U.S. Climate Change Science Program

Synthesis and Assessment Product 2.3

January 2009

Atmospheric Aerosol

Properties and

Climate Impacts
FEDERAL EXECUTIVE TEAM

Director, Climate Change Science Program: ................................William J. Brennan

Director, Climate Change Science Program Office: .....................Peter A. Schultz

Lead Agency Principal Representative to CCSP, 

Associate Director for Research, Earth Science Division,

National Aeronautics and Space Administration: .........................Jack Kaye

Lead Agency Point of Contact, Earth Science Division,

National Aeronautics and Space Administration: ...........................Hal Maring

Product Lead, Laboratory for Atmospheres,

Earth Science Division, Goddard Space Flight Center, 

National Aeronautics and Space Administration: ..........................Mian Chin

Chair, Synthesis and Assessment Product Advisory Group

Associate Director, National Center for Environmental

Assessment, U.S. Environmental Protection Agency: ....................Michael W. Slimak

Synthesis and Assessment Product Coordinator, 

Climate Change Science Program Office: ......................................Fabien J.G. Laurier

EDITORIAL AND PRODUCTION TEAM

Editors: ..........................................................................................Mian Chin, NASA

 ..........................................................................................Ralph A. Kahn, NASA

 ..........................................................................................Stephen E. Schwartz, DOE

Graphic Design: ............................................................................Sally Bensusen, NASA

 ............................................................................Debbi McLean, NASA

This document, part of the Synthesis and Assessment Products described in the U.S. Climate Change Science 

Program (CCSP) Strategic Plan, was prepared in accordance with Section 515 of the Treasury and General 

Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554) and the information quality act 

guidelines issued by the National Aeronautics and Space Administration pursuant to Section 515. The CCSP 

Interagency Committee relies on National Aeronautics and Space Administration certifications regarding 

compliance with Section 515 and Agency guidelines as the basis for determining that this product conforms with 

Section 515. For purposes of compliance with Section 515, this CCSP Synthesis and Assessment Product is an 

“interpreted product” as that term is used in National Aeronautics and Space Administration guidelines and is 

classified as “highly influential”. This document does not express any regulatory policies of the United States or 

any of its agencies, or provides recommendations for regulatory action.
Synthesis and Assessment Product 2.3 

 Report by the U.S. Climate Change Science Program 

and the Subcommittee on Global Change Research

COORDINATING LEAD AUTHOR: 

Mian Chin, NASA Goddard Space Flight Center

LEAD AND CONTRIBUTING AUTHORS:

Ralph A. Kahn, Lorraine A. Remer, Hongbin Yu, NASA GSFC;

David Rind, NASA GISS;

Graham Feingold, NOAA ESRL; Patricia K. Quinn, NOAA PMEL;

Stephen E. Schwartz, DOE BNL; David G. Streets, DOE ANL;

Philip DeCola, Rangasayi Halthore, NASA HQ

Atmospheric Aerosol

Properties and

Climate Impacts
January 2009,

Members of Congress:

On behalf of the National Science and Technology Council, the U.S. Climate Change Science Program (CCSP) is pleased to transmit to the President and the Congress this Synthesis and Assessment 

Product (SAP) Atmospheric Aerosol Properties and Climate Impacts. This is part of a series of 21 

SAPs produced by the CCSP aimed at providing current assessments of climate change science to 

inform public debate, policy, and operational decisions. These reports are also intended to help the 

CCSP develop future program research priorities.

The CCSP’s guiding vision is to provide the Nation and the global community with the science-based 

knowledge needed to manage the risks and capture the opportunities associated with climate and 

related environmental changes. The SAPs are important steps toward achieving that vision and help 

to translate the CCSP’s extensive observational and research database into informational tools that 

directly address key questions being asked of the research community.

This SAP reviews current knowledge about global distributions and properties of atmospheric aerosols, as they relate to aerosol impacts on climate. It was developed in accordance with the Guidelines 

for Producing CCSP SAPs, the Information Quality Act (Section 515 of the Treasury and General 

Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554)), and the guidelines issued by the National Aeronautics and Space Administration pursuant to Section 515.

We commend the report’s authors for both the thorough nature of their work and their adherence to 

an inclusive review process.

Sincerely,

Carlos M. Gutierrez

Secretary of Commerce

Chair, Committee on Climate Change 

Science and Technology Integration

Samuel W. Bodman

Secretary of Energy

Vice Chair, Committee on Climate 

Change Science and Technology 

Integration

John H. Marburger III

Director, Office of Science and 

Technology Policy

Executive Director, Committee

on Climate Change Science and

Technology Integration
TABLE OF CONTENTS

Executive Summary ...............................................................................................1

ES 1. Aerosols and Their Climate Effects .......................................................................................1

 ES 1.1. Atmospheric Aerosols .......................................................................................................1

 ES 1.2. Radiative Forcing of Aerosols ..........................................................................................1

 ES 1.3. Reducing Uncertainties in Aerosol Radiative Forcing Estimates .............................2

ES 2. Measurement-Based Assessment of Aerosol Radiative Forcing ......................................2

 ES 2.1. Assessments of Aerosol Direct Radiative Forcing ......................................................3

 ES 2.2. Assessments of Aerosol Indirect Radiative Forcing ....................................................3

ES 3. Model Estimated Aerosol Radiative Forcing and Its Climate Impact .............................4

 ES 3.1. The Importance of Aerosol Radiative Forcing in Climate Models ..........................4

 ES 3.2. Modeling Atmospheric Aerosols .....................................................................................4

 ES 3.3. Aerosol Effects on Clouds................................................................................................5

 ES 3.4. Impacts of Aerosols on Climate Model Simulations ..................................................5

ES 4. The Way Forward ......................................................................................................................5

CHAPTER 1 .............................................................................................................................9

Introduction

1.1 Description of Atmospheric Aerosols .....................................................................................9

1.2 The Climate Effects of Aerosols ............................................................................................. 12

1.3. Reducing Uncertainties in Aerosol-Climate Forcing Estimates ...................................... 16

1.4 Contents of This Report .......................................................................................................... 20 2 ...........................................................................................................................21

Remote Sensing and In Situ Measurements of Aerosol Properties, Burdens, 

and Radiative Forcing

2.1. Introduction ............................................................................................................................... 21

2.2. Overview of Aerosol Measurement Capabilities ............................................................... 22

 2.2.1. Satellite Remote Sensing .................................................................................................. 22

 2.2.2. Focused Field Campaigns ................................................................................................. 27

 2.2.3. Ground-based In situ Measurement Networks ......................................................... 27

 2.2.4. In situ Aerosol Profiling Programs ................................................................................. 28

 2.2.5. Ground-based Remote Sensing Measurement Networks ....................................... 29

 2.2.6. Synergy of Measurements and Model Simulations..................................................... 32

2.3. Assessments of Aerosol Characterization and Climate Forcing .................................... 34

 2.3.1. The Use of Measured Aerosol Properties to Improve Models ............................... 34

 2.3.2. Intercomparisons of Satellite Measurements and Model Simulation of 

 Aerosol Optical Depth .................................................................................................... 37

 2.3.3. Satellite Based Estimates of Aerosol Direct Radiative Forcing ............................... 38

 2.3.4. Satellite Based Estimates of Anthropogenic Component of Aerosol Direct 

 Radiative Forcing ............................................................................................................... 44

 2.3.5. Aerosol-Cloud Interactions and Indirect Forcing ...................................................... 44

2.4. Outstanding Issues ................................................................................................................... 49

2.5. Concluding Remarks ................................................................................................................ 52
3 ...........................................................................................................................55

Modeling the Effects of Aerosols on Climate Forcing

3.1. Introduction ............................................................................................................................... 55

3.2. Modeling of Atmospheric Aerosols ...................................................................................... 56

