ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-7625-2018On ozone trend detection: using coupled chemistry–climate simulations
to investigate early signs of total column ozone recoveryInvestigating early signs of total column ozone recoveryKeebleJamesjames.keeble@atm.ch.cam.ac.ukhttps://orcid.org/0000-0003-2714-1084BrownHannahAbrahamN. Lukehttps://orcid.org/0000-0003-3750-3544HarrisNeil R. P.https://orcid.org/0000-0003-1256-3006PyleJohn A.Department of Chemistry, University of Cambridge, Cambridge, UKNational Centre for Atmospheric Science, Cambridge, UKCentre for Environmental and Agricultural Informatics, Cranfield
University, Cranfield, UKJames Keeble (james.keeble@atm.ch.cam.ac.uk)1June201818107625763720December201722December201727April20189May2018This 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/18/7625/2018/acp-18-7625-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/7625/2018/acp-18-7625-2018.pdf
Total column ozone values from an ensemble of UM-UKCA model simulations are
examined to investigate different definitions of progress on the road to
ozone recovery. The impacts of modelled internal atmospheric variability are
accounted for by applying a multiple linear regression model to modelled
total column ozone values, and ozone trend analysis is performed on the
resulting ozone residuals. Three definitions of recovery are investigated:
(i) a slowed rate of decline and the date of minimum column ozone, (ii) the
identification of significant positive trends and (iii) a return to historic
values. A return to past thresholds is the last state to be achieved. Minimum
column ozone values, averaged from 60∘ S to 60∘ N, occur
between 1990 and 1995 for each ensemble member, driven in part by the solar
minimum conditions during the 1990s. When natural cycles are accounted for,
identification of the year of minimum ozone in the resulting ozone residuals
is uncertain, with minimum values for each ensemble member occurring at
different times between 1992 and 2000. As a result of this large variability,
identification of the date of minimum ozone constitutes a poor measure of
ozone recovery. Trends for the 2000–2017 period are positive at most
latitudes and are statistically significant in the mid-latitudes in both
hemispheres when natural cycles are accounted for. This significance results
largely from the large sample size of the multi-member ensemble. Significant
trends cannot be identified by 2017 at the highest latitudes, due to the
large interannual variability in the data, nor in the tropics, due to the
small trend magnitude, although it is projected that significant trends may
be identified in these regions soon thereafter. While significant positive
trends in total column ozone could be identified at all latitudes by
∼ 2030, column ozone values which are lower than the 1980 annual mean
can occur in the mid-latitudes until ∼ 2050, and in the tropics and
high latitudes deep into the second half of the 21st century.
Introduction
The year 2017 marked the 30th anniversary of the Montreal Protocol, which was
implemented to protect the stratospheric ozone layer from the harmful effects
of ozone depleting substances (ODSs). These gases, mostly inert in the
troposphere, breakdown when they reached the stratosphere, with the
subsequent products then leading to chemical ozone depletion (e.g. Molina and
Rowland, 1974; Stolarski and Cicerone, 1974; Rowland and Molina, 1975).
Controls introduced under the Montreal Protocol and its subsequent amendments
first slowed the rate of accumulation of these halogenated ODSs in the
atmosphere, and since the late 1990s their atmospheric concentrations have
begun to decline (Newman et al., 2006; Mäder et al., 2010; WMO, 2011;
2014). A reduction in equivalent stratospheric chlorine (ESC; Eyring et al.,
2007) concentrations should lead to an increase in atmospheric ozone as the
strength of the halogen catalysed ozone destruction cycles declines. However,
detecting recovery of the stratospheric ozone layer is complicated by a
number of additional factors which affect the year-to-year variability in
total column ozone values. These factors include volcanic eruptions, such as
the eruption of Mt. Pinatubo in 1991 (e.g. Randel et al., 1995; Telford et
al., 2009), changes in the solar cycle (e.g. Brasseur, 1993; van Loon and
Labitzke, 2000; Austin et al., 2007; Calisesi and Matthes, 2007) and
variability in ozone resulting from a range of factors affecting dynamical
variability, including the quasi-biennial oscillation (QBO; e.g.
Hollandsworth et al., 1995; Baldwin et al., 2001; Leblanc and McDermid, 2001)
and variations in sea surface temperatures, particularly those related to the
El Niño–Southern Oscillation (ENSO; e.g. Braesicke and Pyle, 2004;
Manzini, 2009; Randel et al., 2009). In addition, long-term total column
ozone trends are driven in part by emissions of other non-chlorinated
anthropogenic species, such as CO2, CH4 and N2O, which affect
stratospheric ozone concentrations by altering stratospheric temperatures and
dynamics (Haigh and Pyle, 1982; Avallone and Prather, 1996; Plumb, 1996;
Eyring et al., 2010, 2013; Iglesias-Suarez et al., 2016), and in the case of
CH4 and N2O by acting as source gases for reactive HOx and
NOx species (Chipperfield and Feng, 2003; Ravishankara et al., 2009;
Revell et al., 2012; Meul et al., 2014). Identification of significant trends
is also made problematic by the difference in year-to-year variability in
total column ozone values in different regions. For example, high northern
latitudes exhibit very large interannual variability in winter and spring,
while variability in the Southern Hemisphere is comparatively smaller.
