climatology and enso-related interannual variability of 2 ...alexand/publications/sato16.pdf125 airs...
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 1
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Climatology and ENSO-related interannual variability of 1
gravity waves in the southern hemisphere subtropical 2
stratosphere revealed by high-resolution AIRS observations 3
Kaoru Sato1, Chikara Tsuchiya14, M. Joan Alexander2, and Lars Hoffmann3
Key points 4
z Climatology and interannual variability of gravity waves in the middle 5
stratosphere were examined. 6
z A new temperature retrieval from Atmospheric Infrared Sounder was 7
used. 8
z Gravity waves are clearly synchronized with ENSO in the South Pacific 9
in austral summer. 10
Corresponding author: Kaoru Sato, Department of
Earth and Planetary Science, The University of
Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033,
Japan. ([email protected])
1Department of Earth and Planetary Science, The
University of Tokyo, Tokyo, Japan.
2NorthWest Research Associates, CoRA Office,
Boulder, Colorado, USA.
3Jülich Supercomputing Centre, Forschungszentrum
Jülich GmbH, Jülich, Germany.
4Now at Japan Coast Guard, Tokyo, Japan.
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 2
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Abstract. A new temperature retrieval from Atmospheric Infrared 11
Sounder with a fine horizontal resolution of 13.5 km was used to 12
examine gravity wave (GW) characteristics in the austral summer at an 13
altitude of 39 km in the subtropical stratosphere over eight years from 14
2003/2004−2010/2011. Using an S-transform method, GW components 15
were extracted, and GW variances, horizontal wavenumbers and their 16
orientations were determined at each grid point and time. Both 17
climatology and interannual variability of the GW variance were large 18
in the subtropical South Pacific. About 70 % of the interannual variation 19
in the GW variance there was regressed to El Niño-Southern Oscillation 20
(ENSO) index. The regression coefficient exhibits a geographical 21
distribution similar to that of the precipitation. In contrast, the regression 22
coefficient of the GW variance to the quasi-biennial oscillation of the 23
equatorial lower stratosphere was not significant in the South Pacific. 24
These results indicate that the interannual variability of GW variance in 25
the South Pacific is controlled largely by the convective activity 26
modulated by the ENSO. An interesting feature is that the GW variance 27
is maximized slightly southward of the precipitation maximum. Possible 28
mechanisms causing the latitudinal difference are (1) dense distribution 29
of islands, which effectively radiate GWs with long vertical 30
wavelengths, to the south of the precipitation maximum, (2) selective 31
excitation of southward propagating GWs in the northward vertical 32
wind shear in the troposphere, and (3) southward refraction of GWs in 33
the latitudinal shear of background zonal wind in the stratosphere. 34
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1. Introduction
It is well known that the meridional circulation in the middle atmosphere is driven 35
by atmospheric waves, which maintain a temperature structure that is significantly different 36
from that expected from radiative equilibrium [e.g., Holton, 1983]. Synoptic-scale waves are 37
important to form the shallow branch of the Brewer-Dobson circulation (BDC), which is the 38
meridional circulation in the stratosphere, both in the summer and winter hemispheres, while 39
planetary waves are a main driver of the deep branch of the BDC in the winter hemisphere 40
[e.g., Plumb, 2002]. In the mesosphere, gravity waves (GWs) are primary waves providing 41
wave force to drive the meridional circulation [e.g., Andrews et al., 1987]. However, GWs 42
play an important role to drive the BDC as well, particularly for the summer hemispheric 43
part of the winter circulation where dominant westward mean winds prohibit upward 44
propagation of planetary waves, and for the shallow branches of the BDC through the 45
westward forcing deposited in the weak wind layer above the middle latitude jet [Okamoto 46
et al., 2011; Butchart, 2012; Stephan et al., submitted to the Journal of Atmospheric Science]. 47
Studies using recently available high-resolution satellite observations and general circulation 48
models suggest that the origins of GWs in the summer hemisphere are convection in the 49
subtropical regions, particularly summer monsoon regions, while those in the winter 50
hemisphere are topography and jet-front systems [Sato et al., 2009; Geller et al., 2013]. 51
Satellites can detect GWs globally. However, the observable range of horizontal and 52
vertical wavelengths by satellites are limited, and the limitations largely depend on the 53
viewing geometry [Alexander and Barnet, 2007]. Limb-viewing satellite instruments such 54
as the Limb Infrared Monitor of the Stratosphere (LIMS), the Cryogenic Infrared 55
Spectrometers and Telescopes for the Atmosphere (CRISTA), and the High Resolution 56
Dynamics Limb Sounder (HIRDLS) are able to detect GWs with relatively short vertical but 57
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long horizontal wavelengths. Nadir-viewing or sublimb-viewing satellite instruments and 58
such as the Advanced Microwave Sounding Unit (AMSU) and the Atmospheric Infrared 59
Sounder (AIRS) can observe GWs with relatively short horizontal but long vertical 60
wavelengths. Such limitation in the detectable wavelength and/or frequency range is called 61
the observational filter [Alexander, 1998]. 62
Several previous studies estimated absolute momentum flux associated with GWs 63
using satellite data. Geller et al. [2013] conducted the first comparison among absolute 64
momentum fluxes estimated using satellite, super pressure balloon, and radiosonde 65
observations, those simulated by high resolution general circulation models (GCMs), and 66
those parameterized in climate models. They showed that the parameterized GW momentum 67
flux is largely different from those estimated by satellite observations and those explicitly 68
simulated in high resolution GCMs, and indicated that particularly, non-orographic GWs are 69
not sufficiently well expressed in the GW parameterizations and that a principal problem in 70
models with parameterized gravity waves was in specifying the source distribution. Such 71
differences may cause systematic model biases that are observed in the jet structure in middle 72
atmosphere models. Thus, the characteristics of GWs originating from nonorographic 73
sources need be further investigated using high-resolution observations. According to the 74
Geller et al. [2013] study, the GW momentum flux shows two peaks latitudinally: one is at 75
subtropical latitudes in the summer hemisphere and the other is at high latitudes in the winter 76
hemisphere. The former is considered to be due to GWs originating from monsoon 77
convection [e.g. Sato et al., 2009]. 78
The horizontal distribution of GW variance at an altitude of 38 km with short 79
horizontal and long vertical wavelengths has been investigated using high-horizontal 80
resolution data from the Microwave Limb Sounder (MLS) [Wu and Waters, 1996; 81
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McLandress et al., 2000; Jiang et al., 2004]. MLS detected fluctuations with vertical 82
wavelengths longer than 10 km. Hence part of the observed GW distributions, such as 83
those around the polar night jet where strong winds refract waves to long vertical 84
wavelengths, were attributable to the observational filter. However, the longitudinal 85
distribution of GW variances in the summer subtropical regions may reflect the real nature 86
of GWs, because large GW variance regions compare well with small outgoing longwave 87
radiation (OLR), and because the background wind that can modify GW vertical 88
wavelengths is zonally almost uniform. These GWs are likely originating from the 89
convection in the subtropical region. Enhancement of the GW activity over the summer 90
subtropical monsoon regions was also observed by HIRDLS, which can detect GWs with 91
short vertical wavelengths and long horizontal wavelengths [Wright and Gille, 2011]. GWs 92
originating from convection are expected to have short horizontal wavelengths comparable 93
to individual convection and/or convective systems. Thus, it is important to examine nadir-94
view satellite observation data as well. Currently, AIRS has the highest horizontal resolution, 95
that is 13.5 km across and 18 km along the satellite orbit at nadir. Several previous studies 96
using the AIRS radiance data examined the GW characteristics by applying a wavelet 97
