Strong Impacts of Vertical Velocity on Cloud Microphysics and Implications for Aerosol Indirect Effects: Despite widely recognized importance, aerosol.

Slides:

Advertisements

Similar presentations

Sampling Design, Spatial Allocation, and Proposed Analyses Don Stevens Department of Statistics Oregon State University.

Advertisements

1 Do Polluted Clouds Have Sharper Cloud Edges? Christine Chiu, Julian Mann, Robin Hogan University of Reading Alexander Marshak, Warren Wiscombe NASA Goddard.

Paolo Tuccella, Gabriele Curci, Suzanne Crumeyrolle, Guido Visconti

Impact of entrainmnet and mixing on optical properties of boundary layer clouds Wojciech W. Grabowski, Hugh Morrison, NCAR, Boulder, Colorado, USA Joanna.

Microphysics and the simulation of MCSs in the TWP. Progress in the representation of Mesoscale Convective Systems (MCS) within cloud-resolving models.

Regression, Correlation. Research Theoretical empirical Usually combination of the two.

Correlation and Regression

Probing continental boundary layer clouds using first-time, extended-term aircraft observations: Low-level boundary layer clouds include stratus, stratocumulus,

Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009 Geophysical Fluid Dynamics Laboratory Review June 30 - July 2, 2009.

Aerosol Lifetimes at High Latitudes Betty Croft 1, Jeff Pierce 1,2 and Randall Martin 1,3 1 Dalhousie University, Halifax, Canada 2 Colorado State University,

Preliminary Experiments with a Dynamics-Based PDF Parameterization for Boundary Layers and Associated Clouds in GCMs Leo Donner, Huan Guo, and Chris Golaz.

Clouds and Climate: Cloud Response to Climate Change SOEEI3410 Ken Carslaw Lecture 5 of a series of 5 on clouds and climate Properties and distribution.

Brief review of my previous work in Beijing Xiaofeng Wang Directed by : Guoguang Zheng Huiwen Xue 1.

9. SIMPLE LINEAR REGESSION AND CORRELATION

Aerosol and Cloud Microphysics Working Group Dietrich Althausen, Andrea Riede, Herman Russchenberg, Aldo Amodeo, Susanne Crewell, Paolo Di Girolamo, Stephen.

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,

ARM Atmospheric Radiation Measurement Program. 2 Improve the performance of general circulation models (GCMs) used for climate research and prediction.

The Radiative Budget of an Atmospheric Column in Tropical Western Pacific Zheng Liu 1 Thomas Ackerman 1,2, Sally McFarlane 2, Jim Mather 2, University.

MODULE 12 RANDOM VIBRATION.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

Robert Wood Atmospheric Sciences, University of Washington Image: Saide, Carmichael, Spak, Janechek, Thornburg (University of Iowa) Image: Saide, Carmichael,

Climate model grid meshes are too coarse to explicitly simulate storm system winds and therefore must rely on simplified models referred to as parameterizations.

Unit 1 Lesson 4 Representing Data

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

Chapter 13 Statistics © 2008 Pearson Addison-Wesley. All rights reserved.

The Examination of Residuals. The residuals are defined as the n differences : where is an observation and is the corresponding fitted value obtained.

Simulation of Below-cloud and In-cloud Aerosol Scavenging in ECHAM5-HAM Betty Croft, Ulrike Lohmann, Philip Stier, Sabine Wurzler, Sylvaine Ferrachat,

Aerosol Microphysics: Plans for GEOS-CHEM

Understanding the effects of aerosols on deep convective clouds Eric Wilcox, Desert Research Institute, Reno NV Tianle.

Horizontal Distribution of Ice and Water in Arctic Stratus Clouds During MPACE Michael Poellot, David Brown – University of North Dakota Greg McFarquhar,

ISCCP at 30. Influence of aerosols on mesoscale convective systems inferred from ISCCP and A-Train datasets Rong Fu & Sudip Chakraborty Jackson School.

