Gravity wave in uences on mesoscale divergence:...

manuscript submitted to Geophysical Research Letters

Gravity wave influences on mesoscale divergence: An1

observational case study2

C. C. Stephan1, T. P. Lane2,4, C. Jakob3,43

1Max Planck Institute for Meteorology, Hamburg, Germany42School of Earth Sciences, The University of Melbourne, Melbourne, Australia5

3School of Earth, Atmosphere and Environment, Monash University, Melbourne, Australia64ARC Centre of Excellence for Climate Extremes, Sydney, Australia7

Key Points:8

• 1. Mesoscale divergence profiles near Darwin show similar long-lived fine-scale ver-9

tical structure to that reported over the tropical Atlantic.10

• 2. High-resolution soundings are used to test if gravity waves can serve as a plau-11

sible explanation for the observed divergence variability.12

• 3. Results imply that mesoscale patterns of horizontal divergence may follow scal-13

ing laws that are determined by gravity wave properties.14

Corresponding author: Claudia C. Stephan, [email protected]

–1–


Abstract15

Characteristics of tropospheric low-frequency gravity waves are diagnosed in radiosonde16

soundings from the Tropical Warm Pool-International Cloud Experiment (TWP-ICE)17

near Darwin, Australia. The waves have typical vertical wavelengths of 3 - 4 km, hor-18

izontal wavelengths of 200 - 600 km and intrinsic periods of 8 - 12 hours. These scales19

match those of the vertical, horizontal and temporal variability found in area-averaged20

horizontal wind divergence over the same domain. Vertical profiles of divergence show21

wave-like structures with variability of the order of 2×10−5s−1 in the free troposphere.22

The results for Darwin help explain previously reported observed mesoscale patterns of23

divergence/convergence over the tropical Atlantic. The findings imply that tropical di-24

vergence on spatial scales of a few hundred kilometers, which are typical scales of orga-25

nized convection, may follow universal scaling laws that are determined by a background26

gravity wave forcing.27

Plain Language Summary28

On horizontal scales of several tens to hundreds of kilometers, which we call ”mesoscale”,29

mean vertical motion is very small compared to mean horizontal motion. Yet, the ver-30

tical motion exerts a critical influence on the formation of clouds: large-scale descent is31

associated with fair weather, ascent is associated with cloudiness. Weather and climate32

modeling often assumes that mesoscale vertical motion varies slowly in the three spa-33

tial dimensions and with time. Recent observations over the tropical Atlantic, however,34

showed strong variability in mesoscale vertical motion, implying that clouds do not only35

respond to vertical motion but may themselves trigger vertical motion in their vicinity.36

This study reports similar variability also near Darwin, Australia. One way in which clouds37

can trigger remote vertical motion is by emitting waves, similar to stones that are thrown38

into a pond. This study examines vertical profiles of horizontal wind speed that were mea-39

sured by instruments on ascending balloons near Darwin, Australia. These observations40

do indeed show waves that can provide a plausible explanation for the patterns of note-41

worthy variability in mesoscale motions. These findings suggest a two-way coupling of42

clouds to their environment with potentially important consequences for our understand-43

ing of weather and climate phenomena.44

1 Introduction45

Although gravity waves (GWs) are generated mainly in the troposphere, most re-46

search has been concerned with their effects on the middle and upper atmosphere, where47

they are key drivers of the large-scale circulation (e.g. , J. R. Holton, 1982; J. Holton,48

1983; Alexander et al., 2010). Yet, it has been known for many decades that GWs also49

constitute an important component of tropospheric dynamics across a range of spatial50

and temporal scales. For instance, GWs can promote the growth of shallow convection51

into long-lived convective bands (Shige & Satomura, 2001), initiate severe convection (Balaji52

& Clark, 1988; Su & Zhai, 2017), induce quasi-regularly spaced cloud-systems (Lane &53

Zhang, 2011), or provide a mechanism for the longevity of convective systems (Stechmann54

& Majda, 2009; Lane & Zhang, 2011). Diabatic heating from condensation is an efficient55

source of GWs, which may in turn suppress, favor or trigger remote convection by in-56

ducing large scale rising or sinking motion, respectively (e.g., Mapes, 1993; Stephan et57

al., 2016). In this sense, clouds are sources as well as sinks of wave energy and able to58

remotely condition the surrounding environment for subsequent convection.59

Here, we diagnose the characteristics of low-frequency GWs from radiosonde ob-60

servations in the troposphere, taken during the Tropical Warm Pool-International Cloud61

