An Analysis of Observational Cloud Data to Determine Major Sources of Variability Katie Antilla Mentor: Yuk Yung October 18, 2014

Outline Introduction—why this is important, related work Background info—on data and terminology Methods used Example plots Key results Summary 2

Introduction Climate 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 3

Introduction Previous 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 4

Data Atmospheric InfraRed Sounder (AIRS), Version 6: Instrument suite on NASA’s Aqua satellite Shorter time span ( ) than ISCCP & TOMS, but more reliable Community Atmosphere Model Version 5.0 (CAM5): Predicted data for ~same time period as AIRS ( ) 5

Background El 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) 6

Background Variables: Cloud cover = Relative humidity = Specific humidity = 7 area of AIRS grid pixel area 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)

Methods Empirical 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”) 8

Methods Linear 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 9

Plots—EOF analysis 10 cloud cover percent AIRS high cloudCAM5 high cloud

11 Plots—EOF analysis CAM5 mid. relative humidityCAM5 mid. specific humidity

Plots—SST Regression 12 cloud cover percent AIRS high cloud CAM5 high cloud

Results Classic 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 13

Summary Improving 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 14

Acknowledgments Huge thanks to everyone who helped me with this project: Professor Yuk Yung Sze Ning (Hazel) Mak Dr. Hui Su, Tiffany Chang, Dr. King-Fai Li, Dr. Run-Lie Shia, and the rest of Professor Yung’s group Samuel N. Vodopia and Carol J. Hasson Caltech SFP Program 15