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An Analysis of Observational Cloud
Data to Determine Major Sources of Variability
Katie AntillaMentor: Yuk YungOctober 18, 2014
OutlineIntroduction—why this is important, related work
Background info—on data and terminology
Methods used
Example plots
Key results
Summary
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IntroductionClimate models—simulate and predict
weather/climate changes
Clouds—important aspect of climate models, but currently not very well understood
Can analyze observational cloud (and humidity) data to determine major sources of variability & compare with current models
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IntroductionPrevious work done on:
International Satellite Cloud Climatology Project (ISCCP)
Total Ozone Mapping Spectrometer (TOMS)
Showed that the El Niño Southern Oscillation (ENSO) is the leading factor influencing cloud distribution over time
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DataAtmospheric InfraRed Sounder (AIRS), Version
6:Instrument suite on NASA’s Aqua satellite
Shorter time span (2003-2012) than ISCCP & TOMS, but more reliable
Community Atmosphere Model Version 5.0 (CAM5):Predicted data for ~same time period as AIRS
(2001-2012)
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BackgroundEl Niño Southern
Oscillation (ENSO):
Regular inter-annual variations in sea surface temperatures (SST) & air surface pressures in the Pacific Ocean
2 different modes—classic ENSO & ENSO Modoki (a variant)
Background
Variables:
Cloud cover =
Relative humidity =
Specific humidity =
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area of AIRS grid pixelarea covered by clouds
partial pressure of water vapor
vapor pressure of water at current temp.
mass of water vapor
total mass of wet air
3 altitudes/pressure levels: high (200 hPa), mid (500 hPa), and low (850 hPa)
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MethodsEmpirical Orthogonal Function (EOF) Analysis:
Decomposes a data set into orthogonal basis functions
Each basis function captures a portion of the variability among the data
Each function consists of a spatial pattern (“EOF”) and a temporal pattern (principal component/“PC”)
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MethodsLinear regression with Sea Surface Temperature
(SST) data—to see degree of correlation between EOF’s and SST
Used Matlab to perform EOF analysis & linear regression on cloud & humidity data, from both AIRS & CAM5, at high, mid, & low levels
Plots—EOF analysis
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clou
d co
ver
perc
ent
AIRS high cloud CAM5 high cloud
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Plots—EOF analysis
CAM5 mid. relative humidity CAM5 mid. specific humidity
Plots—SST Regression
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clou
d co
ver
perc
ent
AIRS high cloud CAM5 high cloud
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ResultsClassic ENSO & ENSO Modoki have a strong
influence on both clouds & humidity
Both are also closely linked to SST variations under classic ENSO, but less under ENSO Modoki
Model (CAM5) data seemed to correspond well with observational (AIRS) data
For clouds, high-altitude data appears most closely linked to ENSO; for humidity, the middle-altitude data does
SummaryImproving cloud modeling will lead to better
future predictions
EOF & regression analysis of AIRS & CAM5 cloud & humidity data shows that the El Niño Southern Oscillation is the primary driver of both
The CAM5 model matches observational [AIRS] data quite well
Future research—why mid-level humidity is most closely linked to ENSO
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AcknowledgmentsHuge thanks to everyone who helped me with
this project:Professor Yuk YungSze Ning (Hazel) MakDr. Hui Su, Tiffany Chang, Dr. King-Fai Li, Dr. Run-
Lie Shia, and the rest of Professor Yung’s groupSamuel N. Vodopia and Carol J. HassonCaltech SFP Program
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