Introduction
Although the internal gravity wave (GW) sourcing
e.g., adjustment processes;, propagation and
breaking is governed to some extent by processes in the stratosphere,
there is a significant portion of the GW spectra created in the
troposphere mostly orography and
convection;. The highest-amplitude upward-propagating modes can break already in the troposphere and lower or
middle stratosphere . Model experiments with
gravity wave drag (GWD) parameterization showed that the orographic GWD in
the lower stratosphere can significantly affect the development of the sudden
stratospheric warming and the large-scale flow in the lower
stratosphere and troposphere in general . In the global climate
models, non-orographic GWs are usually considered to break in the upper stratosphere and higher above . It is well recognized that there is a need for
continued and additional research efforts on stratospheric dynamics
as complex understanding and unbiased modeling of stratospheric
conditions is vital for climate research .
From sensitivity simulations with a mechanistic model,
demonstrated the dynamical impact of the artificially enhanced GWD in the
stratosphere and most importantly the significant impact of the spatial GWD
distribution. This can open new horizons for research on teleconnections
between tropospheric (e.g., El Niño–Southern Oscillation, North Atlantic
Oscillation, Pacific Decadal Oscillation) and stratospheric (e.g., polar
vortex stability) phenomena taking into account that the tropospheric
variability can affect the distribution of GW sources and therefore the GWD
distribution (and strength) in the stratosphere. This is also the main
hypothesis that we investigate in this study. It is not possible to compute
the GWD from current satellite observations alone . Only by employing substantial approximation and neglecting
observational filter effects, gave a methodology to estimate
absolute values of a “potential acceleration” caused by GWs. Some
information can also be derived using ray-tracing simulations .
However, numerical simulations remain the major source of the GWD variability
global description. This is also the reason why we study the interannual
variability in the GWD using output from the Canadian Middle Atmosphere Model
with specified dynamics (CMAM-sd) in this paper. Although the orographic GW
parameterization schemes present a severe simplification of reality
e.g., assuming vertical propagation only;, they are the
only available source providing three-dimensional decadal-long information on
the GWD that is necessary to test the hypothesis of a connection between
climate oscillations and GWD distribution. To our knowledge the interannual
variability in GW model parameterization outputs has not been studied before.
The study is structured as follows. The next section introduces the model,
CMAM-sd simulation and the orographic GWD (OGWD) parameterization
scheme together with statistical methods used in our study. The second
section is dedicated to the OGWD analysis, assessing the realism of its
climatology first. This is followed by an interannual variability analysis
where significant differences in the distribution of OGWD depending on the
Southern Oscillation (SO), North Atlantic Oscillation (NAO) and
quasi-biennial oscillation (QBO) are illustrated. In the third section we
examine the correspondence of OGWD to tropospheric conditions and analyze the
variability in orographic gravity wave (OGW) momentum fluxes at the
850 hPa level. Finally, a summary of results and a discussion of the
uncertainties and implications of our paper are given.
Methodology
CMAM-sd and its GWD parameterizations
The CMAM chemistry climate model with
71 levels up to about 100 km with variable vertical resolution
and a triangular spectral truncation of T47, corresponding to
a 3.75∘ horizontal grid, has been used for producing the
specified dynamics (sd) simulation of the time period between 1979 and
2010. Up to 1 hPa the horizontal winds and temperatures are
nudged to the 6 hourly horizontal winds and temperatures from
ERA-Interim , as described in more detail in
. Due to this nudging, CMAM not only realistically
reproduces the climate characteristics of real atmosphere but also
follows its historical trajectory in a deterministic sense. This also
applies to the activity of internal climate variability modes and
their spatial response patterns, as illustrated by the samples in
Figs. S1 and S2 in the Supplement.
OGWD is parameterized using the scheme of . This OGWD
scheme employs two vertically propagating zero-phase-speed GWs to transport
the horizontal momentum to the left and right of the resolved horizontal
velocity vector at the launch layer, which extends from the surface to the
height of the subgrid topography and the static stability. Functional
dependence is on the near-surface wind speed, relative orientation of the
subgrid topography (determining the orientation of the GW momentum flux) and
the static stability in the source region. There are also two dimensionless
parameters in the OGWD scheme allowing us to arbitrarily control the total
value of launch momentum and the vertical flux of horizontal momentum
more detail in indirectly also influencing the breaking
level. The setting used in CMAM-sd has been tuned for polar-ozone chemistry
studies in CMAM since it produces reasonable zonal-mean zonal
winds and polar temperatures in the winter lower stratosphere
. As the parameterized orographic GWs propagate upward
they are subject to both critical-level filtering and nonlinear saturation
(using a convective instability threshold), where the functional dependence
is on the resolved horizontal wind speed and direction and static stability
in the place refer tofor exact description.
