Introduction
Stratospheric heating rates are enhanced between the minimum and maximum phases of the approximately 11-year solar cycle
through two main effects: (1) absorption of enhanced incoming ultraviolet (UV) radiation and (2) enhanced ozone
concentrations (brought about by increased photochemical production) (e.g. ). These radiative
changes can drive feedbacks onto stratospheric dynamics, leading to amplified signals of solar cycle variability in
regional surface climate via stratosphere–troposphere dynamical coupling (e.g. ). To understand and
model the impacts of solar cycle variability on the atmosphere, it is therefore necessary to account for the
characteristics of solar spectral irradiance (SSI) variability and the associated solar cycle ozone response (SOR)
(e.g. ).
In Part 1 of this study, examined the SOR in a number of recently updated and merged satellite ozone.
The present Part 2 focuses on the representation of the
SOR in global climate and chemistry–climate models (CCMs). At a minimum, models must include a sufficiently detailed
representation of SSI and the SOR to realistically simulate solar cycle impacts on the atmosphere. The global models
routinely employed in international scientific assessments (e.g. IPCC, WMO/UNEP Scientific Assessments of Ozone
Depletion) typically represent
atmospheric ozone in one of two ways. CCMs include interactive stratospheric chemistry and
explicitly simulate a SOR that is consistent with their photolysis, radiation and transport schemes provided that SSI
variations are adequately (i.e. with sufficiently high spectral resolution) represented. A small but growing number of
CCMs also include the chemical effects of galactic cosmic rays and solar energetic particles, though these effects are not
explicitly considered in this study. Conversely, global climate models do not routinely include interactive chemistry and
must therefore prescribe a pre-calculated ozone distribution to the radiation scheme, which is usually taken from
observations and/or chemical models. Thus, if climate models without chemistry are to capture the full atmospheric
response to solar cycle variability, they must prescribe an ozone dataset that includes a representation of the SOR.
Understanding and constraining the SOR is a long-standing scientific issue, and numerous studies have analysed its
representation in observations (see and references therein) and CCMs
(e.g. ). Older generations of CCMs
(e.g. CCMVal-1/2: Chemistry–Climate Model Validation Activity) showed a positive annual mean SOR of up to ∼ 2.5 % peaking in the tropics between
∼ 3–5 hPa and a maximum tropical mean temperature response in the upper stratosphere of ∼ 0.5–1.1 K
. Since these earlier studies the CCMs from different centres have been significantly revised and
developed (e.g. ). The more recent study by only analysed a small number of CCMs that
participated in the World Climate Research Programme fifth Coupled Model Intercomparison Project (CMIP5). Thus there has
been no detailed comparison of the SOR in a larger set of CCMs since . Furthermore, compared
the few CMIP5 models with older versions of satellite datasets that have since been updated and extended, leading to
pronounced changes in their representation of the SOR . Hence one goal of this study is to provide
an important update by evaluating the SOR in the latest models from the SPARC/IGAC Chemistry–Climate Model
Initiative (CCMI-1) and comparing them to some of the recently updated and extended satellite datasets
discussed in Part 1.
A further motivation for this study is the recent analysis of the climate response to the solar cycle in CMIP5, which
included models with and without interactive stratospheric chemistry. The CMIP5 models showed a large spread
(∼ 0.3–1.2 K) in the peak amplitude of the tropical stratospheric temperature response between the minimum and
maximum phases of the 11-year solar cycle . This spread may be due to differences in the prescription
of SSI, in the accuracy of model radiative transfer schemes , and/or in the representation of the
SOR. However, the quantitative importance of any one of these factors is unclear. All CMIP5 models were recommended to use
the Naval Research Laboratory Spectral Solar Irradiance 1 (NRLSSI-1) dataset . Those without chemistry
were further recommended to prescribe ozone from the SPARC/Atmospheric Chemistry and Climate (AC&C;
www.igacproject.org, last access: 17 May 2018) ozone database (; hereafter referred to as CMIP5 ozone database). The
historical part of this ozone database was largely based on a multiple-regression model fit to satellite and ozonesonde observations (see
Sect. ). It is therefore plausible that differences in the representation of SOR made an important contribution
to the spread in atmospheric temperature and dynamical responses to the solar cycle in CMIP5 models; we investigate this
hypothesis further in this study.
As was the case in CMIP5, CMIP6 will include a mix of models with and without explicit stratospheric chemistry. A new
ozone database has been created for CMIP6 models without chemistry (hereafter referred to as CMIP6 ozone database; see
https://esgf-node.llnl.gov/projects/input4mips, last access: 17 May 2018). It
is therefore important to document the SOR in the new CMIP6 ozone database and compare it to the previous CMIP5 database,
since these fields are routinely employed in climate models and differences may lead to changes in the modelled responses
to solar forcing. In addition to documenting the SOR in the CMIP6 ozone database, this study is
published with the ozone coefficients derived from the analysis (10.5518/348), so they can be used in other
modelling projects (e.g. ).
Another factor to consider for modelling solar cycle effects on the atmosphere is the representation of the annual cycle
in the SOR . found that the three CMIP5 CCMs that simulated large horizontal
gradients in the SOR in the upper stratosphere in early winter also showed Northern Hemisphere high-latitude dynamical
responses over the 11-year solar cycle that compared more closely with reanalysis data. The enhancement of the SOR at
high latitudes is related to coupling between chemistry and transport processes for ozone and may play a role in driving
the “top-down” mechanism for the solar cycle influence on high-latitude regional surface climate (see
e.g. ). It is therefore also important to compare the representation of the annual cycle in the SOR in
current CCMs and in the pre-calculated ozone databases used in climate models.
The objectives of this study are therefore as follows:
to provide an update to by analysing the SOR in CCMI-1
models,
to document the SOR in the new CMIP6 ozone database and compare this to previous pre-calculated ozone databases
including CMIP5,
to compare the SOR in CCMs and ozone databases with recently updated and extended satellite
records,
to perform atmospheric model experiments to quantify the impact of differences in the SOR between CMIP5 and CMIP6 on
the simulated atmospheric response to the 11-year solar cycle.