Furthermore, there is a dynamical response to changes in chemical ozone
depletion in the stratosphere, which may enhance or impede future recovery by
altering the transport of ozone (e.g. McLandress et al., 2011; Braesicke et
al., 2013; Keeble et al., 2014). In comparison, the chemical ozone depletion
signal in the tropics is small and total column ozone variability is
dominated by features such as the solar cycle, QBO and ENSO. As a result of
all of these factors, identifying robust recovery of total column ozone and
ascribing that recovery to a decline in stratospheric halogen species is a
complex issue.
For past trends, recovery of the stratospheric ozone layer could be detected
using two different methodologies: process-oriented studies and statistical
analysis of datasets. For the first, observations can be compared with a
detailed chemistry-transport model which includes all known processes. If
good agreement is found between the model and observations when all processes
are included, then evidence of ozone recovery due to decreasing stratospheric
halogen loadings can be identified by excluding other processes. For example,
Solomon et al. (2016) found evidence for healing of the Antarctic ozone layer
in September when polar halogen chemistry is included but interannual
dynamical variability and volcanic factors are excluded. For the second
method, a statistical approach can be followed in which data are used to
detect significant changes between time periods. The impact of confounding
changes (QBO, solar cycle, etc.) can be quantified using multiple
linear regression and removed from the statistical analysis of the data in
order to provide a better estimate of long-term trends (e.g. Staehelin et
al., 2001; Reinsel et al., 2005; WMO, 2007; Harris et al., 2015; Chipperfield
et al., 2017). These statistical approaches rely on the assumption of a
linear relationship between a proxy variable and its impact on total ozone.
Using this method, a number of recent studies have started to explore if
observed total column ozone and ozone profile values show signs of recovery
(e.g. Pawson et al., 2014; Harris et al., 2015; Steinbrecht et al., 2017;
Ball et al., 2018; Weber et al., 2018). These studies have indicated that
statistically significant recovery of column ozone values can be identified
in some datasets at some latitudes, but that this is not true for all
datasets (e.g. Weber et al., 2018). As recovery trends are calculated over
relatively short time frames (< 20 years), identification of trend
magnitude and trend significance from observations can be affected by high or
low values at the beginning or end of the observational record (compare, for
example, the trends derived by Pawson et al., 2014, with those of Weber et
al., 2018).
To explore future ozone trends and recovery, data from coupled
chemistry–climate model (CCM) simulations are required. Each CCM simulation
constitutes a possible future evolution of stratospheric ozone. In order to
sample the effect of internal atmospheric variability on ozone and to derive
an estimate of uncertainty in future trends, multiple ensemble members can be
run in which the initial conditions of each simulation are modified but the
same forcings are prescribed (e.g. GHG – greenhouse gas, evolution, aerosol
loadings). Greater confidence can be assigned to significance of the mean
trend as the number of ensemble members increases. Multiple ensemble members
also give information about the possible range of future trends and as a
result are not as sensitive to high or low values at the beginning or end of
the record of any individual ensemble member, in contrast to single member
simulations and observational records. Thus, using an ensemble of future
projections from a single CCM (here UM-UKCA) can provide additional insight
into the detection of different phases of ozone recovery.
In this study, we use results from a chemistry-climate model coupled with
statistical approaches to explore different definitions of ozone recovery
(see Reinsel et al., 2005; Weatherhead and Andersen, 2006; Chipperfield et
al., 2017). In particular we define three stages of total column ozone
recovery:
A reduced rate of decline in ozone and the date of minimum ozone.
Statistically significant increases in column ozone values, after accounting
for natural variability, that can be ascribed to reductions in ESC.
Return of total column ozone values to some specified past value (typically
1980 or 1960).
Identifying when and if each of these stages has occurred at different
latitudes, and being able to assess the confidence with which this can be
done, is fundamental to determining the success of the Montreal Protocol.
For this work we use the ozone fields calculated in an ensemble of UM-UKCA
transient simulations, which are described in Sect. 2. We carry out a
statistical analysis of the model results, as outlined in Sect. 3, to
identify when each of these stages of recovery occurs for different latitude
ranges. These results are presented in Sects. 4, 5 and 6 and implications
are discussed in Sect. 7.
Model configuration and simulations
An ensemble of transient simulations was performed using version 7.3 of the
HadGEM3-A configuration of the Met Office's Unified Model (Hewitt et al.,
2011) coupled with the United Kingdom Chemistry and Aerosol scheme (hereafter
referred to as UM-UKCA). This configuration of the model has a horizontal
resolution of 2.5∘ latitude by 3.75∘ longitude, with 60
vertical levels following a hybrid sigma-geometric height coordinate with a
model top at 84 km. The chemical scheme used in this configuration of the
model is an expansion of the scheme presented in Morgenstern et al. (2009) in
which halogen source gases are considered explicitly, resulting in an
additional 9 species, 17 bimolecular and 9 photolytic reactions.