The CMAM non-orographic GW parameterization scheme
is based on launching a globally uniform isotropic non-orographic GW
spectrum in four cardinal horizontal directions at approximately
125 hPa. The aim is to produce a reasonable seasonal evolution
of the zonal-mean zonal temperature and winds in the mesosphere. The
zonal and meridional asymmetry stems from propagation effects
only. For these reasons it is clear that the resulting non-orographic
GWD (NOGWD) is not suitable for our analysis.
Multiple linear regression and other statistical methods
The specific GW responses to the changes in model atmospheric circulation can
be fairly non-trivial, as their functional dependence on the background
quantities is nonlinear and their extraction and quantification requires the
application of statistical methods able to separate the effects of multiple
simultaneously acting factors. Here, the association between OGWD and
selected prominent climate variability modes has been investigated through
multiple linear regression (MLR), using scalar indices of the NAO (defined as
a normalized pressure difference between Reykjavík,
Iceland, and Gibraltar), the Southern Oscillation (SO, defined as
normalized pressure difference between Darwin, Australia, and Tahiti) and the
QBO (defined as the zonal average of equatorial zonal wind at 30 hPa)
as explanatory variables, along with descriptors of solar forcing (total
solar irradiance), volcanic forcing (global mean stratospheric volcanic
aerosol optical depth) and a linear approximation of the long-term trend
component. The time series of the respective indices were used in the form
available from the KNMI Climate explorer database
(https://climexp.knmi.nl; last access: 29 October 2017). Additional
experiments have also been carried out to investigate the effects of internal
climate variability modes with dominant decadal and multi-decadal components:
the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal
Oscillation (AMO). However, due to their largely statistically nonsignificant
influence on GWD, as well as aliasing with other predictors (particularly the
Southern Oscillation index), only results obtained without considering PDO
and AMO are presented here. The statistical significance of the regression
coefficients has been estimated by moving-block bootstrap, with the block
size chosen to accommodate the autocorrelated structures in the regression
residuals. MLR has also been used to assess the associations between GW
effects and local circulation (characterized by geopotential height or wind
speed at various pressure levels); the stepwise version of linear regression
was used for some of these analysis setups, to identify the predictors most
relevant to the OGWD output. Due to the distinct annual cycle of the activity
of the orographic GWs (with their strongest manifestations typically observed
during the cold part of the year), seasonal specifics need to be considered
in the attribution analysis. While a sub-seasonal setup (such as an analysis
carried out separately for individual months of the year) would be desirable,
it would be difficult to achieve because of the relative shortness (a mere
32 years) of the time series analyzed here and the resulting limited amount
of independent samples. For this reason, the separation into traditionally
defined climatological seasons was used instead.
Results
GW influence on the stratospheric circulation is often estimated and compared
to forcing from resolved waves on the basis of zonal means see,
e.g.,. However, as we show in the first section of results, the
CMAM-sd OGWD climatological horizontal distribution at 100, 50, 30 and
10 hPa is highly zonally asymmetric and OGWD tends to be distributed
in local hotspots. The different dynamical effect of hotspots instead of
zonally symmetric forces has been already shown numerically by . Results in the
next sections illustrate that the studied atmospheric phenomena are connected
with a different OGWD distribution and thus with a potentially different
impact on the stratospheric dynamics.
made a first formal comparison between GW momentum
fluxes from models and observations concluding that the geographical
distribution of the fluxes from models and observations compares
reasonably well, except for certain features connected mainly to
non-orographic GWs. We are interested mainly in the geographical
distribution, and so we simply compare the CMAM-sd OGWD hotspots with
observed GW activity (not only momentum flux) hotspots at selected
pressure levels in the stratosphere. The CMAM-sd momentum flux
climatologies are shown in the Supplement.