 3.2.1. Estimates of Emissions ..................................................................................................... 56

 3.2.2. Aerosol Mass Loading and Optical Depth ................................................................... 58

3.3. Calculating Aerosol Direct Radiative Forcing ..................................................................... 61

3.4. Calculating Aerosol Indirect Forcing .................................................................................... 66

 3.4.1. Aerosol Effects on Clouds ............................................................................................... 66

 3.4.2. Model Experiments ........................................................................................................... 67

 3.4.3. Additional Aerosol Influences ......................................................................................... 69

 3.4.4. High Resolution Modeling ............................................................................................... 70

3.5. Aerosol in the Climate Models .............................................................................................. 72

 3.5.1. Aerosol in the IPCC AR4 Climate Model Simulations .............................................. 72

 3.5.2. Additional considerations ................................................................................................ 77

3.6. Impacts of Aerosols on Climate Model Simulations ......................................................... 78

 3.6.1. Surface Temperature Change .......................................................................................... 78

 3.6.2. Implications for Climate Model Simulations................................................................ 81

3.7. Outstanding Issues ................................................................................................................... 81

3.8 Conclusions ................................................................................................................................ 82

4 ...........................................................................................................................85

The Way Forward

4.1. Major Research Needs ............................................................................................................ 85

4.2. Priorities ..................................................................................................................................... 87

 4.2.1. Measurements .................................................................................................................... 87

 4.2.2. Modeling .............................................................................................................................. 89

 4.2.3. Emissions ............................................................................................................................. 90

4.3. Concluding Remarks ................................................................................................................ 90

Glossary and Acronyms ......................................................................................91

References ............................................................................................................99

TABLE OF CONTENTS
III

Atmospheric Aerosol Properties and Climate Impacts

AUTHOR TEAM FOR THIS REPORT

Executive Summary Lorraine A. Remer, NASA GSFC; Mian Chin, NASA GSFC; Philip DeCola, NASA 

HQ; Graham Feingold, NOAA ERSL; Rangasayi Halthore, NASA HQ/NRL; Ralph 

A. Kahn, NASA GSFC; Patricia K. Quinn, NOAA PMEL; David Rind, NASA GISS; 

Stephen E. Schwartz, DOE BNL; David G. Streets, DOE ANL; Hongbin Yu, NASA 

GSFC/UMBC

Chapter 1 Lead Authors: Ralph A. Kahn, NASA GSFC; Hongbin Yu, NASA GSFC/UMBC

Contributing Authors: Stephen E. Schwartz, DOE BNL; Mian Chin, NASA GSFC; 

Graham Feingold, NOAA ESRL; Lorraine A. Remer, NASA GSFC; David Rind, 

NASA GISS; Rangasayi Halthore, NASA HQ/NRL; Philip DeCola, NASA HQ

Chapter 2 Lead Authors: Hongbin Yu, NASA GSFC/UMBC; Patricia K. Quinn, NOAA PMEL; 

Graham Feingold, NOAA ESRL; Lorraine A. Remer, NASA GSFC; Ralph A. Kahn, 

NASA GSFC

Contributing Authors: Mian Chin, NASA GSFC; Stephen E. Schwartz, DOE BNL

Chapter 3 Lead Authors: David Rind, NASA GISS; Mian Chin, NASA GSFC; Graham Feingold, NOAA ESRL; David G. Streets, DOE ANL

Contributing Authors: Ralph A. Kahn, NASA GSFC; Stephen E. Schwartz, DOE 

BNL; Hongbin Yu, NASA GSFC/UMBC

Chapter 4 David Rind, NASA GISS; Ralph A. Kahn, NASA GSFC; Mian Chin, NASA GSFC; 

Stephen E. Schwartz, DOE BNL; Lorraine A. Remer, NASA GSFC; Graham Feingold, 

NOAA ESRL; Hongbin Yu, NASA GSFC/UMBC; Patricia K. Quinn, NOAA PMEL; 

Rangasayi Halthore, NASA HQ/NRL

III
IV

ACKNOWLEDGMENTS

First, the authors wish to acknowledge the late Yoram J. Kaufman both for his inspiration 

and contributions to aerosol-climate science throughout his career and for his early leadership of the activity that produced this document. His untimely passing left it to the remaining authors to complete this report. Yoram and his contributions to our community are 

greatly missed. 

This Climate Change Science Program Synthesis and Assessment Product (CCSP SAP) 2.3 

has been reviewed by a group of experts, the public, and Federal Agencies. The purpose of 

these independent reviews was to assure the quality of this product.

We wish to thank the following individuals for their expert review of this report: Sundar 

Christopher (University of Alabama Huntsville), Daniel Jacob (Harvard University), Steven 

Ghan (Pacific Northwest National Laboratory), John Ogren (NOAA Earth System Research 

Laboratory), and Susan Solomon (NOAA Earth System Research Laboratory). 

We also wish to thank the following individuals/group for their public/federal agency review of this report: Joel D. Scheraga (EPA), Samuel P. Williamson (NOAA/OFCM), Alan 

Carlin, David L. Hagen, Douglas Hoyt, Forrest M. Mims III (Geronimo Creek observatory), 

John Pittman, Nathan Taylor (Texas A&M University), Werner Weber (Technische University Dortmund, Germany), and the NOAA Research Council.

The work by Bates et al. (2006), Penner et al. (2006), Yu et al. (2006), Textor et al. (2006), 

Kinne et al. (2006), Schulz et al. (2006), and the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (2007) provided important groundwork for the material 

in Chapter 2 and Chapter 3.

IV
V

RECOMMENDED CITATIONS

For the Report as a Whole:

CCSP 2009: Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change Science 

Program and the Subcommittee on Global Change Research. [Mian Chin, Ralph A. Kahn, and Stephen E. Schwartz 

(eds.)]. National Aeronautics and Space Administration, Washington, D.C., USA, 128 pp.

For the Executive Summary:

Remer, L. A., M. Chin, P. DeCola, G. Feingold, R. Halthore, R. A. Kahn, P. K. Quinn, D. Rind, S. E. Schwartz, D. 

Streets, and H. Yu, 2009: Executive Summary, in Atmospheric Aerosol Properties and Climate Impacts, A Report by the 

U.S. Climate Change Science Program and the Subcommittee on Global Change Research. [Mian Chin, Ralph A. Kahn, 

and Stephen E. Schwartz (eds.)]. National Aeronautics and Space Administration, Washington, D.C., USA.

For Chapter 1:

Kahn, R. A., H. Yu, S. E. Schwartz, M. Chin, G. Feingold, L. A. Remer, D. Rind, R. Halthore, and P. DeCola, 2009: 

Introduction, in Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change Science 

Program and the Subcommittee on Global Change Research. [Mian Chin, Ralph A. Kahn, and Stephen E. Schwartz 

(eds.)]. National Aeronautics and Space Administration, Washington, D.C., USA.

For Chapter 2:

Yu, H., P. K. Quinn, G. Feingold, L. A. Remer, R. A. Kahn, M. Chin, and S. E. Schwartz, 2009: Remote Sensing and 

In Situ Measurements of Aerosol Properties, Burdens, and Radiative Forcing, in Atmospheric Aerosol Properties and 

Climate Impacts, A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change 

Research. [Mian Chin, Ralph A. Kahn, and Stephen E. Schwartz (eds.)]. National Aeronautics and Space Administration, 

Washington, D.C., USA.

For Chapter 3:

Rind, D., M. Chin, G. Feingold, D. Streets, R. A. Kahn, S. E. Schwartz, and H. Yu, 2009: Modeling the Effects of 

Aerosols on Climate, in Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change 

Science Program and the Subcommittee on Global Change Research. [Mian Chin, Ralph A. Kahn, and Stephen E. 

Schwartz (eds.)]. National Aeronautics and Space Administration, Washington, D.C., USA.