The Changing Environment. A change occurs when the characteristics or properties of the environment have been altered. The occurrence of change (known.

Improving Black Carbon (BC) Aging in GEOS-Chem Based on Aerosol Microphysics: Constraints from HIPPO Observations Cenlin He Advisers: Qinbin Li, Kuo-Nan.

Chapter 4 Correlation and Regression Understanding Basic Statistics Fifth Edition By Brase and Brase Prepared by Jon Booze.

I.Intro to Statistics II.Various Variables. I.Intro to Statistics A. Definitions -

The Examination of Residuals. Examination of Residuals The fitting of models to data is done using an iterative approach. The first step is to fit a simple.

Cloud fraction in SCMs: Representation of clouds constitutes a major source of uncertainty in climate models and thus future climate change projections.

4. Large-Scale Forcing Datasets Large-scale forcings are obtained: From the ARM variational analysis (ARM VA) for a standard domain (300 km, 25 mb) & reduced.

Estimation of Cloud and Precipitation From Warm Clouds in Support of the ABI: A Pre-launch Study with A-Train Zhanqing Li, R. Chen, R. Kuligowski, R. Ferraro,

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.

Status of the ARM Aerial Vehicle Program at DOE/PNNL Interagency Coordinating Committee for Airborne Geosciences Research and Applications (ICCAGRA) Washington,

Identifying 3D Radiative Cloud Effects Using MODIS Visible Reflectance Measurements Amanda Gumber Department of Atmospherics and Oceanic Sciences/CIMSS.

IACETH Institute for Atmospheric and Climate Sciences Indirect aerosol effects in EC earth Trude Storelvmo and Ulrike Lohmann, ETH-Zurich.

Modelling the radiative impact of aerosols from biomass burning during SAFARI-2000 Gunnar Myhre 1,2 Terje K. Berntsen 3,1 James M. Haywood 4 Jostein K.

Entrainment Rate in Shallow Cumuli : Probabilistic Distribution and Dependence on Dry Air Sources: The rate of cloud engulfing dry air (entrainment rate)

Yuying Zhang, Jim Boyle, and Steve Klein Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Jay Mace University.

Boundary Layer Clouds.

So, what’s the “point” to all of this?….

HAPPY 25 TH !!!! Cloud Feedback George Tselioudis NASA/GISS.

Retrieval of Cloud Phase and Ice Crystal Habit From Satellite Data Sally McFarlane, Roger Marchand*, and Thomas Ackerman Pacific Northwest National Laboratory.

One float case study The Argo float ( ) floating in the middle region of Indian Ocean was chosen for this study. In Figure 5, the MLD (red line),

CCN Variability During LBA-SMOCC-EMfiN! 2002 and Its Role on Precipitation Initiation Over the Amazon Basin.

Modelling and observations of droplet growth in clouds A Coals 1, A M Blyth 1, J-L Brenguier 2, A M Gadian 1 and W W Grabowski 3 Understanding the detailed.

Wanda R. Ferrell, Ph.D. Acting Director Climate and Environmental Sciences Division February 24, 2010 BERAC Meeting Atmospheric System Research Science.

Horizontal Variability In Microphysical Properties of Mixed-Phase Arctic Clouds David Brown, Michael Poellot – University of North Dakota Clouds are strong.

Aerosol Indirect Effects in CAM and MIRAGE Steve Ghan Pacific Northwest National Laboratory Jean-Francois Lamarque, Peter Hess, and Francis Vitt, NCAR.

Lecture 8 Sections Objectives: Bivariate and Multivariate Data and Distributions − Scatter Plots − Form, Direction, Strength − Correlation − Properties.

Multiscale Variations of Decade-long Cloud Fractions from Six Different Platforms over the SGP: For decades, researchers have intensively studied observationally.

Towards parameterization of cloud drop size distribution for large scale models Wei-Chun Hsieh Athanasios Nenes Image source: NCAR.