CMAM-sd GWD climatology
First, we examine if the orographic GW parameterization scheme from CMAM-sd
distributes the OGWD realistically. Figure shows the OGWD
climatology at 100, 50, 30 and 10 hPa levels. The 100 hPa
level is traditionally below the level taken into account in the GW analyses
from satellite observations e.g.,. At this level, in the DJF season, the OGWD is dominated by a
Himalayan hotspot, which has not received significant attention in
observational analyses yet (probably due to its emergence at rather lower
levels). However, enhanced momentum fluxes have already been observed in this
region, e.g., by . Another hotspot emerging in the Northern
Hemisphere (NH) is connected with the Rocky Mountains. These hotspots are not
visible at the higher levels. In the Southern Hemisphere (SH), during
southern summer conditions, we see comparable magnitudes of OGWD (up to
20 ms-1 day-1) as for the NH connected with the southern
tip of the Andes, Tasmania and New Zealand. Those high OGWD values in the
summer hemisphere vanish at higher levels, which is in line with
, who attributed the small amount of summertime potential
energy to lower-level critical filtering.
Mean seasonal wind tendency due to OGWs
(ms-1 day-1) at the 100 hPa (a, b), 50 hPa (c, d), 30 hPa (e, f) and 10 hPa (g, h) level, during DJF (a, c, e, g) and
JJA (b, d, f, h) seasons.
Standard deviation (SD) of the monthly series of wind tendency due to OGW
(ms-1 day-1), displayed in a vector-like form and assigning the value for the eastward component to the x axis and
value for the northward component to the y axis.
In the JJA season at 100 hPa, there is no dominant hotspot in the NH,
while the SH OGWD distribution is dominated by the hotspot connected to the
Andes. At 50 hPa in the DJF, there is a dominating hotspot in the
region of eastern Asia corresponding to the eastern Asia/northern Pacific
(EA/NP) hotspot observed by
or referred to as Mongolian orography in . In the SH in the JJA season, the Andes are dominant, but note that
the OGWD magnitude is smaller than for the EA/NP in the DJF season.
Interestingly, at 30 hPa we see the dominance of the same hotspots as
at 50 hPa but with a smaller magnitude of OGWD. In the SH in the JJA,
the OGWD around the Drake Passage and Antarctic Peninsula begins to gain strength. At 10 hPa, in the NH in
DJF, the Scandinavian hotspot starts to be dominant . In the SH in JJA, the southern Andes , Drake Passage and Antarctic Peninsula hotspots
dominate. Interestingly, we can also see moderately strong OGWD (Fig. 1c,
50 hPa, DJF) over small remote islands in the SH . We conclude that the OGWD distribution from CMAM-sd gives a
sufficiently realistic distribution of the OGWD for our analysis, given the
assumptions employed in the parameterization and the lack of direct
observational information on the OGWD and GWD in general.
Response of the OGWD (ms-1 day-1) at the
50 hPa (a, b, c), 30 hPa (d, e, f) and 10 hPa (g, h, i) level related to the
activity of the Southern Oscillation (a, d, g), North
Atlantic Oscillation (b, e, h) and quasi-biennial
oscillation (c, f, i). The responses correspond to the
increase in the oscillation index by four times its SD, i.e., to the transition of the respective oscillation from a highly negative to a highly positive phase; red symbols pertain to locations with at
least one wind tendency component response statistically
significant at the 95 % confidence level; bright red indicates
at least one component significant at the 99 % confidence
level. Analysis period: 1979–2010, monthly data, DJF season.
Figure gives an illustration of how much the OGWD is changing on
the interannual scale. Note that the arrows do not show the drag direction
but illustrate a ratio of the meridional and zonal SD (both always positive).
We see that at 10 hPa, large OGWD variations correspond to
Scandinavia, central Asia and Greenland in the NH and the southern tip of the
Andes together with the region of Antarctic Peninsula in the SH winter.
standard deviation (SD) values reach 20 ms-1 day-1 in both hemispheres with a
prevalence of the zonal OGWD component (except the Antarctic Peninsula). The
OGWD variability at the 30 hPa level is dominated by the EA/NP and
Scandinavian hotspots with maximum values of SD below
5 ms-1 day-1. This magnitude is reached only in the
Antarctic Peninsula region in JJA in the SH. In the NH, the meridional
component has relatively lower variability than at 10 hPa.
