Recent observational and modeling studies suggest that
stratospheric ozone depletion not only influences the surface climate in the
Southern Hemisphere (SH), but also impacts Northern Hemisphere (NH) spring,
which implies a strong interaction between dynamics and chemistry. Here, we
systematically analyze the importance of interactive chemistry with respect
to the representation of stratosphere–troposphere coupling and in particular
the effects on NH surface climate during the recent past. We use the
interactive and specified chemistry version of NCAR's Whole Atmosphere Community
Climate Model coupled to an ocean model to investigate differences in the
mean state of the NH stratosphere as well as in stratospheric extreme events,
namely sudden stratospheric warmings (SSWs), and their surface impacts. To be
able to focus on differences that arise from two-way interactions between
chemistry and dynamics in the model, the specified chemistry model version
uses a time-evolving, model-consistent ozone field generated by the
interactive chemistry model version. We also test the effects of zonally
symmetric versus asymmetric prescribed ozone, evaluating the importance of
ozone waves in the representation of stratospheric mean state and
variability.
The interactive chemistry simulation is characterized by a significantly
stronger and colder polar night jet (PNJ) during spring when
ozone depletion becomes important. We identify a negative feedback between
lower stratospheric ozone and atmospheric dynamics during the breakdown of
the stratospheric polar vortex in the NH, which contributes to the different
characteristics of the PNJ between the simulations. Not only the mean state,
but also stratospheric variability is better represented in the interactive
chemistry simulation, which shows a more realistic distribution of SSWs as
well as a more persistent surface impact afterwards compared with the
simulation where the feedback between chemistry and dynamics is switched off.
We hypothesize that this is also related to the feedback between ozone and
dynamics via the intrusion of ozone-rich air into polar latitudes during
SSWs. The results from the zonally asymmetric ozone simulation are closer to
the interactive chemistry simulations, implying that under a
model-consistent ozone forcing, a three-dimensional (3-D) representation of the
prescribed ozone field is desirable. This suggests that a 3-D ozone forcing,
as recommended for the upcoming CMIP6 simulations, has the potential to
improve the representation of stratospheric dynamics and chemistry. Our
findings underline the importance of the representation of interactive
chemistry and its feedback on the stratospheric mean state and variability
not only in the SH but also in the NH during the recent past.
Introduction
Ozone is a key constituent of the stratosphere and is important not only for
stratospheric chemistry, but also for transport and dynamics. Ozone is
mainly produced in the tropics and transported towards higher latitudes by
the large-scale meridional circulation in the middle atmosphere, i.e., the
Brewer–Dobson circulation (BDC). This transport, which is directed towards
the winter hemisphere, leads to a larger concentration of ozone at high
latitudes compared with lower latitudes. The production of ozone and the
absorption of UV radiation by stratospheric ozone leads to the characteristic
increase of the stratospheric temperature with height, resulting in a stable
stratification. Hence, ozone and its photochemical characteristics are
important for the seasonal cycle of stratospheric temperatures and due to
their influence on the meridional temperature gradient also affect
stratospheric circulation and dynamics through the thermal wind balance. Consequently,
a large interannual variability or anomalous trends in stratospheric ozone
have the potential to influence the stratospheric mean dynamical
state, its variability, and stratosphere–troposphere coupling (STC),
which can impact the surface climate. The importance of the interactive
representation of stratospheric ozone in a state-of-the-art climate model for
STC is addressed here.
It is well known that polar ozone depletion during spring leads to a cooling
of the lower stratosphere via radiative heating anomalies
(Fig. ). This cooling then enhances catalytic ozone
depletion, as heterogeneous chemistry is more efficient at lower
temperatures (A in Fig. ). Thus, it describes a positive
feedback based on the interaction between ozone chemistry and the absorption of
solar radiation . However, there is also a dynamical response
to ozone depletion: lower polar temperatures enhance the meridional
temperature gradient and, therefore, increase the strength of the polar night jet
(PNJ) through thermal wind balance which in turn influences planetary wave
propagation and dissipation. Depending on the strength of the PNJ, upward
planetary wave propagation and dissipation can either be enhanced or
diminished . This has opposing effects on the state of the
polar vortex and can lead to either positive or negative feedbacks between
ozone depletion and stratospheric dynamics (B and C in
Fig. ) e.g.,. Therefore, the
strength of the background wind determines the impact of ozone depletion
on planetary wave propagation and dissipation and, in turn, the sign of the
feedback.
Scheme of possible feedbacks between ozone chemistry and
dynamics/transport. A negative anomaly in ozone (O3) will lead to a
negative anomaly in temperature (T) which favors ozone depletion (A –
positive feedback). It also increases the strength of the polar night jet
(U). Depending on the strength of the background westerlies, an increase in
U can lead to either an increase or decrease in upward planetary wave
propagation (PWs). A strong (weak) westerly background wind can lead to a
decrease (increase) in PWs, which is connected to a less (more) disturbed
polar vortex, coupled to (B) a cooling (warming) of the polar vortex,
and (C) to less (more) transport of ozone into the polar vortex. Consequently, strong
(weak) background westerlies are connected to positive (negative)
feedbacks between ozone chemistry and dynamics/transport (B and
C).
If we consider an initial cooling due to ozone depletion and strong westerly
background winds, the cooling would result in a further strengthening of the
background winds; this, in turn, would hinder upward planetary wave
propagation and result in a positive feedback. If the cooling from ozone
depletion was accompanied by weak westerly background winds, it would also
result in a strengthening of the background winds; however, in that case it
would favor planetary waves propagating upward and would consequently result
in a negative feedback. Stronger (weaker) upward planetary wave propagation
results not only in a weakening (strengthening) of the PNJ but also in a
strengthening (weakening) of the downwelling branch of the BDC, which can
both directly or indirectly influence stratospheric ozone concentrations. A
stronger (weaker) descent over the pole leads to adiabatic warming (cooling)
that counteracts (enhances) the negative temperature anomalies induced by
ozone depletion (B in Fig. ). Stronger (weaker) descent also
increases (decreases) the transport of ozone from higher altitudes to lower
altitudes, increasing (decreasing) lower stratospheric ozone concentrations
(C in Fig. ). The same effect is achieved by the weaker
(stronger) PNJ, which allows for more (less) mixing between ozone depleted
polar air masses and the relatively ozone-rich surrounding air masses. These
feedbacks would therefore be negative (positive) (B and C in
Fig. ).