Collectively these objectives provide a comprehensive assessment of the representation of the SOR in current CCMs and global
climate models. The outline of the remainder of the paper is as follows: Sect. describes the data and methods
used to analyse the SOR, Sect. presents the results and Sect. summarizes our findings.
Details of the CCMI-1 models used in this study and the number of ensemble members available for the REFC1 experiment for the period 1960–2009. See for more details.
Model
No. ensembles
QBO
No. shortwave bands
Reference
CMAM
3
No
4
CESM1(WACCM)
3
Nudged
19
CCSRNIES-MIROC3.2
3
Nudged
20
CNRM-CM5-3
1
No
6
; http://www.cnrm-game-meteo.fr/
(last access: 17 May 2018)
EMAC(L90)
1
Nudged
55 in the stratosphere (< 70 hPa)
LMDz-REPROBUS-CM5 (L39)
1
No
2
MRI-ESM1r1
1
Internal
22
SOCOL3
3
Nudged
6
Methods
Models and ozone datasets
The CCMI-1 models
Data are analysed from eight CCMI-1 models downloaded from the British Atmospheric Data Centre . The models
analysed are CCSRNIES-MIROC3.2, CESM1(WACCM),
CMAM, CNRM-CM5-3, EMAC(L90), LMDz-REPROBUS-CM5 (L39), MRI-ESM1r1 and
SOCOL3 (see Table 1). These models include the minimum requirements for capturing the SOR (i.e. a prescription of SSI
variability in the chemistry scheme). A detailed description of the models is given by .
Data are analysed from the hindcast simulations (REF-C1), which include observed time-varying sea surface temperatures (SSTs) and sea
ice, well-mixed greenhouse gases, volcanic aerosols and SSI forcing from NRLSSI-1 . Thus, in contrast
to the coupled atmosphere–ocean CMIP5 models analysed by , the CCMI-1 REF-C1 simulations do not include an
interactive ocean. The REF-C1 simulations start in January 1960 but terminate in different years for each model, so for
consistency we analyse the 50-year period 1960–2009, which is common to all the simulations. All available ensemble
members are analysed for each model (see Table 1).
The representation of the Quasi-Biennial Oscillation (QBO) differs across the CCMI-1 models . Some of the models simulate
a spontaneous QBO (MRI-ESM1r1, EMAC(L90)), some models include a QBO by nudging tropical stratospheric zonal winds towards
observations (CCSRNIES-MIROC3.2, CESM1(WACCM), SOCOL3) and some include no representation of the QBO (CMAM, CNRM-CM5-3,
LMDz-REPROBUS-CM5). In EMAC(L90) a weak nudging towards the observed QBO with a relaxation timescale of 58 days is applied
to ensure the same phasing as the observed QBO, whereas in CCSRNIES-MIROC3.2, CESM1(WACCM) and SOCOL3 the QBO is nudged
more strongly (5–10-day timescale). For those models that include QBO variability, two additional orthogonal QBO indices
are included in the multiple linear regression (MLR) model calculated from the modelled zonal mean zonal wind fields (see
Sect. ).
The CMIP5 ozone database
The CMIP5 ozone database consists of monthly mean ozone mixing ratios on 24 pressure levels spanning 1000–1 hPa for the
period 1850–2100 . Data are provided on a regular 5∘ × 5∘ longitude–latitude
grid. Ozone values are provided as a 2-D (i.e. zonal mean) field in the stratosphere (at pressures less than 300 hPa) and
as a 3-D field in the troposphere, with a blending across the tropopause. The tropospheric part of the database was
constructed from CCM simulations. For the stratosphere, the historical portion of the database (1850–2009) was
constructed from observations using an MLR model (that includes solar variability as one of the independent variables) fit
to Stratospheric Aerosol and Gas Experiment (SAGE) I and II version 6.2 satellite data and polar ozonesondes following the method of . A SOR is
therefore implicitly included in the historical portion of the CMIP5 ozone database that will resemble the observations
input to the MLR model. However, owing to the paucity of long-term ozone measurements at high latitudes, the SOR was only
included within ±60∘ latitude. This limitation led some CMIP5 modelling groups to make alterations to the
CMIP5 ozone database, including extrapolation of the SOR coefficients at ±50∘ latitude to the poles using
a cosine latitude weighting. The CMIP5 models known to have employed this “Extended CMIP5 ozone database” include
HadGEM2-CC , MPI-ESM and CMCC-CC (Chiara Cagnazzo, personal communication, 2016). We note that the
historical portion of the CMIP5 ozone database did not include a representation of QBO variability in ozone.
The future portion of the CMIP5 ozone database for the stratosphere was based on CCM simulations from
CCMVal-2 models . However, owing to uncertainties in how individual CMIP5 models would represent SSI variations over the
21st century, the future portion of the CMIP5 ozone database did not include a SOR. For consistency, a SOR was thus added
to the future period in the Extended CMIP5 ozone database using regression coefficients for the SOR derived from the
historical period (; Chiara Cagnazzo, personal communication, 2016).
The CMIP5 ozone database is described in full by and is available at the time of writing from
https://cmip.llnl.gov/cmip5/forcing.html#ozone_forcing. A description
of the CMIP5 models that employed the CMIP5 ozone database is given by .
The CMIP6 ozone database
The CMIP6 ozone database for the historical period (1850–2014) consists of monthly mean ozone mixing ratios on
66 pressure levels spanning 1000–0.0001 hPa. Data are provided as a 3-D field on a regular 2.5∘ × 1.9∘
longitude–latitude grid. The database is constructed using a weighted average of simulations from two CCMs (CESM1(WACCM)
and CMAM) (). The CMIP6 ozone database was downloaded for this study
from https://esgf-node.llnl.gov/projects/input4mips.
The simulations from the two constituent CCMs include prescribed SSTs, sea ice, well-mixed greenhouse gas concentrations
and aerosols. Surface emissions of NOx and other tropospheric ozone precursor gases are also prescribed. Both
CCMs represent SSI variability in their radiation and chemical schemes. However, only CESM1(WACCM) includes the chemical
effects of energetic particle precipitation.