At 50 hPa in the NH winter, we see the largest OGWD variations
in the EA/NP hotspot and surprisingly large values also locally in the
SH in the southern Andes. This is also the only region with pronounced
variation in OGWD in the SH winter. The Rocky Mountains and especially the Himalayas
and southern Andes with SD values around
5 ms-1 day-1 dominate the 100 hPa level in
DJF. At 100 hPa, the relative contribution of the meridional
OGWD component variability is bigger than at 50 and 30 hPa.
In SH in DJF and JJA, the variability in the Andes dominates.
Response of the OGWD (ms-1 day-1) at the
50 hPa (a, b, c), 30 hPa (d, e, f) and 10 hPa (g, h, i) level related to the
activity of the Southern Oscillation (a, d, g), North
Atlantic Oscillation (b, e, h) and quasi-biennial oscillation (c, f, i). The responses correspond to the
increase in the oscillation index by 4 times its SD, i.e., to the transition of the respective oscillation from a highly negative to a highly positive phase; red symbols pertain to locations with at
least one wind tendency component response statistically
significant at the 95 % confidence level; bright red indicates
at least one component significant at the 99 % confidence
level. Analysis period: 1979–2010, monthly data, JJA season
Generally, the OGWD varies interannually by about half of the
climatological OGWD magnitude (even reaching it at 10 hPa),
with respective hotspots dominating the variability at the particular
pressure levels of their climatological influence.
MLR results
Responses of the OGWD to the phase of major internal climate oscillations are
shown at the 50, 30 and 10 hPa levels. In the NH in DJF, the
variability connected with NAO dominates by far (Fig. , middle
column) in the sense that it is distributed across the whole hemisphere with many
significant regions and responses of up to 5 ms-1 day-1. As
could be expected from the NAO definition, it is most pronounced in regions
surrounding the North Atlantic. Note especially that at all analyzed isobaric
levels, there is a dipole-like structure between Greenland and Scandinavia
together with coastal areas in other places in western Europe. That indicates
that during the positive NAO phase the GW activity suppresses the eastward
wind above Greenland and enhances it above western Europe while the opposite
is true of the negative phase. A similar dipole can be found at the western
coast of North America, but only at the 50 hPa level. At higher
levels, the signal above Alaska is more pronounced. The NAO signal is also
pronounced in northeastern America, central Asia and partly in the EA/NP
region (at 50 and 30 hPa levels) and in northern Asia for the
10 hPa level. There is also a significant signal exceeding
2 ms-1 day-1 in northern Africa for the 50 hPa
level. The SO signal in the DJF season in the NH is mostly pronounced at 50
and 10 hPa level. At 50 hPa it constitutes a ring of
significant OGWD responses higher than 2 ms-1 day-1,
whereas at the 10 hPa level the signal in northeastern America,
Turkey, Iran and the Caucasus region dominates. At 50 hPa there is
also a strong localized signal in the southern tip of South America. The QBO
signal in DJF is mostly pronounced in central Asia in the NH and southern
Andes together with the Antarctic Peninsula at 50 and 30 hPa in the
SH.
During austral winter (JJA; Fig. ), the largest signal
found in the OGWD belongs to the SO, with Antarctica dominating at the
50 hPa level. At higher levels there is a dipole-like feature
between Antarctica and the southern tip of the Andes. There is also a strong
(more than 2 ms-1 day-1) significant signal
connected with the QBO at the 50 hPa level over
the Andes. Somewhat surprisingly we can also find a significant NAO signal
(ca. 1 ms-1 day-1) around southern Australia and New
Zealand at 50 hPa. The results of the regression of solar
activity and volcanic forcing were not shown because they gain a mostly
insignificant OGWD signal. Only at 50 hPa is there a weak (up
to 1 ms-1 day-1) significant solar signal in
northeastern America and Antarctica in their respective winter
periods.
To illustrate that it is necessary to consider geographical
distribution for the analysis of the interannual variability in OGWD, we
show the MLR results also for zonal means of OGWD (shown for DJF
only). For the zonal OGWD component (Fig. ), we can see that
there is only a weak positive significant NAO signal at all levels and
a very small positive significant SO signal at 50 hPa between
20–30∘ N corresponding to the belt described in the discussion of
the Fig. . The magnitude of the detected signal is lower
than 1 ms-1 day-1 everywhere. For the QBO and also
for the meridional component (Fig. ) of all indices, the
signal is not significantly positive or negative or lower than
0.1 ms-1 day-1 almost anywhere. The situation is similar
also for the JJA season (not shown).