analysis method for a specific height level in the stratosphere [Alexander and Barnet, 2007; 98
Alexander and Teitelbaum, 2007; 2011]. In this paper, we analyze high-resolution AIRS 99
temperature data from a new retrieval [Hoffmann and Alexander, 2009] focusing on GWs in 100
the subtropical region. 101
There are few studies on the interannual and intraseasonal variability of the GW 102
activity except for the relation to the equatorial quasi-biennial oscillation (QBO), using 103
radiosondes [Sato et al., 1994; Sato and Dunkerton, 1997], satellites [Ern and Preusse, 2009; 104
Gong et al., 2012; John and Kumar, 2012; Zhang et al., 2012], and high resolution numerical 105
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models [Kawatani et al., 2010; Evan et al., 2012]. However, the interannual and 106
intraseasonal variability of GW variance can be affected by other dominant phenomena in 107
the tropical and subtropical regions such as the El Niño-Southern Oscillation (ENSO) and 108
Madden-Julian Oscillation (MJO), having characteristic horizontal structure in precipitation 109
and horizontal winds. As AIRS started its observation in 2002, the observation duration is 110
sufficient to examine such interannual and intra-seasonal variations. In the present study, 111
AIRS data over nine years from 2003 to 2011 were used to examine the climatology of GWs 112
in the summer subtropical region and the interannual variability of GWs in terms of ENSO. 113
The intraseasonal variability in terms of MJO is investigated in a companion paper [Tsuchiya 114
et al., submitted to the Journal of Geophysical Research]. 115
In section 2, details of the AIRS observation data and the method of analysis are 116
described. The climatology of GWs in the tropical and subtropical regions in summer is 117
presented in section 3. In section 4, the interannual variability of GWs and its relation to 118
ENSO is shown focusing on the SH subtropical region. In section 5, modulation of GWs by 119
the QBO as another factor causing the interannual variability, and possible mechanism of 120
the latitudinal difference between GW and convection maxima that is elucidated in the 121
present study are also examined and discussed. Summary and concluding remarks are given 122
in section 6. 123
2. Data description and method of analysis
2.1. Data description 124
AIRS [Aumann et al., 2003] is one of six instruments onboard the Aqua satellite 125
[Parkinson, 2003]. Aqua was launched on May 4, 2002. It has a Sun-synchronous nearly 126
polar orbit with 98° inclination at 705 km altitude. Aqua crosses the equator at 01:30 127
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(descending orbit) and 13:30 (ascending orbit) local time. 128
AIRS measures the thermal emissions from atmospheric constituents in the nadir 129
and sub-limb directions. The scan angle across the measurement track is ±49.5°, 130
corresponding to a distance on the ground of 1765 km [Hoffmann et al., 2013]. Each across-131
track scan consists of 90 foot prints. The extent of a granule, which is consisted of 135 scans, 132
is about 2400 km along the track. Thus, the highest horizontal resolution is 13.5 km across 133
and 18 km along the satellite orbit at nadir. Kernel functions of CO2 channels with radiances 134
of 15 and 4.3 m typically have a peak in the stratosphere and a depth of about 12 km 135
[Alexander and Barnet, 2007; Hoffmann and Alexander, 2009]. 136
The AIRS operational level 2 temperature product has a horizontal resolution 137
coarser than the original radiance measurements by a factor of 3×3 (namely 39 kmx54 km), 138
which corresponds to the horizontal resolution of Advanced Microwave Sounding Unit 139
(AMSU) on board Aqua. However, the level 2 temperature data may be not sufficient to 140
detect such short horizontal wavelengths as convectively-generated GWs have. To overcome 141
this shortage, Hoffmann and Alexander [2009] developed a new retrieval of atmospheric 142
temperature, which provides data with a native high resolution of the AIRS radiance 143
measurements. They used 23 channels of 4.3 m radiance and 12 channels of 15 m 144
radiance for retrievals at the nighttime when the solar zenith angle is larger than 96°, while 145
only 12 channels of 15 m radiance are used for retrievals at the daytime, because the 146
assumption of local thermodynamic equilibrium for 4.3 m radiance is not valid. Thus, the 147
noise level of the retrievals at nighttime is lower than that at daytime. For this reason, the 148
present study used the new retrieval of temperature at nighttime only. In addition, the noise 149
of the AIRS high resolution retrieval of temperature is minimized for an altitude range 25–150
45 km. Following previous studies, this study focused on a specific height level of 39 km 151
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approximately corresponding to 3 hPa. Vertical resolution is about 9 km at that level. The 152
analyzed time period is nine years from 2003 to 2011. A validation of the new AIRS retrieval 153
is presented by Meyer and Hoffmann [2014]. 154
A reanalysis data, NASA’s Modern-Era Retrospective Analysis for Research and 155
Applications (MERRA) [Rienecker et al., 2011] is used for the analysis of the background 156
field of GWs. MERRA data is generated with the Goddard Earth Observing System (GEOS) 157
atmospheric model and data assimilation system (DAS) where AIRS data is also assimilated. 158
Although the original MERRA data is available three hourly, daily mean temperature and 159
horizontal wind values are used for the analysis. The ocean fraction at each grid point in the 160
numerical model used for MERRA is also used to see the surface condition. 161
In addition, we used daily 1×1° gridded precipitation data from the Global 162
Precipitation Climatology Project (GPCP) version 1.2 [Huffman et al., 2001] as an index of 163
convection. The NINO.3 index, which is defined as SST anomalies averaged over the region 164
which is 5°S to 5°N and 150°W to 90°W, from the Japan Meteorological Agency 165
(http://www.data.jma.go.jp/gmd/cpd/data/elnino/nino3irm.html) is used as an ENSO time 166
series. We also used daily 0.25×0.25° gridded SST values from the Optimum Interpolation 167
Sea Surface Temperature (OISST) data [Reynolds et al., 2009] to examine a horizontal 168
distribution of SST. 169
170
2.2. Method of analysis 171
In this section, the method used in the present study to analyze the horizontal 172
propagation characteristics of GWs is described. S-transform [Stockwell et al., 1996] is a 173
one-dimensional wavelet-type analysis and is suitable for the estimation of local 174
characteristics of GWs. Several previous studies [Alexander and Barnet, 2007; Alexander et 175
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al., 2008; Alexander et al., 2009; Alexander and Grimsdell, 2013] applied the S-transform 176
to the satellite data of AIRS and HIRDLS to detect localized GW packets. 177
First, a large-scale field was obtained as follows: The data scans of temperature 178
across the orbit were regressed to a second-order polynomial function and the regressed data 179
scans were further smoothed along the orbit by the 31-point (i.e., 558 km) running mean. 180
The deviation of the original data from the large-scale field was designated as the GW 181
components. Original sampling interval of the data scans across the orbit varies from 13.5 182
km at nadir to 39.6 km at its edge. So as to make the analysis easier, the GW components 183
are interpolated at the same interval of 13.5 km by a spline fit across the track. As a 184
consequence, the number of data series across the track becomes 130 (=1755 km). 185
GW parameters such as two-dimensional horizontal wavenumbers were estimated 186
in two ways. First, the estimation was performed using an S-transform method for cross-187
track data series and cross-spectra for adjacent cross-track data, as developed by Alexander 188
and Barnet (2007). With this method, cross-track wavenumbers are more accurately 189
estimated than along-track wavenumbers. Second, the same method was applied to along-190
track data series which provides more accurate along-track wavenumbers compared with 191
cross-track wavenumbers. We took better estimates between the two depending on the 192
direction of the two-dimensional wavenumber vector for further analysis. An example of 193
satellite data are shown in Figures 1a–1c. The detailed method is as follows. 194
The S-transform spectra are calculated using the Fourier transform and hence 195
affected by difference between the first and last ends of the data series. So as to avoid this 196
edge effect and to obtain statistically stable S-transform spectra, a window function, which 197
