GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L20801, doi:10.1029/2012GL053071, 2012

The vertical and spatial structure of ENSO in the upper
troposphere and lower stratosphere from GPS radio
occultation measurements
B. Scherllin-Pirscher,1,2 C. Deser,3 S.-P. Ho,2 C. Chou,4 W. Randel,3 and Y.-H. Kuo2
Received 12 July 2012; revised 3 September 2012; accepted 5 September 2012; published 17 October 2012.

[1] The vertical and spatial structure of the atmospheric El
Niño-Southern Oscillation (ENSO) signal is investigatedusing radio occultation (RO) data from August 2006 to
December 2010. Due to their high vertical resolution andglobal coverage, RO data are well suited to describe the full
3-dimensional ENSO structure in the troposphere and lower
stratosphere. We find that interannual temperature anomalies
in the equatorial region show a natural decomposition into
zonal-mean and eddy (deviations from the zonal-mean) components that are both related to ENSO. Consistent with previous studies, we find that during the warm phase of ENSO,
zonal-mean temperatures increase in the tropical troposphereand decrease in the tropical stratosphere. Maximum warming
occurs above 8 km, and the transition between warming andcooling occurs near the tropopause. This zonal-mean responselags sea surface temperature anomalies in the eastern equatorial Pacific by 3 months. The atmospheric eddy component, incontrast, responds rapidly (within 1 month) to ENSO forcing.
This signal features a low-latitude dipole between the Indianand Pacific Oceans, with off-equatorial maxima centeredaround 20 to 30 latitude in both hemispheres. The eddy
response pattern attains maximum amplitude in the upper
troposphere near 11 km and (with opposite polarity) in ashallow layer near the tropopause at approximately 17 km.
The eddy ENSO signal tends to be out-of-phase between
low and middle latitudes in both the troposphere and lower
stratosphere. Citation: Scherllin-Pirscher, B., C. Deser, S.-P.
Ho, C. Chou, W. Randel, and Y.-H. Kuo (2012), The verticaland spatial structure of ENSO in the upper troposphere and lower
stratosphere from GPS radio occultation measurements, Geophys.
Res. Lett., 39, L20801, doi:10.1029/2012GL053071.

1. Introduction[2] Global weather and climate are influenced by theinterannual El Niño-Southern Oscillation (ENSO) phenomenon, which emerges from atmosphere-ocean interaction in
the tropical Pacific. Atmospheric temperature variations
1
Wegener Center for Climate and Global Change and Institute forGeophysics, Astrophysics, and Meteorology/Institute of Physics,
University of Graz, Graz, Austria.
2
COSMIC Project Office, University Corporation for AtmosphericResearch, Boulder, Colorado, USA.
3
National Center for Atmospheric Research, Boulder, Colorado, USA.
4
Research Center for Environmental Changes, Academia Sinica, Taipei,
Taiwan.

Corresponding author: B. Scherllin-Pirscher, Wegener Center for
Climate and Global Change, University of Graz, Brandhofgasse 5, A-8010
Graz, Austria. (barbara.pirscher@uni-graz.at)©2012. American Geophysical Union. All Rights Reserved.
0094-8276/12/2012GL053071

associated with ENSO are pronounced not only at low latitudes within the troposphere, but also at middle and highlatitudes as well as in the stratosphere [e.g., Seager et al.,
2003; Free and Seidel, 2009; Randel et al., 2009].
[3] The warm phase of ENSO is associated with tropospheric warming, which is attributable to anomalous heat
flux from the tropical Pacific ocean to the atmosphere [Seager
et al., 2003] and is a well-known feature of interannual tropical temperature variability. Relative to the sea surface temperature (SST) forcing in the eastern equatorial Pacific, thistropospheric response is lagged by one or two seasons[Su et al., 2005; Chou and Lo, 2007]. In the lower tropicalstratosphere, a significant cooling signal is linked to the warm
phase of ENSO [Lau et al., 1998; Free and Seidel, 2009],
produced by enhanced tropical upwelling [Randel et al., 2009;
Calvo et al., 2010]. Modeling studies confirm the existence of
this stratospheric cooling signal [García-Herrera et al., 2006].
[4] Using observations from the Microwave Sounding Unit
(MSU), Yulaeva and Wallace [1994] and Calvo Fernández
et al. [2004] found different ENSO signatures for the zonalmean and eddy (deviations from the zonal-mean) temperaturecomponents within the upper troposphere and lower stratosphere. However, since MSU provides information only forthick (>10 km) atmospheric layers these studies were unable
to extract the detailed vertical structure of the zonal-mean andeddy ENSO signals.
[5] Trenberth and Smith [2006, 2009] and Zhou andZhang [2011] used global reanalysis and model data to
analyze the full 3-dimensional temperature structure associated with ENSO. They identified two distinctive modes
which together capture the full character of the atmospheric
ENSO signal. These two patterns and their correspondingtime-series are in general agreement with the observed vertical structures of the zonal-mean and eddy ENSO signals
obtained from the coarse-resolution MSU data [Yulaeva andWallace, 1994; Calvo Fernández et al., 2004].
[6] Building upon these previous studies, we analyze thefull vertical and spatial structure of the ENSO signal inatmospheric temperatures within the troposphere and lower
stratosphere up to 20 km using Global Positioning System(GPS) radio occultation (RO) data. The unprecedented vertical resolution and global coverage of the RO data provide amore detailed view of the full 3-dimensional ENSO structure
than other observational data sets. Early studies of ENSO
using RO data have been performed in the framework ofclimate change detection studies [Steiner et al., 2011].

