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The Influence of Tropospheric Biennial Oscillation on Mid-tropospheric CO2
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Jingqian Wang,1* Xun Jiang1, Moustafa T. Chahine2, Mao-Chang Liang,3,4, Edward T.
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Olsen2, Luke L. Chen2, Stephen Licata2, Thomas Pagano2, and Yuk L. Yung5
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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 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr.,
Pasadena, CA 91109, USA.
3 Research Center for Environmental Changes, Academia Sinica, Taipei, Taiwan.
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4 Graduate Institute of Astronomy, National Central University, Jhongli, 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 GRL, May 15, 2011
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Abstract:
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[1] We used the mid-tropospheric CO2 retrieved from the Atmospheric Infrared Sounder
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(AIRS) to investigate its interannual variability over the Indo-Pacific region. A signal
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with periodicity around two years was found in the power spectrum of AIRS mid-
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tropospheric CO2 timeseries over the Indo-Pacific region, which might be related to the
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Tropospheric Biennial Oscillation (TBO) associated with the strength of the monsoon.
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During a strong monsoon year, the Western Walker Circulation is strong, resulting in
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enhanced CO2 transport from the surface to the mid-troposphere. As a result, there are
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positive CO2 anomalies at mid-troposphere over the Indo-Pacific region. During a weak
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monsoon year, the Western Walker Circulation is weak. Thus, less CO2 will be
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transported to the mid-troposphere. The MOZART-2 model is used to simulate the
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influence of the TBO on the mid-tropospheric CO2 over the Indo-Pacific region. Model
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results are consistent with the observations, although the magnitude is smaller in the
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model than that in the AIRS observations.
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1. Introduction
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a positive trend ~2 ppm/year, which is mainly due to the fossil fuel emission [Keeling et
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al., 1995; IPCC, 2007]. In addition to fossil fuel emission, biomass burning, exchange
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between biosphere and atmosphere, and ocean, variations of the atmospheric circulation
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can also influence the mid-tropospheric CO2 concentration [see, e.g., Jiang et al., 2010;
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Li et al., 2010]. Over Indo-Pacific region, Tropospheric Biennial Oscillation (TBO) is
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one of the climate systems that influences atmospheric circulation. TBO is defined as a
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tendency for a relatively strong monsoon to be followed by a relatively weak one over
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India and Australia [Mooley and Parthasarathy, 1984; Yasunari and Suppiah, 1988;
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Yasunari, 1990, 1991; Tian and Yasunari, 1992; Shen and Lau, 1995; Webster et al.,
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1999]. TBO occurs in the season prior to the monsoon and involve coupled land–
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atmosphere–ocean processes over a large area of the Indo-Pacific region [Meehl, 1997].
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Observations showed that the signals of the TBO appear not only in the Indian-Australian
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rainfall records, but also in the tropospheric circulation, sea surface temperature (SST),
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and upper-ocean thermal fields [Yasunari, 1991; Ropelewski et al., 1992; Lau and Yang,
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1996; Chang and Li, 2001]. TBO is an important component of the tropical ocean–
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atmosphere interaction system, which is separated from the El Niño–Southern Oscillation
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[Chang and Li, 2000]. From the theory of Chang and Li [2000], the warming in the
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western Pacific induces not only a strong monsoon year but also a stronger Western
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Walker Cell and thus a surface westerly anomaly over the Indian Ocean. This westerly
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anomaly helps the cold sea surface temperature anomalies (SSTA) to persist through the
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succeeding seasons, leading to a weaker Asian monsoon and weaker Western Walker
[2] Carbon dioxide, one of the most important greenhouse gases in the atmosphere, has
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Cell in the following summer. The Western Walker Cell blows from the Indian Ocean to
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the western Pacific and creates a convergence area with the Easter Walker Cell at the
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Indo-Pacific region [Meehl and Arblaster, 2002]. The SSTA resemble the El Niño–La
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Niña conditions [Chang and Li, 2000]. El Niño can influence atmospheric CO2 in the
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mid-troposphere as a result of a change in the circulation [Jiang et al., 2010]. TBO is
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expected to influence the atmospheric CO2 in the mid-troposphere as well. In this paper,
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we used AIRS mid-tropospheric CO2 data, surface CO2 data, and a chemistry-transport
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model to investigate the influence of TBO on the mid-tropospheric CO2 over the Indo-
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Pacific region. Details on data and model are introduced in Section 2. Results and
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discussions follow in Section 3.