The impact of ozone depletion on stratospheric dynamics is strongest during
spring (when solar irradiance is available to initiate ozone depletion) and,
following our discussion above, is very sensitive to the background
state of the polar vortex. In fact, previous studies have suggested a dominance of
the negative feedback during the vortex breakdown
e.g.,.
Although other trace gases, such as water vapor, can also be affected by
these feedbacks, we concentrate our discussion on ozone in this publication.
The effects of ozone can be represented differently in climate models: the
most sophisticated representation is to calculate ozone interactively within
the model's chemistry scheme. Ozone, as well as many other trace gases and
chemicals, is thereby directly and interactively linked to the radiation and
dynamics. These climate models are called chemistry–climate models (CCMs)
and are used for stratospheric applications such as in the WCRP/SPARC
initiatives. However, fully interactive atmospheric chemistry schemes are
computationally expensive, particularly if an interactive ocean is also used
for long-term climate model simulations. Therefore, an alternative way of representing
the effects of ozone chemistry in a climate model is to prescribe
ozone fields which can be based on either observed or modeled ozone
concentrations. These ozone fields can be of different temporal and
horizontal resolution. The majority of climate models that participated in
the Climate Model Intercomparison Project, Phase 5, (CMIP5), prescribe ozone
as monthly mean, zonal mean values based on the
recommended IGAC/SPARC ozone database .
When prescribing ozone as monthly mean, zonal mean fields, some aspects of
ozone variability, such as zonal asymmetries in ozone, are neglected. Using a
monthly climatology has been shown to introduce biases in the model's ozone field
that reduce the strength of the actual seasonal ozone cycle due to the
interpolation of the prescribed ozone field to the model time step
. To avoid these biases, a daily ozone forcing can be
applied. Furthermore, ozone is not distributed in a zonally symmetric fashion in the real
atmosphere; therefore, prescribing zonal mean ozone values inhibits the effect
that zonal asymmetries in ozone, also referred to as ozone waves, can have
on the dynamics. Different studies have shown that including zonal asymmetries
in ozone in a model simulation leads to a warmer and weaker
stratospheric polar vortex in the NH, which has also been associated with a higher
frequency of SSWs
e.g.,. The
recommended ozone forcing for CMIP6 now includes zonal asymmetries, but does
not include variability on timescales shorter than a month
.
As the interactive chemistry module in a climate model is computationally
very expensive, it is necessary to elucidate alternative representations of
ozone for long-term climate simulations. To date, the importance
of interactive chemistry in climate models has mainly been evaluated for
experimental settings that have focused on the effect of an altered external
forcing, such as a change in solar irradiance or CO2
concentrations
e.g.,.
In these studies CCM simulations were compared to model simulations forced
with a constant ozone field (e.g., based on preindustrial control
conditions), which did not include the ozone response to the changing
external forcing. It was shown that the ozone response to the external
forcing has an important damping effect on the surface climate response to
the external forcing. That is to say, under such conditions, including interactive
chemistry reduces the model's climate sensitivity
e.g., and connected
surface responses, such as the tropospheric jet e.g.
or El Niño–Southern Oscillation trends e.g.,. Here, we use a different
approach. We are interested in how feedbacks between ozone chemistry and
model dynamics can impact the stratospheric mean state and variability, given
that the variability in stratospheric ozone is the same between the
interactive and specified chemistry experiments. This question will be
addressed in the present study using a time-evolving, model-consistent
ozone forcing in the specified chemistry version of the model.
When considering the impact of ozone on stratospheric dynamics one has to
distinguish between the two hemispheres. During Antarctic winter,
temperatures are very low and reach the threshold for polar stratospheric
cloud (PSC) formation every winter. This allows the heterogeneous chemical
loss of polar ozone via ozone depleting substances (ODSs) once sunlight
returns in spring and leads to the well-known formation of the Antarctic
ozone hole every austral spring. Although the Montreal Protocol regulated the
emissions of ODSs, they have a very long lifetime and continue to deplete
ozone every winter, which is most prominently seen in the last 2 decades of
the 20th century. The ozone hole contributes to a positive trend in the
southern annular mode during austral summer (December to February, DJF),
which influences the position and strength of the tropospheric jet and
consequently impacts the surface wind stress forcing on the Southern Ocean
e.g.,.
Recently, evaluated the representation of the observed SH
ozone trend and the resulting poleward shift of the tropospheric jet in the
latest CCMs and high-top CMIP5 models (model top at or above 1 hPa).
They argued that irrespective of the representation of stratospheric ozone
(prescribed or interactive) the poleward shift of the tropospheric jet due to
ozone depletion was captured in all model ensembles. Separating the CMIP5
models with and without interactive chemistry showed a slightly stronger
poleward trend in zonal mean zonal wind during DJF in the models with
interactive chemistry. However, also point out that the inter-model
spread in the tropospheric jet latitude trend is rather high; it is
positively correlated with the strength of the ozone trend in individual CCMs
but also dependent on different model dynamics. Hence, it is more
convenient to use one model with the same dynamics to investigate the effect
of interactive chemistry. For example, focused on one model,
the Goddard Earth Observing System Model version 5 (GEOS-5), to assure that
the same dynamical background was used between simulations; their study found a significantly
stronger trend in zonal mean zonal wind in austral summer and a more
significant surface response in surface wind stress and ocean circulation to
the same ozone trends when ozone was calculated interactively in the model.
