ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-1091-2018Ozone sensitivity to varying greenhouse gases and ozone-depleting substances in CCMI-1 simulationsOzone sensitivity to varying GHGs and ODSsMorgensternOlafolaf.morgenstern@niwa.co.nzhttps://orcid.org/0000-0002-9967-9740StoneKane A.https://orcid.org/0000-0002-2721-8785SchofieldRobynhttps://orcid.org/0000-0002-4230-717XAkiyoshiHideharuhttps://orcid.org/0000-0001-6463-9004YamashitaYousukehttps://orcid.org/0000-0002-6813-4668KinnisonDouglas E.GarciaRolando R.https://orcid.org/0000-0002-6963-4592SudoKengohttps://orcid.org/0000-0002-5013-4168PlummerDavid A.https://orcid.org/0000-0001-8087-3976ScinoccaJohnOmanLuke D.ManyinMichael E.ZengGuanghttps://orcid.org/0000-0002-9356-5021RozanovEugenehttps://orcid.org/0000-0003-0479-4488StenkeAndreahttps://orcid.org/0000-0002-5916-4013RevellLaura E.https://orcid.org/0000-0002-8974-7703PitariGiovanniManciniEvaDi GenovaGlaucoVisioniDanielehttps://orcid.org/0000-0002-7342-2189DhomseSandip S.https://orcid.org/0000-0003-3854-5383ChipperfieldMartyn P.National Institute of Water and Atmospheric Research (NIWA), Wellington, New ZealandSchool of Earth Sciences, University of Melbourne, Victoria, AustraliaARC Centre of Excellence in Climate System Science, University of New South Wales, Sydney,
AustraliaNational Institute of Environmental Studies (NIES), Tsukuba, JapanNational Center for Atmospheric Research (NCAR), Boulder, Colorado, USAGraduate School of Environmental Studies, Nagoya University, Nagoya, JapanEnvironment and Climate Change Canada, Montréal, CanadaCCCMA, University of Victoria, Victoria, CanadaNASA Goddard Space Flight Center, Greenbelt, Maryland, USAScience Systems and Applications, Inc., Lanham, Maryland, USAPhysikalisch-Meteorologisches Observatorium Davos – World Radiation Center, Davos, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich, SwitzerlandBodeker Scientific, Christchurch, New ZealandDipartimento di Scienze Fisiche e Chimiche, Università dell'Aquila, L'Aquila, ItalyCETEMPS, Università dell'Aquila, L'Aquila, ItalySchool of Earth and Environment, University of Leeds, Leeds, UKnow at: Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USAnow at: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, JapanOlaf Morgenstern (olaf.morgenstern@niwa.co.nz)29January20181821091111418June20173July201716October201715December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/1091/2018/acp-18-1091-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/1091/2018/acp-18-1091-2018.pdf
Ozone fields simulated for the first phase of the Chemistry-Climate Model
Initiative (CCMI-1) will be used as forcing data in the 6th Coupled Model
Intercomparison Project. Here we assess, using reference and sensitivity
simulations produced for CCMI-1, the suitability of CCMI-1 model results for
this process, investigating the degree of consistency amongst models
regarding their responses to variations in individual forcings. We consider
the influences of methane, nitrous oxide, a combination of chlorinated or
brominated ozone-depleting substances, and a combination of carbon dioxide
and other greenhouse gases. We find varying degrees of consistency in the
models' responses in ozone to these individual forcings, including some
considerable disagreement. In particular, the response of total-column ozone
to these forcings is less consistent across the multi-model ensemble than
profile comparisons. We analyse how stratospheric age of air, a commonly
used diagnostic of stratospheric transport, responds to the forcings. For
this diagnostic we find some salient differences in model behaviour, which may
explain some of the findings for ozone. The findings imply that the ozone
fields derived from CCMI-1 are subject to considerable uncertainties
regarding the impacts of these anthropogenic forcings. We offer some
thoughts on how to best approach the problem of generating a consensus ozone
database from a multi-model ensemble such as CCMI-1.
Introduction
The Chemistry-Climate Model Initiative (CCMI), in its first phase, has
produced an unprecedented wealth of simulations by 20 chemistry–climate and
chemistry–transport models . All of them comprise
interactive chemistry schemes focused on the simulation of stratospheric
and/or tropospheric ozone, but there are significant differences in their
formulations that affect chemistry as well as many other aspects
. One purpose of CCMI-1 is to inform the upcoming 6th
Coupled Model Intercomparison Project CMIP6;, and
particularly to provide pre-calculated ozone climatologies to those CMIP6
general circulation models (GCMs) that do not simulate ozone interactively.
This is complicated by significant inter-model differences amongst the CCMI-1
models as well as the fact that CMIP6 will explore a variety of shared
socio-economic pathways SSPs; that expand on the
representative concentration pathways RCPs;
forming the basis of CMIP5 and CCMI-1. Hence there is a requirement for a
robust mechanism to turn the CCMI-1 ozone fields into merged climatologies
that are consistent with those SSPs. The feasibility of this processing step
hinges upon the degree of consistency with which the CCMI-1 models respond to
variations in forcing fields; this is the topic of the present paper. More
generally, the presence of targeted sensitivity simulations in the CCMI-1
ensemble allows us to study in detail the model responses to forcings by
individual gases, which are of significant scientific interest irrespective
of applications in CMIP6.
Here we only assess the model responses to long-lived gas forcings. Regarding
short-lived climate agents, there are large inter-model differences in the
representation of tropospheric ozone chemistry as
well as spatially very heterogeneous emissions of ozone precursors. Due to
these additional complexities, comprehensively assessing the consistency of
the simulation of tropospheric ozone in CCMI-1 models needs to be the topic
of a separate paper. Notwithstanding this, large-scale global climate and
composition change can influence surface ozone through in situ chemistry,
long-range transport, stratosphere-troposphere exchange, changes in
temperature and humidity, and radiative transfer.
We consider separately the influences of the following four different
anthropogenic forcings on ozone (O3): methane (CH4), nitrous
oxide (N2O), ozone-depleting substances (ODSs, comprising
chlorofluorocarbons, other organic chlorine compounds, methyl bromide,
halons, and other organic bromine compounds), grouped together as
“equivalent chlorine” (Cleq.), and a group of greenhouse gases
(GHGs) comprising CO2 and fluorinated compounds (hydrofluorocarbons,
HFCs, perfluorocarbons, PFCs, and sulfur hexafluoride, SF6) that do
not act as ODSs. These gases are grouped together here as
“CO2-equivalent” (CO2eq.) using the ratios of their
“radiative efficiencies” to that of CO2Table 2.14
of as conversion factors. All of these influences have been
studied before (see below), but not all of them in a multi-model context. In
all cases these forcings have both direct radiative (as GHGs) and chemical
impacts. For the RCPs, the combined radiative impacts of GHGs can be
summarized as warming the troposphere and cooling the stratosphere, with
associated dynamical consequences, but the chemical impacts are more
complicated and also induce secondary effects such as perturbations to
stratospheric water vapour and ozone, which themselves link to dynamics. This
complexity opens up the potential for differences in model behaviour, the
topic of this paper.
Several previous studies have investigated the linkages between CH4
and O3e.g.. Generally, these studies have
found that methane increases lead to ozone increases in most of the lower and
middle atmosphere (below 1 hPa) which amplify the global warming
associated with methane. These increases are associated with a few different
mechanisms, including methane's role as an ozone precursor in the troposphere
and a slow-down of chlorine-catalysed ozone depletion by Cl+CH4→HCl. Since , this link between CH4 and
O3 has been accounted for by stating an effective global warming
potential for CH4 that takes into account those chemical feedbacks,
also due to stratospheric water vapour production by methane oxidation. We
will assess here the consistency to which the methane–ozone link is simulated
in CCMI-1 models.
The impact of N2O on O3 is thought to be well understood
e.g.. N2O is
generally chemically inactive in the troposphere. In the stratosphere it
decays to form nitrogen oxides (NOx=NO+NO2) in a
minor loss channel. NOx then participates in catalytic ozone
depletion . It is the third most important anthropogenic
greenhouse gas after CO2 and CH4 and is now
the leading ODS by emissions .
The impact of organic halogens on stratospheric ozone is likewise well
understood for a review see. Essentially, these gases
rise into the stratosphere, where they release their halogen atoms which then
engage in ozone depletion. This is particularly pronounced in the polar
regions, where chlorine is “activated” on polar stratospheric clouds,
causing the Antarctic ozone hole to form and also causing
usually less severe but highly variable ozone depletion in the Arctic. This
means their chemical impacts occur mostly in the “chlorine layer” around
40 km and in the lower stratosphere over the poles .
However, through dynamical feedbacks, transport, and impacts on ultraviolet
and longwave radiation, such ozone depletion affects atmospheric composition
throughout the troposphere and stratosphere .
Southern Hemisphere climate change is thought to have been dominated in
recent decades by ozone depletion for a review see,
but there is limited evidence for an effect of Arctic ozone depletion on the
Northern Hemisphere circulation . Under the Montreal
Protocol, halogen-catalysed ozone depletion is anticipated to reverse
; a recovery of the Antarctic ozone hole is now unambiguously
identified in observations .
For analysis purposes, the ODSs are combined into a single index, equivalent
chlorine (Cleq.), which is the sum of all chlorinated and brominated
organic compounds as imposed at the Earth' surface, weighted by the number of
halogen atoms per molecule and multiplied by 60 for brominated compounds
. Cleq. excludes here di- and tribromomethane
(CH2Br2, CHBr3) which significantly impact stratospheric
ozone levels . They are imposed as invariant constants
and hence are thought not to contribute to any
trends. Cleq. is shifted by 4 years relative to the A1 scenario
to represent the time it takes for the turn-around in
halogens caused by the implementation of the Montreal Protocol to propagate
to middle and high latitudes of the stratosphere.
