Oh et al.
Progress in Earth and Planetary Science(2022) 9:25
https://doi.org/10.1186/s40645-022-00485-4

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Impact of stratospheric ozone
on the subseasonal prediction in the southernhemisphere spring
Jiyoung Oh1,2, Seok‑Woo Son1* , Jung Choi1, Eun‑Pa Lim3, Chaim Garfinkel4, Harry Hendon5, Yoonjae Kim2 andHyun‑Suk Kang2

Abstract
Antarctic ozone has been regarded as a major driver of the Southern Hemisphere (SH) circulation change in the
recent past. Here, we show that Antarctic ozone can also affect the subseasonal-to-seasonal (S2S) prediction during
the SH spring. Its impact is quantified by conducting two reforecast experiments with the Global Seasonal Forecasting
System 5 (GloSea5). Both reforecasts are initialized on September 1st of each year from 2004 to 2020 but with different
stratospheric ozone: one with climatological ozone and the other with year-to-year varying ozone. The reforecast withclimatological ozone, which is common in the operational S2S prediction, shows the skill re-emergence in Octoberafter a couple of weeks of no prediction skill in the troposphere. This skill re-emergence, mostly due to the strato‑
sphere–troposphere dynamical coupling, becomes stronger in the reforecast with year-to-year varying ozone. The
surface prediction skill also increases over Australia. This result suggests that a more realistic stratospheric ozone could
lead to improved S2S prediction in the SH spring.
Keywords: Antarctic ozone, Downward coupling, Subseasonal-to-seasonal prediction
1 Introduction
Stratosphere–troposphere coupling, associated with thepolar vortex variability, is a prominent dynamical process
in the extratropics. In the Southern Hemisphere (SH)(Baldwin et al. 2003; Byrne et al. 2018; Hio and Yoden,
2005; Lim et al. 2018; Seviour et al. 2014; Thompson et al.
2005), such downward coupling occurs in the austral
spring and significantly modulates the zonal mean circulation in the troposphere as evidenced in the southern
annular mode (SAM). As such, it has been considered as
one of the most important sources of tropospheric predictability on the subseasonal-to-seasonal (S2S) timescale. Seviour et al. (2014) and Byrne et al. (2019), forinstance, showed that a significant prediction skill of the*Correspondence: seokwooson@snu.ac.kr
1
School of Earth and Environmental Sciences, Seoul National University, 1
Gwanak‑ro, Gwanak‑gu, Seoul 08826, South Korea
Full list of author information is available at the end of the article

SAM re-appears in early October after a period of noprediction skill in their model simulations. This skill reemergence has been attributed to the polar vortex variability and its downward influence (Seviour et al. 2014).
The models which have participated in the S2S prediction project (http://​s2spr​edict​ion.​net) also show anenhanced prediction skill in early October, although theskill re-emergence is not always statistically significant(not shown).
It is well documented that stratospheric ozone (hereafter referred to as ozone unless otherwise specified)undergoes substantial interannual variation (e.g., Salbyet al. 2011; Son et al. 2013). This ozone variation canaffect the polar vortex and its downward coupling. Son
et al. (2013) showed that September polar ozone anomaly
is significantly correlated with October SAM on interannual time scale: The chances of hot and dry weather
in southern Australia are increased when polar stratospheric ozone is anomalously high in the austral spring.

© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Oh et al. Progress in Earth and Planetary Science(2022) 9:25

However, their result does not necessarily indicate thatozone anomaly is a driving factor for the troposphericcirculation change, as the ozone anomaly itself is primarily determined by the polar vortex. In general, strong
upward propagating waves act to weaken and warm thepolar vortex in the late winter. The associated meridional
circulation transports ozone from low latitudes to thepole, increasing ozone concentration over the Antarctic stratosphere in late winter to early spring (Lim et al.
2018; Salby et al. 2011; Shaw et al. 2011; Solomon, 1999).
The increased ozone concentration then can further
weaken the polar vortex through an increased shortwave
radiative heating and possibly enhance the downward
coupling. This vortex–ozone relationship is well observed
during stratospheric sudden warming events. In 2002
and 2019 spring when the polar vortex broke down, total
column ozone (TCO) has sharply increased (Hendon
et al. 2020; Lim et al. 2019; Son et al. 2013).
Although the ozone radiative feedback could help
strengthen the downward coupling, by modulating thepolar vortex, its relative importance against the dynamic
coupling has only recently been examined. Hendon et al.
(2020) showed that prescribing the observed ozone during the 2002 SH sudden warming event—when ozone wasanomalously plentiful over the polar cap in the SH—canfacilitate the vortex weakening via radiative heating andenhance the surface response (negative SAM) in October in their model simulation. Yook et al. (2020) moregenerally showed that interactive ozone acts to increasestratospheric variability. Although they did not discuss
its impact on tropospheric predictability, they showedthat interactive ozone enhances the persistence of stratospheric variability, from which it can be inferred that it
also acts to increase predictability of the stratosphereand possibly the troposphere. These results suggest thata realistic ozone could improve the S2S prediction in the
SH in October–November when the downward couplingis prominent. However, most operational S2S prediction models use zonally and monthly averaged climatological ozone and hence ignore its year-to-year variation(Domeisen et al. 2020).
In this study, we evaluate the impact of year-to-year
varying ozone in the S2S prediction using the Global
Seasonal Forecasting System version 5 (GloSea5, MacLachlan et al. 2015). To extend and generalize Hendon et al.
(2020), multi-year model experiments are conducted.
Specifically, the two sets of reforecast experiments arecarried out by prescribing either climatological ozone or
year-to-year varying ozone for the period of 2004–2020.
As shown below, the reforecasts with time-varying ozone
show an improved prediction skill in October, comparedto those with climatological ozone.