There are only a few studies, such as , which are designed
to systematically compare the effect of including or excluding interactive
chemistry in the same model, i.e., also using the ozone forcing from the CCM
in the specified chemistry version of the model. However, there is still a great
need to better understand the role that feedbacks between chemistry and
dynamics may play in representing recent and also future climate conditions
on different timescales.
Recently, discussed the negative feedback between ozone depletion
and dynamics (recall Fig. ) in detail for the observed SH
ozone trend, showing that the lower stratospheric dynamical response to ozone
depletion depends on the timing of the climatological vortex breakdown
during spring. They also claimed that models with a cold-pole bias overestimate
the effect of SH ozone depletion due to an underestimation of the negative
feedback. Here, we want to investigate how important the representation of
such feedbacks in a climate model is for Northern Hemisphere (NH)
stratospheric dynamics and whether it can impact the tropospheric circulation
via extreme stratospheric events.
In the NH, where the stratospheric polar vortex is much more disturbed and,
therefore, warmer during winter, a clear trend in either total column or
lower stratospheric ozone is not as prominent as in the SH. Very low ozone
concentrations dominated in the 1990s , but more recent years,
such as 2011, have also exhibited extremely low Arctic spring ozone
concentrations . This event (in 2011) in particular initiated discussions about the
possibility of an Arctic ozone hole and also about the possible impact of NH
ozone depletion events on the surface
. Using different models but all
with prescribed ozone, these studies did not find a significant surface
impact from observed ozone anomalies. In particular,
reported that significantly higher NH ozone depletion than that observed in
2011 would be needed to cause a detectable surface impact. Conversely,
reported statistically significant impacts of NH ozone
depletion events on tropospheric winds, surface temperatures, and
precipitation in April and May using the same CCM (WACCM) as used in this
study. This suggests that feedbacks between dynamics and chemistry are
necessary to induce a tropospheric signal due to ozone depletion in the NH.
In this study, we will test the importance of two-way feedbacks between ozone
chemistry and dynamics for NH STC in recent decades.
Extreme events in the NH stratosphere can have strong and relatively
long-lasting impacts on the troposphere e.g. and are
therefore of great interest, for example, for seasonal weather prediction
e.g.. Different pathways have been
proposed to explain the coupling between the stratosphere and the
troposphere, including the wave–mean-flow interaction, wave refraction and
reflection mechanisms
e.g.,, as well as
potential vorticity change . Understanding the
relative contribution of these mechanisms to STC in detail is still the subject
of recent research. Here, we focus on sudden stratospheric warmings (SSWs) as
a prominent example of NH STC. SSWs are characterized by a strong
wave-driven disturbance or breakdown of the stratospheric polar vortex and
result in a surface response a few days after the onset of the stratospheric
event that resembles the pattern of the negative phase of the North Atlantic
Oscillation (NAO) . A systematic investigation of
interactive vs. prescribed ozone in the same climate model family on NH STC
effects has to our knowledge not yet been performed and is the goal of the
present study.
Apart from the representation of two-way feedbacks between chemistry and
dynamics, also zonal asymmetry in ozone is often not included when ozone and
other radiatively active species are prescribed. However, earlier
publications have shown that zonally asymmetric ozone is associated with a
warmer and weaker stratospheric polar vortex in the NH
e.g. compared with
zonal mean ozone conditions. , for example, showed that
the NH polar stratospheric vortex is warmer when using zonally asymmetric
ozone compared with zonal mean ozone in the radiation scheme. In their model
setup feedbacks between dynamics and zonal mean ozone concentrations were
possible, only the effects of ozone waves were inhibited. A significant
warming of the polar stratosphere was only found in early winter (November
and December). Using a similar model setup, found a
more significant warming in February when including zonally asymmetric ozone
in their model and associated it with the more prevalent occurrence of SSWs
in their experiments The total number of SSWs was rather low with only five
out of thirty ensemble members: four out of the five SSWs occurred in the
zonally asymmetric simulations. prescribed ozone in both
simulations and also found a higher occurrence of SSWs in the zonally
asymmetric ozone run, with the largest difference in SSW occurrence in
November. Furthermore, a recent study by points to the
importance of the Quasi-Biennial Oscillation (QBO) for the NH high-latitude
response to ozone waves. To test the sensitivity of using either a zonal mean
ozone field or a zonally asymmetric field, we additionally include a
sensitivity experiment using a 3-D ozone forcing in the specified chemistry
simulation.
The paper is organized as follows: Sect. 2 introduces the model and the
simulations performed in this study in addition to the methodologies applied.
After discussing the differences in the climatological mean state between
interactive and prescribed chemistry model simulations in Sect. 3, we
analyze the differences in SSW characteristics and downward influences
between the simulations in Sect. 4. Finally, we conclude the paper with a discussion
of our results.
Data and methodsModel simulations
To asses the importance of interactive chemistry on the mean state and
variability of the stratosphere as well as on STC, we use a model that is
capable of utilizing an interactive chemistry scheme as well as specified
chemistry.
We use the Community Earth System Model (CESM), version 1, from NCAR with
WACCM, version 4, as the atmospheric component; this setting is referred to
as CESM1(WACCM). This version of CESM1(WACCM) is documented in detail
in .
WACCM is a fully interactive chemistry–climate model, with a horizontal
resolution of 1.9∘ latitude × 2.5∘ longitude. It uses a
finite volume dynamical core, has 66 vertical levels with variable spacing,
and an upper lid at 5.1×10-6 hPa (about 140 km) that
reaches into the lower thermosphere . Stratospheric
variability, such as SSW properties and the evolution of the SH ozone hole
are well captured in CESM1(WACCM) . In the SH, CESM1(WACCM)
has a strong cold-pole bias in the middle atmosphere, which could influence
the feedbacks discussed in Fig. . In the NH, the
strength of the PNJ agrees well with observations ;
therefore, the NH is better suited to investigate these feedbacks.