Finally, the gases grouped as CO2eq., comprising CO2,
hydrogenated fluorocarbons (HFCs), perfluorocarbons (PFCs), and SF6,
are not thought to have a significant direct chemical impact on ozone, but as
greenhouse gases have substantial impacts on temperature, humidity, and
circulation, which in turn affect ozone . Under the REF-C2
scenario assumed here (which merges RCP 6.0 for non-ODSs with the
A1 scenario for ODSs), the fluorinated gases do not
contribute much to global warming; i.e. the reference simulations described
below assume moderate emissions of them . CO2,
the leading gas in this group, undergoes roughly a doubling between 1960 and
2100 in this scenario. show graphs of all the
long-lived forcings used here. While these gases, for the purposes of this
paper, are combined into one measure (CO2eq.), their actual
treatment varies by model, with some models considering or not considering
certain minor GHGs in their radiation schemes . Some
others use lumping, which in itself has certain limitations. For example,
increases in CO2 are cooling the stratosphere whereas increases in
HFCs would warm it , meaning that CO2 is not a
perfect analogue for HFCs in our model simulations. However, simulations that
would target separately the impacts of HFCs do not exist in the CCMI-1
ensemble.
In this paper, we assess the degree of consistency found across the CCMI-1
ensemble with regard to the impact of these forcings on ozone. We will do so by using
sensitivity simulations performed for CCMI-1. One limitation of this approach
is that it does not account for nonlinear interactions between the forcings
e.g. stratospheric cooling caused by CO2 slows down gas-phase
ozone depletion;. We will address this further in
Sect. .
Models and dataExperiments used in this paper
Here we use simulations performed under the following experiments as
requested for CCMI-1. The simulations generally cover 1960–2100 unless
stated otherwise :
REF-C2: in this experiment, GHGs, CH4, and N2O follow
the RCP 6.0 scenario , and ODSs follow the A1 scenario of .
SEN-C2-fCH4: same as REF-C2, except CH4 is held fixed at its 1960 value .
SEN-C2-fN2O: same as REF-C2, except N2O is held fixed at its 1960 value .
SEN-C2-fODS: same as REF-C2, except all chlorinated and brominated ODSs are held at their 1960 values.
SEN-C2-fGHG: same as REF-C2, except CO2, CH4, N2O,
and other non-ozone-depleting GHGs are held at their 1960 values.
SEN-C2-RCP26/45/85: same as REF-C2, except the GHGs, CH4 and N2O
follow the RCP 2.6, 4.5, or 8.5 scenarios . These
simulations cover 2000–2100.
SEN-C2-fCH4, SEN-C2-fN2O, SEN-C2-fODS, and SEN-C2-fGHG simulations address
the sensitivities to individual forcings, whereas the SEN-C2-RCP experiments
assess the impacts of the variant RCP scenarios that can be seen as
simultaneous variations of multiple forcings relative to the reference
simulation. For example, we use RCP 8.5 here because it is characterized by
the largest anthropogenic forcings. In particular, CH4 growth is much
more pronounced than in REF-C2/RCP 6.0 .
Models used in the paper
We use CCMI-1 model simulations for which ozone has been archived for REF-C2
and any of the other four sensitivity experiments. For the assessment of the
influences of GHGs, we require simulations covering REF-C2, SEN-C2-fGHG,
SEN-C2-fCH4, and SEN-C2-fN2O (see below). Table lists the models
and the number of simulations used for the sensitivity analysis in
Sect. .
Models used in this paper, with associated ensemble sizes of CCMI-1
simulations conducted.
ACCESS-CCM also conducted two SEN-C2-fGHG simulations, but because of the
missing SEN-C2-fCH4 and SEN-C2-fN2O simulations, these will not be
considered here.
These 10 models are described by and references
therein. Except for ACCESS-CCM and NIWA-UKCA, they all use hybrid-pressure
(or actual pressure, in the case of ULAQ-CCM) as their vertical coordinate.
ACCESS-CCM and NIWA-UKCA use hybrid-height levels. Apart from differences
in coupling (ACCESS-CCM is an atmosphere-only model, whereas NIWA-UKCA
includes a deep ocean), these two models are identical. In the following,
where we display vertically resolved results from these two models, these
will be based on fields interpolated onto a 126-level grid, equally spaced in
logp and spanning 1000 to 0.01 hPa. The underlying pressure climatology
is taken from a NIWA-UKCA REF-C2 simulation.
The CCSRNIES-MIROC3.2 simulations were conducted on two different computers
(REF-C2 (1), SEN-C2-fODS, SEN-C2-fGHG, and SEN-C2-RCP85 on an NEC SX9
machine, and REF-C2 (2), SEN-C2-fCH4, and SEN-C2-fN2O on an NEC SX-ACE). This
resulted in some differences between the two REF-C2 simulations. We have
therefore repeated all calculations detailed below now assuming that the
CCSRNIES-MIROC3.2 simulations represent two different models. The results are
essentially unchanged versus what is presented here. Hence, for the purposes
of this paper, CCSRNIES-MIROC3.2 is treated as one model.
UMSLIMCAT and CCSRNIES-MIROC3.2 have prescribed or only partially interactive
tropospheric composition . This affects the
sensitivity of total column ozone to the external forcings considered here.
There are numerous differences in the formulations of the models that
influence how they respond to external forcings. Stratospheric gas-phase
chemistry is handled relatively consistently by the models. For example,
their chemistry schemes all include ozone depletion by the HOx,
NOx, ClOx, and BrOx loss cycles, with rates
taken from compilations such as . Differences exist in the
treatment of heterogeneous chemistry on polar stratospheric clouds. Also,
photolysis is handled in various different ways by the models, and there are
differences in dynamics that also impact on how these models respond to
external forcings . We will present a limited analysis
of how stratospheric age of air (AOA), a salient diagnostic often used to
characterize stratospheric transport, relates to the responses in ozone
produced by the models. A comprehensive analysis of which aspects of the
models' formulation is responsible for differences in behaviour is, however,
beyond the scope of this paper.
Method of analysis
We form zonally averaged ozone on model levels as represented by the CCMI-1
models. Next, we perform a linear expansion around the reference case
defined by REF-C2. This means
ΔO3=aΔCH4+bΔN2O+cΔCleq.+dΔCO2eq.+ϵ.
Here, ΔO3 is the difference in zonal-mean simulated ozone
between two different scenarios, ΔCH4 and ΔN2O
are the differences in surface methane and nitrous oxide, respectively, and
ΔCO2eq. and ΔCleq. are the differences in
surface carbon dioxide-equivalent and equivalent chlorine as defined above.
a, b, c, and d are determined using least-squares linear regression.
Functions of latitude, level, and month of the year, they minimize the
residual ϵ. For example, to determine a we use the difference in
the zonal-mean ozone fields from REF-C2 and SEN-C2-fCH4,
ΔO3=aΔCH4+ϵ,
and determine a by regressing, at every latitude, model level, and month,
the 140- or 141-year time series of ΔO3 against the same-length
time series of ΔCH4, which is the global-mean surface methane
mixing ratio as defined under RCP 6.0 minus its value in 1960. Equivalent
analyses yield b, using REF-C2 and SEN-C2-fN2O, and c, using REF-C2 and
SEN-C2-fODS. The SEN-C2-fGHG simulation keeps all GHGs including CH4
and N2O, but excluding ODSs, fixed at their 1960s levels. To account
for the effects of fixing CH4 and N2O, we form a modified
ozone field
O3′=O3(SEN-C2-fGHG)+aΔCH4+bΔN2O
which is derived from the ozone field produced by the SEN-C2-fGHG experiment,
O3(SEN-C2-fGHG), but with the impacts of differences in CH4
and N2O added. We then use the difference ΔO3=O3(REF-C2)-O3′ in our regression analysis as before
to determine d.
In this formulation, the forcings (except Cleq.) are as imposed at
the surface, so transport-related delays are not accounted for. Such delays
primarily result from the time it takes for a long-lived tracer, emitted at
the surface, to reach the stratosphere. For the forcings other than
Cleq. this is not critical as their tendencies are only slowly
varying; i.e. they do not display the sharp turn-around characterizing
Cleq..
In cases where multiple simulations are available for a given scenario and
model, the ensemble average is used in the analysis.
In the below, we only display the coefficients a, b, c, or d where
these are significantly (at the 95 % confidence level) different from 0.
Details on this process are in the Appendix.
ResultsSensitivity of ozone to methane
Ratio of zonal-mean ozone volume mixing ratio changes to VMR changes
in surface CH4 (a) as derived from the REF-C2 and SEN-C2-fCH4
simulations. a is dimensionless. The colour white indicates that a is not
significantly different from 0 at the 95 % confidence interval. The
plots for CHASER-MIROC-ESM and ULAQ-CCM have no data above 0.5 and
0.04 hPa, respectively.
Figure shows the sensitivity of zonal-mean ozone with respect to
changes in CH4 (i.e. a) as derived from the REF-C2 and SEN-C2-fCH4
experiments. Nine models have conducted both experiments. The models agree
on some general features of the signal, namely an increase in ozone in much
of the lower and middle atmosphere, and a decrease in the mesosphere. In the
middle and upper stratosphere, in all models there is a region where
CH4 increases cause ozone increases by around 10 to 40 % of the
increase in the prescribed surface methane mixing ratio. This may be because
of the CH4+Cl→HCl reaction which returns chlorine to
HCl not involved in ozone depletion. Higher up, above the stratopause
at approximately 1 hPa, methane increases cause ozone to decline, due to
increases in HOx-related ozone depletion under increasing methane
and references therein. There is considerable
uncertainty regarding the size of this feedback. CCSRNIES-MIROC3.2, CMAM,
and GEOSCCM simulate extensive regions where seasonally or in all seasons
the ozone decline exceeds 10 % of the methane difference, whereas in
ULAQ-CCM this effect is generally smaller than 5 %. In the tropical
upper-troposphere/lower stratosphere (UTLS) region, most of the models
simulate a negative feedback for at least some months; i.e. methane
increases cause a decrease in ozone, but the size and spatial extent of this
effect is highly uncertain, with NIWA-UKCA producing ozone decreases of
10–20 % of the methane difference. In most of the other models, there
are some decreases, but the trends are insignificant in parts of the
latitude–pressure domain at the 95 % confidence level, peaking at less than
10 % of the applied methane increase in CCSRNIES-MIROC3.2, CESM1-WACCM,
GEOSCCM, SOCOL3, and UMSLIMCAT. CMAM exhibits no significant influence
of methane on ozone in this region, and ULAQ-CCM even produces some
significant increases.