2. Data and Methods[7] We use RO temperature and water vapor pressure
profiles from three satellite missions: CHAllenging Mini-

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L20801SCHERLLIN-PIRSCHER ET AL.: THE VERTICAL AND SPATIAL ENSO STRUCTURE

Satellite Payload (CHAMP), Formosa Satellite Mission 3/
Constellation Observing System for Meteorology, Ionosphere, and Climate (FORMOSAT-3/COSMIC, F3C), andGravity Recovery And Climate Experiment (GRACE) (for areview on RO missions and data applications, see Anthes[2011]). We use data for the period August 2006 to
December 2010 when there are more than 40000 profiles ina month. During this period warm ENSO (El Niño) eventsoccurred in 2006/2007 and 2009/2010 and cold ENSO (La
Niña) events in 2007/2008 and at the end of 2010. Neutral
ENSO conditions were observed in 2008/2009. We formed
monthly averages of all profiles which passed the processing
center’s quality control procedures, and gridded them into 5
latitude by 5 longitude boxes and a vertical resolution of
100 m. If no profiles were available for a particular 5  5
grid box, we bi-linearly interpolated the profiles fromneighboring grid-cells to fill the missing data. Due to thestrong confounding effects of the Quasi Biennial Oscillation(QBO) and ENSO in the short RO record, we only use RO
temperature profiles below 20 km.
[8] In humid regions (i.e., in the lower troposphere), a
priori knowledge of the state of the atmosphere is needed toderive temperature and water vapor from RO measurements.
Since lower tropospheric temperature can be significantly
affected by the background data, we focus the interpretationof our results on the upper troposphere and lower stratosphere region. Total column water vapor, which is derived
from RO water vapor pressure profiles, was found to beconsistent with those from ground-based GPS observations
and those from ECMWF [e.g., Ho et al., 2010].
[9] Monthly mean anomalies are obtained from subtracting the mean annual cycle at each grid point. The latter iscalculated from January 2007 to December 2010. The
monthly anomalies were then smoothed with a 3-point
binomial (1-2-1) filter to reduce month-to-month variations.
[10] We apply Principal Component Analysis (PCA) andmultiple linear regression analysis to identify the ENSO
signal. For multiple linear regression, we regress monthly
temperature anomalies against the monthly Niño 3.4 (N3.4)
SST index (the N3.4 region ranges from 5 S to 5 N and120 W to 170 W) provided by the NOAA Climate Prediction Center, and against the first and second principal components (PCs) of Singapore zonal winds [Wallace et al.,1993], which serve as proxies for QBO variability. QBO
regression is important in the lower stratosphere because thesignal penetrates below 20 km. N3.4 and QBO indices arealso smoothed with a 1-2-1 filter prior to the regression
analysis.
[11] Uncertainty estimates for the regression coefficientsare calculated using a bootstrap resampling technique but we
did not account for field significance, which tests the patternitself. This may overestimate significance in regions, wherefeatures are only marginally significant. The statisticalsignificance of the correlations is assessed by applying aStudent’s t-test at the 95% confidence level and accounting
for autocorrelations [see Oort and Yienger, 1996].

