Introduction
Internal gravity waves (GWs) exchange energy and momentum between the
troposphere and the middle atmosphere . Especially the
interaction of GWs with the mean flow plays an important role in atmospheric
dynamics, as dissipating GWs deposit momentum, e.g. by wave breaking, which
changes the background flow . This momentum
deposition drives the meridional Brewer–Dobson circulation
or the periodically changing westerly and easterly
winds in the tropic stratosphere, known as the quasi-biennial oscillation
QBO; e.g..
Due to their importance for atmospheric flows from the boundary layer to the
middle atmosphere, GWs have been studied intensively in the past by means of
analytical and numerical models
e.g. and a large number of field
campaigns like the Momentum Budget over the Pyrénées experiment
PYREX;, the Mesoscale Alpine Programme
MAP;, the Terrain-induced Rotor EXperiment
T-REX; or the Deep Propagating Gravity Wave Experiment (DEEPWAVE) campaign .
An overview of some previous GW field campaigns is given in
. In December 2013 the Gravity Wave Life Cycle I
(GW-LCYCLE I) campaign took place over northern Scandinavia to observe the
whole life cycle of GWs from their excitation via propagation to dissipation.
The Scandinavian coastal mountain range together with the wintertime synoptic
situation over northern Scandinavia and the proximity to the polar vortex
represent favourable conditions for the generation and propagation of
mountain waves. In the stratosphere, these waves can form ice clouds
e.g.
and regions of gravity wave breaking as observed by at
altitudes of 30 km. The principal idea of the GW-LCYCLE I campaign was to
conduct observations whenever the meteorological conditions favoured mountain
wave excitation and vertical wave propagation. GW-LCYCLE I was
a forerunner experiment to test flight strategies and synergies between the
different instruments, which were afterwards applied during the two
consecutive gravity wave campaigns DEEPWAVE in New Zealand during austral
winter 2014 and GW-LCYCLE II in Scandinavia during winter
2015/2016.
Despite the large number of field campaigns and modelling studies, the
accurate simulation of propagating and breaking GWs is still a challenge for
weather and climate models. Simulations with high model grid resolutions can
help to understand the complex interaction of different kinds of waves on a
multitude of scales during a GW event by a detailed analysis of the
respective meteorological situation. These simulations are, however,
dependent on initial and boundary conditions of larger-scale models, whose GW
parameterization schemes have to be improved .
investigated the parameterization of GW-induced
pressure drag and momentum fluxes depending on the horizontal model grid
resolution Δx. They showed a discrepancy between resolved and
parameterized drag processes, as parameterization schemes typically only
represent processes due to subgrid-scale orography. In addition, they should
also include processes from longer wavelengths of up to 10Δx, which
contribute to GW-induced drag but cannot be resolved by numerical models.
This issue is important for simulations with model grid resolutions in the
order of tens of kilometres where the topography and related GW processes are
only partly resolved. By tuning their parameterization scheme, were able to improve the GW-induced pressure drag but
not the mountain wave momentum fluxes.
GW-induced momentum and energy fluxes have been investigated in several
studies by means of observations e.g. and
simulations e.g.. A comparison of simulated and observed
momentum fluxes is presented in for the DEEPWAVE
campaign. On average, model simulation with a 6 km horizontal resolution
resulted in 5 % weaker momentum fluxes compared to observations, while a
reduction in the horizontal resolution to 2 km led to 50 % stronger momentum
fluxes. Along single flight legs the deviation of simulated fluxes from
observed fluxes was higher, with up to 60 and 85 % for the 6 and 2 km
run, which shows according to that momentum fluxes are
not predictable in a complex 3-D wave field even for model resolutions of up
to 2 km. Therefore, we investigate if simulated fluxes can be improved by
model grid resolutions in the sub-kilometre range during the GW-LCYCLE
campaign. In addition, this enables us to study systematically the influence of
model resolution and model topography on simulated GWs in a similar way as
presented in for simulations of trapped waves in the
lee of the Pyrénées. In contrast to , who used
surface stations and vertical profiles from a wind profiler and two
radiosondes to verify their model results, our simulations are compared to 2-D
lidar and in situ measurements on flight legs across the Scandinavian
mountain range.
The overall goal of this paper is to test the ability of a state-of-the-art
mesoscale model to simulate three-dimensional GW structures in the upper
troposphere and lower stratosphere (UTLS) region. Especially, we investigate
the impact of horizontal model grid and topography resolutions on the
simulation results. The accurate simulation of
GW-induced energy (EF) and momentum fluxes (MF) is of a certain interest. For this purpose the
horizontal model grid resolution is increased to 800 m to resolve single
mountain peaks and to investigate first whether a sub-kilometre horizontal
resolution significantly improves the representation of waves and secondly
whether it is possible to compute vertical winds whose magnitude and spatial
structure are comparable to lidar and in situ observations. Sensitivity runs
with increased vertical grid distances and increased turbulent diffusion are
performed to study possible impacts of unresolved, nonlinear processes on
energy and momentum fluxes. Simulation results are compared to vertical
sections of airborne Doppler wind lidar (DWL) measurements in the tropopause and to in situ
observations at flight level.
The paper is organized as follows. Section gives an overview
of the GW-LCYCLE I campaign and the available data sets. In
Sect. the numerical simulations and methods, which are used
to analyse GWs are presented. The general synoptic situation during the
campaign is described in Sect. by means of numerical
simulations. Observed and simulated GW structures in the UTLS are compared
and analysed in Sect. , and the conclusion is presented
in Sect. .
Topographic maps of Scandinavia and operational areas of the
GW-LCYCLE I campaign. The coloured lines indicate DLR Falcon flight tracks
during IOP1 and IOP5. In panel (a) the region shown and the black
boxes mark the modelling domains for mesoscale WRF simulations with
horizontal grid sizes Δx of 7.2 km (D1), 2.4 km (D2) and 0.8 km
(D3). The topography of domain D3 is shown in
panel (b) for the CTRL and in panel (c) for the SMTOPO
simulations (see Table ). The red dots mark the position of
Andenes (A), Abisko (Ab), Kiruna (K), Esrange (E) and Sodankylä
(S).
Campaign and data set overview
GW-LCYCLE I campaign
The GW-LCYCLE I campaign took place from 2 to 14 December 2013 in Kiruna,
northern Sweden (68∘ N, 20∘ E; see Fig. ).
The principal observational platform was the Deutsches Zentrum für Luft-
und Raumfahrt (DLR) research aircraft Falcon, based at Kiruna airport in the
lee of the Scandinavian mountain range. Airborne observations were
complemented by radiosonde measurements launched at the windward side of the
mountains at Andenes (69∘ N, 16∘ E; Norway), at the leeward
side at the European Space and Sounding Rocket Range Esrange (68∘ N,
21∘ E, Sweden) at Kiruna airport (Sweden) and further downstream at
Sodankylä (67∘ N, 27∘ E; Finland). In addition, ground-based
lidar systems were operated at the Arctic Lidar Observatory for Middle
Atmosphere Research (ALOMAR) in Andenes and at Esrange and provided time
series of temperature and wind profiles at altitudes between 30 and 90 km.
