for reference.
2.1.1 GFDL-ESM4
The atmospheric component of the GFDL-ESM4 (Dunne
et al., 2019) called AM4.1 includes an interactive tropo-
spheric and stratospheric gas-phase and aerosol chemistry
scheme (Horowitz et al., 2020). The model includes 56
prognostic (transported) tracers and 36 diagnostic (non-
transported) chemical species, with 43 photolysis reactions,
190 gas-phase kinetic reactions and 15 heterogeneous reac-
tions. The tropospheric chemistry includes reactions for the
NOx--HOx--Ox--CO--CH4 system and oxidation schemes
for other NMVOCs. The stratospheric chemistry accounts
for the major ozone loss cycles (Ox, HOx , NOx , ClOx and
BrOx) and heterogeneous reactions on liquid and solid strato-
spheric aerosols as in Austin et al. (2012). The chemical sys-
tem is solved using an implicit Euler backward method with
Newton–Raphson iteration. Photolysis rates are calculated
interactively using the FAST-JX version 7.1 code, account-
ing for the radiative effects of simulated aerosols and clouds.
Emissions of BVOCs, including isoprene and monoterpenes,
are calculated online in AM4.1 using the Model of Emissions
of Gases and Aerosols from Nature (MEGAN; (Guenther
et al., 2006)), as a function of simulated air temperature and
shortwave radiative fluxes. Details on the chemical mecha-
nism are included in Horowitz et al. (2020). The gas-phase
and heterogeneous chemistry configuration is similar to that
used by Schnell et al. (2018). Anthropogenic and biomass
burning emissions are prescribed from the dataset of Hoesly
et al. (2018) and van Marle et al. (2017a) developed in sup-
port of CMIP6. Natural emissions of ozone precursors not
calculated interactively are prescribed in the same way as in
Naik et al. (2013).
The bulk aerosol scheme, including 18 transported aerosol
tracers, is similar to that in AM4.0 (Zhao et al., 2018), with
the following updates: (1) ammonium and nitrate aerosols are
treated explicitly, with ISORROPIA (Fountoukis and Nenes,
2007) used to simulate the sulfate–nitrate–ammonia thermo-
dynamic equilibrium; (2) oxidation of sulfur dioxide and
dimethyl sulfide to produce sulfate aerosol is driven by the
gas-phase oxidant concentrations (OH, H2O2 and ozone) and
cloud pH simulated by the online chemistry scheme; and
(3) the rate of ageing of black and organic carbon aerosols
from hydrophobic to hydrophilic forms varies with calcu-
lated concentrations of hydroxyl radical (OH). Sources of
secondary organic aerosols (SOAs) include an anthropogenic
source from oxidation of the simulated C4H10 hydrocarbon
tracer by hydroxyl radical and a biogenic pseudo-emission
scaled to BVOC emissions from vegetation.
2.1.2 UKESM1-0-LL
UKESM1-0-LL (also abbreviated to “UKESM1” here) is the
UK’s Earth system model (Sellar et al., 2019). It is based on
the Global Coupled 3.1 (GC3.1) configuration of HadGEM3
(Williams et al., 2018), to which various Earth system com-
ponents have been added, e.g. ocean biogeochemistry, ter-
restrial carbon–nitrogen cycle and atmospheric chemistry.
The atmospheric and land components are described in
Walters et al. (2019). The chemistry scheme included in
UKESM1 is a combined stratosphere–troposphere chemistry
scheme (Archibald et al., 2020b) from the UK Chemistry
and Aerosol (UKCA) model, combining the stratospheric
chemistry scheme of Morgenstern et al. (2009) with the tro-
pospheric (TropIsop) chemistry scheme of O’Connor et al.
(2014). A paper describing and evaluating this stratosphere–
troposphere scheme in UKESM1 is currently in discussion
(Archibald et al., 2020b). The aerosol scheme is a two-
moment scheme from UKCA, called GLOMAP mode, and
is part of the Global Atmosphere 7.0/7.1 configuration of
HadGEM3 (Walters et al., 2019). It models sulfate, sea salt,
organic carbon and black carbon. Some improvements to
the aerosol scheme for GA7.1 were required to address the
strong negative aerosol forcing found with GA7.0 and are
documented in Mulcahy et al. (2018). Dust is modelled sep-
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 5
arately in six size bins following a variant of the Woodward
scheme. Further discussion of the aerosol radiative forcing
UKESM1 is given in Mulcahy et al. (2020).
Anthropogenic and biomass burning emissions are pre-
scribed (Hoesly et al., 2018; van Marle et al., 2017a), but
emissions of isoprene and monoterpenes are interactive and
are based on the interactive biogenic VOC (iBVOC) emission
model (Pacifico et al., 2011). Lightning emissions of NOx
(LNOx) are also interactive using the cloud top height param-
eterization of Price and Rind (Price and Rind, 1992, 1993).
Other natural emissions are prescribed as climatologies and
will be discussed fully in Archibald et al. (2020b). For vol-
canic eruptions, internally consistent stratospheric aerosol
optical depth (AOD) and surface area density (SAD) are
prescribed for both the volcanic forcing and for the UKCA
stratospheric heterogeneous chemistry.
2.1.3 CESM2-WACCM
CESM2-WACCM uses the Community Earth System Model
version 2 (Emmons et al., 2020) and is a fully coupled
Earth system model. The Whole Atmosphere Community
Climate Model version 6 (WACCM6) is coupled to the
other components in CESM2. The Parallel Ocean Program
version 2 (POP2) (Smith et al., 2002; Danabasoglu et al.,
2012) includes several improvements compared to earlier
versions, including ocean biogeochemistry represented by
the Marine Biogeochemistry Library (MARBL), which in-
corporates the biogeochemical elemental cycle (BEC) ocean
biogeochemistry–ecosystem model (e.g. Moore et al., 2013).
Additional components are the sea-ice model CICE version
5.1.2 (CICE5) (Hunke et al., 2015) and the Community Ice
Sheet Model version 2.1 (CISM2.1) (Lipscomb et al., 2019).
The Community Land Model version 5 (CLM5) also in-
cludes various updates, including interactive crops and irri-
gation for the land and the Model for Scale Adaptive River
Transport (MOSART).
CESM2-WACCM has a good representation of the tro-
pospheric dynamics and climate and also simulates internal
variability in the stratosphere, including stratospheric sud-
den warming (SSW) events on the intraseasonal timescales
and the explicitly resolved Quasi-Biennial Oscillation (Get-
telman et al., 2019). The CESM2-WACCM model includes
interactive chemistry and aerosols for the troposphere, strato-
sphere and lower thermosphere with 228 chemical com-
pounds, including the four-mode Modal Aerosol Model
(MAM4) (Emmons et al., 2020). In particular, it includes an
extensive representation of secondary organic aerosols based
on the Volatility Basis Set (VBS) model framework (Tilmes
et al., 2019) following the approach by Hodzic et al. (2016).
The scheme includes both updates to the SOA formation and
removal pathways. MAM4 has been further modified to in-
corporate a new prognostic stratospheric aerosol capability
(Mills et al., 2016). The modifications include mode width
changes, growth of sulfate aerosol into the coarse mode, and
the evolution of stratospheric sulfate aerosols from natural
and anthropogenic emissions of source gases, including car-
bonyl sulfide (OCS) and volcanic sulfur dioxide (SO2). An-
thropogenic and biomass burning emissions are prescribed
(Hoesly et al., 2018; van Marle et al., 2017a). Biogenic emis-
sions including BVOC are produced from MEGAN version
2.1 (Guenther et al., 2012) and are also used for SOA forma-
tion.
2.1.4 GISS-E2-1-G
GISS-E2-1-G is the NASA Goddard Institute for Space
Studies (GISS) chemistry–climate model version E2.1 us-
ing the GISS Ocean v1 (G01) model. The model config-
urations submitted for CMIP6 are described in detail by
Kelley et al. (2020) and Miller et al. (2014). Here, we use
the subset of model configurations that ran with online in-
teractive chemistry. The atmospheric component was run
with a horizontal resolution of 2◦ latitude by 2.5◦ longi-
tude with 40 hybrid sigma–pressure vertical layers extended
from the surface to 0.1 hPa (∼ 28 in the troposphere). On-
line interactive chemistry follows the GISS Physical Under-
standing of Composition-Climate INteractions and Impacts
(G-PUCCINI) mechanism for gas-phase chemistry (Shindell
et al., 2001, 2003, 2006, 2013; Kelley et al., 2020) and ei-
ther the One-Moment Aerosol (OMA) or the Multiconfigu-
ration Aerosol TRacker of mIXing state (MATRIX) model
for the condensed phase (Bauer et al., 2020). The gas-phase
mechanism includes 146 reactions (including 28 photodis-
sociation reactions) acting on 47 species throughout the tro-
posphere and stratosphere including five heterogeneous re-
actions. The model advects 26 (OMA) or 51 (MATRIX)
aerosol particle tracers and 34 gas-phase tracers. Anthro-
pogenic and biomass burning emissions are prescribed fol-
lowing the CMIP6 guidelines. Lightning NOx emissions are
calculated online in deep convection as described by Kelley
et al. (2020). Soil microbial NOx emissions are prescribed
from climatology. Biogenic emissions of isoprene are cal-
culated online and respond to temperature (Shindell et al.,
2006) but are prescribed for alkenes, paraffins and terpenes.
Methane is prescribed as a surface boundary condition but al-
lowed to advect and react with the chemistry in the historical
runs and a subset of the SSP simulations; some future simu-
lations used interactive online methane emissions following
Shindell et al. (2004). The atmosphere is coupled to the GISS
Ocean v1 (GO1) model (Kelley et al., 2020) with a horizontal
resolution of 1◦ latitude by 1.25◦ longitude with 40 vertical
levels.