Stratospheric aerosol concentrations are prescribed using a climatology based
on observations (from SPARC, 2006; described by Eyring et al., 2008) for the
historical part of the run, after which background concentrations of
stratospheric aerosol loadings are prescribed. HadGEM3-A includes an
internally generated quasi-biennial oscillation (QBO), which in this
configuration of the model has a period of ∼ 27 months while the
magnitude of modelled easterly (westerly) equatorial zonal wind speed is
∼ 25 m s-1 (10 m s-1), both aspects in good agreement
with observed zonal winds at Singapore (e.g. Lee and Smith, 2003). The
configuration of the model used for this study includes the effects of the
11-year solar cycle in both the radiation and photolysis schemes. The top of
atmosphere solar flux follows historical observations from 1960 to 2009,
after which a repeating solar cycle is imposed which is an amplitude
equivalent to the observed cycle 23 (as detailed in Bednarz et al., 2016).
The transient simulations were performed following the experimental design of
the WCRP/SPARC CCMI REF-C2 experiment (Eyring et al., 2013), which adopts the
RCP6.0 scenario for future GHG and ODS emissions. Two of these ensemble
members were run from 1960 to 2099 and an additional five were run from 1980
to 2080. All ensemble members have identical time-dependent boundary
conditions, but differ in their atmospheric initial conditions, thereby
providing an estimate of internal atmospheric variability. The simulations
were performed in an atmosphere-only configuration, and each ensemble member
uses prescribed sea surface temperatures and sea ice fields taken from a
parent coupled atmosphere–ocean HadGEM2-ES simulation as lower boundary
conditions. The simulations used for this study are described in more detail
in Bednarz et al. (2016) and Keeble et al. (2017), and were performed in
support of phase 1 of the Chemistry–Climate Model Initiative (CCMI;
Morgenstern et al., 2017).
Deseasonalised total column ozone anomalies (in DU)
relative to the 1980 mean, averaged over 60∘ S–60∘ N, for
the seven UM-UKCA transient ensemble members (light blue lines) and ensemble
mean (dark blue line). Also shown are the ozone residuals calculated when
natural cycles are removed from each ensemble member (light red lines) and
the mean of the ozone residuals (dark red line). The inset shows total column
ozone anomalies for the transient UM-UKCA simulations and v2.8 of the Bodeker
dataset (Bodeker et al., 2005; black line) from 1975 to 2015.
Removing natural cycles
Identifying an increase in total column ozone resulting from reductions in
stratospheric chlorine requires removing the effects of natural processes
(such as volcanic eruptions, the QBO, ENSO and solar cycle) from the modelled
total column ozone data, as these cycles may impose short-term trends in the
data which are wrongly interpreted as signs of recovery. In order to identify
the impacts of these natural processes on modelled total column ozone we
create a statistical model using multiple linear regression (MLR) analysis.
This process assumes that the modelled total column ozone values, TO3, can be
reproduced by combining some constant value of total column ozone, i, which
corresponds to the intercept term of the MLR, with a number of explanatory,
or predictor, variables. This statistical model can be expressed as
TO3e,l,t=ie,l+αe,lQBO50QBO50e,t+αe,lQBO30QBO30e,t+αe,lsolarsolart+αe,lENSOENSOt+αe,laerosolaerosolt+αe,lESCESCe,l,t+Ne,l,t,
in which the α values are the coefficients returned from the MLR for
each explanatory variable (denoted by the superscript) and vary between
latitude range and ensemble member. The explanatory variables included in the
MLR are the QBO, solar cycle, ENSO, volcanic aerosols and ESC. The subscripts
e, l and t indicate that the alpha value or explanatory variable differs
with ensemble member, latitude and/or time, respectively. For the QBO, two
terms are included, QBO50 and QBO30, which correspond to
equatorial westerly winds at 50 and 30 hPa, respectively. Two QBO terms are
included to account for the phase shift in the total column ozone response
with respect to QBO changes at different altitudes. The solar cycle is
represented by the top of atmosphere solar flux, represented in the MLR as
solart. ENSO effects on column ozone are represented by ENSOt,
the detrended sea surface temperature anomalies in the NINO3.4 region.
Volcanic aerosols are included as hemispheric aerosol optical depths, and so
are different for the Northern and Southern hemispheres to account for the
lack of interhemispheric transport of aerosols emitted into the stratosphere
from high-latitude eruptions. The final term included in the MLR, ESC,
represents stratospheric chlorine concentrations. This term is equal to the
ESC concentration at 30 km for each latitude bin to account for the time
taken for ODS to be transported to higher latitudes. Any month-to-month
variation not accounted for by the explanatory variables in the MLR is
represented by the noise term Ne,l,t. The Solart,
ENSOt and aerosolt terms are all prescribed forcings in the
model and do not vary between ensemble members. In this study we use
deseasonalised monthly mean total column data, and so there is no need for a
seasonal cycle term in the MLR model.
This statistical model can then be used to remove the component of total
column ozone variations related to the QBO, solar cycle and volcanic aerosol
changes from the raw model data to leave a set of ozone residuals, RO3,
which retain the long-term trend and any interannual variability not
explained by the MLR:
RO3e,l,t=TO3e,l,t-αe,lQBO50⋅QBO50e,t+αe,lQBO30⋅QBO30e,t+αe,lsolar⋅solart+αe,lENSO⋅ENSOt+αe,laerosol⋅aerosolt.
