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.
Introduction
Daily weather fluctuations are thought to have a deterministic
predictability horizon of around 2 weeks due to the sensitivity of the
evolution of the atmospheric state to small errors in initial conditions
(Lorenz, 1969) – the so-called “butterfly effect”. Recent estimates (Leung et
al., 2020; Domeisen et al., 2018) as well as tests of the predictability of
midlatitude daily weather using the latest global prediction models (Zhang et
al., 2019; Son et al., 2020) produce similar estimates for this
predictability limit. However, this does not preclude skilful forecasts of
the statistics (most notably the average) of conditions at long range beyond this
timescale (e.g. Shukla, 1981). This predictability owes its existence to
slowly varying predictable components of the climate system in the ocean
and in some cases the atmosphere, as well as externally forced changes such
as volcanic or solar variability effects (e.g. Kushnir et al., 2019). Some
of the more prominent examples of stratospheric variability such as sudden
stratospheric warmings and their subsequent impact on the stratosphere and
the troposphere (Baldwin et al., 2021) or the quasi-biennial oscillation
and its associated teleconnections (Scaife et al., 2014a) have been shown
to be predictable out to timescales well beyond the traditional 2-week
predictability horizon from initial tropospheric conditions alone. Other
examples involve stratospheric pathways for teleconnections originating in
the troposphere or ocean (e.g. Schwartz and Garfinkel, 2017; Byrne et al.,
2019) and are shown in Fig. 1. On longer timescales, boundary forcing, for
example from composition changes such as ozone depletion and recovery, allows
the stratosphere to provide relatively slowly varying conditions to guide
the turbulent troposphere and hence provide long-range predictability (e.g.
Thompson et al., 2011). The relative importance of stratospheric initial
conditions to boundary conditions decreases with lead time as shown in the
schematic in Fig. 1.
Schematic representation of the role of the stratosphere in long-range prediction showing the transition from initial-condition predictability in the atmosphere (blue) and the ocean (green) to boundary-condition predictability at longer timescales (orange). Individual mechanisms involving the stratosphere are labelled in black. The width of the ellipses in the timescale direction shows the approximate range over which each phenomenon provides predictability. The width of the ellipses in the variance direction shows their relative contributions to forecast variance.
The extension of long-range prediction systems to explicitly include
the representation of the stratosphere follows many years of development of
stratosphere-resolving general circulation models (GCMs). By the late
20th century many leading centres for climate research had started to
include the stratosphere in versions of their GCMs (Pawson et al., 2000;
Gerber et al., 2012). Much of the early model development was motivated by
the discovery of the ozone hole in the 1980s (Farman et al., 1985) and the
need for simulations of ozone depletion and potential recovery of the ozone
hole following the 1987 Montreal Protocol, which required atmospheric models
that represented both the atmospheric dynamics and chemistry of
stratospheric ozone depletion (Molina and Rowland, 1974; Crutzen, 1974). In
most cases this was achieved by adding further quasi-horizontal layers to
the domain of existing climate models to extend their representation of the
atmosphere to the stratopause or beyond (e.g. Rind et al., 1988; Beagley et al., 1997; Swinbank et al., 1998; Sassi et al., 2002) while also
incorporating key radiative (e.g. Fels et al., 1985), chemical (e.g. Steil
et al., 1998), and dynamical (e.g. Scaife et al., 2000) processes.
The early development of so-called “high-top” climate models, which
represent the whole depth of the stratosphere, in general preceded the
discovery of the main body of evidence that the variability of the
stratosphere is not only affected by but also interacts with the lower
atmosphere and surface climate. Pioneering early studies suggested that the
stratosphere might have direct effects on the troposphere and surface
climate (e.g. Labitzke, 1965; Boville, 1984; Kodera et al., 1990, 1995; Haynes
et al., 1991; Perlwitz and Graf, 1995). In subsequent years, as reliable
observational records lengthened and large enough samples of stratospheric
variability were amassed, it was unequivocally demonstrated that
stratospheric variability precedes important tropospheric changes in the
extratropics (Baldwin and Dunkerton, 1999, 2001). There was debate about
causality and whether the stratosphere really does affect the atmosphere
below (e.g. Plumb and Semeniuk, 2003). However, experiments where the
stratosphere is perturbed in numerical models show changes in surface
climate and reproduce similar patterns of response at the surface to those
found in real-world observations (e.g. Polvani and Kushner, 2002; Norton et
al., 2003; Scaife et al., 2005; Joshi et al., 2006; Scaife and Knight, 2008; Douville, 2009; Hitchcock and Haynes, 2016; White et al., 2020). These involve changes to
planetary-scale waves and also baroclinic eddies in the troposphere that are
consistent with changes in baroclinicity near the tropopause (Kushner and
Polvani, 2004; Song and Robinson, 2004; Wittman et al., 2004, 2007; Scaife et
al., 2012; Domeisen et al., 2013; Hitchcock and Simpson, 2014; White et al.,
2020). Importantly, as we discuss below, the same mechanisms also appear to
be at work across a broad range of timescales (Kidston et al., 2015).
In recent years, motivated by the evidence of surface effects of
stratospheric variability in the midlatitudes, the high-top model
configurations used for stratospheric research were incorporated into
leading prediction systems. Improved vertical resolution was already known
to improve the atmospheric data assimilation of satellite instrument
observations whose sensitivity was often heavily weighted towards
stratospheric altitudes. This also provided initial stratospheric conditions
for sets of retrospective forecasts, some of which were internationally
coordinated (e.g. Butler et al., 2016; Tompkins et al., 2017). A growing
number of operational systems are now producing regular ensembles of
predictions at lead times of months or years with coupled ocean–atmosphere
models that extend to the stratopause or beyond, for example at Environment
Canada (Merryfield et al., 2013), the Met Office in the UK (MacLachlan et
al., 2015), the German Weather Service (DWD; Baehr et al., 2015), the Japan
Meteorological Agency (Takaya et al., 2017), and the European Centre for
Medium-Range Weather Forecasts (Johnson et al., 2019). In the following
sections we document the emerging impacts and benefits of this new
capability for surface climate predictions at monthly, seasonal, and annual-to-decadal lead times starting with the shorter-range initial-condition
cases and ending with the longer-range boundary-condition cases.