At Andenes, middle atmospheric winds were measured with the Middle Atmosphere
Alomar Radar System (MAARSY). The flight strategy during the campaign focused
on synoptic situations with strong westerly cross-mountain flow, which are
favourable for the excitation of mountain waves
. The planning of the
respective research flights was facilitated by the usage of the Mission
Support System MSS;, which is a software tool to
compute meteorological parameters along virtual flight legs on the basis of
numerical weather prediction model output.
Overview of intensive observation periods (IOPs) during GW-LCYCLE I.
Start and end times of research flights are indicated in UTC. For
each flight the number of airborne lidar profiles in both nadir and
scanning mode (see Sect. ) and the corresponding
coverage of useable data in
percentage is shown. Radiosondes were released at Andenes (A),
Esrange (E), Kiruna (K) and Sodankylä (S)
(see Fig. ).
IOP
Date
Research flights
Airborne lidar profiles
Radiosondes
Description
Start
End
Nadir
Scan
A
E
K
S
1
03.12.2013
09:13
11:26
2379 (41.93 %)
6 (26.24 %)
9
9
7
10
Mountain wave event
13:25
16:22
1328 (39.24 %)
100 (67.56 %)
2
05.12.2013
08:33
11:48
7222 (36.24 %)
21 (36.85 %)
–
–
–
–
Storm Xaver
3
09.12.2013
12:08
14:58
2408 (7.84 %)
–
–
–
–
–
Trace gas/pollution event
4
11.12.2013
–
–
–
–
6
7
7
11
Mountain wave event
5
13.12.2013
06:10
09:37
2250 (38.33 %)
68 (53.72 %)
5
5
8
8
Mountain wave event
12:19
15:24
2305 (47.97 %)
39 (44.19 %)
Altogether, there were five intensive observation periods (IOPs) with a total of
six research flights and 92 radiosoundings (see Table ). IOP 1
and 5 were mountain wave events which were studied with four research flights
and are investigated in this paper. No research flight could be conducted
during a strong mountain wave event on 11 December 2013 (IOP4) as a downslope
windstorm with gale force cross winds made take-off and landing impossible
at Kiruna airport. The event during IOP2 was dominated by the winter storm
“Xaver”, which passed over northern Germany .
This storm caused mountain and jet-induced GWs over southern Scandinavia and
deep propagating convective GWs in a strong, convective cold-air outbreak
with polar low formation over the Norwegian Sea. Finally, relatively calm
conditions prevailed during IOP3, enabling the measurement of polluted air
(mainly of SO2), which was advected towards northern Scandinavia from
midlatitudes with sources in the US and China (H. Schlager, personal
communication, 2013).
Airborne observations
The DLR Falcon aircraft was equipped with a downward-looking coherent DWL which operates at a wavelength of 2 µm. Within the last
years, this lidar system was successfully deployed in several ground-based
and airborne field campaigns for instance for measuring aircraft wake
vortices , aerosol optical
properties and the three-dimensional wind field over
the Atlantic Ocean . Details about the DWL
hardware configuration are given by and
, and details about the retrieval procedure can be
found in .
During the GW-LCYCLE I campaign, the DWL was operated in scanning or nadir
modes aiming to measure the vertical profiles of the three-dimensional wind
vector or to measure the vertical wind speed, respectively. While operating
in scanning mode, a conical step-and-stare scan around the vertical axes with
an off-nadir angle of 20∘ is performed, which results in a
horizontal resolution of 9 km. During nadir mode operation, the laser beam is
pointed in nadir direction and the measured LOS wind equals the vertical wind
speed with a horizontal resolution of 200 m. The vertical resolution for both
measurement modes is determined by the laser pulse length and is set to be
100 m. A detailed technical description of the DWL measurements during
GW-LCYCLE I is given in .
Flight legs and altitudes of the four research flights during
(a, b) IOP1 and (c, d) IOP5. The yellow and green shaded
areas indicate regions where the airborne lidar operated in scanning and
nadir-pointing mode, respectively. Blue and red thick lines indicate
respective flight altitudes below and above the tropopause, which was
determined by in situ trace gas measurements of N2O with a threshold value
of 326.6 ppbv (see Sect. ). The black dashed and dotted
lines mark flight legs used for data analysis and indicate complete flight
legs and leg sections limited to mountain regions, respectively. The
topography along the flight tracks is based on the ASTER data
set.
During the GW-LCYCLE I campaign the nadir-operating mode was used most
frequently, as it was suitable to detect small-scale gravity waves over the
complex orography. An overview of the flight altitudes and flight legs where
the lidar operated in nadir and scanning modes during IOP1 and IOP5 is given
in Fig. . In this figure the topography height along the
flight legs is represented by the Advanced Spaceborne Thermal Emission and
Reflection Radiometer (ASTER) data set , which has a
horizontal resolution of 30 m.
Besides wind lidar observations, in situ measurements of standard
meteorological parameters were conducted at flight level during all research
flights with a time resolution of 1 s. The measurement of in situ
three-dimensional wind is described in and the
verification of airborne pressure measurements in . Trace
gas measurements were performed at flight level with the water vapour
analyser (WARAN) hygrometer and a mass spectrometer for water vapour and SO2, respectively. The University of
Mainz Airborne Quantum Cascade Laser-spectrometer
UMAQS; was applied to measure nitrous oxide (N2O),
which is virtually inert in the UTLS. The transition from nearly constant
tropospheric (327.5 ± 0.9 ppbv) to decreasing stratospheric N2O
mixing ratios allows for the determination of the chemical tropopause, which
was defined by mixing ratios of 326.6 ppbv during GW-LCYCLE I. The main
objective of trace gas measurements was to detect GW-induced vertical mixing
processes in the tropopause region.
Ground-based observations
Airborne measurements were complemented by ground-based meteorological
observations. During the five IOPs a total of 92 radiosondes was released
both on the wind and leeward sides of the mountain range at Andenes (Norway),
Esrange (Sweden), Kiruna (Sweden) and Sodankylä (Finland). Depending on
wind conditions the sondes drifted up to 390 km horizontally and reached
altitudes of more than 30 km in most cases. This data set allows us to study
tropospheric GWs and their propagation through the tropopause into the lower
stratosphere. In addition to radiosonde observations, the Esrange Rayleigh
lidar provided 130 h of temperature profiles of the middle atmosphere
between altitudes of 30 to 65 km. The lidar measurements were conducted
during the period of 24 November to 14 December 2013. The analysis of the
lidar and radiosonde data in combination with mesoscale numerical modelling is
described in .
