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El Niño-Southern Oscillation in Tropical Column Ozone and A
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3.5-year signal in Mid-Latitude Column Ozone
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Jingqian Wang,1* Steven Pawson,2 Baijun Tian,3 Maochang Liang,4 Run-Lie Shia,5
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Yuk L. Yung,5 and Xun Jiang1
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1 Department of Earth and Atmospheric Sciences, University of Houston, TX 77204,
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USA.
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2 Global Modeling and Assimilation Office, NASA GSFC, Code 610.1, Greenbelt, MD
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20771, USA.
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3 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr.,
Pasadena, CA 91109, USA.
4 Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.
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5 Division of Geological and Planetary Sciences, California Institute of Technology,
1200 East California Boulevard, Pasadena, CA 91125, USA.
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* To whom all correspondence should be addressed. E-mail: jwang3@mail.uh.edu
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To be submitted to JGR, Jan 27, 2010
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Abstract
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[1] Impact of El Niño-Southern Oscillation (ENSO) on the tropical column ozone,
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tropical tropopause pressure, and the 3.5-year ozone signal [first identified by Jiang et al.
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2008] in the mid-latitude column ozone are examined by Version 1 of the Goddard Earth
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Observation System Chemistry-Climate Model (GEOS CCM) [Pawson et al., 2008].
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Observed monthly-mean sea surface temperature and sea ice between 1951 and 2004
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were used as boundary conditions for the model. ENSO signal is the dominant mode in
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the tropical column ozone variability in the GEOS CCM, capturing 65.8% of the total
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variance. The spatial pattern of this mode is similar to that in Total Ozone Mapping
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Spectrometer (TOMS) observations between Nov 1978 and Apr 1993. However, there
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are some discrepancies in ozone spatial pattern in the southern hemisphere between the
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GEOS CCM and TOMS. There is also a clear ENSO signal in the tropical tropopause
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pressure in the GEOS CCM, which may be the cause of the ENSO signal in the column
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ozone in the GEOS CCM. The regression coefficient between the model O3 and the
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model tropopause pressure is 0.78 DU/hPa.
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[2] GEOS CCM is also used in this paper to investigate possible mechanism for the 3.5-
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year signal in the mid-latitude column ozone. Since the 3.5-year signal also appears in the
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GEOS CCM column ozone as those in the ozonesondes, it suggests that a model with
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realistic ENSO can reproduce the 3.5-year signal. Hence, it is likely that the 3.5-year
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signal is from the ENSO signal.
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1. Introduction
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[3] The influence of El Niño-Southern Oscillation (ENSO) on the sea surface
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temperature (SST), surface pressure, winds, and convection has been well known [see,
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e.g., Trenberth and Shea, 1987; Trenberth, 1997]. ENSO can also affect the column
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ozone abundance. Bojkov [1987] first found low column ozone anomalies in the middle
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and high latitudes within a few months after El Niño episodes. Shiotani [1992] studied
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the asymmetric ENSO signal in the equatorial column ozone by removing the zonal
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means. Kayano [1997] carried out an empirical orthogonal function (EOF) study of the
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total ozone between 70º N and 70º S and found that the first two modes are related to
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ENSO. Zerefos [1992] found a correlation between the total column ozone and the ENSO
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in the tropical troposphere. The decreases in total ozone in middle and sometimes polar
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latitude follow the ENSO by several months. It has been proposed that the warming in the
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tropics over the Pacific Ocean causes increases in the upper troposphere air temperature
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and tropopause height and an upwelling in the lower stratosphere [Kalicharran, 1993].
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More recently, Camp et al. [2003] used the column ozone observed by Total Ozone
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Mapping Spectrometer (TOMS) and revealed that there is an ENSO signal in the fourth
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mode of the column ozone in the tropical region. During the El Niño periods, there are
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negative column ozone anomalies in the eastern tropical Pacific and positive column
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ozone anomalies in the western Pacific. The influence of ENSO on column ozone can
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mostly be explained by the variation of tropopause height driven by changes of tropical
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deep convection [Schubert and Munteanu, 1988; Shiotani, 1992; Hasebe, 1993]. Such a
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causal relationship between column O3 and the tropopause height is also evident in the
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Madden-Julian Oscillation, as recently pointed out by Tian et al. [2007]. However, a
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quantitative demonstration of the impact of ENSO on column O3 and tropopause height
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has not been carried out using model simulations.