Experiment (TWP-ICE) that took place in Darwin, Australia (130◦E, 12◦S) from Jan62

17 to Feb 13, 2006 (May et al., 2008). Our study is motivated by more recent observa-63

tions over the tropical Atlantic near Barbados (Bony & Stevens, 2019). They diagnosed64

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vertical profiles of divergence from dropsonde measurements of horizontal wind veloc-65

ities. The sondes were released along circular flight paths of 180 km diameter. The re-66

sulting vertical profiles of area-averaged divergence showed a noteworthy wave-like struc-67

ture (i.e., divergence and convergence) with magnitudes of variability of around 2×10−5s−168

in the free troposphere. This is four times the climatological divergence required to bal-69

ance radiative cooling. Furthermore, Bony and Stevens (2019) analyzed high-resolution70

nested simulations that were nudged to the observed conditions. The simulations pro-71

duced autocorrelation scales of the divergence in space and time of the order of 200 km72

and 4 h, respectively. Of the potential explanations for the observed wave-like vertical73

structure of the mesoscale divergence profiles and the simulated scaling laws, GWs are74

one potential candidate.75

The TWP-ICE data set provides an excellent opportunity to investigate the influ-76

ences of GWs on vertical profiles of mesoscale divergence. During the campaign 3-hourly77

radiosondes were launched from five stations which were situated around an area of ap-78

proximately 200 km diameter (Fig. 1). Thus, the experimental domain is of similar size79

as the circular regions studied by Bony and Stevens (2019), and the soundings are of a80

sufficiently high temporal resolution to sample the relevant time scales. We will provide81

evidence that i) vertical profiles of divergence near Darwin exhibit similar structures as82

those observed near Barbados, ii) the previously reported autocorrelation timescales in-83

ferred from modeling can also be found in observations, and iii) low-frequency gravity84

waves can serve as a potential explanation for the vertical, horizontal and temporal struc-85

tures of mesoscale divergence patterns.86

Section 2 introduces the data from TWP-ICE that are analyzed for the properties87

of divergence and GWs in Section 3. The results are discussed and summarized in Sec-88

tion 4.89

2 Data90

The following is a brief summary of the synoptic and convective environment dur-91

ing the TWP-ICE campaign. More information can be found in May et al. (2008), where92

the TWP-ICE field experiment is described in detail. The campaign began with an ac-93

tive monsoon period that was characterized by rainfall rates of 17 mm d−1 averaged over94

the 300-km-diameter area sampled with a polarimetric radar at Darwin. The degree of95

convective organization differed widely and a major mesoscale convective system devel-96

oped on Jan 23. A suppressed monsoon period followed from Jan 28 to Feb 6 before deep97

convection featuring intense afternoon thunderstorms and some squall lines commenced98

again. The suppressed period was characterized by surface westerly winds and mid-level99

advection of dry continental air, which limited convection to isolated cells or short lines100

with only few deep single cells reaching up to 10 km. The average rain rates were 5 - 7101

mm d−1 and originated from relatively shallow convection. The three days following Feb102

3 were completely clear with no rain. We will focus on this 10-day suppressed period Jan103

28 to Feb 6, as it presents the most undisturbed conditions in the troposphere, allow-104

ing the extraction of wave signatures from tropospheric wind perturbations that at other105

times would be obfuscated by convection or turbulence. Moreover, the atmospheric con-106

ditions during the suppressed period are similar to the environment sampled by the cir-107

cle flights analyzed in Bony and Stevens (2019), where intermittent shallow cumulus con-108

vection was observed.109

Area-averaged divergence is taken from variational analysis data. By following the110

objective method of Zhang and Lin (1997), Xie et al. (2010) created a variational anal-111

ysis for the TWP-ICE domain, which we will refer to as the large domain, or ”L-Domain”,112

as well as separate variational analyses for subdomains (Fig. 1a). The variational anal-113

ysis technique combines a background from the European Centre for Medium-Range Weather114

Forecasts (ECMWF) analysis with constraints from satellite, surface, and radiosonde mea-115

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surements taken during TWP-ICE. It includes as boundary forcing the observations from116

radiosondes launched inside the respective domains. The technique applies small per-117

turbations to the observed profiles of temperature, wind, and humidity, while conserv-118

ing column-integrated mass, moisture, energy, and momentum. In this way the area-averaged119

time-evolving vertical profiles of thermodynamic variables are obtained. In addition, Davies120

et al. (2013) repeated the variational analysis for L-Domain based on ECMWF data only121