The stratosphere and monthly prediction
The best-established phenomenon that gives rise to the predictability of surface
climate from the stratosphere is the tropospheric circulation changes that
follow strong and weak conditions in the stratospheric polar vortex (Baldwin and Dunkerton, 1999, 2001). For example, weak vortex conditions such as those
found in a sudden stratospheric warming (SSW; Baldwin et al., 2021) are
typically followed by a weakening and southward shift of the tropospheric
midlatitude jet stream (see e.g. Kidston et al., 2015, and references
therein) and thus the negative polarity of the North Atlantic Oscillation
(NAO), Arctic Oscillation (AO), and Northern Annular Mode (NAM). These
fluctuations also show a tendency to vacillate between strong westerly and
weak (SSW) states on subseasonal timescales (Kuroda and Kodera, 2001;
Hardiman et al., 2020a). The changes in the troposphere persist roughly as
long as those in the lower stratosphere and last for around 2 months
(Baldwin and Dunkerton, 2001; Baldwin et al., 2003; Hitchcock et al., 2013;
Son et al., 2020; Domeisen, 2019). The impacts on surface climate also
include changes in the frequency of extremes of temperature and rainfall
(Scaife et al., 2008; King et al., 2019; Cai et al., 2016; Domeisen et al.,
2020b).
Although major SSW events, involving a complete reversal of the zonal flow in the
mid stratosphere, are rare in the Southern Hemisphere (Wang et al., 2020;
Jucker et al., 2021), variations of the Antarctic polar vortex are likewise
followed by similar signatures in the underlying tropospheric flow, in this
case via the Southern Annular Mode (SAM). Weakening of the vortex is
typically followed by a negative shift in the SAM and associated changes in
rainfall and near-surface temperature (Thompson et al., 2005; Lim et al.,
2018, 2019a, 2021; Rao et al., 2020d). These changes in Southern Hemisphere
circulation typically take longer to reach the surface than their Northern
Hemisphere counterparts (Graverson and Christiansen, 2003), perhaps due to
the stronger stratospheric polar vortex and weaker wave driving in the
Southern Hemisphere, but they are nonetheless better predicted by improving
stratospheric resolution of forecast models (Roff et al., 2011). The
timescale of weeks for the predictability of sudden warmings is limited by
the predictability of weather patterns in the troposphere which might
trigger SSW events (e.g. Mukougawa et al., 2005; Taguchi, 2016; Garfinkel and
Schwarz, 2017; Jucker and Reichler, 2018; Lee et al., 2020a). However, if we
add this timescale to the timescale of a month or more for the persistence
of lower-stratospheric anomalies and their surface effects (e.g. Baldwin et
al., 2003; Butler et al., 2019a), we arrive at the conclusion that on these
occasions at least, initial conditions in the atmosphere can provide
predictability well beyond the usual 2-week horizon for daily weather in
either hemisphere.
Predictability of the atmosphere at monthly lead times is also known to
originate in part from the Madden–Julian oscillation (MJO) in the
troposphere and its teleconnection to the extratropics (e.g. Vitart, 2017).
The circulation pattern associated with the MJO resembles a poleward- and
eastward-propagating Rossby wave with centres of action over the Pacific and
extending into the Atlantic sector where it also maps strongly onto the
North Atlantic Oscillation. The lead time of around 10 d for the impact
of a change in the MJO to appear in the extratropical flow (e.g. Cassou,
2008; Lin et al., 2009) is also consistent with the timescale for the poleward
propagation of Rossby waves (e.g. Scaife et al., 2017). It turns out that
this tropospheric MJO teleconnection on monthly timescales also interacts
with the stratosphere (Garfinkel and Schwartz, 2017). The MJO teleconnection
to the North Pacific affects the region most strongly associated with
tropospheric precursors to SSW events, and consistent with this, SSWs in the
observational record have tended to follow certain MJO phases. The
subsequent weak vortex anomaly then propagates down to the troposphere
(Garfinkel et al., 2012b), where it may strengthen and prolong any existing
negative NAO signal that is directly linked to the MJO via the troposphere
(Schwartz and Garfinkel, 2017, 2020; Barnes et al., 2019).
In addition to the interaction of the MJO with the extratropical
stratosphere, a further, completely different link between the stratosphere
and the MJO has recently been uncovered which modulates MJO amplitude and
persistence in the troposphere via the phase of the quasi-biennial
oscillation (QBO) in the tropical lower stratosphere (Liu et al., 2014; Yoo
and Son, 2016; Martin et al., 2021). In this case, easterly phases of the QBO
appear to energize the MJO compared to westerly QBO phases, likely due to
changes in temperature and hence static stability close to the tropopause
(Hendon and Abhik, 2018; Martin et al., 2019) with a potential contribution
of cloud–radiation feedbacks (Son et al., 2017; see Martin et al., 2021, for
a review). This modulation of the MJO is in turn important for
predictability, as it gives rise to higher monthly prediction skill of the
MJO and its surface teleconnections during the easterly phase of the QBO
(Marshall et al., 2017; Abhik and Hendon, 2019; Lim et al., 2019b).
The traditional view of stratosphere–troposphere interaction involves upward
propagation of planetary-scale Rossby waves (Charney and Drazin, 1961), but
this linear theory applies equally well to downward propagation. Harnik and
Lindzen (2001) and Perlwitz and Harnik (2003) identified a possible source
of downward-propagating planetary waves in the form of reflecting surfaces
in the winter stratosphere. Examples of specific reflection events, showing
upward and then downward propagation have since been observed (e.g. Kodera
et al., 2008; Harnik, 2009; Kodera and Mukougawa, 2017; Mukougawa et al.,
2017; Matthias and Kretschmer, 2020). These results suggest that the details
of the stratospheric circulation such as regions of negative vertical wind
shear could be important for the formation of reflecting conditions
(Shaw and Perlwitz, 2013) and may yet provide a further mechanism by which
the stratosphere can affect the troposphere (Domeisen et al., 2019; Butler
et al., 2019b).