2.1.5 MRI-ESM2-0
MRI-ESM2-0 is the Meteorological Research Institute
(MRI) Earth System Model (ESM) version 2.0. Detailed
descriptions of the model and evaluations are given by
Yukimoto et al. (2019a), Kawai et al. (2019) and Oshima
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6 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
et al. (2020). MRI-ESM2-0 consists of four major com-
ponent models: an atmospheric general circulation model
with land processes (MRI-AGCM3.5), an ocean–sea-ice gen-
eral circulation model (MRI Community Ocean Model ver-
sion 4, MRI.COMv4), an aerosol chemical transport model
(Model of Aerosol Species in the Global Atmosphere mark-
2 revision 4-climate, MASINGAR mk-2r4c) and an atmo-
spheric chemistry model (MRI Chemistry Climate Model
version 2.1, MRI-CCM2.1). A coupler is used to interac-
tively couple each component model (Yoshimura and Yuki-
moto, 2008). MRI-ESM2-0 uses different horizontal reso-
lutions in each atmospheric component model but employs
the same vertical resolution: MRI-AGCM3.5, the aerosol
model and the atmospheric chemistry model use TL159
(approximately 120 km or 1.125◦× 1.125◦), TL95 (approxi-
mately 180 km or 1.875◦× 1.875◦) and T42 (approximately
280 km or 2.8125◦× 2.8125◦), respectively, and all models
employ 80 vertical layers (from the surface to the model top
at 0.01 hPa) in a hybrid sigma–pressure coordinate system.
MRI.COMv4 uses a tripolar grid with a nominal horizontal
resolution of 1◦ in longitude and 0.5◦ in latitude with 60 ver-
tical layers (Tsujino et al., 2017). Detailed descriptions of
the CMIP6 CMIP historical experiments by MRI-ESM2-0
are given by Yukimoto et al. (2019b).
MRI-ESM2-0 includes interactive chemistry and aerosols
in the atmosphere. The atmospheric chemistry model, MRI-
CCM2.1, calculates the evolution and distribution of the
ozone and other trace gases in the troposphere and mid-
dle atmosphere (Yukimoto et al., 2019b; Deushi and Shi-
bata, 2011). The model includes 64 prognostic chemical
species and 24 diagnostic chemical species, with 184 gas-
phase reactions, 59 photolysis reactions and 16 heteroge-
neous reactions. It considers the Ox–HOx–NOx–CH4–CO
chemical system and NMVOC oxidation reactions, as well
as the major stratospheric chemical system. Anthropogenic
and biomass burning emissions are prescribed (Hoesly et al.,
2018; van Marle et al., 2017b). Lightning emissions of NOx
are diagnosed at 6 h intervals following the parameterization
of Price and Rind (Price and Rind, 1992, 1993). Other nat-
ural emissions such as biogenic, soil and ocean emissions
are prescribed as climatologies (Deushi and Shibata, 2011).
The aerosol component model, MASINGAR mk-2r4c, cal-
culates the physical and chemical processes of the atmo-
spheric aerosols and treats the following species: non-sea-
salt sulfate, black carbon, organic carbon, sea salt, mineral
dust and aerosol precursor gases (Yukimoto et al., 2019b;
Oshima et al., 2020). The size distributions of sea salt and
mineral dust are divided into 10 discrete bins, and the sizes
of the other aerosols are represented by lognormal size dis-
tributions.
2.2 Simulations
For this review, we used available data from the CMIP6
CMIP Historical experiments from UKESM1 (Tang et al.,
2019), GFDL-ESM4 (Krasting et al., 2018), GISS-E2-1-G
(NASA Goddard Institute For Space Studies (NASA/GISS),
2019), MRI-ESM2-0 (Yukimoto et al., 2019b) and CESM2-
WACCM (Danabasoglu, 2019a). For ScenarioMIP ssp370
experiments we used data archived by UKESM1 (Good
et al., 2019), GFDL-ESM4 (John et al., 2018), GISS-E2-1-G
(NASA Goddard Institute For Space Studies (NASA/GISS),
2020), MRI-ESM2-0 (Yukimoto et al., 2019c) and CESM2-
WACCM (Danabasoglu, 2019b).
We analysed those models that had archived sufficient
data to the Earth System Grid Federation Peer-to-Peer sys-
tem to permit accurate characterization of the tropospheric-
ozone burden. In practice this meant we used archived ozone
data from the AERmon characterization of the tropospheric-
ozone burden (variable name: “o3”) on native model grids,
along with data on the tropopause pressure using the
World Meteorological Organization (WMO) definition of the
tropopause (variable name: “ptp”). For the budget calcula-
tions, dry deposition (variable name: “dryo3”), chemical pro-
duction (variable name: “o3prod”) and chemical destruction
(variable name: “o3loss”) along with “air mass”, air tempera-
ture (variable name: “ta”) and pressure diagnostics (variables
such as “ps” and “phalf” where required) were used from the
AERmon realm.
2.3 Emissions
Figure 1 shows the emissions and methane forcing used in
the CMIP6 models. Data for the period 1850 to 2014 were
taken from the CMIP6 CMIP Historical experiment, and for
the period 2015 to 2100 they are taken from the ScenarioMIP
ssp370 experiment.
CO emissions were calculated using the “emico” variable
output by each model. Anthropogenic NOx emissions used in
each model were calculated as follows: for UKESM1-0-LL,
the “eminox” variable was used, which is the sum of anthro-
pogenic, open-burning, soil and aircraft NOx emissions; for
GFDL-ESM4 and GISS-E2-1-G, the eminox variable repre-
sents anthropogenic, open-burning, soil, aircraft and light-
ning NOx emissions, so the accompanying “emilnox” (light-
ning) output for these models was subtracted to calculate an-
thropogenic NOx ; finally, for CESM2-WACCM, the eminox
variable consists of anthropogenic, open-burning and soil
NOx emissions, so a small fraction of total NOx emissions
in the form of anthropogenic aircraft are missing. Biogenic
non-methane volatile organic compound emissions were cal-
culated using the “emibvoc” variable.
All five models used a version of the Price and Rind
(1992) lightning flash parameterization that assumes light-
ning activity increases with increasing convective cloud
height; since most models predict increases in convective
depths with increasing greenhouse gas levels, this scheme
generally predicts monotonic increases in lightning over
time, from a multi-model mean of 4.9± 1.9 Tg(N) yr−1 in
1850–1859 to 5.1± 2.0 Tg(N) yr−1 in 2005–2014 and to
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 7
Figure 1. Diagnosed emissions and burden of tropospheric-ozone precursors. Maroon line: UKESM1; light blue line: CESM2-WACCM;
dark blue line: GFDL-ESM4; dark red line: GISS-E2-1-G; green line: MRI-ESM2-0.
6.2± 2.6 Tg(N) yr−1 by 2090–2099 (dashed lines of top left
panel of Fig. 1). However, how lightning may respond to
a warming world remains unknown (e.g. Williams, 2005;
Price, 2013; Murray, 2016). Since lightning NOx has a dis-
proportionately strong impact on tropospheric-ozone bur-
dens relative to surface emissions (e.g. Murray et al., 2013),
this remains an important source of uncertainty both between
models and in the temporal evolution of tropospheric ozone.
The CO and NOx tropospheric burdens were calculated
by applying a tropospheric mask derived from each model’s
tropopause pressure or height output. The NOx burden was
determined as the sum of the NO and NO2 burdens.
The prescribed methane lower boundary concentrations
are described in Meinshausen et al. (2020). Over the ssp370
period, global methane concentrations increase monotoni-
cally.
3 Evaluation of tropospheric ozone over recent decades
Figure 2 shows the present-day spatial distribution of ozone
and its inter-model variability in the CMIP6 ensemble. The
spatial patterns are broadly consistent with observations (see
Sect. 3.1–3.4) and those of earlier model intercomparison
studies (e.g. Stevenson et al., 2006; Young et al., 2013).
Zonal mean mixing ratios are highest in the upper tropo-
sphere, especially in the extratropics, reflecting longer chem-
ical lifetimes at higher altitude (Fig. 2a). Ozone is also
higher in the NH relative to the SH, reflecting higher rates
of stratospheric downwelling (e.g. Rosenlof, 1995) and sur-
face ozone precursor emissions. The model ensemble mem-
bers are in relatively good agreement, with a standard devi-
ation of less than 25 % throughout most of the troposphere.
There is improved multi-model agreement in the Northern
Hemisphere and a slight degradation in the Southern Hemi-
sphere relative to Young et al. (2013), although it is hard
to assess given the different number of ensemble members
in the two assessments (15 then vs. 5 here). The greatest
absolute and relative differences in mixing ratio occur in
the upper troposphere. This reflects relatively large inter-
model variability in the simulated mean tropopause pres-
sure (±30 hPa). The tropopause acts as a dynamical barrier
that separates the high-ozone air of the stratosphere from the
low-ozone air of the troposphere. Therefore, simulated dif-
ferences in tropopause height manifest themselves as large
differences in ozone mixing ratio in the upper-troposphere
and lower-stratosphere (UT/LS) region. Furthermore, varia-
tions in tropopause pressure allow for more or less air mass to
exist in the troposphere (±∼3 %), also contributing to vari-
ations in tropospheric columns of ozone (TCOs) between
models, especially in the northern extratropics (Fig. 2e–f).
