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![Page 1: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/1.jpg)
An Analysis of Observational Cloud
Data to Determine Major Sources of Variability
Katie AntillaMentor: Yuk YungOctober 18, 2014
![Page 2: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/2.jpg)
OutlineIntroduction—why this is important, related work
Background info—on data and terminology
Methods used
Example plots
Key results
Summary
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![Page 3: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/3.jpg)
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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![Page 4: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/4.jpg)
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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
![Page 5: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/5.jpg)
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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)
![Page 6: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/6.jpg)
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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)
![Page 7: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/7.jpg)
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)
![Page 8: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/8.jpg)
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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”)
![Page 9: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/9.jpg)
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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
![Page 10: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/10.jpg)
Plots—EOF analysis
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clou
d co
ver
perc
ent
AIRS high cloud CAM5 high cloud
![Page 11: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/11.jpg)
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Plots—EOF analysis
CAM5 mid. relative humidity CAM5 mid. specific humidity
![Page 12: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/12.jpg)
Plots—SST Regression
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clou
d co
ver
perc
ent
AIRS high cloud CAM5 high cloud
![Page 13: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/13.jpg)
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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
![Page 14: An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014.](https://reader030.fdocuments.in/reader030/viewer/2022032415/56649f285503460f94c400c2/html5/thumbnails/14.jpg)
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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