Numerical models and analysis methods for GWs
Real-case simulations
Mesoscale numerical simulations of the two mountain wave events IOP1 and IOP5
are performed with the Weather Research and Forecasting (WRF) model, version 3.7 . Up to three nested domains (D1, D2 and D3)
with horizontal resolutions of 7.2, 2.4 and 0.8 km (see
Fig. ) are used. For the two coarse domains 138 terrain-following levels with stretched level distances of 80 m near the surface,
160 m near the tropopause and 300 m in the upper stratosphere are used, and
the model top is set to 2 hPa (about 39 km altitude). For the innermost
domain only 78 vertical levels are applied and the model top is set to 50 hPa
(about 20 km altitude) to save computational resources. To avoid numerical
instabilities adaptive time stepping was used with a maximum time step of
15 s and a maximum Courant number of 1.2. At the model top a 7 km thick
Rayleigh damping layer is applied to prevent wave
reflection. Physical parameterizations contain the Rapid Radiative Transfer
Model longwave scheme , the Goddard shortwave scheme
, the Mellor–Yamada–Nakanishi–Niino boundary layer
scheme , the Noah land surface model
, the WRF single-moment six-class microphysics scheme
WSM6; and the Kain–Fritsch cumulus parameterization
scheme . The latter is switched off for the innermost
domain. Horizontal diffusion (WRF parameter diff_opt) was not applied in the
two innermost domains to increase GW amplitudes in vertical wind fields. The
initial and boundary conditions are supplied by ECMWF (T1279 L137, cycle 40r1) operational analyses on 137 model levels with a horizontal resolution
of 16 km and a temporal resolution of 6 h. WRF and ECMWF fields are
interpolated in space and time on aircraft flight tracks to compare with
observational data. For this purpose, a temporal output interval of 5 min
is used in the WRF simulations. For ECMWF a 1-hourly output interval is
realized by performing short-term forecasts with the ECMWF integrated forecasting system (IFS). In order to
compute GW-induced energy and momentum fluxes the diagnostic filtering method
of is applied to WRF output.
Overview of real-case simulations for cases with full (CTRL),
smoothed (SMTOPO) and without (NOTOPO) topography and for cases with a flat
water surface (OCEAN) and increased vertical grid resolution (CTRLVR).
Horizontal turbulent diffusion (H) is switched on in the HVDIFF case, and
vertical diffusion (V) is doubled in the H2VDIFF case.
Case
Type
Topo. D1
Topo. D2
Topo. D3
Land use
Diffusion
Vert. resolution
(m)
CTRL
real-case
full
full
full
land–ocean
V
80–300
SMTOPO
real-case
full
smoothed
smoothed
land–ocean
V
80–300
NOTOPO
real-case
flat
flat
flat
land–ocean
V
80–300
OCEAN
real-case
flat
flat
flat
ocean
V
80–300
CTRLVR
real-case
full
full
full
land–ocean
V
80
HVDIFF
real-case
full
full
full
land–ocean
H+V
80–300
H2VDIFF
real-case
full
full
full
land–ocean
H+2V
80–300
In addition to the WRF control simulation (CTRL), six sensitivity runs are
performed (see Table ): in the NOTOPO and OCEAN simulations
the topography height is set to zero everywhere in the domain. In addition,
the land use type has properties of a water surface with a roughness length of
0.0001 m everywhere in the domain in the OCEAN runs. The NOTOPO and OCEAN
simulations aim to define an atmospheric background state without mountain
waves and to investigate the influence of changing surface roughnesses
(transition from an ocean to a land surface) on GW excitation. In the SMTOPO
simulations the two innermost domains use a smoothed topography, which is the
same as in the outermost (D1) model domain. This is done to analyse the
effect of unresolved topography on GW structures in a high-resolution model.
In the CTRLVR runs the vertical grid resolution is increased, and 188 levels
with constant level distances of 80 m and a model top at 50 hPa are used.
Horizontal turbulent diffusion is switched on in the HVDIFF runs (WRF
parameter diff_opt = 1), and vertical turbulent diffusion is additionally
increased by a factor of 2 in the H2VDIFF case by doubling the tendency
terms obtained from the boundary layer scheme, which is responsible for
subgrid-scale turbulent mixing in the whole atmospheric column (not just the
boundary layer). The latter three sensitivity runs are performed to improve
simulated energy and momentum fluxes by damping wave amplitudes by unresolved
nonlinear processes.
Comparison of topography along leg 2 of flight 1 during (a, c) IOP1 and (b, d) IOP5 for the high-resolution ASTER data set,
CTRL and ECMWF topographies. In panels (c) and (d)
power spectra of the topographies in panels (a) and (b) are
shown.
All WRF topographies are based on the Global 30 Arc-Second Elevation
(GTOPO30) digital elevation model with a maximum horizontal resolution of
about 1 km, while the ASTER data set with a horizontal resolution of 30 m is
used as reference topography. Figure illustrates
the different representation of the Scandinavian mountain range in the
different model runs by means of two example flight legs during IOP1 and
IOP5. The innermost WRF domains D2 and D3 (Δx= 2.4 and 0.8 km)
resolve the individual mountain peaks very well in terms of amplitude and
horizontal wavelength (note the local peak in the power spectra at about
20 km). Domain D1 and the ECMWF model do not capture the fine-scale mountain
peaks and represent the topography as a compact, smooth mountain ridge. The
influence of topography resolution on the simulated vertical wind field is
investigated in Sect. .
Idealized simulations
To investigate the complex wave patterns and especially the occurrence of
trapped waves in the troposphere, which developed during IOP1 and IOP5,
idealized 2-D simulations (IOP1ID and IOP5ID) were performed along the two
example cross sections of flight 1, leg 2, during IOP1 and IOP5 (the same as
in Fig. ). The simulations were run without moisture
and were initialized with averaged upstream profiles of horizontal wind speed
and potential temperature of the CTRL D3 real-case simulations at 12:00 UTC on
3 and 13 December. Wind speed was projected to a wind direction
of 300∘, which is perpendicular to the Scandinavian mountain range
and represents the cross-mountain flow.
The model top was set to an altitude of 20 km with a damping layer thickness
of 5 km. A horizontal and vertical grid resolution of 800 and 50 m was
chosen, respectively, and simulations were run for 10 h.
Computation of fluxes and diagnostic variables
The computation of EF and MF at flight level according to the method of
provides information about GWs in the UTLS region and
is applied for both observations and simulations in this study. The
leg-averaged fluxes are computed by the following formulas:
MFx=ρ‾L∫w′u′dx,MFy=ρ‾L∫w′v′dx andEF=1L∫w′p′dx,
with zonal and meridional momentum fluxes MFx and MFy, GW-induced
perturbations of zonal wind u′, meridional wind v′, vertical wind w′,
pressure p′, leg-averaged density ρ‾, and leg length L.