Inter-model variability in TCOs (Fig. 2e) is about twice as
high as in earlier model intercomparison studies (e.g. Young
et al., 2013) due to our use of the thermal tropopause rather
than a chemical tropopause (see Sect. 3.4). Ozone also has
relatively large inter-model variability in the southern ex-
tratropical free troposphere, likely resulting from the rela-
tively large variability in southern lower-stratospheric ozone
and subsequent transport across the tropopause. In addition,
ozone mixing ratios vary relatively greatly between models
in the tropics, especially in the surface boundary layer (espe-
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8 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 2. CMIP6 ensemble mean, annual mean ozone climatologies and their inter-model variability in the present day (2005–2014 CE) of
the historical simulation. Panel (a–c) shows zonal mean ozone, the middle row shows the tropospheric-ozone column, and the bottom row
shows surface ozone. For each row, the left-hand panel shows the absolute values of the ozone variable: ppbv for the zonal mean and surface
concentrations and Dobson units (DU) for the tropospheric column. The middle column shows the absolute inter-model standard deviations
in the same units. The right column shows the standard deviation as a percentage of the ensemble mean value. The top row of panels (a, b,
c) also shows the multi-model zonal mean tropopause pressure (a, d, g), and the mean± 1 standard deviation of the multi-model variability.
Note that each panel has a different scale. This is an updated version of Fig. 3 of Young et al. (2013).
cially in regions of high biogenic emissions such as the Ama-
zon) and the UT/LS region. The latter is of interest due to the
importance of absorption of outgoing longwave radiation for
radiative forcing in this region (e.g. Forster and Shine, 1997).
3.1 Surface ozone
Figure 3 compares the CMIP6 model ensemble to five re-
mote surface ozone stations with the longest available in situ
sampling record: Mauna Loa, Hawai‘i, USA (MLO; 19.5◦ N,
155.6◦W; 3397 m a.s.l.; 1957–present), the South Pole (SPO;
90◦ S, 59◦ E; 2840 m a.s.l.; 1961–present), Barrow, Alaska,
USA (BRW; 71.3◦ N, 156.6◦W; 11 m a.s.l.; 1973–present),
Cape Matatula, Tutuila, American Samoa (SMO; 14.2◦ S,
170.6◦ E; 42 m a.s.l.; 1975–present) and Cape Grim, Tasma-
nia, Australia (CGO; 40.7◦ S, 144.7◦ E; 94 m a.s.l.; 1982–
present). The figure provides the respective trends, tempo-
ral correlation and mean normalized bias error for the model
ensemble and observations. These measurements in remote
background locations are useful constraints for the evaluation
of trends in the tropospheric-ozone budget. Mauna Loa is es-
pecially useful for evaluating trends in tropospheric ozone.
In addition to a long historical record, it is a remote moun-
tain site that frequently samples free-tropospheric air masses.
For a more thorough evaluation and examination of surface
ozone in the CMIP6 simulations, including implications for
surface air quality, we refer the reader to the CMIP6 surface
ozone companion paper (Turnock et al., 2020).
For Mauna Loa, we use monthly average surface ozone
measured using a Regener type potassium iodide (KI) au-
tomatic ozone analyser for 1957–1959 and a UV photo-
metric analyser for 1974–2014. At Barrow and American
Samoa, surface ozone was measured using a UV photo-
metric analyser for 1973–2014 and 1975–2014, respectively.
At the South Pole, ozone was measured using a Regener
type potassium iodide (KI) automatic ozone analyser for
1961–1963, a corrected Regener type chemiluminescent au-
tomatic ozone analyser for 1964–1966, an electrochemical
cell analyser for 1967–1973 and a UV photometric analyser
for 1975–2014. Data for these four stations are archived at
ftp://aftp.cmdl.noaa.gov/data/ozwv/SurfaceOzone/ (last ac-
cess: 1 February 2019). Cape Grim surface ozone was mea-
sured using a UV photometric analyser for 1982–2014 and
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 9
Figure 3. Comparison of annual mean surface observations with the multi-model mean at five stations: Barrow, Alaska, USA (71.3◦ N,
156.6◦W; 11 m a.s.l.), Mauna Loa, Hawai‘i, USA (19.5◦ N, 155.6◦W; 3397 m a.s.l.), Cape Matatula, Tutuila, American Samoa (14.2◦ S,
170.6◦ E; 42 m a.s.l.), Cape Grim, Tasmania, Australia (40.7◦ S, 144.7◦ E; 94 m a.s.l.) and the South Pole (90.0◦ S, 59.0◦ E; 2840 m a.s.l.).
The models are sampled from the surface level, except for Mauna Loa, which is sampled at 680 hPa. The pink shading represents the
multi-model mean and± 1 standard deviation at each location. The red circles indicate the multi-model mean sampled at the month of the
observations. The blue squares represent the observations. The solid lines show an ordinary least-squares regression for the multi-model mean
and the observations, with the respective slope printed in the lower right of the panel. The temporal correlation (r) and mean normalized bias
error (mnbe) are shown in black for each panel.
is available as hourly averages from the WMO World Data
Centre for Reactive Gases at https://www.gaw-wdcrg.org
(last access: 1 February 2019). Monthly observations were
converted to annual averages for those with 9 months or more
of data. Corrections to the data to account for the different
ozone analysers operated during the historical period have
been applied to the SPO data using the framework described
by Tarasick et al. (2019). We sample the models at the sur-
face level for Barrow, American Samoa, Cape Grim and the
South Pole and at the 680 hPa level for Mauna Loa.
The models overestimate surface ozone concentrations at
the two NH sites by 2–3 ppbv and the tropical SH site by
6 ppbv while underestimating surface ozone at the two ex-
tratropical SH sites by 1–7 ppbv. In particular, the models
significantly underestimate surface ozone at the South Pole.
In the time before and after polar sunrise at Barrow there
are significant ozone-depletion events in surface air that are
large enough to affect annual mean ozone levels (e.g. Olt-
mans and Levy, 1994; Helmig et al., 2007) and perhaps sug-
gest one reason for the model–observation difference. These
discrepancies may also reflect biases associated with com-
paring point data to a much coarser model grid cell.
At Barrow, Mauna Loa, American Samoa and Cape Grim,
observed surface ozone has increased on average by 0.5–
2.0 ppbv per decade (2 %–4 % per decade) since measure-
ments began. Despite the mean bias, the models capture well
the magnitude of the decadal trends in response to climate
and emission forcings. In the Southern Hemisphere part of
the trend in tropospheric ozone can be explained by the pole-
ward expansion of the Hadley circulation (Lu et al., 2019).
Over Antarctica, observations show an initial decrease from
the 1960s through the mid-1990s before ozone began rising,
resulting in no significant trend during this period. The mod-
els underestimate the magnitude of the observed reduction
and, consequently, simulate a small growth here.
3.2 Vertical, meridional and seasonal ozone
distribution
Figure 4 compares the vertical, meridional and seasonal dis-
tribution of ozone in the CMIP6 ensemble to climatological
measurements from ozonesondes (balloons). We use sonde
measurements archived by the World Ozone and Ultravio-
let Radiation Data Centre (WOUDC) of the World Meteo-
rological Organization/Global Atmosphere Watch Program
(WMO/GAW). The data were accessed on 4 November 2019
from https://doi.org/10.14287/10000008. A total of 23 392
profiles using carbon–iodine (Komhyr, 1969), electrochemi-
cal concentration cell (ECC) (Komhyr, 1971), and Brewer–
Mast (Brewer and Milford, 1960) sondes from 82 sites
worldwide were aggregated over the period 2005–2014. Son-
des show a modest high bias in the troposphere of about 1 %–
5 %± 5 % when compared to more accurate UV-absorption
measurements (Tarasick et al., 2019). Measurement preci-
sion is ±3 %–5 % and the overall uncertainty in ozone con-
centration is less than 10 % in the troposphere (Kerr et al.,
1994; Smit et al., 2007; Tarasick et al., 2016, 2019).
The models reproduce the increase in ozone with altitude
and from south to north and reproduce the seasonal cycle of
ozone in the tropics and northern extratropics well (r2 all
greater than 0.72). Note that the northern hemispheric over-
estimate and southern hemispheric underestimate seen at the
surface (Sect. 3.1) extends into the lower free troposphere.
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10 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 4. Comparison of the annual cycle of ozone, between ozonesonde observations (black circles) and the CMIP6 ensemble mean (solid
orange line), CMIP6 ensemble median (dashed orange line), the ACCMIP ensemble mean (red line; Young et al., 2013) and the ACCENT
ensemble mean (blue line; Stevenson et al., 2006). CMIP6 model data are from the years 2005 to 2014 of the historical experiment. Model
and observational data were grouped into four latitude bands (90 to 30◦ S, 30◦ S to 0◦, 0 to 30◦ N and 30 to 90◦ N) and sampled at three
altitudes (700, 500 and 250 hPa), with the models sampled at locations and months of the ozonesonde measurements before averaging
together. The individual CMIP6 models and ensemble members are represented by the thin grey lines, with the grey shaded area indicating
±1 standard deviation about the CMIP6 ensemble mean. Error bars on the observations indicate the average interannual standard deviation
for each group of stations. The correlation (r) and mean normalized bias error (mnbe) for the CMIP6 (orange), ACCMIP (red) and ACCENT
(blue) ensemble means versus the observations are also indicated in each panel. This figure is an update of Fig. 4 of Young et al. (2013).
The ensemble mean is biased high by about 10 % in the NH,
although it always falls within the range of interannual vari-
ability in the observations (vertical lines). The ensemble re-
produces the magnitude and seasonality of the southern trop-
ics better than the other regions, although it fails to reproduce
the timing and magnitude of the October peak associated
with the zonal wave-one South Atlantic ozone maximum
(Fishman et al., 1990, 1991; Shiotani, 1992; Thompson and
Hudson, 1999; Thompson et al., 2000; Thompson, 2003b;
Sauvage et al., 2006). The model ensemble performs worst in
the southern extratropics, resulting from seasonal behaviour
anti-correlated with the observations in one model (GISS-
E2-1-G); when that model is removed from the ensemble,
the seasonal correlation at 500 hPa becomes r = 0.96, but the
mean bias increases in magnitude to −3 %. CMIP6 shows
nominal improvements in certain regions such as the south-
ern tropics with respect to biases and correlations reported by
the earlier ACCMIP (Young et al., 2013) and Atmospheric
Composition Change: the European Network of excellence
(ACCENT) (Stevenson et al., 2006) studies, although it is
difficult to evaluate given the smaller number of models in
the CMIP6 (5) versus ACCMIP (15) and ACCENT (26) stud-
ies and given different periods of evaluation.