Page 2 of 9

2 Methods/experimental
The GloSea5, which is the operational ensemble seasonal
prediction system of the UK Met Office (MacLachlan
et al. 2015), is used in this study. This model became
operational in January 2014 as the joint seasonal prediction system of the UKMO and its partner the Korea
Meteorological Administration. The GloSea5 is fully
coupled with atmosphere–land–ocean–sea ice components. The horizontal resolution of the atmosphereis ~ 0.83 degrees of longitude and ~ 0.56 degrees of latitude. In the vertical, a total of 85 levels with the model
top at 0.01 hPa are used. The ocean and sea ice are initialized with the Forecast Ocean Assimilation Model Ocean
Analysis (Blockley et al. 2014). The atmospheric and land
surface initial conditions are obtained from the European
Centre for Medium-Range Weather Forecasts (ECMWF)
Interim reanalysis data (ERA-Interim, Dee et al. 2011)
for the period of 2004–2016. After 2017, the initial conditions are taken from the analysis of the KMA/UKMO
numerical weather prediction data assimilation system.
See MacLachlan et al. (2015) for the full details of the
GloSea5.
A monthly zonally averaged ozone climatology is prescribed in the model. As a default, the Atmospheric
Chemistry and Climate (AC&C)/Stratospheric Processes
and their Role in Climate (SPARC) ozone for the periodof 1994–2005 is used in the operational mode. In thisstudy, the latest ozone data from the Stratospheric Water
and OzOne Satellite Homogenized (SWOOSH) ozone ata horizontal resolution of 2.5° are used. These data combine the multiple satellite observations such as SAGE-II
and III, HALOE, UARS MLS, and EOS Aura MLS observations (Davis et al. 2016). Since SWOOSH ozone isavailable only for 12 pressure levels from 261 to 1 hPa,
it is combined with AC&C/SPARC ozone below 261 hPa
and above 1 hPa.
All experiments are initialized on September 1st ofeach year for the period of 2004–2020 and integrated
for 61 days. Although SWOOSH ozone is available since
1984, its spatial and temporal coverages are coarse until
2003. Hence, only the last 17 years (2004–2020) are considered in this study. Because of the limitation of computing resources, only 18 ensemble members are used. In allreforecasts, the monthly zonal mean SWOOSH ozone isinterpolated to daily timescale in order to allow a smoothtransition from one month to another. In the operationalversion of GloSea5, ozone is prescribed on the 360-day
calendar. This could lead to radiative heating or coolingerrors in the polar regions in the spring when the ozone
concentration changes rapidly (Hendon et al. 2020). In
this study, we prescribe ozone data based on the Gregorian calendar and update it every day.

Oh et al. Progress in Earth and Planetary Science(2022) 9:25

Here, it should be emphasized that only the zonal
mean ozone is considered in this study. It has beenreported that the zonally asymmetric ozone distribution can affect the zonal mean circulation in both thestratosphere and troposphere (e.g., Rae et al. 2019).
However, three-dimensional ozone distribution is notconsidered in this study as the zonally averaged ozone
is prescribed in the operational GloSea5. More importantly, the ozone datasets used in this study, i.e., AC&C/
SPARC and SWOOSH ozone, are available only for thezonal mean value.
The two sets of experiments are conducted with different stratospheric ozone concentrations. The reference
run prescribes the climatological zonal mean SWOOSH
ozone (COZ), while the sensitivity run uses the year-toyear varying zonal mean SWOOSH ozone (YOZ) for
17 years. Except for the interannual variation of stratospheric ozone above 261 hPa and below 1 hPa, all otherconfigurations are identical between the two experiments. Note that COZ ozone is derived for the period of
2004–2018. This causes a subtle difference of YOZ ozone
climatology from COZ ozone as the former includes therecent two years (i.e., 2019 and 2020).
The reference meteorological fields for model evaluation, such as geopotential height and surface air temperature, are obtained from the fifth generation of the
ECMWF atmospheric reanalysis (ERA5, Hersbach andDee, 2016; Copernicus Climate Change Service, 2017;https://​clima​te.​coper​nicus.​eu). The extratropical circulation is quantified by the polar cap index (PCI). The
PCI is defined as the geopotential height anomaly integrated south of 60°S at every level in the vertical. Since
the PCI well corresponds to the SAM index not only inthe troposphere but also in the stratosphere (Baldwin andThompson 2009), it is a useful metric to diagnose polarcap stratospheric variability and its downward coupling
to the troposphere.
The model prediction skill is quantified by computing the temporal anomaly correlation coefficient (ACC)
between GloSea5 ensemble mean prediction and ERA5.
The ACC is defined as follows:


n 
Oi.τ − Oτ
i=1 fi,τ − fτ
ACC(τ ) = 
2  
2
n 
n
f
−
fτ
i=1 i,τ
i=1 Oi,τ − Oτ
where i is the year, n is the total number of years (n = 17),and τ is the forecast lead time from τ = 0 to τ = 61 days.
The ensemble mean prediction and ERA5 are denotedwith fi and Oi, respectively. Overbar indicates the timemean over 17 years. In general, ACC decreases rapidly
with the forecast lead time. A statistically significant
ACC is typically shorter than 2 weeks in the troposphere

