Aquaplanet simulations of the MJO: Windevaporation feedback and horizontal
moisture advection
Eric D. Maloney, Walter Hannah
Department of Atmospheric Science
Colorado State University
Adam Sobel
Columbia University
Funded by: NSF Climate and Large-Scale Dynamics Program, NOAA Climate Program Office,
CMMAP
The Madden-Julian Oscillation (MJO)
• Salient features:
– Convectively coupled
disturbance that propagates
eastward across the Tropics
– 5-10 m s-1 propagation speed
in the Indian and west Pacific,
where MJO convective
variability is strongly coupled
to the large-scale flow.
– Simple baroclinic wind
structure, with 850 hPa and
200 hPa wind perturbations
180o out of phase
– Characteristic timescales of
30-90 days.
Madden and Julian 1972
Theory: Moisture Modes and Wind-Induced Surface
Heat Exchange (WISHE)
Moisture modes:
• A moisture mode is a balanced disturbance in which the large-scale dynamics
are regulated by the weak temperature gradient approximation (e.g. Sobel et al.
2001; Majda and Klein 2003; Raymond and Fuchs 2007, 2009; Sugiyama 2009)
• Properties are strongly regulated by local interactions between convection and
tropospheric moisture
• Essential dynamics of the mode involve processes that control the tropical
moisture field, including latent heat flux and horizontal advection
WISHE:
• Early models of WISHE: Mean easterly flow and enhancement of surface fluxes
to the east of convection supported disturbance amplitude and eastward
propagation (Neelin et al. 1987, Emanuel 1987)
• Increasing evidence exists that some form of WISHE may be importance for MJO
maintenance and possibly propagation, although it must operate in a state of
basic state westerlies, unlike the early linear models (e.g. Sobel et al. 2009)
SST Boundary Condition for Our Primary Simulation
• Resembles the observed mean December-April SST, but with the meridional
SST gradient poleward of 10o reduced to ¼ of observed.
• Modified version of NCAR CAM3
• T42 horizontal resolution (2.8o x 2.8o), and 26 vertical levels
• Perpetual March 21 insolation and ozone
• 16-year simulation
4
Unfiltered Precipitation and Winds vs.
Longitude
5 m/s
Even in unfiltered data, many salient features of the MJO apparent,
including 5 m s-1 eastward propagation, and a period of 40-60 days.
Wavenumber-Frequency Spectra (Precip)
Observations
Model
90 days
90 days
30 days
30 days
A strong spectral peak exists in the model at same zonal
wavenumber and frequency as observations.
6
Mean Wind and Precipitation Variance
Variance units: mm2 day-2
Intraseasonal variance peaks in regions of mean westerly flow at
low-levels. Variance is stronger than observed.
7
Composite Precipitation and U850 (Unfiltered)
8
Precipitation is an increasing and strongly non-linear function of
saturation fraction of the troposphere (e.g. compare to Bretherton
9
et al. 2004)
Composite PW Anomalies
PW Units: mm
Column precipitable water anomalies are sizeable, and in phase
with precipitation anomalies, as would be expected given the strong
relationship between saturation fraction and precipitation.
Precipitation contour interval 4 mm day-1.
10
dPW/dt in quadrature with precipitation signal. Precipitation contour 4 mm day-1.
Intraseasonal Vertically-Integrated MSE Budget
LW
Precip

 h  v

 v  h
h
t
LH+SH
h
  h  v  v  h  LH  SH  LW  SW
t
e.g. Neelin and Held (1987)
• Horizontal advection is the leading
term and is (nearly) in quadrature
with PW and precipitation in the
intraseasonal MSE budget
• Latent heat flux slightly lags
precipitation, and has a positive
covariance with precipitation
• 80-90% of MSE tendency due to
latent heat component
• Vertical advection causes
anomalous MSE export during
enhanced precipitation, although is
overcompensated by LH and LW
anomalies
12
Composite Vertically-Integrated Moisture Budget
q
  q  v  v  q  E  P
t
• Horizontal advection is (nearly) in quadrature with precipitation
(and PW) and in phase with the humidity tendency.
• Surface evaporation slightly lags the precipitation anomalies, with
a strong positive covariance
Composite Zonal Advection (Phase 5)


q 
q
q  q 

 u
  u
  u   u


x

x

x


 x 
•
a = 50-day mean, a= deviation from 50-day mean
• Eastward zonal advection of moisture anomalies is supported by u u 
q
x
 q 
 u

 x 
Total
• At time of peak moistening
in the model, total zonal
winds are on the order of 5
m s-1.
q
 q  

  u 


x


U850
 q 
  u 
 x 
Precip
• That u u 
is helping to
x
regulate eastward
propagation in the model
 might be testable by
changing the SST boundary
condition in the model in
order to reduce the basic
state westerly flow in the
model.
• Meridional advection
appears to act more as15a
damping mechanism on
moisture anomalies, mainly
through  q
v 
 y 
Sensitivity Tests
1) Set surface fluxes at climatological mean to test
influence of WISHE
2) Use SST distribution with reduced zonal gradient to
test influence of reduced zonal advection through
reduction in u  u