MLR analyses and ozone residuals are produced for each individual ensemble
member of the raw model data, resulting in seven RO3 time series. For both
the raw model data and the ozone residuals, decline and recovery trends are
calculated using independent linear trend fits for the periods 1980–1997 and
2000–2017, respectively. When calculating trends for both the raw model data
and ozone residuals, a single linear fit is produced using data from all
seven ensemble members rather than producing a fit for each individual
ensemble. In order to make any robust conclusions about the statistical
significance of modelled trends, some measure of the trend uncertainty is
required. Here we calculate trend uncertainties following the methodology of
Weatherhead et al. (1998), in which the standard deviation of the uncertainty
in the linear trend is calculated by
σtrend=σdatan3/21+φ1-φ,
where σdata is the standard deviation of the time series
in question (either raw model data or RO3), n is the number of
months in the time series and φ is the autocorrelation coefficient
for a 1-month lag. As discussed by Weatherhead et al. (1998), autocorrelation
can be substantial for monthly mean time series, particularly in low
latitudes, and failure to account for it results in an underrepresentation of
trend uncertainty. As for the trend calculations, trend uncertainties are
calculated using data from all seven ensemble members together, and as such
for the 17-year periods considered for the decline and recovery phases n is
very large (n=17× 12 ×7=1428).
Modelled global column ozone and minimum values
Figure 1 shows deseasonalised monthly mean total column ozone anomalies
relative to 1980 values, averaged over 60∘ S–60∘ N, from
1960 to 2100 for each individual ensemble member (light blue lines) and the
ensemble mean (dark blue line). A sharp decrease in total column ozone is
modelled from 1980 to the late 1990s, consistent with increased ESC loadings
resulting from the use and emission of ODSs. From the late 1990s until
∼ 2070 column ozone values gradually increase, exceeding their 1980s
values by ∼ 2030, and their 1960s values by ∼ 2050. Beyond 2070
total column ozone values remain relatively constant until the end of the
century. Superimposed on these long-term trends is the effect of the solar
cycle, which imprints a distinctive 11-year oscillation in the data.
Alongside the modelled total column ozone anomalies are shown values from
version 2.8 of the Bodeker Scientific total-column ozone dataset in black
(Bodeker et al., 2005). There is generally good agreement between modelled
total column ozone anomaly values and the Bodeker dataset; decadal total
column ozone changes, the response of column ozone to the solar cycle and the
magnitude of interannual variability are all well captured by the model
ensemble throughout the time period during which the observations and model
data overlap.
Also shown in Fig. 1 are the ozone residuals calculated when the effects of
natural cycles are removed, as detailed in Sect. 3 (red lines). This dataset
follows the long-term trends of the raw UM-UKCA data, but the cyclic short-term
trends in column ozone values have been removed. Most obvious from
Fig. 1 is the removal of the 11-year solar cycle signal, leading to a much
smoother, monotonically increasing trend from 2000 to 2060 compared to the
raw model data.
As discussed above, the first signs of detectable ozone recovery would be
identified as a reduced rate of decline in column ozone and the date of
minimum ozone. Modelled total column ozone values generally decrease from
1980 to the late 1990s (blue line, Fig. 1), consistent with the increase in
ESC amounts. However, this decrease is not constant; rapid decline is
modelled from 1980 to 1985 and from 1990 to 1995, while between these periods
total column ozone abundances are relatively constant, or even increase (see
inset in Fig. 1). This feature is also seen in the Bodeker dataset, and
predominantly results from the impact of the solar cycle on stratospheric
ozone concentrations. As top of atmosphere solar flux decreases from solar
maximum to solar minimum, a rapid decline of total column ozone occurs as this
effect combines with the impacts of increasing ESC. Conversely, as top of
atmosphere solar flux increases, an enhanced stratospheric ozone production
temporarily offsets the chemical ozone destruction resulting from increased
ESC concentrations. This is confirmed by analysing the ozone residuals (red
line Fig. 1), which show a much smoother decline from 1980 to the late 1990s,
and highlights the importance of understanding the drivers of short-term
trends in raw total column ozone values when trying to assess longer-term
trends.
Ozone trends from 1980 to 1997 (blue points) and 2000–2017 (red
points) for the raw UM-UKCA data (dark points) and ozone residuals calculated
when the effects of natural cycles are removed (light points). Error bars
associated with each trend represent the 95 % confidence intervals,
calculated as described in Sect. 3.
As well as influencing the trajectory of declining column ozone abundances,
natural cycles also affect the timing and magnitude of minimum total column
ozone values. In the raw model data, the minimum total column ozone values
averaged over 60∘ S–60∘ N are reached between 1992 and 1994,
depending on the ensemble member, which is several years before the peak
loading of ESC in 1997 (e.g. Mäder et al., 2010; WMO, 2011, 2014). This
offset in timing between peak ESC and total column ozone minimum results from
the impact of the solar cycle, as discussed above, and the eruption of
Mt. Pinatubo on total column ozone. The early 1990s was a time of low top of
atmosphere solar flux, while the eruption of Mt. Pinatubo increased
stratospheric sulphate surface area density, both reducing total column ozone
abundances. When the effects of these natural cycles are removed, ozone
residuals (red line Fig. 1) are larger than modelled total column ozone
values throughout the early 1990s.