For Chapter 4:

Rind, D., R. A. Kahn, M. Chin, S. E. Schwartz, L. A. Remer, G. Feingold, H. Yu, P. K. Quinn, and R. Halthore, 2009: 

The Way Forward, in Atmospheric Aerosol Properties and Climate Impacts, A Report by the U.S. Climate Change 

Science Program and the Subcommittee on Global Change Research. [Mian Chin, Ralph A. Kahn, and Stephen E. 

Schwartz (eds.)]. National Aeronautics and Space Administration, Washington, D.C., USA.
The U.S. Climate Change Science Program

VI

Earth observed from space. Much of the information contained in this image came from the MODIS instrument on the NASA Terra 

satellite. This 2002 “Blue Marble” features land surfaces, clouds, topography, and city lights. Credit: NASA (image processed by 

Robert Simmon and Reto Stöckli).
Authors: Lorraine A. Remer, NASA GSFC; Mian Chin, 

NASA GSFC; Philip DeCola, NASA HQ; Graham Feingold, NOAA ERSL; Rangasayi Halthore, NASA HQ/NRL; 

Ralph A. Kahn, NASA GSFC; Patricia K. Quinn, NOAA 

PMEL; David Rind, NASA GISS; Stephen E. Schwartz, 

DOE BNL; David G. Streets, DOE ANL; Hongbin Yu, 

NASA GSFC/UMBC

EXECUTIVE SUMMARY

1

Atmospheric Aerosol Properties and Climate Impacts

This report critically reviews current knowledge about global distributions 

and properties of atmospheric aerosols, as they relate to aerosol 

impacts on climate. It assesses possible next steps aimed at substantially 

reducing uncertainties in aerosol radiative forcing estimates. Current 

measurement techniques and modeling approaches are summarized, 

providing context. As a part of the Synthesis and Assessment Product 

in the Climate Change Science Program, this assessment builds upon 

recent related assessments, including the Fourth Assessment Report of the Intergovernmental Panel on Climate Change 

(IPCC AR4, 2007) and other Climate Change Science Program reports. The objectives of this report are (1) to promote 

a consensus about the knowledge base for climate change decision support, and (2) to provide a synthesis and integration 

of the current knowledge of the climate-relevant impacts of anthropogenic aerosols for policy makers, policy analysts, 

and general public, both within and outside the U.S government and worldwide.

ES 1. AEROSOLS AND THEIR

CLIMATE EFFECTS

ES 1.1. Atmospheric Aerosols

Atmospheric aerosols are suspensions of solid 

and/or liquid particles in air. Aerosols are ubiquitous in air and are often observable as dust, 

smoke, and haze. Both natural and human 

processes contribute to aerosol concentrations. On a global basis, aerosol mass derives 

predominantly from natural sources, mainly 

sea salt and dust. However, anthropogenic 

(manmade) aerosols, arising primarily from a 

variety of combustion sources, can dominate 

in and downwind of highly populated and 

industrialized regions, and in areas of intense 

agricultural burning.

The term “atmospheric aerosol” encompasses 

a wide range of particle types having different compositions, sizes, shapes, and optical 

properties. Aerosol loading, or amount in the 

atmosphere, is usually quantified by mass 

concentration or by an optical measure, aerosol 

optical depth (AOD). AOD is the vertical integral through the entire height of the atmosphere 

of the fraction of incident light either scattered 

or absorbed by airborne particles. Usually 

numerical models and in situ observations use 

mass concentration as the primary measure of 

aerosol loading, whereas most remote sensing 

methods retrieve AOD.

ES 1.2. Radiative Forcing of Aerosols

Aerosols affect Earth’s energy budget by scattering and absorbing radiation (the “direct 

effect”) and by modifying amounts and microphysical and radiative properties of clouds (the 

“indirect effects”). Aerosols influence cloud 

properties through their role as cloud condensation nuclei (CCN) and/or ice nuclei. Increases in 

aerosol particle concentrations may increase the 

ambient concentration of CCN and ice nuclei, 

affecting cloud properties. A CCN increase can 

lead to more cloud droplets so that, for fixed 

cloud liquid water content, the cloud droplet 

size will decrease. This effect leads to brighter 

clouds (the “cloud albedo effect”). Aerosols can 

also affect clouds by absorbing solar energy and 

altering the environment in which the cloud 

develops, thus changing cloud properties without actually serving as CCN. Such effects can 

change precipitation patterns as well as cloud 

extent and optical properties.

The addition of aerosols to the atmosphere alters the intensity of sunlight scattered back to 

space, absorbed in the atmosphere, and arriving 

Aerosols affect 

Earth’s energy budget 

by scattering and absorbing radiation (the 

“direct effect”) and 

by modifying amounts 

and microphysical 

and radiative properties of clouds (the 

“indirect effects”).
The U.S. Climate Change Science Program Executive Summary

2

at the surface. Such a perturbation of sunlight 

by aerosols is designated aerosol radiative forcing (RF). Note that RF must be defined as a 

perturbation from an initial state, whether that 

state be the complete absence of aerosols, the 

estimate of aerosol loading from pre-industrial 

times, or an estimate of aerosol loading for today’s natural aerosols. The RF calculated from 

the difference between today’s total aerosol 

loading (natural plus anthropogenic) and each 

of the three initial states mentioned above will 

result in different values. Also, the aerosol RF 

calculated at the top of the atmosphere, the 

bottom of the atmosphere, or any altitude in 

between, will result in different values. Other 

quantities that need to be specified when reporting aerosol RF include the wavelength range, 

the temporal averaging, the cloud conditions 

considered for direct effects, and the aerosolcloud interactions that are being considered 

for the broad classifications of indirect and 

semi-direct effects. Regardless of the exact 

definition of aerosol RF, it is characterized by 

large spatial and temporal heterogeneity due to 

the wide variety of aerosol sources and types, 

the spatial non-uniformity and intermittency of 

these sources, the short atmospheric lifetime of 

aerosols, and the chemical and microphysical 

processing that occurs in the atmosphere.

On a global average basis, the sum of direct 

and indirect forcing by anthropogenic aerosols at the top of the atmosphere is almost 

certainly negative (a cooling influence), and 

thus almost certainly offsets a fraction of the 

positive (warming) forcing due to anthropogenic greenhouse gases. However, because of 

the spatial and temporal non-uniformity of the 

aerosol RF, and likely differences in the effects 

of shortwave and longwave forcings, the net effect on Earth’s climate is not simply a fractional 

offset to the effects of forcing by anthropogenic 

greenhouse gases.

ES 1.3. Reducing Uncertainties in 

Aerosol Radiative Forcing Estimates

The need to represent aerosol influences on 

climate is rooted in the larger, policy related 

requirement to predict the climate changes 

that would result from different future emission strategies. This requires that confidence 

in climate models be based on their ability to 

accurately represent not just present climate, 

but also the changes that have occurred over 

roughly the past century. Achieving such 

confidence depends upon adequately understanding the forcings that have occurred over 

this period. Although the forcing by long-lived 

greenhouse gases is known relatively accurately 

for this period, the history of total forcing is 

not, due mainly to the uncertain contribution 

of aerosols. 

Present-day aerosol radiative forcing relative 

to preindustrial is estimated primarily using 

numerical models that simulate the emissions of 

aerosol particles and gaseous precursors and the 

aerosol and cloud processes in the atmosphere. 

The accuracy of the models is assessed primarily by comparison with observations. The key 

to reducing aerosol RF uncertainty estimates 

is to understand the contributing processes 

well enough to accurately reproduce them in 

models. This report assesses present ability to 

represent in models the distribution, properties and forcings of present-day aerosols, and 

examines the limitations of currently available 

models and measurements. The report identifies 

three specific areas where continued, focused 

effort would likely result in substantial reduction in present-day aerosol forcing uncertainty 

estimates: (1) improving quality and coverage 

of aerosol measurements, (2) achieving more 

effective use of these measurements to constrain model simulation/assimilation and to test 

model parameterizations, and (3) producing 

more accurate representation of aerosols and 

clouds in models. 