Momentum and energy fluxes of linear GWs are related by the Eliassen–Palm
relation :
EF=-U×MF, withU×MF=u×MFx+v×MFy,
with leg-averaged zonal, meridional and total horizontal wind speeds u, v
and U, respectively. The wind and pressure perturbations u′, v′, w′
and p′ are computed by subtracting linear regressions from full wind and
pressure fields along flight legs. For pressure, a hydrostatic correction is
applied in advance of computing the pressure perturbations as described in
.
Further diagnostic variables are used in this study to describe flow and GW
characteristics. The gradient Richardson number is the ratio of buoyancy to
shear force and is defined as Ri=N2(dUdz)-2 with
Brunt–Väisälä frequency N. Typically a flow is dynamically unstable for
Ri<0.25.
The Scorer parameter l=N2/U2-1Ud2Udz2
can be used to distinguish between evanescent and
vertically propagating waves. Trapped lee waves occur in layers where l is
decreasing with height, which means that only waves with horizontal wave
numbers smaller than l can propagate vertically. In this study the
curvature term is neglected for simplification, and l is computed as
l=N2/U2.
Synoptic situation and respective flight tracks during
(a–c) IOP1 and (d–f) IOP5. Shown are CTRL D1 (Δx=7.2 km) horizontal wind speed and geopotential height at
700 hPa (a, d) and 300 hPa (b, e). GW-induced vertical
energy fluxes are plotted at 5 km altitude in panels (c) and
(f). The red dots mark the locations of Andenes, Abisko, Kiruna and
Sodankylä (cf. Fig. ). The first and second flight of
IOP1 and IOP5 are plotted with black and grey lines, respectively. The
example flight legs used in this study are marked with a green line. The
chosen times at 12:00 UTC, 3 December, and 12:00 UTC, 13 December, are valid
between the respective two research flights (see Table and
Fig. ). The areas marked with black lines in
panels (a) and (b) indicate regions used for the
computation of averaged vertical profiles in
Fig. .
Meteorological conditions during IOP1 and IOP5 from simulations
Synoptic situation
Meteorological situations favourable for the generation of mountain waves
occurred at the beginning (IOP1) and end (IOP4, IOP5) of the GW-LCYCLE I
campaign (see Table ).
Vertical time series of CTRL D2 simulations at Abisko
(68∘ N, 19∘ E) during (a–d) IOP1 and
(e–h) IOP5 for horizontal wind speed, vertical energy flux,
gradient Richardson number and Scorer parameter. The vertical solid and
dashed lines mark the different mountain wave phases and periods of research
flights. The dashed horizontal line indicates the height of the sponge layer
at the model top. Thin black contour lines mark the nonlinearity ratio (NLR)
of in panels (b) and (f) and isentropes
in the remaining figures.
The meteorological condition during IOP1 (3 December 2013) was dominated by a
strong synoptic low-pressure system, which was located over the northern
Norwegian Sea and travelled eastwards from the coast of Greenland towards
northern Norway (see Fig. ). At the tropopause level, which was
located at an altitude of about 5 km on the upstream side of the mountains, a
strong westerly jet moved southwards during IOP1. The cross-mountain flow
excited GWs and the related vertical energy fluxes were enhanced in the
middle troposphere along the whole Scandinavian mountain range with largest
fluxes occurring over the Kiruna region (Fig. c).
The GW event during IOP1 can be divided into three phases, which are marked
in the time–height sections for horizontal wind speed, gradient Richardson
number, vertical energy flux and Scorer parameter at the location Abisko
(68∘ N, 19∘ E; Fig. a to d), which is
situated in the centre of the mountain range between Andenes and Kiruna (see
the dots in Fig. ). The first GW phase (P1) from 20:00 UTC
on 2 December to 03:00 UTC on 3 December was dominated by moderate westerly
cross-mountain flow at low levels (30 m s-1 at 850 hPa) within the
warm air sector of the synoptic low (not shown) and moderate vertical energy
fluxes. The second phase (P2) from 03:00 to 06:00 UTC on 3 December was
characterized by weaker low-level winds (15 m s-1) and low GW activity
due to calm conditions after the passage of a cold front. At upper levels the
tropopause jet was located directly over northern Scandinavia. At about
06:00 UTC on 3 December the third phase (P3) started when low-level winds
intensified due to the approaching low-pressure system (cf.
Fig. ). During this phase the tropopause dropped to an
altitude of about 5 km upstream of the mountains and stratospheric air
descended to nearly 2 km altitude in the lee of the mountains, which is
visible in a cross section of the Brunt–Väisälä frequency along leg 2 of
flight 1 in Fig. a. The stratospheric intrusion on the
eastern side of the mountains was also present in the NOTOPO and OCEAN
simulations (not shown), which means that it was jet-induced
cf. and not generated by mountain waves. Within this
tropopause fold weak interfacial waves could develop in the CTRL D3 run
(Fig. a) due to decreasing Scorer parameters (not shown). GW
excitation stopped at about 03:00 UTC on 4 December when the low-pressure
system moved further east. The two research flights of IOP1 took place during
phase P3 (see Fig. ).
Cross sections of CTRL D3 Brunt–Väisälä frequency (coloured
contours) of flight 1, leg 2, during (a) IOP1 and (b) IOP5.
Potential temperature is indicated with black contour lines.
During IOP5 (13 December 2013) the situation was less complex as northern
Scandinavia was located below a strong and quasi-stationary northwesterly
tropopause jet (Fig. e) in polar air masses far north of
the polar front (not shown). The low-level forcing was weaker than during
IOP1 and dominated by a polar-low-like short-wave trough, which developed
into a cold-air outbreak south of Svalbard reaching the Norwegian coast at about 06:00 UTC on 13 December. Mountain wave generation was restricted to northern
Scandinavia (Fig. f) and started at about 00:00 UTC on 13 December, and EF stopped immediately when the
polar low dissolved at about 17:00 UTC on 13 December (Fig. e and f). In the troposphere, interfacial waves formed at a layer with
increased stratification between about 2.5 and 5 km altitude. This layer was
the residual of a tropopause fold, which passed over northern Scandinavia the
day before (not shown) and is visible in the Scorer parameter maximum at
about 2.5 to 3 km in Fig. h and in a local maximum of
the Brunt–Väisälä frequency in Fig. b. Interfacial waves
are visible in this layer by means of potential temperature contour lines,
which show wave structures with vertical phase lines. As the upstream
profile was already favourable for interfacial waves, wave trapping was stronger
during IOP5 than during IOP1.