Page 3 of 9

but over 20 days in the stratosphere (Mariotti et al. 2018;
Son et al. 2020).
The significance of ACC is tested by a nonparametricbootstrap method (Goddard et al. 2013; Smith et al. 2013;
Wilks, 2006). This method is widely used in the seasonalto-decadal prediction when the sample size is limited.
We randomly select 17 years from reanalysis and ensemble mean forecasts and then calculate ACC at each leadtime. By repeating this process 1000 times by allowing
overlapping selection, a probability distribution of ACC
is constructed. The p value is defined as the ratio of negative value from bootstrap-generated 1000 ACCs on thebasis of a one-tailed test of the hypothesis that ACC isgreater than 0. If p value is smaller than 0.05, the prediction skill is determined to be statistically significant at the
95% confidence level.
To evaluate the statistical significance of the YOZ-COZ
skill difference, the same approach is applied to ACC difference (YOZ minus COZ). The ratio of negative value
from bootstrap-generated ACC differences serves as thep value (Goddard et al. 2013).

3 Results and discussion
The Antarctic springtime ozone anomaly is significantly
linked to the tropospheric circulation anomaly on interannual timescale (Son et al. 2013). Figure 1 shows theirconnection in the last three decades. It depicts the timepressure evaluation of lagged correlation coefficients
between September mean TCO and daily PCI of ERA5
at all levels as a function of calendar day for the period of
1991–2020. The detrended data are used. To focus on S2Stimescale, ERA5 PCI is smoothed by applying a 14-day
moving average. Here, we use ERA5 TCO instead of
SWOOSH ozone in order to extend the analysis period.
The ERA5 TCO is derived from the modified version of
the ozone parameterization of Cariolle and Deque (1986)
as delineated by Cariolle and Teyssedre (2007). Various
satellite observations over different time periods, such
as MIPAS, MLS, OMI, GOME, GOME-2, and SBUV, areassimilated with variational bias corrections (Hersbach
et al. 2019, 2020). Although TCO represents the ozone
in a column of air extending from the surface to the topof the atmosphere, it is dominated by ozone within thestratosphere.
Figure 1a shows that September TCO is positively correlated with September PCI in the stratosphere. This
relationship can be explained by the stratospheric circulation as introduced earlier. A positive PCI (or negative SAM index) is typically associated with a weak polar
vortex which results from the upward wave propagationfrom the troposphere. The associated meridional circulation transports ozone from low latitudes to the pole,increasing polar stratospheric ozone.

Oh et al. Progress in Earth and Planetary Science(2022) 9:25

Page 4 of 9

Fig. 1 a Time pressure distribution of lagged correlation coefficient between detrended September total column ozone anomaly and 14-day
running averaged PCI of ERA5 during the period of 1991–2020. Statistically significant values are hatched at the 95% confidence level. b Same as (a)but for the PCI at 700 hPa. Open circles denote the values which are statistically significant at the 95% confidence level. c, d Same as (a, b) but for
PCI predicted by a linear regression model based on 10-hPa PCI on 1st September

Unlike in the stratosphere, a negative correlation
appears in the troposphere in early to mid-September(Fig. 1a). Although not statistically significant, this vertical dipole, i.e., a positive correlation in the stratosphereand a negative correlation in the troposphere, could be
partly explained by ozone radiative forcing in the stratosphere. Jucker and Goyal (2022) argued that the enhanced
static stability at the mid- to high-latitudes in the lower
stratosphere due to ozone shortwave heating could drive
an equatorward wave deflection at the tropopause level.
The resultant wave divergence in mid- to high latitudes
then could lead to a thermally indirect circulation in the
troposphere through the downward control (Haynes
et al. 1991) and cause adiabatic cooling in the subpolar
region. The net result is negative PCI at the high latitudes
in the troposphere (see Fig. 4 of Jucker and Goyal, 2022).
The vertical dipole structure, which was referred to as“fast response” in Jucker and Goyal (2022), disappears
in late September. A statistically significant positive correlation then emerges in the troposphere from weeks 5
to 8 (29th of September to 19th of October; Fig. 1a, b)
while a positive correlation is maintained in the stratosphere (Fig. 1a). This skill re-emergence results from thedownward coupling. It is evident from Fig. 1a that a positive correlation propagates downward in time from theupper stratosphere to the tropopause and becomes connected to the troposphere. Such a time-lagged downward

connection, which was referred to as “slow response” inJucker and Goyal (2022), represents a canonical stratosphere–troposphere dynamical coupling (e.g., Seviour
et al., 2014; Saggioro and Shepherd, 2019).
Seviour et al. (2014) showed that a simple regression
model utilizing the polar vortex variability can explain
this skill re-emergence (see Fig. 7b of Seviour et al. 2014).
When the similar regression model, based on 10-hPa PCI
on ­1st September, is applied to ERA5 data, the downward coupling is well captured at weeks 6 to 7 (Fig. 1c,d). However, compared to Fig. 1a, b, the vertical dipolein early and mid-September is weak and the time of skill
re-emergence is delayed to mid-October. This result
may indicate that the ozone radiative forcing leads to astronger fast response and an earlier slow response.
It is worth to note that the SAM index exhibits thelongest timescale in November (Baldwin et al. 2003; Gerber et al. 2012). This November peak has typically been
explained by the stratospheric variability and its downward coupling. However, the downward coupling shownin Fig. 1 starts to appear in October. This result is consistent with Lim et al. (2019) who showed that the stratosphere–troposphere coupling in the SH is strongest in
October (see their Fig. 1b, c). Lim et al. (2019) suggested
that the timing of the downward coupling is determinedby the polar vortex weakening. When the polar vortex
substantially weakens earlier than normal during spring,Oh et al. Progress in Earth and Planetary Science(2022) 9:25