There are some differences in the set-up of the CCM simulations used to create the CMIP6 ozone database compared to the
CCMI-1 versions of the same models (see Sect. ), which may affect the representation of the SOR. The version of
CMAM for the CMIP6 ozone database used historical stratospheric aerosols and solar variability, similar to REF-C1,
extended back to 1850. However, SSTs and sea ice were prescribed from a CanESM2 historical simulation performed for CMIP5
rather than from observations. The temporal variability in SSI for CMAM was taken from the CMIP6 SSI dataset
, but the variations were applied to the long-term background spectrum from NRLSSI-1. This is in slight
contrast to the CCMI-1 version of CMAM that used both SSI variability and the background spectrum from NRLSSI-1. However,
showed that the slightly weaker variability over the solar cycle at shorter UV wavelengths in NRLSSI-1 only
reduced the amplitude of the tropical mean SOR in a CCM by ∼ 0.3 % compared to a reference of
∼ 2 %. This difference is therefore likely to have only a small effect on the SOR in the configurations of CMAM
implemented for CCMI-1 and the CMIP6 ozone database. Neither CMAM simulation includes nudging of the QBO.
There are also some differences in the configuration of CESM1(WACCM) used for the CMIP6 ozone database compared to
CCMI-1. The CESM1(WACCM) CCMI-1 runs prescribed the NRLSSI-1 data at daily resolution, whereas the version run for the CMIP6
ozone database used annual values as these extend back to 1850. In the lower thermosphere, values of the F10.7cm flux and
Kp index used to parametrize the chemical effects of energetic particle precipitation were taken from observations in
CCMI-1 and from a proxy record in the simulation for the CMIP6 ozone database. Furthermore, the simulation for the CMIP6
ozone database did not include solar proton events or galactic cosmic ray effects. Both versions of CESM1(WACCM) used
observed SSTs and include a nudged QBO towards observed tropical winds. In summary, there are some differences in the
experimental set-ups of the two CCMs used to create the CMIP6 ozone database, in particular that they use slightly
different representations of SSI variability, they do not both include QBO variability and they use different SST
datasets.
The Bodeker ozone database
describe a new observation-based ozone database for climate models covering the period 1979–2007. Monthly
and zonal mean ozone mixing ratios are provided on 70 pressure levels spanning 878–0.05 hPa on a regular 5∘
latitude grid. The ozone field is constructed from a large number of satellite and ozonesonde observations from the Binary
DataBase of Profiles (BDBP; ) used to fit an MLR model that includes terms for equivalent effective
stratospheric chlorine (EESC), a linear trend, the QBO, the El Niño–Southern Oscillation (ENSO), the solar cycle
and the Mt Pinatubo volcanic eruption. We note that, since the BDBP contains SAGE II v6.2 mixing ratio data, this is likely
to provide a strong constraint on the SOR in the tropics and subtropics. See and for
a discussion of the differences in the SOR in SAGE II v6.2 and v7.0 data. To generate a spatially complete ozone
field, a single MLR fit is performed for all points on a given pressure surface to enable regression coefficients to be derived
for latitudes where the observations are relatively sparse (e.g. in polar regions). We use the Tier 1.4 product from the
Bodeker ozone database, which includes contributions from all the MLR basis functions. The Bodeker ozone database was
downloaded from
http://www.bodekerscientific.com/data/monthly-mean-global-vertically-resolved-ozone.
Time series of the six basis functions used in the MLR analysis.
(a) Solar forcing based on F10.7cm flux; (b) CO2;
(c) equivalent effective stratospheric chlorine; (d) ENSO
index; (e, f) two orthogonal QBO indices defined as the first two
principal component time series of tropical zonal mean zonal winds (in this
case taken from observations). The time series are in units of standard
deviation.
The multiple linear regression (MLR) model
MLR models have been used to analyse drivers of secular trends and variability in stratospheric
ozone for many decades (see e.g. , and references therein). In the context of extracting the SOR
from ozone time series, there is a long history of similar methods being applied to both satellite observations (e.g.,
)
and CCMs
. Here we follow the methodology described
by , which is very similar to the methods described in those earlier studies. Briefly, the zonal mean ozone
data are deseasonalized by removing the long-term monthly mean at each latitude and pressure level. As in past studies, we
then perform an MLR analysis on the time series of monthly mean anomalies at each location, O3′(t), to
diagnose the solar cycle component:
O3′(t)=A×F10.7(t)+B×CO2(t)+C×EESC(t)+D×ENSO(t)+E×QBOA(t)+F×QBOB(t)+r(t),
where r(t) is a residual. The annual-mean SOR is calculated by regressing all months as a single time series. The monthly
SOR is calculated by regressing time series of year-to-year anomalies for individual months. The monthly mean basis
functions in Eq. () are the F10.7cm radio solar flux, the CO2 concentration at Mauna Loa, the EESC
and the Niño 3.4 index to represent ENSO. The F10.7cm flux is used to represent
solar activity because it has been shown to be well correlated with indices for UV radiation (e.g. ), the key
driver of the stratospheric ozone response. The results presented in Sect. assume a difference of 130 solar
flux units (1 SFU = 10-22 Wm-2Hz-1) as a representative amplitude of the 11-year solar
cycle. The Niño 3.4 index is computed as the standardized mean SST averaged over the region
5∘ S–5∘ N, 120–170∘ W. For those CCMI-1 models and ozone databases that include QBO variability (see Table 1), the QBO
indices are calculated as the first two principal component time series of the tropical (±10∘, 5–70 hPa)
zonal mean zonal winds. The ozone response to volcanic aerosols is non-linear through time owing to changing levels of
inorganic chlorine in the atmosphere . Thus, rather than including a term in the MLR model to represent
volcanic effects on ozone, data from the 2-year periods following the three major tropical volcanic eruptions since 1960
are excluded from the analysis: Mount Agung (February 1963), El Chićhon (March 1982) and Mount Pinatubo
(June 1991). Figure shows example time series of the MLR basis functions from 1960 to 2009 in arbitrary units. In
this example the ENSO and QBO indices are based on observations. The coefficients A–F in Eq. () are calculated
using linear least squares regression.