Following studies demonstrating enhanced tropospheric predictability after
SSW events in individual climate models (e.g. Kuroda, 2008; Mukougawa et al.,
2009; Marshall and Scaife, 2010; Sigmond et al., 2013), subseasonal-forecast
systems which explicitly represent the stratosphere in the climate system
were developed and implemented at operational-prediction centres worldwide.
It is often difficult to demonstrate significant increases in overall skill
(e.g. Richter et al., 2020a), but routinely produced ensembles of subseasonal
predictions show that both stratospheric variability and its subsequent
tropospheric signature are predictable at monthly lead times (Domeisen et
al., 2020a, b). The strongest surface impacts occur if the polar vortex
in the lower stratosphere is in a weakened state at the time of the SSW
(Karpechko et al., 2017), and there appears to be a roughly linear
relationship between the strength of these lower-stratospheric anomalies and
the tropospheric response (e.g. Runde et al., 2016; White et al., 2020; see
Baldwin et al., 2019, for a review). We should note however that there is no
one-to-one correspondence between stratospheric variability and tropospheric
events, and some prominent examples of sudden stratospheric warmings are
followed by differing tropospheric anomalies (e.g. Charlton-Perez et al.,
2018; Knight et al., 2020; Butler et al., 2020; Rao et al., 2020a).
Nevertheless, the canonical response is seen in the majority
(∼ 70 %) of cases, and periods of intense wintertime
stratospheric variability are important windows of opportunity to provide
skilful monthly forecasts (Mariotti et al., 2020; Tripathi et al., 2015a).
These forecast systems are now important tools for national meteorological
and hydrological services to monitor impending stratospheric variability and
associated surface impacts in real time. Recent extreme examples illustrate
the importance of this activity. In February 2018 a major SSW occurred and
was followed by a strong negative NAO-like pattern at the surface with
easterly wind anomalies over Europe and multiple cold-air outbreaks over the
following weeks, including extreme snowfall across northern Europe (Fig. 2; Karpechko et al., 2018; Knight et al., 2020; Rao et al., 2020a) and an
abrupt end to Iberian drought in southern Europe (Ayarzagueña et al.,
2018b). Studies of monthly ensemble predictions of this event with
operational stratosphere-resolving systems showed that the stratospheric
event was predictable at least 2 weeks in advance (Fig. 2) and that the
ensemble forecasts indicated an increased likelihood of cold surface conditions
for several weeks after the event (Karpechko, 2018; Butler et al., 2020;
Statnaia et al., 2020; Rao et al., 2020a). Again, as in the analysis of
previous events, there was also a strong association with the MJO entering
phase 7 with increased convection in the West Pacific (cf. Garfinkel and
Schwartz, 2017) in the 2018 event. Finally, we should also note that cases of
monthly forecasts where the stratosphere plays an important role are not
restricted to winters with sudden stratospheric warmings; periods when
the stratospheric polar vortex is above normal strength also provide
opportunities for skilful monthly forecasts (Tripathi et al., 2015b; Scaife
et al., 2016). In this case an opposite but symmetric surface response
results, with a strong positive NAO. A very recent example occurred in February 2020,
when, following an extremely strong polar vortex (Hardiman et al., 2020b;
Lee et al., 2020b; Lawrence et al., 2020; Rao and Garfinkel, 2021), the
tropospheric jet in the Atlantic sector strengthened, and the associated
increased storminess and rainfall in this case resulted in UK monthly
rainfall reaching a new record high (Davies et al., 2021).
Monthly forecasts prior to the 2018 sudden stratospheric warming and severe cold event over northern Europe. Forecast polar cap index (a) and February sea level pressure anomalies (b). Ensemble mean anomalies are shown for the average of forecasts initialized between 8 and 22 January 2018 relative to hindcasts over the 1993–2016 period using the Met Office Hadley Centre GloSea (global seasonal) prediction system (MacLachlan et al., 2015). Sea level pressure is measured in hectopascals (hPa), and the polar cap index is the geopotential height anomaly (m) averaged over 65∘ N to the North Pole.
The stratosphere and seasonal prediction
Prior to the advent of dynamical forecast systems which explicitly represent
the stratosphere, seasonal forecasts using empirical relationships and
statistical methods were proposed. These relied on the prior state of the
polar vortex and other predictable factors such as the QBO that are known to
have links to surface climate (Thompson et al., 2002; Charlton et al., 2003;
Christiansen, 2005; Boer and Hamilton, 2008). In some cases they
indicated additional predictability that was absent in existing operational
forecast systems, providing further evidence of predictability involving the
stratosphere and further motivating the extension of dynamical forecast
systems to properly represent the stratosphere. Similar empirical forecast
studies continue, and although they cannot provide evidence of
predictability that is as strong as from GCM experiments based on
fundamental physical principles, they do continue to be useful to indicate
sources of predictability that need to be properly represented in
comprehensive forecast systems (e.g. Folland et al., 2012; Wang et al.,
2017; Hall et al., 2017; Byrne and Shepherd, 2018).
Following the introduction of dynamical seasonal-forecast systems with a
good representation of the stratosphere, clear links between successful
seasonal prediction of the North Atlantic Oscillation, the closely related
Arctic Oscillation, and the state of the stratospheric polar vortex have been
identified in forecast output (e.g. Scaife et al., 2014b; Stockdale et al.,
2015; Jia et al., 2017). Similar signals are also seen in the Southern
Hemisphere in relation to predictability of the Southern Annular Mode
(Seviour et al., 2014; Byrne et al., 2019; Lim et al., 2021). Statistically
significant increases in overall skill directly attributable to the
inclusion of the stratosphere in prediction systems is sometimes difficult
to demonstrate (e.g. Butler et al., 2016), especially given that other
factors such as horizontal resolution and physical parametrizations are
often simultaneously changed. Nevertheless, the body of evidence now weighs
heavily in favour of predictability of the NAO and SAM from the
stratospheric polar vortex and from analyses showing reduced surface
prediction skill in the absence of stratospheric variability (e.g. Hardiman
et al., 2011; Sigmond et al., 2013; Scaife et al., 2016).