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[4] In this paper, we will focus on the ENSO signal in the tropopause pressure and
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column ozone simulated by Version 1 of the Goddard Earth Observation System
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Chemistry-Climate Model (CCM) [Pawson et al., 2008], and try to investigate the
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mechanism for the mid-latitude column ozone 3.5-year signal. The 3.5-year signal is first
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found in Arosa ozonesonde. The same signal is also found in the first leading mode of
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column ozone in the northern hemisphere [Jiang et al., 2008]. However, mechanism for
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the 3.5-year signal in ozone is not known, which will be explored in this paper.
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2. GEOS-4 Chemistry-climate Model and Data
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[5] The GEOS CCM (Version 1) [Stolarski et al., 2006; Pawson et al., 2008] combines
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the GEOS-4 general circulation model [Bloom et al., 2005] with a stratospheric chemistry
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model [Douglass et al., 2003; Stolarski et al., 2006]. Interaction between atmospheric
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composition and the circulation is through use of predicted O3, H2O, CO2, CH4, N2O,
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CFC-11, and CFC-12 in the radiative heating computation. The model run analyzed in
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this study, which is the run P1 of Pawson et al. [2008], spanned 54 years (1951-2004) at
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2º 2.5º (latitude by longitude) with 55 layers between the surface and about 80km. At
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the lower boundary, monthly mean sea surface temperature and sea ice are prescribed
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from observations [Rayner et al., 2003], along with time-dependent emissions of
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greenhouse gases and chloroflorocarbons, as described in Eyring et al. [2006]. Neither
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the solar cycle nor volcanic aerosol variations were included in the model and it does not
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capture a QBO [see Horinouchi et al., 2003], so the signals of these sources in ozone
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variability are all absent from the simulation.
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[6] Isolation of the ENSO signal in the GEOS CCM will be performed using gridded
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model output on a 2 latitude by 2.5 longitude grid. Two simulated fields will be
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examined: the tropopause pressure and the column ozone. The tropopause pressure, PT,
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was determined from the simulations using the lapse-rate definition of tropopause.
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Column ozone, ΩS, is the integrated profile of the total amount of ozone in atmosphere. A
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corresponding analysis of the observed SSTs between 1951 and 2004 will also be
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performed.
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[7] Ozonesonde data, which have relatively long data record, will be investigated in this
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paper. (Data can be downloaded from http://www.woudc.org/data_e.html). Twenty-one
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stations with long records will be used in this paper. These ozonesonde data will be
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compared with model simulation from GEOS CCM.
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3. Methods
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[8] The following filtering and analysis techniques were applied to three time series of
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gridded, monthly mean data. No spatial smoothing was performed. First, the mean annual
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cycle was computed by independently evaluating the 54-year mean value for each month.
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This mean annual cycle and the linear trend were subtracted from the original time series
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to create the detrended monthly anomalies for each month. Second, a spectral filter was
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applied to the monthly anomalies, to isolate the interannual variability (IAV) from higher
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frequency oscillations. The filter was constructed as the convolution of a step function
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with a Hanning window and chosen to obtain a full signal from periods above 15 months
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and no signal from periods below 12.5 months.
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[9] Finally, principal component analysis (PCA) [Preisendorfer, 1988] was performed on
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the filtered tropical (25 ºS to 25 ºN) anomalies of SST, ΩS, and PT. PCA provides a
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decomposition of a multivariate dataset into orthogonal spatial patterns, known as
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empirical orthogonal functions (EOF), with associated time-dependent amplitude, known
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as principal component time series (PC). The EOFs are the eigenfunctions of the
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covariance matrix of the dataset sorted by the decreasing values of associated
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eigenvalues. Since these eigenvalues represent the variance captured by each EOF, PCA
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guarantees that the leading EOFs capture most of the variance of the dataset.
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[10] For the observation data (ozonesonde data), We first remove the seasonal cycle
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from the data then remove the trend. Power spectral analysis is applied to deseasonalized
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and detrended ozonesondes. Spectral analysis will also be applied to the deseasonalized
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model O3 simulations. To obtain the statistical significance of signals in a power
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spectrum, we will compare the amplitude of a spectral peak to the red noise spectrum
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[Gilman et al., 1963]. The 15%, 10%, and 5% significance levels for the power spectrum
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are found using F-statistics to compare the spectrum to the red noise spectrum.
