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No Robust Evidence of Future Changes in Major Stratospheric Sudden Warmings: A Multi-model Assessment from CCMI

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Major mid-winter stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Because SSWs are able to cause significant surface weather anomalies on intra-seasonal timescales, several previous studies have focused on their potential future change, as might be induced by anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to an actual decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs and the impact of large climatological biases in single-model studies. To bring some clarity, we here revisit the question of future SSW changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry–Climate Model Initiative. Our analysis reveals that no statistically significant change in the frequency of SSWs will occur over the 21st century, irrespective of the metric used for the identification of the event. Changes in other SSW characteristics – such as their duration, deceleration of the polar night jet, and the tropospheric forcing – are also assessed: again, we find no evidence of future changes over the 21st century.
Atmos. Chem. Phys., 18, 11277–11287, 2018
https://doi.org/10.5194/acp-18-11277-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
No robust evidence of future changes in major stratospheric sudden
warmings: a multi-model assessment from CCMI
Blanca Ayarzagüena1,2,a, Lorenzo M. Polvani3, Ulrike Langematz4, Hideharu Akiyoshi5, Slimane Bekki6,
Neal Butchart7, Martin Dameris8, Makoto Deushi9, Steven C. Hardiman7, Patrick Jöckel8, Andrew Klekociuk10,11,
Marion Marchand6, Martine Michou12, Olaf Morgenstern13, Fiona M. O’Connor7, Luke D. Oman14,
David A. Plummer15, Laura Revell16,17, Eugene Rozanov18,16 , David Saint-Martin12, John Scinocca15,
Andrea Stenke16, Kane Stone19,20,b, Yousuke Yamashita5,c, Kohei Yoshida9, and Guang Zeng13
1Dpto. Física de la Tierra y Astrofísica, Universidad Complutense de Madrid, Madrid, Spain
2Instituto de Geociencias (IGEO), CSIC-UCM, Madrid, Spain
3Columbia University, New York, USA
4Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany
5National Institute for Environmental Studies (NIES), Tsukuba, Japan
6LATMOS, Institut Pierre Simon Laplace (IPSL), Paris, France
7Met Office Hadley Centre (MOHC), Exeter, UK
8Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
9Meteorological Research Institute (MRI), Tsukuba, Japan
10Australian Antarctic Division, Kingston, Tasmania, Australia
11Antarctic Climate and Ecosystems Cooperative Research Centre, Hobart, Tasmania, Australia
12CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
13National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
14National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), Greenbelt, Maryland, USA
15Environment and Climate Change Canada, Montréal, Canada
16Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
17Bodeker Scientific, Christchurch, New Zealand
18Physikalisch-Meteorologisches Observatorium Davos/World Radiation Centre, Davos, Switzerland
19School of Earth Sciences, University of Melbourne, Melbourne, Australia
20ARC Centre of Excellence for Climate System Science, Sydney, Australia
apreviously at: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
bnow at: Massachusetts Institute of Technology (MIT), Boston, Massachusetts, USA
cnow at: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Correspondence: Blanca Ayarzagüena (bayarzag@ucm.es)
Received: 21 March 2018 – Discussion started: 26 March 2018
Revised: 29 June 2018 – Accepted: 3 July 2018 – Published: 13 August 2018
Abstract. Major mid-winter stratospheric sudden warm-
ings (SSWs) are the largest instance of wintertime variabil-
ity in the Arctic stratosphere. Because SSWs are able to
cause significant surface weather anomalies on intra-seasonal
timescales, several previous studies have focused on their po-
tential future change, as might be induced by anthropogenic
forcings. However, a wide range of results have been re-
ported, from a future increase in the frequency of SSWs to
an actual decrease. Several factors might explain these con-
tradictory results, notably the use of different metrics for the
identification of SSWs and the impact of large climatological
biases in single-model studies. To bring some clarity, we here
revisit the question of future SSW changes, using an identical
set of metrics applied consistently across 12 different mod-
Published by Copernicus Publications on behalf of the European Geosciences Union.
11278 B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings
els participating in the Chemistry–Climate Model Initiative.
Our analysis reveals that no statistically significant change in
the frequency of SSWs will occur over the 21st century, irre-
spective of the metric used for the identification of the event.
Changes in other SSW characteristics – such as their dura-
tion, deceleration of the polar night jet, and the tropospheric
forcing – are also assessed: again, we find no evidence of
future changes over the 21st century.
1 Introduction
Stratospheric sudden warmings (SSWs) are the largest man-
ifestation of the internal variability of the wintertime po-
lar stratosphere in the Northern Hemisphere, consisting of
a very rapid temperature increase accompanied by a reversal
of the westerly wintertime circulation (the polar vortex). In
observations, SSWs occur roughly with a frequency of six
SSWs per decade (e.g. Charlton and Polvani, 2007). How-
ever, large variability on intra- and inter-decadal timescales
has been reported (Labitzke and Naujokat, 2000; Schimanke
et al., 2011).
SSWs also play an important role in the dynamical cou-
pling between the stratosphere and troposphere. Although
other mechanisms are possible, SSWs are usually related
to precursors in the troposphere that lead to an anoma-
lously high injection of tropospheric waves that propagate
into the stratosphere, where they deposit momentum and en-
ergy, decelerating the mean flow (Matsuno, 1971; Polvani
and Waugh, 2004). More importantly, however, their effects
are not restricted to the stratosphere: SSWs can also impact
the tropospheric circulation and surface climate for up to 2
months (e.g. Baldwin and Dunkerton, 2001). Given their im-
portance for seasonal forecasting, SSWs have been studied
with great interest, as they are likely to provide a source
of improved weather forecasts on intra-seasonal scales (Sig-
mond et al., 2013).
One question of particular relevance is whether SSWs will
change in the future, as a consequence of increasing green-
house gas (GHG) concentrations and ozone recovery. The an-
swer to this question has proven elusive since the first studies
over 2 decades ago. While Mahfouf et al. (1994) found an
increase in the frequency of SSWs under doubled-CO2con-
ditions, Rind et al. (1998) reported a decrease, and Butchart
et al. (2000) did not find any change that might be attributed
to increasing GHG concentrations. And, in spite of an im-
proved stratospheric representation and more realistic model
features in the last decade, a clear consensus as to future SSW
changes is still missing (Charlton-Perez et al., 2008; Bell et
al., 2010; SPARC CCMVal, 2010; Mitchell et al., 2012a, b;
Hansen et al., 2014; Kim et al., 2017).
Several potential reasons that might explain the dispar-
ity in the projected SSW changes have been proposed in
the literature. One is the combination of different aspects of
future climate change with opposing effects on the Arctic
stratosphere, such as the projected ozone recovery, increas-
ing GHG concentrations and their induced changes in global
sea surface temperatures. These result in a weak polar strato-
spheric response to climate change (Mitchell et al., 2012a;
Ayarzagüena et al., 2013). Consequently, individual models
yield different future projections of SSW changes, depend-
ing on the relative importance of these competing effects in
each model. Hence, any result obtained with a single model
needs to be taken with much caution.