3.3 Tropospheric-ozone column abundance
Satellites provide high-frequency near-global coverage of
TCOs, the amount of ozone integrated from the sur-
face to the tropopause, typically given in Dobson units
(1 DU≡ 2.69×1020 molecules m−2). Figure 5 compares the
seasonality of TCOs in the model ensemble to that of
the Ozone Monitoring Instrument/Microwave Limb Sounder
(OMI/MLS) product (Ziemke et al., 2006). The OMI/MLS
product is the residual of the OMI total ozone column and
the MLS stratospheric ozone column, available as gridded
1◦× 1.25◦ monthly means, and is provided from 60◦S to
60◦N due to its reliance on solar backscattered UV radiation.
Here we use the data for 2005–2014 downloaded in Novem-
ber 2019 from https://acd-ext.gsfc.nasa.gov/Data_services/
cloud_slice/new_data.html (last access: 1 November 2019).
The model ensemble captures the salient features of
spatial–seasonal patterns in TCO from OMI/MLS. This in-
cludes zonal-wide maxima in the subtropics (where isen-
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 11
Figure 5. Comparison of the seasonal cycle of tropospheric column of ozone (TCO) abundances with satellite climatology for the period
2005 to 2014. Each row shows a separate meteorological season, from top to bottom: December to February (DJF), March to May (MAM),
June to August (JJA) and September to November (SON). The left column shows the inter-model standard deviation of seasonal mean TCO
in the CMIP6 ensemble in Dobson units (DU). The second column from the left shows the multi-model seasonal mean TCO in DU. The
second column from the right shows the seasonal mean TCO in the OMI/MLS product (Ziemke et al., 2006). The right column shows the
relative bias in the multi-model seasonal mean relative to the OMI/MLS product in percent (%).
tropes intersect the tropopause), greater TCO in the NH, min-
ima over the remote Pacific and Antarctic, and the zonal-
wave pattern over the South Atlantic Ocean. On average, the
models overestimate TCO in the NH and Indian Ocean by
up to 25 % versus OMI/MLS and underestimate ozone in the
remote Pacific and Southern Ocean, yielding small net pos-
itive biases when integrated over the whole region (+2 DU
or 7 %–10 % in all seasons). The models show greatest dis-
agreement in summertime extratropical TCO, especially in
the high Arctic, but OMI/MLS is not available here.
Figure 6 evaluates annual mean TCO in the model
ensemble versus OMI/MLS and the Trajectory-mapped
Ozonesonde dataset for the Stratosphere and Troposphere
(TOST). TOST is a global three-dimensional dataset of
tropospheric and stratospheric ozone, derived from the
ozonesonde record (Liu et al., 2013a, b). TOST deter-
mines TCO using 96 h forward and backward trajectory cal-
culations of the ozone profiles using the Hybrid Single-
Particle Lagrangian Integrated Trajectory (HYSPLIT) parti-
cle dispersion model (Draxler and Hess, 1997, 1998) driven
by the global NOAA National Centers for Environmen-
tal Prediction/National Center for Atmospheric Research
(NCEP/NCAR) pressure level meteorological reanalysis. By
assuming ozone production and loss to be negligible, the
ozone is mapped to other locations and times using a 3-
dimensional grid of 5◦× 5◦× 1 km. TCO is calculated from
the surface to the tropopause, which is defined using the
WMO 2 K/km lapse-rate definition applied to the NCEP re-
analysis. Over mountainous areas a topographic correction
is made in order to address an apparent bias in TCO over
high mountains. TOST has been evaluated using individual
ozonesondes, excluded from the mapping, by backward and
forward trajectory comparisons and by comparisons with air-
craft profiles and surface monitoring data (Tarasick et al.,
2010; Liu et al., 2013a, b). Differences are typically about
10 % or less, but there are larger biases in the UT/LS, the
boundary layer and in areas where ozonesonde measure-
ments are very sparse. The accuracy of the TOST product
depends largely on the accuracy of HYSPLIT and the mete-
orological data on which it is based.
The TOST data presented here use the troposphere-only
dataset, which explicitly excludes trajectories originating in
the stratosphere. This avoids including stratospheric air, with
its very high ozone content, when the NCEP tropopause
is higher than the climatological tropopause (i.e. the ozone
tropopause). If the same calculations are made using the full-
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12 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 6. Comparison of the annual tropospheric column of ozone (TCO) abundance with satellite (OMI/MLS) and ozonesonde-derived
(TOST) climatologies for the period 2005 to 2014. The leftmost panel shows the inter-model standard deviation of annual mean TCO in the
CMIP6 ensemble in Dobson units (DU). The second panel from the left shows the multi-model annual mean TCO in DU. The middle panel
shows the annual mean TCO in the OMI/MLS product (Ziemke et al., 2006). The second panel from the right shows the annual mean TCO
in the TOST product (Liu et al., 2013b, a). The rightmost panel shows the relative bias in the multi-model mean relative to the TOST product
in percent (%).
profile TOST dataset, the calculated burden is on average
42 Tg (about 15 %) larger.
The models agree with the TOST product in much of
the tropics, except in the remote Pacific, where they are bi-
ased low, qualitatively consistent with the OMI/MLS prod-
uct. Since the TOST product is on average lower than
OMI/MLS, especially in higher latitudes, the models are
biased even higher with respect to the TOST data than
OMI/MLS (+6 DU and 22 %).
3.4 Tropospheric-ozone burden
Figure 7 compares the present-day tropospheric-ozone
burden to seven space-based satellite products and the
ozonesonde-derived TOST product. The satellite-derived
products include the annual mean burdens for 60◦ S–60◦ N
from OMI/MLS, IASI (Infrared Atmospheric Sounding
Interferometer)-FORLI (Fast Optimal Retrievals on Lay-
ers), IASI-SOFRID (SOftware for a Fast Retrieval of IASI
Data), GOME (Global Ozone Monitoring Experiment)/OMI-
SOA (Smithsonian Astrophysical Observatory), OMI-RAL
(Rutherford Appleton Laboratory), SCIAMACHY (SCan-
ning Imaging Absorption SpectroMeter for Atmospheric
CHartographY) and TES (Tropospheric Emission Spectrom-
eter) reported by Gaudel et al. (2018). The TOST record
has been calculated since 1980 but is most accurate begin-
ning in 1998 when sonde measurements began in the tropics
as part of the Southern Hemisphere Additional OZoneson-
des (SHADOZ) campaign (Thompson, 2003a). The satel-
lite burdens span a range of values (∼ 250–350 Tg) con-
sistent with the multi-model mean (MMM) and standard
deviation, reflecting uncertainties in the tropopause defi-
nition (Gaudel et al., 2018). TOST is consistently lower
than most satellite products and the model ensemble. De-
spite the spread in mean value, the models and observations
largely agree on the magnitude of the increasing trend fol-
lowing 1997 (0.82± 0.13 Tg yr−1 in the CMIP6 ensemble
vs. 0.70± 0.15 Tg yr−1 in TOST vs. 0.83± 0.85 Tg yr−1 in
the satellite ensemble).
Figure 7b and c demonstrate the sensitivity of the
tropopause burden to the definition of the tropopause ap-
plied. Earlier model intercomparison studies generally uti-
lized a chemical tropopause defined at the 150 ppbv ozone
isopleth, since most models did not archive TCO calculated
as an online diagnostic or tropopause pressure, and there is
no clear tropopause definition for tracers. However, there is a
relatively large amount of ozone by mass in the upper tropo-
sphere, and the local column and global burden is sensitive
to the exact definition applied. Model groups taking part in
the CMIP6 experiments were asked to archive both monthly
mean tropopause pressure as well as monthly mean TCO as
calculated online with the dynamically varying tropopause
and ozone concentrations. We calculate the tropospheric-
ozone burden using the monthly mean tropopause pressure in
two different ways: first, excluding the mass of ozone in the
layer containing the tropopause (as commonly implemented;
“exclusive”; yellow); second, including the mass of ozone
between the bottom of the layer containing the tropopause
and the tropopause itself (“inclusive”; orange). The ozone
mixing ratio in the layer containing the tropopause reflects a
mixture of tropospheric and stratospheric air and may be bi-
ased toward the higher stratospheric values. However, there
is a potentially non-negligible amount of tropospheric-ozone
mass in this level, as reflected in the difference between
the inclusive and exclusive calculations of the tropospheric
burden in Fig. 7b–c. Either way, the inter-model spread
in tropospheric burdens is much higher when calculated
with the pressure tropopause than the chemical tropopause
(red). This is because there is large inter-model variability
in the tropopause pressure (Fig. 2), and because the chemi-
cal tropopause by definition somewhat limits the amount of
ozone mass in the troposphere. That being said, TCO calcu-
lated using the monthly mean chemical tropopause ends up
being most similar in mean and variability to the online TCO
diagnostic in the three models (GFDL-ESM4; MRI-EMS2-0;
UKESM1-0-LL) that archived it using the dynamically vary-
ing online pressure tropopause and ozone (orange-red). In
this study, we elect to use the exclusive pressure tropopause
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 13
Figure 7. Evaluation of the present-day tropospheric-ozone burden. (a) Time series of tropospheric-ozone burden integrated from 60◦ S
to 60◦ N for the period 1980 to 2014 (CE). The black line shows the CMIP6 ensemble mean using the pressure tropopause excluding the
layer which contains the tropopause. The grey shading shows the mean± 1 standard deviation of the ensemble inter-model variability for
each year. The coloured lines show the annual mean tropospheric burdens reported by seven satellite products aggregated by Gaudel et al.