For our investigations we run the model under historical forcing conditions
for the period from 1955 to 2005 and under the Representative Concentration
Pathway 8.5 (RCP8.5) from 2006 to 2019. Therefore, we capture a 65-year period
that features the years with the lowest ozone concentrations before ozone
recovery starts. We include all external forcings based on the CMIP5
recommendations: greenhouse gas (GHG) and ODS concentrations , spectral
solar irradiances , and volcanic aerosol concentrations
including the eruptions of Agung (1963), El
Chichón (1982), and Mount Pinatubo (1991). As the QBO is not generated internally by this version of WACCM,
it was nudged following the methodology of .
CESM1(WACCM) incorporates an active ocean (Parallel Ocean Program version 2,
POP2), land (Community Land Model version 4, CLM4), and sea ice (Community Ice
CodE version 4, CICE4) model. POP2 and CICE4 have a nominal
latitude–longitude resolution of 1∘; the ocean model has 60 vertical
levels. A central coupler is used to exchange fluxes between the different
components. For more details on the different model components the reader is
referred to and references therein.
As mentioned above, WACCM incorporates an interactive chemistry scheme in its
standard version. It uses version 3 of the Model for Ozone and Related
Chemical Tracers (MOZART) . Within MOZART ozone
concentrations and concentrations of other radiatively active species are
calculated interactively, which allows for feedbacks between dynamics and
chemistry as well as radiation. It includes the Ox,
NOx, HOx, ClOx, and
BrOx chemical families, along with CHx and
its degradation products. A total of 59 species and 217 gas-phase chemical
reactions are represented, and 17 heterogeneous reactions on three aerosol
types are included .
The specified chemistry version of WACCM (SC-WACCM), in which interactive
chemistry is turned off, does not simulate feedbacks between chemistry and
dynamics. This version of WACCM is documented in . Here,
ozone concentrations are prescribed throughout the whole atmosphere. Above
approximately 65 km, in addition to the ozone concentrations,
concentrations of other species, namely atomic and molecular oxygen, carbon
dioxide, nitrogen oxide, and hydrogen, as well as the total shortwave and
chemical heating rates are also prescribed. validated SC-WACCM
by prescribing monthly mean zonal mean values of the aforementioned species
and heating rates from a companion WACCM run. Following the procedure in
, we use the output from our transient WACCM integration to
specify all of the necessary components in SC-WACCM (i.e., O, O2, O3,
NO, H, CO2, and total shortwave and chemical heating rates). We use
transient, monthly mean zonal mean values for all variables, except ozone,
for which we use daily zonal mean transient data. The use of daily ozone data
reduces a bias that is introduced by linear interpolation of the prescribed
ozone data to the model time step when using monthly ozone values
. Using daily data also allows for extreme ozone anomalies
to occur in the specified chemistry run.
In the following we will refer to the interactive chemistry version of
CESM1(WACCM) as “Chem ON” and to the specified version, that uses SC-WACCM
as the atmosphere component, as “Chem OFF”. Additionally, we include
results from a sensitivity run, prescribing daily zonally asymmetric (3-D)
transient ozone in SC-WACCM, which will be referred to as Chem OFF 3D. All
other settings in Chem OFF 3D are equal to that of the Chem OFF simulation.
The model simulations and settings are summarized in Table .
Model experiments carried out with CESM1(WACCM) in Chem ON, Chem
OFF, and Chem OFF 3D mode. For more details see text.
Experiment/Ozone settingYearsSSWs during winters of data1955/56 to 2018/191958/59 to 2016/17Chem ONInteractive1955 to 20192624Chem OFFPrescribed* zonal mean1955 to 20194140Chem OFF 3DPrescribed* zonally asymmetric1955 to 20193028ERA–1958 to 2017–32
* The ozone data used for prescription originate from the
Chem ON run.
Climatological zonal mean (a) zonal wind at 10 hPa
in ms-1, (b) temperature at 30 hPa in K,
and (c)w‾* at 70 hPa in mms-1
with month and latitude for Chem OFF (contours) and for the difference
between Chem ON and Chem OFF (shading). Contour intervals are
(a) 20 ms-1, (b) 10 K, and
(c) 0.2 mms-1. Statistically insignificant areas are
hatched at the 95 % level.
February–April (FMA) zonal mean (a) zonal wind in ms-1 and
(b) temperature in K with latitude and height for the NH for
Chem OFF (contours) and for the difference between Chem ON and Chem OFF
(shading). Contour intervals are (a) 10 ms-1 and
(b) 10 K. Solid contours are used for positive values,
and dashed contours are used for negative values. The zero line is omitted.
Statistically insignificant areas are hatched at the 95 % level.
Methods
The results presented in this paper are largely based on climatological mean
values of model output. When variability is considered we use deseasonalized
daily or monthly data by removing a slowly varying climatology after removing
the global mean from each grid point each year. This follows the procedure
described in and is used to omit the effect that may arise
from variability on timescales longer than 30 years, such as the signature of
global warming. The slowly varying climatology is produced as follows: first,
a 60-day low-pass filter is applied. Then, for each time step and grid point,
a 30-year low-pass filter is applied to the smoothed time series.
describe this procedure in detail and apply it
exemplarily. We confine the results presented to altitudes below
5 hPa, as it is the lower stratospheric ozone and its effects on
the circulation that we are most interested in.