A second clear example of seasonal predictability originating in the
stratosphere is the quasi-biennial oscillation (QBO). The QBO has such
inherently long timescales that it persists for several months in seasonal
forecasts from initial atmospheric conditions alone, and its regularity means
that it can be predicted from simple composites of earlier cycles.
Nevertheless, a growing number of numerical models used in seasonal-forecast
systems can now simulate and predict the oscillation within climate
forecasts (Garfinkel et al., 2018; Richter et al., 2020b; Stockdale et al.,
2021) with the aid of forcing from parametrized non-orographic gravity
waves, and there is skill in predicting QBO phase changes at lead times of a
few months (e.g. Pohlman et al., 2013; Scaife et al., 2014a). The surface
impact of the QBO is also well established and has stood the test of time
since it was first identified in the 1970s (Ebdon, 1975; Thompson et al.,
2002; Anstey and Shepherd, 2014; Gray et al., 2018). Yet again this response
projects closely onto the North Atlantic Oscillation (and hence the Arctic
Oscillation–Northern Annular Mode) and the Southern Annular Mode. The
favoured mechanism involves refraction of vertically propagating Rossby
waves in the lower stratosphere (Holton and Tan, 1980), although other
pathways may also be involved (e.g. Inoue et al., 2011; Yamazaki et al.,
2020; Rao et al., 2020b, 2021). The observed magnitude of the QBO
teleconnection is also large enough to provide seasonal predictability of
surface climate (Boer and Hamilton, 2008), but its modelled amplitude at the
surface appears to be underrepresented in current operational-prediction
systems and models (Scaife et al., 2014b; Garfinkel et al., 2018; O'Reilly et
al., 2019; Rao et al., 2020b; Anstey et al., 2021).
In addition to the stratosphere acting as a source of predictability, other
mechanisms by which the stratosphere plays a role in seasonal predictions
involve a pathway for global-scale teleconnections. These often originate in
the tropics where the longer timescales of coupled ocean–atmosphere
variability such as the El Niño–Southern Oscillation (ENSO; L'Heureux et
al., 2020) provide a predictable source of low-frequency variability. Effects
on the extratropics can occur by tropical excitation of anomalous Rossby
waves which propagate not only poleward but also upward into the stratosphere, as
in the case of ENSO (Manzini et al., 2006; Domeisen et al., 2019), giving
two pathways for extratropical influence (Butler et al., 2014; Kretschmer et
al., 2021). These highly predictable tropical sources of climate variability
alter the strength and position of the stratospheric polar vortex in the
extratropics as well as the frequency of SSWs (Polvani et al., 2017), and
these are followed by changes in the seasonal westerly jets in the
troposphere and surface climate via the North Atlantic Oscillation (Ineson
and Scaife, 2009; Cagnazzo and Manzini, 2009) or the Southern Annular Mode
(Byrne et al., 2019). As might be expected, both the QBO and ENSO
teleconnections are best represented in seasonal-forecast systems which
contain a well-resolved stratosphere (Butler et al., 2016). We note that new
examples of the stratosphere acting as a conduit for seasonal
teleconnections are still being uncovered (Hurwitz et al., 2012; Woo et al.,
2015). For example, the Indian Ocean Dipole (IOD) received little attention
in this context until the recent record event of late 2019, when it appears
to have driven an extreme winter strengthening of the Northern Hemisphere
stratospheric polar vortex. This strengthening took many weeks to decay,
giving rise to extreme yet highly predictable conditions in the stratosphere
and around the Atlantic sector in late boreal winter (Hardiman et al.,
2020b; Lee et al., 2020b). The same event was also implicated in extreme
changes in the polar vortex and the near SSW in the Southern Hemisphere (Rao
et al., 2020d), an event that itself likely helped to drive the extreme
summer conditions and wildfires over Australia that year (Lim et al., 2021).
Apparent links between Arctic sea ice and seasonal winter climate in the midlatitudes have also been suggested to be mediated by the stratosphere, with
increased Rossby wave activity and a weakening of the stratospheric polar
vortex in response to reduced sea ice, especially in the Barents–Kara Sea
(Honda et al., 2009; Jaiser et al., 2013; Kim et al., 2014; King et al., 2016; Kretschmer et al.,
2016). Some studies also reproduced surface signals in response to sea ice
anomalies in seasonal forecasts of particular years that are in apparent
agreement with observational estimates (e.g. Balmaseda et al., 2010;
Orsolini et al., 2012). However, recent updates to observational records
show a weakening of these apparent effects (Blackport and Screen, 2020) and
significant non-stationarity (Kolstad and Screen, 2019). Subsequent modelling
studies with larger samples of simulations have provided mixed results
(Zhang et al., 2018; Dai and Song, 2020), and some have
argued that the atmospheric response to sea ice is weak (Smith et al., 2022) and that while the
sensitivity to Barents–Kara sea ice may be stronger, the stratospheric
response in particular is highly variable (McKenna et al., 2018). While there
may well be a longer-term effect via the stratosphere from sea ice decline
(Sun et al., 2015; Screen and Blackport, 2019; Kretschmer et al., 2020),
sensitivity of the response to the background state complicates the issue
(Labe et al., 2019; Smith et al., 2017), as do possible confounding
influences from the tropics (Warner et al., 2020), and to date there is no
clear consensus for strong enough year-to-year effects to provide
significant seasonal predictability.