Although this work indicates minimum column ozone values occurred in the
1990s, this is a poor metric for making robust conclusions about ozone
recovery. Firstly, the ozone minimum may occur because there is no more
capacity for increased chemical depletion despite increasing ESC. This is the
case over Antarctica during springtime during the 1990s, where near-complete
destruction of polar lower stratospheric ozone occurs and any additional
increase in ESC would have a negligible effect. Secondly, minimum column
ozone values are very sensitive to dynamical conditions. For example, Bednarz
et al. (2016) have shown that even under much lower stratospheric halogen
loadings significant ozone depletion can occur in the Arctic lower
stratosphere during conditions which favour a cold, stable polar vortex. Even
outside the high latitudes, where interannual variability in total column
ozone values is largest, identification of the year of minimum ozone is
uncertain, with each of the seven residual ozone time series having minimum
values at different times between 1992 and 2000 (light red lines, Fig. 1).
Regional trends
The decline and subsequent recovery of total column ozone is often calculated
using linear trends for two periods either side of an inflection time (e.g.
Newchurch et al., 2003; Reinsel et al., 2005; Jones et al., 2009; Nair et
al., 2013; Chehade et al., 2014). Previous studies have identified 1997 as
the inflection time for long-term total column ozone observed trends (e.g.
Harris et al., 2008), and as a result we define the decline phase as
1980–1997 with the recovery phase defined from 2000 to 2017. Here we calculate
independent linear trends for both the decline and recovery phases firstly
using the raw total column ozone data from the UM-UKCA model (discussed
below) and then using model data in which the effects of the natural
processes discussed above have been removed using the statistical model
introduced in Sect. 3.
Figure 2 shows total column ozone trends (in DU yr-1) obtained from
both the raw data from the UM-UKCA simulation and the ozone residuals for the
decline (1980–1997) and recovery (2000–2017) phases, averaged over
10∘ latitude bands. Error bars associated with each trend represent
the 95 % confidence intervals (2σtrend), calculated as
described in Sect. 3. During the decline phase, ozone trends for both the raw
model data and ozone residuals are greatest at high latitudes due to the
heterogeneous activation of chlorine on polar stratospheric clouds (PSCs) within the polar vortex and the
transport of mid-latitude ozone depletion signals to high latitudes by the
Brewer–Dobson circulation (BDC). The uncertainty associated with the trends is also largest at high
latitudes, due to the higher year-to-year variability in chemical and
dynamical processes at high latitudes compared with the tropics. Negative
trends in the raw column ozone data from 1980 to 1997 are significant at all
latitudes, although when natural cycles are removed the trends from
10∘ S–10∘ N are not significant. At all latitudes there is
a more negative trend in the raw UM-UKCA data compared with the dataset in
which the natural cycles have been removed. This is the result of the
eruption of Mt. Pinatubo and the pronounced solar minimum during the 1990s,
both of which resulted in lower column ozone values and so a greater trend
from 1980. This can be clearly seen in Fig. 1 by comparing the blue and red
lines. However, the close agreement in trends between the raw modelled values
and the ozone residuals calculated when natural cycles are accounted for
indicates that natural cycles have had only a small contribution to the
trends during the period 1980–1997 (consistent with the findings of Gillett
et al., 2011). Trends for the decline phase, calculated for both the raw
model data and ozone residuals, agree within the uncertainty estimates with
those obtained from observation datasets by Weber et al. (2018) and those
obtained from CCMVal-2 models (e.g. Pawson et al., 2014).
Year in which recovery trend of the ozone residuals becomes
significant. A distinction is made for the first time significance can be
determined (blue points) and the time after which trends remain significant
(red points). Here trend significance is calculated using the trend
uncertainty obtained when all ensemble members are used, as discussed in
Sect. 3, but the trend magnitude is calculated for each ensemble member
individually to reflect the impact of unaccounted for noise on the trend
magnitude. Error bars represent the 95 % confidence intervals, calculated
as twice the standard deviation of the seven values obtained for the year
that trend significance is identified (one for each ensemble member).
When considering the recovery phase, positive trends are modelled at all
latitudes from 2000 to 2017 for both the raw model data and ozone residuals.
For the raw model values, these trends are only significant at the 95 %
confidence interval in the Southern Hemisphere between 80 and 50∘ S.
However, when the effects of natural cycles are removed from the data,
significant positive trends can be identified in the Southern Hemisphere
between 80 and 30∘ S, and also in the Northern Hemisphere from
20–70∘ N. Significant trends cannot be identified by 2017 at the
highest latitudes, in either dataset, due to the large interannual
variability in the data, nor in the tropics due to the small trend magnitude.
As for the decline phase, while accounting for nature cycles in the ozone
residuals reduces the trend uncertainties, it does not significantly affect
the trend magnitudes, indicating that natural cycles do not significantly
contribute to recent increases in column ozone values.