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4. ENSO Signals and 3.5-year Column Ozone Signal
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4.1 ENSO Signals
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[11] Principal component analysis (PCA) [Preisendorfer, 1988] was performed on the
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filtered tropical (25 ºS to 25 ºN) anomalies of SST, ΩS, and PT. The first modes of
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variability determined by the PCA capture 46.9%, 33.9%, and 63.8% of the total variance
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in SST, PT, and ΩS, respectively (see Table 1). Evidence is presented that the first modes
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of variability captured by the PCA are related to ENSO. The Southern Oscillation Index
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(the pressure difference between Tahiti and Darwin) is used to establish this
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correspondence. Visual comparison of time series of the SOI and the amplitude of the
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first mode (PC1) of the SST (Fig 1) reveals a good correspondence, with in-phase
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variations. Note that negative/positive SOI values denote the El Niño/La Niña events.
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The correlation between the two time series is 0.88 and is significant at the 0.1% level.
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The significance level was generated by a Monte Carlo method [Press et al., 1992]: a
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small numerical value denotes high statistical confidence. Time series of PC1 for P T and
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ΩS reveal a similar low-frequency variability as for SOI, with some higher-frequency
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variations superimposed. This leads to somewhat lower correlations (0.53 and 0.65)
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between these time series and the SOI, but these remain highly significant (see Table 1).
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The correlation between PC1s of PT and ΩS is 0.82. Power spectra of the PC1s of SST, PT
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and ΩS (Fig. 2) all reveal strong spectral peaks near 3-7 years, which are typical of ENSO
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variations. The higher frequency variations evident in PT and ΩS also lead to prominent
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peaks near 17 months, which might be a beat frequency between annual cycle and ENSO
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signal.
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[12] Figure 3 shows the spatial patterns of the first modes of the PCA analysis for the
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three fields, which describe their ENSO-related variability. For SST, the spatial pattern
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(Fig. 3a) is similar to the first mode of ENSO-related variability described in Trenberth et
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al. [2002], which corresponds to the SST anomalies in the Niño-3.4 region. Negative
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amplitudes of this mode correspond to the El Niño events, with warm SST anomalies in
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the central and eastern Pacific and cold anomalies in the western Pacific. During El Niño
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periods, the convection will move eastward to the central Pacific and impact the
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longitudinal structure of the tropopause [e.g., Gage and Reid, 1987]. The spatial structure
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of the first mode of PT (Fig. 3b) is consistent with this behavior. For the negative
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amplitudes of this mode, corresponding to El Niño events, there are large negative
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tropopause pressure anomalies (higher tropopause height) in the central and eastern
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Pacific and weak positive tropopause pressure anomalies (lower tropopause heights) in
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the equatorial western Pacific. Please note the tropopause pressure anomalies are
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relatively small over the equatorial regions. On the other hand, the large tropopause
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pressure anomalies are found over the subtropics, which is the Rossby-wave response of
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the equatorial ENSO convection anomaly [Trenberth et al., 1998]. In the positive
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amplitude of this mode, that is, La Niña events, there are weak negative tropopause
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pressure anomalies (higher tropopause heights) in the equatorial western Pacific and large
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positive tropopause pressure anomalies (lower tropopause heights) in the central and
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eastern Pacific. The present results are in general agreement with previous studies [e.g.,
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Kiladis et al., 2001; Gettelman et al. 2001] on the tropopause pressure and height
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variability associated with the ENSO based on reanalysis data. This indicates that the
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vertical movement of the tropopause associated with the ENSO is reasonably captured by
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the GEOS CCM.
