1. Baijun Tian, Jet Propulsion Laboratory, M/S. 183-501, California Institute of Technology, 4800 Oak Grove Dr., Pasadena CA 91109. Email: baijun.tian@jpl.nasa.gov. I. Introduction (e.g., Li et al. 2010; Tian et al. 2007; 2008; 2010; Weare 2010; Wong and Dessler 2007; Ziemke and Chandra 2003; Ziemke et al. 2007) The Madden-Julian Oscillation (MJO) is the dominant form of intraseasonal variability in the tropical atmosphere, characterized by slowly eastward-propagating, large-scale oscillations in tropical deep convection and baroclinic wind field, especially during the boreal winter (November-April) over the warmest tropical waters in the equatorial Indian Ocean and western Pacific (Madden and Julian 1971; 1972; Lau and Waliser 2005; Zhang 2005). Since its discovery, the MJO has continued to be a topic of significant interest due to its extensive interactions with other components of the global climate system and the fact that it represents a connection between the better-understood weather and seasonal-to-interannual climate variations. To date, influences of the MJO on the physical components of the climate system have been well recognized, documented, and in same cases, also well understood (e.g., monsoon, ENSO, hurricane, and extratropical weather). However, the impacts of the MJO on the chemical component of the climate system have been realized only recently and have not been well documented and understood (e.g., Li et al. 2010; Tian et al. 2007; 2008; 2010; Weare 2010; Wong and Dessler 2007; Ziemke and Chandra 2003; Ziemke et al. 2007). In this poster, we highlight our ongoing exploratory activities on the chemical reach of the MJO using the modern atmospheric composition data from the A-Train satellite constellation. References: 1) Tian, B., D. E. Waliser, E. J. Fetzer, and Y. L. Yung (2010), Vertical moist thermodynamic structure of the Madden- Julian Oscillation in Atmospheric Infrared Sounder retrievals: An update and a comparison to ECMWF interim reanalysis, Mon. Wea. Rev., 138, doi:10.1175/2010MWR3486.1, in press. 2) Tian, B., D. E. Waliser, R. A. Kahn, and S. Wong (2010), Modulation of Atlantic aerosols by the Madden-Julian Oscillation, J. Geophys. Res., under review. 3) Li, K.-F., B. Tian, D. E. Waliser, and Y. L. Yung, 2010: Tropical mid-tropospheric CO2 variability driven by the Madden-Julian Oscillation. Proc. Nat. Acad. Sci., 107, 19171-19175, doi:10.1073/pnas.1008222107. 4) Li, K.-F., B. Tian, D. E. Waliser, Y. L. Yung, M. J. Schwartz, J. L. Neu, and J. R. Worden (2010), Vertical structure of MJO-related subtropical ozone variations from MLS, TES and SHADOZ data, J. Geophys. Res., in prep. 5) Tian, B., and D. E. Waliser, 2010: Chemical and biological impacts. Chapter 13.4 of Intraseasonal Variability of the Atmosphere-Ocean System (2 nd Edition), Edited by K.-M. Lau and D. E. Waliser, in press. V. Summary  The MJO is the dominant component of the intraseasonal (30–90 day) variability in the tropical atmosphere.  The currently available A-Train atmospheric composition data provide us an unprecedented opportunity to study the MJO’s impacts on atmospheric composition.  Our results indicate that the MJO can impact a number of important atmospheric constituents, such as H 2 O, aerosols, O 3, and CO 2.  Our results provide a better understanding of the intraseasonal variability of atmospheric composition and an important test for chemical transport models.  Our results imply that some atmospheric constituents may be predictable with lead times of 2-4 weeks. III. MJO Analysis Method The combined Empirical Orthogonal Function (EOF) analysis method as described in Wheeler and Hendon (2004) and Waliser et al. (2009) was used. Briefly, the intraseasonal anomalies of the daily data were obtained by removing the annual cycle and filtering via a 30–90-day band pass filter. Then, average anomalies for each MJO phase of a composite MJO cycle were calculated. The MJO phase for each day was determined by the Real-time Multivariate MJO (RMM) index (a pair of principal component time series, called RMM1 and RMM2) developed by Wheeler and Hendon (2004). This RMM index is based on a pair of EOFs of the combined fields of near-equatorially averaged (15°S-15°N) 850-hPa zonal wind and 200-hPa zonal wind from NCEP/NCAR reanalysis, and satellite-observed outgoing longwave radiation (OLR) data. Only strong MJO events (RMM1 2 + RMM1 2 >=1) in the boreal winter (November-April) were considered. Figure 1 shows the (RMM1, RMM2) phase space for all days in boreal winter from 2002 to 2009. Exploring the Chemical Impacts of the Madden-Julian Oscillation using the A-Train Data Baijun Tian 1, King-Fai Li 2, Duane Waliser 1, and Yuk Yung 2 1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA. 2. Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA. National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Figure 1: (RMM1, RMM2) phase space for all days in boreal winter from 2002 to 2009. Eight defined phases of the phase space are labeled to indicate the eastward propagation of the MJO in one MJO cycle. Also labeled are the approximate locations of the enhanced convective signal of the MJO for that location of the phase space, e.g., the ‘‘Indian Ocean’’ for phases 2 and 3. Acknowledgment: This work was supported under the National Science Foundation (NSF) grant ATM-0840755 to University of California, Los Angeles (UCLA) and NSF grant ATM-0840787 to California Institute of Technology (Caltech) as well as the Atmospheric Infrared Sounder (AIRS) project at Jet Propulsion Laboratory (JPL). We thank many colleagues, particularly, Ralph Kahn, Eric Fetzer, Sun Wong, Michael Schwartz, John Worden, and Jessica Neu for valuable help. II. A-Train Atmospheric Composition Data SensorsProductsResolution (H, V)Height RangeRecord Length AIRS/AquaH 2 O profile Temp profile O 3 CO CO 2 Dust 45 km, 2 km 45 km 90 km 45 km >=300hPa >=10hPa Total Col. Mid-Trop Total Col. 09/2002-pres CALIOP/CALIPSOAerosol profile40km, 120m/360m<20 km/20-30km06/2006-pres MODIS/Aqua MODIS/Terra AOT 10 km Total Col. 07/2002-pres 12/1999-pres MLS/AuraH 2 O profile O 3 profile CO profile 160 km, 3 km 300 km, 4.5 km <=316hPa <=215hPa <=316hPa 08/2004-pres OMI/AuraTotal O 3 O 3 profile AI/AOT 13 x 24 km 13 x 48 km, 10 km 13 x 24 km Total Col. <60km Total Col. 08/2004-pres TES/AuraO 3 profile HDO 175 km, 8 km700-10hPa >550hPa 08/2004-pres MISR/TerraAOT Aerosol particle properties 17.6 kmTotal Col.12/1999-pres Figure 2: Composite boreal winter 20–100-day CMAP precipitation (color) and NCEP/NCAR surface wind anomalies vectors) as a function of MJO phase. Zonal wind anomalies statistically significant at 99% based on Student’s t test are drawn. The reference vector in units of m/s is shown at the bottom right. The number of days used to generate the composite for each phase is shown to the right of each panel. Result I. Vertical Moist Structure of the MJO  Science question: What is the large-scale vertical moist thermodynamic structure of the MJO?  A-Train contribution: AIRS/Aqua AIRS provides twice daily global water vapor (H 2 O) and temperature profiles with vertical resolution of 1–2 km, horizontal resolution of 45 km, and radiosonde accuracy for cloud cover up to about 70%.  Results: AIRS data provide a well-sampled global vertical moist thermodynamic structure of the MJO. This is demonstrated by the right figure: Equatorial mean (8°S-8°N) pressure- longitude cross sections of AIRS specific humidity anomalies (see Ref 1 for details).  Implications: These results helps to better understand the MJO dynamics and also offers a useful observation-based metric for climate models. Result II. MJO-related Atlantic Aerosol Variations  Science question: Does the MJO influence aerosol variability (Tian et al. 2008)?  A-Train contribution: MODIS/Aqua MODIS/Aqua provides high-quality daily AOT over cloud-free oceans.  Results: Our analysis shows that negative (positive) AOT anomalies over the equatorial Atlantic are associated with low-level westerly (easterly) anomalies there (see Ref 2 for details).  Implications: This indicates that the MJO does modulate the Atlantic aerosol concentration through its influence on the Atlantic low-level zonal wind anomalies and westward aerosol transport from Africa. This implies that Atlantic aerosol concentration might have predictable components with lead times of 2-4 weeks. Result III. MJO-related CO 2 Variations  Science question: Does the MJO influence CO 2 variations?  A-Train contribution: AIRS/Aqua AIRS provides the first global daily mid-tropospheric (~400 hPa) CO 2 with horizontal resolution of 90 km for cloud cover up to about 70%.  Results: There is a MJO signal in the AIRS mid-tropospheric CO 2 data that appears to be driven by the lower-tropospheric large-scale vertical motions associated with the MJO (see Ref 3 and A54D-01 for details).  Implications: These findings will provide a robustness test for coupled carbon–climate models. They also imply that surface CO 2 values are higher than those in the upper- troposphere. Result IV. MJO-related Ozone Variations  Science question: Tian et al. (2007) found that the MJO-driven total O 3 variations are mainly evident in the subtropics and related to the vertical movement of subtropical tropopause. Do the O 3 profiles from satellites support such results?  A-Train contribution: MLS/Aura MLS/Aura provide O 3 profiles in the upper troposphere and lower stratosphere.  Results: Our analysis indicates that the MJO-driven subtropical O 3 anomalies have maximum variability around 70 hPa with positive (negative) anomalies to the east (west) of equatorial rainfall anomalies. In particular, these subtropical O 3 anomalies are dynamically driven by the vertical movement of subtropical tropopause. (see Ref 4 for details).  Implications: This indicates that the subtropical total column O 3 anomalies are mainly from the O 3 anomalies in the lower stratosphere rather the troposphere which is consistent with the conjecture of Tian et al. (2007). A43B-0224