ES 2. MEASUREMENT-BASED

ASSESSMENT OF AEROSOL 

RADIATIVE FORCING

Over the past decade, measurements of aerosol 

amount, geographical distribution, and physical and chemical properties have substantially 

improved, and understanding of the controlling 

processes and the direct and indirect radiative 

effects of aerosols has increased. Key research 

activities have been:

• Development and implementation of new and 

enhanced satellite-borne sensors capable of 

observing the spatial and temporal characteristics of aerosol properties and examine 

aerosol effects on atmospheric radiation. 

• Execution of focused field experiments examining aerosol processes and properties in 

various aerosol regimes around the globe; 

Forcing by anthropogenic aerosols at the 

top of the atmosphere is negative 

(cooling) and offsets 

a fraction of the 

positive (warming) 

forcing by greenhouse gases. However, because of the 

spatial and temporal 

non-uniformity of 

aerosol forcing, the 

net effect is not simply a fractional offset.
3

Atmospheric Aerosol Properties and Climate Impacts

• Establishment and enhancement of groundbased networks measuring aerosol properties and radiative effects; 

• Development and deployment of new and 

enhanced instrumentation including devices 

to determine size dependent particle composition on fast timescales, and methods for 

determining aerosol light absorption coefficients and single scattering albedo. 

ES 2.1. Assessments of Aerosol Direct 

Radiative Forcing

Over the past 15 years, focused field campaigns 

have provided detailed characterizations of 

regional aerosol, chemical, microphysical and 

radiative properties, along with relevant surface 

and atmospheric conditions. Studies from these 

campaigns provide highly reliable characterization of submicrometer spherical particles such 

as sulfate and carbonaceous aerosol. In situ

characterization of larger particles such as dust 

are much less reliable.

For all their advantages, field campaigns are 

inherently limited by their relatively short 

duration and small spatial coverage. Surface 

networks and satellites provide a needed longterm view, and satellites provide additional extensive spatial coverage. Surface networks, such 

as the Aerosol Robotic Network (AERONET), 

provide observations of AOD at mid-visible 

wavelengths with an accuracy of 0.01 to 0.02, 

nearly three to five times more accurate than 

satellite retrievals. These same remote sensing 

ground networks also typically retrieve column 

integrated aerosol microphysical properties, but 

with uncertainties that are much larger than in 

situ measurements.

The satellite remote sensing capability developed 

over the past decades has enabled the estimate 

of aerosol radiative forcing on a global scale. 

Current satellite sensors such as the MODerate 

resolution Imaging Spectroradiometer (MODIS) 

and Multi-angle Imaging SpectroRadiometer 

(MISR) can retrieve AOD (τ) under cloud free 

conditions with an accuracy of ±0.05 ± 0.20τ

over land and better than ±0.04 ± 0.1τ over ocean 

at mid-visible wavelength. In addition, these and 

other satellite sensors can qualitatively retrieve 

particle properties (size, shape and absorption), 

a major advance over the previous generation of 

satellite instruments. Much effort has gone into 

comparing different observational methods to 

estimate global oceanic cloud-free aerosol direct 

radiative forcing for solar wavelengths at the 

top of the atmosphere (TOA). Applying various 

methods using MODIS, MISR and the Clouds 

and Earth’s Radiant Energy System (CERES), 

the aerosol direct RF at TOA derived above 

ocean converges to -5.5 ± 0.2 W m-2, where 

the initial state of the forcing perturbation is 

a completely aerosol-free atmosphere. Here, 

the uncertainty is the standard deviation of the 

various methods, indicating close agreement between the different satellite data sets. However, 

regional comparisons of the various methods 

show greater spread than the global mean. Estimates of direct radiative forcing at the ocean 

surface, and at top and bottom of the atmosphere 

over land, are also reported, but are much less 

certain. All these measurement-based estimates 

are calculated for cloud-free conditions using an 

initial state of an aerosol-free atmosphere. 

Although no proven methods exist for measuring the anthropogenic component of the observed aerosol over broad geographic regions, 

satellite retrievals are able to qualitatively 

determine aerosol type under some conditions. 

From observations of aerosol type, the best 

estimates indicate that approximately 20% of 

the AOD over the global oceans is a result of 

human activities. Following from these estimates of anthropogenic fraction, the cloud-free 

anthropogenic direct radiative forcing at TOA 

is approximated to be -1.1 ± 0.4 W m-2 over the 

global ocean, representing the anthropogenic 

perturbation to today’s natural aerosol. 

ES 2.2. Assessments of Aerosol Indirect Radiative Forcing

Remote sensing estimates of aerosol indirect 

forcing are still very uncertain. Even on small 

spatial scales, remote sensing of aerosol effects on cloud albedo do not match in situ

observations, due to a variety of difficulties 

with the remote sensing of cloud properties at 

fine scales, the inability of satellites to observe 

aerosol properties beneath cloud base, and the 

difficulty of making aerosol retrievals in cloud 

fields. Key quantities such as liquid water path, 

cloud updraft velocity and detailed aerosol size 

distributions are rarely constrained by coincident observations.

Most remote sensing observations of aerosolcloud interactions and aerosol indirect forcing 
The U.S. Climate Change Science Program Executive Summary

4

are based on simple correlations among variables, which do not establish cause-and-effect 

relationships. Inferring aerosol effects on clouds 

from the observed relationships is complicated 

further because aerosol loading and meteorology are often correlated, making it difficult to 

distinguish aerosol from meteorological effects. 

As in the case of direct forcing, the regional nature of indirect forcing is especially important 

for understanding actual climate impact.

ES 3. MODEL ESTIMATED

AEROSOL RADIATIVE FORCING 

AND ITS CLIMATE IMPACT

Just as different types of aerosol observations 

serve similar purposes, diverse types of models 

provide a variety of approaches to understanding aerosol forcing of climate. Large-scale 

Chemistry and Transport Models (CTMs) are 

used to test current understanding of the processes controlling aerosol spatial and temporal 

distributions, including aerosol and precursor 

emissions, chemical and microphysical transformations, transport, and removal. CTMs 

are used to describe the global aerosol system 

and to make estimates of direct aerosol radiative forcing. In general, CTMs do not explore 

the climate response to this forcing. General 

Circulation Models (GCMs), sometimes called 

Global Climate Models, have the capability of 

including aerosol processes as a part of the climate system to estimate aerosol climate forcing, 

including aerosol-cloud interactions, and the 

climate response to this forcing. Another type 

of model represents atmospheric processes on 

much smaller scales, such as cloud resolving 

and large eddy simulation models. These smallscale models are the primary tools for improving understanding of aerosol-cloud processes, 

although they are not used to make estimates 

of aerosol-cloud radiative forcing on regional 

or global scales.

ES 3.1. The Importance of Aerosol Radiative Forcing in Climate Models

Calculated change of surface temperature due 

to forcing by anthropogenic greenhouse gases 

and aerosols was reported in IPCC AR4 based 

on results from more than 20 participating 

global climate modeling groups. Despite a wide 

range of climate sensitivity (i.e. the amount of 

surface temperature increase due to a change 

in radiative forcing, such as an increase of CO2) 

exhibited by the models, they all yield a global 

average temperature change very similar to that 

observed over the past century. This agreement 

across models appears to be a consequence of 

the use of very different aerosol forcing values, 

which compensates for the range of climate sensitivity. For example, the direct cooling effect of 

sulfate aerosol varied by a factor of six among 

the models. An even greater disparity was seen 

in the model treatment of black carbon and 

organic carbon. Some models ignored aerosol 

indirect effects whereas others included large 

indirect effects. In addition, for those models 

that included the indirect effect, the aerosol 

effect on cloud brightness (reflectivity) varied 

by up to a factor of nine. Therefore, the fact that 

models have reproduced the global temperature 

change in the past does not imply that their future forecasts are accurate. This state of affairs 

will remain until a firmer estimate of radiative 

forcing by aerosols, as well as climate sensitivity, is available.