Fig. 2 Anomaly correlation coefficients (ACCs) of 14-day runningaveraged PCI as a function of forecast lead time in a COZ and b
YOZ experiments for the period of 2004–2020. The values which
are statistically significant at the 95% confidence level are dotted.
c ACCs of 700-hPa PCI in COZ experiment in black and those in
YOZ experiment in red. Open circles denote the values which arestatistically significant at the 95% confidence level. The forecast time
when the ACC difference (YOZ minus COZ) is statistically significant
at the 95% confidence level is shaded in pink. Note that October 6 in
x-axis refers to the ACC at the forecast week 6–7

the resultant wind anomaly tends to propagate downward to the surface in October and November although
the mechanism of downward propagation still remains to
be determined.
The downward coupling shown in Fig. 1 is well captured by the reforecast experiments. Figure 2a shows
the ACCs of PCI in the COZ experiment. High ACCs,
which are statistically significant at the 95% confidence
level, are maintained at all lead times in the stratosphere,
indicating persistent stratospheric anomalies. However,Page 5 of 9

the ACCs in the troposphere rapidly decrease after week
3. This result is consistent with Son et al. (2020) who
reported that the stratospheric prediction skill is muchhigher than the tropospheric skill in austral spring.
In the troposphere, a significant prediction skill reappears later in COZ experiment (i.e., 6 weeks aftermodel initialization). This skill re-emergence in midOctober is not sensitive to the ensemble size and the
choice of the variable. The same result is obtained when
only half ensemble members are randomly selected.
Although not shown, similar results are robustly found inother S2S prediction models which differ in the horizontal and vertical resolutions, ensemble size, and reforecast
period (see also Seviour et al. 2014; Byrne et al. 2019).
Here, we note that week 6 is not necessarily the optimallead time of the tropospheric skill re-emergence. It could
slightly change for 1 week when the initialization date isvaried. For instance, the skill re-emergence appears inlate September in the S2S prediction models initialized inlate August or early September (not shown).
Although Fig. 2a clearly shows a downward influence in prediction skill, it does not delineate the role ofozone radiative forcing. To quantify the ozone impact on
S2S prediction, we compare the COZ experiment to the
YOZ experiment. Figure 2b shows the ACCs of the YOZ
experiment. As described in the Method section, thisexperiment is identical to the COZ experiment except
in how stratospheric ozone is specified; that is, the only
difference is that stratospheric ozone is prescribed with
year-to-year varying observation. The overall prediction
skill does not change much in the first few weeks. However, a notable difference appears in late September andOctober when the downward influence is pronounced
in the COZ experiment (Fig. 2a). Most importantly, thetropospheric prediction skill of the YOZ experimentis higher than that of the COZ experiment during thewhole forecast lead time (Fig. 2c). The ACC difference
between YOZ and COZ experiments which is statisticallysignificant at the 95% confidence level appears at week
1–2 and week 6–7 (pink shading). It is noteworthy from
Fig. 2a that the lowest prediction skill in the troposphereis found in mid- to late September when the vertical
dipole disappears or when the fast response is switched
into the slow response (Fig. 1a). Although statisticallyinsignificant, the lowest prediction skill is also improvedin the YOZ experiment presumably due to a more realistic ozone downward coupling.
This result indicates that a more realistic ozone helps
to improve tropospheric prediction in October. Lim et al.
(2018) indicated that the stratosphere–troposphere coupling mode in the SH is highly correlated with ozone
concentration in spring and could affect the troposphericpredictability in October. By integrating a model similarOh et al. Progress in Earth and Planetary Science(2022) 9:25

Page 6 of 9

Fig. 3 a Root-mean-squared error (RMSE) (m) and b ACC of 700-hPa PCI at the forecast week 6–7 in COZ and YOZ experiments. c, d ACCs of week
6–7 PCI700 as a function of ensemble size and number of validation years. Each line indicates the average value of bootstrap-generated 1000 ACCs.
Square indicates the practical ACC shown in (b). Bars represent p values for the ACC difference (YOZ minus COZ). The ACC difference is statisticallysignificant at the 95% confidence level when the p value is less than 0.05, which is denoted by red color

to the one used in this study, Hendon et al. (2020) showedthat SH circulation in October is better captured when arealistic ozone is prescribed in the model during the 2002
stratospheric sudden warming event. In this regard, ourresult confirms the finding of Lim et al. (2018) and generalizes the case study of Hendon et al. (2020).
The skill difference between COZ and YOZ experiments at week 6–7 is further quantified in Fig. 3a, b for
700-hPa PCI. The root-mean-squared error (RMSE) issignificantly reduced from COZ to YOZ experiments,
while the ACC is significantly increased. The ACC difference and its sensitivity to the ensemble size or numberof validation years are further tested in Fig. 3c, d by conducting a nonparametric bootstrap resampling method.
The sensitivity to the ensemble size (M) is first tested byrandomly selecting M ensemble members when computing the ensemble mean prediction. This resampling is

conducted 1,000 times, allowing multiple counts. Theiraverage is then considered as a skill score for a given
ensemble size M. It turns out that the YOZ experimentshows a higher ACC than the COZ experiment for allcases from M = 1 to M = 18 (Fig. 3c). Its difference fromthe COZ experiment becomes statistically significant
if the ensemble size is equal to or greater than 10. Notefrom Fig. 3c that the ACC difference for M = 18 is slightly
smaller than the practical ACC difference (compare lines
and squares) because a bootstrapping allows multiple
counts.
A similar test is also conducted for a number of validation years. The number of validation years, N, is randomly selected. This process is also repeated 1,000 times
and their average is considered as a skill score for a given
number of validation years. Figure 3d shows that the
YOZ-COZ skill difference is robust for N = 8 to N = 17. It

