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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