(”L-Domain-EC”). Instead of ingesting radiosonde measurements, they replaced them122

with vertical profiles from ECMWF grid points near the respective radiosonde launch123

locations.124

Measurements from 3-hourly radiosonde soundings at five stations (Ship, Cape Don,125

Garden Point, Mount Bundy, and Point Stuart; Fig. 1a,b) are analyzed for GW signa-126

tures. Winds, air temperature, mixing ratio, pressure and the altitude of the balloons127

were recorded every 2 s. As in Hankinson et al. (2014b), we use area-averaged vertical128

profiles of U and V from the variational analysis on L-Domain to derive perturbations129

u′ and v′ from radiosonde measurements, which are then analyzed for GWs (Fig. 1c).130

Furthermore, we intercompare divergence profiles from variational analyses on different131

domains to test how their characteristics vary in space and with domain size and bound-132

ary forcing (i.e., L-Domain versus L-Domain-EC).133

Several studies have shown that a rich spectrum of stratospheric GWs existed dur-134

ing the suppressed monsoon period of TWP-ICE. For example, Evan and Alexander (2008)135

identified southeastward-propagating inertia-GWs in radiosonde observations with ver-136

tical wavelengths λz ∼ 6 km, horizontal wavelengths λh ∼ 5500 - 7000 km and peri-137

ods τ ∼ 2 days. A subsequent modeling study confirmed that convection near Indone-138

sia likely generated these stratospheric waves (Evan et al., 2012). Satellite images showed139

waves with λh =200 - 400 km near an altitude of 40 km (Hecht et al., 2009). Air glow140

images revealed both waves with τ =1 - 2 h and τ =15 - 25 min, λh =30-40 km at 80141

km altitude (Hecht et al., 2009). Hankinson et al. (2014b) and Hankinson et al. (2014a)142

analyzed radiosonde measurements for low and high-frequency GWs in the stratosphere,143

respectively. During the suppressed monsoon period they detected inertia-GWs with ori-144

gins near Indonesia in the middle stratosphere (λz =5 - 6 km, λh =3000 - 6000 km, τ =145

35 h). During the monsoon break period they reported inertia-GWs with origins near146

New Guinea in the lower stratosphere (λz ∼2 km, λh =2000 - 4000 km, τ = 45 h). High-147

frequency GWs (τ = 10 - 50 min) appeared mostly below 22 km in the stratosphere and148

were generated by local convection in the afternoon during the suppressed period. Our149

study is the first to investigate GWs during TWP-ICE with a specific focus on the tro-150

posphere.151

3 Results152

3.1 Vertical profiles of divergence153

Fig. 2a shows the temporal evolution of vertical profiles of divergence from the vari-154

ational analysis on L-Domain. Typical magnitudes of divergence are ±2×10−5s−1, as155

can also be seen from Fig. 2f. The profiles show a rich spectrum of rather persistent or156

oscillating wave-like signals. At a 3-hour lag, temporal autocorrelations for L-Domain157

are ∼0.5 for divergence averaged between the surface and 5 km (Fig. 2b), ∼0.7 for av-158

erages between 5 - 10 km (Fig. 2c), and close to 0.8 for 10 - 15 km (Fig. 2d). At a 9-hour159

lag time, they drop to zero below 5 km and to 0.2 at higher levels.160

It is insightful to intercompare the divergence profiles of the different subdomains,161

as they are spatially separated and ingest data from independent soundings. The ver-162

tical profiles of all subdomains are strongly correlated with that of L-Domain at all times163

(Fig. 2e). In contrast, L-Domain-EC does not capture the vertical structure of divergence.164

This suggests that all soundings are subject to a similar large-scale forcing of divergence,165

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which is not included in the ECMWF analysis. The ECMWF analysis used for L-Domain-166

EC assimilated only the 6-hourly soundings from Darwin, and was available for inges-167

tion into the variational analysis algorithm at a longitude-latitude resolution of 0.5◦×168

0.5◦ and 15 vertical levels between 1000 hPa and 10 hPa.169

Unsurprisingly, all subdomains have similar autocorrelation timescales (Fig. 2b-d).170