has a cosine shape at both ends for one tenth of the total length (=1755 km), was multiplied 198
to the GW data series across the track. The S-transform spectra were calculated at respective 199
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GW data series across the track. Cross spectra for respective two adjacent data series were 200
obtained using the S-transform spectra. A wavenumber vector , of the GWs is 201
estimated at each grid point. Here, and are the wavenumber components in the 202
direction of the cross-track data series and perpendicular to that direction, respectively. First, 203
the wavenumber is determined as the wavenumber at which the magnitude of the cross 204
spectral density is maximized in the meaningful wavenumber range. Here the meaningful 205
wavenumber range was estimated at 2π/(70 km) to 2π/(700 km) based on an analysis of 206
noise spectra (see Appendix for details). The wavenumber component across the data series 207
(i.e., along the track), ′, is estimated from the phase shift of the cross spectra at . Note 208
that ′ is different from , because the two data series which are respectively across and 209
along the track are not right-angled as seen in Figure 1a which shows the angle between the 210
two data series as a function of the latitude. The relation among , , , and is 211
illustrated in Figure 1b. The wavenumber component is estimated using , , and as 212
cossin . (1)
The wavenumber vectors were estimated for a central cross-track data series with a width of 213
1215 km, because the above-mentioned window function was applied to each one tenth end 214
of the cross-track data series. Similar analysis can be made by applying the S-transform to 215
the data series “along” the track with a length of 196 (i.e., 3528 km), and the estimation of 216
wavenumber vector was made for the central 135 grid points (i.e., 2430 km). Note that the 217
analysis using cross-track data series (along-track data series) provides better estimates for 218
waves with | | | | (| | | |). 219
In this way, we obtained a pair of horizontal wavenumber vectors for each grid point 220
for the same GW, i.e., one from the two adjacent data series across the track and the other 221
from that along the track. Through tests with idealized wave patterns, a better estimate of 222
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( , ) vector was selected with a criteria based on the angle of the horizontal wavenumber 223
vector ( ), where a positive (negative) value means an angle counter-clockwise 224
(clockwise) from the cross-track direction (see Figure 1b). We selected the estimate from the 225
along-track data series when 45° 45°, and that from the cross-track one when 226
90° 45° or 45° 90°. The direction of the horizontal wavenumber vector 227
in the Cartesian coordinate, namely, the angle with counter-clockwise rotation from the 228
eastward direction, , is then estimated using the cross-track direction in the Cartesian 229
coordinate, , as . Note that there is an ambiguity of 180° in . According 230
to a GW-resolving general circulation model study by Sato et al. [2009], dominant GWs tend 231
to have negative (positive) vertical flux of zonal momentum in the eastward (westward) 232
background wind. This means that the zonal component of horizontal wavenumber vector 233
has opposite sign to the background zonal wind. Thus, based on this fact, we determined the 234
direction of horizontal wavenumber using the zonal wind from MERRA at each grid point. 235
The horizontal wavelength is calculated as 2 / . 236
GW amplitude squared was estimated as the absolute value of the cross spectra at 237
with a unit of K2, which is hereafter referred to as the GW variance. Note that this GW 238
variance is equal to twice as much as conventional variance. 239
In addition, it was seen that data were quite noisy and temperature perturbation 240
signals were quite weak in the regions with weak background winds. This is probably 241
because in such weak background winds, vertical wavelengths of GWs are not sufficiently 242
long to be detected by AIRS. Thus, we simply omitted the data in regions where the 243
background wind slower than 10 m s-1 for the analysis. This threshold for the background 244
wind is somewhat arbitrary, however it was confirmed that the results are not sensitive to 245
slight changes of the threshold. 246
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3. Climatology of GWs in the summer subtropics
Figures 2a and 2b show maps of the climatology of GW variance in the summer 247
subtropics for the Southern Hemisphere (SH) averaged over December to February (DJF) 248
and for the Northern Hemisphere (NH) over June to August (JJA), respectively. The GW 249
variances are large over continents such as South Africa, Australia, South America, North 250
Africa, South and Southeast Asia, and North America and over the western to central South 251
Pacific. This feature is consistent with MLS observations [McLandress et al., 2000; Jiang et 252
al., 2004]. 253
Figure 2c (2d) shows maps of the standard deviation of seasonal mean GW variance 254
showing interannual variability in the SH (NH). The numbers of years to obtain the 255
interannual variability is eight and nine for Figures 2c and 2d, respectively. 256
An interesting feature is that the interannual variability of the GW variance in the 257
summer subtropics is larger in the SH than that in the NH, although the climatological GW 258
variances are comparable. The standard deviation of the DJF-mean GW variance in the 259
Australian monsoon region amounts to about 20 % of the climatology (Figure 2c), while in 260
JJA Asian monsoon region it is about 12 % (Figure 2d). Thus, in the following, we mainly 261
analyze the climatology and interannual variability of DJF-mean GW characteristics in SH 262
subtropics. 263
Figure 3 shows DJF-mean climatology of (a) precipitation and zonal winds at 100 264
hPa, (b) GW variance and zonal winds at 3 hPa, (c) GW horizontal wavelength averaged 265
with a weight of the GW variance, and (d) GW horizontal wavenumber direction 266
averaged with a weight of the GW variance in the SH tropical and subtropical region. The 267
South Pacific Convergence Zone (SPCZ) is defined as the latitudes of the precipitation 268
maxima for respective bins from 150°E−140°W and denoted by a red curve in all maps of 269
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Figure 3. 270
As expected, the GW variance maxima are observed in strong precipitation regions 271
such as in South Africa, Australia, and South America continents and in the South Pacific. 272
This indicates that the GWs at 39 km observed by AIRS are originating from strong 273
convection in the troposphere. It is interesting that the GW variance maxima are located 274
southward of the precipitation maxima by a few degrees at respective longitudes. Similar 275
differences in the locations of precipitation and GW variance maxima are also seen in South 276
Africa and South America. 277
The zonal wind at 3 hPa is mainly zonally uniform in Figure 3b, although it is 278
slightly stronger southward of the SPCZ. This fact indicates that the characteristic 279
longitudinal distribution of GW variance observed in Figure 3b (or Figure 2a) is not solely 280
due to the observational filter of AIRS, but is reflecting true differences in GW properties. 281
The mean GW horizontal wavelengths are long (>200 km) over southeastern Africa, 282
Australia, and southwestern America where the GW activity is high, while those in the other 283
regions are ~150 km (Figure 3c). The mean horizontal wavenumber direction is eastward or 284
slightly southward in most regions (Figure 3d). The direction tends to more southward to the 285
west of the precipitation. It was confirmed by a nonparametric method (Fisher, 1993) that 286
the observed directional departures of several degrees from the zonal direction are 287
statistically significant at 95 % confidence intervals (0.32 degrees at most) (not shown). 288
4. ENSO-related interannual variability of GW variance
As a possible cause of the GW interannual variability observed by AIRS in the 289
austral summer season, we examined the relation with ENSO. DJF-mean GW variance and 290
precipitation were made in respective years and binned at each 2.5° 10° latitude-longitude 291
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box area. To see the interannual variability of ENSO, the NINO.3 index is used (Figure 4) 292
(e.g. Trenberth, 1997). Note that a five-monthly mean was applied to the NINO.3 index by 293
its definition. Values of the NINO.3 index in January of 2004 to 2011 were used as a 294
reference time series for our analysis. It is seen that DJF periods of 2003/2004, 2004/2005, 295