Other proposed teleconnections acting via the stratosphere have been found
in observations but remain to be confirmed with successful reproduction in
physically based climate models. A prominent example involves a proposed
link between Eurasian snow amounts and the stratosphere, followed by a
return influence on the NAO and surface climate. In this case, enhanced snow
cover or depth is associated with high pressure over northern Eurasia, an
increase in the flux of Rossby wave activity into the stratosphere, and a
subsequent weakening of the stratospheric polar vortex, followed by the
expected negative shift in the NAO and AO (Cohen and Entekhabi, 1999; Cohen
and Jones, 2011; Cohen et al., 2014; Furtado et al., 2015). However, the
strength of this link in climate models and seasonal predictions is modest
(Fletcher et al., 2009; Riddle et al., 2013; Tyrrell et al., 2018, 2019) and
does not agree with apparent links to the AO in observations (Kretschmer et
al., 2016; Garfinkel et al., 2020) even when model mean state biases are
corrected (Tyrrell et al., 2020). It has also been suggested that
teleconnections to snow are non-stationary or non-causal, and there is
continued debate about its long-term robustness (Peings et al., 2013;
Henderson et al., 2018).
In summary, a number of mechanisms by which the stratosphere acts to provide
seasonal predictability by acting directly either as a source of predictable
variability (e.g. the QBO and SSWs) or as a conduit for teleconnections (e.g.
ENSO, MJO, and IOD) have now been established in observations and have been
confirmed using climate model simulations based on first principles. These
operate in seasonal-forecast systems, albeit with remaining errors such as
the weakness of the QBO connection to surface climate. Meanwhile, other
mechanisms involving the stratosphere (for example the response to snow
cover variations) have been proposed based on apparent observed
relationships, but until we have agreement between these observations and
theory (model simulations), scientists remain sceptical of whether they
represent actual sources of seasonal predictability, and these remain topics
of current research.
The stratosphere and annual-to-decadal prediction
In recent years, initialized predictions on longer timescales were developed
on the premise of multiyear memory in the ocean (e.g. Smith et al., 2007), and
following the development pathway mapped out by seasonal forecasts in the
past, these are now being run operationally to produce real time multimodel
forecasts (Smith et al., 2013). Kushnir et al. (2019) mapped out this
operational development of annual-to-decadal predictions and highlighted a
number of sources of predictability, some of which involve the stratosphere
(Fig. 3) but not all of which are fully represented in climate prediction
systems.
Sources of annual-to-decadal predictability, some of which involve the stratosphere through the response to external forcing, internal atmospheric dynamics, or ozone chemistry changes. After Kushnir et al. (2019).
Despite common misconceptions, not all annual-to-decadal predictability
stems from the ocean. Indeed, it has been clearly demonstrated that
multiyear predictability of the QBO exists in current decadal predictions
systems out to lead times of several years (Pohlman et al., 2013; Scaife et
al., 2014a). This offers the prospect of a stratospheric contribution to
multiyear predictability of the extratropics through the teleconnection with
the Arctic Oscillation (Anstey and Shepherd 2014; Gray et al., 2018) and to
tropical predictability through links to the MJO (e.g. Martin et al., 2021)
and wider tropical climate variability (Haynes et al., 2021).
Although it is more important on multidecadal timescales (see below),
external forcing of the stratosphere can also act as a source of decadal
predictability. Forced climate signals from changes in greenhouse gases or
stratospheric effects such as ozone depletion occur on a much longer
timescale than the lead time of decadal forecasts, but their contribution to
the skill of predictions is not trivial. For example, it is not immediately
obvious whether the slow changes from multidecadal forced signals would
simply be swamped by unpredictable internal variability on decadal
timescales, rendering long-term external forcing changes useless for decadal
predictions. However, this is not the case and long-term forcing is now
known to be an important source of decadal prediction skill (Smith et al.,
2019, 2020).
External forcing involving the stratosphere on shorter timescales is also
important for annual-to-decadal predictions. The stratosphere has long been
known to be influenced by volcanic eruptions, particularly in the case of
tropical volcanic eruptions which are powerful enough to inject significant
quantities of sulfur dioxide into the atmosphere. Here it reacts with water
to form sulfuric acid and persists in aerosol form, leading to predictable
multiyear global surface cooling, tropical stratospheric warming, and an
intensification of the westerly stratospheric polar vortex in the
extratropics (Robock and Mao, 1992). Although the sample of observed events
is limited, modelling studies have reproduced an observed post-eruption
intensification of the westerly winds in the stratosphere and some impacts
on the surface Arctic Oscillation. However, generations of models have
struggled to reproduce the 2-year persistence of volcanic effects seen in
observations and the observed magnitude of the effect on the winter AO (e.g.
Stenchikov et al., 2006; Marshall et al., 2009; Charlton-Perez et al., 2013;
Bittner et al., 2016). In addition to these changes in the atmosphere, the
intensification of stratospheric westerlies and hence Arctic Oscillation
also combines with surface cooling of the ocean to generate predictable
changes in the Atlantic meridional overturning circulation (Reichler et al.,
2012) which can extend the volcanic influence to decadal timescales
(Swingedouw et al., 2015). Finally, although the mechanism is debated, there
is also evidence of a multiyear effect of tropical volcanic eruptions on
ENSO, presumably requiring the persistent radiative forcing that arises
through the long residence time of volcanic products, particularly sulfate
aerosols, in the stratosphere. This reportedly increases the frequency of El Niño events by a factor of 2 in the years following volcanic eruptions
(Adams et al., 2003), again suggesting an important source of multiannual
predictability via the stratosphere.