In addition, their magnitudes of temporal variability are very similar (Fig. 2f). This sug-171

gests that any mechanism responsible for the observed variability of divergence must be172

sufficiently large in scale to simultaneously affect all TWP-ICE domains used here.173

3.2 Gravity waves174

We next investigate if the presence of GWs may explain the observed character-175

istics of the vertical profiles of large-scale divergence. To first order, the intrinsic frequen-176

cies of GWs are bound by the inertial frequency f , which at Darwin would correspond177

to a period of 57.6 h, and the buoyancy frequency N , which would correspond to peri-178

ods of less than 10 min in the mid-troposphere during TWP-ICE. We focus on low-frequency179

waves, as the typical periods and horizontal wavelengths of high-frequency GWs are too180

short to simultaneously affect an area of the size of L-Domain. Consider the wind per-181

turbations associated with a plane wave with wave vector (k, l,m), frequency ω and phase182

φ,183

(u, v, w) = (Ax, Ay, Az)ei(kx+ly+mz−ωt+φ). (1)

Mass conservation implies that the amplitudes of the wind components (Ax, Ay, Az) are184

related through185

Az = −Axk +Ayl

m. (2)

Therefore, Az grows with decreasing vertical and increasing horizontal wavenumber rel-186

ative to the perturbations of the horizontal winds. For this reason, vertical motion can187

be used to diagnose high-frequency, typically short horizontal-wavelength waves, while188

horizontal motion can be used to diagnose low-frequency waves (Lane et al., 2003). As189

described earlier, we obtain wave perturbations of the zonal and meridional velocities,190

u′ and v′, by subtracting the respective vertical profiles from the variational analysis on191

L-domain. Examples of the resulting profiles of u′ for Point Stuart are shown in Fig. 1c.192

For illustrative purposes Fig. 1d also shows vertical velocity perturbations, defined as193

deviations from the mean ascent rate of the balloon. The comparison between the ver-194

tical and horizontal wind perturbations confirms that the latter exhibit more consistency195

between neighboring profiles, i.e., they are associated with lower-frequency, longer-wavelength,196

waves. That these wind perturbations are not mere noise but contain coherent physi-197

cal features is also clear from a visual inspection that reveals vertical phase propagation198

between subsequent soundings.199

The main spectral peaks of the low-frequency GW spectrum are identified by com-200

puting the quadrature spectrum of u′ and v′ as a function of the ground-based frequency201

ωgb and the vertical wavenumber m. Since this requires evenly spaced data points in time202

and height, u′ and v′ are first interpolated to a 10-m resolution in the vertical and to a203

10,000 s resolution in time, which roughly corresponds to the average radiosonde launch204

interval. The TWP-ICE soundings were not evenly spaced in time and the number of205

soundings differ between the stations. The spectral analysis is carried out between 2 -206

14 km altitude to avoid the boundary layer and the tropopause. Not all soundings reach207

14 km and there are intervals of missing data, as can be seen from Fig. 1c. We treat miss-208

ing observations by linearly interpolating in time from values observed at the same al-209

titude.210

Fig. 3a shows the quadrature power spectrum of u′ and v′ averaged over all sta-211

tions. We separately analyze the peaks at λz = 3 km and λz = 4 km. To isolate per-212

turbations in the relevant spectral regions, u′ and v′ are first filtered in Fourier space.213

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For λz = 3 km we retain ground-based periods τgb = 1.0 - 2.0 days and λz = 2.5 - 3.5214

km, and for λz = 4 km we retain τgb = 0.5 - 1.5 days, λz = 3.5 - 4.5 km. The filtered215

wind perturbations are subjected to the Stokes parameter method developed by Eckermann216

and Vincent (1989). The horizontal wind perturbation vector of low-frequency GWs ro-217

tates with height, such that the wave’s hodograph follows a polarization ellipse. The Stokes218

algorithm diagnoses key parameters of these ellipses. The angle θ of the counter-clockwise219

rotation of the major axis from the eastward direction corresponds to the propagation220

direction of the wave (with a 180◦ ambiguity). Here, the average θ is -2◦ for λz = 3 km221

and -12 ◦ for λz = 4 km (Table 1). Thus, the waves are propagating either nearly due222

eastward or westward. Studies investigating stratospheric waves showed eastward prop-223

agation (see Section 2) and, as we will argue below, the tropospheric waves are likely also224

propagating eastward. The ratio R of the major to minor axis of the polarization ellipse225

is used to estimate the typical intrinsic frequency of the waves as226

ω∗ =f

R. (3)

Background wind shear transverse to propagation direction of a wave can create an ad-227

ditional eccentricity (e.g., Cho, 1995; Hines, 1989). Here, we can neglect the correspond-228

ing correction term, as shear in the transverse direction, i.e., the meridional direction is229

close to zero (Fig. 3b).230

The horizontal wavenumber k is calculated from the GW dispersion relation as231

k2 =

(ω2∗ − f2

) (m2 + 1

4H2s

)N2

, (4)

where Hs is the density scale height and N the mean buoyancy frequency. To evaluate232