ES 3.2. Modeling Atmospheric Aerosols 

Simulations of the global aerosol distribution by 

different models show good agreement in their 

representation of the global mean AOD, which 

in general also agrees with satellite-observed 

values. However, large differences exist in 

model simulations of regional and seasonal 

distributions of AOD, and in the proportion of 

aerosol mass attributed to individual species. 

Each model uses its own estimates of aerosol 

and precursor emissions and configurations 

for chemical transformations, microphysical 

properties, transport, and deposition. Multimodel experiments indicate that differences 

in the models’ atmospheric processes play a 

more important role than differences in emissions in creating the diversity among model 

results. Although aerosol mass concentration 

is the basic measure of aerosol loading in the 

models, this quantity is translated to AOD via 

mass extinction efficiency in order to compare 

with observations and then to estimate aerosol 

direct RF. Each model employs its own mass 

extinction efficiency based on limited knowledge of optical and physical properties of each 

aerosol type. Thus, it is possible for the models 

to produce different distributions of aerosol 

loading as mass concentrations but agree in 

their distributions of AOD, and vice-versa.

Model calculated total global mean direct anthropogenic aerosol RF at TOA, based on the 

difference between pre-industrial and current 

The fact that models 

have reproduced the 

global temperature 

change in the past 

does not imply that 

their future forecasts 

are accurate. This 

state of affairs will 

remain until a firmer 

estimate of radiative 

forcing by aerosols, 

as well as climate 

sensitivity, is available.
5

Atmospheric Aerosol Properties and Climate Impacts

aerosol fields, is -0.22 W m-2, with a range from 

-0.63 to +0.04 W m-2. This estimate does not 

include man-made contributions of nitrate and 

dust, which could add another -0.2 W m-2 estimated by IPCC AR4. The mean value is much 

smaller than the estimates of total greenhouse 

gas forcing of +2.9 W m-2, but the comparison of 

global average values does not take into account 

immense regional variability. Over the major 

sources and their downwind regions, the modelcalculated negative forcing from aerosols can be 

comparable to or even larger than the positive 

forcing by greenhouse gases.

ES 3.3. Aerosol Effects on Clouds

Large-scale models are increasingly incorporating aerosol indirect effects into their calculations. Published large-scale model studies 

report calculated global cloud albedo effect RF 

at top-of-atmosphere, based on the perturbation 

from pre-industrial aerosol fields, ranging from 

-0.22 to -1.85 W m-2 with a central value of -0.7 

W m-2. Numerical experiments have shown that 

the cloud albedo effect is not a strong function 

of a model’s cloud or radiation scheme, and 

that although model representations of cloud 

physics are important, the differences in modeled aerosol concentrations play a strong role in 

inducing differences in the indirect as well as 

the direct effect. Although small-scale models, 

such as cloud-resolving or large eddy simulation models, do not attempt to estimate global 

aerosol RF, they are essential for understanding 

the fundamental processes occurring in clouds, 

which then leads to better representation of 

these processes in larger-scale models.

ES 3.4. Impacts of Aerosols on Climate 

Model Simulations

The current aerosol modeling capability demonstrated by chemical transport models has not 

been fully incorporated into GCM simulations. 

Of the 20+ models used in the IPCC AR4 assessment, most included sulfate direct RF, but 

only a fraction considered other aerosol types, 

and only less than a third included aerosol indirect effects. The lack of a comprehensive representation of aerosols in climate models makes 

it difficult to determine climate sensitivity, and 

thus to make climate change predictions.

Although the nature and geographical distribution of forcings by greenhouse gases and 

aerosols are quite different, it is often assumed 

that to first approximation the effects of these 

forcings on global mean surface temperature 

are additive, so that the negative forcing by 

anthropogenic aerosols has partially offset the 

positive forcing by incremental greenhouse 

gas increases over the industrial period. The 

IPCC AR4 estimates the total global average 

TOA forcing by incremental greenhouse gases 

to be 2.9 ± 0.3 W m-2, where the uncertainty 

range is meant to encompass the 90% probability that the actual value will be within the 

indicated range. The corresponding value for 

aerosol forcing at TOA (direct plus enhanced 

cloud albedo effects), defined as the perturbation from pre-industrial conditions, is -1.3 (-2.2 

to -0.5) W m-2. The total forcing, 1.6 (0.6 to 

2.4) W m-2, reflects the offset of greenhouse 

gas forcing by aerosols, where the uncertainty 

in total anthropogenic RF is dominated by the 

uncertainty in aerosol RF.

However, since aerosol forcing is much more 

pronounced on regional scales than on the 

global scale because of the highly variable 

aerosol distributions, it would be insufficient 

or even misleading to place too much emphasis 

on the global average. Also, aerosol RF at the 

surface is stronger than that at TOA, exerting 

large impacts within the atmosphere to alter 

the atmospheric circulation patterns and water 

cycle. Therefore, impacts of aerosols on climate 

should be assessed beyond the limted aspect of 

globally averaged radiative forcing at TOA.

ES 4. THE WAY FORWARD

The uncertainty in assessing total anthropogenic greenhouse gas and aerosol impacts on 

climate must be much reduced from its current 

level to allow meaningful predictions of future 

climate. This uncertainty is currently dominated by the aerosol component. In addition, 

evaluation of aerosol effects on climate must 

take into account high spatial and temporal 

variation of aerosol amounts and properties as 

well as the aerosol interactions with clouds and 

precipitation. Thus, the way forward requires 

more certain estimates of aerosol radiative forcing, which in turn requires better observations, 

improved models, and a synergistic approach.

From the observational perspective, the high 

priority tasks are:

• Maintain current and enhance future 

satellite capabilities for measuring geographical and vertical distribution of aerosol 

The uncertainty in 

total anthropogenic 

radiative forcing 

(greenhouse gases 

+ aerosols) is dominated by the uncertainty in aerosol 

radiative forcing.

Impacts of aerosols 

on climate should 

be assessed beyond 

the limited aspect 

of globally-averaged 

radiative forcing at 

top-of-atmosphere.
The U.S. Climate Change Science Program Executive Summary

6

amount and optical properties, suitable for 

estimating aerosol forcing over multi-decadal time scales and for evaluating global 

models.

• Maintain, enhance, and expand the surface observation networks measuring aerosol optical properties for satellite retrieval 

validation, model evaluation, and climate 

change assessments. Observation should 

be augmented with routine measurements 

of other key parameters with state-of-art 

techniques.

• Execute a continuing series of coordinated 

field campaigns aiming to study the atmospheric processes, to broaden the database 

of detailed aerosol chemical, physical, and 

optical/radiative characteristics, to validate 

remote-sensing retrieval products, and to 

evaluate chemistry transport models.

• Initiate and carry out a systematic program of simultaneous measurement of 

aerosol composition and size distribution, 

cloud microphysical properties, and precipitation variables.

• Fully exploit the existing information in 

satellite observations of AOD and particle type by refining retrieval algorithms, 

quantifying data quality, extracting greater 

aerosol information from joint multi-sensor 

products, and generating uniform, climatequality data records.

• Measure the formation, evolution, and 

properties of aerosols under controlled 

laboratory conditions to develop mechanistic and quantitative understanding of aerosol 

formation, chemistry, and dynamics.

• Improve measurement-based techniques 

for distinguishing anthropogenic from 

natural aerosols by combining satellite 

data analysis with in situ measurements and 

modeling methods.