Wave propagation into the stratosphere
Because of different cross-mountain flow during IOP1 and IOP5, the vertical
wave propagation in the stratosphere was different. While during IOP5
continuously propagating GWs developed, GW breaking occurred at altitudes
between about 25 and 30 km during IOP1, which is visible from convective
overturning, reduced Richardson numbers Ri, increased nonlinearity ratios
NLR; and decreasing energy fluxes in this altitude range
during phase P3 in Fig. . As breaking mountain waves slow
down the background flow , this turbulent region could
also be observed by radiosondes starting at Andenes, which measured strongly
reduced horizontal wind speeds (smaller than 10 m s-1) between altitudes
of 25 to 30 km (not shown).
To investigate the reason for different propagation conditions in the
stratosphere the NOTOPO and OCEAN simulations are analysed. Wind speeds in
these simulations can be regarded as atmospheric background state without
perturbations due to mountain waves. The solid lines in
Fig. show time series of horizontal wind speeds
averaged between 25 and 30 km at Abisko obtained from the NOTOPO and OCEAN
simulations and reveal about 10 m s-1 lower wind speeds during IOP1
compared to IOP5. The grey shading in Fig.
indicates maximum and minimum GW-induced wind speed perturbations obtained by
subtracting OCEAN from CTRL run fields. During IOP1 weaker background winds
and stronger wind perturbations generated regions with winds below
10 m s-1, which favour local mountain wave breaking due to the formation
of critical levels. This means that the growing wave amplitude generates
regions with nearly zero winds while the vertical wavelength approaches zero.
This leads to convective overturning and turbulent wave breaking
, which is visible in the regions of reduced
Richardson number and increased NLR in Fig. b. During
IOP5 wind speeds stayed above 20 m s-1 at this altitude range and
enabled wave propagation to altitudes above 30 km
(Fig. f). In addition, the comparison of NOTOPO and
OCEAN simulations shows nearly no difference in horizontal wind speeds. This
indicates that GWs are not generated by changes in roughness lengths in the
NOTOPO run, when the flow passes over the ocean and reaches the land surface,
as NOTOPO (water and land surfaces) and OCEAN (only water surfaces)
simulations generate a similar wind field.
Time series of background horizontal wind speed averaged between 25
to 30 km (solid lines) of the OCEAN and NOTOPO simulation at Abisko
(68∘ N, 19∘ E) for (a) IOP1 and (b) IOP5.
The grey shaded area marks the range of minimum and maximum wind speed
perturbations (mountain-wave-induced) at 25 to 30 km determined by
subtracting OCEAN from CTRL simulation fields. All data are based on domain
D2. The vertical solid and dashed lines mark the different mountain wave
phases and periods of research flights as in Fig. .
GWs in the UTLS region
Beside the GW evolution in the lower and middle stratosphere the focus of
this study is on GW structures in the UTLS, as this part of the atmosphere
was observed by airborne measurements. Differences in the atmospheric
background conditions during IOP1 and IOP5 are shown by means of average
vertical profiles of wind speed, Scorer parameter and vertical energy flux in
Fig. . CTRL upstream profiles of horizontal wind
speed were averaged over a region between 69 and 70∘ N and
10 to 15∘ E (see small black boxes over the ocean in
Fig. ) and indicate relatively strong and constant
horizontal wind speeds between 25 and 30 m s-1 in the
troposphere during IOP1. In contrast, a strong increase in wind speed from
10 m s-1 near the surface to 50 m s-1 at the tropopause existed
during IOP5. Upstream profiles of the Scorer parameter show continuously
increasing values in the troposphere during IOP1 (with a minor local maximum
below 2 km altitude), which is not favourable for the formation of trapped
waves. During IOP5 maximum values of l occurred at about 2 km altitude and
the Scorer parameter was strongly decreasing between 2 and 4 km and
between 5 and 8 km altitude during IOP5. This means that during IOP5
background atmospheric conditions were favourable for the formation of
interfacial waves. Regionally averaged profiles over the mountain region
between 67 to 69∘ N and 15 to 25∘ E (see large
black box in Fig. ) of vertical energy fluxes show upward-propagating waves with relatively constant energy fluxes with a height of up to
15 km altitude during IOP1 and a strong peak in energy fluxes in the
jet stream region during IOP5.
Area-averaged vertical profiles during (a–c) IOP1 and
(d–f) IOP5. Cross-mountain horizontal wind speed (from a direction
of 300∘) and Scorer parameter are averaged over the upstream area
from 69 to 70∘ N and 10 to 15∘ E, while vertical energy
fluxes are averaged over the mountain area within 67 to 69∘ N and 15
to 25∘ E (see black boxes in Fig. a and b). The
thick horizontal black line marks the maximum mountain peak height within the
mountain box.
Cross sections of idealized 2-D simulations (IOP1ID and IOP5ID) for
horizontal and vertical wind speed along leg 2 of flight 1 during (a, b) IOP1 and (c, d) IOP5. Profiles of EF (dashed lines) and MF
(dotted lines) are plotted in panel (e). Simulations were
initialized with upstream profiles shown in Fig.
for horizontal wind speed.
To simplify the meteorological conditions and to investigate principal
differences in wave patterns during IOP1 and IOP5 in the UTLS region,
idealized 2-D simulations (IOP1ID and IOP5ID) were performed in addition to
real-case simulations. The formation of waves in the idealized 2-D simulations
(IOP1ID and IOP5ID) is only determined by the upstream wind profiles, thermal
stratification and the mountain peaks at the surface. Effects of horizontal
wind shear, convection or 3-D wave propagation are not included. The idealized
wave patterns during IOP1 and IOP5 are visualized in
Fig. . As already seen in Fig.
the situation during IOP1 was characterized by relatively constant horizontal
wind speeds with height in the upstream region. Under these conditions
hydrostatic waves formed over the Lofoten Islands and the main mountain range
with horizontal and vertical wavelengths of 20 and 6 km and maximum wave
amplitudes of 3.3 m s-1 and propagated through the low tropopause into
the stratosphere. In the lee of the mountains no tropopause fold with
interfacial waves as in Fig. a is visible because the
idealized simulations were initialized with upstream CTRL D3 profiles and the
tropopause fold was associated with the synoptic upper-level frontal system
low approaching from the north.
