Three-Dimensional Water Vapor and
Cloud Variations Associated with the
MJO during Northern Hemisphere
Winter
By: David S. Myers and Duane E. Waliser
Presented by: Emily M. Riley
24 April 2007
Outline
•
•
•
•
•
Motivation
Data and Methodology
Digression to examine weighting function
Results
Summary
Motivation
• Improve GCMs simulation of the MJO
• How?
• Characterize typical co-evolution of the moisture,
rainfall, and cloud field associated with various phases
of the MJO
• Why?
• Most studies focus on moisture through the interaction
of dynamics with convection
• Understand role of hydrological cycle with tropical
dynamics, particularly within intraseasonal variations
(e.g. the MJO)
Data
• Moisture profiles from TOVS Pathfinder-A
•
•
•
•
High-Resolution IR Sounder 2 (HRIS2)
Microwave Sounding Unit (MSU)
Stratospheric Sounding Unit (SSU)
Resolution:
• 5-day avgs
• 1° X 1°
• Standard pressure heights
Data
• Clouds from ISCCP
• 2.5° X 2.5°
• July 1983 - February 1994
• Precipitation from CMAP
• 1979 - 1999
Methodology
• TOVS soundings calculate relative &
specific humidity
• Precipitation based indexing scheme:
• EOF trickery with filtered rainfall data from
CMAP averaged from 10°N - 10°S to capture
MJO events
• Leading EOF explains 39% of the intraseasonal
variance, which apparently is good…
A picture shows it better!
Leading EOF of bandpassed, equatorially averaged rainfall in NH winter.
Contour interval is 0.5 mm per day
• 46 events were identified
• Signal confined to E. Hemisphere
• Noticeable disruption around Maritime Continent
Great, but how does TOVS
measure moisture?
•
•
•
•
Recall Temperature profiles and the weighting
function (Petty, 8.3.2)
Consider a satellite able to measure radiant
intensities for a series of closely spaced
wavelengths, located on the edge of a strong
absorption line for constituent X.
Each channel measures thermal emission from a
different level.
Wavelengths closer to the center of the absorption
band will peak at higher heights
Intensity of emission is determined by temperature
within layer
Ideal Weighting Function
TOVS Weighting Function
Results: How good is the TOVS
data?
Specific Humidity Radiosonde Data
Specific Humidity TOVS Data
• Surface dry bias. Also found in comparison with
ECMWF data.
• In all, captures essential moisture features well
Results: Evolution of composite
MJO humidity variations
500-mb composite
specific humidity
anomalies
Results: Evolution of composite
MJO humidity variations
300-mb
500-mb
700-mb
surface
Results: Equatorially Avg.
composite moisture analysis
Results: Vertical profile of Equatorially
averaged moisture during the MJO life cycle
Results: Timing between precipitation and
moisture anomalies
90°E • Surface level moisture leads
precipitation
• 90°E upper level moisture
lags precipitation
• 150°E upper level moisture
and precip. practically in
150°E
phase for both dry and wet
phases
• 90°W upper and lower level
moisture variations are
roughly out of phase
90°W
Results: Timing between precipitation and
total cloud fraction anomalies
• Cloud fraction and precipitation lag near-surface moisture
• While precipitation and moisture are in phase in time, they
are not in phase in space.
• Higher cloudiness at trailing edge, despite lower
precipitation rate.
Results: Equatorially avg. ISCCP cloud
fraction by cloud-top heights
• Over Indian Ocean
transition from
moist to dry phase
appears to be
quicker than dry to
moist
• Western Hemi. is
dominated by low
cloud amounts,
consistent with low
moisture
• Low-top clouds
vulnerable to
biasing
Summary
• Clear westward tilt with height of the
moisture maximum across the Indian
Ocean, which become almost vertical in W.
Pacific
• Near surface (upper-level) moisture leads
(lags) precipitation
• Higher cloudiness at the western (trailing)
edges of precip. Maximum
• Dry anomalies dominate over moist
anomalies