One important issue for MLR analysis is the handling of possible autocorrelation in the regression residuals, r(t), and
the effect on the estimation of statistical uncertainty in the results. A Durbin–Watson test reveals significant serial
correlation in the regression residuals in many locations for lags of 1 and 2 months, particularly in the middle and
polar lower stratosphere. Such serial correlation can lead to spurious overestimation of the statistical significance of
the regression coefficients, and we therefore include an autoregressive term in the regression model. Given the significant
serial correlations in some regions up to a lag of 2 months, a second-order autoregressive noise process (AR2) is used,
which assumes the residuals r(t) have the form
r(t)=ar(t-1)+br(t-2)+w(t),
where a and b are constants and w(t) is a white noise process. This is identical to the approach employed in
and the recent SPARC SI2N analysis of ozone trends . The autocorrelation term is
not included in the analysis of the monthly SOR because the residuals are approximately uncorrelated from
year to year. Unless otherwise stated, the statistical significance of the SOR extracted using the MLR model is assessed
using a two-tailed Student's t test with a null hypothesis that the magnitude of the SOR is indistinguishable from
zero. We apply a threshold to determine whether the null hypothesis can be rejected at a 95 % confidence level.
It is a challenge in geophysical science to develop statistical methods to extract forced signals from complex
time series. The implementation of MLR analysis as described above may have a number of limitations,
including (but not limited to) assumption that the input basis functions have zero uncertainty, difficulties in
separating a signal from noise in relatively short or sparse records and issues arising from
degeneracy between basis functions . We have not attempted to account for these factors in our results.
Atmospheric model sensitivity experiments
To explore the atmospheric impacts of different representations of the SOR, simulations were carried out with the
atmospheric general circulation model ECHAM6.3, which is an update of the ECHAM6.1 model used
as the atmospheric component of the Max Planck Institute Earth System Model in CMIP5 simulations. Model changes
from version 6.1 to 6.3 are mainly related to fixes of bugs described by ; efforts to ensure energy
conservations; an update of the radiation scheme, which is now the PSrad version of the RRTMG code
; and retuning. If the same forcings are used, temperature effects of solar cycle variability in ECHAM6.3
compare well to those described for ECHAM6.1 . The model experiments performed here use a horizontal
resolution of T63 (∼ 140 km × 210 km) with 47 vertical levels up to a lid of 80 km.
It is known that the ECHAM6.3 radiation code does not cover wavelengths below 200 nm, and therefore the important
Schumann–Runge bands and Lyman-alpha lines of ozone are not captured . This results in a too-weak
radiative response to the imposed solar forcing particularly in the mesosphere. Therefore we focus our analysis on the
stratospheric response, where most of the absorption occurs at higher UV wavelengths, and the performance of ECHAM6.3 is
comparable to models with a more comprehensive radiative code .
We have performed five time-slice simulations with ECHAM6.3, each lasting for 50 years. The control simulation uses
average boundary conditions as specified for the CMIP5 AMIP simulation; i.e. for all boundary conditions such as SSTs,
greenhouse gas concentrations, SSI and prescribed atmospheric ozone we have used multi-year averages of the CMIP5
recommended values for the years 1978–2008. Four sensitivity simulations have then been performed in which solar maximum
minus solar minimum differences in either atmospheric ozone concentrations or both ozone and SSI have been added to the
respective fields of the control simulation. For solar maximum and minimum conditions we have used average values over the
years 1985–1986 and 1981–1982, respectively. According to the solar irradiance recommendations for CMIP6
these are characterized by a difference of 126.1 SFU and are therefore closely comparable to the results presented for
the SOR, which assume a representative solar cycle amplitude of 130 SFU. Ozone anomalies were calculated either from the
respective years of the Extended CMIP5 ozone database () or using the monthly SOR coefficients from the
CMIP6 ozone database shown in Sect. . The corresponding SSI anomalies are calculated either from the CMIP5
recommended NRLSSI-1 dataset or from the CMIP6 recommended solar forcing dataset .
Time series of
deseasonalized percent tropical (30∘ S–30∘ N) ozone
anomalies in CCMI-1 models for 1960–2009 and two satellite datasets at
(a) 1 hPa, (b) 3 hPa, (c) 5 hPa,
(d) 10 hPa and (e) 30 hPa. The lowest panel shows the
F10.7cm solar flux for reference. Anomalies are shown relative to
a baseline period 1985–2009.
Results
The SOR in CCMI-1 models
Figure shows time series of deseasonalized tropical (30∘ S–30∘ N) and monthly mean percent
ozone anomalies at select pressure levels (1, 3, 5, 10, 30 hPa) for the eight CCMI-1 models described in
Sect. . Anomalies are defined relative to the period 1985–2009. Also plotted in Fig. are time series
from two satellite datasets discussed in Part 1 of this study: SBUVMOD VN8.6 (black dashed) and SAGE-GOMOS 1
(black solid). For completeness, the time series of absolute ozone mixing ratios from the models are shown
in the Supplement (Fig. S1).
The CCMI-1 models show a long-term decline in stratospheric ozone abundances, particularly in the mid- and upper
stratosphere. This is the result of increasing atmospheric loading of inorganic chlorine and bromine over this period and
is consistent with results from earlier CCM studies (e.g. ). At 1 hPa, the trend in ozone
between 1979 and 1997 computed by linear regression ranges from -1.9 to -2.6 %decade-1 across the
models. At 3 hPa, the range in trends is -4.1 to -5.1 %decade-1. These values are within the
uncertainty bounds of satellite-observed ozone trends over this period .
In addition to a long-term decline, Fig. shows quasi-decadal variations in ozone in the upper stratosphere that
are approximately in phase with the 11-year solar cycle. These are a marker of the SOR which is evident in the raw ozone
time series and can be seen as a peak around the decadal timescale in the modelled ozone power spectra (see Fig. S2).