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[13] One important aspect of the spatial pattern of the PT anomaly is that it has the same
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sign everywhere except for a confined region centered over the Equator to the North of
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Indonesia (Fig. 3b). This contrasts with the existence of positive and negative anomalies
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in the SST pattern. This means that almost the entire tropical tropopause is displaced
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upwards (to lower pressure) in El Niño events, with a discernable signal in the zonal
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mean. A similar result is true for the ΩS pattern (Fig. 3c), in that substantial zonal
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anomalies are superimposed on a monotonic signal. Note that the ENSO signal is the
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dominant pattern of variability in ΩS because irradiance changes associated with the solar
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cycle were not imposed in the simulation and because this version of GEOS CCM does
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not simulate a QBO. The pattern is very similar to the ENSO spatial pattern in the TOMS
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column ozone data in Camp et al. [2003], although there are some discrepancies in the
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southern hemisphere. The spatial pattern is also consistent with the second mode (mature
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stages of the El Niño) for column ozone from Kayano et al. [1997]. There is a double-
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ridged symmetric structure with ridges at 12º N and 15º S. The value is relatively low in
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the western Pacific and relatively high in the eastern Pacific.
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[14] A broadly consistent picture arises of the leading-order anomalies, with (in El Niño
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events) an eastward migration of warm SST anomalies that enhance convection over the
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eastern Pacific where the tropopause rises and ΩS decreases. This decrease in column
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ozone is consistent with the reduction in depth of the stratophere. It is also consistent
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with a faster vertical flow through the ozone source region in the tropical stratosphere,
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which tends to reduce the ozone column.
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[15] We also regress the first mode of the column ozone on the first mode of the
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tropopause pressure. The mean value for the first mode of the column ozone in the Niño-
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3.4 region is plotted against the mean value for the first mode of the tropopause pressure
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in Fig. 4a. These two time series correlate well. The correlation coefficient is 0.82. The
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scatter plot for the two time series is shown in Fig. 4b. The regression coefficient
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between the O3 and tropopause pressure is 0.71 DU/hPa. Assuming that the tropopause
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height is 10.5 km and scale height is 7 km, the regression coefficient is about -15.5
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DU/km, which is consistent with the previous results varying from -10 to -25 DU/km
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[Meetham, 1937; Reed, 1950; Hoinka et al., 1996; Steinbrecht et al., 1998].
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4.2 3.5-year Ozone signal
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[16] There were 21 observation stations that have long time record, but only eight
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stations have significant 3.5-year signal. These eight stations are Potsdam, Belsk, Hradec,
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Hohenpeissenberg, Arosa, Toronto, Sappor, Nashiville (see Table 2). We layout the
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observation stations and their positions, and found the signal has no preference for the
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spatial distributions. Power spectra and statistical significances of the peaks are shown in
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Figure 5 for the eight stations. Dotted lines are the mean red-noise spectra. Dash-dot lines
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and dashed lines correspond to 10% and 5% significance levels, respectively. In Fig. 5a,
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there are about five significant spectral peaks (10% significance level) in the low-
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frequency regions: residual annual cycle, QBO-Annual beat, QBO, 3.5-year signal, and
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decadal oscillation. Since the mechanisms for the QBO-Annual beat, QBO, and decadal
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oscillation are well known, we will only focus on the 3.5-year signal in this paper.
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Among eight stations, there are significant peaks between 3-4 years. The power spectra
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of Fig. 5a-Potsdam, d-Hohenpeissenberg, and g-Sappor are within 10% to 5%
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significance levels. The 3.5-year signals in the other five stations, Fig. 5b-Belsk, c-
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Hradec, e- Arosa, f-Toronto, h-Nashiville, are within 5% significance level.
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[17] In order to reveal the mechanism for the 3.5-year cycle and explain the results from
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the above, we investigated the signal in GEOS CCM. GEOS CCM is forced by realistic
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sea surface temperature and sea ice. The ENSO signal is well captured at the model
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surface. There is no QBO and solar cycle in the model. We applied the same spectral
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analysis to the model output. Results for model column ozone are shown in Figure 6.
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There are significant 3.5-year signals in all stations. The significance levels are within
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10%-5% for column ozone at Potsdam, Belsk, Hradec, Hohenpeissenberg, Arosa, and
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Sapporo (Figs. 6a-6e and Fig. 6g). At Toronto and Nashville (Fig. 6f and Fig. 6h), the
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significance levels are within 5% for column ozone. It is obvious that the 3.5-year signal
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could also be found in the model column ozone and is significant. Since the model only
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has realistic interannual variability from ENSO and no other interannual variability is
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available, it suggests that the 3.5-year signal in ozone might origin from ENSO signal
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from surface.