Another potential explanation for the discrepancies stems
from the criterion chosen for the identification of SSWs. As
shown in Butler et al. (2015), the identification of SSWs can
be sensitive to the method used. It was found to depend on
the meteorological variable chosen for analysis, and also on
whether the identification criterion entails total fields and a
fixed threshold (absolute criterion) or anomalies relative to
a changing climatology (relative criterion). For instance, the
traditional criterion of the World Meteorological Organiza-
tion (hereafter WMO criterion; McInturff, 1978) requires the
reversal of both zonal-mean zonal wind at 60N and 10 hPa
and the meridional gradient of zonal-mean temperature be-
tween 60N and the pole at the same level. This criterion
was empirically developed from the observations in the last
several decades and was applied in historical stratospheric
analyses (e.g. Labitzke, 1981). Recent studies have contin-
ued using the WMO criterion, although many of them have
only imposed the reversal of the wind for the SSW identifi-
cation (e.g. Charlton and Polvani, 2007). Because of its sim-
plicity and its dynamical insight, the WMO criterion (and its
recent simplified version) is the most commonly used crite-
rion in modelling studies as well. However, such an absolute
metric might not always be the best choice to measure the
polar stratospheric variability in these studies, as it does not
account for potential model biases in the polar vortex clima-
tology or possible changes in this climatology in the future
projections (McLandress and Shepherd, 2009; Mitchell et al.,
2012a; Butler et al., 2015; Kim et al., 2017). An analysis with
the Canadian Middle Atmosphere Model by McLandress and
Shepherd (2009) showed that the frequency of SSWs may or
may not change depending on the detection index.
The purpose of this study, therefore, is to revisit the ques-
tion of possible future SSW changes, taking these issues into
consideration. Seeking a robust answer, we employ three dif-
ferent SSW identification criteria (both absolute and relative)
and apply them consistently to the output from 12 state-
of-the-art climate models (contributing to the Chemistry–
Climate Model Initiative, CCMI). Interactive stratospheric
chemistry, which is present in all the CCMI models, makes
them the most realistic in terms of stratospheric processes. In
addition, the CCMI models are improved compared to their
counterparts which participated in the previous Chemistry–
Climate Model Validation-2 programme (CCMVal-2). In
particular, several CCMI models are coupled to interactive
ocean modules, and the vertical resolution of many mod-
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B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings 11279
Table 1. Main characteristics relative to the models and their REF-C2 simulations used in this study.
CCMI models Model resolution QBO Solar variability SSTs
GEOS-CCM 2.5×2, L72
(top: 0.01 hPa)
Internally generated Yes Prescribed (CESM1)
CNRM-CCM T42L60
(top: 0.07 hPa)
Internally generated Yes Prescribed (CNRM)
NIWA-UKCA 3.75×2.5, L60
(top: 84 km)
Internally generated No Coupled to ocean model
CCSRNIES-MIROC 3.2 T42L34
(top: 0.012 hPa)
Nudged Yes Prescribed (MIROC 3.2)
IPSL-LMDZ-REPROBUS 3.75×2.5, L39
(top: 70 km)
Nudged Yes Prescribed (SRES A1b IPSL)
ACCESS CCM 3.75×2.5, L60
(top: 84 km)
Internally generated No Prescribed (HadGEM2-ES)
HadGEM3-ES 1.875×1.25,
L85 (top: 85 km)
Internally generated Yes Coupled to ocean model
SOCOL3 T42L39
(top: 0.01 hPa)
Nudged Yes Prescribed (CESM1(CAM5))
MRI-ESM1r1 TL159L80
(top: 0.01 hPa)
Internally generated Yes Coupled to ocean model
EMAC-L47 T42L47
(top: 0.01 hPa)
Nudged Yes Prescribed (HadGEM2-ES)
EMAC-L90 T42L90
(top: 0.01 hPa)
Internally generated
(slightly nudged)
Yes Prescribed (HadGEM2-ES)
CMAM T47L71
(top: 0.0575 hPa)
No No Prescribed (CanCM4)
els has been increased (Morgenstern et al., 2017). More-
over, unlike other previous studies such as Kim et al. (2017),
our analysis is not only restricted to the mean frequency of
SSWs; we also examine the possible future changes in other
characteristics, such as the duration of events, the related de-
celeration of the polar night jet, or the wave activity preced-
ing their occurrence. To our knowledge this is the first time
that a multi-model assessment of these different SSW fea-
tures is performed. The structure of the paper is as follows:
in Sect. 2 the data and methodology used in the analysis are
described. The main results are shown in Sect. 3, and Sect. 4
includes the discussion and the most important conclusions
derived from the analysis.
2 Data and methodology
2.1 Data description
Our study is based on the analysis of the transient REF-C2
simulation of 12 CCMI models (cf. Table 1; for more de-
tails see Morgenstern et al., 2017). The REF-C2 runs ex-
tend from 1960 to 2099 or 2100 for most models (except for
the IPSL-LMDZ-REPROBUS model, which terminates the
run in 2095) and include natural and anthropogenic forcings
following the CCMI specifications (Eyring et al., 2013). In
particular, GHG concentrations and surface mixing ratios of
ozone-depleting substances are based on observations until
2000, as well as on the Representative Concentration Path-
way 6.0 (RCP6.0; Meinshausen et al., 2011) and A1 (WMO,
2011) scenarios, respectively, from 2000 to 2100. Solar vari-
ability is included in most of the models. Depending on
the characteristics and performance of the models, sea sur-
face temperatures (SSTs) and the Quasi-Biennial Oscillation
(QBO) are prescribed or internally generated. Future changes
in frequency and other features of SSWs are obtained by
comparing the last 40 winters of each run (denoted as “the
future”) to the first 40 winters (denoted as “the past”). Un-
less otherwise stated, anomalies are calculated from the cli-
matology of the corresponding 40-year period. A Student’s
t-test is applied to determine if the future changes are statisti-
cally significant in all cases except for the duration of SSWs,
where we applied a Wilcoxon ranked-sum test. The perfor-
mance of the models in reproducing SSW characteristics for
the past period (1960–2000) is assessed by comparing the
models to the ERA-40 and JRA-55 reanalyses (Uppala et al.,
2005; Kobayashi et al., 2015). Both reanalyses extend back
to before 1979; ERA-40 data starts in September 1957 and
JRA-55 begins in January 1958. Thus, they cover the past pe-
riod of our study. Among the few reanalyses that have avail-
able data in the pre-satellite era, ERA-40 and JRA-55 are the
most suitable for middle-atmosphere analyses because they
have a higher top level and vertical resolution (Fujiwara et
al., 2017).
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11280 B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings
2.2 Criteria for the detection of SSWs
As the detection of SSWs is somewhat sensitive to the cho-
sen criterion, we use three different criteria to ensure that the
conclusions regarding future changes are the same irrespec-
tive of the metric. The criteria we use are described in Butler
et al. (2015) and as follows.
1. WMO criterion
SSWs are identified when the zonal-mean zonal wind at
10 hPa and 60N and the zonal-mean temperature dif-
ference between 60N and the pole at the same level
are simultaneously reversed. Two events must be sepa-
rated by at least 20 consecutive days of westerly winds.
Only events from November to March are considered.
Stratospheric final warmings are excluded by imposing
at least 10 days with westerly winds after the occurrence
of a SSW and before 30 April, to ensure the recovery of
the polar vortex before its final breakup. The onset date
of the event corresponds to the first day of the wind re-
versal.
2. Polar cap zonal wind reversal (u6090N)
SSWs are identified when the area-weighted zonal wind
at 10 hPa averaged over the polar cap (60–90N) re-
verses. The separation of events and the exclusion of
stratospheric final warmings are done in the same way
as for the WMO criterion.
3. Polar cap 10 hPa geopotential (ZPOL)
SSWs are identified based on the polar cap standardized
anomalies of 10 hPa geopotential height. The anoma-
lies are detrended and computed following Gerber et
al. (2010). A SSW is detected if the anomalies exceed
3 standard deviations of the climatological January–
March geopotential height (Thompson et al., 2002).
Note that WMO and u6090N are absolute SSW criteria,
whereas ZPOL is a relative one.