A second source of multiannual predictability from external forcing
originates from solar variability and in particular the 11-year solar
activity cycle. Although a number of alternative mechanisms have been
proposed (see Gray et al., 2010, for a review), the established mechanism for
surface effects via the stratosphere is the change in the polar vortex that
results from changes in upper-stratospheric heating over the course of each
cycle between solar minimum and solar maximum. Interactions of atmospheric waves and mean flow amplify the initial radiatively driven change and drive its
descent to the troposphere (Kodera and Kuroda, 2002; Marsh et al., 2007; Ineson
et al., 2011; Givon et al., 2021), where changes in the extratropical jets
result in a negative (positive) Arctic Oscillation pattern following solar
minimum (maximum). There is also evidence that it contributes to interannual
prediction skill (Dunstone et al., 2016), and an interesting aspect that has
emerged in recent years is the integrating effect of the ocean on solar-induced changes in the NAO via interannual persistence of ocean heat content
anomalies which lead to a lag of around 3 years (π/2 cycles) in the
peak response, as would be expected if the ocean is integrating the effects
of a periodic solar forcing (Scaife et al., 2013; Gray et al., 2013; Andrews
et al., 2015; Thiéblemont et al., 2015). However, debate continues as to
whether the solar signal is indeed large enough to be detectable in
observations in the presence of large internal tropospheric variability
(Chiodo et al., 2019).
Perhaps the longest known timescale for predictability from initial
conditions, which also involves the stratosphere, is the interaction of
Atlantic multidecadal variability (AMV) with the stratospheric circulation.
The Atlantic has followed pronounced multidecadal variations over the last
century (Mann et al., 1995), and these variations are predictable out to
years ahead (Hermanson et al., 2014). Some studies link these variations to
the stratosphere and the NAO–AO (Reichler et al., 2012; Omrani et al.,
2014). Indeed, the pronounced multidecadal increase in the surface NAO
between the 1960s and 1990s is strongly coupled to changes in the strength
of the stratospheric polar night jet (Scaife et al., 2005). Although current
models simulate weak coupling between the AMV and the free atmosphere, this
coupling appears to increase with model resolution (Lai et al., 2021),
suggesting that the links between AMV, the stratosphere, and the NAO offer
potential for improved decadal-scale prediction involving the stratosphere.
The currently recognized role of the stratosphere in decadal forecasts of
surface climate again appears mainly via the impact on annular modes and, in
the Northern Hemisphere, the North Atlantic Oscillation. Indeed, while
current decadal prediction systems are now able to produce skilful
predictions of variations in the NAO on multiyear lead times (Smith et al.,
2019, 2020; Athanassiadis et al., 2020), much work is still needed to
attribute these variations to external forcing or internal variability and
to understand the interaction between boundary and initial conditions, which
blurs the simple distinction between the two. These new results are
important because they indicate newfound decadal predictability of events
like the high NAO of the 1990s which yielded a run of mild but wet and
stormy winters in northern Europe and the eastern USA. These winters are
well known to have caused significant impact for example on the insurance
sector (Leckebusch et al., 2007) and coincided with the longest observed
absence of SSW events (Pawson and Naujokat, 1999; Domeisen, 2019). Given the
indications of coupled stratosphere–troposphere variations on decadal
timescales (Scaife et al., 2005; Omrani et al., 2014; Garfinkel et al.,
2017; Woo et al., 2015), understanding the role of the stratosphere in
extratropical decadal predictions needs further investigation.
The stratosphere and multidecadal projection
The importance of the stratosphere for climate projections on multidecadal
timescales was generally recognized before its role in predictions on
shorter timescales. This is in part a legacy of the early development of
stratosphere–troposphere models for ozone depletion studies described in the
Introduction. On these longer timescales, coupling between stratospheric
composition, thermal structure, and atmospheric circulation gives rise to
improved climate projections.
Perhaps the best-known case for the stratosphere affecting multidecadal
projections of surface climate is the influence of ozone depletion on the
Southern Annular Mode (SAM; Thompson and Solomon, 2002; Thompson et al., 2005; McLandress et
al., 2011; Polvani et al., 2011; Son et al., 2008, 2018), where decreasing ozone in
the late 20th century led to a strengthened pole-to-Equator temperature
gradient, a stronger stratospheric polar vortex, and a shift to strong
positive SAM phases at the surface. In this case, studies again show the
importance of stratospheric resolution to generate the full response,
consistent with a genuine downward influence (Karpechko et al., 2008). The
associated poleward shift in the tropospheric jet is connected to a delay in
the spring breakdown of the stratospheric polar vortex (Byrne et al., 2017)
and delivered significant and prolonged changes in rainfall across many
regions of the Southern Hemisphere (Kang et al., 2011b; Purich and Son, 2012).
Implementation of the Montreal Protocol in 1987 and subsequent reductions in
the rate of ozone depletion mean that recovery of the ozone layer is now
expected over the coming decades, and the reversible effects of this on the
surface climate form an important element of current multidecadal
projections (Thompson et al., 2011; Previdi and Polvani, 2014; Solomon et
al., 2016; Banarjee et al., 2020; Zambri et al., 2021), where they are
expected to play an important role alongside other changes in the southern
stratosphere due to continuing increases in greenhouse gases (Son et al.,
2009; Barnes et al., 2014), some of which occur via the stratospheric polar
vortex in a similar way to those from ozone depletion and recovery (Ceppi
and Shepherd, 2019).
The more limited effects of ozone depletion in the Northern Hemisphere meant
that the role of the stratosphere in multidecadal projections took longer to
become established. Some early studies found potential amplification of
positive Arctic Oscillation trends under climate change when the
stratosphere was included (Shindell et al., 2001). However, this was not
borne out in later studies as simulations with other fully coupled
ocean–troposphere–stratosphere models, suggesting weakening of the
stratospheric polar vortex (e.g. Huebener et al., 2007). Subsequent studies
with multiple models also indicated a southward shift in the polar night jet
with weakening high-latitude winds and strengthening subtropical winds
(Scaife et al., 2012; Manzini et al., 2014). 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 (Scaife et al., 2012). There is still significant
uncertainty due to the diversity of modelled stratospheric responses to
greenhouse gas increases (Manzini et al., 2014; 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, 2020; Rao and Garfinkel, 2020). This is perhaps due to the competition
between strengthening latitudinal temperature gradients near the tropopause
and enhanced meridional overturning in the mid stratosphere. There is also
strong inherent unpredictable variability from decade to decade in the
frequency of SSW occurrence (Butchart et al., 2000; McLandress and Shepherd,
2009).