Individual sensors or instruments have both 

strengths and limitations, and no single strategy is adequate for characterizing the complex 

aerosol system. The best approach is to make 

synergistic use of measurements from multiple 

platforms, sensors and instruments having 

complementary capabilities. The wealth of 

information coming from the variety of today’s sensors has not yet been fully exploited. 

Advances in measurement-based estimates of 

aerosol radiative forcing are expected in the 

near future, as existing data sets are more fully 

explored. Even so, the long-term success in reducing climate-change prediction uncertainties 

rests with improving modeling capabilities, and 

today’s suite of observations can only go so far 

towards that goal.

From the modeling perspective, the high priority tasks are: 

• Improve the accuracy and capability of 

model simulation of aerosols (including 

components and atmospheric processes) 

and aerosol direct radiative forcing. Observational strategies described above must be 

developed to constrain and validate the key 

parameters in the model.

• Advance the ability to model aerosolcloud-precipitation interaction in climate 

models, particularly the simulation of 

clouds, in order to reduce the largest uncertainty in the climate forcing/feedback 

processes.

• Incorporate improved representation 

of aerosol processes in coupled aerosolclimate system models and evaluate the 

ability of these models to simulate present 

climate and past (twentieth century) climate 

change.

• Apply coupled aerosol-climate system 

models to assess the climate change that 

would result from alternative scenarios of 

prospective future emissions of greenhouse 

gases and aerosols and aerosol precursors.

In addition to the above priorities in measurements and modeling, there is a critical need to:

• Develop and evaluate emission inventories 

of aerosol particles and precursor gases. 

Continuous development and improvement 

of current emissions, better estimates of past 

emissions, and projection of future emissions 

should be maintained.

Progress in improving modeling capabilities 

requires effort on the observational side, to 

reduce uncertainties and disagreements among 

observational data sets. The way forward 

will require integration of satellite and in situ

measurements into global models. However, 

understanding the strengths and weaknesses 

of each observational data set must be clear in 

order for the constraints they provide to improve 

confidence in the models, and for efforts at data 

assimilation to succeed. 

The way forward 

requires more 

certain estimates 

of aerosol radiative 

forcing, which in 

turn requires better 

observations, improved models, and a 

synergistic approach.
7

Atmospheric Aerosol Properties and Climate Impacts

Narrowing the gap between the current understanding of long-lived greenhouse gas and that 

of anthropogenic aerosol contributions to RF 

will require progress in all aspects of aerosolclimate science. Development of new spacebased, field and laboratory instruments will be 

needed, and in parallel, more realistic simulations of aerosol, cloud and atmospheric processes must be incorporated into models. Most 

importantly, greater synergy among different 

types of measurements, among different types 

of models, and especially between measureMost importantly, 

greater synergy 

among different 

types of measurements, among 

different types of 

models, and especially between 

measurements and 

models is critical.

ments and models is critical. Aerosol-climate 

science will naturally expand to encompass 

not only radiative effects on climate, but also 

aerosol effects on cloud processes, precipitation, 

and weather. New initiatives will strive to more 

effectively include experimentalists, remote 

sensing scientists and modelers as equal partners, and the traditionally defined communities 

in different atmospheric science disciplines will 

increasingly find common ground in addressing 

the challenges ahead. 

Several massive wildfires were across southern California during October 2003. MODIS, on the NASA 

Terra satellite, captured smoke spreading across the region and westward over the Pacific Ocean on 

October 26, 2003. Credit: NASA.
The U.S. Climate Change Science Program Executive Summary

8

Mexico city, located in a basin surrounded by mountains, often accumulates air pollution—anthropogenic 

combustion particles, sometimes mixed with wildfire smoke and mineral dust from the surrounding 

region. Photo taken from the NASA DC-8 aircraft during the INTEX-B field experiment in spring 2006. 

Credit: Cameron McNaughton, University of Hawaii.

Los Angeles in the haze at sunset. Pollution aerosols scatter sunlight, shrouding the region in an intense 

orange-brown glow, as seen through an airplane window, looking west across the LA River, with the city 

skyline in the background. Credit: Barbara Gaitley, JPL/NASA.
Introduction

Lead Authors: Ralph A. Kahn, NASA GSFC; Hongbin Yu, 

NASA GSFC/UMBC

Contributing Authors: Stephen E. Schwartz, DOE BNL; 

Mian Chin, NASA GSFC; Graham Feingold, NOAA ESRL; 

Lorraine A. Remer, NASA GSFC; David Rind, NASA 

GISS; Rangasayi Halthore, NASA HQ/NRL; Philip DeCola, 

NASA HQ

CHAPTER

1

9

Atmospheric Aerosol Properties and Climate Impacts

This report highlights key aspects of current knowledge about the global distribution of aerosols and their properties, as they relate to climate change. Leading measurement techniques 

and modeling approaches are briefly summarized, providing context for an assessment of the 

next steps needed to significantly reduce uncertainties in this component of the climate change 

picture. The present assessment builds upon the recent Inter-governmental Panel on Climate 

Change Fourth Assessment Report (IPCC AR4, 2007) and other sources.

1.1 Description of Atmospheric Aerosols

Although Earth’s atmosphere consists primarily 

of gases, aerosols and clouds play significant 

roles in shaping conditions at the surface and 

in the lower atmosphere. Aerosols are liquid 

or solid particles suspended in the air, whose 

typical diameters range over four orders of 

magnitude, from a few nanometers to a few 

tens of micrometers. They exhibit a wide range 

of compositions and shapes, that depend on 

the their origins and subsequent atmospheric 

processing. For many applications, aerosols 

from about 0.05 to 10 micrometers in diameter 

are of greatest interest, as particles in this size 

range dominate aerosol direct interaction with 

sunlight, and also make up the majority of the 

aerosol mass. Particles at the small end of this 

size range play a significant role in interactions 

with clouds, whereas particles at the large end, 

though much less numerous, can contribute 

significantly near dust and volcanic sources. 

Over the ocean, giant salt particles may also 

play a role in cloud development.

A large fraction of aerosols is natural in origin, 

including desert and soil dust, wildfire smoke, 

sea salt particles produced mainly by breaking 

bubbles in the spray of ocean whitecaps, and 

volcanic ash. Volcanoes are also sources of sulfur dioxide, which, along with sulfur-containing 

gases produced by ocean biology and the decomposition of organic matter, as well as hydrocarbons such as terpenes and isoprene emitted 

by vegetation, are examples of gases that can be 

converted to so-called “secondary” aerosols by 

chemical processes in the atmosphere. Figure 

1.1 gives a summary of aerosol processes most 

relevant to their influence on climate. 

Table 1.1 reports estimated source strengths, 

lifetimes, and amounts for major aerosol types, 

based on an aggregate of emissions estimates 

and global model simulations; the ranges provided represent model diversity only, as the 

global measurements required to validate these 

quantities are currently lacking.

Aerosol optical depth (AOD) (also called aerosol 

optical thickness, AOT, in the literature) is a 

measure of the amount of incident light either 

scattered or absorbed by airborne particles. 

Formally, aerosol optical depth is a dimensionless quantity, the integral of the product 

of particle number concentration and particle 

extinction cross-section (which accounts for 

individual particle scattering + absorption), 
The U.S. Climate Change Science Program Chapter 1

10

along a path length through the atmosphere, 

usually measured vertically. In addition to 

AOD, particle size, composition, and structure, 

which are mediated both by source type and 

subsequent atmospheric processing, determine 

how particles interact with radiant energy and 

influence the heat balance of the planet. Size 

and composition also determine the ability of 

particles to serve as nuclei upon which cloud 

droplets form. This provides an indirect means 

for aerosol to interact with radiant energy by 

modifying cloud properties.