Redemann, ICARTT J-31 data workshop, Boulder, CO, Mar. 9-10, 2005 Aerosol horizontal structure – variability Jens Redemann (BAER Institute) + J-31 team.

Mesoscale variability and drizzle in stratocumulus Kim Comstock General Exam 13 June 2003.

12 th annual CMAS conference Impact of Biomass Burning Aerosols on Regional Climate over Southeast US Peng Liu, Yongtao Hu, Armistead G. Russell, Athanasios.

UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.

Mayurakshi Dutta Department of Atmospheric Sciences March 20, 2003

What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.

Derek Ortt1 and Shuyi S. Chen, RSMAS/University of Miami

H. Morrison, A. Gettelman (NCAR) , S. Ghan (PNL)

Stat 112 Notes 4 Today: Review of p-values for one-sided tests

Turbulence-induced broadening of cloud-droplet size distributions: Implications for aerosol effects on climate Tuesday, April 25, :00-12:00 Noon.

Unit 1 Lesson 4 Representing Data

Presentation transcript:

Strong Impacts of Vertical Velocity on Cloud Microphysics and Implications for Aerosol Indirect Effects: Despite widely recognized importance, aerosol indirect effects are still full of uncertainty, even controversy for some aspects. One reason for the uncertainty/controversy is that aerosol effects are often intertwined with changes in cloud dynamics such as vertical velocity and separation of aerosol indirect effects from dynamical effects poses a confounding challenge, esp., in observations. DOE scientists at the Brookhaven National Laboratory and collaborators have taken on the issue using data collected in cumulus clouds during the Routine AAF [Atmospheric Radiation Measurement (ARM) Aerial Facility] Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign, with a focus on the effect of vertical velocity on cloud droplet number concentration and relative dispersion, two cloud microphysical properties critical for quantifying aerosol indirect effects. This observational study confirms their early theoretical prediction that variation of vertical velocity leads to a negative correlation between cloud droplet number concentration and relative dispersion: an increase in vertical velocity leads to an increase in cloud droplet number concentration but a decrease in relative dispersion, opposite to that caused by changes in aerosol loading. The study demonstrates that one can capitalize on the opposing effects of aerosol loading and vertical velocity to help discern their relative importance. Reference: Lu, C., Y. Liu, S. Niu, and A. M. Vogelmann (2012), Observed impacts of vertical velocity on cloud microphysics and implications for aerosol indirect effects, Geophys. Res. Lett.,doi: /2012GL (accepted) Contact: Dorothy Koch, SC23.1,

2 Motivation ● One reason for the uncertainty in aerosol effects is that aerosol effects are often intertwined with changes in other factors, such as vertical velocity. Separating aerosol effects from dynamical effects poses a confounding challenge, and such observational studies are particularly rare. Approach ● Simultaneous measurements of vertical velocity and cloud droplet size distributions in cumuli collected during the RACORO field campaign are analyzed to empirically quantify the effect of vertical velocity on cloud droplet number concentration, relative dispersion and their relationship. Result ● Effect of vertical velocity on relative dispersion and its relationship with droplet concentration is opposite to that associated with aerosol loading. The opposing relationships can be used to help discern the relative importance of aerosol and dynamical effects. Observed Impacts of Vertical Velocity on Cloud Microphysics and Implications for Aerosol Indirect Effects Lu, C., Y. Liu, S. Niu, and A. M. Vogelmann (2012), Observed impacts of vertical velocity on cloud microphysics and implications for aerosol indirect effects, Geophys. Res. Lett.,doi: /2012GL (accepted) Joint probability density functions (PDF) of relative dispersion (ε) vs. vertical velocity (w) along horizontal aircraft legs for each cumulus flight (date given in legend). The color (contour) represents the frequency of occurrence; Only the mesh grids (0.1 for ε and 0.5 m s -1 w)that have percentages ≥ 0.3% are shown. the total numbers of samples in the six flights are 3,641, 4,586, 2,829, 3,525, 776 and 4,115, respectively. The red lines denote weighted least squares power-law fits of the data.