Other aspects of future climate change where the stratosphere plays a role
have also been identified, for example, in the debate over the response to
future levels of Arctic sea ice. In this case it seems that the response of
the midlatitude circulation involves a negative shift in the Arctic
Oscillation (Screen et al., 2018; Zappa et al., 2018; McKenna et al., 2018).
This could again be amplified by interaction with the stratosphere, as some
studies suggest that the stratospheric response is necessary for a large
surface response (Kim et al., 2014), while others highlight that the
stratospheric interaction is sensitive to the regional pattern of sea ice
decline (McKenna et al., 2018), and still others show evidence of nonlinear
stratospheric and stratosphere-mediated surface response (Manzini et al.,
2018), coincident with the time when the Barents and Kara seas become
ice-free (Kretschmer et al., 2020). Furthermore, studies also indicate that
the surface climate response to sea ice decline depends systematically on
the phase of the stratospheric QBO (Labe et al., 2019).
Although it is much less certain than anthropogenic climate change, there
have also been suggestions of a multidecadal decline of external solar
irradiance which can impact multidecadal climate projections via the
stratosphere. Previous multidecadal solar minima, so-called “grand minima”,
have occurred in sunspot records and have been connected to the Little Ice
Age period around the end of the 17th century using proxy and other
data (Owens et al., 2017). Given recent weak-amplitude 11-year solar cycles,
there are now suggestions of a future solar “grand minimum”, where the 11-year cycle described above could become muted or even absent for a prolonged
period (Lockwood et al., 2010). In this case, the upper-stratospheric
cooling in the tropics and summer hemisphere can change the meridional
temperature gradient in a similar fashion to the 11-year cycle (Maycock et
al., 2015) and leads to a negative shift in the AO and the NAO and hence
affects regional climate (Ineson et al., 2015). However, in this case it
appears that while regional changes could be significant, they are generally
much smaller than the surface warming due to anticipated levels of
anthropogenic greenhouse gases (Anet et al., 2003; Ineson et al., 2015;
Maycock et al., 2015).
Finally, we note that although low-frequency variability in teleconnections
is observed (e.g. Garfinkel et al., 2019), it is often unclear whether this
is a systematic variation or simply due to sampling variability of an
underlying stationary process (Jain et al., 2019). Nevertheless, there is
growing evidence for systematic climate change in some of the
teleconnections by which the stratosphere enables surface predictability.
Under future climate change it appears that some of the teleconnections
discussed above may strengthen in amplitude. For example, the strengthening of ENSO-induced
anomalies in the extratropical Atlantic–European sector increases in future
climate projections (Müller and Roeckner, 2006; Fereday et al., 2020).
Similarly, recent analyses suggest that the MJO teleconnection to the
extratropics increases in amplitude under climate change (Samarasinghe et
al., 2021). The same is also true of the extratropical effects of the
stratospheric QBO, where in this case, the amplitude of the teleconnection
in composite anomalies doubles under future climate change (Rao et al.,
2020c) despite the QBO itself becoming weaker (Richter et al., 2020c).
Outlook
Long-range prediction has evolved quickly in recent years (Merryfield et
al., 2017, 2020; Butler et al., 2019b; Meehl et al., 2021), and this rapid
development is due in part to the improved representation of stratospheric
processes and stratospheric initial conditions in ensemble prediction
systems. The long-range forecast community originally focused on
predictability from initial ocean conditions, and this remains the primary
source of long-range predictability, for example from ENSO, but some of
these long-range prediction systems contained poor representations of the
stratosphere. In the meantime, those working in parallel on climate
modelling of the stratosphere were rarely involved in initialized long-range
prediction, instead being driven primarily by the ozone depletion problem.
Knowledge exchange across fields is important in science and precursors to a
new paradigm often occur when a topic is investigated by researchers from
outside the field (Kuhn, 1970). The crossover and collaboration between long-range prediction and stratospheric research communities is no exception, and
the interaction between these communities has yielded rapid progress and new
insights. Examples where initial atmospheric conditions can provide
predictability beyond the usually assumed limit have been demonstrated,
not only for the extratropics but also for the tropics, and we now know
that in some situations, for example when sudden stratospheric warmings
occur, the initial conditions in the stratosphere can have more impact than
initial conditions in the ocean (Thompson et al., 2002; Scaife and Knight,
2008; Polvani et al., 2017). This suggests that initial atmospheric conditions
in the stratosphere are likely to be more important for long-range forecasts
than previously assumed (Mukougawa et al., 2005, 2009; Stockdale et al.,
2015; Noguchi et al., 2016, 2020a; Choi and Son, 2019; O'Reilly et al., 2019;
Nie et al., 2019), not least because the overturning and breaking of Rossby
waves in the stratosphere is followed by long-lived atmospheric anomalies
due to synoptic-scale eddy feedbacks that prolong the effects in the
troposphere (Kunz and Greatbatch, 2013; Kang et al., 2011a; White et al., 2020).
More research on the initial conditions in the stratosphere might therefore
help to reveal potential for further improvements in prediction skill.
A notable simplification to understanding the role of the stratosphere, at
least in extratropical long-range predictions, is its apparently seamless
mechanism across different timescales and different phenomena. Following the
early ground-breaking studies showing surface impacts of stratospheric
variability (e.g. Labitzke, 1965; Boville, 1984) and a multitude of studies on
individual teleconnections between the stratosphere and surface climate, the
projection of stratospheric variability onto the Arctic Oscillation–North
Atlantic Oscillation–Northern Annular Mode circulation patterns across timescales and
hemispheres is now well established (see the review by Kidston et al.,
2015). This suggests that similar coupling processes occur between the
stratosphere and troposphere from months to decades, and these processes lead
to some of the most intense extratropical climate extremes, in winter in the
Northern Hemisphere and in late spring–early summer in the Southern
Hemisphere (Karpechko et al., 2018; Fereday et al., 2012; Kautz et al.,
2019; Domeisen and Butler, 2020). While studies point to changes in upper-tropospheric baroclinicity and tropospheric eddy feedbacks as crucial in
these teleconnections, a full mechanistic understanding of how this occurs
is still lacking.