2.3 Other SSW characteristics
Beyond their frequency, we also study if the other key char-
acteristics of SSWs – such as duration, deceleration of the
polar night jet, and tropospheric forcing – will change in the
future. The considered events in all features are those identi-
fied by the WMO criterion, because it is a popular criterion
and, as will be shown later, the conclusions relative to the fre-
quency results are not different from those obtained for the
other two criteria. The following three subsections describe
the metrics/diagnostics applied.
2.3.1 Duration
The duration of the events is computed by the number of con-
secutive days of easterly wind regime at 60N and 10hPa as
in Charlton et al. (2007).
2.3.2 Deceleration of the polar night jet
The deceleration of the polar night jet associated with the
occurrence of SSWs is defined as the difference in the zonal-
mean zonal wind at 60N and 10 hPa, 15–5 days before the
SSWs minus 0–5 days after the SSW as in Charlton and
Polvani (2007).
2.3.3 Tropospheric forcing
The analysis of the tropospheric forcing is based on the evo-
lution of the anomalous eddy heat flux at 100 hPa averaged
between 45 and 75N (aHF100) before and after the occur-
rence of SSWs. aHF100 is a measure of the injection of tro-
pospheric wave activity into the stratosphere (Hu and Tung,
2003).
3 Future changes in the main characteristics of SSWs
3.1 Mean frequency
We start by considering the frequency of SSWs and whether
it is projected to change as a consequence of anthropogenic
forcings. For this purpose, we have identified SSWs in the
12 models listed in Table 1, for the past and future periods,
according to the three criteria presented in Sect. 2.2. Figure 1
shows the mean frequency of SSWs for each case.
In spite of some differences among the criteria, there ap-
pears to be a suggestion of a small increase in frequency in
the multi-model mean (hereafter MM), but this tendency is
not statistically significant at the 95 % confidence level for
any of the criteria, either absolute (WMO, u6090N) or rela-
tive (ZPOL). Also, while most models show a small increase
in the frequency of SSWs in the future (10 of 12 models for
the WMO criterion, 9 of 12 in the u6090N criterion, and 7
of 12 for the ZPOL), most of those changes are not statis-
tically significant. Specifically, none of the models displays
a statistically significant future change for the relative cri-
terion (ZPOL) (Fig. 1c); only 3 out of 12 models show a
significant increase for the WMO criterion (NIWA-UKCA,
EMAC-L90, and CMAM) (Fig. 1a), and only 2 out of 12
models for the u6090N criterion (SOCOL3, EMAC-L90)
(Fig. 1b). It is, however, important to note that the NIWA-
UKCA and CMAM models do not simulate a realistic fre-
quency of SSWs when compared to reanalyses for the cur-
rent climate, so they may not be a reliable indicator of pos-
sible future changes. Additionally, none of the four models
(NIWA-UKCA, SOCOL3, EMAC-L90, and CMAM) shows
an increase in SSWs for the three criteria simultaneously, in-
dicating the lack of consistency for those models across the
different methods. This confirms the absence of a robust fu-
ture signal regarding changes in the frequency of SSWs.
A further comparison of the results for the different criteria
for the past period confirms the findings of previous studies
(e.g. McLandress and Shepherd, 2009) which showed that
Atmos. Chem. Phys., 18, 11277–11287, 2018 www.atmos-chem-phys.net/18/11277/2018/
B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings 11281
MSWs/decade
WMO criterion
u6090N criterion
0
5
10
15
MSWs/decade
ZPOL criterion
(a)
(b)
(c)
Past
Future
CCSRNIES-MIROC3.2
NIWA-UKCA
GEOS-CCM
CNRM-CCM
ACCESS CCM
HadGEM3-ES
MRI-ESM1r1
SOCOL3
IPSL
EMAC-L90
EMAC-L47
CMAM
MM
JRA-55
ERA-40
0
5
10
15
CCSRNIES-MIROC3.2
NIWA-UKCA
GEOS-CCM
CNRM-CCM
HadGEM3-ES
MRI-ESM1r1
SOCOL3
IPSL
EMAC-L90
EMAC-L47
CMAM
MM
JRA-55
ERA-40
MSWs/decade
0
5
10
15
ACCESS CCM
CCSRNIES-MIROC3.2
NIWA-UKCA
GEOS-CCM
CNRM-CCM
HadGEM3-ES
MRI-ESM1r1
SOCOL3
IPSL
EMAC-L90
EMAC-L47
CMAM
MM
JRA-55
ERA-40
ACCESS CCM
Figure 1. (a) Mean frequency of stratospheric sudden warmings
per decade for the past (blue bars) and the future (red bars) for all
models, the multi-model mean (MM), and JRA-55 and ERA-40 re-
analyses (black bars) according to the WMO criterion. (b, c) Same
as (a) but for the u6090N and ZPOL, respectively. Green stars on
top of the future bar denote a statistically significant change in the
frequency of SSWs in the future at the 95 % confidence level.
models’ biases in mean state and variability affect the fre-
quency values for the absolute criteria, since the different
models show a wide range of SSW frequency values in the
past period (see Fig. S1 in the Supplement). For instance,
CCSRNIES-MIROC3.2 and NIWA-UKCA show very low
SSW frequencies in agreement with the fact that the polar
vortex in these models is much stronger than in the reanaly-
ses, and the opposite is seen for ACCESS CCM, CMAM and
CNRM-CCM (Fig. S2). Note the good agreement between
the JRA-55 and ERA-40 reanalyses. Conversely, SSW fre-
quencies computed with the relative ZPOL criterion are more
similar across the models, as they are less affected by cli-
matological model biases. Interestingly, note how the values
for the relative criterion are somewhat lower in models than
in the reanalyses. Since the threshold for selecting events is
based on the latter, this suggests that models may be underes-
timating the variability of the Arctic polar stratosphere. Nev-
ertheless, regardless of the biases of models and their differ-
ent representations of the underlying processes, the null fu-
ture change in the frequency of SSWs is a robust result across
all examined models.
Finally, it is worth highlighting that nearly identical re-
sults to the ones obtained with the WMO criterion are found,
for both past and future periods, when only the reversal of
the wind at 60N and 10 hPa (Charlton and Polvani, 2007)
is used as the identification criterion. It is reassuring to re-
port that the additional temperature constraint imposed in the
WMO criterion does not significantly alter the frequency of
SSWs, even for the future climates. This means that most
recent studies, which have used the simpler method and con-
sidered the reversal of the wind as the sole quantity for identi-
fying SSWs, would have likely reached the same conclusions
had they used the more precise WMO criterion and can thus
be considered valid.
3.2 Duration
Next, we turn to the duration of SSWs, for which the results
are shown in Fig. 2a, for the past and future. In each period,
we notice a considerable spread across the models; nonethe-
less, the MM value for the past period falls within the interval
of reanalyses values ±1.5 standard error. Note, however, the
variability within each model is larger than that across the
models. This is particularly true for the NIWA-UKCA and
CCSRNIES-MIROC3.2 models, possibly as a consequence
of the low number of SSWs simulated by these two models.
MRI-ESM1r1 also shows a large variability in SSW duration,
but only in the past period.
The key message from Fig. 2a is that the duration of SSWs
does not change in the future, using the canonical 95 % con-
fidence level for each individual model. In fact, even at the
90 % confidence level SSWs show a statistically significant
change in only one model (HaddGEM3-ES). Nevertheless, as
in the case of the mean frequency, more than half of the mod-
els (7 out of 12) agree on the sign of the future change in the
SSW duration (they indicate that it will be slightly shorter),
but this change in the MM is not statistically significant at
the 95 % confidence level.