We calculated the vertical component of the meridional residual circulation
(w‾*) using the transformed Eulerian mean framework defined,
for example, in :
w‾*=w‾+1Acosϕ(cosϕv′Θ′‾Θ‾z′)ϕ,
where the overbar indicates zonal mean values, subscripts refer to partial
derivatives, A denotes the Earth's radius
(A= 6 371 000 m), and w‾* is used to estimate
the difference in tropical upwelling and polar downwelling between the model
simulations. We refer to major sudden stratospheric warmings as
“SSWs” or “major warmings” in the following. SSWs are defined based on
the definition from the World Meteorological Organization (WMO)
e.g.,; according to this definition,
they occur (between November and March) when the two following criteria are
fulfilled: (1) the predominantly westerly zonal mean zonal wind reverses sign
at 60∘ N and 10 hPa, i.e., changes from westerly to
easterly; and (2) the 10 hPa zonal mean temperature difference
between 60∘ N and the pole is positive for at least 5 consecutive
days. The central date (or onset) of SSWs is defined as the first day of wind
reversal. To exclude final warmings (the transition from winter to summer
circulation), a switch from westerly to easterly winds at the given location
is only considered a SSW if the westerly wind recovers for at least 10
consecutive days prior to 30 April and exceeds a
threshold of 5 ms-1. To avoid double
counting of events, there have to be at least 20 days of westerlies in
between two major warmings .
We compare the modeled major warming frequency to the European Centre for
Medium-Range Weather Forecasts Re-Analysis (ERA) products ERA40
and ERA-Interim . These two products were
combined into one data set following (here merged on
1 April 1979), which resolves the stratosphere up to 1 hPa and
spans the period from 1958 to 2017.
Regarding the uncertainty estimate for the SSW frequencies, we use the
standard error for the monthly frequencies and the 95 % confidence
interval based on the standard error for the winter mean frequency.
Atmospheric variability linked to SSWs is evaluated in the form of composites
for selected variables before, during, and after the SSW onset. The statistical
significance of the composites is tested using a Monte Carlo approach
see for example. Therefore, 10 000 randomly chosen
central dates are used to calculate random composites. Statistical
significance at the 95 % level is reached when the actual composites
exceed the 2.5th or 97.5th percentiles of the distribution drawn from the
random composites.
The differences between Chem ON and Chem OFF are displayed as
Chem ON minus Chem OFF, and they are depicted along with the climatological
field of the Chem OFF run to display the effect of including interactive
chemistry. For these differences, the statistical significance at the 90 % or
95 % level is tested using a two-sided t test.
Climatological NH (a) polar cap (70 to 90∘ N)
temperature in K, (b) zonal mean zonal wind (55 to
75∘ N) in ms-1, (c) polar cap
longwave heating rates in Kday-1, and (d) polar cap
dynamical heating rates in Kday-1 with month and height for Chem
OFF (contours) and for the differences between Chem ON and Chem OFF
(shading). Contour intervals are (a) 10 K,
(b) 5 ms-1, (c) 1 Kday-1, and
(d) 0.5 Kday-1. Solid contours are used for positive
values, and dashed contours are used for negative values. The zero contour is
omitted. Statistically insignificant areas are hatched at the 95 %
level.
Correlation between polar cap (70 to 90∘ N) ozone at
50 hPa and polar cap dynamical heating rates in (a) Chem ON
and (b) Chem OFF for ozone lagging by 15 days (LAG 15 days) and
ozone leading by 15 days (LAG -15 days). Statistically insignificant areas
are hatched at the 95 % level.
The impact of interactive chemistry on the stratospheric mean state
To assess the importance of interactive chemistry on stratospheric dynamics
we first consider zonal mean zonal wind at 10 hPa (U10) and zonal
mean temperature at 30 hPa (T30) to characterize the stratospheric
polar vortex in our model simulations (Fig. a, b). The
stratospheric PNJ is characterized by strong westerlies around 70∘ N
and 60∘ S (Fig. a) and low polar cap temperatures
(Fig. b). The PNJ is significantly stronger and colder in the
Chem ON run. In both hemispheres, this feature is especially significant
during spring, when ozone chemistry becomes important for the temperature
budget of the lower stratosphere and hence for the dynamics. This difference
already hints at the relevance of representing feedbacks between ozone
chemistry and dynamics for the climatological state of the PNJ during spring.
In the NH, the difference between the runs is also significant during fall
and early winter, which is connected to a weaker downwelling, i.e., weaker
adiabatic warming, indicated by the statistically significant positive
anomaly in w‾* at 70 hPa (Fig. c) from
June to December. At the same time, Chem ON is characterized by a slightly
weaker tropical upwelling at 70 hPa, indicating that at least the
shallow branch of the BDC (below 50 hPa) is weaker in Chem ON
compared with Chem OFF.
In the following we will focus on the NH spring season as this is the period
when the effect of ozone depletion and possible feedbacks between chemistry
and dynamics become important. Figure shows February to April
(FMA) NH zonal mean zonal wind and zonal mean temperature with height.
Consistent with Fig. , north of 70∘ N, we find a
stronger PNJ (up to 4.5 ms-1 stronger at about 10 hPa)
when interactive chemistry is included (Fig. a) and a colder
polar vortex, with a maximum difference between Chem ON and Chem OFF of
-2.8K at about 60 hPa directly at the pole
(Fig. b). While temperature differences between Chem ON and Chem
OFF are mainly restricted to the lower stratosphere, statistically
significant differences in zonal mean zonal wind reach up to about
4 hPa and even down to the surface.
As the temperature differences are decisive for the differences in zonal
wind, we now consider the differences in polar cap heating rates between Chem
ON and Chem OFF to investigate why the models differ in their climatological
stratospheric state (Fig. ). As already seen in
Figs. and , including interactive chemistry leads
to a stronger PNJ and colder polar vortex, especially during spring but also
during early winter (Fig. a, b).