2006/2007 and 2009/2010 (2005/2006, 2007/2008, 2008/2009 and 2010/2011) are in El 296
Niño (La Niña) or similar conditions, which are hereafter referred to as the El Niño (La Niña) 297
years. 298
Figure 5a (5c) and 5b (5d) show composite maps for precipitation (GW variance) 299
in the El Niño and La Niña years, respectively. Both the precipitation and GW variance 300
distributions are largely different over the subtropical South Pacific between the El Niño and 301
La Niña years. They are large northward (southward) of climatological SPCZ shown by a 302
red curve in the El Niño (La Niña) years. The GW variance is stronger in Maritime Continent, 303
North Australia, and Indochina in the La Niña years than in the El Niño years. In contrast, 304
the difference is not significant in other regions such as African and American continents 305
and western Indian Ocean. It is also worth noting that the GW variance is large over the 306
continents and not necessarily very large along SPCZ where significant precipitation is 307
observed, as consistent with the fact recently indicated by Geller et al. (2015). The contrast 308
in the precipitation and GW variance distribution between the El Niño and La Niña years is 309
more clearly seen in the results from a regression analysis in the following. 310
Figure 6a (6b) shows correlation coefficients of the DJF-mean precipitation (GW 311
variance) with the NINO.3 index. Figure 6c (6d) represents regression coefficients of the 312
DJF-mean precipitation (GW variance) to the NINO.3 index in the region where the 313
magnitude of correlation coefficients with the NINO.3 index is larger than 0.62 314
corresponding to a confidence level of 90%. Positive correlation and regression coefficient 315
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values indicate an increase in the precipitation and GW variance in the El Niño years. 316
Magnitudes of the correlation coefficients are greater than 0.83 over the subtropical 317
South Pacific for both precipitation and GW variance. The regressed component accounts 318
for about 70% of the GW interannual variability at most. The precipitation has positive 319
regression and correlation coefficients eastward of SPCZ and in the central equatorial South 320
Pacific, and negative regression and correlation coefficients westward of SPCZ and in the 321
eastern South Indian Ocean. The coefficients for the GW variance exhibit similar 322
distributions. An exception is seen at the Maritime Continent where the correlation and 323
regression coefficients are significantly negative for the precipitation, while they are small 324
for the GW variance. 325
So as to examine the cause of the difference in precipitation and hence likely the 326
GW variance, we made correlation coefficients with and regression coefficients to the 327
NINO.3 index of the DJF-mean sea surface temperature (SST) and horizontal wind 328
divergence at 850 hPa (Figure 7). Only values with a confidence level of 90% are plotted. 329
The SST has positive regression and correlation coefficients eastward of SPCZ and in the 330
central equatorial South Pacific. However, significant values are not seen westward of SPCZ. 331
Instead, the correlation and regression coefficients of the horizontal wind divergence are 332
negative eastward of SPCZ and positive westward of SPCZ and in the central equatorial 333
South Pacific. These characteristics are consistent with the distributions observed in the 334
precipitation shown in Figure 6. 335
The characteristic modulation of the GW variance by ENSO observed in Figures 6b 336
and 6d is likely due to modulation of GW sources (i.e. convection). However, we need to 337
scrutinize carefully the possibility of virtual modulation by the observational filter. As 338
horizontal wavenumber vectors are oriented mainly zonally (Figure 3d), GWs tend to have 339
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longer vertical wavelengths in stronger zonal winds. Thus, even if the horizontal phase speed 340
spectra of GWs propagating into the middle stratosphere are the same, the GW variance 341
observed by AIRS may exhibit virtual interannual variability by inter-annually varying 342
background zonal winds. 343
To examine this possibility, further analysis is made for three regions where 344
characteristic interannual variability of the GW variance is observed, synchronized with 345
ENSO: (A) the equatorial western South Pacific region (0°S to 10°S, 150°E to 150°W), (B) 346
the subtropical region (10°S to 30°S, 150°E to 110°W) to the east of the SPCZ, and (C) the 347
subtropical region to the west of the SPCZ that are denoted in Figure 6c. We calculated DJF-348
mean background wind zonal wind at 3 hPa for respective years and averaged over respective 349
A, B, and C regions. Results are shown in Figure 8 together with the time series of DJF-350
mean GW variance and precipitation averaged over respective regions. Regional dependence 351
is clear for the GW variance and precipitation: both the GW variance and precipitation values 352
are large (small) in the A and C regions and small (large) in the B region in the El Niño years 353
except 2003/2004 (the La Niña years). In contrast, the mean zonal wind exhibits similar 354
variation for all regions and does not seem to be modulated much by ENSO. Thus, we can 355
exclude the possibility of the observational filter alone causing the interannual variability 356
observed in the GW variance. It is therefore concluded that the interannual variability of 357
stratospheric GWs in the SH summer subtropical region is largely due to the modulation of 358
tropical convective GW sources by ENSO. 359
The regression to the NINO.3 index is also performed for the mean horizontal 360
wavelengths and the horizontal wavenumber direction (Figure 9). The correlation and 361
regression coefficients for the horizontal wavelengths exhibit similar patterns to those for 362
the GW variance (Figures 6b and 6d): They are largely positive in the A and C regions and 363
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negative in the B region. The rate of change in the horizontal wavelength is about 20 km per 364
1 K NINO.3 SST at most. In contrast, the regressed pattern of horizontal wavenumber 365
direction shows different features: Significant negative correlation and regression 366
coefficients are observed along the SPCZ. The rate of change in the direction is about 2 367
degrees per 1 K NINO.3 SST. This means that the GWs over the SPCZ propagate slightly 368
more southward relative to the mean wind in the El Niño phase than in the La Niña phase. 369
As described in section 3, the DJF-mean GW variance climatology is maximized 370
slightly to the south of the precipitation maximum (Figures 3a and 3b). This feature is further 371
examined by making a composite separately for the El Niño years and for the La Niña years. 372
Figure 10a shows composite profiles of the GW variance (black) and precipitation (blue) as 373
a function of the latitude relative to the climatological SPCZ latitude that are averaged over 374
longitudes from 150°E−150°W for all years (i.e., climatology), while Figures 10b and 10c 375
represent the same composite profiles but for the El Niño and La Niña years, respectively. A 376
profile of a mean ocean fraction in each grid box for the same longitude region is also plotted 377
by a green curve in Figure 10, which will be referred to in the discussion in section 5.2. 378
The precipitation maximum shifts northward (southward) in the El Niño (La Niña) 379
years compared with the climatology. However, it is commonly seen for both phases that the 380
GW variance is maximized southward of the precipitation maximum. It is interesting that 381
the latitudinal difference between the precipitation and GW variance maxima is larger in the 382
El Niño years than in the La Niña years. This feature is at least qualitatively consistent with 383
the fact that the mean horizontal wavenumber vector direction is more southward in the El 384
Niño years (Figures 9b and 9d). 385
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5. Discussions
5.1 Possibility of interannual variability modulation by the QBO 386
As described in section 1, the interannual variability of stratospheric GWs in the 387
tropical region has been discussed in terms of the QBO in previous studies. It seems, however, 388
that the QBO does not largely modulate the interannual variability of the GWs observed by 389
AIRS over this subtropical South Pacific region, as is shown below. 390
As a QBO index, we used a time series of DJF-mean zonal-mean zonal wind at 10 391
hPa at the equator from MERRA. Figures 11a and 11b respectively show correlation and 392
regression coefficients between the GW variance at respective locations and the QBO index. 393