Eq. 3 we insert for m wavenumbers corresponding to λz = 3 km or 4 km, respectively.233

Intrinsic periods and horizontal wavelengths vary substantially from station to station234

(Table 1). This may in part be due to noise and the effects of missing data. But it is also235

physically plausible, as we expect the tropospheric GW background to consist of a wide236

spectrum of waves and different waves may dominate at a given location. The average237

intrinsic period is 7.6 h for λz = 3 km and 12.5 h for λz = 4 km. The intrinsic period238

for λz = 3 km is shorter than the lower bound of the filtering interval for the ground-239

based period (1 day). The zonal wind averaged between 2 - 14 km of altitude is close240

to zero, whereas the Doppler shift required to explain the difference between the intrin-241

sic and ground-based periods would imply propagation against a wind of 6 ms−1. Nev-242

ertheless, the results are not necessarily inconsistent, because we neglect the temporal243

variability of the background wind. The horizontal wavelengths are on average λh = 200244

km and 580 km, respectively. Thus, areas spanning roughly 12λh ∼ 100 km - 300 km would245

experience similar conditions in terms of GW-driven divergence. This is consistent with246

the high correlations between the vertical divergence profiles from the variational anal-247

yses on different subdomains (Fig. 2e). It is also consistent with a lack of sensitivity of248

the magnitude of temporal variability to the domain area, as all domains fit well within249

half a horizontal wavelength.250

In calm wind conditions rising or sinking motion at any given location would pre-251

vail for the duration of half the intrinsic period, i.e., four to six hours. This timescale252

is consistent with the temporal autocorrelations of divergence (Fig. 2b-d). A wave crest253

would take approximately 7.7 h (λz = 3 km) or 5.6 h (λz = 4 km) to traverse a region254

of 200 km diameter. This timescale is rather insensitive to the station (Table 1), because255

it depends only on the phase speed ch = khω∗

, which is similar for all stations (8 ms−1256

for λz = 3 km, 10 ms−1 for λz = 4 km). In addition to the period of a wave, the lat-257

ter timescale may also be important for explaining the autocorrelation times of diver-258

gence, as it implies that a crest of one wave can dominate the divergence field averaged259

over areas of 200 km diameter for roughly five to eight hours.260

–6–


4 Conclusion261

We demonstrated that vertical profiles of divergence near Darwin obtained during262

the TWP-ICE campaign exhibit similar wave-like structures as those observed over the263

tropical Atlantic. Bony and Stevens (2019) reported values of 2×10−5s−1 for the mag-264

nitudes of divergence variability in layers of a few km thickness. This magnitude of vari-265

ability is in excellent agreement with TWP-ICE data (Fig. 2f). The observations dis-266

cussed by Bony and Stevens (2019) were taken over the ocean while the TWP-ICE ob-267

servations were mainly taken over land. Both studies examined convectively suppressed268

conditions during local summer at mirroring latitudes. The similar absolute value of the269

Coriolis parameter may partly explain the similarities between the different locations,270

as it enters the gravity wave (GW) dispersion relation and acts as a lower bound on the271

range of wave intrinsic frequencies. The autocorrelations reported by (Bony & Stevens,272

2019) were based on high-resolution simulations, which were initialized and nudged at273

their boundary using data from the Integrated Forecast System (IFS) of the ECMWF.274

They inferred temporal autocorrelation scales of 4 h for circular regions of 100 km ra-275

dius, which is confirmed by the observations from TWP-ICE. The autocorrelation timescale276

of divergence agrees very well with the characteristic GW timescales derived here. TWP-277