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2. Data and Model
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2.1. Data
[3] In this paper, we used mid-tropospheric CO2 retrievals from Atmospheric Infrared
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Sounder (AIRS) to investigate the influence of TBO on the mid-tropospheric CO2.
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Mixing ratios of AIRS mid-tropospheric CO2 are retrieved using the Vanish Partial
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Derivative Method (VPD) [Chahine et al., 2005; Chahine et al., 2008]. The maximum
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sensitivity of AIRS mid-tropospheric CO2 is between 500 hPa to 300 hPa. AIRS Version
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2 CO2 retrievals are available globally over land and ocean, day and night. Time period
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of AIRS CO2 is from September 2002 to February 2011. In this paper, we regridded
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AIRS mid-tropospheric CO2 to 2º 10º from CO2 cluster retrievals (latitude by
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longitude). Olsen et al. [2011] found that the AIRS CO2 accuracy is within 2 ppm under
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clear and cloudy conditions and over both land and ocean between 30°S and 80°N after
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comparing AIRS mid-tropospheric CO2 with in-situ aircraft measurements.
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[4] Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Climatology
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Project (GPCP V 2.1) precipitation data were used in this paper to construct the Indian
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monsoon rainfall index. TRMM precipitation data are available at 0.25º 0.25º (latitude
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by longitude) from 50ºS to 50ºN from 1998 to 2010. TRMM calibrated precipitation data
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combine precipitation estimates from different instruments (TMI, AMSR-E, SSM/I,
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AMSU-B) [Huffman et al., 2007]. GPCP Version 2.1 precipitation data are obtained by
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merging infrared and microwave satellite estimates of precipitation with rain gauge data
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from more than 6,000 stations [Huffman et al., 2009]. GPCP global monthly mean
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precipitation data are from 1979 to 2009 with spatial resolution 2.5 2.5 (latitude by
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longitude). Precipitation data in the monsoon season (June to September, JJAS) were
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used to calculate the Indian monsoon rainfall index (area mean values of JJAS rainfall in
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5N ~ 40N, 60E ~ 100E; Meehl and Arblaster, 2002), which could be used to
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determine monsoon strengths in different years [Meehl and Arblaster, 2002]. A relatively
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strong monsoon is defined when the precipitation (Pi) is higher than the nearby two years
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(Pi-1 < Pi > Pi+1). A relatively weak monsoon is defined when the precipitation is lower
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than the nearby two years (Pi-1 > Pi < Pi+1).
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2.2. Model
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[5] We used a three-dimensional (3-D) chemistry and transport model, Model of Ozone
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and Related Chemical Tracers version 2 (MOZART-2), to investigate the TBO signal in
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the mid-tropospheric CO2. This model is a global 3-D chemical transport model, which
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was developed jointly at the National Center of Atmospheric Research (NCAR) in
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Boulder, Colorado, the General Fluid Dynamics Laboratory (GFDL) in Princeton, New
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Jersey, and the MPI-Met in Hamburg. It is designed to simulate chemical and transport
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processes. It can be driven by many kinds of standard meteorological field outputs, such
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as the National Centers for Environmental Prediction (NCEP), European Centre for
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Medium Range Weather Forecasts (ECMWF), or Global Modeling and Assimilation
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Office (GMAO). In this paper, we used ECMWF-Interim meteorological data to drive the
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model. The horizontal resolution is 2.8° (latitude)  2.8° (longitude). There are 45
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vertical levels extending up to approximately 50 km altitude [Horowitz et al. 2003].
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MOZART-2 is built on the framework of the Model of Atmospheric Transport and
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Chemistry (MATCH). MATCH includes representations of advection, convective
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transport, boundary layer mixing, and wet and dry deposition. The surface boundary
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condition for the MOZART-2 is the climatological CO2 surface fluxes from biomass
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burning, fossil fuel emission, ocean, and biosphere used in Jiang et al. [2008a].