During IOP5 the strong jet stream and the stratospheric intrusion layer
around 5 km altitude dominated the ambient conditions. Over the mountain
range waves with maximum amplitudes of 7 m s-1 and horizontal
wavelengths of 13 km with nearly vertical phase lines formed between 5 and
10 km altitude due to the strong increase in horizontal wind speed, which
caused the increase in the vertical wavelength. Below 5 km altitude waves
with shorter horizontal wavelengths of 6 km are visible. On the eastern side
of the mountains, interfacial waves with horizontal wavelengths of 8 km formed
along the stratospheric intrusion layer and propagated horizontally far in
the lee of the mountains. Horizontal wavelengths of interfacial waves in
real-case simulations were 10 km (Fig. b) and therefore in
the same order as in the IOP5ID simulation. The extremely stable boundary
layer in the lee of the mountains represents a typical situation for the
development of resonant trapped lee waves . The
stratification and the Scorer parameter are continuously decreasing with
height, which induces the formation of trapped waves with horizontal
wavelengths of 6 km in the IOP5ID case (Fig. d) and 8 km
in the CTRL D3 run (Fig. b). Profiles of idealized EF and
MF computed along the cross sections (Fig. e) show
nearly constant fluxes with height during IOP1. In the IOP5ID case fluxes
were very small below an altitude of 5 km due to wave trapping in this
altitude range and strongly increased in the jet stream region. Idealized
flux profiles are qualitatively similar to fluxes of the CTRL simulations
(Fig. ).
Cross sections for vertical velocity of flight 1, leg 2, during IOP1.
Lidar and in situ measurements (data at 7.3 km) are shown in
panel (a) for the complete flight leg and in panel (b) for
a smaller region over the mountains to enlarge the wave structures. Model
results for vertical wind and potential temperature (contour interval 2 K)
of the CTRL D1, CTRL D2, CTRL D3 and SMTOPO D3 simulations are shown in
panels (c) to (f). The thick black line in
panels (c) to (f) marks the flight altitude.
As in Fig. , but for flight 1, leg 2, during
IOP5. The flight level was at 5.6 km.
Observed versus simulated GWs in the UTLS region
Sensitivity of simulated GWs to grid and topography resolution
To study the agreement between observed and simulated wave structures,
vertical wind speeds along flight leg 2 of the respective first research
flights during IOP1 and IOP5 are shown in Figs.
and . In all panels the complete flight leg is shown
except in panel b where a smaller part of the leg is shown to enlarge the wave
structures. Airborne lidar and in situ measurements in panels a and b show
alternating up- and downward motions along the flight legs with amplitudes of
2 to 4 m s-1. The strongest signals occur directly over the
mountains and extend horizontally up to 300 km eastward in the lee of the
mountain range during both IOP1 and IOP5. White areas in lidar observations
mark regions where no measurements are available due to cloud coverage
. It can be recognized in Figs.
and a and b that waves in the lower troposphere were
nearly vertically oriented with weak upstream tilts of the phase lines.
During IOP1 (Fig. b) the combination of lidar
observations with in situ observations, which were close to the tropopause,
enables us to identify a stronger phase tilt in the UTLS compared to waves in
the lower troposphere. During IOP5 (Fig. b) both lidar
and in situ observations were conducted below the tropopause on the flight leg shown, which means that nearly vertical phase lines observed by the
lidar continued in the in situ measurements.
The simulated vertical wind fields in Figs. and
depend strongly on the grid and topography resolution.
As the CTRL D1 run does not resolve single mountain peaks (cf.
Fig. ), the vertical wind field is large-scale. With
a horizontal resolution of 2.4 and 0.8 km, the mountain peaks and related
waves are resolved in the CTRL D2 and D3 simulations, however, with weaker
maximum amplitudes of 2.4 and 5.3 m s-1 (2.5 and
4.1 m s-1) compared to observed amplitudes of 5.6 m s-1
(4.7 m s-1) during IOP1 (IOP5) mainly due to numeric diffusion. As in
the observations waves show nearly vertical phase lines in the troposphere
over the mountains, while upward-propagating waves with stronger phase tilts
are visible in the stratosphere in the CTRL D2 and D3 runs. The combination
of high model resolution with smoothed topography in the SMTOPO D3 simulation
(Figs. and f) results in a
vertical wind field, which is very similar to the coarse-resolution CTRL D1
run (Figs. and c). This shows that
realistic vertical wind fields can only be simulated with a model topography
that includes single mountain peaks (Fig. ). Similar
results were found in for GWs over southern
Greenland and in , who analysed WRF simulations of a
trapped lee wave event over the Pyrénées. In both cases GWs were not
captured appropriately in simulations with grid distances larger than 1.3
and 1 km due to smoothed topography and numerical diffusion.
Comparison of vertical winds at flight level along leg 2 of flight 1
during (a, c) IOP1 and (b, d) IOP5. To improve readability,
vertical wind speeds are shifted by 5 m s-1 in panels (a) and
(b). In panels (c) and (d) power spectra of
vertical winds in panels (a) and (b) are shown. The
corresponding spectra of the topography along the flight legs are plotted in
Fig. .
On the upstream side of the mountains all simulations reveal no vertical
winds at flight level in contrast to lidar and in situ observations, which
show vertical wind perturbations of up to 1.6 and 0.6 m s-1
during IOP1 and IOP5, respectively. This can be explained by missing
perturbations in simulations, e.g. due to convective GWs excited further
upstream. It is assumed that the east–west extent of the WRF modelling
domains (see Fig. ) would have to be much larger to
allow the development of convection-induced GWs in the westerly flow over the
ocean. This was, however, not possible in this study due to limitations in
computational resources. In addition, missing wave structures in the ECMWF
analysis data, which are used as initial and boundary conditions for the WRF
model, contribute to the smooth upstream vertical wind fields.
As in Fig. , but for horizontal wind from a
direction of 300∘ at flight level.
The relation between topography resolution and simulated vertical wind field
is demonstrated by comparing Figs. and
. The latter shows vertical wind speeds at flight
level in panels a and b along the two example flight legs during IOP1 and IOP5
(the same legs as in Figs. and ). As
seen in Figs. and , the wave
structures cannot be computed in the ECMWF, CTRL D1 and SMTOPO runs but
occur in the CTRL D2 and D3 simulations with weaker amplitudes over and in
the lee of the mountains compared to observations. The power spectra of
observed vertical velocity reveal dominant wavelengths between 15 and 30 km. Similar wavelengths were obtained from the corresponding topographies
in Fig. , which indicates that waves observed in
vertical wind fields over the Scandinavian mountains were connected to single
mountain peaks. The CTRL D2 and D3 runs partially reproduce these
wavelengths, however, with significantly smaller amplitudes, while they
cannot be resolved by CTRL D1, SMTOPO and ECMWF.
Figure shows the horizontal wind component from a
direction of 300∘, which represents the flow across the mountains
along the same flight legs as in
Fig. and . During both
IOPs a strong jump in horizontal wind speed is visible in the lee of the
mountains (Fig. a, b). All WRF simulations compute
very similar horizontal winds independently of the horizontal resolution with
maximum deviations from the in situ observations of 6.4 and
8.1 m s-1 during IOP1 and IOP5, respectively. In situ observations
indicate strong fluctuations in the horizontal wind with amplitudes of up to
5 and 3.6 m s-1 during IOP1 and IOP5. In contrast to the
vertical wind, the spectra of the horizontal wind component are dominated by
larger wavelengths of 60 to 120 km. In addition, power spectra of in situ
observations show secondary maxima for smaller wavelengths of about 25 to
40 km, which are also computed by the CTRL D2 and CTRL D3 simulations during
IOP5. The good agreement of horizontal winds and related spectra between all
CTRL and SMTOPO simulations indicates that horizontal winds are less
dependent on the grid and the topography resolution compared to the waves
observed in vertical wind fields.