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11282 B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings
CCSRNIES-MIROC3.2
NIWA-UKCA
GEOS-CCM
CNRM-CCM
ACCESS CCM
HadGEM3-ES
MRI-ESM1r1
SOCOL3
IPSL
EMAC-L90
EMAC-L47
CMAM
MM
JRA-55
ERA-40
0
5
10
15
Duration [days]
Duration of SSWs
Past
Future
0
5
10
15
20
25
30
35
40
Zonal-mean zonal wind [m s
-1
]
Past
Future
Deceleration of PNJ
CCSRNIES-MIROC3.2
NIWA-UKCA
GEOS-CCM
CNRM-CCM
ACCESS CCM
HadGEM3-ES
MRI-ESM1r1
SOCOL3
IPSL
EMAC-L90
EMAC-L47
CMAM
MM
JRA-55
ERA-40
(a) (b)
Figure 2. (a) Duration of SSWs (in days) and (b) deceleration of the PNJ associated with SSWs (in m s1) in each model for both periods
of study. Bars denote ±1.5 standard error, and green stars indicate future values that are statistically significantly different from the past ones
at the 95 % confidence level.
3.3 Deceleration of the polar night jet
The next step is the assessment of future changes in the de-
celeration of the polar night jet (PNJ) associated with SSWs
(Fig. 2b). Similar to the duration and mean frequency, the
MM value of the PNJ deceleration does not change in the
future at the 95 % confidence level, with only two models
(EMAC-L90 and CMAM) showing a significant future re-
duction. These are the same models that show a significant
though small increase in SSWs in the future with the WMO
criterion (an absolute criterion), but at least in one of these
models (CMAM) the climatological polar vortex is unrealis-
tically weak.
It is also worth noting that the MM value for the past pe-
riod falls out of the interval of reanalysis values ±1.5 stan-
dard error. Thus, one could question the reliability of the fu-
ture projections of the deceleration of the polar night jet dur-
ing SSWs. Half of the models show values included in the
reanalysis interval, and only two of these six models display
a statistical change in the future. Thus, we are quite confi-
dent in concluding that the PNJ deceleration during SSWs
does not change in the future.
3.4 Tropospheric forcing
Since SSWs are usually triggered by anomalously high
tropospheric wave activity entering the stratosphere in the
weeks preceding the events (Matsuno, 1971; Polvani and
Waugh, 2004), we have analysed the possible future changes
in the injection of wave activity during the course of the oc-
currence of these events for the MM. Thus, as indicated in
Sect. 2.3.3, Fig. 3 displays the anomalous eddy heat flux at
100 hPa averaged between 45 and 75N (aHF100). The re-
sults do not show a statistically significant change in any as-
pect of the anomalous wave activity preceding SSWs in the
MM and in the individual models (not shown). In particular,
neither the strong peak of aHF100 of the MM in the 10 days
prior to the occurrence of events nor the general time evo-
lution of the aHF100 is projected to change in the future
(Fig. 3a). Hence the common, but not statistically signifi-
cant, trend of models towards shorter future SSWs mentioned
above cannot be explained by changes in tropospheric forc-
ing. Additionally, when examining the two first zonal wave
number components of the anomalous HF100, no significant
future changes are found either (Fig. 3b). This would also im-
ply little change in the distribution of split and displacement
SSWs.
Model projections of future aHF100 are reliable because
models are able to simulate the tropospheric forcing of these
events reasonably well (Fig. 3). Only a few discrepancies can
be seen between the MM and the mean of JRA-55 and ERA-
40 reanalyses (reanalysis mean, RM; black curve). Note that
we include the average of JRA-55 and ERA-40 because they
show very similar results, and we avoid confusion by includ-
ing too many lines in the same plot. One of the discrepan-
cies between MM and RM is that the strong peak in aHF100
in the 5 days prior to the occurrence of SSWs is weaker in
the models than in observations. The reanalyses also show
a secondary peak of aHF100 between 20 and 10 days
that does not appear in the MM. Additionally, the contribu-
tion of the wave number 1 (WN1) component to the strongest
wave pulse is similar or even stronger than in the reanalyses
(Fig. 3b), but the wave number 2 (WN2) component in the
models is much weaker than in the RM. This explains the
weaker total value of aHF100 in the MM than in the RM.
Nevertheless, the RM is only one realization averaged over
40 years, and the MM corresponds to the average over many
more realizations. Thus, the multi-model/individual realiza-
tion spread possibly accounts for at least some of the mis-
match between MM and RM. In any case, the models show
no statistically significant changes between the past and the
future.
Atmos. Chem. Phys., 18, 11277–11287, 2018 www.atmos-chem-phys.net/18/11277/2018/
B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings 11283
aHF100 (all wave numbers)
aHF100 (WN1 and WN2)
Days
Anom. heat flux [K m s
-1
]
Past WN1
Future WN1
Past WN2
Future WN2
RM WN1
RM WN2
(a) (b)
-30 -25 -20 -15 -10 -5 10
-10
-5
0
5
10
15
20
25
Anom. heat flux [K m s
-1
]
Days
50 -10
-5
0
5
10
15
20
-30 -25 -20 -15 -10 -5 1050
Past
Future
RM
Figure 3. (a) Multi-model mean of anomalous heat flux (K m s1) at 100hPa averaged over 45–75N from 30 days before until 10 days
after the occurrence of SSWs. (b) Same as (a) but for WN1 (solid lines) and WN2 (dashed lines) wave components. Thick lines denote
statistically significant future values different from the past ones at the 95 % confidence level. RM stands for reanalysis mean (JRA-55 and
ERA-40).
4 Discussion and conclusions
We have revisited the question of whether SSWs will change
in the future, analysing 12 state-of-the-art stratosphere re-
solving models that participated in CCMI. To obtain robust
results, we have used three different identification criteria
(two absolute and one relative) and have applied them consis-
tently across all 12 models. Moreover, unlike most previous
multi-model comparison studies, we have not restricted our
analysis to the mean frequency of SSWs, but we have also
analysed other SSW characteristics that are important for the
stratosphere–troposphere coupling. In summary, our analysis
reveals the following:
No statistically significant changes in the frequency of
occurrence of SSWs are to be expected in the coming
decades and until the end of the 21st century. This result
is robust, as it is obtained with three different identifica-
tion criteria.
Other features of SSWs – such as their duration, decel-
eration of the polar night jet, and the tropospheric pre-
cursor wave fluxes – do not change in the future either
in the model simulations, in agreement with other stud-
ies, such as McLandress and Shepherd (2009) and Bell
et al. (2010).
The absence of a future change in SSWs is a robust re-
sult across all models examined here, regardless of their
biases or different representation of the QBO, coupling
to the ocean, solar variability, etc.
Despite the lack of statistically significant changes in the
frequency of SSWs, both the MM and the majority of the
models analysed show a slight increase in frequency across
all criteria (Fig. 1). A similar result was reported by Kim
et al. (2017), who analysed the change in SSW frequency
in some Coupled Model Intercomparison Project Phase 5
(CMIP5) models by identifying the events based either on
the reversal of the wind or on the vortex deceleration. Look-
ing at changes in the daily climatology of the zonal-mean
zonal wind at 10 hPa (Figs. 4a and S3), the MM and indi-
vidual model simulations also provide a consistent picture,
with a robust weakening of the PNJ from mid-December un-
til mid-March, the deceleration being particularly strong be-
tween mid-December and mid-February; this is in agreement
with previous CMIP5 results (Manzini et al., 2014). This de-
celeration is, however, only statistically significant in less
than half of the models (Fig. S3), explaining why we do not
find a significant change in the tropospheric forcing of SSWs
(Fig. 3). To determine whether these changes in the climatol-
ogy of wintertime PNJ might be associated with changes in
SSW frequency, the future-minus-past difference plots of the
climatological wind are shown separately for winters with
and without SSWs (Fig. 4b and c, respectively). We find a
weakening of the PNJ in mid-winter in both cases: this allows
us to conclude that the future deceleration of the PNJ is not a
consequence of a higher frequency of SSWs. This decelera-
tion might be related to a general increase in the total strato-
spheric variability that, in the case of winters without SSWs,
would correspond to a higher frequency of minor warmings.