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3. Results and Discussion
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[6] To investigate the variability of the mid-tropospheric CO2 over the Indo-Pacific
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region, we calculated the deseasonalized and detrended AIRS mid-tropospheric CO2 over
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5S-20N, 100E-150E. Result was shown in Figure 1a. Seasonal cycle was removed by
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subtracting monthly mean CO2 from the data. Then we removed a linear trend from the
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deseasonalized CO2. The power spectrum of the deseasonalized and detrended CO2 is
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shown in Figure 1b. In addition to the high frequency signals, there is also a signal
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around two years in the power spectrum, which is within 5% significance level. The two-
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year signal in the deseasonalized and detrended mid-tropospheric CO2 may be related to
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the TBO. The statistical significance of signals in the power spectrum was obtained by
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comparing the amplitude of a spectral peak to the mean red noise spectrum [Gilman et
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al., 1963; Jiang et al., 2008b].
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[7] To better investigate the possible relation between the TBO and the mid-
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tropospheric CO2, we calculated AIRS mid-tropospheric CO2 during monsoon season
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(JJAS), and compared it with the Indian monsoon rainfall index derived from TRMM
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precipitation in JJAS. Results are shown in Figure 2. Correlation coefficient between the
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two time series is 0.58 (4% significance level). The in-phase variations show that there is
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more CO2 in the mid-troposphere during the strong monsoon years (2003, 2005, 2007,
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and 2010), and less CO2 during the weak monsoon years (2004, 2006, and 2008). The
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transport of CO2 into the mid-troposphere over the Indo-Pacific region is determined by
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the strength of the upwelling. During a strong monsoon year, the Western Walker Cell is
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strong [Chang and Li, 2000], resulting in enhanced CO2 transportation from the surface
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to the mid-troposphere due to the strong upwelling. During a weak monsoon year, less
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CO2 is transported to the mid-troposphere. Results shown in Figure 2 are not
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contaminated by influence from El Niño or La Niña, for the El Niño and La Niña occur in
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the winter seasons of 2005, 2008, and 2010, which don’t overlap with Monsoon seasons
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(JJAS).
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[8] We chose the strong and weak monsoon years to investigate the spatial patterns of the
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AIRS mid-tropospheric CO2. The result of the AIRS mid-tropospheric CO2 in the strong
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monsoon years (mean value of CO2 in JJAS of 2003, 2005, 2007, and 2010) is shown in
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Figure 3a. The result of the AIRS mid-tropospheric CO2 in weak monsoon years (mean
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value of CO2 in JJAS of 2004, 2006 and 2008) is shown in Figure 3b. In these two figures
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(Fig. 3a and Fig. 3b), high concentrations of the mid-tropospheric CO2 over the Indo-
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Pacific region correspond to the upwelling area of the Western and Eastern Walker Cells.
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During the strong monsoon years, there is more mid-tropospheric CO2 over the Indo-
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Pacific region. However, in weak monsoon years, though there is still a high value of
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CO2 over Indo-Pacific region, the CO2 value is smaller comparing with that in the strong
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monsoon years. Figure 3c reveals the difference in mid-tropospheric CO2 between strong
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and weak monsoon years. There are more mid-tropospheric CO2 over the Indo-Pacific
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region and the South sea of China during the strong monsoon years. The Student-t test
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was used to calculate the statistical significance of the difference for mid-tropospheric
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CO2 concentrations in strong and weak monsoon years. Mid-tropospheric CO2
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differences between strong and weak monsoon years are statistically significant when t is
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larger than a certain value t0. The number of degrees of freedom for the CO2 difference
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between two groups is 26. When the t-value is larger than 1.7, the results are within 10%
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significance level, which are highlighted by blue areas in Fig. 3d.
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[9] MOZART-2 model was used to investigate the TBO signal in the model mid-
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tropospheric CO2. AIRS mid-tropospheric CO2 weighting function was applied to
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MOZART-2 CO2 vertical profiles. Weighted MOZART-2 CO2 were averaged over 5S-
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20N, 100E-150E in JJAS from 1991 to 2008. Figure 4a is the time series of
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MOZART-2 mid-tropospheric CO2 concentration averaged over 5S-20N, 100E-150E
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in JJAS and Indian monsoon rainfall index calculated from GPCP from 1991 to 2008.