Ratio of zonal-mean total-column ozone changes to VMR changes in
surface CH4 (in Dobson units ppmv-1) as derived from the REF-C2
and SEN-C2-fCH4 simulations. The colour white indicates insignificantly
differences from 0 at the 95 % confidence interval.
The equivalent analysis for zonal-mean total-column ozone (TCO;
Fig. ) indicates that indeed CH4 increases generally
cause a TCO increase almost everywhere (apart from over the South Pole in the
ULAQ-CCM). The weak responses in TCO by UMSLIMCAT and CCSRNIES-MIROC3.2 are
as expected, considering the simplified treatment of tropospheric ozone in
both models mentioned above. Figure S1 shows the response of ozone to methane
changes, expressed in terms of ozone concentrations. From this figure, it is
clear that apart from CCSRNIES-MIROC3.2 (and UMSLIMCAT, not shown) in all
models the tropospheric response is a substantial albeit quite
model-dependent fraction of the total-column response. In the tropics, the
increase in TCO in response to CH4 increases is smaller in
CESM1-WACCM, CHASER-MIROC-ESM, and NIWA-UKCA than in the other models.
CESM1-WACCM, CHASER-MIROC-ESM, GEOSCCM, and NIWA-UKCA also have larger TCO
increases during winter/spring over the Arctic than the other models. This
anticorrelation of trends in the two regions may be indicative of differences
in the strength of the response of stratospheric overturning in these models,
the subject of Sect. .
Ratio of zonal-mean surface ozone changes to changes in surface
CH4 (in ppbv ppmv-1) as derived from the REF-C2 and SEN-C2-fCH4
simulations.
Figure shows the zonal-mean sensitivity a at the surface
as a function of month of the year and latitude. The seven models exhibit
some common features but also some considerable qualitative and quantitative
differences in their responses to methane increases. Commonalities include
that methane increases cause statistically significant ozone increases
everywhere. This is as expected, given the role of methane as an ozone
precursor. In all seven models, the increase maximizes in northern
midlatitudes, but the seasonality of this feature varies by model. There is
a secondary maximum in the Southern Hemisphere winter. In four of the
models (CESM1-WACCM, CMAM, GEOSCCM, NIWA-UKCA) the response minimizes at
the South Pole during summer. CHASER-MIROC-ESM, SOCOL3, and ULAQ-CCM have a
very small seasonal cycle of this feature over the South Pole. In
CESM1-WACCM, there are three distinct minima in the response of ozone to
methane increases, located at around 65∘ S in January, in the
tropics throughout the year, and in the Arctic from June to September.
Differences that divide these results are partly about magnitude of the signal (NIWA-UKCA simulations show
the smallest sensitivity of surface ozone to methane increases, followed roughly in order by
CHASER-MIROC-ESM, CESM1-WACCM, CMAM, SOCOL3, GEOSCCM, and ULAQ-CCM). Details of the annual cycle
also differ. For example, CESM1-WACCM, CMAM, and SOCOL3 produce a minimum over the Arctic in summer; there is no
sign of this occurring in CHASER-MIROC-ESM, NIWA-UKCA, and ULAQ-CCM. The
relatively strong response of SOCOL3 surface ozone to CH4 increases
may be related to a general overestimation of tropospheric ozone in the
Northern Hemisphere by that model .
Sensitivity of ozone to nitrous oxide
Same as Fig. but for N2O.
Figure shows the sensitivity to zonal-mean N2O changes
(b) as derived from the REF-C2 and SEN-C2-fN2O experiments. The same nine
models as discussed in Sect. also conducted SEN-C2-fN2O. The
sensitivity to N2O increases is more coherently simulated by the
models than that to CH4, with the models largely agreeing on the main
features. In the upper stratosphere, N2O increases cause a decrease
in O3 of about 5 to 10 times the increase in N2O, peaking
in all seasons in the tropics. Above 1 hPa, there is disagreement on the
sign of the ozone response, with CCSRNIES-MIROC3.2 and ULAQ-CCM producing
mostly increasing ozone for increases in N2O, whereas in
CESM1-WACCM, GEOSCCM, NIWA-UKCA, SOCOL3, and UMSLIMCAT, the decreases
dominate the increases in spatial extent. In CMAM, the co-variance of ozone
with surface N2O appears to be insignificant almost everywhere above
1 hPa. In the lower stratosphere, all models produce some increases in
ozone for increases in N2O. This may be the result of a self-healing
process, whereby ozone depletion higher up caused by increased N2O
allows more UV light to penetrate to this level, producing more ozone there.
The meridional extent and magnitude of the ozone increase vary by model. In
CESM1-WACCM, CCSRNIES-MIROC3.2, CHASER-MIROC-ESM, GEOSCCM, NIWA-UKCA,
SOCOL3, and UMSLIMCAT, the ozone increase covers the whole or almost the
whole latitude range, whereas in CMAM and ULAQ-CCM the belt does not
consistently extend to the poles.
Same as Fig. but for N2O, in units of
DU ppmv-1, derived from the REF-C2 and SEN-C2-fN2O simulations.
Like for methane, the response of TCO to N2O changes is highly
model dependent (Fig. ). (Figure S7 in the Supplement gives the
concentration-weighted ozone responses that visualize height-dependent
contributions to the TCO changes.) Best agreement in the TCO response
across the nine-model ensemble is achieved in the tropics, where all
models find decreases in TCO for increases in N2O ranging around
-0.075 to -0.05 DU ppbv-1 in CCSRNIES-MIROC3.2 to roughly -0.03
Dobson units (DU) ppbv-1 in GEOSCCM, NIWA-UKCA, SOCOL3, and ULAQ-CCM.
In the northern extratropics, several of the models agree on the phasing of
the annual cycle, with TCO decreases maximizing in late winter/spring and
minimizing in late summer. In the southern extratropics, a similar
seasonality is evident. SOCOL3 exhibits significant increases under
N2O increases over Antarctica in spring (the result of large
increases in ozone in the lowermost stratosphere and UTLS, Fig. S2), and
NIWA-UKCA has relatively weak decreases and some seasonal increases under
N2O increases, particularly in the Arctic in summer. Both are
associated with anomalously large increases in the lower stratosphere evident
in Figs. and S2, suggesting that dynamical/chemical feedbacks in
the lower stratosphere overcompensate for the additional chemical depletion
that all models show in the middle stratosphere. Even for this forcing, to
which the models simulate a generally consistent response in the middle
stratosphere, the extratropical TCO response remains quantitatively
uncertain.
Same as Fig. but for N2O, in
ppbv ppmv-1, as derived from the REF-C2 and SEN-C2-fN2O simulations.
Figure shows b evaluated at the surface. Generally, as
N2O is chemically inert in the troposphere, six of the models show
large areas of insignificant covariance between N2O and surface
O3, particularly in the extratropics. As for significant features,
the same six models agree on a decrease in ozone in the tropics, also
extending into northern midlatitudes in summer, of -0.002 to -0.004 times
the increase in N2O, and an increase in ozone by roughly 0.002 times the
increase in N2O in southern midlatitudes during winter. In CESM1-WACCM,
this feature in more pronounced, covering much of the southern extratropics,
and is significant year-round. The feature is insignificant in CMAM.
ULAQ-CCM, by contrast, shows significant increases in surface ozone almost
everywhere for an increase in N2O, peaking in northern midlatitudes; i.e. it is in disagreement with the other models regarding both magnitude and
shape of the annual cycle of b.
Sensitivity of ozone to equivalent chlorine
Same as Fig. but for Cleq..
Figure shows the sensitivity of zonal-mean ozone to changes in
Cleq. (Sect. ), as derived from the REF-C2 and
SEN-C2-fODS experiments. Eight models have conducted both of these
experiments. In the upper stratosphere, there is a significant decrease in
ozone by up to 300 to 1000 times the Cleq. increase. This is
consistently simulated by all models, and is the consequence of global
halogen-catalysed ozone depletion maximizing at around 1 to 10 hPa. Higher
up, above approximately 1 hPa, the models simulate mostly a decrease of 0
to 50 times the Cleq. increase. There also are consistent
decreases in ozone in the lower stratosphere/tropopause region of the
southern high latitudes during spring and summer, associated with the
Antarctic ozone hole. In January, in what is likely a dynamical feedback,
there is an increase in ozone (for an increase in ODSs) between about 50 and
10 hPa.
In CCSRNIES-MIROC3.2, CESM1-WACCM, CHASER-MIROC-ESM, CMAM, and UMSLIMCAT, Antarctic October polar ozone
depletion occupies the entire lower stratosphere, between ∼ 200 and 10 hPa,
with ozone loss reaching 1000 times the difference in Cl eq.
Same as Fig. but for Cleq., in units of
DU ppbv-1 (Cleq.), derived from the REF-C2 and SEN-C2-fODS
simulations.
Regarding the response of the TCO to Cleq. changes, the models
uniformly exhibit decreases in TCO for an increase in Cleq.
(Fig. ). In the tropics, there is reasonable agreement regarding
the size of the effect. In the extratropics, there is some quantitative
disagreement. Best agreement is found over the Antarctic in spring, where
most models in October agree to within
±10 DU ppbv-1(Cleq.) with each
other. This general agreement may be the result of a long-term focus on
this region for the impact of ozone depletion. By contrast, in the Arctic
significant quantitative differences are apparent regarding this effect,
also evident in Fig. S3. In all models except ACCESS-CCM and NIWA-UKCA,
the reduction of TCO in the Arctic is significantly weaker than in the
Antarctic.