However, this possibility is unlikely because we do not find
a robust future increase in the standard deviation of zonal-
mean zonal wind at 10 hPa across the models or a change in
the shape of the distribution of the zonal-mean zonal wind as
shown in Fig. S4. Perhaps the future deceleration of the PNJ
might explain the statistically significant increase in SSWs in
a few models, using the absolute criteria in agreement with
McLandress and Shepherd (2009). In any case, these signals
are small, and it is nearly impossible to untangle the cause
and the effect, as these changes occur simultaneously.
More importantly our findings dispel, to a large degree,
the confusion in the literature regarding future SSW changes
and suggest that previous reports of significant changes are
www.atmos-chem-phys.net/18/11277/2018/ Atmos. Chem. Phys., 18, 11277–11287, 2018
11284 B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings
All years
m s
-1
(a)
With SSWs
(b)
Without SSWs
(c)
90° N
60° N
30° N
90° N
60° N
30° N
90° N
60° N
30° N
Figure 4. (a) Multi-model mean of future-minus-past differences in the daily climatology of 5-day running mean of zonal-mean zonal wind
at 10 hPa. (b) Same as (a) but only for winters with SSWs. (c) Same as (a) but for winters without SSWs. Shading interval: 1m s1. Dots
indicate where at least 75 % of the models coincide in sign with the multi-model mean.
likely to be artefacts, caused by biases associated with indi-
vidual models or by flaws in the identification methods used
(or both). In addition, the analysis of other features of SSWs
besides the mean frequency supports the key finding of our
study, i.e. that anthropogenic forcings will not affect SSWs
over the 21st century. Our results confirm and expand the
findings of Kim et al. (2017), who did not find a statistically
significant future change in the frequency of SSWs in CMIP5
models. Note that, although the key finding of our study is
a null result, it is by no means uninteresting. Just to offer
one example: Kang and Tziperman (2017) have recently pro-
posed that future changes in the Madden–Julian Oscillation
(which are expected to occur with increased levels of CO2in
the atmosphere) will cause an increased occurrence of SSWs.
While their conclusion may be correct, our findings indicate
that it can be misleading to project changes in the SSWs on
the basis of a single mechanism: the complexity of the cli-
mate system is such that multiple mechanisms may be at
play, with likely opposite effects, which may result in net
changes that are not statistically significant.
One may argue that the lack of a statistically significant
future change in our study could be explained, at least par-
tially, by the high interannual variability of the boreal polar
stratosphere in 40-year periods (e.g. Langematz and Kunze,
2006), or perhaps by the natural variability on longer time-
scales coming from other subcomponents of the climate sys-
tem (e.g. Schimanke et al., 2011). As shown in a recent pa-
per, 10 identically forced model simulations over the 50-year
period 1952–2003 exhibit great differences in the number of
SSWs, and these differences are solely due to internal vari-
ability (Polvani et al., 2017). This means that the 40 years
of observations at our disposal may not represent the mean
of a distribution but could happen to be an outlier. Needless
to say, we have no means of determining whether this is the
case, as we do not have long enough observations.
One might also object that the forcing in the scenario used
of our runs (RCP6.0) is not extreme enough to produce a sig-
nificant signal in the frequency and duration of SSWs, but
that a significant change would occur with stronger forcing,
such as the RCP8.5 scenario. Although we cannot rule out
this possibility, it seems improbable based on a similar lack
of significance in the results documented for that very ex-
treme scenario by several previous studies (Mitchell et al.,
2012a; Ayarzagüena et al., 2013; Hansen et al., 2014; Kim
et al., 2017). Nevertheless, it would be hard to verify the hy-
pothesis because of the low number of CCMI RCP8.5 simu-
lations available.
Finally, in recent years much activity has been devoted to
searching for novel criteria for the identification of SSWs
(Butler et al., 2015). One of the reasons given to justify
the implementation of a new metric was that the traditional
WMO criterion was not appropriate for modelling studies, as
it was based on observationally chosen parameters, such as
the location of the polar night jet. However, our results show
that this criterion performs well under a changing climate,
provided models are able to reproduce correctly the past
stratospheric variability. Thus, considering the good agree-
ment among the three criteria used here on the lack of change
in future SSWs, and given the dynamical implications for the
propagation of planetary waves into the stratosphere, we sug-
gest that the WMO criterion is appropriate for the study of
SSWs in the future if the model can represent well the strato-
spheric variability. Furthermore, since the simplest (and most
commonly used) criterion, involving only the zonal winds
(Charlton and Polvani, 2007), yields identical results to those
of the WMO criterion, one could argue that the simplest
method may suffice in most cases for the study of SSWs, and
that more complex criteria might not be worth the trouble.
A similar conclusion was reached, independently, by Butler
and Gerber (2018), who methodically assessed different met-
Atmos. Chem. Phys., 18, 11277–11287, 2018 www.atmos-chem-phys.net/18/11277/2018/
B. Ayarzagüena et al.: No robust evidence of future changes in major stratospheric sudden warmings 11285
rics and concluded that the simplest algorithm is within the
optimal range.
Data availability. Data in this paper were mostly downloaded
from the Centre for Environmental Data Analysis (http://catalogue.
ceda.ac.uk/uuid/9cc6b94df0f4469d8066d69b5df879d5, last access:
21 March 2018) or supplied directly by the co-authors. For instruc-
tions on how to access this archive, see http://blogs.reading.ac.uk/
ccmi/badc-data-access (last access: 21 March 2018). The data sup-
plied by the co-authors will in due course be uploaded to the CEDA
archive.
The Supplement related to this article is available
online at https://doi.org/10.5194/acp-18-11277-2018-
supplement.
Author contributions. BA, LMP, and UL designed the analysis and
wrote the paper. BA carried out the analysis of the model output and
drafted all the figures. The other authors ran the individual model,
contributed the output, and helped revise the paper.
Competing interests. The authors declare that they have no conflict
of interest.
Special issue statement. This article is part of the special
issue “Chemistry–Climate Modelling Initiative (CCMI)
(ACP/AMT/ESSD/GMD inter-journal SI)”. It is not associ-
ated with a conference.
Acknowledgements. We acknowledge the modelling groups for
making their simulations available for this analysis, the joint WCRP
SPARC/IGAC Chemistry–Climate Model Initiative (CCMI) for
organizing and coordinating the model data analysis activity,
and the British Atmospheric Data Centre (BADC) for collecting
and archiving the CCMI model output. Blanca Ayarzagüena
was funded by the European Project 603557-STRATOCLIM
under the FP7-ENV.2013.6.1-2 programme and “Ayudas para la
contratación de personal postdoctoral en formación en docencia
e investigación en departamentos de la Universidad Complutense
de Madrid”. Blanca Ayarzagüena and Ulrike Langematz wish to
acknowledge the Deutsche Forschungsgemeinschaft (DFG) within
the research programme SHARP under the grant LA 1025/15-1.