Figure a and c show that lower (higher) temperatures go
along with weaker (stronger) longwave (LW) cooling in the Chem ON run. The
difference in LW cooling between Chem ON and Chem OFF is directly connected
to the temperature difference and works as a damping factor. By construction,
there are no significant differences in the shortwave (SW) heating rates
between Chem ON and Chem OFF that could explain the different temperatures
between the models in this region, nor can differences in temperature
tendencies due to gravity waves (not shown). The dynamical heating rates,
which describe the total adiabatic heating rates in the model dominated by
advection through the vertical component of the residual circulation,
(w‾*), (Fig. d) seem to be the dominant
factor in shaping the climatological differences in polar cap temperature
between Chem ON and Chem OFF. Although the spring season is characterized by
a stronger PNJ and lower polar cap temperatures in the lower stratosphere in
Chem ON, a stronger dynamical heating in April and May leads to higher
temperatures in Chem ON in the middle stratosphere peaking in May
(Fig. a, d). Statistically significant dynamical heating
differences between Chem ON and Chem OFF reach down to the troposphere
resulting in a strong reduction of the temperature difference between Chem ON
and Chem OFF in the lower stratosphere in May. These features are
characteristic of a later but more intense breakdown of the polar vortex
when interactive chemistry is present. The differences in temperature between
Chem ON and Chem OFF during early winter can also be explained by the differences
in dynamical heating. In the Chem ON run there is statistically
significant weaker dynamical warming compared with the Chem OFF run with a
maximum difference between the runs in November (Fig. d)
that leads to lower temperatures in Chem ON in December. This agrees with the
earlier finding that the shallow branch of the BDC is weaker in the Chem ON
simulation (Fig. c). However, this poses the question as to why the signal in dynamical heating
differs between early winter and late spring. We suggest feedbacks between
ozone chemistry and dynamics as the reason for this and will discuss these
factors in more detail in the following.
Same as Fig. but using Chem OFF 3D for comparison
with Chem ON.
Monthly SSW frequency (a) and winter SSW
frequency (b) for the combined ERA data (gray), Chem ON (blue), Chem
OFF (light green), and Chem OFF 3D (dark green). Error bars are shown in the
figure. They indicate the standard error for the monthly frequencies and the
95 % confidence interval based on the standard error for the mean winter
frequency.
To illustrate the relation between ozone and dynamical heating we calculated
the correlation between polar cap ozone concentrations at 50 hPa and
polar cap dynamical heating rates in Chem ON and Chem OFF. A similar analysis
using ozone and temperature was carried out by for the SH.
Figure shows this correlation for ozone lagging and
leading the dynamical heating rates by 15 days. As the dynamical heating is
only available at a monthly resolution, daily ozone data were shifted by ±15 days with respect to the dynamical heating time axis. The contours show
the climatological zonal mean zonal wind as a reference. The shading shows
the correlation coefficients. Two different states are represented in
Fig. : (1) the dependence of ozone on the dynamics
(Fig. , top row) and (2) the effect ozone can have on
the dynamics (Fig. , bottom row). When ozone lags behind
dynamical heating (Fig. a, top row), positive
correlation coefficients occur in late autumn and early winter indicating
that low (high) ozone concentrations follow low (high) dynamical heating
rates. In this case, ozone concentrations and dynamical heating are caused by
reduced (enhanced) downwelling which leads to adiabatic cooling (warming)
as well as to lower (higher) ozone concentrations. When ozone leads dynamical
heating (Fig. a, bottom row), positive correlation
coefficients are not significant anymore. Instead, a statistically
significant negative correlation between ozone and dynamical heating
throughout the lower stratosphere is found in April and May, setting in
earlier at higher altitudes (above 10 hPa). By only looking at the
dynamical heating rates here, we do not capture possible positive feedbacks
caused by radiative heating and ozone chemistry indicated under (A) in
Fig. . Using this analysis we also do not identify a
positive feedback between ozone chemistry and dynamics (recall B and C,
Fig. ). However, we clearly find a negative feedback between
ozone and dynamics during the vortex breakdown phase in correspondence to
earlier studies e.g.. The westerly background wind
is weak enough that a decrease in ozone concentrations leads to an
increase in dynamical heating, which would consequently increase ozone
concentrations via the aforementioned pathways (B and C,
Fig. ). This negative feedback indicates that during weak
zonal mean zonal wind conditions, ozone depletion, which leads to an initial
cooling of the lower polar stratosphere and strengthening of the PNJ,
eventually leads to a faster breakdown of the vortex by allowing upward wave
propagation to take place at a higher rate than would occur during weaker
westerlies. In this analysis, the negative feedback clearly dominates and
leads to a more abrupt breakdown of the polar vortex in the Chem ON
simulation. A statistically significant correlation signature between ozone
and dynamical heating is only found in Chem ON (compare
Fig. a and b). Hence, we conclude that interactive
chemistry is indeed contributing to the different climatological
characteristics of the PNJ between Chem ON and Chem OFF.
Apart from the lack of feedbacks between chemistry and dynamics, Chem OFF is
also missing zonal asymmetry in the prescribed ozone field. Hence, the
missing effect of ozone waves in the Chem OFF simulation could potentially
contribute to the differences that we find between Chem ON and Chem OFF. Therefore, we
also include a sensitivity run, for which we used a zonally
asymmetric daily ozone forcing, Chem OFF 3D (Table ).
When including ozone waves, no significant
correlation signature is found between ozone and dynamical heating (similarly to Chem OFF; not shown).
Nevertheless, the absolute climatological differences between Chem ON and
Chem OFF 3D are smaller than what we found for a zonally symmetric
ozone forcing (Figs. and ). The PNJ is
still colder and stronger with interactive chemistry
(Fig. a, b), and significant differences of the same
sign as above are found for LW and dynamical heating rates in spring
(Fig. c, d). The lower amplitude of the
differences between Chem ON and Chem OFF 3D compared with Chem ON and Chem
OFF do indicate that other processes (apart from the feedbacks discussed
so far) are also important for the generally stronger and colder PNJ in Chem ON.