There is larger interannual and multi-year variability in ozone at 10 and 30 hPa, where some models show
enhanced variability associated with the QBO. The modelled evolution of the tropical ozone anomalies is generally in good
agreement with the observation data in Fig. , with some exceptions where the satellite records show
larger-amplitude short-term fluctuations that may be related to incomplete spatial and temporal sampling.
The percent (%) differences in stratospheric ozone mixing ratios
per 130 SFU derived for the period 1960–2009 in the CCMI-1 models listed in Table 1. The solid
contours denote 1 % intervals. Hatching denotes regions where the
regression coefficients are not significantly different from zero at the
95 % confidence level. Panel (i) shows the multi-model mean
(MMM), with hatching denoting where the MMM response is smaller than
±2 SD of the intermodel spread. The number of ensemble members used
for each model is shown in parentheses in the titles.
Figure shows latitude–pressure cross sections of the annual mean SOR in the eight CCMI-1 models
(Fig. a–h) along with the multi-model mean (Fig. i). For the individual models, the statistical
significance of the SOR is assessed using a two-tailed Student's t test with a threshold for rejecting the null
hypothesis at the 95 % confidence level (see Sect. ). The robustness of the CCMI-1 multi-model mean SOR is
assessed by distinguishing regions where the magnitude of the SOR is greater than ±2 SD of the intermodel
spread. Figure can be compared with Fig. 1 in and Fig. 1 in , which show similar
plots for the CCMVal-1 and CMIP5 models, respectively.
All of the CCMI-1 models analysed show a significant positive SOR of up to ∼ 2 % between 1 and 10 hPa. This is
less than half the peak amplitude of the SOR in the SAGE II v6.2 mixing ratio dataset and is more comparable to the SOR
amplitude in SAGE II v7.0 mixing ratios and the SBUVMOD VN8.6 dataset (see Figs. 4 and 12 in ). The
results from the CCMI-1 models are broadly consistent with the results from CCMVal-1 models . The main
exception is the absence in the multi-model mean of a strong SOR in the tropical lower stratosphere. An enhanced SOR in
the tropical lower stratosphere has been identified in satellite observations, albeit with large uncertainties
, and it has been postulated this may be associated with a change in the strength
of the Brewer–Dobson circulation. The CCMVal-1 multi-model mean showed a SOR of around 5 % per 130 SFU at
∼ 50 hPa (see Fig. 4d in ), as compared to around 1 % in the CCMI-1 multi-model mean
(Fig. i). However, there was large intermodel spread in this signal across the CCMVal-1 models, and the
multi-model mean SOR was dominated by strong responses in a few models that only ran for a short period
(1980–2004), during which aliasing effects with other climatic factors can be significant . Since the analysis shown here
extends for a longer period and excludes the post-volcanic epochs, this is a plausible reason for the apparent difference
in the SOR in the tropical lower stratosphere between the CCMI-1 and CCMVal-1 models. One of the CCMI-1 models (SOCOL3)
appears to show an enhanced SOR in the tropical lower stratosphere, which is similar in amplitude to that seen in some
CCMVal-1 models. However, this feature shows some sensitivity to the choice of autoregressive model in the MLR model
probably because the decorrelation timescale for the regression residuals in the tropical lower stratosphere is longer
than 2 months in SOCOL3 and some of the other CCMs (not shown). Further analysis of the transformed Eulerian mean
residual vertical velocity does not reveal a substantial change in the rate of upwelling in the tropical lower
stratosphere in any of the models (not shown).
Time series of deseasonalized percent tropical
(30∘ S–30∘ N) ozone anomalies from two satellite
observation datasets (black) and the Bodeker (orange), CMIP5
(red) and CMIP6 (blue) ozone databases over the
period 1960–2011 at (a) 1 hPa, (b) 3 hPa,
(c) 5 hPa, (d) 10 hPa and (e) 30 hPa. The
lowest panel shows the F10.7cm solar flux for reference. Anomalies are
shown relative to a baseline period 1985–2009.
The month-by-month SORs in the individual CCMI-1 models (see Figs. S3–S10) show a significant positive
SOR in the tropical upper stratosphere throughout the year but enhanced SOR amplitudes at high latitudes, particularly in
the winter and spring seasons. This behaviour, which is also seen in some satellite ozone datasets
(e.g. ), cannot be understood from photochemical processes alone and must therefore be related to
stratospheric circulation changes (e.g. ). Such localized changes in ozone will be associated with
a radiative perturbation that could lead to feedbacks onto circulation , and thus it may be important to
account for this seasonal variation in the SOR in model simulations.
The SOR in ozone databases for climate models
Figure shows time series from 1960 to 2011 of deseasonalized tropical and monthly mean percent ozone anomalies
at select stratospheric levels (1 to 30 hPa) from the Bodeker (orange line), CMIP5 (red) and CMIP6 (blue) ozone
databases. Also plotted in black are the same satellite datasets shown in Fig. . Anomalies are defined
relative to the period 1985–2007. The Extended CMIP5 ozone database is not shown because it is identical to the original
CMIP5 database in the tropics.
Although the time series have been deseasonalized, the CMIP5 and Bodeker ozone databases show a residual annual cycle
particularly in the upper stratosphere. This is because in these databases the amplitude of the ozone annual cycle is
larger in the early part of the record, when the background levels are higher, and smaller in the latter part of the
record following the long-term decline in ozone. Since the ozone anomalies in Fig. are shown as anomalies from
the 1985–2007 mean, there is therefore a residual annual cycle particularly in the period before 1985. Conversely, the
CMIP6 database, which is constructed from CCM simulations, does not show a significant change in the amplitude of the
ozone annual cycle over time.