3. Results[12] Figure 1 shows results for the equatorial plane basedon averages from 5 S to 5 N. Interannual temperature
variability, given by the standard deviation field, is largest
above the tropopause across all longitudes (>1.5 K), and is

L20801attributable to the QBO. High variability (>1.0 K) also
occurs in the upper troposphere (7 km to 13 km) in thelongitude sector 90 W to the date line. This sector corresponds approximately to the N3.4 region where SST variations associated with ENSO are largest.
[13] The dominant pattern of interannual equatorial temperature variability in the longitude-height plane, given by
the leading empirical orthogonal function (EOF1), exhibits azonally-symmetric structure of warming within the troposphere (maximum values at upper levels) and cooling within
the lower stratosphere (Figure 1b). In addition, there is apronounced zonally-asymmetric signal over the N3.4 longitudinal sector with maximum warming (>1 K) between 8 km
and 12 km and stratospheric cooling that penetrates below thetropopause. This EOF is closely associated with ENSO: thecorrelation coefficient between its corresponding PC timeseries and the N3.4 index is 0.79 at a lag of 3 months.
[14] Given the natural split of EOF1 into zonally symmetric and asymmetric components, we apply EOF analysis
to each component separately following Calvo Fernández
et al. [2004]. EOF1 of the zonal-mean temperature anomaly field shows tropospheric warming and stratosphericcooling, with the nodal line at the altitude of the climatological tropopause (16.5 km) (Figure 1c). This vertical profilereflects the quasi-moist adiabatic behavior of the tropicalatmosphere. On the other hand, EOF1 of the eddy field shows
maximum amplitude over the N3.4 longitudinal sector, withwarming below 14 km and maximum cooling at and directly
above the tropopause (17 km to 18 km; Figure 1d). The
corresponding PC time series are again highly correlated with
the N3.4 index: r = 0.75 for the zonal-mean PC1 at a lag of
3 months and r = 0.93 for the eddy PC1 at zero lag.
[15] Figures 1e and 1f show the temporal evolutions ofzonal-mean temperature anomalies and eddy temperature
anomalies averaged across the N3.4 longitudinal sector,
respectively. Both records reveal positive temperature
anomalies during El Niño conditions and negative anomalies
during La Niña conditions. Above the tropopause, thezonally-symmetric QBO phenomenon dominates the zonalmean field. Consistent with the EOF results, the zonal-meantemperature anomalies lag the eddy anomalies by several
months and exhibit a different vertical structure, with thetransition between positive and negative anomalies occurring near the tropopause in the zonal-mean field and at
approximately 14 km altitude in the eddy field.
[16] We further analyze the interannual temperature variability by applying a separate PCA to each altitude level forboth the zonal-mean and eddy anomaly fields over thelatitude-longitude domain 30 S to 30 N and 0 to 360 E.
The leading EOF at each altitude is associated with ENSO,
as shown by the correlation and regression coefficients
between the corresponding PC time series and the N3.4
index at the lag at which these coefficients are maximized(Figures 1g and 1h, respectively). The correlations are generally positive in the troposphere, where they exceed 0.8,
and negative in the stratosphere where they are less than
0.9 ( 0.6) for the eddy (zonal-mean) component. Due tothe QBO signal, zonal-mean correlations are lower (but still
statistically significant) in the lower stratosphere. Consistent
with the results shown in Figure 1 (top panels), the transition
between positive and negative values is more abrupt andoccurs at a lower altitude (approximately 14 km compared to
17 km) in the eddy field than the zonal-mean field. Due to2 of 6