Ratio of zonal-mean surface ozone changes to changes in surface
Cleq. (in ppbv ppbv-1) as derived from the REF-C2 and
SEN-C2-fODS simulations.
As for surface ozone, there is little agreement as to the impacts of this
stratospheric ozone depletion (Fig. ). In ACCESS-CCM and
NIWA-UKCA, there is a widespread decrease in surface ozone associated with
stratospheric ozone depletion, with maxima in both midlatitude regions
during autumn. The southern one is larger, reaching the size of the
difference in Cleq.. The near symmetry between the two hemispheres is
in agreement with the pronounced Arctic ozone depletion produced by
ACCESS-CCM and NIWA-UKCA (Fig. ). CESM1-WACCM and CMAM produce a
Southern Hemisphere maximum of similar magnitude, but CMAM produces a
secondary maximum over the South Pole in austral spring, and the response in
the Northern Hemisphere in both models is much smaller than in ACCESS-CCM
and NIWA-UKCA. CHASER-MIROC-ESM shows a much weaker response to
Cleq. and also only minor asymmetries between the hemispheres.
ULAQ-CCM disagrees with the other five models in that in the Northern
Hemisphere and the tropics, ozone mostly increases under increases in
Cleq.; in the southern extratropics, this model largely produces
decreases but the effect maximizes in austral summer; i.e. the seasonality
disagrees with the other five models.
It is noteworthy that four of the six models display their peak response
of surface ozone to stratospheric ozone depletion in austral autumn,
approximately 6 months after the onset of the Antarctic ozone hole.
Sensitivity of ozone to GHGs
Same as Fig. but for CO2eq. Here units are
10-3 ppmv ppmv-1.
Here we assess the sensitivity of ozone to increases in CO2eq.
(Sect. ). Increases in CO2eq. cause increases in ozone
peaking between roughly 10 and 1 hPa; these increases are of similar
magnitude in all models (Fig. ). They also cause decreases in ozone
in the tropical and subtropical lower stratosphere; again there largely is
agreement about the magnitude of this effect. Both the decrease and the
increase may be aspects of an upward displacement and associated acceleration
of the Brewer–Dobson circulation (BDC) Sect. . Stratospheric cooling, through its impact on
ozone-depleting chemical cycles, also leads to an increase in stratospheric ozone.
In the mesosphere, there is quantitative disagreement regarding the impact of
increases in CO2eq., with CESM1-WACCM, CMAM, ULAQ-CCM, and UMSLIMCAT
exhibiting mostly or generally increases, whereas in NIWA-UKCA and
CCSRNIES-MIROC3.2 increases cause ozone to decline. The models generally
agree on a region of ozone decrease in the tropical and subtropical lower
stratosphere, which reaches -0.5×10-3 to -2×10-3 times
the increase in the CO2eq. VMR.
Same as Fig. but for CO2eq., in units of
10-3 DU ppmv-1 (CO2eq.), derived from the REF-C2,
SEN-C2-fGHG, SEN-C2-fCH4, and SEN-C2-fN2O simulations.
Regarding the TCO response to CO2eq. increases (Fig. ),
there is reasonable agreement across the models. In all models, there is significant cancellation in the tropics between decreases in ozone in the lower stratosphere with increases in the middle and upper stratosphere and (for some models) in the troposphere (Fig. S4). In five of the models (CCSRNIES-MIROC3.2, CHASER-MIROC-ESM, CMAM, NIWA-UKCA, and ULAQ) this tropical TCO decreases under increasing CO2eq., whereas in two (CESM1-WACCM, UMSLIMCAT) it
increases.
In order to assess whether for CESM1-WACCM the finding is the result of the
linear analysis conducted here, whose limitation is that nonlinear
interactions between increases in CO2eq., N2O, and
CH4 are ignored, we analyse here a simulation using CESM1-WACCM,
which is identical to the REF-C2 simulations except that CO2 is held
fixed at 1960 levels. In this simulation, actually we find that CESM1-WACCM
does produce a small decrease in tropical TCO for increasing CO2 in
much of the tropics, much of the time (Fig. ). This decrease is
still smaller than in most other models, but the finding does indicate that
the tropical ozone feedback is subject to substantial nonlinear coupling
between the forcings, which we cannot fully diagnose here. UMSLIMCAT
also produces increases in tropical TCO for increasing CO2eq.; we
attribute this partly to the prescribed tropospheric ozone in this model.
Increases in the northern extratropics during boreal winter and spring are
consistent across the seven models; they exceed those in the south. There
is no agreement regarding the seasonality of the effect in the southern
extratropics. CHASER-MIROC-ESM, CMAM, and UMSLIMCAT produce some
significant decreases in TCO in response to CO2eq. increases over
the South Pole in austral winter and/or spring; the other models do not
simulate this feature.
Same as Fig. but for actual CO2, in units of
10-3 DU ppmv1 (CO2), derived from the REF-C2 and
fixed-CO2 simulations of CESM1-WACCM.
As for surface ozone, CMAM, CHASER-MIROC-ESM, NIWA-UKCA, and ULAQ-CCM
mostly produce decreases in surface ozone for an increase in
CO2eq., but also some increases at northern high latitudes during
autumn, winter, and spring (Fig. ). CESM1-WACCM produces
smaller changes in ozone under climate change; they are negative (0 to
-5 ppbv ppmv-1) in the tropics and in the SH during summer, also in
the Arctic from late spring to autumn and positive (0 to
5 ppbv ppmv-1) at other times and seasons. In ULAQ-CCM, increases
are restricted to late winter and spring in the Arctic and to October in
the Antarctic. While the models agree about decreases in ozone in the
tropics and midlatitudes, there is disagreement about the magnitude, with
decreases in CESM1-WACCM and NIWA-UKCA smaller than in the other models.
CESM1-WACCM, CHASER-MIROC-ESM, and NIWA-UKCA simulate relatively large
ozone decreases over the Arctic in summer. These may be the result of
reductions of sea ice cover and associated decreased tropospheric ozone
formation in an ice–albedo feedback on photochemistry
. Note that three of the models used here
(CESM1-WACCM, CHASER-MIROC-ESM, and NIWA-UKCA) are coupled atmosphere–ocean
models, but this has no direct bearing on this ice–albedo feedback because
the other models use prescribed ocean-surface fields that also have sea ice
generally decreasing in spatial extent as global warming progresses
.
Ratio of zonal-mean surface ozone changes to changes in surface
CO2eq., times 106, as derived from the REF-C2, SEN-C2-fGHG,
SEN-C2-fCH4, and SEN-C2-fN2O.
What is causing the differences in the responses of ozone?
In the previous sections, we have shown that the responses of total-column,
lower-stratospheric, and surface ozone to the anthropogenic forcings studied
here vary considerably by model. By contrast, in the middle and upper
stratosphere, we find a more consistent response. This indicates that broadly
speaking, gas-phase chemistry schemes appear to be relatively consistent
across the model ensemble studied here, but dynamical feedbacks (which
influence the responses in the lower stratosphere) are not. In this context
we assess how stratospheric AOA responds to these forcings
for a review of AOA see. AOA is the average time it
takes an air parcel to travel from the troposphere to any given location in
the stratosphere. It is a measure of the strength of the BDC. Essentially, we explore the hypothesis that differences in
the response of the BDC to anthropogenic forcings are behind some of the
differences in the response of ozone to these forcings. Hence we repeat the
analysis formulated in Sect. but now replacing ozone with AOA.
Of the 10 models considered here, 6 have produced sufficient output for
this, i.e. AOA from the REF-C2 and at least one of the sensitivity
simulations. These models are ACCESS-CCM, CCSRNIES-MIROC3.2, CESM1-WACCM,
CMAM, NIWA-UKCA, and ULAQ-CCM. Of these models, ACCESS-CCM,
CCSRNIES-MIROC3.2, CMAM, and ULAQ-CCM use prescribed sea surface forcing,
with identical forcing used for REF-C2 and the SEN-C2 simulations. This
restricts the climate response particularly in the troposphere to the variant
forcings explored in the SEN-C2 simulations.
In summary, we find the following (the figures discussed here are in the
Supplement):
Increases in N2O in REF-C2 produce mostly insignificant
differences in AOA in all five models considered here, versus the corresponding
SEN-C2-fN2O simulations (Fig. S5). This suggests that the impact of N2O
changes on ozone is caused mostly directly by chemistry, with only a minor role
for dynamical feedbacks. Speculatively, such a minor role for dynamics might be
the result of a cancellation of the impacts on stratospheric dynamics of the
radiative forcing exerted by N2O increases with those due to ozone
depletion associated with such increases. Such a cancellation would mean that
dynamical feedbacks do not interfere much with the relatively good agreement
in the chemical model responses to N2O increases discussed in
Sect. , which results from the similar gas-phase chemistry
schemes employed by the models. However, the CMAM SEN-C2-fN2O experiment did not use
the reduced N2O in the radiation scheme (for radiation,
N2O in this model follows the same scenario as in REF-C2). CMAM
still exhibits a near-zero impact of reduced N2O on AOA, suggesting
that this mechanism may not hold for all models.
Increases in CH4 lead to significant reductions in AOA above roughly
100 hPa in CESM1-WACCM and NIWA-UKCA, weaker or insignificant changes in
CCSRNIES-MIROC3.2 and CMAM, and some increases in age in much of the stratosphere
in ULAQ-CCM (Fig. S6). This behaviour corroborates Fig. , where
CESM1-WACCM and NIWA-UKCA show relatively small sensitivities of tropical
column ozone to increases in CH4 and large sensitivities of springtime
Arctic ozone, suggesting that in these models the speed-up of the BDC
accompanying CH4 increases contributes to the sensitivity of TCO to
CH4 increases. Such a speed-up removes ozone from the tropics and
transports it to the winter/spring pole, contributing to this contrast in
sensitivity. By contrast, CMAM and ULAQ-CCM are characterized by a relatively
weak contrast in the trend in AOA between the tropics and the polar
latitudes, consistent with their response in AOA to increasing CH4
(Fig. ). In the case of CMAM, this may be because in this
model, actually the reduced CH4 characterizing the SEN-C2-fCH4
experiment was only used in chemistry and not in radiation. The radiation
scheme saw a similar CH4 evolution as in the REF-C2 simulations.