At 1 hPa, the CMIP5 and Bodeker databases show a larger linear trend in ozone over 1979–2007 (diagnosed using linear
regression) of around -3.5 %decade-1 compared to -1 %decade-1 in the CMIP6
database. The latter is, as expected, similar to the long-term ozone trends in the CCMI-1 models shown in
Fig. . At 3 hPa, the CMIP5 database also shows a larger long-term decrease in ozone by around a factor of
2 compared to the Bodeker and CMIP6 databases. Thus, a model that uses the recommended CMIP6 ozone database might be
expected to show weaker upper-stratospheric cooling over recent decades than an equivalent simulation using the
CMIP5 database, owing to the smaller negative trend in upper-stratospheric ozone.
At 10 and 30 hPa, the Bodeker and CMIP6 databases show a QBO signal in ozone, whereas the CMIP5 database does not include
QBO variability. This is an important distinction because a model that employs the CMIP6 ozone database, but which does
not simulate a dynamical QBO, will impose a QBO-ozone signal that may alter the model's behaviour. Alternatively, a model
that internally generates a dynamical QBO that is not in phase with the prescribed QBO-ozone signal in the CMIP6 ozone
database will be subject to a diabatic heating anomaly from ozone that is inconsistent with the model's dynamical
evolution. Both of these cases would be physically unrealistic. However, a model that nudges a QBO towards observations
and uses the CMIP6 ozone database should have a more consistent representation of temporal variability in winds and ozone
associated with the QBO. Modelling groups may therefore choose to post-process the CMIP6 ozone database in order to treat
the QBO-ozone signal in a consistent manner for their model. Note that since the CMIP6 ozone database is produced by
averaging two CCMs, one that does include QBO-ozone variability (CESM1(WACCM)) and one that does not (CMAM), the QBO-ozone
signal is weaker in the CMIP6 ozone database than in the CESM1(WACCM) model alone (compare blue line in Fig.
with dark pink line in Fig. ). The absence of a QBO-ozone signal in the CMIP5 ozone database means that CMIP5
models that simulated a QBO would have neglected any radiative feedback from ozone on the QBO.
The annual mean percent (%) differences in ozone per 130 SFU for the
(a) Bodeker (1979–2007), (b) CMIP5 (1960–2005),
(c) Extended CMIP5 (1960–2005) and (d) CMIP6 (1960–2011)
ozone databases. The contour interval is 1 %. The hatching in
(d) is as in Fig. (a–h).
Figure shows latitude–pressure cross sections of the annual mean SOR in the three ozone databases shown in
Fig. and the Extended CMIP5 ozone database. In the tropics, the Bodeker ozone database, Fig. a,
shows a positive SOR of up to 4 % peaking at around 2–3 hPa with a distinct minimum at ∼ 10 hPa. The
latitudinal structure of the SOR is smoother than in the SAGE II v6.2 mixing ratio data (cf. Fig. 4d of Part 1) probably
because the construction of the Bodeker database fits an MLR model to all data points along pressure surfaces rather than
to individual latitude bands. At high latitudes, the magnitude of the SOR in the Bodeker database is small and the spatial
structure is noisy likely because of the small number of observations there. In the lower stratosphere, the Bodeker
database indicates a positive SOR at most latitudes, as was found in a number of satellite ozone datasets in
Part 1. However, the uncertainty in the magnitude of the SOR at these levels is comparatively large (see below).
The SOR in the CMIP5 ozone database, Fig. b, shows a very similar structure to that found in SAGE v6.2 mixing
ratios (cf. Fig. 4d in Part 1), consistent with those data forming the backbone for the historical portion of the dataset
. Note that the MLR fits were applied separately at each latitude band in the construction of the CMIP5
database, and this likely explains why the horizontal structure of the SOR is more heterogeneous than in the Bodeker ozone
database. In particular the three-peaked structure of the SOR found in the tropical upper stratosphere in the SAGE II v6.2
mixing ratio dataset is evident in the CMIP5 ozone database. The sharp cut-offs in the SOR at ±60∘ latitude
are spurious and result from a lack of data points to define a SOR at high latitudes. As described in Sect. ,
the Extended CMIP5 ozone database, Fig. c, applied an extrapolation to introduce a SOR in the
extratropics. The details of this structure, which shows a positive SOR extending into the northern extratropics and
a negative SOR at pressures greater than ∼ 5 hPa in
the Southern Hemisphere poleward of 60∘ S, is likely to
be subject to considerable uncertainties owing to the simple spatial filling method employed.
Vertical profiles of the tropical (30∘ S–30∘ N) average annual SOR per 130 SFU (%). The range of the best estimates across the eight CCMI-1 models is shown in the grey shading.
The lines show the best-estimate tropical mean annual SOR in the three climate model ozone databases discussed in Sect. and two satellite ozone datasets from (SBUVMOD VN8.6 and SAGE-GOMOS 1). The whiskers denote 2.5–97.5 % confidence intervals on the estimated SOR.
In the CMIP6 ozone database, Fig. d, the amplitude of the SOR is around 1–2 % in the upper stratosphere,
which is, as expected, broadly consistent with the CCMI-1 results shown in Fig. . The peak amplitude of the SOR is
therefore 2–3 times smaller, and is considerably smoother in latitude, than in the CMIP5 ozone database. In the lowermost
tropical stratosphere, the CMIP6 database shows a positive SOR of up to ∼ 3 % in the southern tropics. This is
slightly larger than the SOR in the tropical lower stratosphere simulated by the CCMI-1 versions of the two CCMs used to
produce the CMIP6 ozone database (CESM1(WACCM) and CMAM) (see Fig. b and c). To further investigate the vertical
structure of the tropical SOR and its uncertainties, Fig. shows the best-estimate tropical
(30∘ S–30∘ N) mean SOR along with the 2.5–97.5 % confidence intervals for the climate model ozone
databases and the two satellite datasets from Fig. (see Part 1). Also shown in grey shading is the range of the
best-estimate SORs from the eight CCMI-1 models. The best-estimate SOR in the tropical lower stratosphere ranges from
a small negative signal in the CMIP5 ozone database to +6 % in the Bodeker ozone database. In the CMIP6 ozone
database, the best-estimate tropical SOR is 2 % at 80 hPa, which is, as expected, within the range of the signals in
the CCMI-1 models. The substantial spread amongst the estimates along with the large uncertainties reinforces the
challenge of constraining the SOR in the tropical lower stratosphere (e.g. ). Despite the relatively
large uncertainties, the SOR in the tropical lower stratosphere is larger in the CMIP6 database than in CMIP5; this
may be important for the modelled atmospheric response to solar variability in CMIP5 and CMIP6 models (see
Sect. ). Figure further confirms that the two climate model ozone databases that include
SAGE II v6.2 mixing ratio data (CMIP5 and Bodeker) show a significantly stronger SOR in the tropical upper
stratosphere. This is likely to be unrealistically large since the updated SAGE II v7.0 mixing ratio data, which show
a smaller SOR in the tropical upper stratosphere , exhibit a more realistic representation of the
relationship between upper-stratospheric ozone and temperature than the v6.2 data .