Oh et al. Progress in Earth and Planetary Science(2022) 9:25

Fig. 4 RMSE and ACC of week 6–7 maximum surface air temperature(T2Max) in a COZ and b YOZ experiments. In the right column, thevalues which are statistically significant at the 95% confidence levelare dotted

becomes statistically significant when the number of validation years is equal to or greater than 14. These results
suggest that the ACC difference between COZ and YOZ
experiments shown in Fig. 2c is robust and not caused bychance.
An improved prediction skill in YOZ experiment isalso evident at the surface for the regions that exhibit astrong impact of polar vortex variability (e.g., Lim et al.
2019). Figure 4 illustrates the spatial distribution of
RMSEs and ACCs at week 6–7 for maximum surface airtemperature over Australia. Due to SAM-related surface
climate variability, a reliable prediction skill appears insome regions in the COZ experiment (Fig. 4a), consistent with the SAM-related surface air temperature variability (e.g., Hendon et al. 2007). The prediction skill isenhanced in most regions when the year-to-year varying
ozone is prescribed (Fig. 4b). A large error reduction isespecially found in eastern and southern Australia, whileskill improvement is negligible in western and northern
Australia.

4 Conclusions
It has been suggested that S2S prediction could be
improved if the stratospheric state is represented more
realistically (Seviour et al. 2014; Domeisen et al. 2020;
Hendon et al. 2007). However, not many modeling studies have explored the impact of stratospheric conditions
on the SH S2S prediction. In particular, the possible
impact of stratospheric ozone has been rarely reported in
the literature. Hendon et al. (2020) recently showed that

Page 7 of 9

stratospheric ozone can affect the surface prediction skill
for the 2002 stratospheric sudden warming event, thefirst major sudden warming event in the SH. However,their results remain to be generalized with long-term
reforecasts.
To assess the impact of stratospheric ozone on the S2S
prediction, we performed two sets of GloSea5 reforecasts in which the stratospheric ozone concentration
is prescribed with the long-term climatology (COZ) or
year-to-year varying observation (YOZ). While the skill
difference between the two experiments is relatively
minor in the stratosphere, it is significant in the troposphere. Most importantly, the YOZ experiment outperforms the COZ experiment at all forecast lead times in
October. Such an improvement is also evident at the surface over Australia.
Our result confirms that the ozone radiative forcing
plays a critical role in the S2S prediction in the austral
spring, generalizing Hendon et al. (2020). This finding
suggests that more realistic ozone is critical not only forthe long-term climate simulation (e.g., Haase et al. 2020;
Ivanciu et al. 2021; Li et al. 2016) but also for the operational S2S prediction. This is particularly true in the austral spring when the ozone radiative forcing is important.
Here, it should be stated that the ozone radiative feedback is not fully included in this study. The stratosphericozone anomalies are prescribed as an external forcing to
explore their thermodynamical and dynamical effects.
The interplay between ozone and circulation is not taken
into account. To evaluate the role of interactive ozone in
the S2S prediction, further studies are required.
What determines the timing of the downward coupling(e.g., October) is a question yet to be answered, althoughobservational data and model simulations show abouta month lag (e.g., Seviour et al. 2014; Shaw et al. 2011).
The related physical process is presumably similar to thelong-term tropospheric response to the ozone depletion(e.g., Son et al. 2018) which is not yet fully understood.
Several mechanisms have been proposed, which include
downward control, eddy–mean flow interaction related
to planetary and synoptic scale waves, eddy–eddy interactions, or combination of them (e.g., Hitchcock and
Haynes, 2016; Orr et al. 2012; Yang et al. 2015; Martineau
and Son, 2015). This topic needs more comprehensive
analyses beyond ozone sensitivity tests.
Abbreviations
SH: Southern Hemisphere; S2S: Subseasonal-to-seasonal; GloSea5: Global
Seasonal Forecasting System 5; SAM: Southern Annular mode; UKMO: United
Kingdom Met Office; ECMWF: European Centre for Medium-Range Weather
Forecasts; AC&C: Atmospheric Chemistry and Climate; SPARC​: Stratospheric
Processes and their Role in Climate; SWOOSH: Stratospheric Water and OzOne
Satellite Homogenized; COZ: Climatological zonal mean SWOOSH ozone; YOZ:
Year-to-year varying zonal mean SWOOSH ozone; PCI: Polar Cap Index; ACC​:Oh et al. Progress in Earth and Planetary Science(2022) 9:25