Hence only differences in ozone have affected the AOA response in this model.
An additional analysis of the temperature response to CH4 increases (not shown)
indicates that the models also exhibit considerable variations in their temperature
trends in response to methane changes. Most indicate stratospheric cooling of varying
magnitude but some also warming of the stratosphere. This might begin to explain
the differences in AOA.
Increases in Cleq. lead to significant and similar decreases in age
throughout most of the stratosphere in five of the models but not in CCSRNIES-MIROC3.2;
this model produces mostly no significant change in response to this forcing
(Fig. S7). The only region that shows consistent increases in age is the
Antarctic polar vortex, which in all models shows increasing AOA during summer,
suggesting an increasing persistence into summer. A comparison with Fig. indicates
that the region of increasing age during January coincides with the region of
ozone depletion at the base of the polar vortex. Of the five models considered here,
CCSRNIES-MIROC3.2 has the largest difference in sensitivity between tropical and
Antarctic springtime total-column ozone (Fig. ), which is consistent
with the lack of speed-up of the BDC in this model, compared to the other five.
The role of ozone depletion in driving a decrease in AOA, shown by most of the
models analysed here, has been found before e.g..
In ACCESS-CCM and NIWA-UKCA, the region of increasing age for increasing
Cleq. in January is located somewhat higher in the atmosphere than
in the other models. This has been noted before, in the context of the
evaluation of ozone depletion in the ACCESS-CCM . (Note
again ACCESS-CCM and NIWA-UKCA share the same atmosphere model.)
Increases in CO2eq. cause consistent decreases in AOA above
about 100 hPa in all five models shown here, with CMAM and CCSRNIES-MIROC3.2
exhibiting a larger response than CESM1-WACCM, NIWA-UKCA, and ULAQ-CCM (Fig. S8).
Below 100 hPa, all models show decreases in age in the extratropical lowermost
stratosphere, except for CCSRNIES-MIROC3.2, which also shows some significant and
substantial increases in age around the 100 hPa pressure level.
CESM1-WACCM, CMAM, NIWA-UKCA, and ULAQ-CCM exhibit a region of weak increases
in age, or insignificant sensitivity of age, in response to increasing
CO2eq., in the tropical upper troposphere. In CMAM, NIWA-UKCA,
and ULAQ-CCM, this “tongue” extends to roughly 200 hPa, but in CESM1-WACCM it
extends significantly above the tropical tropopause, to about 80 to 100 hPa.
This difference in behaviour is a contributing factor in the weak response of
tropical TCO in CESM1-WACCM to increasing CO2eq. Conversely, the
large difference in sensitivity of TCO in CMAM between the tropics and the
extratropics is related to the relatively large speed-up of the BDC in
response to CO2eq. forcing in this model.
These considerations do not constitute a complete discussion of the
differences in model behaviour found in this paper. But they do corroborate
the hypothesis that dynamics and transport contribute to the sensitivity of
modelled ozone to the anthropogenic forcings considered here. Some
interesting inconsistencies in model behaviour are found here that require
further analysis.
Linearity of the ozone response to greenhouse gas forcing
Based on the previous sections, we calculate, assuming linear scaling and
ignoring nonlinear coupling , the ozone
fields that would result from GHG scenarios other than the RCP 6.0 forcing
used in REF-C2. For the moderate-emissions scenarios RCP 2.6 and 4.5, this
can be seen as a consistency test. For the more extreme RCP 8.5, where
forcings are partially outside the range spanned by RCP 6.0/REF-C2 and the
total ozone abundance is larger than in REF-C2, this exercise will help
highlight nonlinear couplings between the forcings. The scaling is possible
for those models that have produced the REF-C2, SEN-C2-fGHG, SEN-C2-fN2O, and
SEN-C2-fCH4 simulations. We produce scaled ozone fields for
CCSRNIES-MIROC3.2, CESM1-WACCM, CMAM, ULAQ-CCM, and UMSLIMCAT
(CHASER-MIROC-ESM and NIWA-UKCA did not produce any SEN-C2-RCP simulations
needed for comparison here). For the more moderate RCPs 2.6 and 4.5, the
ozone fields resulting from such scaling in the zonal mean relatively
accurately match those simulated by the five models. Significant relative
differences occur in the troposphere, where the scaling method is not
applicable (see above) and in the UTLS region, where changes in the
tropopause height constitute a nonlinear feedback not well captured by
simple scaling of the ozone fields (Supplement, Figs. S9 and S10). Larger
differences, generally of opposite sign relative to RCP2.6 and RCP 4.5, occur
for RCP 8.5 (Fig. 14). Here, the models fall into two groups: one group, comprising
CCSRNIES-MIROC3.2, CESM1-WACCM, and CMAM, overestimates ozone in this scaling
in the mid- and upper stratosphere and underestimates it in the mesosphere
(above 1 hPa). A second group, comprising ULAQ-CCM and UMSLIMCAT,
overestimates ozone almost everywhere above the UTLS region, ULAQ-CCM more so
than UMSLIMCAT. In all cases, the analysis quantifies that
nonlinear interactions play a significant role, particularly in the RCP8.5 scenario.
Some general thoughts on the generation of a consensus ozone database
As noted in Sect. , the CMIP6 activity requires prescribed ozone
fields to drive simulations by CMIP6 models that do not interactively compute
ozone. Out of 20 models participating in CCMI-1, only two were actually
used in the generation of the ozone climatology provided to CMIP6
participants, namely CMAM and CESM1-WACCM (Michaela Hegglin, personal
communication, 2017). Such a narrow base was chosen because these two modelling
groups were ready to provide pre-industrial and pre-1960 ozone fields that
are also required for CMIP6 but fall outside the period spanned by CCMI-1
simulations. A larger and more representative base of model simulations might
have been possible to use here, had the production of CMIP6 ozone
climatologies been identified early on as a key deliverable of the CCMI-1
activity, particularly in view of the several coupled atmosphere–ocean CCMs
participating in CCMI-1 that would have had to conduct spin-up simulations
covering the pre-1960 period.
It is not the purpose of the present paper to actually produce such a merged
ozone climatology. Nevertheless, we offer some thoughts on how one might go
about producing such a climatology.
All ozone fields are interpolated to a common pressure-based grid, as is a
reference ozone climatology derived from satellite data and in situ observations.
Single-model ensemble means are formed for those models that have produced more
than one ensemble member.
It is clear that not every model is equally suitable for representing ozone
in every region. For example, some models have prescribed ozone in the troposphere
or do not extend into the mesosphere. This can be accounted for by introducing, for
every model i, weighting functions ζi(p) that are zero outside the pressure
interval where model i should be considered. The weights can also include information
on ensemble size. This accounts for the idea that the statistical uncertainty in model
projections reduces with increasing ensemble size. In addition to such elementary
considerations, it is possible to give models weights based on skill scores, but
these depend on metrics chosen to measure skill, which can be contentious.
The multi-model mean is formed, using the above weights:
O3‾=∑ζiO3i∑ζi.
Forming a multi-model mean already has the effect of dampening interannual variations.
These can be further reduced by applying a filter.
Bias-correcting the ozone fields versus observational ozone climatologies is possible.
However, here a few caveats apply. First, available ozone climatologies have their own shortcomings,
particularly in the troposphere where space-borne measurements are difficult or subject to large
uncertainty. Second, in the stratosphere, and to some extent in the troposphere, the dependence
of ozone on variations in long-lived constituents can be expressed in terms of a regression
model. Using a modelling approach, it is possible, as demonstrated here, to identify the
contributions made by individual long-lived gases to long-term ozone trends. However,
the satellite record may not be straightforwardly amenable to such an approach because
multiple forcings are acting simultaneously whose effects likely cannot be separated using
multi-variate regression – the record may be too short, meteorological noise too large,
or impacts of different forcings too similar for this to be a viable strategy.
This means only a simpler approach may be possible, consisting of subtracting the bias
in the mean annual cycle of ozone, determined for the satellite era, off the multi-model mean.
The problem here is that the bias may be a function of the anthropogenic forcings.
If that is the case, simply subtracting off the mean bias could result in inappropriate
“corrections”, particularly before and after the satellite era.
Unlike previous CMIP rounds, for CMIP6 zonally resolved ozone will be requested.
Stratospheric ozone is subject to zonal asymmetries caused by dynamical anomalies, e.g. due to orographic forcing. For example, there is a significant trend in the orientation of the
Antarctic polar vortex during the satellite era, which some models fail to reproduce
. Given the inability to attribute such misbehaviour to individual
anthropogenic forcings as discussed above, it appears difficult though to consistently account for this in a correction.
With these considerations in mind, apart from the restricted database, taking
a simple weighted average of available modelled ozone fields (Michaela Hegglin,
personal communication, 2017) appears to be the most practical and
straightforward approach to the problem. In comparison to the process adopted
for CMIP5 ozone , for CMIP6 there will not be any
discontinuity between stratospheric and tropospheric ozone, and the ozone
climatology now will be zonally resolved everywhere.
Conclusions
We have analysed the sensitivities of ozone to changes in CH4,
N2O, halogenated ODSs, and a combination of CO2 and other
greenhouse gases in 10 CCMI-1 models. In all cases we find some
qualitative and quantitative agreement, mainly about the impacts in the
middle stratosphere, but also considerable disagreements in other regions,
particularly the troposphere, the UTLS region, and the mesosphere. The
middle-stratospheric impact of CH4 increases is largely consistently
simulated by the nine models studied here, but significant differences
occur in the lower stratosphere, the troposphere, and in the total-column
impacts of increasing CH4. The impacts on ozone of increasing
N2O are relatively consistently simulated, in particular regarding
decreases in the middle stratosphere and increases in the lower stratosphere.