Observed versus simulated energy and momentum fluxes
Airborne observations during IOP1 and IOP5 enable us to verify the simulation of
GW-induced energy and momentum fluxes in the UTLS for different grid
resolutions. EF and MF at flight level are computed according to the method
of (see Sect. ) and are shown in
Fig. . During both IOP1 and IOP5, the linear Eliassen–Palm
relation between EF and MF is satisfied nearly perfectly in all WRF
simulations and indicates upward-propagating mountain waves. Observed EF and
MF values show, however, an offset from the identity line and include
negative values during IOP5 (Fig. b), which means that
nonlinear effects seem to be underestimated by the WRF model. The
Eliassen–Palm relation was fulfilled already in other GW campaigns, e.g.
over the Sierra Nevada and New Zealand
. Note that only data directly above the mountains (cf.
black dotted lines in Fig. ) are used for flux
computations to avoid nonlinearity effects in observational data in upstream
regions. For ECMWF the relatively short lengths of the mountain legs cause
inadequate linear Eliassen–Palm relations. Along complete legs (cf. black
dashed lines in Fig. ), the linear relation is achieved
well for ECMWF (not shown).
Leg-averaged Eliassen–Palm relation between energy flux (EF) and
momentum flux (MF) multiplied by leg-averaged horizontal wind speed U along
all flight legs during (a) IOP1 and (b) IOP5 for observed
and simulated data at flight level. The dash–dotted line marks the identity
line and the thick dashed line indicates the linear regression of observed
data. The grey dots (In-situSM) mark observed data for wavelengths larger
than 15 km. To exclude effects of non-orographic GWs, only data directly over
the mountains are utilized (see Fig. for leg
locations).
Surprisingly, energy and momentum fluxes are significantly larger in the WRF
CTRL runs during both IOPs compared to in situ observations (up to
10 W m-2 during IOP5) in spite of smaller wave amplitudes (cf.
Figs. and ). As already seen in
Fig. , fluxes were strongest for D3 simulations due
to higher vertical wind speeds compared to D1 and D2 simulations (cf.
Figs. and ).
Correlation coefficients between airborne in situ and lidar observations and numerical
models for potential temperature Θ, horizontal wind speed U, vertical wind speed w, and
wind direction dd along all flight legs during both IOP1 and IOP5 (numbers in bold).
Model
In situ Θ
In situ U
In situ w
In situ dd
Lidar U
Lidar w
CTRL D3
0.992 0.996
0.877 0.963
0.154 0.343
0.892 0.868
0.755 0.917
0.242 0.408
CTRL D2
0.993 0.996
0.872 0.964
0.169 0.361
0.883 0.858
0.735 0.913
0.300 0.417
CTRL D1
0.993 0.997
0.872 0.964
0.156 0.213
0.868 0.858
0.712 0.916
0.205 0.218
ECMWF
0.992 0.998
0.891 0.967
0.116 0.076
0.858 0.897
0.414 0.882
-0.064 -0.092
To study the reason for increased fluxes in the model, sensitivity runs with
an increased vertical grid resolution of 80 m (CTRLVR) and increased
horizontal and vertical turbulent diffusion (HVDIFF, H2VDIFF) were performed
(see Table ). The idea of these sensitivity experiments was
to improve the representation of small-scale nonlinear effects like wave
breaking, which reduces vertical wind speeds and contributes to a reduction in the energy and momentum fluxes. By comparing EF and MF from different
simulations, Fig. shows that an increased vertical grid
resolution (CTRLVR) slightly reduces the EF and MF values in the order of
2 W m-2. Probably, an additional increase in the horizontal resolution
towards the order of a large-eddy simulation (LES) would be necessary to
reduce the simulated fluxes by explicitly resolving wave breaking. By
switching on horizontal turbulent diffusion in the HVDIFF simulation, vertical
fluxes were reduced by about 2 W m-2, which is similar to the CTRLVR
simulation. A clear improvement of vertical flux computation was attained by
both applying horizontal diffusion and doubling the vertical mixing tendency
term obtained from the boundary layer parameterization scheme in the H2VDIFF
simulation. The propagation of wave energy was effectively damped by up to
6 W m-2 compared to the CTRL run.
Grey dots in Fig. mark smoothed observed fluxes
(In-situSM), which were computed by using horizontal wavelengths larger than
15 km. These fluxes are nearly identical to the original in situ fluxes,
which means that waves with wavelengths smaller than 15 km did not contribute
significantly to GW fluxes at flight level. Similar results were found by
for “fluxless” waves with wavelengths between 6 and 15 km over New Zealand during the DEEPWAVE campaign.
Profiles of EF and MF of D3 simulations, which were averaged over all flight
legs during IOP1 and IOP5 are shown in
Fig. . For the CTRLVR, HVDIFF and H2VDIFF simulations EF
and MF fluxes are reduced over the atmospheric column between 2 and 15 km
and agree better with in situ observations than fluxes obtained from CTRL
runs. CTRLVR and HVDIFF simulations indicate similar flux profiles, while the
H2VDIFF runs show clearly reduced fluxes. During IOP5 the largest improvement
of the H2VDIFF fluxes compared to CTRL run fluxes can be found in the lower
troposphere at altitudes between about 2.5 and 7.5 km. This is at
altitudes of the layer where wave trapping occurred (see
Fig. ) and localized regions of wave breaking increased
turbulent mixing. These processes seemed to be underestimated in the CTRL
simulations.
Profiles of EF (dashed lines) and MF (dotted lines) averaged over
all flight legs during (a) IOP1 and (b) IOP5 for different
sensitivity runs of domain D3. Red and blue dots indicate EF and MF obtained
from in situ measurements of single flight legs (same as in
Fig. ).
Correlation between in situ and simulated (a) potential
temperature, (b) wind direction, (c) horizontal and
(d) vertical wind speed at flight level along all flight legs during
IOP1 and IOP5. The correlation between simulations and lidar observations is
shown in panel (e) for horizontal and in panel (f) for
vertical wind speed along all lidar cross sections during IOP1 and IOP5. The
identity line is marked by the dashed line. Correlation coefficients (COR)
and root mean square errors (RMSEs) are shown at the bottom of each figure.
Model verification
In order to verify the model results of the previous sections, CTRL
simulations are compared quantitatively to in situ and lidar observations.