Regression coefficients of the GW variance time series to the QBO index are only shown in 394
regions where the correlation coefficient magnitudes are greater than 0.62 corresponding to 395
a confidence level of 90%. Significant modulation by the QBO is observed in longitudes 396
from 120°W eastward to 60°E at latitudes lower than 10°S. The negatively large regression 397
coefficients in this region mean that the GW variances are larger in the westward phase of 398
the QBO at 10 hPa than the eastward phase. In contrast, significant modulation by the QBO 399
is not observed in the west and central South Pacific region even near the equator which is 400
the focus in the present study. Similar results were obtained for the correlation and regression 401
analysis performed using zonal-mean zonal wind at the equator at 30, 40, 50, and 70 hPa 402
(not shown). Thus, this result also strongly suggests that the interannual variability of 403
stratospheric GWs over the western and central parts of the subtropical South Pacific in 404
austral summer is largely affected by ENSO. 405
5.2. Possible mechanisms of latitudinal difference between GW and convection maxima 406
An interesting result from the analysis of climatology in Section 4 is that the DJF-407
mean GW variance takes its maximum southward of the precipitation maximum by about 408
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3°. In this section, we discuss three possible mechanisms causing the latitudinal gap of 409
stratospheric GWs and tropical convection in the South Pacific. They are (1) island 410
distribution, (2) selective excitation of southward propagating waves in the troposphere, and 411
(3) southward refraction due to background wind shear in the stratosphere. Other 412
mechanisms and a combination of these mechanisms are also discussed. 413
5.2.1 Island distribution 414
Island distribution may affect the GW climatology because the occurrence 415
frequency of deep convection over the land is higher than that over the ocean [Takayabu, 416
2002]. Thus, it is expected that GWs with long vertical wavelengths are generated more 417
effectively over the islands. Such GWs with long vertical wavelengths have fast intrinsic 418
phase speed and hence less frequently encounter their critical levels compared with those 419
with short vertical wavelengths. In addition, such GWs with long wavelengths are more 420
easily detectable by AIRS. 421
As a proxy of the existence of islands, we used mean ocean fraction for each bin, 422
which is hereafter referred to as MOF. A map of MOF is plotted in Figure 12a. MOF values 423
are zero over continents and one over oceans by its definition. Small but nonzero MOF 424
values are observed around and in particular southward of SPCZ indicating that a number of 425
islands and/or islands with large areas are distributed there. 426
Figure 12b shows a histogram as a function of the DJF-mean climatology of GW 427
variance versus that of precipitation for a region of (0°S−30°S, 160°E−160°W) which is 428
denoted by a rectangle on the map in Figure 12a. It is clear that the two quantities are 429
positively correlated. Figure 12c shows the mean of MOF values at respective bins of this 430
plot by the same color scale as used for Figure 12a. The mean MOF values are smaller at 431
larger GW variance for a particular precipitation value. This result indicates that GWs are 432
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effectively generated from convection over islands. 433
In Figure 10, composite MOF values were shown as a function of the latitude 434
relative to the climatological SPCZ. It is seen for the climatology in Figure 10a that the MOF 435
takes its minimum slightly southward of the precipitation maximum and slightly northward 436
of the GW variance maximum. It is also seen from Figures 10b and 10c that the GW variance 437
maximum does not move much and remains close to the MOF minimum, although latitudinal 438
movement of the precipitation maximum is largely depending on the ENSO phase. This 439
result is consistent with our inference that convection over islands effectively generates GWs 440
with long vertical wavelengths, and suggests that the island distribution is partly attributable 441
to the difference in the dominant latitude between the observed GW variance and 442
precipitation. In addition, it is worth noting that the diurnal cycle of convection near islands 443
has a peak in evening [Mori et al., 2004; Ichikawa et al., 2008], while convection over the 444
tropical Pacific and Atlantic Oceans is maximized in the morning [Serra and McPhaden, 445
2004]. Night time observations by AIRS which are used in the present study maybe more 446
apt to detect GWs originating from convection near islands rather than those over the ocean. 447
However, as the MOF minimum is always located slightly northward of the GW variance 448
maximum (Figures 10a, 10b and 10c), additional mechanisms causing southward shift of the 449
GW variance maximum are necessary. 450
5.2.2. Selective excitation of GWs in the background wind shear 451
Beres et al. [2002] showed from a series of numerical simulation using a two-452
dimensional model that GWs propagating opposite to the upper tropospheric wind shear are 453
effectively excited by convection in squall lines. Figure 13a shows vertical profiles of 454
composite meridional winds for longitudes of 160°E−160°W as a function of the latitude 455
relative to SPCZ. Composites of precipitation and GW variance for the same longitude 456
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region are respectively shown in Figures 14b and 14c as a function of the latitude relative to 457
the SPCZ. As was also shown in Figure 10a, the latitude of GW variance maximum is 458
observed southward of the precipitation maximum by 3°. 459
Northward wind is observed in the upper troposphere with a maximum around 200 460
hPa and hence the vertical wind shear in the troposphere is northward. This wind structure 461
suggests that southward propagating GWs should be more effectively excited from the SPCZ. 462
This implication is qualitatively consistent with the relative location of the GW variance and 463
precipitation maxima. 464
A rough but quantitative estimation is made for a possible latitudinal propagation 465
distance of GWs using typical wave parameters obtained from the S-transform analysis: The 466
mean horizontal wavenumber 2 / (225 km) (Figure 3c) and a mean horizontal 467
wavenumber direction of −4.5° (Figure 3d) around the location (15°S, 180°E) near the SPCZ. 468
A typical observable vertical wavenumber is assumed as 2 /(15 km). The inertial 469
frequency at 15°S is 2 /(46.4 h) and a typical stratospheric Brunt-Väisälä frequency 470
is assumed as 2 /(5 min). From the linear theory of non-hydrostatic internal inertia-gravity 471
wave, vertical ( ) and meridional group velocity components ( ) are expressed as; 472
, (2)
sin , (3)
where is meridional wavenumber (≡ sin , and estimated using the above-473
mentioned parameters at 3.31 m s-1 and 3.89 m s-1. Thus, the time period needed 474
for propagation from the upper troposphere ( = 9 km) to the stratosphere ( = 39 km) is 475
estimated at about 2.52 h and the latitudinal distance over which the GWs migrate is 35.3 476
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km. This value is not sufficient to explain the observed distance of 3° (~330 km). Thus, a 477
preference for excitation of southward waves is not the only mechanism causing the large 478
latitudinal distance between the GW variance and precipitation maxima, although it is 479
qualitatively consistent. 480
5.2.3 Refraction due to the latitudinal gradient of zonal wind 481
GWs tend to propagate meridionally by refraction in a background zonal wind 482
having latitudinal shear [Dunkerton, 1984; Sato et al., 2009]. Meridional cross sections of 483
the mean zonal wind are shown for two longitudes of 160°E and 160°W (Figure 14) instead 484
of a composite, because changes largely depend on longitude (see Figure 3a). The latitudinal 485
gradient of zonal wind, / , is mainly positive in the stratosphere in the latitude range 486
between the GW variance and precipitation maxima. Assuming that the zonal wavenumber 487
( ) is positive in the westward background wind, the tendency is negative from the ray 488
tracing theorem, 489
dd (4)
This means that the GW packets would tend to propagate southward. This fact is at least 490
qualitatively consistent with the difference in the latitude between the GW variance and 491
precipitation maxima. A rough but quantitative estimation is next made. Acceleration of the 492
GW packet in the latitudinal direction is written as, 493
dd ∼ d
ddd
.