Six of the models also agree to some extent on the relatively small impact
on surface ozone. However, as with CH4, quantitative differences in
the sensitivity of lower-stratospheric ozone to increases in N2O mean
that the response of the TCO to N2O increases remains uncertain. The
impact of changing ODSs on stratospheric ozone is well simulated, with some
general agreement regarding the middle-stratospheric response and also the
impact on polar ozone. There remain quantitative differences regarding the
impact on the TCO, globally, and particularly regarding the impact of
stratospheric ozone depletion on surface ozone. Lastly, we have studied the
effect of a combination of CO2 and other GHGs on ozone. Essentially,
global warming causes ozone in the middle stratosphere to increase and in the
low-latitude lower stratosphere to decrease. The TCO impacts are relatively
consistently simulated, but the response of surface ozone to global warming
remains highly uncertain, with the five CCMI-1 models suitable for this
analysis disagreeing on major aspects of the impact. They exhibit larger
differences regarding the impact of global warming on surface ozone than were
found in a recent study using a different ensemble . This
may reflect uncertainties related to stratosphere–troposphere coupling that
were suppressed in the large subset of the models examined by
, which used prescribed stratospheric ozone. This may thus be
an example of additional model complexity causing increased divergence of
results .
Rows 1, 3, 5, 7, and 9: zonal-mean ozone (ppmv), averaged
individually for the months of January, April, July, and October, for the
years 2090–2099 of the RCP 8.5 scenario, as simulated by the
CCSRNIES-MIROC3.2, CESM1-WACCM, CMAM, ULAQ-CCM, and UMSLIMCAT models. Rows
2, 4, 6, 8, and 10: percentage difference between the rescaled and
simulated ozone fields. The rescaling is based on the REF-C2 simulations and
the a, b, and d coefficients as derived versus the SEN-C2-fCH4, -fN2O,
and -fGHG simulations.
Note that the ODSs evolve identically in REF-C2 and in SEN-C2-RCP85.
In an effort to further investigate the dynamical feedbacks causing some
differences in model response to these anthropogenic feedbacks, we have
analysed AOA in a subset of the models studied here. Here we find some
distinct consistencies and inconsistencies in the response of AOA to these
forcings. With further analysis, the results might help shed light on the
actual causes of these inter-model variations. Considering that greenhouse
gases interact with dynamics via their impact on radiation, the consistency
of the impact of greenhouse gases on radiative heating might be worth
assessing in more detail.
In essence, it appears that mid- and upper-stratospheric impacts of the four
gaseous anthropogenic forcings are relatively consistently simulated by the
subset of CCMI-1 models studied here, but lower-stratospheric, tropospheric,
and mesospheric impacts often are not. The total-column response is affected
by dynamical feedbacks which are not consistent in the CCMI-1 model ensemble.
We have linked these to differences in the impact on stratospheric
overturning. These inconsistencies in the CCMI-1 ensemble need to be
considered and may have consequences for the fidelity of any merged ozone
climatologies produced from the CCMI-1 results.
It is possible that the results presented here are subject to a sampling bias
in the sense that they require a relatively large number of sensitivity
simulations to be available, which some more expensive, higher-resolution
models in the CCMI-1 ensemble have not performed. It is regrettable that even
though the CCMI-1 ensemble nominally comprises 20 models
, only 10 models have been considered here, and of
these, some are unsuitable for certain diagnoses, e.g. because tropospheric
composition is prescribed or because required simulations or diagnostics do
not exist. Nonetheless, the results point to the need to better characterize
quantitatively the lower-stratospheric climate-ozone feedbacks that are the
likely cause for the discrepancies found here. The impact of methane on ozone
occurs significantly in the troposphere. Here differences in formulation and
sophistication of tropospheric chemistry also impact the models' responses to
methane changes. Such differences may also play into the responses to the
other forcings, although the surface ozone responses to N2O increases
are surprisingly consistent across most of the models, despite such
differences in formulation.
The ozone fields as used
in Sects. 3 to 5 are mostly as downloaded from the Centre for Environmental Data Analysis
(CEDA, 2017;
ftp://ftp.ceda.ac.uk). CESM1-WACCM data were downloaded from
http://www.earthsystemgrid.org. For instructions for access to both
archives see http://blogs.reading.ac.uk/ccmi/badc-data-access. Some
data were also supplied directly by the co-authors; these data will in due
course be uploaded to the CEDA archive.
Calculation of significance intervals
In the calculation of the regression coefficients a, b, c, and d of
Eq. () confidence intervals are critical for understanding where
the regression coefficients differ from 0, i.e. where the uncertainty in
them exceeds the amplitude. For this a standard statistical approach is used,
which essentially assumes that the residual ϵ consists of “white
noise”; i.e. there is no autocorrelation.
For this we use an IDL routine “trend.pro” (see http://www.harrisgeospatial.com/docs/trend.html). The regression coefficients simply come out of a
least-squares regression which uses the difference time series in ozone versus
the various external forcing (Sect. ).
Given are the original time series y of simulated ozone differences at a
given latitude, pressure level, and month of the year, n years in length,
and the associated external forcing x (such as an annual global-mean
methane mixing ratio). Then let yfit be the vector of best-fit
regression values. Next we define
se=∑ϵ2n-2
and
sxx=∑x-x‾2,
where x represents one of the four forcings considered here. We calculate
the confidence interval κ that characterizes the distribution:
κ=tcvf(0.025,n-2)sesxx.
Here, tcvf is the cut-off value of Student's t distribution
with n-2 degrees of freedom. The numerical value 0.025 means that
κ refers to the 95 % confidence interval.
More details on this process are in the routine used here
(http://web.csag.uct.ac.za/~daithi/idl_lib/pro/trend.pro) and in the
documentation of the tcvf function (e.g.
http://northstar-www.dartmouth.edu/doc/idl/html_6.2/T_CVF.html).
For the above approach to be robust, the residual ϵ
(Eq. ) needs to be free of autocorrelation. We test this using
the Durbin–Watson criterion :
d=∑i=2nϵi-ϵi-12∑i=1nϵi2.
In all situations 0≤d≤4. d=2 would characterize a dataset
without autocorrelation. For n=140 or 141, the case considered here, and
at 95 % confidence,
1.6≤d≤2.4
would characterize a dataset very likely free of autocorrelation
(https://www3.nd.edu/~wevans1/econ30331/Durbin_Watson_tables.pdf). In
Figs. S1–S4, violations of the Durbin–Watson criterion are marked with
stippling. Autocorrelation does indeed play a role in all models,
diagnostics, and seasons, but to varying extents. In principle,
autocorrelation can have two different origins, namely genuine modes of
variability that operate on scales of a year or longer, e.g. the
quasi-biennial oscillation, or alternatively nonlinear aspects to the
response of the model to the forcings, which might mean that the linear
regression fit systematically over- or underpredicts the model behaviour for
extended periods of time. The first cause would recede with increasing
ensemble size, the second might increase relative to the random noise that is
suppressed by increasing ensemble sizes. The Figs. S1–S4 indicate that the
models with larger ensemble sizes are equally or more affected by
autocorrelation than those with small ensemble sizes, suggesting that
nonlinearities may well play a role in this. However, a more in-depth
analysis of this aspect is needed.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-1091-2018-supplement.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Chemistry-Climate
Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We thank the Centre for Environmental Data Analysis (CEDA) for hosting the
CCMI-1 data archive. We acknowledge the modelling groups for making their
simulations available for this analysis, and the joint WCRP SPARC/IGAC
Chemistry-Climate Model Initiative (CCMI) for organizing and coordinating
this model data analysis activity. We acknowledge the UK Met Office for use
of the MetUM. This research was supported by the NZ Government's Strategic
Science Investment Fund (SSIF) through the NIWA programme CACV. Olaf Morgenstern
acknowledges funding by the New Zealand Royal Society Marsden Fund (grant
12-NIW-006) and by the Deep South National Science Challenge
(http://www.deepsouthchallenge.co.nz). The authors wish to acknowledge
the contribution of NeSI high-performance computing facilities to the results
of this research. New Zealand's national facilities are provided by the New
Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI's
collaborator institutions and through the Ministry of Business, Innovation &
Employment's Research Infrastructure programme
(https://www.nesi.org.nz). ACCESS-CCM runs were supported by Australian Research Council's Centre of
Excellence for Climate System Science (CE110001028), the Australian
Government's National Computational Merit Allocation Scheme (q90) and
Australian Antarctic science grant program (FoRCES 4012). WACCM is a component of
NCAR's Community Earth System Model (CESM), which is supported by the
National Science Foundation (NSF). Computing resources (ark:/85065/d7wd3xhc)
were provided by the Climate Simulation Laboratory at NCAR's Computational
and Information Systems Laboratory, sponsored by the National Science
Foundation and other agencies. The SOCOL team acknowledges support from the
Swiss National Science Foundation under grant agreement CRSII2_147659
(FUPSOL II). CCSRNIES's research was supported by the Environment Research
and Technology Development Fund (2-1303 and 2-1709) of the Ministry of the
Environment, Japan, and computations were performed on NEC-SX9/A(ECO) and NEC
SX-ACE computers at the CGER, NIES.Edited by:
Paul Young Reviewed by: two anonymous referees
ReferencesAkiyoshi, H., Nakamura, T., Miyasaka, T., Shiotani, M., and Suzuki, M.: A
nudged chemistry-climate model simulation of chemical constituent
distribution at northern high-latitude stratosphere observed by SMILES and
MLS during the 2009/2010 stratospheric sudden warming, J. Geophys. Res.
Atmos., 121, 1361–1380, 10.1002/2015JD023334, 2016.