(2018) and the ozonesonde trajectory product (TOST; Liu et al., 2013b, a). (b) Tropospheric-ozone burden distribution for 60◦ S to 60◦ N
for the period 1997 to 2014 CE, corresponding to the space between the two vertical dashed lines of panel (a). Box-and-whisker plots show
the distribution of the various satellite products (green) and TOST (blue), alongside the CMIP6 ensemble using four different tropopause
definitions (see main text for details). (c) The same as panel (b) but showing the burden integrated from 90◦ S to 90◦ N in the TOST product
and models. All units are in Tg O3.
definition for defining the tropopause for purposes of the
following budget calculations but recommend future studies
archive and explore the sensitivity of results to multiple def-
initions of the tropopause, especially with online TCO diag-
nostics.
4 Evolution of tropospheric-ozone burden and budget
over the period 1850–2100
4.1 Evolution of tropospheric-ozone burden from 1850
to 2100
Figure 8 shows the evolution of the tropospheric-ozone bur-
den for the five models together with the multi-model mean.
The burdens were calculated using the exclusive pressure
tropopause definition, as discussed above, using the o3 vari-
able defined in the AERmon CMIP6 table, on native model
grids, and using the WMO tropopause pressures as archived
in the ptp variable. All models show an increased burden over
the period 1850–2100, with the largest rate of increase seen
in the second half of the 20th century and a decreased rate in
the second half of the 21st century in response to declining
emissions of ozone precursors.
The figure shows a large increase in tropospheric-ozone
burden, consistent with the increase in emissions of ozone
precursors from the preindustrial (PI) to the present-day pe-
riod (PD). The burden increases by 109 Tg from the PI
(MMM 247± 36 Tg) to the PD (356± 31 Tg), with the most
rapid change to burden occurring between 1950 and 1990.
Figure 8. Evolution of tropospheric-ozone burden integrated from
90◦ S to 90◦ N for the period 1850–2100. Models are shown as
coloured lines as in the caption. Thick blue line: multi-model mean
for CMIP Historical experiment. Red line: multi-model mean for
ScenarioMIP ssp370 experiment. TOST burden is show as black
line, TOAR multi-model mean as a green triangle and ACCMIP
multi-model mean for time slice experiments as dark green circles.
Figure 8 shows that the burdens calculated in CMIP6 models
are consistent with those from ACCMIP time slice experi-
ments for 1850, 1930, 1980 and 2000. There is good agree-
ment between the two datasets, with a similar range in calcu-
lated model burden.
Good agreement is seen between the CMIP6 multi-
model mean burden and separate estimates from TOAR
(336± 8 Tg) derived from observational estimates of the
whole-troposphere ozone burden using IASI and TOST data
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14 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
for the year 2000. The CMIP6 burden for the period 1990–
2014 is, however, significantly higher than the TOST burden
data presented above (Sect. 3.4) for the same period. The
origin of this discrepancy is not yet clear and may emerge
as more models with varying ozone distributions and tro-
pospheric extent become available. Despite the high model
bias with respect to these observational data, it is clear that
a similar trend is observed for both model and observations,
with both the TOST-derived burden and the CMIP6 histor-
ical mean burden increasing by around 15 Tg over the pe-
riod 2000–2015. Further observational constraints are pro-
vided by the study of Yeung et al. (2019), who used iso-
tope data to estimate that the change in tropospheric-ozone
burden was no more than 40 % over the period 1850–2014,
and the TOAR analysis that concluded a change in surface
ozone concentrations of 32 %–71 % over this period (Tara-
sick et al., 2019). In CMIP6, the change in MMM, from
283 Tg to 356 Tg over this period, i.e. a change of 25 %, is
consistent with this constraint.
The evolution in burden from 2014 to 2100 is shown for
the ssp370 scenario. The burden increases by a further 60 Tg
over the period 2015–2100. The major ozone precursors are
projected to increase in the early part of ssp370 up to 2030
before beginning to level off after 2050 as in Fig. 1. As
anthropogenic NOx and CO emissions in ssp370 are pro-
jected to stabilize, the continued increase in ozone burden
indicates an increasingly significant role for other ozone pre-
cursors, such as methane, which continues to increase until
2100 in this scenario, BVOCs, which increase due to chang-
ing climate, and CO2, and a likely increase in stratosphere-
to-troposphere transport of ozone.
Figure 8 shows that the ozone burden in GISS-E2-
1G shows the strongest response to increasing emissions
and consequently, after approximately 1950, the largest
tropospheric-ozone burden. While the source of this strong
increase in ozone burden with respect to other models is dif-
ficult to attribute, Kelley et al. (2020) note that there is a sig-
nificant bias in the stratospheric ozone column, which is also
connected to a positive tropospheric-ozone column bias, and
too cold a tropopause in this model configuration.
The response of UKESM1 is more muted, with UKESM1
showing the largest ozone burden in 1850 of all models and
continuing into the early stages of the simulations, which is
consistent with the largest LNOx and BVOC emissions. The
present-day ozone burden is affected by a strong decrease in
downward transport of ozone from the stratosphere (Skeie
et al., 2020), as discussed below. Together, these two factors
contribute to the increase in ozone burden from 1850 to 2014
being the smallest in this model.
The range in simulated burden varies little across the his-
torical period in the five simulations, being 36 Tg in PI condi-
tions, with UKESM1 showing the highest burden, and 31 Tg
for 2005–2015, with GISS-E2-1-G the highest.
4.2 Regional changes
Figures 9–10 show the historical changes in tropospheric-
ozone distribution in the CMIP6 ensemble since the prein-
dustrial. Over the historic period, ozone increases through-
out the troposphere, with the greatest increases occurring in
the NH. The largest relative changes occur near the surface
in the NH, especially downwind of eastern North America
and East Asia, where the rise in ozone precursor emissions
(Fig. 1) was predominantly located. Of the three periods ex-
plored, the bulk of the increase in ozone occurred between
the 1930s and 1980s. Since the 1980s, most of the increases
were located in South and East Asia and the southern tropics
and subtropics, reflecting the implementation of aggressive
precursor emission controls in North America and Europe.
The recent increase in ozone in South and East Asia has led
to an increase in the inter-model spread of ozone relative to
earlier periods and other regions.
Figures 11–12 show the future changes in tropospheric-
ozone distribution in the CMIP6 ensemble relative to the
present day. Future changes are expected to be less dra-
matic than the 1850 to 2014 increase, reflecting the reduc-
tion in NOx emissions and relative stabilization of CO emis-
sions in the ssp370 scenario (Fig. 1). Despite the global non-
methane precursor emission reductions, tropospheric ozone
still increases across the 21st century possibly driven by a
combination of enhanced stratospheric downwelling associ-
ated with a GHG-driven acceleration to the BDC, increas-
ing methane in the ssp370 scenario (see lower right panel of
Fig. 1) and increasing tropopause height. In particular, the
increase in the subtropical upper troposphere likely reflects
the influence of increased stratospheric downwelling coupled
with stratospheric ozone super-recovery (cf. Fig. 10, Keeble
et al., 2020). The models predict that TCO decreases over the
remote Pacific, likely reflecting precursor emission reduc-
tions coupled with a temperature-driven increase in ozone-
destroying tropospheric water vapour.
4.3 Global ozone budget
We report here data for models that diagnosed the required
chemical ozone production (o3prod), chemical ozone loss
(o3loss) and ozone dry-deposition (dryo3) outputs for both
the Historical and the ssp370 experiment. Figure 13 shows
the evolution of globally integrated annual mean ozone
dry deposition (DD), net chemical ozone production (NCP
=PO3−LO3 ) and the inferred net stratospheric to tropospheric
transport (STE: derived as the “residual” in the ozone budget;
i.e. “Residual = o3loss − o3prod + dryo3”). For this anal-
ysis, we used the CMIP6 data request for ozone production
and loss: PO3 is defined as the sum of reaction tendencies
through HO2 /CH3O2 /RO2+NO reactions and LO3 as the
sum of O(1D)+H2O, O3+HO2 and OH and O3+ alkenes.
The tropospheric-ozone budget terms, burden and lifetime
for the historical and future ssp370 simulations are reported
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 15
Figure 9. Historic change in zonal decadal mean ozone relative to the preindustrial era. Each row shows the change in decadal zonal (i.e.
pressure altitude versus latitude) statistics in the CMIP6 historical simulations relative to those of 1850–1859 CE. From top to bottom: the
change at 1930–1939, at 1980–1989, and at 2005–2014 CE The left two columns show the absolute and relative change in the ozone mixing
ratio in nmol mol−1 (ppbv) and in percent (%). Both columns show the multi-model decadal mean tropopause pressure for the relevant
decade as a solid black line and from 1850–1859 CE as a dashed black line. The second column from the right shows the absolute inter-
model standard deviation in the simulated change in nmol mol−1 (ppbv) and the mean± 1 standard deviation in tropopause pressure height
in the respective decade (solid line) versus 1850–1859 CE (dashed line). The rightmost column is the same as the second column from the
right but normalized by the multi-model mean in percent (%).
in Tables 1 and 2, respectively. The results are averaged over
10 years for each period. As with the tropospheric burden
calculation, we used monthly mean output for each variable
and the WMO tropopause definition to define the limit of the
tropopause using monthly mean output and mask the reaction
tendency data accordingly.