Monthly mean percent (%) ozone
anomalies per 130 SFU for (a) January to (l) December in
the Extended CMIP5 ozone database. The solid contours denote 2 %
intervals.
Monthly mean percent (%) ozone anomalies per 130 SFU for (a) January to (l) December
in the CMIP6 ozone database. The solid contours denote 2 % intervals. Hatching denotes regions where the regression coefficients are not significantly different
from zero at the 95 % confidence level.
Average tropical (30∘ S–30∘ N) solar cycle
(max–min) temperature anomalies as simulated by ECHAM6.3. Anomalies have been
calculated between four sensitivity experiments representing different solar
maximum conditions and a reference experiment representing solar minimum
conditions. The sensitivity experiments are performed by prescribing (red
solid) SOR from the recommended Extended CMIP5 ozone database; (red dashed) recommended
SOR and solar spectral irradiance anomalies for CMIP5; (blue solid)
historical SOR from the recommended CMIP6 ozone database; and (blue dashed)
recommended SOR and solar spectral irradiance anomalies for CMIP6. The shaded
regions denote 2.5–97.5 % confidence intervals on the combined forcing
responses.
Comparison of SOR annual cycle in CMIP5 and CMIP6 ozone databases
Earlier studies have shown evidence for an annual cycle in the structure and amplitude of the SOR in satellite
observations (e.g. ). Figure shows the monthly mean SOR in the Extended CMIP5 ozone
database, and Fig. shows the same for the CMIP6 ozone database. The SOR in the CMIP5 database has a fixed
structure and constant amplitude in all months; the small annual cycle in the fractional SOR amplitude arises purely from
the annual cycle in background ozone concentrations. There are well-understood photochemical arguments for why the
structure of the SOR is expected to track the position of the Sun through the year . Furthermore, the
coupling between ozone and stratospheric dynamics may lead to variations in the SOR at high latitudes in some months due
to the formation in winter of the polar vortices and their subsequent break-up in spring . For these reasons
a complete absence of seasonal variation in the SOR as found in the CMIP5 ozone database is unrealistic. In contrast, the
SOR in the CMIP6 ozone database, Fig. , shows greater seasonal variation. Locally enhanced signals in the SOR
are found in the high latitudes in winter and spring, which may be linked to variations in the strength of the polar
vortex . Thus, including some semblance of an annual cycle in the SOR, as seen in Fig. , is
likely to be a truer reflection of the behaviour of the real atmosphere than the complete absence of an SOR annual cycle
as in Fig. . However, the associated uncertainties in the monthly SORs are larger than the annual mean
results presented in the previous section, and there are quantitative differences between the SOR annual cycle in the
CMIP6 ozone database and that estimated from satellite observations (cf. e.g. Fig. 13 of ). Such
differences may result from uncertainties in estimating the SOR from relatively short observational records, from errors
in the representation of the SOR in the models used to construct the CMIP6 ozone database or a combination of
factors. Thus we should not consider the evolution of the monthly SOR in the CMIP6 ozone database as a precise
representation of the true SOR, but it is likely an improvement compared to the representation in the CMIP5 ozone
database.
Atmospheric impact of change in SOR between CMIP5 and CMIP6 ozone databases
We now explore the atmospheric impacts of the differences between the SOR in the CMIP5 and CMIP6 ozone databases using the
ECHAM6.3 model sensitivity experiments described in Sect. . Figure shows the tropical average
annual mean temperature differences in the four perturbation experiments representing 11-year solar cycle maximum
conditions with respect to the control solar minimum simulation. Note that the tropospheric temperature
responses in all simulations are small because the model includes fixed SSTs, and therefore the troposphere does not fully
adjust to the imposed solar forcing (e.g. ).
The experiments performed to capture the total (i.e. SSI + SOR) solar cycle impact (dashed lines) show considerable
differences in the tropical mean stratospheric temperature response between the recommended CMIP5 (red line) and CMIP6
(blue line) solar forcings. In the CMIP5 case, the maximum temperature response is around 1.25 K near the stratopause,
which can be compared to a much smaller response to the CMIP6 solar forcing inputs of 0.8 K. The SOR-only sensitivity
experiments (solid lines) reveal that much of the difference in the total temperature response can be attributed to the
differences in the SOR between the CMIP5 and CMIP6 ozone databases. The SOR in the Extended CMIP5 ozone database induces
a peak tropical temperature response of 0.85 K (solid red), which is nearly 3 times larger than the maximum response
to the SOR in the CMIP6 ozone database with an amplitude of 0.3 K (solid blue). In addition to the marked differences in
the maximum temperature response, there are also distinct differences in vertical structure. In the CMIP5 case, there is
a stronger vertical gradient in the temperature response to the imposed solar forcing, which can be attributed to the
highly peaked structure of the SOR in the CMIP5 ozone database at the stratopause compared to the smoother vertical structure of
the SOR in the CMIP6 database (cf. Fig. c and d). The simulation forced with the SOR from the
CMIP6 ozone database also shows a small secondary peak in tropical lower-stratospheric temperature of ∼ 0.3 K due
to the presence of a locally enhanced SOR of ∼ 3 %, which is not present in the CMIP5 ozone database. The
results show that the change in the representation of the SOR between the recommended CMIP5 and CMIP6 ozone databases
induces a much larger difference in the stratospheric temperature response between solar cycle minimum and maximum than do changes in
the recommended SSI forcing (see also Fig. 8 in ).