Brasseur, G. P., Orlando, J. J., and Tyndall, G. S. (Eds.): Atmospheric
Chemistry and Global Change, Oxford University Press, Oxford, United Kingdom,
and New York, NY, USA, 1999.Butchart, N.: The Brewer-Dobson circulation, Rev. Geophys., 52, 157–184,
10.1002/2013RG000448, 2014.Centre for Environmental Data Analysis: CCMI archive, available at: ftp://ftp.ceda.ac.uk/badc/wcrp-ccmi/data/CCMI-1/output, last access: 20 September 2017.Cionni, I., Eyring, V., Lamarque, J. F., Randel, W. J., Stevenson, D. S., Wu,
F., Bodeker, G. E., Shepherd, T. G., Shindell, D. T., and Waugh, D. W.: Ozone
database in support of CMIP5 simulations: results and corresponding radiative
forcing, Atmos. Chem. Phys., 11, 11267–11292,
10.5194/acp-11-11267-2011, 2011.Dennison, F., McDonald, A., and Morgenstern, O.: The evolution of zonally
asymmetric austral ozone in a chemistry–climate model, Atmos. Chem. Phys.,
17, 14075–14084, 10.5194/acp-17-14075-2017, 2017.Dhomse, S. S., Chipperfield, M. P., Damadeo, R. P., Zawodny, J. M., Ball,
W. T., Feng, W., Hossaini, R., Mann, G. W., and Haigh, J. D.: On the
ambiguous nature of the 11 year solar cycle signal in upper stratospheric
ozone, Geophys. Res. Lett., 43, 7241–7249, 10.1002/2016GL069958, 2016.Durbin, J. and Watson, G. S.: Testing for serial correlation in least squares
regression, I, Biometrika, 37, 409–428, 10.1093/biomet/37.3-4.409, 1950.Eyring, V., Cionni, I., Bodeker, G. E., Charlton-Perez, A. J., Kinnison, D.
E., Scinocca, J. F., Waugh, D. W., Akiyoshi, H., Bekki, S., Chipperfield, M.
P., Dameris, M., Dhomse, S., Frith, S. M., Garny, H., Gettelman, A., Kubin,
A., Langematz, U., Mancini, E., Marchand, M., Nakamura, T., Oman, L. D.,
Pawson, S., Pitari, G., Plummer, D. A., Rozanov, E., Shepherd, T. G.,
Shibata, K., Tian, W., Braesicke, P., Hardiman, S. C., Lamarque, J. F.,
Morgenstern, O., Pyle, J. A., Smale, D., and Yamashita, Y.: Multi-model
assessment of stratospheric ozone return dates and ozone recovery in CCMVal-2
models, Atmos. Chem. Phys., 10, 9451–9472,
10.5194/acp-10-9451-2010, 2010.
Eyring, V., Lamarque, J.-F., Hess, P., Arfeuille, F., Bowman, K.,
Chipperfield,
M. P., Duncan, B., Fiore, A., Gettelman, A., Giorgetta, M. A., Granier, C.,
Hegglin, M. I., Kinnison, D., Kunze, M., Langematz, U., Luo, B. P., Martin,
R., Matthes, K., Newman, P. A., Peter, T., Robock, A., Ryerson, T.,
Saiz-Lopez, A., Salawitch, R., Schultz, M., Shepherd, T. G., Shindell, D.,
Staehelin, J., Tegtmeier, S., Thomason, L., Tilmes, S., Vernier, J.-P.,
Waugh, D. W., and Young, P. J.: Overview of IGAC/SPARC Chemistry-Climate
Model Initiative (CCMI) community simulations in support of upcoming ozone
and climate assessments, SPARC Newsletter, 40, 48–66, 2013.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R.
J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project
Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9,
1937–1958, 10.5194/gmd-9-1937-2016, 2016.Farman, J. C., Gardiner, B. G., and Shanklin, J. D.: Large losses of total
ozone in Antarctica reveal seasonal ClOx/NOx interaction,
Nature, 315, 207–210, 10.1038/315207a0, 1985.
Fuglestvedt, J. S., Jonson, J. E., and Isaksen, I. S. A.: Effects of
reductions
in stratospheric ozone on tropospheric chemistry through changes in
photolysis rates, Tellus, 46B, 172–192, 1994.Fuglestvedt, J. S., Jonson, J. E., Wang, W.-C., and Isaksen, I. S. A.:
Responses in tropospheric chemistry to changes in UV fluxes, temperatures and
water vapour densities, in: Atmospheric Ozone as a Climate Gas: General
Circulation Model Simulations, edited by: Wang, W.-C. and Isaksen, I. S. A.,
Springer Verlag, Berlin, Heidelberg, 145–162,
10.1007/978-3-642-79869-6_10, 1995.Garcia, R. R., Smith, A. K., Kinnison, D. E., de la Cámara, A., and Murphy,
D.: Modifications of the gravity wave parameterization in the Whole
Atmosphere Community Climate Model: Motivation and results, J. Atmos. Sci.,
74, 275–291, 10.1175/JAS-D-16-0104.1, 2017.
Hegglin, M. I., Lamarque, J.-F., Duncan, B., Eyring, V., Gettelman, A., Hess,
P., Myhre, G., Nagashima, T., Plummer, D., Ryerson, T., Shepherd, T., and
Waugh, D.: Report on the IGAC/SPARC Chemistry-Climate Model Initiative (CCMI)
2015 science workshop, SPARC Newsletter, 46, 37–42, 2016.Hurwitz, M. M., Fleming, E. L., Newman, P. A., Li, F., Mlawer, E.,
Cady-Pereira, K., and Bailey, R.: Ozone depletion by hydrofluorocarbons,
Geophys. Res. Lett., 42, 8686–8692, 10.1002/2015GL065856, 2015.
IPCC: Climate Change 2007: The Physical Science Basis. Contribution of
Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M.,
Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2007.
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of
Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M.,
Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.
M.,
Cambridge University Press, 2013.
Madronich, S.: Tropospheric photochemistry and its response to UV changes,
in:
The role of the stratosphere in global change, edited by: Chanin, M.-L.,
vol. 18 of NATO-ASI Series, Springer Verlag, Amsterdam,
Netherlands, 437–461, 1993.
Madronich, S. and Granier, C.: Impact of recent total ozone changes on
tropospheric ozone photodissociation, hydroxyl radicals, and methane trends,
Geophys. Res. Lett., 19, 465–467, 1992.Meinshausen, M., Smith, S. J., Calvin, K. V., Daniel, J. S., Kainuma, M.
L. T.,
Lamarque, J.-F., Matsumoto, K., Montzka, S., Raper, S., Riahi, K., Thomson,
A. M., Velders, G. J. M., and van Vuuren, D. P.: The RCP greenhouse gas
concentrations and their extensions from 1765 to 2300, Climatic Change, 109,
213–241, 10.1007/s10584-011-0156-z, 2011.Morgenstern, O., Braesicke, P., O'Connor, F. M., Bushell, A. C., Johnson, C.
E., Osprey, S. M., and Pyle, J. A.: Evaluation of the new UKCA
climate-composition model – Part 1: The stratosphere, Geosci. Model Dev., 2,
43–57, 10.5194/gmd-2-43-2009, 2009.Morgenstern, O., Akiyoshi, H., Bekki, S., Braesicke, P., Butchart, N.,
Chipperfield, M. P., Cugnet, D., Deushi, M., Dhomse, S. S., Garcia, R. R.,
Gettelman, A., Gillett, N. P., Hardiman, S. C., Jumelet, J., Kinnison, D. E.,
Lamarque, J.-F., Lott, F., Marchand, M., Michou, M., Nakamura, T., Olivié,
D., Peter, T., Plummer, D., Pyle, J. A., Rozanov, E., Saint-Martin, D.,
Scinocca, J. F., Shibata, K., Sigmond, M., Smale, D., Teyssèdre, H., Tian,
W., Voldoire, A., and Yamashita, Y.: Anthropogenic forcing of the Northern
Annular Mode in CCMVal-2 models, J. Geophys. Res.-Atmos., 115, D00M03,
10.1029/2009JD013347, 2010.Morgenstern, O., Zeng, G., Abraham, N. L., Telford, P. J., Braesicke, P.,
Pyle, J. A., Hardiman, S. C., O'Connor, F. M., and Johnson, C. E.: Impacts of
climate change, ozone recovery, and increasing methane on surface ozone and
the tropospheric oxidizing capacity, J. Geophys. Res.-Atmos., 118,
1028–1041, 10.1029/2012JD018382, 2013.Morgenstern, O., Zeng, G., Dean, S. M., Joshi, M., Abraham, N. L., and
Osprey, A.: Direct and ozone-mediated forcing of the Southern Annular Mode by
greenhouse gases, Geophys. Res. Lett., 41, 9050–9057,
10.1002/2014GL062140, 2014.Morgenstern, O., Hegglin, M. I., Rozanov, E., O'Connor, F. M., Abraham, N.
L., Akiyoshi, H., Archibald, A. T., Bekki, S., Butchart, N., Chipperfield, M.
P., Deushi, M., Dhomse, S. S., Garcia, R. R., Hardiman, S. C., Horowitz, L.
W., Jöckel, P., Josse, B., Kinnison, D., Lin, M., Mancini, E., Manyin, M.
E., Marchand, M., Marécal, V., Michou, M., Oman, L. D., Pitari, G.,
Plummer, D. A., Revell, L. E., Saint-Martin, D., Schofield, R., Stenke, A.,
Stone, K., Sudo, K., Tanaka, T. Y., Tilmes, S., Yamashita, Y., Yoshida, K.,
and Zeng, G.: Review of the global models used within phase 1 of the
Chemistry–Climate Model Initiative (CCMI), Geosci. Model Dev., 10, 639–671,
10.5194/gmd-10-639-2017, 2017.Naik, V., Voulgarakis, A., Fiore, A. M., Horowitz, L. W., Lamarque, J.-F.,
Lin, M., Prather, M. J., Young, P. J., Bergmann, D., Cameron-Smith, P. J.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R., Eyring, V.,
Faluvegi, G., Folberth, G. A., Josse, B., Lee, Y. H., MacKenzie, I. A.,
Nagashima, T., van Noije, T. P. C., Plummer, D. A., Righi, M., Rumbold, S.