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MOZART-2 mid-tropospheric CO2 is highly correlated with the Indian monsoon rainfall
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index. The correlation coefficient is 0.56 (4% significance level). We chose two strong
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monsoon years (1996 and 2007) and two weak monsoon years (1999 and 2002) from the
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MOZART-2 model to investigate the influence of the TBO on mid-tropospheric CO2.
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Differences of the MOZART-2 mid-tropospheric CO2 between strong and weak monsoon
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years (Figure 4b) demonstrate that there are more mid-tropospheric CO2 over the Indo-
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Pacific area during strong monsoon years, which is similar to that from AIRS mid-
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tropospheric CO2. However, mid-tropospheric CO2 difference amplitude is smaller in the
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MOZART-2 compared with that from AIRS. Mid-tropospheric CO2 differences in
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MOZART-2 model are relatively high over the central Africa, which are not shown in
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AIRS CO2. This might due to the model was sampled in different years as AIRS CO2.
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The number of degrees of freedom for the model CO2 difference between two groups is
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14. When the t-value is larger than 1.75, the results are within 10% significance level,
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which are highlighted by blue areas in Fig. 4c.
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4. Conclusions
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[10] This study reveals the relationship between the TBO and variations of mid-
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tropospheric CO2 concentrations over the Indo-Pacific region. The power spectrum of the
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time series of AIRS mid-tropospheric CO2 over Indo-Pacific region contains a signal with
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a period of about 2 years, which is related to the TBO. Time series of AIR mid-
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tropospheric CO2 correlate with the TBO index, which shows that during the strong
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(weak) monsoon years, there are more (less) CO2 in the mid-troposphere at Indonesia due
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to the strong (weak) Western Walker Cell. It suggests that the strength of the circulation
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influences CO2 concentration in the mid-troposphere. MOZART-2 mid-tropospheric CO2
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results are consistent with those from the observation, although the signal simulated in
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the model is smaller than that from AIRS CO2. This work reveals that concentrations of
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mid-tropospheric CO2 can be influenced by the strength of the monsoon for the first time.
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Variations in AIRS mid-tropospheric CO2 as a response to monsoon offer a unique
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opportunity to diagnose the deficiency in the chemistry-transport model and can help
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improve the model in the future.
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Acknowledgments. We specially acknowledge Alexander Ruzmaikin and Runlie Shia,
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who gave helpful comments on this research.
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Figure 1. (a) Deseasonalized and detrended AIRS CO2 averaged over 5S - 20N, 100E -
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150E. (b) The power spectrum of deseasonalized and detrended AIRS CO2 averaged
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over 5S - 20N, 100E - 150E. Dotted line is the mean red-noise spectrum, Dash-dot
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line and dashed line shows 10% and 5% significance levels.
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Figure 2. AIRS detrended mid-tropospheric CO2 averaged at 5S-20N, 100E-150E in
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JJAS from 2003 to 2010 (black solid line) and detrended Indian monsoon index
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calculated from TRMM precipitation data (red dashed line). Red dots are strong monsoon
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years and blues dots are weak monsoon years. Correlation coefficient between AIRS CO2
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and monsoon index is 0.58 (4%).
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Figure 3. (a) The mean value of AIRS CO2 concentration in strong monsoon years (JJAS
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of 2003, 2005, 2007, and 2010), (b) The mean value of AIRS CO2 concentration in weak
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monsoon years (JJAS of 2004, 2006 and 2008), (c) CO2 difference between the strong
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and weak monsoon years, (d) CO2 differences within 10% significance level are
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highlighted in blue.
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Figure 4. (a) MOZART-2 mid-tropospheric CO2 averaged at 5S-20N, 100E-150E
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(Black solid line) and Indian monsoon index derived from GPCP precipitation data (Blue
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dashed line). Correlation coefficient between two time series is 0.56 (4%). (b) MOZART-
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2 CO2 difference between strong monsoon years (1996 and 2007) and weak monsoon
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years (1999 and 2002) in JJAS. (c) MOZART-2 CO2 differences within 10% significance
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level are highlighted in blue.
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.
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