Including zonal asymmetries in ozone generally allows for stronger anomalies in
ozone, as no averaging is applied, and for anomalies that do not
center over the pole but also affect lower latitudes. Hence, ozone waves
can influence wave propagation and dissipation pathways, possibly leading to a
better representation of the effect that ozone has on wave–mean-flow
interactions in our model setup.
SSW composites for (a) the polar cap (60 to 90∘ N)
temperature anomaly in K, (b) zonal mean zonal wind at
60∘ N in ms-1, and for (c) the polar cap ozone
anomaly in ppm with lag in days with respect to the SSW central date
(lag 0) and height. Contour lines show the composite for the Chem OFF run.
Shading shows the difference between Chem ON and Chem OFF SSW composites.
Contour intervals are (a) 2 K,
(b) 5 ms-1, and (c) 0.05 ppmv. Solid
contours are used for positive values, and dashed contours are used for negative
values. The zero contour is omitted. Statistically insignificant areas are
hatched at the 90 % level (two-sample t test).
How does interactive chemistry influence stratosphere–troposphere coupling?
We found a stronger PNJ during NH spring when interactive chemistry and
feedbacks between ozone and dynamics are included in a climate model. This
stronger PNJ exhibits a boundary for upward planetary wave propagation which
influences the occurrence of SSWs. Figure shows the
frequency of SSWs for ERA re-analysis data (gray), the Chem ON (blue), Chem
OFF (light green), and Chem OFF 3D (dark green) simulations for each month of
the extended winter season individually (Fig. 7a) and the average over the
whole winter season (Fig. 7b) (see also
Table ). Chem ON represents the observed monthly frequency of
SSWs very well with the exception of January where it significantly
underestimates the occurrence of SSWs. Chem OFF, in comparison,
underestimates SSWs significantly in February and shows an unrealistic
increase in the occurrence of SSWs towards the end of the extended winter
season (March). Overall there is a tendency for fewer SSWs when interactive
chemistry is included in the model (Chem ON: 0.41±0.12 warmings per
winter; Chem OFF: 0.64±0.12 warmings per winter; and
Table ), which is likely due to the stronger background
westerlies in Chem ON. The SSW frequency in Chem OFF 3D is much closer to
that in Chem ON compared with Chem OFF, which we attribute to the smaller
climatological differences between Chem ON and Chem OFF 3D. However, this
poses the question as to how interactive chemistry impacts the downward
influence of SSWs.
The downward propagation of anomalies connected to the vortex breakdown is
stronger in the Chem ON simulation (Fig. ). Polar cap
temperature anomalies are stronger and persist longer in Chem ON
(Fig. a). Furthermore, the zonal mean wind at 60∘ N
(Fig. b) shows a longer-lasting easterly anomaly connected
to SSWs that reaches further down to the surface. Figure a
and b also demonstrate that the SSW signal in the Chem ON run is more sudden
compared with the Chem OFF run: the polar cap temperature anomaly is
significantly weaker before and significantly stronger after the SSW onset
compared with the Chem OFF run. Also, the easterly wind at 60∘ N is
preceded by stronger westerlies in the Chem ON simulation. Both criteria show
a more abrupt change from before to after the central date. To consider the
possible impact of ozone chemistry, we also show a composite of ozone
volume mixing ratio anomalies during the SSWs (Fig. c). A
strong intrusion of ozone from surrounding air masses during the SSWs, as
described in , is only evident in the Chem ON
simulation. No significant signal is found in the Chem OFF run (contours in
Fig. c). This suggests that the increase in lower
stratospheric ozone in Chem ON contributes to the longer persistence of the
SSW signal in the lower stratosphere.
The stronger and more persistent SSW signal in the Chem ON run in the
stratosphere also appears at the surface in the sea level pressure (SLP)
response to SSWs (Fig. ). The well-known negative NAO-like
surface response after SSWs is stronger in the Chem ON simulation (averaged
over 30 days after the SSW onset, Fig. a) and longer lasting
(averaged over 30 to 60 days after the SSW onset, Fig. e)
compared with the Chem OFF simulation (Fig. b, f). This
larger persistence of SLP anomalies after SSWs, which we also find in the
combined ERA data set (Fig. d, h), could be due to the
intrusion of ozone into the lower stratosphere which is only represented with
interactive chemistry (Fig. c). Prescribing zonally
asymmetric ozone does not significantly improve the surface response
(Fig. c, g). The NAO signal averaged over 30 days after
the SSWs is similar to Chem OFF, and restricted to a significant positive
anomaly over the pole 30 to 60 days after the SSW. Hence, a prescribed 3-D
ozone forcing is not sufficient to simulate the persistent NAO-like SLP
signal after SSWs.
SSW composite of SLP anomalies in hPa averaged over 0 to
30 days (a, b, c, d) and over 30 to 60 days (e, f, g, h)
following the central date of the SSW for (a) and (e) Chem
ON, (b) and (f) Chem OFF, (c) and
(g) Chem OFF 3D, and (d) and (h) combined ERA
data. Contour lines show the full composites, whereas only statistically
significant areas at the 95 % level are colored. Solid contours are used
for positive values, and dashed contours are used for negative values. The zero
contour is a bold solid line. The contour line interval is 1 hPa.
Conclusions
In this study we systematically investigated the effect of interactive
chemistry on the characteristics of the stratospheric polar vortex in
CESM1(WACCM) during the second half of the 20th century and the beginning of
the 21st century with a focus on the NH climatology as well as on its
interannual variability. Therefore, an interactive chemistry–climate model
was compared to the specified chemistry version of the same model using a
time-evolving, model-consistent, daily ozone forcing. We found that including
interactive chemistry (Chem ON) results in a colder and stronger polar night
jet (PNJ) during spring and early winter. We attribute the spring difference
to feedbacks between the model dynamics and ozone chemistry
(Fig. ). The inability to include a dynamically consistent
ozone variability when prescribing ozone (Chem OFF), inhibits the two-way
interaction between ozone chemistry and model dynamics. We found a negative
feedback between ozone chemistry and dynamics similar to that described by
for the SH to be very important during the breakdown of the NH
polar vortex in our Chem ON simulation: an initial polar cap temperature
decrease due to ozone depletion during NH spring occurs in correspondence
with an increase in the strength of the PNJ, which, during weak background
westerlies, leads to an increase in upward planetary wave propagation and
dissipation; this, in turn, results in adiabatic warming and an increase in
ozone due to the stronger descent of air masses. This negative feedback,
which only appears in the Chem ON simulation (Fig. ),
leads to a more abrupt transition from the winter to the summer circulation.