T., Skeie, R., Shindell, D. T., Stevenson, D. S., Strode, S., Sudo, K.,
Szopa, S., and Zeng, G.: Preindustrial to present-day changes in tropospheric
hydroxyl radical and methane lifetime from the Atmospheric Chemistry and
Climate Model Intercomparison Project (ACCMIP), Atmos. Chem. Phys., 13,
5277–5298, 10.5194/acp-13-5277-2013, 2013.Newman, P. A., Daniel, J. S., Waugh, D. W., and Nash, E. R.: A new
formulation of equivalent effective stratospheric chlorine (EESC), Atmos.
Chem. Phys., 7, 4537–4552, 10.5194/acp-7-4537-2007, 2007.Oberländer-Hayn, S., Gerber, E. P., Abalichin, J., Akiyoshi, H.,
Kerschbaumer,
A., Kubin, A., Kunze, M., Langematz, U., Meul, S., Michou, M., Morgenstern,
O., and Oman, L. D.: Is the Brewer-Dobson circulation increasing or moving
upward?, Geophys. Res. Lett., 43, 1772–1779, 10.1002/2015GL067545,
2016.Oman, L. D., Douglass, A. R., Ziemke, J. R., Rodriguez, J. M., Waugh, D. W.,
and Nielsen, J. E.: The ozone response to ENSO in Aura satellite measurements
and a chemistry-climate simulation, J. Geophys. Res.-Atmos., 118, 965–976,
10.1029/2012JD018546, 2013.Oman, L. D., Douglass, A. R., Salawitch, R. J., Canty, T. P., Ziemke, J. R.,
and Manyin, M.: The effect of representing bromine from VSLS on the
simulation and evolution of Antarctic ozone, Geophys. Res. Lett., 43,
9869–9876, 10.1002/2016GL070471, 2016.Pitari, G., Aquila, V., Kravitz, B., Robock, A., Watanabe, S., Cionni, I.,
De Luca, N., Di Genova, G., Mancini, E., and Tilmes, S.: Stratospheric ozone
response to sulfate geoengineering: Results from the Geoengineering Model
Intercomparison Project (GeoMIP), J. Geophys. Res.-Atmos., 119, 2629–2653,
10.1002_2013JD020566, 2014.Polvani, L. M., Wang, L., Aquila, V., and W, W. D.: The impact of
ozone-depleting substances on tropical upwelling, as revealed by the absence
of lower-stratospheric cooling since the late 1990s, J. Climate, 30,
2523–2534, 10.1175/JCLI-D-16-0532.1, 2017.Portmann, R. W., Daniel, J. S., and Ravishankara, A. R.: Stratospheric ozone
depletion due to nitrous oxide: influences of other gases, Philos. T. Roy.
Soc. B, 367, 1256–1264, 10.1098/rstb.2011.0377, 2012.
Prather, M., Ehhalt, D., Dentener, F., Derwent, R., Dlugokencky, E., Holland,
E., Isaksen, I., Katima, J., Kirchhoff, V., Matson, P., Midgley, P., and
Wang, M.: Atmospheric Chemistry and Greenhouse Gases, in: Climate Change
2001: The Scientific Basis. Third Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Houghton, J., Ding, Y., Griggs, D.,
Noguer, M., van der Linden, P., Dai, X., Maskell, K., and Johnson, C.,
Cambridge University Press, Cambridge, UK, 2001.Ravishankara, A. R., Daniel, J. S., and Portmann, R. W.: Nitrous oxide
(N2O): the dominant ozone-depleting substance emitted in the
21st century, Science, 326, 123–125, 10.1126/science.1176985,
2009.Revell, L. E., Bodeker, G. E., Huck, P. E., Williamson, B. E., and Rozanov,
E.: The sensitivity of stratospheric ozone changes through the 21st century
to N2O and CH4, Atmos. Chem. Phys., 12, 11309–11317,
10.5194/acp-12-11309-2012, 2012a.Revell, L. E., Bodeker, G. E., Smale, D., Lehmann, R., Huck, P. E.,
Williamson,
B. E., Rozanov, E., and Struthers, H.: The effectiveness of N2O in
depleting stratospheric ozone, Geophys. Res. Lett., 39, l15806,
10.1029/2012GL052143, 2012b.Revell, L. E., Tummon, F., Stenke, A., Sukhodolov, T., Coulon, A., Rozanov,
E., Garny, H., Grewe, V., and Peter, T.: Drivers of the tropospheric ozone
budget throughout the 21st century under the medium-high climate scenario RCP
6.0, Atmos. Chem. Phys., 15, 5887–5902,
10.5194/acp-15-5887-2015, 2015.Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., Samir KC, Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva,
L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T.,
Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M.,
Takahashi, K., Baumstark, L., Doelman, J., Kainuma, M., Klimont, Z.,
Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.:
The Shared Socioeconomic Pathways and their energy, land use, and greenhouse
gas emissions implications: an overview, Global Environ. Chang., 42,
153–168, 10.1016/j.gloenvcha.2016.05.009, 2016.Sander, S. P., Abbatt, J., Barker, J., Burkholder, J., Friedl, R., Golden,
D., Huie, R., Kolb, C., Kurylo, M., Moortgat, G., Orkin, V., and Wine, P.:
Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies,
Evaluation No. 17, JPL Publication 10-6, Jet Propulsion Laboratory, Pasadena,
available at: https://jpldataeval.jpl.nasa.gov/, 2011.Scinocca, J. F., McFarlane, N. A., Lazare, M., Li, J., and Plummer, D.:
Technical Note: The CCCma third generation AGCM and its extension into the
middle atmosphere, Atmos. Chem. Phys., 8, 7055–7074,
10.5194/acp-8-7055-2008, 2008. Sekiya, T. and Sudo, K.: Roles of transport and
chemistry processes in global ozone change on interannual and 830
multidecadal time scales, J. Geophys. Res., 119, 4903–4921, 2014.Solomon, S.: Stratospheric ozone depletion: A review of concepts and history,
Rev. Geophys., 37, 275–316, 10.1029/1999RG900008, 1999.Solomon, S., Ivy, D. J., Kinnison, D., Mills, M. J., Neely, R. R., and
Schmidt,
A.: Emergence of healing in the Antarctic ozone layer, Science, 353,
269–274, 10.1126/science.aae0061, 2016.Stenke, A., Schraner, M., Rozanov, E., Egorova, T., Luo, B., and Peter, T.:
The SOCOL version 3.0 chemistry–climate model: description, evaluation, and
implications from an advanced transport algorithm, Geosci. Model Dev., 6,
1407–1427, 10.5194/gmd-6-1407-2013, 2013.
Stevenson, D., Johnson, C., Collins, W., Derwent, R., and Edwards, J.:
Future estimates of tropospheric ozone radiative forcing and methane
turnover – the
impact of climate change, Geophys. Res. Lett., 27, 2073–2076, 2000.Stolarski, R. S., Douglass, A. R., Oman, L. D., and Waugh, D. W.: Impact of
future nitrous oxide and carbon dioxide emissions on the stratospheric ozone
layer, Environ. Res. Lett., 10, 034011,
10.1088/1748-9326/10/3/034011, 2015.Stone, K. A., Morgenstern, O., Karoly, D. J., Klekociuk, A. R., French, W.
J., Abraham, N. L., and Schofield, R.: Evaluation of the ACCESS –
chemistry–climate model for the Southern Hemisphere, Atmos. Chem. Phys., 16,
2401–2415, 10.5194/acp-16-2401-2016, 2016.
Thompson, D. W. J., Solomon, S., Kushner, P. J., England, M. H., Grise,
K. M.,
and Karoly, D. J.: Signatures of the Antarctic ozone hole in Southern
Hemisphere surface climate change, Nat. Geosci., 4, 741–749, 2011.Tian, W. and Chipperfield, M. P.: A new coupled chemistry-climate model for
the
stratosphere: The importance of coupling for future O3 – climate
predictions, Q. J. Roy. Meteor. Soc., 131, 281–303, 2005.Voulgarakis, A., Savage, N. H., Wild, O., Carver, G. D., Clemitshaw, K. C.,
and Pyle, J. A.: Upgrading photolysis in the p-TOMCAT CTM: model evaluation
and assessment of the role of clouds, Geosci. Model Dev., 2, 59–72,
10.5194/gmd-2-59-2009, 2009.Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J.,
Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V.,
Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., MacKenzie, I. A.,
Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S.,
Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and
future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys.,
13, 2563–2587, 10.5194/acp-13-2563-2013, 2013.Waugh, D. W. and Hall, T. M.: Age of stratospheric air: Theory, observations,
and models, Rev. Geophys., 40, 1010, 10.1029/2000RG000101, 2002.
WMO: Scientific Assessment of Ozone Depletion: 2010, vol. 52 of Global
Ozone Research and Monitoring Project, 516 pp., 2011.WMO: Scientific Assessment of Ozone Depletion: 2014, vol. 55 of Global
Ozone Research and Monitoring Project, World Meteorological Organization,
416 pp., 2014.
Young, P. J., Archibald, A. T., Bowman, K. W., Lamarque, J.-F., Naik, V.,
Stevenson, D. S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D.,
Cameron-Smith, P., Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty,
R. M., Eyring, V., Faluvegi, G., Horowitz, L. W., Josse, B., Lee, Y. H.,
MacKenzie, I. A., Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T.,
Skeie, R. B., Shindell, D. T., Strode, S. A., Sudo, K., Szopa, S., and Zeng,
G.: Pre-industrial to end 21st century projections of tropospheric ozone from
the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP),
Atmos. Chem. Phys., 13, 2063–2090, 10.5194/acp-13-2063-2013,
2013.