Transcript Composite Regression Analysis of the Eight Phases of the MJO

Composite Regression Analysis of
the 8 Phases of the MJO
By: Zachary Handlos
Introduction
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MJO – What is it?
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8 phases of MJO
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Convective phases vs. suppressed phases
MJ 1971 – origins of oscillation
MJ 1994 – Brief History of Research (ie:
observational work, Super Clusters, Monsoons, etc...)
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Wheeler and Hendon (2004)
Performed EOF analysis of combined fields (OLR,
zonal wind at 850 hPa and 200 hPa)
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Subtracted out as much seasonal, annual variability
as possible (ie: ENSO, etc...)
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RMM1 and RMM2 = EOF1 and EOF2
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Explain MJO propagation over space
RMM1 – enhanced convection over Maritime Continent
 RMM 2 – enhanced convection over the Pacific
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Goal of This Project:
Understand WH (2004) statistical methods by
recreating some of their work
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Show the significance of the RMM1 and RMM2
EOF's and their relationship to the MJO, forecasting
the MJO
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For this presentation, results regarding the
composite regression analysis of OLR data
considered
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Composite Regression Analysis
ESRL (NCEP) interpolated OLR data (2.5 deg
resolution, daily for May-June)
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Subtracted out mean
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Regressed OLR onto RMM 1 and RMM2
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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:
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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?
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Wheeler and Hendon (2004) phase space diagram
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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
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Want the real part of equation
My Results vs. Wheeler and Hendon
(2004)
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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:
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1990-2004 (Me)
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1974-2003 (WH 2004)
Future Work (Current Research)
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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
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Idea: Look at evolution of latent heating profiles,
clouds within MJO phases (and SCC's)
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Statistical Analysis methods such as composite
analysis, EOF analysis, even spectral analysis could
be useful
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References
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Madden, R. A. and P. R. Julian, 1971: Detection of a 40-50-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 4050-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, 1837-1853, doi:10.1175/202009JCLI3240.1
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.