Understanding Observed Stratocumulus Variability and Implications for Aerosol Indirect Effects Nathan Johnson.

Slides:



Advertisements
Similar presentations
Principal Component Analysis (PCA) or Empirical Orthogonal Functions (EOFs) Arnaud Czaja (SPAT Data analysis lecture Nov. 2011)
Advertisements

1 CIRA/NOAA/ESRL, Boulder, CO
Aerosol, Interhemispheric Gradient, and Climate Sensitivity Ching-Yee Chang Department of Geography University of California Berkeley Lawrence Livermore.
Strong Sensitivity of Aerosol Concentrations to Convective Wet Scavenging Parameterizations in ECHAM5-HAM B. Croft 1, J.R. Pierce 1, R.V. Martin 1,2, C.
Kinematic Studies of the IDV Quasar S. Bernhart T. P. Krichbaum L. Fuhrmann Max-Planck-Institut fϋr Radioastronomie, Bonn.
By : Kerwyn Texeira. Outline Definitions Introduction Model Description Model Evaluation The effect of dust nuclei on cloud coverage Conclusion Questions.
Sensitivity of cloud droplet nucleation to kinetic effects and varying updraft velocity Ulrike Lohmann, Lisa Phinney and Yiran Peng Department of Physics.
Vertical cloud structures of the boreal summer intraseasonal variability based on CloudSat observations and ERA-interim reanalysis Speaker : Li-Chiang.
Investigation of the Aerosol Indirect Effect on Ice Clouds and its Climatic Impact Using A-Train Satellite Data and a GCM Yu Gu 1, Jonathan H. Jiang 2,
Brief review of my previous work in Beijing Xiaofeng Wang Directed by : Guoguang Zheng Huiwen Xue 1.
Ultrafine Particles and Climate Change Peter J. Adams HDGC Seminar November 5, 2003.
INTERDECADAL OSCILLATIONS OF THE SOUTH AMERICAN MONSOON AND THEIR RELATIONSHIP WITH SEA SURFACE TEMPERATURE João Paulo Jankowski Saboia Alice Marlene Grimm.
ARM Atmospheric Radiation Measurement Program. 2 Improve the performance of general circulation models (GCMs) used for climate research and prediction.
Slide 1 EE3J2 Data Mining EE3J2 Data Mining Lecture 9 Data Analysis Martin Russell.
Assessment and Quantification of HF Radar Uncertainty Fearghal O’Donncha Sean McKenna Emanuele Ragnoli Teresa UpdykeHugh Roarty.
Robert Wood Atmospheric Sciences, University of Washington Image: Saide, Carmichael, Spak, Janechek, Thornburg (University of Iowa) Image: Saide, Carmichael,
Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM Betty Croft, Ulrike Lohmann, Philip Stier, Sabine Wurzler, Sylvaine Ferrachat,
Comparative analysis of climatic variability characteristics of the Svalbard archipelago and the North European region based on meteorological stations.
FOREST FIRE AEROSOLS Optical and microphysical properties from EARLINET observations D. Balis Laboratory of Atmospheric Physics, Aristotle University of.
Climate Change: A National and Marine Perspective David Woolf National Oceanography Centre, Southampton.
Radiative Properties of Eastern Pacific Stratocumulus Clouds Zack Pecenak Evan Greer Changfu Li.
1 Hadley Centre The Atlantic Multidecadal Oscillation: A signature of persistent natural thermohaline circulation cycles in observed climate Jeff Knight,
DYNAMO Webinar Series Dynamics of the Madden-Julian Oscillation Field Campaign Climate Variability & Predictability.
Department of Mechanical Engineering The Pearlstone Center for Aeronautical Engineering Studies Ben-Gurion University of the Negev P.O.B. 653, Beer Sheva.
Interannual Variabilities of High Clouds Seen by AIRS and Comparison with CAM5 simulations Yuk Yung, Hui Su, Katie, Hazel et al.
Aerosol-Cloud Interactions and Radiative Forcing: Modeling and Observations Graham Feingold 1, K. S. Schmidt 2, H. Jiang 3, P. Zuidema 4, H. Xue 5, P.
Black Carbon+Organic carbon Sulphate
Sahel Climate Change in the IPCC AR4 models Michela Biasutti in collaboration with : Alessandra Giannini, Adam Sobel, Isaac.
IACETH Institute for Atmospheric and Climate Sciences Indirect aerosol effects in EC earth Trude Storelvmo and Ulrike Lohmann, ETH-Zurich.
Aerosol Size-Dependent Impaction Scavenging in Warm, Mixed, and Ice Clouds in the ECHAM5-HAM GCM Betty Croft, and Randall V. Martin – Dalhousie University,
Betty Croft, and Randall V. Martin – Dalhousie University, Canada
Numerical simulations of optical properties of nonspherical dust aerosols using the T-matrix method Hyung-Jin Choi School.
GUIDELINES FOR STUDENTS. Investigating Correlation Think/ Explore at least 10 real life examples regarding the relationship of two variables Draw scatter.
Influences of In-cloud Scavenging and Cloud Processing on Aerosol Concentrations in ECHAM5-HAM Betty Croft - Dalhousie University, Halifax, Canada Ulrike.
Timothy Logan University of North Dakota Department of Atmospheric Science.
Global Simulations of Below-Cloud and In-Cloud Aerosol Scavenging
X X X Cloud Variables Top pressure Cloud type Effective radius
Zhibo (zippo) Zhang 03/29/2010 ESSIC
Extratropical Sensitivity to Tropical SST Prashant Sardeshmukh, Joe Barsugli, and Sang-Ik Shin Climate Diagnostics Center.
Towards parameterization of cloud drop size distribution for large scale models Wei-Chun Hsieh Athanasios Nenes Image source: NCAR.
Strong Impacts of Vertical Velocity on Cloud Microphysics and Implications for Aerosol Indirect Effects: Despite widely recognized importance, aerosol.
3 “Products” of Principle Component Analysis
UBC/UW 2011 Hydrology and Water Resources Symposium Friday, September 30, 2011 DIAGNOSIS OF CHANGING COOL SEASON PRECIPITATION STATISTICS IN THE WESTERN.
Aerosol 1 st indirect forcing in the coupled CAM-IMPACT model: effects from primary-emitted particulate sulfate and boundary layer nucleation Minghuai.
Central limit theorem - go to web applet. Correlation maps vs. regression maps PNA is a time series of fluctuations in 500 mb heights PNA = 0.25 *
Integration of models and observations of aerosol-cloud interactions Robert Wood University of Washington Robert Wood University of Washington.
June Haiyan Teng NCAR/CGD
Putting the Clouds Back in Aerosol-Cloud Interactions
Unit 3: Science of Psychology
Ice Microphysics in CAM
Science of Psychology AP Psychology
Dynamics of ENSO Complexity and Sensitivity
Precisions of Adjusted Quantities
The Diurnal Temperature Range and its Recent Evolution
Integration of models and observations of aerosol-cloud interactions
Microphysical-macrophysical interactions or Why microphysics matters
Components of the yield trend.
Constraining the aerosol indirect effect
The radiative properties of inhomogeneous cirrus clouds
What Color is it?.
Effects of 3D radiation on cloud evolution
Integration of models and observations of aerosol-cloud interactions
A Bulk Parameterization of Giant CCN
Summary figure illustrating our hypotheses.
Further use and development of the bin-resolved warm phase LEM
(a) A band table where the y axis data represent individual OTUs and the x axis data represent samples. (a) A band table where the y axis data represent.
Canonical Correlation Analysis
Pancreatic cancer cell lines are sensitive to knockdown of outlier kinases. Pancreatic cancer cell lines are sensitive to knockdown of outlier kinases.
Fig. 1 Epigenomic and genomic variations between dwarf and normal whitefish species and their reciprocal hybrids. Epigenomic and genomic variations between.
Presentation transcript:

Understanding Observed Stratocumulus Variability and Implications for Aerosol Indirect Effects Nathan Johnson

Outline Effects of aerosols on Climate Susceptibility Observations of Microphysical Quantities MArine Stratocumulus Experiment Principle Components Analysis

Introduction Indirect Effects of Aerosols on Climate Twomey / Albrecht / Dispersion

Observational effect of N on k k increases with increasing N Pruppacher and Klett (1997) Miles et al. (2000) ??? Yum and Hudson (1997) Lu and Seinfeld (2006) (Modeling) Feingold et al. (1997) (Modeling) Lu et al. (2007) k decreases with increasing N Martin et al. (1994) Ackerman et al. (2000) McFarquhar and Heymsfield (2001) ??? Liu and Daum (2000, 2002) Peng and Lohmann (2003) (Sensitivity)

MASE N L k

Data Description

Principle Components Analysis % Variance Explained by Mode: 1 2 3 CDNC 0.746 0.226 0.028 LWC 0.879 0.001 0.120 k 0.767 0.191 0.042

Principle Components Analysis: N0

EOF: N0

Principle Components Analysis: N

EOF: N

Principle Components Analysis: NR2

EOF: NR2

North Test Weight Color N0 Black N Red NR2 Green

Summary Weighting of the data can influence the result obtained from PCA Scattering - QNR2 Uncertain which method will yield most interesting results Important implications for aerosol indirect effects