Composite Regression Analysis of the 8 Phases of the MJO By: Zachary Handlos

Introduction  MJO – What is it?  8 phases of MJO  Convective phases vs. suppressed phases  MJ 1971 – origins of oscillation  MJ 1994 – Brief History of Research (ie: observational work, Super Clusters, Monsoons, etc...)

Wheeler and Hendon (2004)  Performed EOF analysis of combined fields (OLR, zonal wind at 850 hPa and 200 hPa)  Subtracted out as much seasonal, annual variability as possible (ie: ENSO, etc...)  RMM1 and RMM2 = EOF1 and EOF2  Explain MJO propagation over space  RMM1 – enhanced convection over Maritime Continent  RMM 2 – enhanced convection over the Pacific

Goal of This Project:  Understand WH (2004) statistical methods by recreating some of their work  Show the significance of the RMM1 and RMM2 EOF's and their relationship to the MJO, forecasting the MJO  For this presentation, results regarding the composite regression analysis of OLR data considered

Composite Regression Analysis  ESRL (NCEP) interpolated OLR data (2.5 deg resolution, daily for May-June)  Subtracted out mean  Regressed OLR onto RMM 1 and RMM2  Call the regression vectors r1 and r2

Composite Regression Analysis  Calculate the value of the OLR regression slope values as a combination of RMM1, RMM2: r = r1-i*r2 OLR = Re{r*exp(i*[(9*π/8)+(j*(π/4))])]} where j = phase (1-8) i = sqrt(-1)

Wait Zak...what are you doing here?  Wheeler and Hendon (2004) phase space diagram  Represent MJO phases with combined RMM value  Multiply r by the second term on the RHS of previous equation to composite regression into the 8 phases  Want the real part of equation

My Results vs. Wheeler and Hendon (2004)  Used complex, combined RMM regression vector and composite based on earlier algorithm  WH (2004) composite OLR (and wind) anomalies based on results from phase space diagram (Fig. 7 in paper)  Time Frame analyzed:  (Me)  (WH 2004)

Future Work (Current Research)  Focus: ISCCP cloud regimes (Rossow et al, 2005)  Currently looking at the shape of latent heating profiles, calculating shapes using only precipitation and surface convergence in the ITCZ  Idea: Look at evolution of latent heating profiles, clouds within MJO phases (and SCC's)  Statistical Analysis methods such as composite analysis, EOF analysis, even spectral analysis could be useful

References  Madden, R. A. and P. R. Julian, 1971: Detection of a day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28:702–708.  Madden, R. A. and P. R. Julian, 1972: Description of global scale circulation cells in the Tropics with a day period. J. Atmos. Sci., 29:1109–1123.  Madden, R. A. and P. R. Julian, 1994: Observations of the 40–50-day tropical oscillation—A review. Mon. Wea. Rev., 122:814–837.  Tromeur, E., and W.B. Rossow, 2010: Interaction of tropical deep convection with the large-scale circulation in the MJO. J. Climate, 23, , doi: /202009JCLI  Wheeler, Matthew C., Harry H. Hendon, 2004: An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Mon. Wea. Rev., 132, 1917–1932.