Some, but not all, leading forecast systems now include a well-resolved
stratosphere with a reasonable representation of relevant processes such as
the body force from sub-grid orographic and non-orographic gravity waves.
However, many outstanding problems remain. Although their number is
increasing, only a subset of current GCMs have the ability to simulate a
realistic QBO beyond its decay from initial conditions, and it seems that all
GCMs have problems with the fidelity of modelled QBO teleconnections, which
are either too weak or absent altogether (Scaife et al., 2014a; Kim et al.,
2020; Anstey et al., 2021). Even the relatively well-studied ENSO
teleconnection via the stratosphere to the extratropics still has
outstanding questions, such as whether the Northern Hemisphere stratosphere
exhibits more SSW events during the La Niña phase (Butler and Polvani,
2011; Song and Son, 2018). This is not generally reproduced in modelling
systems (Garfinkel et al., 2012a) but occurred in the recent La Niña
winter of 2020/21. Similarly, while the increased monthly predictability
from the MJO during the easterly phase of the QBO has been detected in
monthly forecast experiments, the QBO–MJO connection does not persist in
longer predictions and simulations with current models (Kim et al., 2020).
Research and model development on stratosphere–troposphere interaction,
including tropical effects (Noguchi et al., 2020b), will no doubt lead to
further progress in resolving this issue (Haynes et al., 2021).
Errors in the modelled climatological mean climate are inevitably present to
varying degrees in even the latest climate models. The common protocol of
running a set of retrospective predictions to allow these mean biases to be
estimated and hence subtracted from real-time predictions may well correct
for much of this error. However, the degree to which biases have a
nonlinear, state-dependent impact on the predictions is not fully
understood. In some contexts, the nonlinear impacts of biases may be minimal
(Karpechko et al., 2021), while others show sensitivity (Sigmond et al.,
2008, 2010) and increases of prediction skill occur under certain background
conditions, for example during easterly QBO phases (Taguchi, 2018). Other
processes generally omitted from long-range predictions include interactive
variations of ozone and other trace gases. Although reports of impacts and
benefits have varied, it is thought that surface signals on interannual
timescales come mainly from dynamical rather than chemical changes (Seviour
et al., 2014; Harari et al., 2019). Nevertheless, some studies suggest
detectable effects from interannual variability of ozone, and it may be that
ozone fluctuations could help to amplify surface signals (Karpechko et al.,
2014; Son et al., 2013; Smith and Polvani, 2014; Oehrlein et al., 2020;
Hendon et al., 2020), providing a further area for future development. Given
that the cost of full atmospheric chemistry schemes remains computationally
expensive, it seems likely that simple parametrizations of ozone chemistry
(e.g. Monge-Sanz et al., 2021) would be valuable in this context.
We end with a pointer to an issue that has now been found to affect
long-range predictions from monthly to seasonal to decadal and multidecadal
timescales, particularly in the extratropics. So-called “perfect model
studies”, which test the ability of models to predict their own ensemble
members, are now known to underestimate the true predictability of climate in some
regions, particularly around the Atlantic basin, and so models are better at
predicting real-world variations than they are at predicting themselves.
This so-called “signal-to-noise paradox” (Scaife and Smith, 2018) is at first
surprising, because perfect model prediction scores are often assumed to
represent an upper (rather than lower) limit for prediction skill of the
real world. The problem can be understood in terms of unrealistically weak
ensemble mean predictions (e.g. Eade et al., 2014), but whether the
stratosphere is involved directly in the cause of this problem remains to be
seen (Saito et al., 2017; Stockdale et al., 2015), as it initially appears
in the troposphere rather than the stratosphere in long-range forecasts
(Domeisen et al., 2020a). Nevertheless, the unrealistically weak amplitude
of ensemble mean predictions may well have the same root cause as the weaker-than-observed amplitude of modelled teleconnections to the stratosphere
discussed in this review, including, for example, the underrepresentation
of the surface impact of the QBO. Resolving this problem will therefore
likely amplify these signals, provide greater levels of prediction skill,
and further strengthen the role of the stratosphere in long-range
predictions of surface climate.
Data availability
No data sets were used in this article.
Author contributions
AAS wrote the draft manuscript. All other co-authors contributed relevant
references and input to revisions and edits of the manuscript. SWS helped
produce Fig. 1.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Financial support
Adam A. Scaife and Steven C. Hardiman were supported by the Met Office Hadley Centre Climate Programme funded by BEIS (Department for Business, Energy & Industrial Strategy) and Defra (Department for Environment, Food & Rural Affairs). Mark P. Baldwin was supported by the Natural Environment
Research Council (grant no. NE/M006123/1). Jadwiga H. Richter was supported by the
Regional and Global Model Analysis (RGMA) component of the Earth and
Environmental System Modeling programme of the U.S. Department of Energy's
Office of Biological and Environmental Research (BER) via the National Science Foundation (NSF; interagency agreement no. 1844590). Shunsuke Noguchi was supported by the Japan Society for the Promotion of Science (KAKENHI; grant no. 19K14798). Eun-Pa Lim was supported by the
Australian government's National Environmental Science Program phase 2 and
the Victorian Water and Climate Initiative phase 2. Seok-Woo Son was supported by the
National Research Foundation of Korea (NRF), funded by the
government of the Republic of Korea (Ministry of Science and ICT; grant no. 2017R1E1A1A01074889). David W. J. Thompson is
supported by the US National Science Foundation Climate and Large-Scale
Dynamics programme. Daniella I. V. Domeisen is supported by the Swiss National Science Foundation
(project nos. PP00P2_170523 and PP00P2_198896). Chaim I. Garfinkel was supported by a European Research
Council starting grant under the European Union Horizon 2020 research and
innovation programme (agreement no. 677756).
Review statement
This paper was edited by Martin Dameris and reviewed by two anonymous referees.
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