Lorenzo M. Polvani is grateful for the continued support of the
US National Science Foundation. The work of Neal Butchart,
Steven C. Hardiman, and Fiona M. O’Connor was supported by the
Joint BEIS/Defra Met Office Hadley Centre Climate Programme
(GA01101). Neal Butchart and Steven C. Hardiman were sup-
ported by the European Community within the StratoClim project
(grant 603557). Olaf Morgenstern and Guang Zeng acknowledge
the UK Met Office for use of the Met Office Unified Model
(MetUM). This research was supported by the New Zealand
Government’s Strategic Science Investment Fund (SSIF) through
the NIWA programme CACV. Olaf Morgenstern acknowledges
funding by the New Zealand Royal Society Marsden Fund
(grant 12-NIW-006) and by the Deep South National Science
Challenge (http://www.deepsouthchallenge.co.nz, last access:
21 March 2018). The authors wish to acknowledge the contribution
of New Zealand eScience Infrastructure (NeSI) high-performance
computing (HPC) facilities to the results of this research. New
Zealand’s national facilities are provided by NeSI and funded
jointly by NeSI’s collaborator institutions and through the Ministry
of Business, Innovation & Employment’s Research Infrastructure
programme (https://www.nesi.org.nz, last access: 21 March 2018).
The EMAC simulations were performed at the German Climate
Computing Centre (DKRZ) through support from the Bundesmin-
isterium für Bildung und Forschung (BMBF). DKRZ and its
Scientific Steering Committee are gratefully acknowledged for
providing the HPC and data archiving resources for the consortial
project ESCiMo (Earth System Chemistry integrated Modelling).
CCSRNIES’s research was supported by the Environment Research
and Technology Development Funds of the Ministry of the Envi-
ronment (2-1303) and Environment Restoration and Conservation
Agency (2-1709), Japan, and computations were performed on
NEC-SX9/A(ECO) and NEC SX-ACE computers at the Center for
Global Environmental Research, NIES. The authors wish to thank
two anonymous referees for their helpful comments.
Edited by: Gunnar Myhre
Reviewed by: two anonymous referees
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... These changes result from increased atmospheric wave driving of the winds which can overwhelm the cooling effect of greenhouse gases (Karpechko and Manzini, 2012) and can lead to important differences in future surface climate, for example in regional rainfall in areas typically affected by the stratosphere via the Arctic Oscillation and NAO . There is still significant uncertainty due to the diversity of modelled stratospheric responses to greenhouse gas increases Simpson et al., 2018;Zappa and Shepherd, 2017), and it has proved difficult to identify any clear change in the frequency of sudden stratospheric warmings (Ayarzagüena et al., 2018a(Ayarzagüena et al., , 2020. This is perhaps due to the competition between strengthening latitudinal temperature gradients near the tropopause and enhanced meridional overturning in the mid stratosphere. ...
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Over recent years there have been concomitant advances in the development of stratosphere-resolving numerical models, our understanding of stratosphere–troposphere interaction, and the extension of long-range forecasts to explicitly include the stratosphere. These advances are now allowing for new and improved capability in long-range prediction. We present an overview of this development and show how the inclusion of the stratosphere in forecast systems aids monthly, seasonal, and annual-to-decadal climate predictions and multidecadal projections. We end with an outlook towards the future and identify areas of improvement that could further benefit these rapidly evolving predictions.
... Another important difference is that in the Isca model the nonlinear term displays larger values for both types of SSWs compared to the reanalysis. This latter behavior might be related to the stronger SSW sensitivity to wave-2 forcing in the idealized models (e.g., Isca model) where most of the SSWs are likely triggered by wave-2 activity (Gerber and Polvani, 2009), in contrast with wave-1 which seems to be more dominant in more complex general circulation models or reanalysis datasets (see for example Fig. 3 in Ayarzagüena et al., 2018). Note that even when classifying an SSW event as a wave-1 event in the model, its wave-2 component, although weaker than the wave-1 component, might still play an important role in the overall evolution of the event. ...
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Major sudden stratospheric warmings (SSWs) are extreme wintertime circulation events of the Arctic stratosphere that are accompanied by a breakdown of the polar vortex and are considered an important source of predictability of tropospheric weather on subseasonal to seasonal timescales over the Northern Hemisphere midlatitudes and high latitudes. However, SSWs themselves are difficult to predict, with a predictability limit of around 1 to 2 weeks. The predictability limit for determining the type of event, i.e., wave-1 or wave-2 events, is even shorter. Here we analyze the dynamics of the vortex breakdown and look for early signs of the vortex deceleration process at lead times beyond the current predictability limit of SSWs. To this end, we employ a mode decomposition analysis to study the potential vorticity (PV) equation on the 850 K isentropic surface by decomposing each term in the PV equation using the empirical orthogonal functions of the PV. The first principal component (PC) is an indicator of the strength of the polar vortex and starts to increase from around 25 d before the onset of SSWs, indicating a deceleration of the polar vortex. A budget analysis based on the mode decomposition is then used to characterize the contribution of the linear and nonlinear PV advection terms to the rate of change (tendency) of the first PC. The linear PV advection term is the main contributor to the PC tendency at 25 to 15 d before the onset of SSW events for both wave-1 and wave-2 events. The nonlinear PV advection term becomes important between 15 and 1 d before the onset of wave-2 events, while the linear PV advection term continues to be the main contributor for wave-1 events. By linking the PV advection to the PV flux, we find that the linear PV flux is important for both types of SSWs from 25 to 15 d prior to the events but with different wave-2 spatial patterns, while the nonlinear PV flux displays a wave-3 wave pattern, which finally leads to a split of the polar vortex. Early signs of SSW events arise before the 1- to 2-week prediction limit currently observed in state-of-the-art prediction systems, while signs for the type of event arise at least 1 week before the event onset.
... Consequently, a greater understanding of these events is crucial in developing climate forecasts. The realistic capture of SSWs is vital in accurately representing climate variability and potential future weather and climate extremes (Ayarzagüena et al., 2018). ...
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Sudden stratospheric warmings (SSWs) can have major impact on surface wintertime weather, especially at mid‐high latitudes. We do not yet have a complete understanding of why some of these events influence our weather more than others, but one factor may be the dynamical nature of the SSW; whether it involves a split or a displacement of the polar vortex, and one way to explore this is through comprehensive climate models. Here, we analyze the stratospheric dynamics of SSWs within models from the sixth Coupled Model Intercomparison Project (CMIP6). All CMIP6 models simulate SSWs to some degree, although we find a persistent bias in the relative underrepresentation of split vortex events. When comparing with CMIP5 models, large biases persist despite significant model improvements in resolution and in representing atmospheric processes. We show that the simulated displacement frequency is strongly related to climatological lower stratospheric eddy heat flux. The split frequency, on the other hand, is not related to lower stratospheric eddy heat flux, but is strongly related to both the vortex geometry (aspect ratio) and lower stratospheric zonal winds. This suggests that those models with a large positive bias in zonal winds may inhibit the propagation of zonal wavenumber 2 planetary waves from the troposphere, which are associated with split events. Our results suggest how future model development may address these longstanding biases.
... In contrast, for November-March, we find virtually no difference in SSWs (109 events vs. 111, not shown) in CHEM and NOCHEM. We note that both of these frequencies, around 5.5 events per decade, are on the lower end of what is seen across reanalyses (Butler et al., 2017;Cao et al., 2019) but very well within the spread among state-of-the-art chemistry-climate models (Ayarzagüena et al., 2018). ...
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Modeling and observational studies have reported effects of stratospheric ozone extremes on Northern Hemisphere spring climate. Recent work has further suggested that the coupling of ozone chemistry and dynamics amplifies the surface response to midwinter sudden stratospheric warmings (SSWs). Here we study the importance of interactive ozone chemistry in representing the stratospheric polar vortex and Northern Hemisphere winter surface climate variability. We contrast two simulations from the interactive and specified chemistry (and thus ozone) versions of the Whole Atmosphere Community Climate Model, which is designed to isolate the impact of interactive ozone on polar vortex variability. In particular, we analyze the response with and without interactive chemistry to midwinter SSWs, March SSWs, and strong polar vortex events (SPVs). With interactive chemistry, the stratospheric polar vortex is stronger and more SPVs occur, but we find little effect on the frequency of midwinter SSWs. At the surface, interactive chemistry results in a pattern resembling a more negative North Atlantic Oscillation following midwinter SSWs but with little impact on the surface signatures of late winter SSWs and SPVs. These results suggest that including interactive ozone chemistry is important for representing North Atlantic and European winter climate variability.