Trends for the recovery phase, calculated for both the raw model data and
ozone residuals, are consistent with those calculated for observational
datasets by Chehade et al. (2014), Pawson et al. (2014) and Weber et
al. (2018), and agree with all three studies within trend uncertainty
estimates. In general, while in agreement with each of these studies, trends
presented here are larger than those presented by Weber et al. (2018), and
are closer in magnitude to the findings of Chehade et al. (2014) and Pawson
et al. (2014). The modelled trends presented here indicate, as Weber et
al. (2018) conclude, that the differences between the latest trends
calculated from observations by Weber et al. (2018) and the earlier trends
estimated by Pawson et al. (2014) result from lower than average column ozone
values at the end of the observational record, which are part of natural
interannual variability and likely do not represent some fundamental shift in
the trajectory of ozone recovery.
Identification of significant trends depends on the gradient of the trend,
the number of data points (in this case the number of modelled monthly means)
and the variance and autocorrelation of the data (e.g. Weatherhead et al.,
2000). Analysing the near-global (60∘ S–60∘ N) raw total
column ozone data (blue lines, Fig. 1), the year 2000 is a solar maximum year and
so total column ozone values are relatively high compared to the following
few years. It is not until ∼ 11 years later, during the next solar
maximum, that trends become positive. Trend analysis on data between 2000 and
2015 could indicate that there is a significant positive trend, which could
in turn lead to the conclusion that significant recovery of the ozone layer
had begun. However, as further years are considered, from 2015 to 2020, total
column ozone values start to decline as the solar cycle moves towards a solar
minimum, and the magnitude of the recovery trend is reduced while the
variance in the residuals increases. Now, trends calculated from 2000 to 2020
are no longer statistically significant. As a result, when assessing recovery
trends it is necessary to use datasets in which the effects of natural cycles
have been accounted for, such as the ozone residuals calculated in Sect. 3.
However, as the MLR analysis performed on the raw data does not capture the
full modelled interannual variability (hence the occurrence of the noise
term, Ne,l,t), trend magnitudes calculated using ozone
residuals from any individual ensemble member are similarly affected by
anomalously low or high values at the start or end of the time series. The
use of multiple member ensemble simulations in this study mitigates this
effect.
The impacts of the noise term on trend magnitudes for each of the individual
ensemble members is explored in more detail in Fig. 3, which shows the year
after 2000 in which trend significance can be identified in the ozone
residuals for either the first time (blue) or final time (red). The year a
trend becomes significant can be calculated for each ensemble member by
identifying the first month after January 2000 in which trends become
significant (initial recovery) and the month after which they remain
significant (robust recovery). Here trend significance is calculated using
the trend uncertainty obtained when all ensemble members are used, as
discussed above, but the trend magnitude is calculated for each ensemble
member individually so as to provide a range of dates that trend significance
can be identified. This range of dates reflects the impact of unaccounted for
noise on the trend magnitude, while maintaining significance thresholds which
are consistent between Figs. 2 and 3. Error bars on Fig. 3 represent the
95 % confidence intervals, calculated as twice the standard deviation of
the seven values obtained for the year trend significance is identified (one
for each ensemble member).
Year at which modelled total column ozone returns to 1980 annual
mean values in each latitude band for the raw data from the seven UM-UKCA
ensemble members. Blue points represent the first time annual mean values
exceed the 1980 mean, while red points represent the final time annual mean
values are lower than the 1980 mean. Error bars represent the 95 %
confidence intervals, calculated as twice the standard deviation of the
return dates calculated for each ensemble member.
Distinguishing between initial and robust recovery significance dates is
necessary since, as discussed above, trends can be significant after a number
of months and then become non-significant as more data are added so that the
variance in the data can increase or the magnitude of the trend can decrease.
The first instance of detection of significant trends can be considered as
false recovery if it does not coincide with the time after which trends never
become non-significant. Note that if the MLR described in Sect. 3 accurately
represented all drivers of interannual variability (i.e. the N term was
zero), there would be no distinction between initial and robust recovery.
For the ensemble of ozone residuals presented here, mid-latitude trends
become significant earlier than those of the tropics or high latitudes. This
is due to the high degree of interannual variability at high latitudes,
particularly in the Arctic, and the small magnitude of the trends in the
tropics. Therefore, it is likely that both initial and robust recovery will
first be observed in the mid-latitudes. In addition, both measures of
recovery occur at similar times, minimising the risk of identifying false
recovery. Correct identification of robust recovery is important when
considering observations of total column ozone and highlights the need to
treat detection of significant recovery for the first time with caution as
additional months or years of observational data may reduce the statistical
significance of any observed trends. It should be noted that no individual
ensemble member shows statistically significant recovery by 2017, and only
when considering data from all the ensembles together when calculating trend
uncertainties can significant trends be identified.
Once the difference in projected recovery trends is accounted for, these
findings are consistent with those of Weatherhead et al. (2000), who also
identified the mid-latitudes as the best location to identify early signs of
ozone recovery. However, there is an offset in when trends are expected to
become significant between the two studies, with projected detection years
modelled in this study generally occurring earlier than those of Weatherhead
et al. (2000), particularly in the tropics. This discrepancy in the tropics
is unsurprising, as recovery of the tropical ozone column is dependent on the
competing influences of declining CFCs, decreasing stratospheric temperatures
and changing BDC speeds (e.g. see Eyring et al., 2013; Meul et al., 2016;
Keeble et al., 2017), with increases to the BDC offsetting ozone recovery in
the lower stratosphere and resulting in smaller column ozone recovery trends.