(5)
To isolate the refraction effect on the meridional propagation direction, d /d is set to zero 494
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at the initial time. The zonal wind shear is simply set to a constant value of 4.5 m s−1 per 495
3°at 30 hPa and (160°E, 7.5°S) during the propagation (Figure 14). The latitudinal 496
propagation distance is estimated at 28 km for GWs using ∼ 2 /(225 km). This 497
value is again not sufficient to explain the observed distance of ~330 km. Thus, the latitudinal 498
propagation due to refraction is not the only mechanism to cause the difference in the 499
latitudes between the GW variance and precipitation maxima. 500
5.2.4. Other possible mechanisms and a combination of multiple mechanisms 501
We considered other mechanisms such as advection by the southward background 502
wind and critical level filtering at the latitudes of the precipitation maximum. However, none 503
of them can explain the southward shift of the GW variance maximum. Meridional 504
background wind is almost zero in the stratosphere below 3 hPa (Figure 13a) and hence 505
cannot cause much advection. The background zonal winds are not very different between 506
the latitudes of the GW variance and precipitation maxima (Figure 14). The eastward wind 507
around the tropopause is rather stronger at higher latitudes (Figure 14d), which means that 508
GWs at higher latitudes can be more effectively filtered. An effect of the observational filter 509
by the stratospheric zonal wind is also possible. However, this is not the case at least around 510
160°W as seen in Figure 14d where the zonal wind around 3 hPa does not largely depend on 511
the latitude around the precipitation maximum. 512
In conclusion, the most important mechanism explaining the latitudinal distance 513
about 330 km between the GW variance and precipitation maxima is the island distribution 514
which is dense (sparse) southward (northward) of SPCZ. The selective GW excitation in the 515
vertical shear of mean meridional wind, and the latitudinal propagation by refraction due to 516
the latitudinal shear of mean zonal wind have secondary contribution (about 63 km in total). 517
Probably a combination of these mechanisms is likely responsible for the latitudinal 518
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difference. 519
6. Summary and concluding remarks
The present study first examined the climatology and interannual variability of GW 520
variance in the subtropical region in the summer middle stratosphere based on satellite nadir 521
sounding data by AIRS over eight years. High-resolution temperature data at 39 km made 522
from the Hoffmann and Alexander [2009] retrieval algorithm were used for the analysis. An 523
S-transform method was applied to extract GW parameters such as temperature variance and 524
the magnitude and direction of horizontal wavenumber. In a climatology, large GW variance 525
is observed over continents and the tropical Maritime Continent in both hemispheres. 526
Precipitation is also dominant over the continents but there is a systematic latitudinal 527
difference between the GW variance maximum and precipitation maximum by about three 528
degrees. 529
The interannual variability in the summer subtropics is larger in the SH than in the 530
NH. Thus further analysis was focused on the SH. Horizontal wavelengths are longer (>200 531
km) over continents and the Maritime Continent and shorter (about 150 km) over the ocean. 532
Assuming that the zonal phase speeds are opposite to the background zonal wind as is 533
consistent with previous studies, the waves propagate primarily eastward, but the latitudinal 534
component of the wavenumber vectors is negative (i.e., southward) for most GWs. 535
An interesting and important feature is that the interannual variability of the GW 536
variance in the western and central South Pacific region in summer is closely related to the 537
ENSO which accounts for 70% of the variation. This variation of GW variance follows the 538
SPCZ latitudinal movement in association with the ENSO. The distribution of both 539
horizontal wavelengths and propagation direction also vary following the ENSO. The 540
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contribution of the equatorial QBO is minor in that region. 541
Last but not least, we examined possible mechanisms causing the systematic 542
latitudinal difference by 3 degrees between the maxima of GW variance and precipitation 543
climatology. An important mechanism is the distribution of islands which are dense 544
southward of SPCZ. It is expected that deep convection excited over islands effectively 545
generates GWs with long vertical wavelengths, which are more easily detectable by AIRS. 546
Selective GW excitation due to vertical shear of the upper tropospheric wind, and GW 547
refraction in the latitudinal shear of the background wind are secondary but important 548
mechanisms for the southward component of propagation of GWs. By using typical GW 549
parameters estimated from AIRS data, the sum of the two mechanisms might account for 550
about 20 % of the latitudinal distance. Combination of the three mechanisms are likely 551
responsible for the latitudinal difference. 552
This study showed a significant inter-annual modulation of stratospheric GW 553
activity by ENSO in the SH subtropical region. It is seen from comparison between Figure 554
3b and Figure 6d that ENSO-modulation of the GW variance is more than ten percent 555
depending on the location. This fact means that the meridional circulation in the middle and 556
upper atmosphere may be also modulated by ENSO. Changes in the meridional circulation 557
also modify the thermal structure and affect the structure of tides, which are dominant in the 558
upper mesosphere and thermosphere. Thus, climate models using a parameterization with 559
constant, prescribed GW sources versus a convection-based GW schemes may not represent 560
the GW interannual variability related to ENSO and its effect on the meridional circulation. 561
For a more quantitative discussion, it is necessary to examine the momentum flux 562
associated with GWs. To do this, the estimation of vertical wavelengths is needed using data 563
from at least two altitudes in addition to the temperature variance. However, generally 564
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speaking, this is difficult to derive from nadir-viewing satellite observations with low 565
vertical resolution like AIRS. The momentum flux is expressed using a formula 566
from observed temperature variances [Ern et al., 2004; Alexander, 2015]. 567
Thus, we assume a typical detectable vertical wavelength of 15 km for a rough estimation. 568
Using a climatological mean GW variance of 1 K2, a background temperature of 250 K, a 569
typical horizontal wavelength of 225 km, and damping due to limited vertical resolution of 570
AIRS retrieval of about 15 % in variance [Hoffmann and Alexander, 2009], the 571
climatological momentum flux observed by AIRS is estimated at about 0.5 mPa and the 572
interannual variability related to ENSO is about 0.05 mPa. This climatological momentum 573
flux value of the GWs observed by AIRS is comparable to the estimate (about 0.5 mPa) 574
around the SPCZ at 40 km in January, 2006 from observations of Sounding of the 575
Atmosphere using Broadband Emission Radiometry (SABER) and HIRDLS, which are 576
sensitive to GWs with short vertical wavelengths unlike AIRS [Geller et al., 2013]. 577
In addition, it is also worth noting that the GW variance dependence on longitude 578
has an interannual variability. This means that the Lagrangian mean circulation in the middle 579
atmosphere may have significant three-dimensional structure, although it has mainly been 580
examined in the two dimensional meridional cross section so far. It should be interesting to 581
examine the three-dimensional structure of the interaction of GWs with the mean flow [e.g., 582
Kinoshita and Sato, 2013; Sato et al., 2013] and the interaction between GWs and planetary-583
scale waves [e.g., Smith, 2003; Lieberman et al., 2013; Sato and Nomoto, 2015] in terms of 584
interannual variability in the future. 585
Acknowledgments
The authors thanks Yukari N. Takayabu, Masaaki Takahashi, Tomoki Tozuka, and 586
Hiroaki Miura for their constructive comments. This work was supported by JSPS 587
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KAKENHI Grant Numbers 25247075 and 24-2641. MJA was supported by the National 588
Science Foundation grant numbers AGS–1318932 and AGS-1519271. 589
Appendix A: Noise spectra
It is expected that there are few significant GW sources such as topography, jet-590