For the Historical and ssp370 coupled experiments, the
GISS-E2-1-G model did not provide the chemical loss term
(L), and so we only include its production (P ) and DD in the
tables (Tables 1 and 2). A notable feature is that P and DD
from the GISS-E2-1-G model are significantly (at least 50 %)
higher than similar data for the other models reported here.
In light of the good agreement between models in terms of
ozone abundance, this is somewhat surprising, but the higher
production is offset by a similarly fast ozone deposition at
the surface, giving ozone burden and abundance that agree
reasonably with other models.
Figure 13 shows the global total dry-deposition tendency
for ozone in Tg yr−1. Global total deposition increases over
the period 1850 to 2100 for all models, increasing gradu-
ally until the 1950s before increasing more steeply until the
late 1990s. The variation in dry deposition largely reflects
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16 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 10. Historic change in tropospheric column ozone (TCO) relative to the preindustrial era. The same as Fig. 9 but for changes in TCO
in Dobson units (DU) or percent (%), as appropriate.
the evolving ozone burden which increases over the PI to
PD period and stabilizes from PD into the later 21st century.
Excluding GISS-E2-1-G, there still remain significant differ-
ences in ozone dry deposition among the models before the
1950s (e.g. from 460 Tg yr−1 in GFDL-ESM4 to 633 Tg yr−1
in UKEMS1 for 1850s), but the differences are smaller after
the year 2000 (815–907 Tg yr−1 over 2005–2014).
Figure 13 shows a more complex behaviour in NCP. There
is a small increase in ozone production over the period 1850–
1950, at which point there is a more rapid rise in the emis-
sion of tropospheric-ozone precursors and hence burden; see
Fig. 1. This rapid increase continues until around 1980 at
which the growth in emissions slows. The projected emis-
sions and NCP reach a maximum between 2030 and 2050
and subsequently stabilize.
GISS-E2-1-G is erroneously missing the loss of ozone
with isoprene and terpenes in its reported o3loss vari-
able, making the net chemical production term erroneously
high. When the online calculation of the stratosphere-to-
troposphere flux of ozone is used instead to calculate the net
chemical production term in that model (not reported to Earth
System Grid Federation (ESGF) but obtained from the orig-
inal simulations), the temporal evolution of the net produc-
tion term is qualitatively consistent with the other four mod-
els. While each term in the GISS-E2-1-G ozone budget has
a larger magnitude than the other models, these components
nevertheless sum to create ozone mixing ratios and burdens
comparable to, albeit still larger than, the other models and
the observations.
The other four models show similar behaviour across
time with NCP peaking around 2030 but different abso-
lute responses to the increase in emissions, with the PI to
PD change in NCP being 585 Tg yr−1 for UKESM1, com-
pared to 460 Tg yr−1 for CESM2-WACCM, 400 Tg yr−1 for
GFDL-ESM4 and 353 Tg yr−1 for MRI-ESM2-0. In
1850–1859, after the GISS-E2-1-G NCP of 1500 Tg yr−1,
UKESM1 shows the highest NCP (of around 250 Tg yr−1),
while the other three models show much smaller NCP around
60 Tg yr−1, which is similar to values reported for 1900 in
Wild and Palmer (2008). The higher NCP in UKESM1 is
consistent with higher LNOx and BVOC emissions in the
early part of the historical period, compared to the other three
models.
Figure 14 shows the variation in vertically integrated zonal
mean net chemical tropospheric-ozone production over the
period 1850 to 2100. GISS-E2-1-G is excluded from this plot
for reasons discussed above. In the 1850s, the main region of
ozone production is located in the tropics and arises from
emissions of NOx due to biomass burning at the surface and
NOx production in the UT from lightning. Over the period
1850–2100, an increase in net ozone production in the mid-
latitudes of NH is observed for all models. In the 20th cen-
tury, ozone production can be seen to commence in NH mid-
latitudes in response to the increase in anthropogenic emis-
sions in these regions. There is a substantial increase in the
extent of regions of strong, positive NCP in the NH extratrop-
ics from the mid-20th century onwards and some expansion
of the region of positive NCP into the southern subtropics
can be seen beginning around 1980. There is good agreement
between the models on these points, but there are some in-
teresting regional differences in this period that merit further
study: UKESM1 shows net positive ozone tendency through-
out the NH across the whole historical period, in contrast to
the other models, and CESM2-WACCM and GFDL-ESM4
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 17
Figure 11. Future change in zonal mean ozone relative to the present day. The same as Fig. 9 but showing future decadal statistics in the
ssp370 future scenario relative to 2005–2014 CE values. From top to bottom: 2025–2034, 2045–2054 and 2090–2099 CE.
show net ozone destruction in high-latitude regions in con-
trast to UKESM1 and MRI-ESM2-0. The figure shows that,
as in Fig. 13, around the year 2010, NCP reaches a max-
imum and then begins to decline, presumably in response
to the projected decrease in emissions of tropospheric-ozone
precursors in the later part of the 21st century (Revell et al.,
2015).
The models also agree in simulating net ozone destruc-
tion across the midlatitudes of the SH, due to a combination
of low emissions and chemical ozone destruction via ozone
photolysis and reaction with HOx radicals in the free tro-
posphere and over the oceans (Cooper et al., 2014). Ozone
destruction in this region reaches a minimum around 2000,
presumably due to a shift in emissions southward during the
later 20th century (Zhang et al., 2016). In the 21st century,
there is a pronounced increase in ozone destruction in the
SH tropics, reflecting a warmer and wetter future climate that
promotes ozone chemical destruction through the reaction of
O(1D) and H2O following ozone photolysis (Stevenson et al.,
2006) and higher concentrations of HOx radicals (Doherty
et al., 2013; Johnson et al., 1999). In the tropics, there is a
strong net ozone destruction in CESM2-WACCM over the
whole period, with an increase towards the end of 21st cen-
tury; this tropical feature is much weaker in the other three
models, and there is even slightly net positive ozone produc-
tion in UKESM1 before around 2020.
Figure 15 shows that both chemical production and loss
terms, PO3 and LO3 , increase over the 20th century, albeit
with terms that increase at different rates over the period.
The chemical production increases rapidly over the 20th
century, particularly in GISS-E2-1-G, CESM2-WACCM and
UKESM1, and the rate of increase slows in the 21st cen-
tury as projected emission reductions begin to have an im-
pact. Chemical destruction also increases over the entire pe-
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18 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 12. Future change in tropospheric column ozone (TCO) relative to the present day. The same as Fig. 11 but for changes in TCO in
Dobson units (DU) or percent (%), as appropriate.
Figure 13. Evolution of net chemical production (red line), dry de-
position (black line) and residual ozone budget (blue line) over the
period 1850–2100 for UKESM1-0-LL, CESM2-WACCM, GFDL-
ESM4 and MRI-ESM2-0.
riod, largely following ozone burden increases but also re-
flecting increases in HOx radicals, as discussed above. After
2030, the destruction rate increases faster than production,
and NCP begins to decrease. The steadily increasing ozone
burden in all models, despite the declining NCP in four of
the five, demonstrates the increasingly large role of down-
ward transport of ozone from the stratosphere to the ozone
burden in the later part of this century.
Ozone production efficiency (OPE) (Liu et al., 1987), de-
fined as moles of ozone produced per mole of NOx emit-
Figure 14. Integrated annual net chemical production of tro-
pospheric ozone for UKESM1-0-LL, CESM2-WACCM, GFDL-
ESM4 and MRI-ESM2-0. Results are historical (1850–2014) and
ssp370 (2015–2100) simulations. Troposphere is masked by the
tropopause pressure calculated in each model using the WMO ther-
mal tropopause definition.
ted, is included here as a way to compare the different model
ozone responses to changes in NOx emissions. It can also be
compared to OPE derived from in situ measurements of O3
and NOx , e.g. Travis et al. (2016). For the experiments pre-
sented here, OPE was calculated from o3prod and eminox or
emilnox variables, so as to include NOx from anthropogenic,
biological and lightning sources. We present the OPE as a
way of comparing model responses to a given change in NOx
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 19
Table 1. Tropospheric-ozone budget terms for the three models averaged over each 10-year historical period. P for chemical production,
L for chemical loss, P -L for net chemical production and DD for dry deposition; Residual is the term balance by Residual=L−P+DD.
Units of P , L, DD and Residual are in Tg(O3)yr−1, Burden in Tg(O3), and Lifetime in days. The Residual quantities for GISS-E2-1-G were
calculated differently from the others, being based on dynamical transport rather than budget closure, and so this is indicated in bold.