There is poor agreement in modelled projections of future BDC speeds, and as
a result projections of tropical column ozone differ significantly between
models (e.g. WMO, 2011). In addition, ozone depletion in the tropics
resulting from increasing ESC concentrations is weak in comparison to the
mid- and high latitudes, and as a result identifying significant recovery trends
is particularly sensitive to any interannual variability not accounted for in
the MLR, which may differ between models.
Return to historic values
While identification of statistically significant increases in total column
ozone is a real sign that ozone recovery is occurring, recovery can be said
to be complete when column ozone values reach their pre-CFC values again.
Traditionally these return thresholds are taken to be either 1980 or 1960
values; here we use 1980. It is likely that future total column ozone values
will initially exceed the 1980s threshold and then fall below this value
again due to interannual variability and the effects of the solar cycle and
QBO. As a result two metrics are considered: the first time total column
ozone exceeds the 1980s threshold, and the last time total column abundances
are below the threshold. Between the two time periods total column ozone
values rise above and fall below the threshold.
Figure 4 shows the year in which raw total column ozone abundances return to
their 1980s values for the first time (blue) and final time (red) for each
10∘ latitude bin. In the tropics, total column ozone exceeds the
1980s threshold as early as 2000 as the amplitude in total column ozone
variations resulting from the solar cycle is greater than the decrease in
total column ozone resulting from ESC changes. However, despite this region
seeing the first values greater than those of the 1980s, it is the only
region in which total column ozone abundances are not greater than their
1980s values by the end of the simulation, consistent with other studies
(e.g. Eyring et al., 2013; Meul et al., 2016). This is due to decreasing
lower stratospheric ozone concentrations resulting from an acceleration of
the BDC under increased greenhouse gas concentrations offsetting increased
upper stratospheric ozone concentrations due to decreased ESC and increased
CO2 (explored in detail in Keeble et al., 2017).
In the Northern Hemisphere mid-latitudes earliest recovery occurs by
∼ 2020, while final recovery occurs by 2040. The closeness of these two
dates is due to the large trend to variability ratio in the mid-latitudes
compared to both the tropics and Arctic. The results are similar in the
Southern Hemisphere mid-latitudes, although both dates are delayed by around
10 years, most likely due to the effects of Antarctic polar ozone depletion
and transport of ozone-poor air masses into these latitudes upon the collapse
of the Antarctic polar vortex.
Earliest recovery to historic values at high southern latitudes occurs by
∼ 2040, with final recovery occurring by 2060. However, the signature
of this recovery is very sensitive to calendar month, and earlier signs of
recovery may be identified in certain months (e.g. September; Solomon et al.,
2016). Arctic column ozone exhibits high interannual variability, with values
exceeding the 1980s threshold as early as 2010. However, final recovery is
not projected until ∼ 2060.
The future evolution of total column ozone is dependent on the emissions
scenario considered, and the exact timings of recovery to historic values
will vary with changes to CO2, N2O and CH4 emissions as well
as ESC reductions. As a result, the expected return dates for each latitude
will evolve as we approach those dates, in line with our increased
understanding of the emissions pathway or if future emission controls come
into effect.
Discussion and conclusions
In this study we analyse total ozone values from an ensemble of UM-UKCA model
runs to investigate different definitions of progress on the road to ozone
recovery. In particular, we have investigated three definitions: (i) a slowed
rate of decline and the date of minimum ozone, (ii) the identification of
significant positive trends and (iii) a return to historic values. The
impacts of natural cycles on modelled internal atmospheric variability are
accounted for by applying a multiple linear regression model to modelled
total column ozone values. The use of multi-member CCM ensembles, in which
each simulation constitutes a possible future evolution of stratospheric
ozone, allows us to better account for the modelled internal atmospheric
variability not captured by the explanatory variables in the MLR, and so
provide greater confidence when assessing the statistical significance of
each definition.
The first and most obvious conclusion is that recovery can be identified in
the first two metrics before a return to past thresholds is achieved. For the
first definition of recovery, minimum total column ozone values averaged from
60∘ S–60∘ N occur between 1990 and 1995 for each ensemble
member, driven in part by the solar minimum conditions during the 1990s. When
natural cycles are accounted for, identification of the year of minimum ozone
in the resulting ozone residuals is uncertain, with minimum values for each
of the seven residual ozone time series occurring at different times between
1992 and 2000. As a result, identification of the date of minimum ozone
values is problematic and a poor measure of ozone recovery.