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5. Conclusion
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[18] In this paper, we have studied the ENSO signal in the tropical column ozone and
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tropical tropopause pressure in the GEOS CCM from Jan 1951 to Dec 2004. The first
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modes in the model column ozone and tropopause pressure capture 63.8% and 33.9% of
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the total variances, respectively. They are related to ENSO. PC1 for the first modes in the
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model column ozone and tropopause pressure correlate well with the SOI index and
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leading PC time series for the SST. The spatial pattern of the first mode for the model
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column ozone is similar to the ENSO signal in the TOMS column ozone, although there
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are some discrepancies for the ozone in the southern hemisphere. The regression
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coefficient between the model O3 and the model tropopause pressure is consistent with
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observations. It can be improved in the future with the inclusion of tropospheric
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chemistry in the chemical module of the GEOS CCM.
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[19] In the middle latitude, we apply spectral analysis to the observed monthly mean
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ozonesonde data and find the 3.5-year signal in eight stations. The 3.5-year signal is also
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found in the GEOS CCM model ozone. Since GEOS-CCM only has realistic ENSO and
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there is no QBO and solar cycle signals in the model, it suggests that the 3.5-year signal
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in column ozone is related to the ENSO signal. The linkage between 3.5-year ozone
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signal and surface ENSO needs to be investigated in more details in the future.
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[20] Acknowledgments. We specially acknowledge Dr. Rappenglueck and Dr. Lefer,
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who gave great commends and help in this research.
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392
393
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394
Table 1: Variances, spectral peaks, and correlations (Lag = 0) for the first modes of the
395
sea surface temperature, tropopause pressure, and column ozone. The numbers in
396
parentheses denote significance levels.
397
Mode 1
Variance
Spectral
Correlations (significance level) with:
of
captured
Peaks
SOI
SST (PC1)
PT (PC1)
SST
46.9%
4-5 years
0.88 (0.1%)


PT
33.9%
17 months
0.65 (0.1%)
0.72 (0.1%)

0.53 (0.1%)
0.57 (0.1%)
0.82
5 years
ΩS
63.8%
17 months
2-5 years
398
399
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400
Table 2. The observation stations that have 3.5-year column ozone signal.
Stations
Latitude
Longitude
Potsdam
52.36
13.08
Belsk
51.84
20.79
Hradec
50.18
5.83
Hohenpeissenberg
47.8
11.02
Arosa
46.7
9.68
Toronto
43.78
-79.47
Sapporo
43.05
141.33
Nashville
36.25
86.57
401
402
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403
404
Figure 1: PC1 time series for the first modes of sea surface temperature (black solid line),
405
tropopause pressure (blue dotted line), and column ozone (red dashed line). SOI index is
406
shown by green dash-dotted line.
407
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408
409
Figure 2: (a) Power spectrum of PC1 for the first mode of SST from GEOS-4 CCM. (b)
410
Power spectrum of PC1 for the first mode of tropopause pressure from GEOS-4 CCM.
411
(c) Power spectrum of PC1 for the first mode of column ozone from GEOS-4 CCM.
412
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413
414
415
416
417
418
419
Figure 3: (a) The spatial pattern of the first mode for sea surface temperature in the
tropics. Units are K. The first mode explains 46.9% of the total variance. (b) The spatial
pattern of the first mode for tropopause pressure from GEOS-4 CCM in the tropics. Units
are Pa. The first mode explains 33.9% of the total variance. (c) The spatial pattern of the
first mode for column ozone from GEOS-4 CCM in the tropics. Units are DU. The first
mode explains 65.8% of the total variance.
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420
421
422
423
424
425
Figure 4: (a) First mode of column O3 averaged in Niño-3.4 region (solid line) and first
mode of tropopause pressure in Niño-3.4 region (dotted line). (b) Scatter plot of O3
against the tropopause pressure. Solid line is the linear fitting of the dots.
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426
427
428
Figure 5: Power spectra of ozonesonde data: (a) Potsdam, (b) Belsk, (c) Hradec, (d)
Hohenpeissenberg, (e) Arosa, (f) Toronto, (g) Sapporo, (h) Nashville.
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429
430
431
432
Figure 6: Power spectra of model column ozone data simulated by GEOS CCM sampled
in the same locations as those in Figure 5.
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