Anomaly correlation coefficient; TCO: Total column ozone; RMSE: Root-meansquare error.
Acknowledgements
We would like to thank Dr. Catherine Deburgh-Day and Dr. Yu-Kyung Hyun for
their valuable advice. We are also grateful to the anonymous reviewers whoshared their time and provided constructive comments to the earlier versionof the manuscript.
Author contributions
J.O., S.W. S., and J. C. conceived of the presented idea, conducted overall
analysis, and took the lead in writing the manuscript. E.-P. L, C.G, H. H, Y. K., and
H. S. K. contributed to expanding the main conceptual idea and to interpret‑
ing the results. All authors discussed the results and reviewed the manuscript.
All authors read and approved the final manuscript.
Funding
This work was supported by the National Research Foundation of Korea (NRF)
grant (2017R1E1A1A01074889) and NRF R&D Program for Oceans and Polar
Regions (NRF-2020M1A5A1110579) funded by the Ministry of Science and ICT.
Availability of data and materials
The SWOOSH ozone data are downloaded from the NOAA (https://​www.​esrl.​noaa.​gov/​csl/​groups/​csd8/​swoosh/). The ERA5 data are obtained fromthe Copernicus (https://​cds.​clima​te.​coper​nicus.​eu/​cdsapp#​!/​home). Model
outputs are available upon request to the first author, Jiyoung Oh (E-mail:projy001@snu.ac.kr or projy@korea.kr).

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
School of Earth and Environmental Sciences, Seoul National University, 1
Gwanak‑ro, Gwanak‑gu, Seoul 08826, South Korea. 2 Korea Meteorological
Administration, Daejeon, South Korea. 3 Bureau of Meteorology, Melbourne,
Australia. 4 The Fredy and Nadine Herrmann Institute of Earth Sciences,
Hebrew University, Jerusalem, Israel. 5 Monash University, Melbourne, Australia.
Received: 21 January 2022 Revised: 18 April 2022 Accepted: 6 May 2022

References
Baldwin MP, Stephenson DB, Thompson DWJ, Dunkerton TJ, Charlton AJ,
O’Neill A (2003) Stratospheric memory and skill of extended-range
weather forecasts. Science 301:636–640. https://​doi.​org/​10.​1126/​scien​ce.​10871​43
Baldwin MP, Thompson DW (2009) A critical comparison of stratospheretroposphere coupling indices. Q J R Meteorol Soc 135:1661–1672. https://​doi.​org/​10.​1002/​qj.​479
Blockley EW, Martin MJ, McLaren AJ, Ryan AG, Waters J, Lea DJ, Mirouze I, Peter‑son KA, Sellar A, Storkey D (2014) Recent development of the Met Office
operational ocean forecasting system: an overview and assessment of
the new Global FOAM forecasts. Geoscientific Model Dev 7:2613–2638.
https://​doi.​org/​10.​5194/​gmd-7-​2613-​2014
Byrne NJ, Shepherd TG (2018) Seasonal persistence of circulation anomalies in
the Southern Hemisphere stratosphere and its implications for the tropo‑
sphere. J Clim 31:3467–3483. https://​doi.​org/​10.​1175/​JCLI-D-​17-​0557.1
Byrne NJ, Shepherd TG, Polichtchouk I (2019) Subseasonal-to-seasonal predict‑ability of the Southern Hemisphere eddy-driven jet during austral springand early summer. J Geophys Res: Atmospheres 124:6841–6855. https://​doi.​org/​10.​1029/​2018J​D0301​73
Cariolle D, Deque M (1986) Southern hemisphere medium-scale waves andtotal ozone disturbances in a spectral general circulation model. J Geo‑phys Res: Atmospheres 91:10825–10846. https://​doi.​org/​10.​1029/​JD091​
iD10p​10825