Response of the zonal-mean OGWD
(ms-1 day-1) at the 50 hPa (a, b, c), 30 hPa (d, e, f) and 10 hPa (g, h, i) level related to the activity of the Southern
Oscillation (a, d, g), North Atlantic Oscillation (b, e, h) and quasi-biennial oscillation (c, f, i). The responses correspond to the increase in the oscillation
index by 4 times its SD, i.e., to the transition of the respective
oscillation from a highly negative to a highly positive phase; the blue curve shows the signal value and blue shading illustrates the
95 % confidence interval. Analysis period: 1979–2010, monthly
data, DJF season.
Response of the meridional mean OGWD
(ms-1 day-1) at the 50 hPa (a, b, c), 30 hPa (d, e, f) and 10 hPa (g, h, i) level related to the activity of the Southern
Oscillation (a, d, g), North Atlantic Oscillation (b, e, h) and quasi-biennial oscillation (c, f, i). The responses correspond to the increase in the oscillation
index by 4 times its SD, i.e., to the transition of the respective
oscillation from a highly negative to a highly positive phase; the blue curve shows the signal value and blue shading illustrates the
95 % confidence interval. Analysis period: 1979–2010, monthly
data, DJF season.
The general finding of the results presented above is that the OGWD varies
locally by a few meters per second per day depending on the phase of
the climate indices and also that the geographical variation in
hotspots can vary from a phase to phase. The analysis also points to
the important finding that the significant signal connected to the
climate oscillations diminishes in the case of the traditional zonal-mean
approach.
Explanatory factors
The results presented above alone cannot confirm our hypothesis on the
tropospheric variability transfer to the stratosphere by altering the GW
activity and its distribution because the MLR results do not illustrate the
causality of the problem considered. It can be argued that the OGWD
variability results are caused simply by the variations in the stratosphere
or upper troposphere (e.g., jet shift, meandering due to anomalous planetary
wave (PW) activity) possibly leading to Doppler shifting effects or
variations in critical lines for the orographic GW propagation e.g.,
the relevance of the occurrence of the Aleutian High for the EA/NP hotspot –
. The modulation of GWs by
PWs receives great attention in the scientific community
e.g.,, and considering this causality mechanism, the
dynamical influence of the OGWD variations would be of secondary importance
only. Therefore, in this subsection we analyze the daily data of wind
direction and speed (the influence of another OGWD parameterization variable
– a stability – was not diagnosed) to show that at least a part of the OGWD
variability is directly influenced by the variability at the surface or in
the lower troposphere.
Figures , and present an analysis of
daily data aimed at estimating how much of the OGWD variability at
a given level can be explained by 850 hPa wind variance. At
50 hPa, we can see that the link between the lower-tropospheric winds and OGWD is strongly expressed in a belt in the
midlatitudes and tropics of the NH. The fraction of variance
explained is maximal and the geographical distribution is also very
similar for the links between zonal wind/the zonal OGWD component and
meridional wind/the meridional OGWD component. In the regions with
significant orography and particularly in the region of the EA/NP
hotspot (which dominates the OGWD field at 50 hPa in the NH), the majority of OGWD variance is explained by lower-tropospheric
winds.
(a, b) Standardized regression coefficients
between orographic gravity wave drag at the 50 hPa level
(predictand) and eastward and northward wind components at the
850 hPa level (predictors), during the DJF
season. (c) Coefficient of determination, i.e., the fraction
of total variance of OGWD explained through the regression
mappings by both components of wind at the 850 hPa
level. Analysis period: 1979–2010, daily data, DJF season.
(a, b) Standardized regression coefficients
between orographic gravity wave drag at the 30 hPa level
(predictand) and eastward and northward wind components at the
850 hPa level (predictors), during the DJF
season. (c) Coefficient of determination, i.e., the fraction
of total variance of OGWD explained through the regression
mappings by both components of wind at the 850 hPa
level. Analysis period: 1979–2010, daily data, DJF season.