Historical UKESM1 CESM2-WACCM GFDL-ESM4 MRI-ESM2-0 GISS-E2-1-G
1850–1859 P 3409 2225 2291 2271 4311
L 3155 2155 2225 2212 –
P -L 254 70 66 58 –
DD 633 459 471 549 1000
Residual 379 387 404 491 1878
Burden 291 204 221 248 272
Lifetime 27.7 28.2 29.5 32.4 –
1895–1904 P 3492 2331 2418 2367 4464
L 3212 2253 2332 2297 –
P -L 279 78 86 70 –
DD 654 481 497 574 1051
Residual 374 403 410 504 1872
Burden 298 211 229 256 279
Lifetime 27.8 27.9 29.1 32.1 –
1945–1954 P 3922 2807 2921 2798 5457
L 3522 2628 2734 2631 –
P -L 400 179 187 167 –
DD 730 579 611 675 1336
Residual 329 400 424 508 1962
Burden 318 239 260 285 315
Lifetime 26.9 26.8 28.0 31.0 –
1975–1984 P 4677 3699 3822 3560 6691
L 4004 3277 3440 3201 –
P -L 673 422 382 359 –
DD 837 725 774 844 1759
Residual 164 303 392 485 2005
Burden 345 282 307 334 355
Lifetime 26.6 26.8 24.6 27.4 –
1995–2004 P 5315 4366 4371 3987 8377
L 4476 3835 3905 3576 –
P -L 839 530 466 411 –
DD 867 791 833 892 1992
Residual 28 261 367 481 1991
Burden 354 310 327 357 387
Lifetime 23.8 24.1 25.4 28.7 –
emissions across the period 1850-2100. By normalizing for
the important driver of NOx emissions, the OPE illustrates
the variation between models of the chemical response to
emissions changes, which can arise from the differing treat-
ment of processes such as photolysis, deposition, transport
and mixing. Figure 16 shows the ozone production efficiency
for the five models. The five models show similar behaviour,
with OPE declining from large initial values to a minimum
over the period 1980–2050 before recovering in the late 21st
century. The trend suggests that models respond less sensi-
tively to NOx emissions as the tropospheric NOx burden in-
creases, with the OPE mirroring somewhat the NOx burden
plots of Fig. 1. Throughout the period 1850-2100, the OPE
of the GISS-E2-1-G model is significantly higher than that
of the other models, consistent with the higher ozone pro-
duction and with the stronger response of ozone to surface
NOx emissions noted in Wild et al. (2020). The OPE for the
other models is lower, and more similar, indicating that the
models’ chemistry has similar ozone responses to increases
in NOx levels. The OPE recovers somewhat in the 21st cen-
tury, during which time ozone production responds more sen-
sitively to increasing NOx , with implications for air quality
control measures. As the OPE is a function of the background
NMVOC mixing ratio, the higher VOC emissions in the pe-
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20 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Table 2. Same as Table 1 but for ssp370. As before, the Residual quantities for GISS-E2-1-G were calculated differently from the others,
being based on dynamical transport rather than budget closure, and so this is indicated in bold.
SSP370 UKESM1 CESM2-WACCM GFDL-ESM4 MRI-ESM2-0 GISS-E2-1-G
2025–2034 P 5867 4996 4805 4327 9106
L 4977 4399 4330 3905 –
P -L 890 597 475 422 –
DD 894 863 879 937 2150
Residual 4 266 404 515 2318
Burden 373 346 355 381 439
Lifetime 22.9 23.6 24.6 28.3 –
2045–2054 P 6114 5311 4974 4498 9434
L 5273 4756 4535 4112 –
P -L 841 555 439 386 –
DD 899 895 898 952 2178
Residual 58 340 459 566 2546
Burden 386 364 371 393 468
Lifetime 22.5 23.2 24.6 27.9 –
2090–2099 P 6763 5909 5324 4828 10350
L 6089 5527 4981 4563 –
P -L 675 382 343 266 –
DD 887 904 898 957 2141
Residual 212 522 555 692 2868
Burden 406 378 389 411 499
Lifetime 20.9 21.2 23.8 26.8 –
riod 1850-1900 in UKESM1 appear to account for the higher
OPE. Similarly, the higher OPE of CESM2-WACCM at the
end of the 21st century is likely to be the result of the higher
biogenic VOC emissions in this model.
Based on the calculated ozone burden, B, and the rates of
ozone removal, the ozone lifetime defined as B/(L+DD) de-
creases across the historical period and into the 21st century.
In 1850, the multi-model mean ozone lifetime is 29.5± 2.1 d,
decreasing by 4 d to 25.5± 2.2 d in the present day. This de-
crease continues in the 21st century to 23.2± 2.7 d in 2100.
The decrease in lifetime is driven partly by an increase in L,
responding to the higher temperatures and humidity impact-
ing the rates of ozone destruction reactions (Young et al.,
2013), and partly by increasing DD, which is a response
to increasing ozone concentration at the surface. Together
these offset the increase in lifetime which would be calcu-
lated from an increase in ozone burden.
Tables 1 and 2 show the residual term in the ozone budget.
Again, it should be noted that the data for GISS-E2-1-G are
not directly comparable with the data from the other mod-
els, being instead an integrated dynamical ozone flux across
the tropopause. While there is a large inter-model spread
in NCP and dry-deposition terms (i.e. substantially higher
values in UKESM1), there are similar residual terms in the
ozone budget (i.e. the inferred net stratospheric influx) before
the 1950s of between 400 and 500 Tg per year. These values
decrease sharply after 1970 partly due to the effect of strato-
spheric ozone depletion by amounts ranging from 300 Tg
(UKESM1) to 60 Tg (GFDL-ESM4). This decline in resid-
ual is a robust feature across models and is consistent with
reduced ozone STE in the present day compared to preindus-
trial times as a result of stratospheric ozone depletion despite
an acceleration of the stratospheric residual circulation and a
potential increase in the troposphere-to-stratospheric flux of
ozone in the northern latitudes (cf. Fig. 9i–j). After the year
2000, the residual terms starts to increase in all models coin-
ciding with the expected ozone recovery, as ozone-depleting
substances decrease, and the BDCs increase, resulting from
increasing GHGs. This is in line with recent studies using
CCMs including a stratospheric ozone tracer which provide
evidence that both the acceleration of the BDC and strato-
spheric ozone recovery will tend to increase the future global
tropospheric-ozone burden through enhanced STE, with the
magnitude of the change depending on the RCP scenario
(Banerjee et al., 2016; Meul et al., 2018; Akritidis et al.,
2019). This projected increase in STE associated with cli-
mate change and ozone recovery offsets decreases in net
chemical production associated with reductions in ozone pre-
cursor emissions, in agreement with, e.g., Sekiya and Sudo
(2014).
Models differ in their simulations of stratospheric
ozone, which inevitably affects tropospheric ozone through
stratosphere–troposphere coupling. Figure 17 shows prein-
dustrial zonal mean ozone (PI: averaged over 1850–1859),
changes in ozone between the PI and the present-day peri-
ods (PD; averaged over 1995–2004), and the change between
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P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 21
Figure 15. Evolution of ozone chemical production (P ) and chemi-
cal loss (L) terms over the period 1850–2100 for the five CMIP6
models (except L from GISS-E2-1-G). ACCENT and ACCMIP
production and loss are also displayed for the year 2000, with a
slight shift for display purposes.
Figure 16. Variation in ozone production efficiency (OPE) for the
five models. Individual models are shown, as in the figure caption
PD and the end of the 21st century (2090–2100) in all five
models. In the PI case, UKESM1 has the largest ozone mix-
ing ratios throughout the troposphere among the five models,
which is associated with its large ozone production (Fig. 14)
and net ozone production (Fig. 15). Figure 17 shows that the
GISS model shows a higher tropopause than other models,
which gives a higher tropospheric-ozone burden for GISS-
E2-1-G (Tables 1 and 2).
The propagation of ozone from the stratosphere to the
troposphere is evident in all five models and this influx of
ozone, to a varying extent, contributes to the tropospheric-
ozone burden. Figure 17 shows strong stratospheric ozone
depletion in UKESM1 between 1850 and 2014 which results
in a reduced net input of stratospheric ozone into the tro-
posphere. This strong ozone depletion is consistent with the
very low residual budget term for this model in the present
day. In Fig. 17 the tropopause height is shown to increase
across all models over the historical period at southern high
latitudes due to the circulation changes associated with in-
creasing GHGs and ozone depletion but with a smaller but
still visible increase in the NH midlatitudes in UKESM1.
Over this period there are substantial ozone increases in the
high-latitude NH lower stratosphere, which would also en-
hance stratosphere-to-troposphere transport of ozone, in all
models except UKESM1. Despite the larger increase in NCP
in UKESM1 (from 279 to 830 Tg yr−1 compared to an in-
crease from 78 to 530 in CESM2-WACCM, from 86 to 466
in GFDL-ESM4 and 58 to 411 Tg yr−1 in MRI-ESM2-0;
Table 1), the decrease in the transport of ozone from the
stratosphere results in UKESM1 showing a smaller increase
in ozone burden from 1850 to 2014, as noted elsewhere (Kee-
ble et al., 2020; Skeie et al., 2020). From the PD into the
future, all models show pronounced stratospheric ozone in-
creases, which visibly impact the tropospheric-ozone abun-
dance. Again, UKESM1 shows the smallest increase in tro-
pospheric ozone among the five models, which may be linked
to the calculated decrease in ozone near the tropopause that
could be linked to the increase in the tropopause height in
future climate. However, such behaviour is not obvious in
the other models which also show a slight increase in the
tropopause height.
More detailed study of the influence of the stratosphere on
the troposphere is difficult in the context of CMIP6 and its
data request. While described well in the literature (Holton
et al., 1995; Appenzeller et al., 1996; Jaeglé et al., 2017),
in the CMIP6 data request there is no diagnostic output for
the dynamical transport of ozone across the tropopause, and
so the residual method has to be employed as in previous
assessments (Stevenson et al., 2006; Young et al., 2018).
This is an acceptable method provided the overall ozone ten-
dency is small (Hu et al., 2017) and has been shown to give
good agreement with the dynamical STE for models such
as UKESM1 (Griffiths et al., 2020). The values determined
here using the residual method agree reasonably with direct
calculation of STE using various tropopause definitions of
410–450 Tg per year (Yang et al., 2016) and the range of
400–500 Tg given in Olsen et al. (2013). The Supplement
shows comparisons between dynamical STE calculations us-
ing a subset of the models described here using an online
tropopause and shows that there is good agreement between
the dynamical calculations and the residual method.
https://doi.org/10.5194/acp-21-1-2021 Atmos. Chem. Phys., 21, 1–32, 2021
22 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Figure 17. Annual and zonal mean ozone distribution in five models over the 1850s (averaged over 1850–1859) (left), the difference between
1850 and 2000 (averaged over 1995–2004) (middle), and the difference between 2000 and 2095 (2090–2099) (right). Thick black lines are
the tropopause height of each model based on the WMO definition. Dashed black lines are the tropopause for the 1850 period (middle) and
for 2000 (right).