For the second definition of recovery, positive trends are modelled at all
latitudes from 2000 to 2017 for both the raw model data and ozone residuals. In
contrast to recent analysis of total ozone measurements (e.g. Chipperfield et
al., 2017; Weber et al., 2018), when the effects of natural cycles are
removed from the data, statistically significant positive trends can be
identified in the Southern Hemisphere between 80 and 30∘ S, and also in
the Northern Hemisphere from 20 to 70∘ N. This increased significance
results largely from the much larger sample size that arises in a
multi-member ensemble and the resulting reduction in the uncertainty
associated with the mean trend. Significant trends cannot be identified by
2017 at the highest latitudes, due to the large interannual variability in
the data, nor in the tropics, due to the small trend magnitude. It was found
that while accounting for the effects of natural cycles impacted trend
uncertainty estimates, it did not significantly affect trend magnitudes for
either the decline or recovery phases, indicating that natural cycles have
played only a minor role in recent trends, consistent with previous studies.
It is important to note that, while a statistically significant, positive
recovery trend could be calculated at a particular point of time, additional
years of data may lead to a reduced significance of trend, due to either a
decrease in the magnitude of the trend or an increase in interannual
variability. This effect results in a need to distinguish between initial
recovery (the time at which trends become significant for the first time) and
robust recovery (the time after which trends remain significant despite
adding further years). Accounting for this, we identify the mid-latitudes as
the best place to find early signs of ozone column recovery. This is due to
the combination of reasonably large trend magnitudes and comparably low
variability (especially in the Southern Hemisphere). Despite the large trend
magnitudes modelled in the high latitudes, interannual variability in these
regions resulting from both the large dynamic interannual variability and the
large changes in chemical ozone loss occurring from year to year is too large
for a statistically significant signal to be easily detected. In contrast,
the small trend magnitudes modelled in the tropics confound identification of
statistically significant ozone recovery.
For the third definition of recovery, a return to historic values, it was
found that, while robust recovery could be identified at all latitudes by
∼ 2030, column ozone values which are lower than the 1980 annual mean
can occur in the mid-latitudes until ∼ 2050, and in the tropics and
high latitudes deep into the
second half of the 21st century. While projected column ozone values for the
mid- and high latitudes reach a point after which they are never lower than
the 1980 annual mean, consistent with the projected super recovery of ozone,
column ozone values lower than the 1980 annual mean occur in the tropics
until the end of the period analysed in this study. This results in part from
the large amplitude ozone response to natural cycles, particularly the solar
cycle, and also the effects of increased BDC speeds offsetting column ozone
recovery resulting from decreased CFCs, as discussed by Keeble et al. (2017).
This work further highlights the need to ensure that the impacts of natural
cycles (e.g. solar cycle, QBO, ENSO) on total ozone are correctly described
when performing MLR analysis. This is a challenge because of a number of
factors. Firstly, the assumption in all MLR analysis of a linear
relationship between the proxy variables used and the impact on ozone is not
accurate, and there is growing evidence that these cycles are not isolated,
but interact with one another (e.g. White and Liu, 2008; Calvo et al., 2009;
Gray et al., 2010). Secondly, cycles with varying amplitudes (e.g. the solar
cycle, which shows differing top of atmosphere solar flux during the last
four solar maximums) or lengths (e.g. the QBO, the period of which may
change in the future and has recently been observed to undergo rapid,
non-periodic reversal) have different impacts on total column ozone which
makes accurate estimates of the coefficients for these variables in the MLR
harder to achieve. Finally, volcanic eruptions are particularly difficult to
account for in the MLR, both because of the infrequent, non-periodic timings
of eruptions and because eruptions have very different impacts on
stratospheric ozone when stratospheric ESC concentrations are high compared
to when ESC is low (e.g. Tie and Brasseur, 1995).
Our analysis has focused solely on interpreting the total ozone column
record. Many studies have recently examined the trends in the vertical
distribution of ozone since ESC maximised (e.g. Harris et al., 2015;
Steinbrecht et al., 2017; Ball et al., 2018). In these studies, factors such
as higher variability, greater uncertainties and poorer data quality add to
the uncertainty in detection of significant trends compared to the total
column. However, similar studies to this one using ensembles of model runs
could provide real insights into the issue, especially in the climatically
important lower stratosphere where ozone may still be decreasing (e.g. Ball
et al., 2018). As a result we recommend the use of multi-member ensemble
simulations, in conjunction with ongoing observational efforts, to better
identify signs of ozone recovery for both the total column and ozone
profiles.
Data from the two 1960–2100 transient simulations are
available as part of the CCMI initiative through BADC:
https://blogs.reading.ac.uk/ccmi/badc-data-access/. All further data
are available upon request.
The authors declare that they have no conflict of interest.
Acknowledgements
The research leading to these results has received funding from the European
Community's Seventh Framework Programme (FP7/2007-2013) under grant
agreement no. 603557 (StratoClim) and the European Research Council
through the ACCI project (project no. 267760). We thank NCAS-CMS for
modelling support. Model simulations have been performed using the ARCHER UK
National Supercomputing Service and MONSooN system, a collaborative facility
supplied under the Joint Weather and Climate Research Programme, which is a
strategic partnership between the UK Met Office and the Natural Environment
Research Council. We would like to thank Greg Bodeker of Bodeker Scientific,
funded by the New Zealand Deep South National Science Challenge, for
providing the combined total column ozone database.
Edited by: Farahnaz Khosrawi
Reviewed by: two anonymous referees
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