Page 8 of 9

Cariolle D, Teyssedre H (2007) A revised linear ozone photochemistry param‑
eterization for use in transport and general circulation models: multiannual simulations. Atmos Chem Phys 7:2183–2196. https://​doi.​org/​10.​
5194/​acp-7-​2183-​2007
Copernicus Climate Change Service (2017) ERA5: Fifth Generation of ECMWF
Atmospheric Reanalyses of the Global Climate. Copernicus Climate
Change Service Climate Data Store (CDS). ECMWF. Available at: https://​cds.​clima​te.​coper​nicus.​eu/​cdsapp#​!/​home
Davis SM, Rosenlof KH, Hassler B, Hurst DF, Read WG, Vömel H, Selkirk H,
Fujiwara M, Damadeo R (2016) The Stratospheric Water and Ozone
Satellite Homogenized (SWOOSH) database: a long-term database forclimate studies. Earth Syst Sci Data 8:461–490. https://​doi.​org/​10.​5194/​
essd8-​461-​2016
Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Bal‑
maseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L,
Bidlot J, Bromann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger
L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matri‑cardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C,
de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim rea‑nalysis: configuration and performance of the data assimilation system. Q
J R Meteorol Soc 137:553–597. https://​doi.​org/​10.​1002/​qj.​828
Domeisen D, Butler A, Charlton-Perez A, Ayarzaguena B, Baldwin M, DunnSigouin E, Furtado JC, Garfinkel CI, Hitchcock P, Karpechko AY, Kim H,
Knight J, Lang AL, Lim EP, Marshall A, Roff G, Schwartz C, Simpson IR,
Son SW, Taguchi M (2020) The role of the stratosphere in subseasonal toseasonal prediction: 1. Predictability of the stratosphere. J Geophys Res:
Atmospheres, 125, e2019JD030920, doi:https://​doi.​org/​10.​1029/​2019J​
D0309​20
Gerber EP, Butler A, Calvo N, Charlton-Perez A, Giorgetta M, Manzini E, Perlwitz
J, Polvani LM, Sassi F, Scaife A, Shaw T, Son S-W, Watanabe S (2012)
Assessing and understanding the impact of stratospheric dynamics andvariability on the earth system. Bull Am Meteor Soc 93:845–859
Goddard L, Kumar A, Solomon A, Smith D, Boer G, Gonzalez P, Kharin V, Mer‑ryfield W, Deser C, Mason SJ, Kirtman BP, Msadek R, Sutton R, Hawkins
E, Fricker T, Hegerl G, Ferro CAT, Stephenson DB, Meehl GA, Stockdale
T, Burgman R, Greene AM, Kushnir Y, Newman M, Carton J, Fukumori I,
Delworth T (2013) A verification framework for interannual-to-decadal
predictions experiments. Clim Dyn 40:245–272. https://​doi.​org/​10.​1007/​
s00382-​012-​1481-2
Haase S, Fricke J, Kruschke T, Wahl S, Matthes K (2020) (2020): Sensitivity of
the Southern Hemisphere circumpolar jet response to Antarctic ozone
depletion: prescribed versus interactive chemistry. Atmos Chem Phys
20:14043–14061. https://​doi.​org/​10.​5194/​acp-​20-​14043-​2020
Haynes PH, McIntyre ME, Shepherd TG, Marks CJ, Shine KP (1991) On the“Downward Control” of extratropical diabatic circulations by eddyinduced mean zonal forces. J Atmos Sci 48(4):651–678. https://​doi.​org/​10.​
1175/​1520-​0469(1991)​048%​3c0651:​otcoed%​3e2.0.​co;2
Hendon HH, Thompson DWJ, Wheeler MC (2007) Australian rainfall andsurface temperature variations associated with the Southern Hemisphere
Annular Mode. J Clim 20:2452–2467. https://​doi.​org/​10.​1175/​JCLI4​134.1
Hendon HH, Lim E-P, Abhik S (2020) Impact of interannual ozone variations on
the downward coupling of the 2002 Southern Hemisphere stratosphericwarming. J Geophys Res: Atmospheres, 125:e2020JD032952. doi:https://​doi.​org/​10.​1029/​2020J​D0329​52
Hersbach H, Dee D (2016) ERA5 reanalysis is in production. ECMWF Newsletter
147, ECMWF, Reading, UK, Available from https://​www.​ecmwf.​int/​en/​newsl​etter/​147/​news/​era5-​reana​lysis-​produ​ction
Hersbach H, Bell B, Berrisford P, Horányi A, Muños-Sabater JM, Nicolas J, Radu R,
Schepers D, Simmons A, Soci C, Dee D (2019) Global reanalysis: goodbye
ERA-Interim, hello ERA5. ECMWF, https ://doi.org/https://​doi.​org/​10.​21957/​vf291​hehd7. https://​www.​ecmwf .int/node/19027
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muños-Sabater J, Nicolas
J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan
X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, De Chiara G,
Dahlgren P, Dee D, Diamantakis M, Dragani R, Flemming J, Forbes R,
Fuentes M, Geer A, Haimberger L, Healy S, Hogan RJ, Hólm E, Janisková
M, Keeley S, Laloyaux P, Lopez P, Lupu C, Radnoti G, de Rosnay P, Rozum I,
Vamborg F, Villaume S, Thepaut J-N (2020) The ERA5 global reanalysis. Q J
R Meteorol Soc. https://​doi.​org/​10.​1002/​qj.​3803
Hio Y, Yoden S (2005) Interannual variations of the seasonal march in theSouthern Hemisphere stratosphere for 1979–2002 and characterization