5 Summary and conclusions
We have analysed the evolution of tropospheric ozone in
CMIP6 CMIP Historical and ScenarioMIP ssp370 experi-
ments, a “regional rivalry” pathway. Ozone has been eval-
uated against a broad range of observations spanning sev-
eral decades, and we have determined the evolution of the
tropospheric-ozone burden over the period 1850-2100. For
this analysis, we have concentrated on coupled atmosphere–
ocean experiments using whole-atmosphere chemistry and
interactive ozone. We excluded those models that use sim-
plified chemistry which have been shown to yield low ozone
burdens, with the availability of data limiting us to an analy-
sis of ozone burden in five models and the ozone budget for
four models.
We evaluated these CMIP6 models against a suite of sur-
face, sonde and satellite products for the recent past. The
models tend to overestimate ozone in the Northern Hemi-
sphere and underestimate ozone in the Southern Hemisphere.
Nevertheless, the models reproduce the spatial and seasonal
variability in the tropospheric-ozone distribution well and
capture the observed increasing trends in tropospheric ozone
since at least 1998.
However, a key uncertainty identified by this analysis re-
gards the definition of the troposphere. We compared def-
initions based on the chemical tropopause (as tradition-
ally applied) versus the pressure tropopause and online
Atmos. Chem. Phys., 21, 1–32, 2021 https://doi.org/10.5194/acp-21-1-2021
P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations 23
tropospheric-ozone diagnostics. All three varied significantly
from one another, and we recommend future model inter-
comparison studies explicitly examine the sensitivity of re-
sults to the tropopause definition applied, including an em-
phasis on online tropospheric-ozone column calculations.
The ozone burden grows by 44 % from PI (247± 36 Tg)
to the PD (356± 31 Tg) and reaches a maximum of
416± 36 Tg in 2100. The inter-model range is roughly con-
stant across the integration, being around 8 %.
The ozone budget has been analysed in terms of ozone
chemical production, loss, deposition and the STE. Deposi-
tion, chemical ozone production and loss have been shown
to increase steadily from the PI into the future, with the
evolution of the ozone burden likely moderated by the be-
haviour of the stratospheric ozone burden (e.g. Morgenstern
et al., 2018), lightning NOx and global methane abundances,
despite any reductions in non-methane precursor emissions.
The variation in the growth rate of the ozone burden is shown
to depend sensitively on the growth rate of emissions and the
STE. There remains wider diversity between modelled ozone
budget terms, with UKESM1 showing the largest tenden-
cies, particularly in net chemical production, and the smallest
STE.
At the start and end of the model period, inter-model di-
versity appears to be affected by differences in emissions
of biogenic VOCs and LNOx. In contrast to the prescribed
anthropogenic NOx and CO emissions, emission fluxes of
BVOCs are calculated online, as a function of environmental
parameters. There is considerable variation in BVOC emis-
sions across the models, and in the PI, UKESM1, the model
with the highest ozone burden, has the largest emissions of
BVOCs. The sensitivity of ozone production to NOx emis-
sions has been calculated in the form of ozone production
efficiency. There is much greater similarity between models
in this case, reflecting similar sensitivities in the underpin-
ning chemical mechanisms, although only four of the models
show very similar OPE. The higher OPE in GISS-E2-1-G re-
flects its greater sensitivity to emissions, resulting in a larger
production term, a larger source of lightning NOx than the
other models and a shorter NOx lifetime. The OPE, which
is large in the PI, reaches a minimum around the PD before
recovering again into the later part of the 21st century.
The impact of the stratosphere on tropospheric-ozone bur-
den has been demonstrated. We find that the residual STE
tendencies are similar among the models in the PI but that
the STE evolves differently in the five models: UKESM1
has the largest ozone depletion in both hemispheres, whereas
in CESM2-WACCM, MRI-ESM2-0 and GFDL-ESM4 there
are ozone increases in the lower-stratosphere northern high
latitudes; this goes along with the inferred STE being very
low in UKESM1, which may contribute to the smallest
ozone burden trend in this model. Differences in strato-
spheric ozone in the models contribute significantly to the
model spread in diagnosing ozone budget. GISS-E2-1-G is
again an outlier in terms of its behaviour, with STE increas-
ing across the period 1850–2100, presumably the result of
tropospheric expansion.
Stratospheric ozone depletion and recovery to tropo-
spheric ozone has the biggest effect on the budget calcu-
lations around the year 2000. In this period, the decline in
stratospheric ozone, and presumably STE, offsets a signifi-
cant increase in net chemical ozone production over the pe-
riod 1980-2000, which partially mitigates the response of
tropospheric ozone to rapidly increasing emissions. The tro-
pospheric burden over this period is therefore lower than it
might otherwise have been, although the precise level of off-
set requires further clarification.
There remains a need to assess these future changes at
the regional scale and to understand which regions of the
troposphere are most affected by future stratospheric ozone
changes.
Looking forward, there is a clear need to improve the diag-
nostic data request for the evaluation of tropospheric-ozone
budgets, especially for multi-model intercomparison studies.
The closure of the ozone budget remains problematic due to
missing terms in the o3prod, o3loss and dryo3 diagnostics
(e.g. photolysis of nitrates, deposition of NOy species) being
of similar magnitude to the residual terms, and their absences
introduce large uncertainties to the budget calculations. We
would propose that a consistent odd-oxygen family first be
defined that accounts for ozone and its fast cycling with NOx
and its reservoirs (e.g. Wang et al., 1998; Bates and Jacob,
2019), for which the net chemical tendency (i.e. P –L) may
then be easily calculated by comparing total family mem-
ber mass before and after each call to the chemical operator.
This will guarantee that all relevant chemical reactions are in-
cluded regardless of each model’s different chemical mech-
anism and minimize the chance of coding errors. The net
odd-oxygen deposition tendency may then be similarly deter-
mined across the dry- and wet-deposition operators. We also
recommend that online ozone and odd-oxygen mass fluxes
across the tropopause be diagnosed and archived to compare
to the residual method from which one may evaluate budget
closure. Lastly, a consistent definition of what mass is con-
sidered tropospheric should be defined; of the possibilities,
we recommend the inclusive definition as the most physical
and appropriate.
Code availability. This work uses simulations from multiple mod-
els participating in the AerChemMIP project as part of the
Coupled Model Intercomparison Project (Phase 6; https://www.
wcrp-climate.org/wgcm-cmip, World Climate Research Program,
2020); model-specific information can be found through references
listed in Table S1. Model outputs are available on the Earth System
Grid Federation (ESGF) website (https://esgf-data.dkrz.de/search/
cmip6-dkrz/, Earth System Grid Federation, 2020). The model out-
puts were preprocessed using the NetCDF Operator (NCO) and
Climate Data Operator (CDO). The analysis was carried out using
Bash, R and Python programming languages.
https://doi.org/10.5194/acp-21-1-2021 Atmos. Chem. Phys., 21, 1–32, 2021
24 P. T. Griffiths et al.: Tropospheric ozone in CMIP6 simulations
Data availability. All of the data from the CMIP and AerChemMIP
simulations analysed in this study have been published on the Earth
System Grid Federation.
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/acp-21-1-2021-supplement.
Author contributions. PTG, LTM, GZ, PJY, YMS, IEG, DT, JL,
OM, ST, MD and NO provided data analysis and contributed to
the writing and discussion of this paper. VN, PZ, ATA, LWH, OW,
JMK, FOC, BH and STT contributed to the writing and discussion.
STT compiled the data in the Supplement table. PTG, FOC, LTM
and VN provided data and data analysis for Fig. 1 of the Supple-
mentary, for which NLA contributed to the preparation of UKESM1
experiments. GZ prepared the data for Fig. S2 of the Supplement.
Competing interests. The authors declare that they have no conflict
of interest.
Special issue statement. This article is part of the special is-
sue “The Aerosol Chemistry Model Intercomparison Project
(AerChemMIP)”. It is not associated with a conference.
Acknowledgements. This work used JASMIN, the UK collabora-
tive data analysis facility. Paul T. Griffiths, Alexander T. Archibald,
James Keeble and N. Luke Abraham thank NCAS and the
Met Office for funding and support of the UKCA project.
Fiona M. O’Connor and Birgit Hassler were supported by the
European Union’s Horizon 2020 Framework Programme for Re-
search and Innovation “Coordinated Research in Earth Systems
and Climate: Experiments, kNowledge, Dissemination and Out-
reach (CRESCENDO)” project under grant agreement no. 641816.
Guang Zeng was supported by the NZ Government’s Strate-
gic Science Investment Fund (SSIF) through the NIWA pro-
gramme CACV. Makoto Deushi and Naga Oshima were supported
by the Japan Society for the Promotion of Science KAKENHI
(grant numbers: JP18H03363, JP18H05292 and JP20K04070), the
Environment Research and Technology Development Fund (JP-
MEERF20172003, JPMEERF20202003 and JPMEERF20205001)
of the Environmental Restoration and Conservation Agency of
Japan, and the Arctic Challenge for Sustainability II (ArCS II), pro-
gramme grant number JPMXD1420318865. We acknowledge the
World Climate Research Programme, which, through its Working
Group on Coupled Modelling, coordinated and promoted CMIP6.
We thank the climate modelling groups for producing and mak-
ing available their model output, the Earth System Grid Federation
(ESGF) for archiving the data and providing access, and the multi-
ple funding agencies who support CMIP6 and ESGF.
Review statement. This paper was edited by Holger Tost and re-
viewed by Mathew Evans and one anonymous referee.
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