front systems and convection in the winter subtropical Pacific. In addition, the background 591
wind there is generally weak at 3 hPa and hence GWs originating from convection are not 592
significantly Doppler shifted. Such GWs have short vertical wavelengths that are hardly 593
detected by AIRS. Thus, we regarded the magnitude of the S-transform cross spectra of 594
adjacent data series in such regions as “noise” spectra which is a function of the location and 595
wavelength. 596
Figure A-1 shows the noise spectra obtained from adjacent two data series across 597
the track for the region of 10°N−30°N, 150°E−120°W on February 12 and the region of 598
0°S−25°S, 180°W−90°W on July 12 of 2003−2011. The spectral densities, which we call 599
variances, are larger at shorter wavelengths. At nadir where the cross track location is 0 km, 600
the variance is maximized at a wavelength of about 30 km. Similar maxima are observed at 601
longer wavelengths for larger distances from the nadir. Such dependence of the maximum 602
wavelength can be explained by the coarser resolution at larger distances from the nadir. 603
These maxima are likely due to the random noise that appears in the temperature retrievals, 604
and hence should be removed. A weak peak is also observed around 1000 km wavelength. 605
The reason of this peak is not clear but may be due to the detrending method used in the 606
present study. This peak should also be removed as noise. Thus, we examined S-transform 607
spectra in the range of wavelengths 70−700 km. Note that the variances are diminished near 608
the edge of a cross track scan. This reflects to the cosine-shaped window function applied to 609
the original data before the S-transform calculation. Thus the edge regions are not examined 610
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 28
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for the analysis either. 611
Two examples of the S-transform spectra including GW signals are shown in Figure 612
A-2. Figures A-2a and A-2b respectively show the results over convection in Australia on 613
January 15, 2007 and over the Andean mountains on May 18, 2006. The latter corresponds 614
to a significant GW event examined by Alexander and Teitelbaum [2011]. Clear GW signals 615
are observed in both examples, occurring at a wavelength of 300 km and a distance of -200 616
km in Figure A-2a and at a wavelength near 100 km at nadir in Figure A-2b. 617
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777
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Figure captions
Figure 1. (a) The angle between the directions of the data series across and along the 778
satellite orbit at the daytime (ascending orbit) shown by a red curve and at the nighttime 779
(descending orbit) by a blue curve as a function of the latitude, calculated using data on 780
December 24, 2003. (b) A schematic illustration of a wave phase structure and directions 781
along and across the satellite track. See text for details. 782
Figure 2. (a) DJF mean climatology of GW variance at a height of 39 km binned with an 783
interval of 2.5° latitude and 10° longitude made from eight years from 2003/04−2010/11 784
in the SH subtropics. (b) The same as (a) but for JJA mean climatology from 2003−2011 785
in the NH subtropical region. (c) ((d)) Standard deviation of the DJF (JJA) GW variance 786
for the eight (nine) years. Black contours show mean zonal winds at 3 hPa at an interval 787
of 10 m s-1. 788
Figure 3. DJF mean climatology of (a) precipitation (color), zonal wind at 100 hPa 789
(contours at an interval of 5 m s-1), (b) GW variance at 39 km (color), zonal wind at 3 790
hPa (contours at an interval of 5 m s-1), (c) horizontal wavelengths averaged with a 791
weight of the GW variance, (d) direction of the horizontal wavenumber vector averaged 792
with a weight of the GW variance shown by angles counter-clockwise from the eastward 793
direction. The thick red curve denotes the latitude of the climatological precipitation 794
maxima for 150°E−140°W. The longitudinal region of 160°E−160°W for which a scatter 795
diagram analysis is made (Figure 12b) is denoted by two vertical thick lines. 796
Figure 4. Time series of SST anomaly from the 30-year climatology in the NINO.3 region 797
(5°S−5°N, 150°W− 90°W). Black dots show NINO.3 data used for the regression 798
analysis. See text for details. 799
Figure 5. Same as Figure 3a and 3b but for composite maps of (a) (b) precipitation and (c) 800
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(d) GW variance at 39 km for (a) (c) El Niño and (b) (d) La Niña years. 801
Figure 6. Maps of correlation coefficients of NINO.3 time series with (a) DJF-mean 802
precipitation and (b) GW variance. Red (blue) contours show positive (negative) 803
correlation. Thin contours show 0.62 corresponding to the 90 % significant level and 804
thick contours show 0.83 corresponding to the 99 % significant level. Regression 805
coefficients for (c) DJF-mean precipitation and (d) GW variance are shown by colors 806
only in regions with correlation coefficient magnitudes larger than 0.62. Thick black 807
lines show the regions of (A) the equatorial central South Pacific (from 150°E to 150°W, 808
from 0°S to 10°S), (B) and (C) the regions respectively to the east and west of SPCZ 809
(from 150°E to 110°W, from 10°S to 30°S). The SPCZ is denoted by a thick red curve. 810
Figure 7. Same as Figure 6 but for (a) (b) SST and (c) (d) horizontal wind divergence. The 811
SPCZ is denoted by a thick orange (red) curve for (a) and (b) ((c) and (d)). 812
Figure 8. DJF-mean time series of precipitation (blue), GW variance (black), and zonal 813
wind at 3 hPa (green) for (a) the equatorial central South Pacific, and the regions (b) 814
west and (c) east of the SPCZ. These regions are shown in Figure 6 as thick black lines. 815
Figure 9. The same as Figure 6 but for ((a) and (c)) horizontal wavelengths averaged with 816
a weight of the GW variance and ((b) and (d)) direction of horizontal wavenumber 817
vector averaged with a weight of the GW variance. 818
Figure 10. Composites of the GW variance (black), precipitation (blue), and mean ocean 819
fraction (green) at longitudes from 150°E−150°W as a function of the latitude relative to 820
the climatological SPCZ. (a) Composites are made for the climatology. (b) Composites 821
are made for the seasonal mean in the El Niño years such as 2003/2004, 2004/2005, 822
2006/2007, and 2009/2010. (c) The same as (b) but for the La Niña years such as 823
2005/2006, 2007/2008, 2008/2009, and 2010/2011. 824
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Figure 11. (a) Correlation coefficients of DJF-mean GW variance with the DJF-mean 825
zonal mean zonal wind at 10 hPa at the equator (QBO time series). Positive (negative) 826
values are shown by red (blue) contours. Thin contours show 0.62 (a significant level 827
of 90%) and a thick contours show 0.83 (a significant level of 99%). (b) Regression 828
coefficients of DJF-mean GW variance to the QBO time series are shown by colors only 829
in the regions where the correlation coefficient magnitudes are larger than 0.62. 830
Figure 12. (a) A map of mean ocean fraction from MERRA. (b) Histogram and contours 831
for the precipitation versus the GW variance at 39 km in the region of (160°E−160°W, 832
0°−20°S). Contour interval is 2. (c) Mean ocean fraction as a function of precipitation 833
and the GW variance at 39 km for the same region as for (b). 834
Figure 13. A composite latitude and height cross section of (a) the mean meridional wind 835
at a contour interval of 0.5 m s−1. Composite latitudinal profiles of (b) precipitation and 836
(c) GW variance averaged for 160°E−160°W. The reference latitude is the latitude of the 837
precipitation maximum between 0°−30°S. 838
Figure 14. Latitude and height cross sections of background zonal winds at (a) 160°E and 839
(d) 160°W. Contour intervals are 2.5 m s−1. Latitudinal distributions of the precipitation 840
at (b) 160°E and (e) 160°W and those of GW variance at (c) 160°E and (f) 160°W. Thin 841
vertical lines denote the latitudes of the maximum precipitation ((b) and (e)) and the 842
maximum GW variance ((c) and (f)). 843
Figure A-1. Noise spectra calculated for the winter subtropics using an S-transform 844
method. See the text for details. Contour intervals are 2.5 dB. Wave characteristics are 845
estimated for the region surrounded by four thin blue lines. 846
Figure A-2. Examples of the S-transform cross spectra in which GW signals are observed: 847
(a) in Australia on January 15, 2007 and (b) over the Andes on May 8, 2006. Displayed 848
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 39