Among the main aerosol properties required to 

evaluate their effect on radiation is the singlescattering albedo (SSA), which describes the 

fraction of light interacting with the particle 

that is scattered, compared to the total that is 

scattered and absorbed. Values range from 0 for 

totally absorbing (dark) particles to 1 for purely 

scattering ones; in nature, SSA is rarely lower 

than about 0.75. Another quantity, the asymmetry parameter (g), reports the first moment 

of the cosine of the scattered radiation angular 

distribution. The parameter g ranges from -1 

for entirely back-scattering particles, to 0 for 

isotropic (uniform) scattering, to +1 for entirely 

forward-scattering. One further quantity that 

must be considered in the energy balance is the 

surface albedo (A), a measure of reflectivity at 

the ground, which, like SSA, ranges from 0 for 

purely absorbing to 1 for purely reflecting. In 

practice, A can be near 0 for dark surfaces, and 

can reach values above 0.9 for visible light over 

snow. AOD, SSA, g, and A are all dimensionless quantities, and are in general wavelengthdependent. In this report, AOD, SSA, and g are 

given at mid-visible wavelengths, near the peak 

of the solar spectrum around 550 nanometers, 

and A is given as an average over the solar 

spectrum, unless specified otherwise. 

About 10% of global atmospheric aerosol mass 

is generated by human activity, but it is concentrated in the immediate vicinity, and downwind 

of sources (e.g., Textor et al., 2006). These anthropogenic aerosols include primary (directly 

emitted) particles and secondary particles that 

are formed in the atmosphere. Anthropogenic 

aerosols originate from urban and industrial 

emissions, domestic fire and other combustion 

products, smoke from agricultural burning, and 

soil dust created by overgrazing, deforestation, 

draining of inland water bodies, some farming 

practices, and generally, land management 

activities that destabilize the surface regolith 

to wind erosion. The amount of aerosol in 

the atmosphere has greatly increased in some 

parts of the world during the industrial period, 

and the nature of this particulate matter has 

substantially changed as a consequence of the 

evolving nature of emissions from industrial, 

commercial, agricultural, and residential activities, mainly combustion-related. 

One of the greatest challenges in studying aerosol impacts on climate is the immense diversity, 

not only in particle size, composition, and 

origin, but also in spatial and temporal distribution. For most aerosols, whose primary source 

is emissions near the surface, concentrations 

are greatest in the atmospheric boundary layer, 

decreasing with altitude in the free troposphere. 

However, smoke from wildfires and volcanic 

effluent can be injected above the boundary 

layer; after injection, any type of aerosol can be 

lofted to higher elevations; this can extend their 

atmospheric lifetimes, increasing their impact 

spatially and climatically. 

Aerosols are removed from the atmosphere 

primarily through cloud processing and wet 

Figure 1.1. Major aerosol processes relevant to their impact on climate. Aerosols can be directly emitted as primary particles and can form secondarily by the 

oxidation of emitted gaseous precursors. Changes in relative humidity (RH) can 

cause particle growth or evaporation, and can alter particle properties. Physical 

processes within clouds can further alter particle properties, and conversely, 

aerosols can affect the properties of clouds, serving as condensation nuclei for 

new cloud droplet formation. Aqueous-phase chemical reactions in cloud drops 

or in clear air can also affect aerosol properties. Particles are ultimately removed 

from the atmosphere, scavenged by falling raindrops or settling by dry deposition. 

Modified from Ghan and Schwartz (2007).

One of the greatest 

challenges in studying aerosol impacts 

on climate is the 

immense diversity, 

not only in particle 

size, composition, 

and origin, but also in 

spatial and temporal 

distribution.
11

Atmospheric Aerosol Properties and Climate Impacts

deposition in precipitation, a mechanism that 

establishes average tropospheric aerosol atmospheric lifetimes at a week or less (Table 1.1). 

The efficiency of removal therefore depends 

on the proximity of aerosols to clouds. For example, explosive volcanoes occasionally inject 

large amounts of aerosol precursors into the 

stratosphere, above most clouds; sulfuric acid 

aerosols formed by the 1991 Pinatubo eruption 

exerted a measurable effect on the atmospheric 

heat budget for several years thereafter (e.g., 

Minnis et al., 1993; McCormick et al., 1995; 

Robock, 2000, 2002). Aerosols are also removed by dry deposition processes: gravitational settling tends to eliminate larger particles, 

impaction typically favors intermediate-sized 

particles, and coagulation is one way smaller 

particles can aggregate with larger ones, leading to their eventual deposition by wet or dry 

processes. Particle injection height, subsequent 

air mass advection, and other factors also affect 

the rate at which dry deposition operates.

Despite relatively short average residence times, 

aerosols regularly travel long distances. For 

example, particles moving at mean velocity of 

5 m s-1 and remaining in the atmosphere for a 

week will travel 3000 km. Global aerosol observations from satellites provide ample evidence 

of this– Saharan dust reaches the Caribbean 

and Amazon basin, Asian desert dust and anthropogenic aerosol is found over the central 

Pacific and sometimes as far away as North 

America, and Siberian smoke can be deposited 

in the Arctic. This transport, which varies both 

seasonally and inter-annually, demonstrates the 

global scope of aerosol influences.

As a result of the non-uniform distribution of 

aerosol sources and sinks, the short atmospheric 

lifetimes and intermittent removal processes 

compared to many atmospheric greenhouse 

trace gases, the spatial distribution of aerosol 

particles is quite non-uniform. The amount and 

nature of aerosols vary substantially with location and from year to year, and in many cases 

exhibit strong seasonal variations. 

One consequence of this heterogeneity is that 

the impact of aerosols on climate must be understood and quantified on a regional rather 

than just a global-average basis. AOD trends 

observed in the satellite and surface-based 

data records suggest that since the mid-1990s, 

the amount of anthropogenic aerosol has decreased over North America and Europe, but 

has increased over parts of east and south Asia; 

on average, the atmospheric concentration of 

low-latitude smoke particles has increased 

(Mishchenko and Geogdzhayev, 2007). The 

observed AOD trends in the northern hemisphere are qualitatively consistent with changes 

in anthropogenic emissions (e.g. Streets et al., 

2006a), and with observed trends in surface 

solar radiation flux (“solar brightening” or 

“dimming”), though other factors could be 

involved (e.g., Wild et al., 2005). Similarly, the 

increase in smoke parallels is associated with 

Table 1.1. Estimated source strengths, lifetimes, mass loadings, and optical depths of major aerosol types. Statistics 

are based on results from 16 models examined by the Aerosol Comparisons between Observations and Models 

(AeroCom) project (Textor et al., 2006; Kinne et al., 2006). BC = black carbon; POM = particulate organic matter. 

See Chapter 3 for more details.

Aerosol Type Total source1

(Tg/yr1) Lifetime (day) Mass loading1 (Tg) Optical depth @ 550 nm

Median (Range) Median (Range) Median (Range) Median (Range)

Sulfate2 190 (100-230) 4.1 (2.6-5.4) 2.0 (0.9-2.7) 0.034 (0.015-0.051) 

BC 11 ( 8-20) 6.5 (5.3-15) 0.2 (0.05-0.5) 0.004 (0.002-0.009) 

POM2 100 (50-140) 6.2 (4.3-11) 1.8 (0.5-2.6) 0.019 (0.006-0.030) 

Dust 1600 (700-4000) 4.0 (1.3-7) 20 (5-30) 0.032 (0.012-0.054)

Sea salt 6000 (2000-120000) 0.4 (0.03-1.1) 6 (3-13) 0.030 (0.020-0.067) 

Total 0.13 (0.065-0.15)

1 Tg (teragram) = 1012 g, or million metric tons.

2 The sulfate aerosol source is mainly SO2 oxidation, plus a small fraction of direct emission. The organic matter source includes 

direct emission and hydrocarbon oxidation.