The climatological differences between Chem ON and Chem OFF during early
winter result from reduced dynamical heating in the Chem ON simulation, which
is associated with weaker polar downwelling
(Figs. c, d).
The climatological differences between the model simulations also influence
stratosphere–troposphere coupling. The distribution of SSWs is very well
captured in Chem ON, whereas Chem OFF significantly overestimates SSWs in
March, when ozone chemistry is most important (Fig. ). The
stratospheric anomalies in polar cap temperature and mid-latitude zonal wind
associated with SSWs as well as the NAO-like SLP response to SSWs are better
captured and persist for longer in the Chem ON simulation
(Figs. , ). Hence, feedbacks between
chemistry and dynamics may also impact the influence that stratospheric
events can have on the troposphere. In Chem ON, ozone-rich air from
surrounding air masses is mixed into the polar vortex during SSWs in
correspondence with . Additional heating due to the
increase in ozone mixing ratios could explain the extended lifetime of the
SSW warming signal in the lower stratosphere in Chem ON and thereby the
persistence of the NAO-like SLP anomaly in association with the
occurrence of SSWs in the Chem ON simulation.
Apart from the lack of feedbacks between chemistry and dynamics, Chem OFF is
also missing the effect of ozone waves in the prescribed zonal mean ozone
field, which contributes to the differences between Chem ON and Chem OFF. Therefore, we
performed a sensitivity run prescribing zonally asymmetric (3-D)
ozone (Chem OFF 3D, Table ). The differences between Chem ON
and Chem OFF 3D agree in sign with the differences between Chem ON and
Chem OFF but are smaller in amplitude overall and are less significant
(Figs. , ). Significant differences
are restricted to early winter and late spring. Hence, we conclude that the
missing effects of ozone waves in Chem OFF contribute to the larger
differences between Chem ON and Chem OFF.
Considering stratospheric variability, the distribution of SSWs throughout
the winter season is still better captured in Chem ON compared with Chem OFF 3D
(Fig. ), whereas the total SSW frequency in Chem OFF 3D is
not significantly different from that in Chem ON (Table ).
Also, the SSW surface impact is better captured in Chem ON compared with
Chem OFF 3D (Fig. ), which we explain with the missing
intrusion of ozone-rich air into higher latitudes in Chem OFF 3D (similar to
Chem OFF; not shown).
Climatological zonal mean (a) zonal wind at 10 hPa
in ms-1, and (b) temperature at 30 hPa in
K with month and latitude for Chem OFF CTRL (contours) and for the
difference between Chem ON CTRL and Chem OFF CTRL (shading). Contour
intervals are (a) 20 ms-1, and
(b) 10 K. Statistically insignificant areas are hatched at
the 95 % level.
Our results demonstrate the importance of chemistry–dynamics interactions
and also hint at the important influence of ozone waves on the differences
between Chem ON and Chem OFF. Prescribing daily zonally asymmetric ozone such
as in Chem OFF 3D, which is not consistent with the dynamics might also
introduce feedbacks that are difficult to interpret. A larger ensemble of
experiments, which was unfortunately not possible for this study, is needed
to better understand the importance of feedbacks between chemistry and
dynamics in the absence and presence of ozone waves. Therefore, a larger
ensemble of simulations is planned for a follow-up study to increase
significance and reduce the effect of internal variability on the results.
However, to further validate the results presented in this study, we show the
difference in the climatological mean state of the middle stratosphere for a
145-year control simulation in Fig. using a constant
external forcing based on 1960s conditions. Zonal wind and temperature show
the same differences between Chem ON CTRL and Chem OFF CTRL as presented in
Fig. for the transient forcing. The amplitude of the
differences is lower, which we attribute to the lower variability in lower
stratospheric ozone in this control setting. Nevertheless, it shows that our
basic results are robust and can be reproduced in a control setting.
It is, however, essential to better understand the role of
chemistry–dynamics interactions in order to improve our decisions about how
ozone is prescribed in upcoming model simulations. A new approach was
recently presented by , who discuss the potential of
machine learning to parameterize the impact of ozone in different standard
scenarios, such as in a 4×CO2 setting. Based on our findings
from prescribing a model-consistent, daily ozone forcing, we argue that a 3-D
ozone forcing as now provided for CMIP6 has the potential to improve the
representation of the impact that ozone chemistry has on model dynamics.
However, such a forcing does not perfectly compare to our experimental
setting, as the more generalized CMIP6 ozone forcing cannot supply
model-consistent ozone fields for different models and is based on monthly
mean data.
Data availability
Re-analysis data used in this paper are publicly available
from the ECMWF for the ERA-40 and ERA-Interim products. CESM1(WACCM) model
data requests should be addressed to Katja Matthes (kmatthes@geomar.de).
Author contributions
SH and KM designed the model experiments, decided on
the analysis, and wrote the paper. SH carried out the model simulations and
data analysis and produced all of the figures.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We want to thank the two anonymous reviewers for their constructive comments that
helped to improve the paper. We thank the computing center at
Christian-Albrechts-University in Kiel for support and computer
time. The article processing charges for this
open-access publication were covered by a Research
Center of the Helmholtz Association. Edited by: Farahnaz Khosrawi Reviewed by: two
anonymous referees
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