ICE data do not allow us to test spatial autcorrelation scales, which were 2.2◦ of lati-278

tude over the tropical Atlantic (Bony & Stevens, 2019). This distance corresponds to the279

typical GW horizontal wavelengths found for Darwin, such that GW dynamics would280

be able to explain the spatial correlations as well. Thus, low-frequency GWs can serve281

as an explanation for the vertical, horizontal and temporal structures of mesoscale di-282

vergence patterns.283

The existence of strong convergence or divergence layers implies that the mesoscale284

environment is potentially strongly influenced by physical processes other than those usu-285

ally included in climate models. Interactions between waves and convection are not cap-286

tured in climate models of coarse resolution. The typical effective horizontal and ver-287

tical resolutions are hardly sufficient for resolving the waves, let alone their sources or288

interaction with subsequent convection. The scales discussed here are large compared289

to single updrafts but correspond well to scales of organized convection. Thus, low-frequency290

GWs might be important for determining the characteristics of cloudiness on the mesoscale.291

Clouds and their coupling to circulation still remain one of the major uncertainties in292

climate predictions (Bony et al., 2015). Further efforts are needed to quantify the wave293

background and its interaction with convection and clouds. The temporal and spatial294

scales reported here can serve as guidance for measurements during future field campaigns,295

such as the field campaign Elucidating the Role of CloudCirculation Coupling in Climate296

(EUREC4A Bony et al., 2017), which will take place in winter 2020, and could be used297

to collect additional data for analyzing the response of convection to GW dynamics in298

relation to other cloud controlling factors. The relatively short inertial periods of the waves299

(Table 1) favor soundings at short intervals, ideally every 2 hours. Given that the hodo-300

graph method can be independently applied to data at single locations, one station with301

high-frequency soundings would already be an improvement over, e.g., an array of lo-302

cations with 6-hourly soundings. Moreover, if soundings at different locations within sev-303

eral hundreds of kilometers were launched simultaneously, this may allow a direct com-304

putation of the horizontal wavelengths by inferring the phase differences between differ-305

ent locations.306

Inertia GWs are able to propagate large distances inside the troposphere. Based307

on their large spatial scales and the fact that convective sources always exist at some lo-308

cation in the tropics, we expect such waves to be present most of the time. Our results309

suggest that GWs are a plausible explanation for the scaling laws of mesoscale divergence310

at two different locations. Thus, it is possible that tropical divergence on spatial scales311

of a few hundred kilometers follows general scaling laws that are determined by the char-312

acteristics of low-frequency gravity waves. Additional measurements are necessary to de-313

–7–


Table 1. Gravity wave properties derived from radiosonde soundings. θ is the propagation

direction measured counter-clockwise from eastward, τ∗ is the intrinsic period, λh the horizon-

tal wavelength, and τ∗200 the time it takes a wave to travel 200 km when there is no background

wind.

(a) Results for λz = 3 kmStation θ (◦) τ∗ (hours) λh (km) τ∗200 (hours)

Garden Point 20 12.4 330 7.6Mount Bundy -18 6.6 170 7.7Point Stuart 37 5.4 140 7.7Ship -38 6.7 180 7.7Cape Don -12 7.1 180 7.7

Average -2 7.6 200 7.7

(b) Results for λz = 4 kmStation θ (◦) τ∗ (hours) λh (km) τ∗200 (hours)

Garden Point 20 20.9 770 5.4Mount Bundy 21 2.3 750 5.4Point Stuart -37 12.4 440 5.7Ship -42 11.3 400 5.7Cape Don -24 15.5 550 5.6

Average -12 12.5 580 5.6

termine to what degree the tropospheric wave background is saturated or consists of in-314

dividual spectral peaks or individual waves, as this would affect the universality of po-315

tential scaling laws.316

Acknowledgments317

C. C. Stephan was supported by the Minerva Fast Track Programme of the Max Planck318

Society. C. C. Stephan is grateful for the joint funding from the ARC Centre of Excel-319

lence for Climate Extremes (CE170100023) and the Max Planck Institute for Meteorol-320

ogy to enable the research visit to Australia that has led to the research reported here.321

The authors also acknowledge the support of the US Department of Energy Atmospheric322

Radiation Measurement (ARM) Program for the TWP-ICE campaign. The data used323

in this study are available from the ARM data archive (https://www.arm.gov/data).324

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–10–


Jan 28 Jan 30 Feb 1 Feb 3 Feb 5 Feb 7time

0

5

10

15

he

igh

t (k

m)

(c) u’ Point Stuart soundings


0

5

10

15

he

igh

t (k

m)