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are the magnitude of cross spectra (dB). Contour intervals are 5 dB. Only contours of 849
larger values than -15 dB with the same unit for Figure A-1 are shown. 850
851
852
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 40
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853
Figure 1. (a) An example of AIRS temperature observation January 15, 2008. (b) A 854
cross-track data series (black) with an adjacent cross-track data series (blue). A blue 855
curve is shifted upward by 3K. (c) An along-track data series (black) with an adjacent 856
along-track data series. A blue curve is shifted upward by 3K. (d) The angle between 857
the directions of the data series across and along the satellite orbit at the daytime 858
(ascending orbit) shown by a red curve and at the nighttime (descending orbit) by a blue 859
curve as a function of the latitude, calculated using data on December 24, 2003. (d) A 860
schematic illustration of directions and angles describing a satellite track and a gravity 861
wave. See text for details. 862
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 41
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863
864
865
Figure 2. (a) DJF mean climatology of GW variance at a height of 39 km binned with 866
an interval of 2.5° latitude and 10° longitude made from eight years from 867
2003/04−2010/11 in the SH subtropics. (b) The same as (a) but for JJA mean 868
climatology from 2003−2011 in the NH subtropical region. (c) ((d)) Standard deviation 869
of the DJF (JJA) GW variance for the eight (nine) years. Black contours show mean 870
zonal winds at 3 hPa at an interval of 10 m s-1. 871
872
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 42
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873
874
Figure 3. DJF mean climatology of (a) precipitation (color), zonal wind at 100 hPa 875
(contours at an interval of 5 m s-1), (b) GW variance at 39 km (color), zonal wind at 3 876
hPa (contours at an interval of 5 m s-1), (c) horizontal wavelengths averaged with a 877
weight of the GW variance, (d) direction of the horizontal wavenumber vector averaged 878
with a weight of the GW variance shown by angles counter-clockwise from the 879
eastward direction. The thick red curve denotes the latitude of the climatological 880
precipitation maxima for 150°E−140°W. The longitudinal region of 160°E−160°W for 881
which a scatter diagram analysis is made (Figure 12b) is denoted by two vertical thick 882
lines. 883
884
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 43
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885
886
Figure 4. Time series of SST anomaly from the 30-year climatology in the NINO.3 887
region (5°S−5°N, 150°W−90°W). Black dots show NINO.3 data used for the regression 888
analysis. See text for details. 889
890
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 44
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891
Figure 5. Same as Figure 3a and 3b but for composite maps of (a) (b) precipitation and 892
(c) (d) GW variance at 39 km for (a) (c) El Niño and (b) (d) La Niña years. 893
894
895
896
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 45
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897
Figure 6. Maps of correlation coefficients of NINO.3 time series with (a) DJF-mean 898
precipitation and (b) GW variance. Red (blue) contours show positive (negative) 899
correlation. Thin contours show 0.62 corresponding to the 90 % significant level and 900
thick contours show 0.83 corresponding to the 99 % significant level. Regression 901
coefficients for (c) DJF-mean precipitation and (d) GW variance are shown by colors 902
only in regions with correlation coefficient magnitudes larger than 0.62. Thick black 903
lines show the regions of (A) the equatorial central South Pacific (from 150°E to 150°W, 904
from 0°S to 10°S), (B) and (C) the regions respectively to the east and west of SPCZ 905
(from 150°E to 110°W, from 10°S to 30°S). The SPCZ is denoted by a thick red curve. 906
907
908
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 46
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909
Figure 7. Same as Figure 6 but for (a) (b) SST and (c) (d) horizontal wind divergence. 910
The SPCZ is denoted by a thick orange (red) curve for (a) and (b) ((c) and (d)). 911
912
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 47
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913
914
Figure 8. DJF-mean time series of precipitation (blue), GW variance (black), and zonal 915
wind at 3 hPa (green) for (a) the equatorial central South Pacific, and the regions (b) 916
west and (c) east of the SPCZ. These regions are shown in Figure 6 as thick black lines. 917
918
919
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 48
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920
921
922
Figure 9. The same as Figure 6 but for ((a) and (c)) horizontal wavelengths averaged 923
with a weight of the GW variance and ((b) and (d)) direction of horizontal wavenumber 924
vector averaged with a weight of the GW variance. 925
926
927
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 49
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928
929
930
Figure 10. Composites of the GW variance (black), precipitation (blue), and mean 931
ocean fraction (green) at longitudes from 150°E−150°W as a function of the latitude 932
relative to the climatological SPCZ. (a) Composites are made for the climatology. (b) 933
Composites are made for the seasonal mean in the El Niño years such as 2003/2004, 934
2004/2005, 2006/2007, and 2009/2010. (c) The same as (b) but for the La Niña years 935
such as 2005/2006, 2007/2008, 2008/2009, and 2010/2011. 936
937
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 50
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938
939
Figure 11. (a) Correlation coefficients of DJF-mean GW variance with the DJF-mean 940
zonal mean zonal wind at 10 hPa at the equator (QBO time series). Positive (negative) 941
values are shown by red (blue) contours. Thin contours show 0.62 (a significant level 942
of 90%) and a thick contours show 0.83 (a significant level of 99%). (b) Regression 943
coefficients of DJF-mean GW variance to the QBO time series are shown by colors 944
only in the regions where the correlation coefficient magnitudes are larger than 0.62. 945
946
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 51
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947
948
949
Figure 12. (a) A map of mean ocean fraction from MERRA. (b) Histogram and 950
contours for the precipitation versus the GW variance at 39 km in the region of 951
(160°E−160°W, 0°−20°S). Contour interval is 2. (c) Mean ocean fraction as a function 952
of precipitation and the GW variance at 39 km for the same region as for (b). 953
954
955
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 52
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956
957
958
Figure 13. A composite latitude and height cross section of (a) the mean meridional 959
wind at a contour interval of 0.5 m s−1. Composite latitudinal profiles of (b) precipitation 960
and (c) GW variance averaged for 160°E−160°W. The reference latitude is the latitude 961
of the precipitation maximum between 0°−30°S. 962
963
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 53
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964
965
966
Figure 14. Latitude and height cross sections of background zonal winds at (a) 160°E 967
and (d) 160°W. Contour intervals are 2.5 m s−1. Latitudinal distributions of the 968
precipitation at (b) 160°E and (e) 160°W and those of GW variance at (c) 160°E and (f) 969
160°W. Thin vertical lines denote the latitudes of the maximum precipitation ((b) and 970
(e)) and the maximum GW variance ((c) and (f)). 971
972
973
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 54
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974
975
976
Figure A-1. Noise spectra calculated for the winter subtropics using an S-transform 977
method. See the text for details. Contour intervals are 2.5 dB. Wave characteristics are 978
estimated for the region surrounded by four thin blue lines. 979
980
981
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SATO ET AL: ENSO-MODULATED GRAVITY WAVES 55
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982
983
984
Figure A-2. Examples of the S-transform cross spectra in which GW signals are 985
observed: (a) in Australia on January 15, 2007 and (b) over the Andes on May 8, 2006. 986
Displayed are the magnitude of cross spectra (dB). Contour intervals are 5 dB. Only 987
contours of larger values than -15 dB with the same unit for Figure A-1 are shown. 988
989