... In contrast, during warmer climates, the circulation is predicted to accelerate due to increased eddy fluxes (McLandress and Shepherd, 2009). In a warmer climate there are competing effects of a colder stratosphere and more wave driving (Ayarzagüena et al., 2018); however, in a model with a strengthened Madden Julian Oscillation, the stratosphere is predicted to exhibit a weaker polar vortex, more sudden stratospheric warming events, and warmer temperatures (Kang and Tziperman, 2017). ...
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According to the Snowball Earth hypothesis, Earth has experienced periods of low‐latitude glaciation in its deep past. Prior studies have used general circulation models (GCMs) to examine the effects such an extreme climate state might have on the structure and dynamics of Earth's troposphere, but the behavior of the stratosphere has not been studied in detail. Understanding the snowball stratosphere is important for developing an accurate account of the Earth's radiative and chemical properties during these episodes. Here we conduct the first analysis of the stratospheric circulation of the Snowball Earth using ECHAM6 general circulation model simulations. In order to understand the factors contributing to the stratospheric circulation, we extend the Statistical Transformed Eulerian Mean framework. We find that the stratosphere during a snowball with prescribed modern ozone levels exhibits a weaker meridional overturning circulation, reduced wave activity, and stronger zonal jets and is extremely cold relative to modern conditions. Notably, the snowball stratosphere displays no sudden stratospheric warmings. Without ozone, the stratosphere displays a complete lack of polar vortex and even colder temperatures. We also explicitly quantify for the first time the cross‐tropopause mass exchange rate and stratospheric mixing efficiency during the snowball and show that our values do not change the constraints on CO2 inferred from geochemical proxies during the Marinoan glaciation (ca. 635 Ma), unless the O2 concentration during the snowball was orders of magnitude less than the CO2 concentration.
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Stratospheric circulation is a critical part of the Arctic ozone cycle. Sudden stratospheric warming events (SSWs) manifest the strongest alteration of stratospheric dynamics. During SSWs, changes in planetary wave propagation vigorously influence zonal mean zonal wind, temperature, and tracer concentrations in the stratosphere over the high latitudes. In this study, we examine six persistent major SSWs from 2004 to 2020 using the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Using the unique density of observations around the Greenland sector at high latitudes, we perform comprehensive comparisons of high-latitude observations with the MERRA-2 ozone dataset during the six major SSWs. Our results show that MERRA-2 captures the high variability of mid-stratospheric ozone fluctuations during SSWs over high latitudes. However, larger uncertainties are observed in the lower stratosphere and troposphere. The zonally averaged stratospheric ozone shows a dramatic increase of 9 %–29 % in total column ozone (TCO) near the time of each SSW, which lasts up to 2 months. This study shows that the average shape of the Arctic polar vortex before SSWs influences the geographical extent, timing, and magnitude of ozone changes. The SSWs exhibit a more significant impact on ozone over high northern latitudes when the average polar vortex is mostly elongated as seen in 2009 and 2018 compared to the events in which the polar vortex is displaced towards Europe. Strong correlation (R2=90 %) is observed between the magnitude of change in average equivalent potential vorticity before and after SSWs and the associated averaged total column ozone changes over high latitudes. This paper investigates the different terms of the ozone continuity equation using MERRA-2 circulation, which emphasizes the key role of vertical advection in mid-stratospheric ozone during the SSWs and the magnified vertical advection in elongated vortex shape as seen in 2009 and 2018.
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This study investigated the influence of the interdecadal changes in the Pacific-North America (PNA) and Western Pacific (WP) teleconnections on the planetary waves and stratospheric sudden warmings (SSWs), based on the National Centre for Environmental Prediction reanalysis and Japanese 55-year reanalysis. Before the 1980s, there were more negative PNA & negative WP combinations (-PNA&-WP), and after the 1980s, there were more +PNA&+WP. +PNA&-WP and -PNA&+WP had no significant interdecadal changes before and after the 1980s. The interdecadal changes in the teleconnection combinations may be related to the Pacific Decadal Oscillation (PDO). During +PNA&+WP (-PNA&-WP), the planetary waves with wavenumber 1 have the maximum (minimum) wave energy and propagating more (less) to the stratosphere, causing a greater (lesser) frequency of SSWs. Thus, the interdecadal changes in the teleconnection combinations are one of the important factors which could contribute to the possible increase in SSWs after the 1980s. Since there are no satellite observations on the stratosphere before 1979, there are still many uncertainties in the interdecadal changes in SSWs.
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Plain Language Summary The stratosphere at 10–50 km height can influence surface weather for several months. In 2002 and 2019, the stratosphere warmed over Antarctica within a few days to weeks. This caused dry and hot summers in Australia and South America, and it reduced the size of the ozone hole. Since these warming events are rare, it is difficult to say how often they occur. We therefore use long computer simulations to answer that question. We find that without climate change, warming events occur about every 22 years, but with climate change, the warming events will happen only once every 300 years. From this, we believe that the quick succession of two events in 2002 and 2019 will remain special in history.
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Sudden stratospheric warmings (SSWs) are impressive fluid dynamical events in which large and rapid temperature increases in the winter polar stratosphere (∼10–50 km) are associated with a complete reversal of the climatological wintertime westerly winds. SSWs are caused by the breaking of planetary‐scale waves that propagate upwards from the troposphere. During an SSW, the polar vortex breaks down, accompanied by rapid descent and warming of air in polar latitudes, mirrored by ascent and cooling above the warming. The rapid warming and descent of the polar air column affect tropospheric weather, shifting jet streams, storm tracks, and the Northern Annular Mode, making cold air outbreaks over North America and Eurasia more likely. SSWs affect the atmosphere above the stratosphere, producing widespread effects on atmospheric chemistry, temperatures, winds, neutral (nonionized) particles and electron densities, and electric fields. These effects span both hemispheres. Given their crucial role in the whole atmosphere, SSWs are also seen as a key process to analyze in climate change studies and subseasonal to seasonal prediction. This work reviews the current knowledge on the most important aspects of SSWs, from the historical background to dynamical processes, modeling, chemistry, and impact on other atmospheric layers.
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Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
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Sudden stratospheric warming (SSW) events influence the Arctic Oscillation and midlatitude extreme weather. Observations show SSW events to be correlated with certain phases of the Madden-Julian oscillation (MJO), but the effect of the MJO on SSW frequency is unknown, and the teleconnection mechanism, its planetary wave propagation path, and time scale are still not completely understood. The Arctic stratosphere response to increased MJO forcing expected in a warmer climate using two models is studied: the comprehensive Whole Atmosphere Community Climate Model and an idealized dry dynamical core with and without MJO-like forcing. It is shown that the frequency of SSW events increases significantly in response to stronger MJO forcing, also affecting the averaged polar cap temperature. Two teleconnection mechanisms are identified: a direct propagation of MJO-forced transient waves to the Arctic stratosphere and a nonlinear enhancement of stationary waves by the MJO-forced transient waves. The MJO-forced waves propagate poleward in the lower stratosphere and upper troposphere and then upward. The cleaner results of the idealized model allow identifying the propagating signal and suggest a horizontal propagation time scale of 10-20 days, followed by additional time for upward propagation within the Arctic stratosphere, although there are significant uncertainties involved. Given that the MJO is predicted to be stronger in a warmer climate, these results suggest that SSW events may become more frequent, with possible implications on tropospheric high-latitude weather. However, the effect of an actual warming scenario on SSW frequency involves additional effects besides a strengthening of the MJO, requiring further investigation.