L20801SCHERLLIN-PIRSCHER ET AL.: THE VERTICAL AND SPATIAL ENSO STRUCTURE

L20801Figure 1. Zonal cross sections from 5 S to 5 N of (a) deseasonalized temperature standard deviation, (b) EOF1 of thetemperature anomaly field, (c) EOF1 of the zonal-mean field, and (d) EOF1 of the eddy field. Temporal evolutions of(e) zonal-mean temperature anomaly between 5 S and 5 N and (f) eddy temperature anomaly calculated in the N3.4
region. In each panel, the lapse-rate tropopause altitude is shown for reference (black curves). Vertical profiles shownin Figures 1g–1i are based on a separate PC analysis over the 30 S to 30 N, 0 to 360 E domain calculated at each
altitude level separately. (g) Correlation between zonal-mean/eddy PC1 and the N3.4 index at the lag at which thesecoefficients are maximized, (h) regression coefficients obtained from regressing zonal-mean/eddy PC1 onto the N3.4
index, and (i) lag (in months) where the maximum correlation (shown in Figure 1g) is obtained. Zonal-mean results are shownin black, eddy results in gray. ENSO lag (Figure 1i) is only plotted if correlations are statistically significant.
the short record, correlations smaller than approximately 0.6
in absolute value are not statistically significant.
[17] The lag at which the correlation/regression coefficientis maximized occurs consistently at approximately 3 months
for PC1 of the zonal-mean field and 0 to 1 month for PC1 ofthe eddy field across a wide range of altitudes (Figure 1i), in
good agreement with the results of Yulaeva and Wallace[1994] and Calvo Fernández et al. [2004]. The 3-monthlag for the zonal-mean field can be attributed to exchange offluxes at the atmosphere-ocean interface and the atmosphericenergy loss to space and to mid latitudes [Su et al., 2005].

The fast response of the eddy ENSO signal is related to local
heating induced by positive SST anomalies. Vertically
propagating Kelvin waves induce a rapid response of thetropical troposphere to an anomalous diabatic heat source[Ryu et al., 2008].
[18] Regression maps of the zonal-mean and eddy temperature anomaly fields in the lower stratosphere (16 km to
17 km) and upper troposphere (9 km to 10 km) upon theN3.4 index are shown in Figures 2a and 2b, respectively. We
used a lag of 3 months for the zonal-mean field and zero lag
for the eddy field, using multiple linear regression analysis

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L20801SCHERLLIN-PIRSCHER ET AL.: THE VERTICAL AND SPATIAL ENSO STRUCTURE

L20801Figure 2. ENSO regression coefficients of the (left) zonal-mean and (right) eddy temperature fields in the altitude layerfrom (a) 16 km to 17 km and (b) 9 km to 10 km. (c) ENSO regression coefficients of total column water vapor fields. Solidblack lines enclose areas of statistically significant regressions.
that includes the two QBO terms. Similar results are
obtained using the zonal-mean/eddy PC1 time-series inplace of the N3.4 index with appropriate lag (not shown). Asa measure of diabatic forcing of these temperature patterns,
we also show the corresponding regression maps of total
column water vapor in Figure 2c.
[19] The zonal-mean regression maps (Figure 2, left) showa narrow equatorial peak in total column water vapor, whichis accompanied by a broad tropical (30 S to 30 N) temperature maximum in the upper troposphere and lower stratosphere. Negative temperature anomalies are also evident
over southern high latitudes (50 S to 80 S) at both altitudelevels, although only the values at 9 km to 10 km are statistically significant. An additional temperature maximum isfound over northern middle latitudes (30 N to 60 N) in thelower stratosphere.
[20] The eddy regression maps feature a narrow equatorial
maximum over the central Pacific surrounded by negativevalues over the maritime continent, the South Pacific Convergence Zone (SPCZ), and the subtropical North Pacific.
Additional signals are evident over the tropical Atlantic andeastern Pacific. The corresponding temperature pattern at
low latitudes is symmetric about the equator and shows aneast-west Indo-Pacific dipole centered slightly west of thedate line, with off-equatorial maxima centered around 20 to
30 in both hemispheres. This dipole reverses polarity
between the tropical middle troposphere (9 km to 10 km
layer) and tropical upper troposphere (16 km to 17 km

layer). This pattern has also been observed by Yulaeva andWallace [1994], Lau et al. [1998], Randel et al. [2000],
and Calvo Fernández et al. [2004]. Idealized model calculations suggest that such a pattern is associated with Rossby
and Kelvin circulations induced by equatorial heating
anomalies [Gill, 1980; Highwood and Hoskins, 1998].
[21] The latitude-height cross section of the zonal-meantemperature regression coefficients (Figure 3a) shows several statistically significant features including: warming
throughout the tropical troposphere with maximum amplitudes above 8 km; warming of the northern high latitude
troposphere with maximum values at 70 N to 80 N; coolingin the lower stratosphere (10 km to 17 km) at southern highlatitudes (50 S to 85 S), some of which penetrates down into
the troposphere; and a stratospheric dipole pattern of negative anomalies in the deep tropics (10 N to 10 S), and positive anomalies at northern middle latitudes (30 N to 60 N)
with maximum amplitude near the upper boundary of ourdomain (20 km). With exception of the southern high latitude cooling and the strength of the tropospheric northernhigh latitude warming, these features have also been foundin other studies, e.g., by Seager et al. [2003], Lu et al.
[2008], Free and Seidel [2009], Randel et al. [2009], andCalvo et al. [2010]. The southern high latitude signal justmight be representative for the period from 2006 to 2010.
[22] Meridional cross sections of the eddy temperature
regression coefficients over the maritime continent (110 E
to 130 E) and central Pacific (140 W to 160 W) reveal