(a, b) Standardized regression coefficients
between orographic gravity wave drag at the 10 hPa level
(predictand) and eastward and northward wind components at the
850 hPa level (predictors), during the DJF
season. (c) Coefficient of determination, i.e., the fraction
of total variance of OGWD explained through the regression
mappings by both components of wind at the 850 hPa
level. Analysis period: 1979–2010, daily data, DJF season.
An interesting pattern can be seen in the SH around the Andes, where the
maximum of the OGWD variance explained is located up- and downwind
from the Andes. Also interestingly, at 50 hPa, in the southern
Andes/Antarctic Peninsula region, a larger fraction of the meridional
OGWD component variance is explained by surface conditions than for
the zonal component. Otherwise, the fraction of the OGWD variability
explained in the Australian/New Zealand hotspot (connected in previous
analyses mainly with the NAO signal) is about two-fifths of the total
variance.
At the 30 hPa level the fraction of variance explained is
lower – around one-third of the variance in eastern Asia and locally
in northern Atlantic coastal regions and in the SH. In the eastern
Asia region this is due to the stratospheric background affecting the
critical line occurrence and propagation of the GWs between 50 and
30 hPa. Interestingly, for the meridional OGWD component, the
fraction of variance explained is slightly higher. At the
10 hPa level, there is a single maximum of explained total
variance (around one-third) in Scandinavia. A similar amount of variance
is also explained by 850 hPa winds for Iceland but for the zonal
OGWD component only.
Another approach allowing us to assess the variability in the orographic
GW sourcing is to analyze the 850 hPa orographic GW fluxes as
a proxy and apply the MLR method. However, with this method it is not
possible to link the results directly with the variability in the OGWD
because processes like the Doppler shifting of amplitudes or critical
line variations can alter the resulting OGWD significantly. Also note,
that this analysis is made with monthly data and the comparability with
previous analyses is limited.
In Fig. we see that in the NH in DJF there is a strong
signal in Greenland and western Europe connected with the NAO and
an equally strong signal in GW sourcing variability in central Asia
(Himalayas), Greenland, Iceland and Svalbard connected with the
QBO. The SO signal is largely insignificant in the NH, but in the SH
in JJA it is most strongly pronounced mainly in the southern tip of the Andes
and Antarctic Peninsula. In the SH in JJA, some
regions of significant signal in GW sourcing variability connected
with the NAO (the Andes, Australia and New Zealand) and QBO (Antarctica) can also be found.
Response of the orographic GW momentum fluxes (Pa) at the
850 hPa level related to the activity of the Southern
Oscillation (a, b), North Atlantic Oscillation (c, d) and quasi-biennial oscillation (e, f) during DJF (a, c, e) and JJA (b, d, f) seasons.
The responses correspond to the increase in the
oscillation index by 4 times its SD, i.e., to the transition of the
respective oscillation from a highly negative to a highly positive
phase; red symbols pertain to locations with at least one
orographic GW flux component response statistically significant at
the 95 % confidence interval. Analysis period: 1979–2010,
monthly data.
Although the strong QBO signal may be surprising, the QBO phase
exhibits a distinct and in some regions statistically significant
influence on the lower-tropospheric winds in CMAM-sd (not shown). The
influence of the QBO on the surface meteorological conditions has been
pointed out in the literature in detail before
e.g.,.
Summary and discussion
The study presented here introduces an analysis of interannual variability in the CMAM-sd OGWD at particular pressure levels in the
stratosphere. Building on the results of , the aim of
our paper has been to evaluate if the tropospheric variability can
affect the OGWD distribution in the stratosphere.
In the first section we show the simulated climatological OGWD distribution
at 100, 50, 30 and 10 hPa levels and estimate its interannual
variability to be about half of the climatological OGWD value at the major
hotspots. The main conclusion of this part is that the distribution can be
regarded as reasonably realistic because the main GW activity hotspots are
detected in a similar way to that described in the GW-observing literature (also considering the practically
missing observational constraints on the OGWD in general). In the second
section, results of the MLR analysis of monthly OGWD data are presented
showing a significant NAO, SO and QBO signal of a few (up to
5) ms-1 day-1 in the OGWD at 50, 30 and 10 hPa.
Depending on the phase of the climate oscillations, OGWD values in the
hotspot regions and also the distribution of OGWD hotspots vary interannually
on the selected pressure levels. However, in the case of the traditional
zonal-mean analysis, the detected signal is small and mostly insignificant.