The ECHAM6.3 model results help to elucidate the findings of , which show a clear difference in the
annual mean stratospheric temperature response to the solar cycle between CMIP5 models that used the CMIP5 ozone database
(HadGEM2-CC, MPI-ESM, CMCC) and those with interactive chemistry that simulated their own internally consistent SOR
(CESM1(WACCM), GFDL-CM3, GISS-E2-H, MIROC-ESM-CHEM, MRI-ESM1). Specifically, models that used the CMIP5 ozone database
exhibit a markedly larger temperature response near the tropical stratopause, with a stronger vertical gradient, than
the models with interactive chemistry (see Fig. 5 in ). One might therefore anticipate that the
difference in the stratospheric temperature response to the solar cycle between models with and without
interactive chemistry will be smaller in CMIP6 than was found in CMIP5 owing to the fact that the SOR in the CMIP6 ozone
database is derived from CCM simulations, albeit without full consistency with the other CMIP6 external forcings such as
SSI.
Conclusions
Changes in stratospheric ozone concentrations constitute an important part of the atmospheric response to variations in incoming solar
radiation over the 11-year solar cycle (e.g. ). The associated solar–ozone response (SOR)
must therefore be included in global model simulations to realistically capture the effects of solar variability on the
atmosphere.
This study has used a multiple linear regression (MLR) model to analyse the SOR in current satellite observations (Part 1;
) and in global models (Part 2). In the present Part 2, the SOR is analysed in eight chemistry–climate
models (CCMs) from the CCMI-1 project: CCSRNIES-MIROC3.2, CESM1(WACCM), CMAM, CNRM-CM5-3, EMAC(L90), LMDz-REPROBUS-CM5,
MRI-ESM1r1 and SOCOL3. These analyses complement earlier studies assessing the SOR in previous generations of CCMs
(e.g. ). In a novel step, we also analyse and compare the SORs in three pre-calculated ozone
databases that are prescribed in climate models without interactive chemistry: the Tier 1.4 ozone database
and the CMIP5 ozone database , which are both based on regression models fit to ozone measurements, and the
CMIP6 ozone database, which is created from simulations from two CCMs (CESM1(WACCM) and CMAM).
The CCMI-1 models simulate an annual mean SOR with a peak amplitude of 1–2 % in the upper stratosphere
(∼ 3–5 hPa). This is more than a factor of 2 smaller than the SOR found in SAGE II v6.2 mixing ratio data and is
more consistent with results from SAGE II v7.0 and SBUV satellite datasets and with previous CCM
studies (e.g. ). Many of the CCMI-1 models
show larger fractional monthly SORs in the high latitudes during winter and spring, which are likely to be strongly
coupled to dynamical processes such as the formation and evolution of the polar vortex. The spread in the best-estimate
SOR across the CCMI-1 models is around 4 times larger in the tropical lower stratosphere than in the middle and upper
stratosphere, and the statistical uncertainties in the SOR are also substantially larger in the lower stratosphere.
There are strong differences in the representations of the SOR in the pre-calculated ozone databases. The peak amplitude
of the SOR in the tropics in the CMIP5 and Bodeker ozone databases is substantially larger (5 %) than in the CMIP6
database (1.5–2 %). This is because the former databases are derived from observations that include SAGE II v6.2 mixing
ratios, which exhibit a larger SOR than found in other satellite ozone datasets (see Part 1). In contrast, the CMIP6 ozone
database was constructed from CCM simulations, and thus its SOR generally resembles the CCMI-1 models in
terms of both its broad structure and magnitude and the fact that it exhibits some variation over the annual cycle. Note that
the amplitude of the SOR in the CMIP6 ozone database may have been slightly larger if both of the constituent CCMs had
used the CMIP6 SSI forcing rather than the NRLSSI-1 forcing from CCMI-1 . The CMIP5 database exhibits
spurious sharp horizontal gradients in the SOR across the extratropics, which were partly alleviated by a simple poleward
extrapolation in the Extended CMIP5 ozone database, albeit with considerable uncertainties in the detailed spatial
structure. Furthermore, the CMIP5 and Extended CMIP5 ozone databases include a fixed SOR throughout the year, which is
unrealistic.
Sensitivity experiments were performed using a comprehensive global atmospheric model without chemistry (ECHAM6.3) to test
how the changes in the recommended SOR and SSI between CMIP5 and CMIP6 affect the simulated annual mean temperature
response over the 11-year solar cycle. The larger amplitude of the SOR in the CMIP5
ozone database compared to CMIP6 causes a larger tropical stratospheric temperature response between 11-year solar
cycle minimum and maximum by up to 0.55 K, or around 80 % of the total amplitude.
This impact on the simulated stratospheric temperature response over the solar cycle is many times larger
than the separate impact (i.e. without ozone feedbacks) of changes in the recommended SSI forcing between CMIP5 and
CMIP6. The results indicate that differences in the representation of the SOR amongst CMIP5 models are likely to be a major
explanatory factor for the large spread in the stratospheric temperature responses to the solar cycle found in CMIP5
models . The broader relevance of different representations of the SOR for atmospheric dynamics and
regional surface climate responses to the solar cycle remains to be explored. However, suggested CMIP5
models with an interactive representation of the SOR showed a stronger high latitude dynamical response to the solar
cycle.
Parts 1 and 2 of this study have shown that uncertainties remain in understanding the SOR, which present a challenge for
including these effects in model simulations. However, given the inclusion of variations in the SOR over the annual cycle,
as well as the greater consistency of the amplitude of the SOR with CCM results, CMIP6 models without chemistry are
encouraged to use the recommended CMIP6 ozone database. The CMIP6 solar–ozone coefficients are published with this paper
(10.5518/348) and have already been used in other modelling projects such as PMIP4 . Nevertheless,
whatever approach is employed, all CMIP6 modelling groups are encouraged to document the representation of the SOR and SSI
in their simulations to facilitate future analysis of solar–climate impacts.