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L20801over the central Pacific. Similar features with opposite sign
and weaker amplitude are found over the maritime continent.
Using a linearized Boussinesq model, Holloway and Neelin[2007] show that a shallow layer of cooling occurs close to
the tropopause in conjunction with tropical troposphericheating. They suggest that this cooling layer results fromhydrostatic horizontal pressure gradients that extend above
the top of the convective heating and cause divergence andadiabatic ascent.

4. Summary

Figure 3. Meridional cross sections of ENSO regression
coefficients of (a) zonal-mean temperature, eddy temperature centered at (b) 120 E, and (c) 150 W. Thin solid blacklines enclose areas of statistically significant regressions.
Thick black lines denote the mean lapse-rate tropopause
altitude.
similar structures with opposite polarity (Figures 3b and 3c).
Taking the central Pacific perspective, off-equatorial (10 to
20 ) maxima are coupled with mid-latitude (30 to 50 )minima within the troposphere of both hemispheres. Thesesignals are generally weaker over the maritime continentcompared to the central Pacific, especially within the lower
troposphere. In addition to these tropospheric responses,
negative (positive) temperature anomalies centered at thetropopause are found within the tropics (middle latitudes)[23] We have used radio occultation (RO) data to investigate the vertical and spatial structure of global atmospherictemperature anomalies associated with ENSO in the troposphere and lower stratosphere. Despite the relatively shortrecord, we were able to demonstrate that the RO data clearly
capture the ENSO signal.
[24] Interannual temperature anomalies in the equatorial
region show a natural decomposition into zonal-mean andeddy components, both of which are related to ENSO. While
the zonal-mean component lags sea surface temperature(SST) anomalies in the eastern equatorial Pacific (N3.4
region) by 3 months, the eddy component responds rapidly(within 1 month). These results are consistent with those ofprevious studies and known mechanisms, as discussed
above.
[25] The unprecedented vertical resolution of the RO data
provided a detailed view of the full 3-dimensional ENSO
structure. During the warm phase of ENSO, zonal-meantemperatures increase in the tropical troposphere anddecrease in the tropical stratosphere. High-resolution RO
data clearly show that maximum warming occurs above
8 km, and that the transition between warming and cooling
occurs near the tropopause.
[26] The atmospheric eddy ENSO signal features an IndoPacific dipole at low latitudes, with off-equatorial maxima
centered around 20 to 30 latitude in both hemispheres. Thispattern has also been found in previous studies and is consistent with Rossby and Kelvin wave circulations induced byequatorial heating anomalies [Gill, 1980; Highwood andHoskins, 1998]. However, the high-resolution RO data
reveal the detailed vertical structure of this pattern, which
attains maximum amplitude in the upper troposphere near
11 km and (with opposite polarity) in a shallow layer near the
tropopause at approximately 17 km. The shallow response
near the tropopause has been shown to result from hydrostatic horizontal pressure gradients associated with deep
convective heating as modeled by Holloway and Neelin[2007]. At mid latitudes, eddy ENSO signals are out-ofphase with those at low latitudes.
[27] So far, a sufficient number of RO measurements isavailable only for a few ENSO cycles. A follow-on multisatellite RO mission, currently in the planning stage, will
provide a longer RO record, which will enable a better separation of the ENSO and QBO signals in the lower stratosphere, and allow for a more robust assessment of thedetailed vertical and spatial structures of interannual temperature variability.
[28] Acknowledgments. We would like to acknowledge UCAR/CDAAC, Boulder, CO, USA, for the provision of RO data products. Furthermore, we want to thank CPC for providing the N3.4 index, and Barbara
Naujokat for Singapore zonal winds. The study was funded by the Austrian

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Science Fund (FWF) under research grant P22293-N21 (BENCHCLIM)and the National Science Foundation under Cooperative Agreement AGS0918398/CSA AGS-0939962. The National Center for AtmosphericResearch is sponsored by the National Science Foundation.
[29] The Editor thanks the two anonymous reviewers for assisting in theevaluation of this paper.

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