16
Control Versus No-WISHE Comparison
Control
No-WISHE
• WISHE appears to destabilize the MJO in the model. 30-90 day, zonal
wavenumber 1-3 variance decreases dramatically without WISHE active
• Small spatial scale precipitation variability that moves slowly east is still
apparent in the model
Effect of Reduced Basic State Westerlies
Control
90 days
30 days
Reduced Zonal Gradient
• The space time-spectrum of
precipitation still indicates a
concentrated spectral peak,
but now it occurs near 100 day
period rather than 40-50 days.
• Propagation speed is 2.5 m s-1
rather than 4-5 m s-1, which is
the approximate total
eastward zonal wind at peak
moistening as well.
q

u


u


• Supports the role of
x
in eastward propagation

18
Lag-Regression Plot vs. 141oE Wind
Reduced Zonal Gradient
Control
• Propagation speed slowed from 4-5 m s-1 to about 2.5 m s-1 in
going to the simulation with reduced zonal gradient and reduced
westerlies.
• Supports the role of u u  q in eastward propagation
19
x
Conclusions
• An MJO in an aquaplanet GCM simulation is analyzed that
shows some characteristics of a moisture mode
• The model MJO is destabilized by wind-evaporation feedback,
and appears to propagate eastward through advection of
anomalous humidity by the sum of perturbation winds and
mean westerly flow
• At the phase of peak moistening, the total zonal wind at 850
hPa is 5 m s-1, approximately the same as the propagation
speed of the intraseasonal disturbances
• A zonally-symmetric aquaplanet does not support a robust MJO
Open Questions/Future Work
• Why is the MJO in a simulation with realistic meridional
temperature gradients so unlocalized in frequency?
• What initiates the MJO in this model?
• Can the horizontal advection mechanism in this model be even
more convincingly proven (e.g. by manipulating moisture
advection?)
• Importance of cloud-radiative feedbacks?
Thanks!
Extra Slides
23
Theory: Wind-Induced Surface Heat Exchange
(WISHE)
• Early models of WISHE: Mean easterly flow and enhancement of surface
fluxes to the east of convection supported eastward propagation (Neelin et
al. 1987, Emanuel 1987)
• Increasing evidence exists that
some form of WISHE may be
importance for MJO maintenance
and possibly propagation,
although it must operate in a state
of basic state westerlies, unlike
the early linear models (e.g. Sobel
et al. 2009)
Araligidad (2007)
Motivation for Studying the MJO: Impacts
(e.g. Tropical Cyclones)
Enhanced MJO Precip
Suppressed MJO Precip
Higgins and Shi (2001)
Maloney and Hartmann (2000)
We Worked Hard to Get a Reasonable MJO in
this Model
Precipitation efficiency
Minimum entrainment
Theory: Moisture Modes and the MSE Budget
• A moisture mode instability can
result if large-scale divergent
motions associated with convection
import MSE into the column (e.g.
Raymond et al. 2009, negative
gross moist stability)
• Alternatively, export by divergent
motions can be positive, but MSE
sources such as latent heat flux and
cloud-radiative feedbacks
overcompensate to produce MSE
increases as a result of convection.
• Such instability is manifest for WTG
as strong positive moistureconvection feedbacks
• The later view appears most
relevant to the simulated MJO we
analyze here
Peters and
Bretherton (2006)
SST Distributions Used
“Realistic” SST
Zonally Symmetric
Quarter Meridional
Gradient
Wavenumber-Frequency Spectra (U850)
Observations
Model
90 days
30 days
90 days
30 days
A concentrated model peak also occurs in zonal wind,
although variance too strong compared to observed.
29
GCM Unfiltered Precipitation vs. Longitude
60 d
40 d
5 m/s
50 d
30
Wavenumber-Frequency Spectra (U850)
Observations
Quarter Gradient
“Realistic” SST
Zonally Symmetric
31
Composite Precipitation and U850 (Unfiltered)
From Wheeler and Hendon (2004)
Precip
U850
(Notice reversed time axis)
Westerly wind maximum lags precipitation maximum by about
5 days (1 MJO phase). Note the approximate 5 m s-1 total wind
at Phases 4 and 5. Composited using the method of Wheeler
33
and Hendon (2004)
Composite Humidity Anomalies
Unlike popular hypothesis in the literature, no initial and
gradually deepening low-level precursor humidity signal in the
lower troposphere precedes MJO convection.
34
Results
Lag Composites
Composite Horizontal Advection (MSE)
Meridional
Zonal
36
Composite U-Wind, T
Contour=1 m s-1
Contour=0.4 K
(Notice reversed time axis)
37
Mean Wind (Vertical Profile)
38
Intraseasonal Precipitable Water Budget

 q  v
q
t

 v  q
E
Precip
q
  q  v  v  q  E  P
t
• Precipitation and the vertical
advection (convergence) terms
largely are equal and opposite
• Anomalous vertical moisture
advection is negative at the time of
peak moistening
• Horizontal advection is (nearly) in
quadrature with precipitation,
consistent with its role suggested in
the MSE budget
39
Composite Meridional Advection (Phase 5)
• Meridional advection tends to be a damping influence
 q
on anomalies. The term v y  is prominent, acting
somewhat like a diffusion in the presence of strong
moisture gradients 
Total
 m 

  v
 y 
 m  

  v 

 y 
 m 
 u

 x 
 m 

  v
 y 
Total
 m  

  u 

 x 
 m 
  u

 x 
41
GCM Unfiltered U850 vs. Longitude
Control
No-WISHE
42
Unfiltered Precipitation vs. Longitude,
Control Versus No-WISHE
Control
Reduced Zonal Gradient
• Get the sense right away that eastward propagation is slower
when the basic state westerlies are changed, supporting the role
of u u  q in eastward advection.
x
Lag-Regression Plot vs. 141oE Wind
Reduced Zonal Gradient
Control
• Propagation speed slowed from 4-5 m s-1 to about 2.5 m s-1 in
going to the simulation with reduced zonal gradient and reduced
westerlies.
• Supports the role of u u  q in eastward propagation
44
x
Precipitation Variance and Mean Wind
45
Mean Wind and Precip
46