Figure shows correlations of airborne in situ and
lidar measurements with numerical models for potential temperature, wind
direction, and vertical and horizontal wind speed for all legs during IOP1 and
IOP5. Except for vertical wind speed, all variables are captured well by both the
WRF and ECMWF model with similar correlation coefficients of up to 0.99 and
root mean square errors (RMSEs) independently of the horizontal resolution.
This good agreement can be explained as fields of potential temperature and
horizontal wind speed are principally dominated by large-scale waves (cf.
Fig. ). Vertical winds on the other hand reflect
small-scale up- and downdrafts , which are linked to
single mountain peaks (cf. Figs. and
) and may be shifted slightly in space and time in
the models, which complicates a pointwise comparison with measurements. As
ECMWF is a hydrostatic model, vertical velocity is a diagnostic variable and
GW-induced vertical winds cannot be resolved, which results in very low
correlation coefficients in Fig. d and f. A
separation of correlation coefficients for both IOPs is listed in
Table and indicates that IOP5 was captured better by
the models than IOP1 probably due to the less complex meteorological
situation (see Sect. ).
To verify vertical velocities in a different way, the distribution of lidar,
CTRL and ECMWF vertical winds is computed. Figure a
shows the distribution of vertical velocity along all flight legs during both
IOP1 and IOP5, where the lidar was operating in nadir-pointing mode. The
observed lidar data exhibit a broad distribution with large wave amplitudes
of maximum up- and downdrafts of 5.0 and -8.1 m s-1. The CTRL
D3, D2, D1 and especially the ECMWF model simulate narrower distributions
with maximum and minimum vertical winds of 8.25 and
-8.23, 4.7 and -5.3, 0.99 and
-0.95 and 0.46 and -0.35 m s-1, respectively.
Lidar and model vertical velocity distribution (a) and mean
vertical velocity amplitude depending on horizontal model grid
resolution (b) for all lidar nadir-pointing flight legs during both
IOP1 and IOP5 (cf. Fig. ).
Figure b shows the relation between the mean vertical
velocity amplitude along all nadir-pointing lidar flight legs and the
horizontal model resolution (for lidar data a resolution of 800 m was
applied; see Sect. ). The largest improvement in simulating
vertical velocities is achieved by reducing the horizontal mesh size from
7.2 to 2.4 km (CTRL D1 and CTRL D2) due to the more realistic
representation of the topography in CTRL D2 (see
Fig. ). The importance of a properly resolved
topography for the simulated vertical wind field is further indicated by the
SMTOPO runs, which show nearly the same amplitudes as the CTRL D1 run
independently of the model grid resolution. Equal values of the NOTOPO and
OCEAN simulations indicate that GWs are not induced by a change in roughness
length when the flow passes the coast line in the NOTOPO simulation. The more
realistic computation of EF and MF in the HVDIFF and H2VDIFF simulations due
to increased turbulent diffusion results in reduced vertical wind speeds of
up to 0.1 m s-1 on average, while higher vertical grid resolutions in
the CTRLVR simulations did not change vertical wind fields significantly.
Conclusions
In this study two mountain wave events were analysed, which occurred during
the GW-LCYCLE I field campaign in December 2013 by means of airborne
observations and numerical simulations. During the campaign the DLR Falcon
was stationed at Kiruna airport to measure GWs above northern Scandinavia.
Airborne in situ and lidar observations were accompanied by ground-based
lidar, radar and radiosonde observations on the wind- and leeward side of the
Scandinavian mountain range. In contrast to , who
analysed the same GW cases with a focus on waves in the middle atmosphere,
this paper concentrated on GW structures in the troposphere and lower
stratosphere.
During both events the situation was dominated by westerly cross-mountain
flow with different atmospheric upstream conditions, which induced variable
GW development over and in the lee of the mountains. Weaker stratospheric
winds during IOP1 caused GW breaking between 25 and 30 km altitude compared
to deeper GW propagation during IOP5 cf.. In the
troposphere, a stratified layer at 5 km altitude formed favourable conditions
for the generation of interfacial waves during IOP5. During IOP1 upstream
conditions were not conducive for wave trapping, but a synoptic tropopause
fold on the eastern side of the mountains enabled weak wave trapping in the
CTRL simulations.
A large number of numerical simulations were performed to test the ability of
a state-of-the-art mesoscale model to capture the meteorological situation
and to properly simulate the observed small-scale GWs. A special focus was on
the correct representation of vertical winds and GW-induced vertical energy
and momentum fluxes. Observations and simulations showed that up- and
downdrafts had a strong linkage to single mountain peaks, and horizontal
wavelengths obtained from vertical winds were in the order of 15 to 30 km.
Wave structures deduced from horizontal wind speeds were dominated by larger
wavelengths between 60 and 120 km and represented GWs excited by the main
mountain range. The intercomparison of numerical simulations revealed that
wave structures in horizontal winds were captured well by all model runs
nearly independently of the horizontal grid resolution. The analysis of
vertical wind fields showed that single mountain peaks must be represented
correctly in the model topography and that a horizontal model grid resolution
of at least 2.4 km is necessary over Scandinavia to compute realistic
vertical winds.
The calculation of energy and momentum fluxes along all flight legs of the
four research flights during IOP1 and IOP5 indicated that the linear
Eliassen–Palm relation was satisfied very well
especially in the model runs. The completion of this relation was already
found in other GW campaigns e.g. During
GW-LCYCLE I, simulated fluxes were generally larger than observed values (up
to 10 W m-2 during IOP5), and this discrepancy was most distinct for
simulations with high horizontal model grid resolutions due to better-resolved vertical winds (cf. CTRL D1 and CTRL D3). Sensitivity runs
demonstrated that simulated fluxes could be reduced by up to 2 W m-2 by
increasing the vertical grid resolution from about 160 to 80 m (CTRLVR) and
by switching on horizontal turbulent diffusion (HVDIFF). A reduction of up to
6 W m-2 was achieved by activating horizontal diffusion and additionally
doubling the tendency terms computed by the boundary layer scheme (H2VDIFF),
i.e. intensifying the effect of vertical turbulent mixing in regions of GW
breaking. In all three cases small-scale nonlinear effects like GW breaking
were amplified, which damped the vertical propagation of waves and related
energy and momentum fluxes. This result makes clear that quasi-linear wave
propagation dominated in the presented simulations even for small grid
distances of 800 m (CTRL D3) and that the used boundary layer scheme
underestimated turbulent mixing induced by GW breaking. A systematic test of
further boundary layer parameterizations would be necessary to study if other
schemes produce similar results. Further investigations could focus on
disagreements between simulated and observed GWs on the upstream side of the
mountains, which were not included in ECMWF and WRF simulations but were strongly
disturbed in observations. WRF runs driven by ECMWF ensemble members could be
a first step to investigate the role of upstream variability in the resulting
GW structures.