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A sudden stratospheric warming (SSW) is often defined as zonal-mean zonal wind reversal at 10 hPa and 60°N. This simple definition has been applied not only to the reanalysis data but also to climate model output. In the present study, it is shown that the application of this definition to models can be significantly influenced by model mean biases (i.e., more frequent SSWs appear to occur in models with a weaker climatological polar vortex). In order to overcome this deficiency, a tendency-based definition is proposed and applied to the multi-model data sets archived for the Coupled Model Intercomparison Project Phase 5 (CMIP5). In this definition, SSW-like events are defined by sufficiently strong vortex deceleration. This approach removes a linear relationship between SSW frequency and intensity of the climatological polar vortex in the CMIP5 models. The models’ SSW frequency instead becomes significantly correlated with the climatological upward wave flux at 100 hPa, a measure of interaction b...
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We present an overview of state-of-the-art chemistry–climate and chemistry transport models that are used within phase 1 of the Chemistry–Climate Model Initiative (CCMI-1). The CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, tropospheric composition, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI-1 recommendations for simulations have been followed is necessary to understand model responses to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI-1 simulations with the aim of informing CCMI data users.
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The climate research community uses atmospheric reanalysis data sets to understand a wide range of processes and variability in the atmosphere, yet different reanalyses may give very different results for the same diagnostics. The Stratosphere–troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP) is a coordinated activity to compare reanalysis data sets using a variety of key diagnostics. The objectives of this project are to identify differences among reanalyses and understand their underlying causes, to provide guidance on appropriate usage of various reanalysis products in scientific studies, particularly those of relevance to SPARC, and to contribute to future improvements in the reanalysis products by establishing collaborative links between reanalysis centres and data users. The project focuses predominantly on differences among reanalyses, although studies that include operational analyses and studies comparing reanalyses with observations are also included when appropriate. The emphasis is on diagnostics of the upper troposphere, stratosphere, and lower mesosphere. This paper summarizes the motivation and goals of the S-RIP activity and extensively reviews key technical aspects of the reanalysis data sets that are the focus of this activity. The special issue The SPARC Reanalysis Intercomparison Project (S-RIP) in this journal serves to collect research with relevance to the S-RIP in preparation for the publication of the planned two (interim and full) S-RIP reports.
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Sudden stratospheric warmings (SSWs) are large, rapid temperature rises in the winter polar stratosphere, occurring predominantly in the Northern Hemisphere. Major SSWs are also associated with a reversal of the climatological westerly zonal-mean zonal winds. Circulation anomalies associated with SSWs can descend into the troposphere with substantial surface weather impacts, such as wintertime extreme cold air outbreaks. After their discovery in 1952, SSWs were classified by the World Meteorological Organization. An examination of literature suggests that a single, original reference for an exact definition of SSWs is elusive, but in many references a definition involves the reversal of the meridional temperature gradient and, for major warmings, the reversal of the zonal circulation poleward of 60° latitude at 10 hPa. Though versions of this definition are still commonly used to detect SSWs, the details of the definition and its implementation remain ambiguous. In addition, other SSW definitions have been used in the last few decades, resulting in inconsistent classification of SSW events. We seek to answer the questions: How has the SSW definition changed, and how sensitive is the detection of SSWs to the definition used? For what kind of analysis is a “standard” definition useful? We argue that a standard SSW definition is necessary for maintaining a consistent and robust metric to assess polar stratospheric wintertime variability in climate models and other statistical applications. To provide a basis for, and to encourage participation in, a communitywide discussion currently underway, we explore what criteria are important for a standard definition and propose possible ways to update the definition.
Article
Various criteria exist for determining the occurrence of a major sudden stratospheric warming (SSW), but the most common is based on the reversal of the climatological westerly zonal-mean zonal winds at 60° latitude and 10 hPa in the winter stratosphere. This definition was established at a time when observations of the stratosphere were sparse. Given greater access to data in the satellite era, a systematic analysis of the optimal parameters of latitude, altitude, and threshold for the wind reversal is now possible. Here, the frequency of SSWs, the strength of the wave forcing associated with the events, changes in stratospheric temperature and zonal winds, and surface impacts are examined as a function of the stratospheric wind reversal parameters. The results provide a methodical assessment of how to best define a standard metric for major SSWs. While the continuum nature of stratospheric variability makes it difficult to identify a decisively optimal threshold, there is a relatively narrow envelope of thresholds that work well-and the original focus at 60° latitude and 10 hPa lies within this window.
Article
Stratospheric conditions are increasingly being recognized as an important driver of North Atlantic and Eurasian climate variability. Mindful that the observational record is relatively short, and that internal climate variability can be large, we here analyze a new 10-member ensemble of integrations of a stratosphere-resolving, atmospheric general circulation model, forced with the observed evolution of sea surface temperature (SST) during 1952-2003. We confirm previous studies, and show that El Niño conditions enhance the frequency of occurrence of stratospheric sudden warmings (SSWs), whereas La Niña does not appear to affect it. We note, however, large differences among ensemble members, suggesting caution when interpreting the relatively short observational record. More importantly, we emphasize that the majority of SSWs are not caused by anomalous tropical Pacific SSTs. Comparing composites of winters with and without SSWs in each ENSO phase separately, we demonstrate that stratospheric variability gives rise to large and statistically significant anomalies in tropospheric circulation and surface conditions over the North Atlantic and Eurasia. This indicates that, for those regions, climate variability of stratospheric origin is comparable in magnitude to variability originating from tropical Pacific SSTs, so that the occurrence of a single SSW in a given winter is able to completely alter seasonal climate predictions based solely on ENSO conditions. These findings, corroborating other recent studies, highlight the importance of accurately foreacasting SSWs for improved seasonal prediction of North Atlantic and Eurasian climate.
Article
The Japan Meteorological Agency (JMA) conducted the second Japanese global atmospheric reanalysis, called the Japanese 55-year Reanalysis or JRA-55. It covers the period from 1958, when regular radiosonde observations began on a global basis. JRA-55 is the first comprehensive reanalysis that has covered the last half-century since the European Centre for Medium-Range Weather Forecasts 45-year Reanalysis (ERA-40), and is the first one to apply four-dimensional variational analysis to this period. The main objectives of JRA-55 were to address issues found in previous reanalyses and to produce a comprehensive atmospheric dataset suitable for studying multidecadal variability and climate change. This paper describes the observations, data assimilation system, and forecast model used to produce JRA-55 as well as the basic characteristics of the JRA-55 product. JRA-55 has been produced with the TL319 version of JMA’s operational data assimilation system as of December 2009, which was extensively improved since the Japanese 25-year Reanalysis (JRA-25). It also uses several newly available and improved past observations. The resulting reanalysis products are considerably better than the JRA-25 product. Two major problems of JRA-25 were a cold bias in the lower stratosphere, which has been diminished, and a dry bias in the Amazon basin, which has been mitigated. The temporal consistency of temperature analysis has also been considerably improved compared to previous reanalysis products. Our initial quality evaluation revealed problems such as a warm bias in the upper troposphere, large upward imbalance in the global mean net energy fluxes at the top of the atmosphere and at the surface, excessive precipitation over the tropics, and unrealistic trends in analyzed tropical cyclone strength. This paper also assesses the impacts of model biases and changes in the observing system, and mentions efforts to further investigate the representation of low-frequency variability and trends in JRA-55.