In the last part we demonstrate that a large fraction (over hotspots like
EA/NP) of the described OGWD variance can be linked to the variance of
850 hPa winds. We also find significant NAO, SO and QBO signals in
the orographic GW momentum fluxes at 850 hPa suggesting different
orographic GW sourcing in a model depending on the phase of these phenomena.
For the CMAM-sd simulation, all of the results support the original
hypothesis of the tropospheric variability transfer into the
stratosphere via OGWD variability. The suggested mechanism depicts
a simplified picture, not taking into account the inner variability in
the stratosphere, PW propagation or mutual interactions between the
troposphere and stratosphere. On the other hand, it has to be noted
that the GWs are arguably the fastest way for communicating information in the vertical (apart from the acoustic and
acoustic gravity waves with effects much higher in the middle and in
the upper atmosphere). Therefore, tropospheric information can be
quickly mediated into the stratosphere and OGWD variability can be
directly influenced by the variability at the surface or in the lower
troposphere. During propagation and in the stratosphere, those fast
and GW-mediated tropospheric contributions interact nonlinearly with
the stratospheric processes (Doppler shifting, critical-level
variations). However, it makes little sense to look for the causality
between GWs and PWs (background field for GWs) when only the steady
state (monthly data) is considered.
There is also a factor of longitudinal variability in the OGWD (and
GWD in general). For the PW breaking there is almost no information in
the literature about the geometry and longitudinal variability in the
imposed drag force. But for the GWs, it has been shown in
that localized forces can lead to dynamical
responses different from the reactions to a zonally averaged
forcing. Although the gravity waves are a small-scale phenomenon, they
are often organized in large-scale hotspots constituting a large-scale
forcing. We argue that incorporating these effects into related
analyses can open new horizons for research on teleconnections between
tropospheric (e.g., SO, NAO or PDO) and stratospheric (e.g., polar
vortex stability) phenomena. The magnitude of the OGWD variations
reaching a few meters per second per day locally can
significantly affect the stratospheric dynamics. It was shown by
that the injection of a localized versus zonally
symmetric GWD of 10 ms-1 day-1 can lead to wind
speed differences of an order of 10 ms-1 at corresponding
vertical levels. For the residual circulation and Eliassen–Palm flux
the localized GW forcing of this magnitude induced differences ranging
up to 50 % of their climatological values in the middle and upper
atmosphere mechanistic model used for the study
.
Our analysis relies on parameterized processes, and thus the results
can be highly model dependent considering that other models use
different OGWD parameterizations than CMAM. The NOGWD in CMAM-sd at
the vertical domain of our analysis is clearly underestimated compared
to the current consensus on the GW distribution and impacts on the
stratosphere e.g.,. It would be
highly interesting to look at NOGWD variations connected with
variability in jets, fronts, etc., in future research. For the real
atmosphere our results strongly suggest that GWs can play a much
bigger and different role in the troposphere–stratosphere coupling and
in shaping the stratospheric dynamics than is currently
acknowledged. However, at the current stage it is impossible to
evaluate the actual details of the connection between climate
oscillations (tropospheric variability) and OGWD changes. This is
partly due to the nudging procedure, which prevents us from analyzing the
GWD impact on the circulation because this impact is weakened by the
relaxation towards ERA-I data. However, as we have only analyzed the
OGWD interannual variability, the nudging is very advantageous for us
(compared to a free running model), since the distribution of gravity
wave momentum fluxes in CMAM-sd can resemble the distribution of real
fluxes .
From a methodological point of view we must also note that GWs and their
effects are handicapped by the use of monthly mean data because the GWs are
very intermittent in the atmosphere e.g.,, and
also in CMAM, the OGWD shows large daily (and shorter, not shown)
variability. Therefore, for example, the monthly mean values may be hiding 1
order stronger intermittent drag values. During the analysis,
there were also indications of noteworthy deviations from linear behavior in
some regions, encouraging future transition to nonlinear regression
techniques.
In our future work, we aim to separate and estimate the dynamical impacts
of the different OGWD distributions belonging to the respective phases of
the NAO, SO and QBO by producing sensitivity simulations with
a mechanistic model with prescribed OGWD values and a distribution
according to MLR results from CMAM.