(d) w’ Point Stuart soundings

Jan 28 Jan 30 Feb 1 Feb 3 Feb 5 Feb 7time (days)

0

1

2

3

4

5

height (km)

(b) Data coverage

Gard

en P

oin

t

2 -

14 k

m

Mount B

undy

2 -

14 k

m

Poin

t S

tuart

2 -

14 k

m

Ship

2 -

14 k

m

Cape D

on

2 -

14 k

m

129 130 131 132 133Longitude (deg E)

-14.0

-13.5

-13.0

-12.5

-12.0

-11.5

-11.0

La

titu

de

(d

eg

N)

(a) TWP-ICE domain

Darwin

Ocean

Mainland

Tiwi

L-Domain

Garden Point

Mount Bundy

Point Stuart

Ship

Cape Don

Figure 1. (a) Locations of Darwin and the five radiosonde launching sites during TWP-

ICE. L-Domain (black) encloses the five boundary forcing stations. Subdomains are shown with

colored lines. (b) For each launch location colored areas show times and heights that are not cov-

ered by soundings. Data were first interpolated to a temporal resolution of 10,000 s. (c) Zonal,

and (d) vertical wind perturbations from radiosonde soundings interpolated to a 10 m vertical

grid but not interpolated in time from Point Stuart. w′ are deviations from a linear fit to the

mean ascent rate of the balloon, scaled such that 10 ms−1 corresponds to 0.85 days on the x-

axis. u′ are deviations from the area-mean horizontal wind speed of the variational analysis on

L-Domain, scaled such that 10 ms−1 corresponds to 1.7 days on the x-axis.

–11–



2

4

6

8

10

12

14h

eig

ht

(km

)

(a) Div profiles of L-Domain

2 4 6 8 10lag (hours)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

au

toco

rre

latio

n

(b) Sfc-5 km

L-DomainMainlandOceanTiwi

2 4 6 8 10lag (hours)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

autocorrelation

(c) 5-10 km

2 4 6 8 10lag (hours)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

autocorrelation

(d) 10-15 km


-1.0

-0.5

0.0

0.5

1.0

co

rre

latio

n

L-Domain-ECMainlandOceanTiwi

(e) Correl. with Div profiles of L-Domain

1.0 1.2 1.4 1.6 1.8 2.0div (10-5s-1)

0

1

2

3

layer

L-DomainMainlandOceanTiwi

Sfc

-5 k

m5

-10

km

10

-15

km

(f) Div magnitude

Figure 2. Characteristics of large-scale divergence from the variational analyses for the sup-

pressed period Jan 28 00:00 UTC - Feb 7 00:00 UTC. (a) 3-hourly vertical profiles of divergence

on L-Domain. Divergence is scaled such that one day on the x-axis corresponds to 104s−1. (b-d)

Autocorrelations of divergence vertically averaged over (a) surface - 5 km, (b) 5 - 10 km and (c)

10 - 15 km for all domains. (e) Correlations of the vertical divergence profiles between 2 - 14 km

with that of L-Domain. L-Domain-EC (dashed) denotes the variational analysis on L-Domain

that is based on virtual soundings from the ECMWF analysis. (f) Time-averaged divergence

amplitude in the vertical layers, which are labeled on the y-axis. Data points for the different

variational analyses are vertically off-set for better visibility.–12–


0.5 1.0 1.5 2.0 2.5period (days)

1

2

3

4

5

6

ve

rtic

al w

ave

len

gth

(km

)

(a) Normalized FFT power

0 10 20 30 40 50 60 70 80 90 100

0

1

-20 -10 0 10 20speed (ms-1)

2

4

6

8

10

12

14

16

he

igh

t (k

m) UV

N

0.005 0.010 0.015 0.020 0.025 0.030N (s-1)

(b) Wind and stability profiles

Figure 3. (a) The quadrature spectrum of u′ and v′ for the suppressed period Jan 28 00:00

UTC - Feb 7 00:00 UTC averaged over all five radiosonde stations. u′ and v′ are the deviations

from the variational analysis on L-Domain and were first interpolated to vertical and temporal

resolutions of 10 m and 10,000 s, respectively. Boxes mark the spectral regions that are sepa-

rately subjected to a Stokes analysis. (b) Average background profiles of U , V and N from the

variational analysis on L-Domain for the suppressed period.

–13–

Gravity wave in uences on mesoscale divergence:...

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