Oh et al. Progress in Earth and Planetary Science(2022) 9:25

of the unprecedented year 2002. J Atmos Sci 62:567–580. https://​doi.​org/​
10.​1175/​JAS-​3333.1
Hitchcock P, Haynes PH (2016) Stratospheric control of planetary waves. Geo‑phys Res Lett 43:11884–11892. https://​doi.​org/​10.​1002/​2016G​L0713​72
Ivanciu I, Matthes K, Wahl S, Harlaß J, Biastoch A (2021) Effects of prescribed
CMIP6 ozone on simulating the Southern Hemisphere atmospheric cir‑
culation response to ozone depletion. Atmos Chem Phys 21:5777–5806.
https://​doi.​org/​10.​5194/​acp-​21-​5777-​2021
Jucker M, Goyal R (2022) Ozone-forced southern annular mode during Antarc‑
tic stratospheric warming events. Geophys Res Lett 49:e2021GL095270.
doi:https://​doi.​org/​10.​1029/​2021G​L0952​70
Li F, Vikhliaev YV, Newman PA, Pawson S, Perlwitz J, Waugh DW, Douglass
AR (2016) Impacts of interactive stratospheric chemistry on antarctic
and southern ocean climate change in the Goddard Earth Observing
System, version 5 (GEOS-5). J Clim 29:3199–3218. https://​doi.​org/​10.​1175/​
JCLI-D-​15-​0572.1
Lim E-P, Hendon HH, Thompson DWJ (2018) Seasonal evolution of strato‑
sphere-troposphere coupling in the Southern Hemisphere and implica‑
tions for the predictability of surface climate. J Geophys Res: Atmos‑
pheres 123:12002–12016. https://​doi.​org/​10.​1029/​2018J​D0293​21
Lim E-P, Hendon HH, Boschat G, Hudson D, Thompson DWJ, Dowdy AJ,
Arblaster JM (2019) Australian hot and dry extremes induced by weaken‑
ings of the stratospheric polar vortex. Nat Geosci 12(11):896–901. https://​doi.​org/​10.​1038/​s41561-​019-​0456-x
MacLachlan C, Arribas A, Peterson KA, Maidens A, Fereday D, Scaife AA, Gordon
M, Vellinga M, Williams A, Comer RE, Camp J, Xavier P (2015) Descriptionof GloSea5: the met office high resolution seasonal forecast system. Q J R
Meteorol Soc. https://​doi.​org/​10.​1002/​qj.​2396
Mariotti A, Ruti PM, Rixen M (2018) Progress in subseasonal to seasonal predic‑
tion through a joint weather and climate community effort. Npj Clim
Atmospheric Sci. https://​doi.​org/​10.​1038/​s41612-​018-​0014-z
Martineau P, Son S-W (2015) Onset of circulation anomalies during strato‑
spheric vortex weakening events: the role of planetary-scale waves. J
Clim 28:7347–7370
Orr A, Bracegirdle TJ, Hosking JS, Jung T, Haigh JD, Phillips T, Feng W (2012)
Possible dynamical mechanisms for Southern Hemisphere climatechange due to the ozone hole. J Atmos Sci 69(10):2917–2932. https://​doi.​
org/​10.​1175/​JASD-​11-​0210.1
Rae CD, Keeble J, Hitchcock P, Pyle JA (2019) Prescribing zonally asymmetricozone climatologies in climate models: performance compared to achemistry-climate model. J Adv Model Earth Syst 11:918–933. https://​doi.​
org/​10.​1029/​2018M​S0014​78
Saggioro E, Shepherd TG (2019) Quantifying the timescale and strength ofSouthern Hemisphere interseasonal stratosphere-troposphere coupling.
Geophys Res Lett 46:13479–13487. https://​doi.​org/​10.​1029/​2019G​L0847​
63
Salby M, Titova E, Deschamps L (2011) Rebound of Antarctic ozone. Geophys
Res Lett 38:L09702. https://​doi.​org/​10.​1029/​2011G​L0472​66
Seviour WJM, Hardiman SC, Gray LJ, Butchart N, MacLachlan C, Scaife AA(2014) Skillful seasonal prediction of the Southern Annular Mode andAntarctic ozone. J Clim 27(19):7462–7474. https://​doi.​org/​10.​1175/​
jcli-d-​14-​00264.1
Shaw TA, Perlwitz J, Harnik N, Newman PA, Pawson S (2011) The impact
of stratospheric ozone changes on downward wave coupling in theSouthern Hemisphere. J Clim 24:4210–4229. https://​doi.​org/​10.​1175/​
2010J​CLI38​04.1
Smith DM, Eade R, Pohlmann H (2013) A comparison of full-field and anomaly
initialization for seasonal to decadal climate prediction. Clim Dyn
41:3325–3338. https://​doi.​org/​10.​1007/​s00382-​013-​1683-2
Solomon S (1999) Stratospheric ozone depletion: a review of concepts andhistory. Rev Geophys 37:275–316. https://​doi.​org/​10.​1029/​1999R​G9000​08
Son S-W, Purich A, Hendon HH, Kim BM, Polvani LM (2013) Improved seasonal
forecast using ozone hole variability? Geophys Res Lett 40:6231–6235.
https://​doi.​org/​10.​1002/​2013G​L0577​31
Son S-W, Han B-R, Garfinkel CI, Kim S-Y, Park R, Abraham NL, Akiyoshi H,
Archibald AT, Butchart N, Chipperfield MP, Dameris M, Deushi M,
Dhomse SS, Hardiman SC, Jöckel P, Kinnison D, Michou M, Morgenstern
O, O’Connor FM, Oman LD, Plummer DA, Pozzer A, Revell LE, Rozanov
E, Stenke A, Stone K, Tilmes S, Yamashita Y, Zeng G (2018) Tropospheric
jet response to Antarctic ozone depletion: an update with Chemistry

Page 9 of 9

Climate Model Initiative (CCMI) models. Environ Res Lett 13:054024.
https://​doi.​org/​10.​1088/​1748-​9326/​aabf21
Son S-W, Kim H, Song K, Kim SW, Martineau P, Hyun YK, Kim Y (2020) Extrat‑
ropical prediction skill of the subseasonal-to-seasonal (S2S) prediction
models. J Geophys Res: Atmospheres, 125:e2019JD031273. doi:https://​doi.​org/​10.​1029/​2019J​D0312​73
Thompson DWJ, Baldwin M, Solomon S (2005) Stratosphere–troposphere
coupling in the Southern Hemisphere. J Atmos Sci 62:708–715. https://​doi.​org/​10.​1175/​JAS-​3321.1
Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn.
Academic, San Diego
Yang H, Sun L, Chen G (2015) Separating the mechanisms of transient
responses to stratospheric ozone depletion-like cooling in an idealized
atmospheric model. J Atmos Sci 72(2):763–773. https://​doi.​org/​10.​1175/​
JAS-D-​13-​0353.1
Yook S, Thompson DWJ, Solomon S, Kim S-Y (2020) The key role of coupledchemistry-climate interactions in tropical stratospheric temperature vari‑ability. J Clim 33:7619–7629. https://​doi.​org/​10.​1175/​JCLI-D-​20-​0071.1

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub‑lished maps and institutional affiliations.