The impact of 

aerosols on climate 

must be understood and quantified on a regional 

rather than just a 

global-average basis.
The U.S. Climate Change Science Program Chapter 1

12

changing biomass burning patterns (e.g., Koren 

et al., 2007a). 

1.2 The Climate Effects of Aerosols

Aerosols exert a variety of impacts on the 

environment. Aerosols (sometimes referred to 

particulate matter or “PM,” especially in air 

quality applications), when concentrated near 

the surface, have long been recognized as affecting pulmonary function and other aspects 

of human health. Sulfate and nitrate aerosols 

play a role in acidifying the surface downwind 

of gaseous sulfur and odd nitrogen sources. Particles deposited far downwind might fertilize 

iron-poor waters in remote oceans, and Saharan 

dust reaching the Amazon Basin is thought to 

contribute nutrients to the rainforest soil. 

Aerosols also interact strongly with solar and 

terrestrial radiation in several ways. Figure 1.2 

offers a schematic overview. First, they scatter 

and absorb sunlight (McCormick and Ludwig, 

1967; Charlson and Pilat, 1969; Atwater, 1970; 

Mitchell, Jr., 1971; Coakley et al., 1983); these 

are described as “direct effects” on shortwave 

(solar) radiation. Second, aerosols act as sites 

at which water vapor can accumulate during cloud droplet formation, serving as cloud 

condensation nuclei or CCN. Any change in 

number concentration or hygroscopic properties 

of such particles has the potential to modify 

the physical and radiative properties of clouds, 

altering cloud brightness (Twomey, 1977) and 

the likelihood and intensity with which a cloud 

will precipitate (e.g., Gunn and Phillips, 1957; 

Liou and Ou 1989; Albrecht, 1989). Collectively 

changes in cloud processes due to anthropogenic aerosols are referred to as aerosol indirect 

effects. Finally, absorption of solar radiation 

by particles is thought to contribute to a reduction in cloudiness, a phenomenon referred to 

as the semi-direct effect. This occurs because 

absorbing aerosol warms the atmosphere, which 

changes the atmospheric stability, and reduces 

surface flux.

The primary direct effect of aerosols is a brightening of the planet when viewed from space, as 

much of Earth’s surface is dark ocean, and most 

aerosols scatter more than 90% of the visible 

light reaching them. The primary indirect effects of aerosols on clouds include an increase 

in cloud brightness, change in precipitation and 

possibly an increase in lifetime; thus the overall 

net impact of aerosols is an enhancement of 

Earth’s reflectance (shortwave albedo). This 

reduces the sunlight reaching Earth’s surface, 

producing a net climatic cooling, as well as a 

redistribution of the radiant and latent heat energy deposited in the atmosphere. These effects 

can alter atmospheric circulation and the water 

cycle, including precipitation patterns, on a 

variety of length and time scales (e.g., Ramanathan et al., 2001a; Zhang et al., 2006).

Figure 1.2. Aerosol radiative forcing. Airborne particles can affect the heat balance of the atmosphere, directly, by scattering and 

absorbing sunlight, and indirectly, by altering cloud brightness and possibly lifetime. Here small black dots represent aerosols, circles 

represent cloud droplets, straight lines represent short-wave radiation, and wavy lines, long-wave radiation. LWC is liquid water content, 

and CDNC is cloud droplet number concentration. Confidence in the magnitudes of these effects varies considerably (see Chapter 

3). Although the overall effect of aerosols is a net cooling at the surface, the heterogeneity of particle spatial distribution, emission 

history, and properties, as well as differences in surface reflectance, mean that the magnitude and even the sign of aerosol effects vary 

immensely with location, season and sometimes inter-annually. The human-induced component of these effects is sometimes called 

“climate forcing.” (From IPCC, 2007, modified from Haywood and Boucher, 2000).)

The primary direct 

effect of aerosols is 

a brightening of the 

planet when viewed 

from space. The primary indirect effects 

of aerosols on clouds 

include an increase in 

cloud brightness and 

possibly an increase 

in lifetime. The 

overall net impact 

of aerosols is an enhancement of Earth’s 

reflectance.
13

Atmospheric Aerosol Properties and Climate Impacts

Several variables are used to quantify the impact 

aerosols have on Earth’s energy balance; these 

are helpful in describing current understanding, 

and in assessing possible future steps. 

For the purposes of this report, aerosol radiative forcing (RF) is defined as the net energy 

flux (downwelling minus upwelling) difference 

between an initial and a perturbed aerosol loading state, at a specified level in the atmosphere. 

(Other quantities, such as solar radiation, are 

assumed to be the same for both states.) This 

difference is defined such that a negative aerosol forcing implies that the change in aerosols 

relative to the initial state exerts a cooling 

influence, whereas a positive forcing would 

mean the change in aerosols exerts a warming 

influence.

There are a number of subtleties associated with 

this definition: 

(1) The initial state against which aerosol forcing is assessed must be specified. For direct 

aerosol radiative forcing, it is sometimes taken 

as the complete absence of aerosols. IPCC AR4 

(2007) uses as the initial state their estimate 

of aerosol loading in 1750. That year is taken 

as the approximate beginning of the era when 

humans exerted accelerated influence on the 

environment. 

(2) A distinction must be made between aerosol RF and the anthropogenic contribution

to aerosol RF. Much effort has been made to 

distinguishing these contributions by modeling 

and with the help of space-based, airborne, and 

surface-based remote sensing, as well as in situ

measurements. These efforts are described in 

subsequent chapters. 

(3) In general, aerosol RF and anthropogenic 

aerosol RF include energy associated with 

both the shortwave (solar) and the long-wave 

(primarily planetary thermal infrared) components of Earth’s radiation budget. However, 

the solar component typically dominates, so 

in this document, these terms are used to refer 

to the solar component only, unless specified 

otherwise. The wavelength separation between 

the short- and long-wave components is usually 

set at around three or four micrometers. 

(4) The IPCC AR4 (2007) defines radiative 

forcing as the net downward minus upward 

irradiance at the tropopause due to an external driver of climate change. This definition 

excludes stratospheric contributions to the 

overall forcing. Under typical conditions, most 

aerosols are located within the troposphere, so 

aerosol forcing at TOA and at the tropopause 

are expected to be very similar. Major volcanic 

eruptions or conflagrations can alter this picture 

regionally, and even globally. 

(5) Aerosol radiative forcing can be evaluated 

at the surface, within the atmosphere, or at topof-atmosphere (TOA). In this document, unless 

specified otherwise, aerosol radiative forcing is 

assessed at TOA. 

(6) As discussed subsequently, aerosol radiative forcing can be greater at the surface than 

at TOA if the aerosols absorb solar radiation. 

TOA forcing affects the radiation budget of the 

planet. Differences between TOA forcing and 

surface forcing represent heating within the 

atmosphere that can affect vertical stability, circulation on many scales, cloud formation, and 

precipitation, all of which are climate effects 

of aerosols. In this document, unless specified 

otherwise, these additional climate effects are 

not included in aerosol radiative forcing. 

(7) Aerosol direct radiative forcing can be 

evaluated under cloud-free conditions or under 

natural conditions, sometimes termed “all-sky” 

conditions, which include clouds. Cloud-free 

direct aerosol forcing is more easily and more 

accurately calculated; it is generally greater 

than all-sky forcing because clouds can mask 

the aerosol contribution to the scattered light. 

Indirect forcing, of course, must be evaluated 

for cloudy or all-sky conditions. In this document, unless specified otherwise, aerosol radiative forcing is assessed for all-sky conditions. 

(8) Aerosol radiative forcing can be evaluated 

instantaneously, daily (24-hour) averaged, or 

assessed over some other time period. Many 

measurements, such as those from polar-orbiting satellites, provide instantaneous values, 

whereas models usually consider aerosol RF as 

a daily average quantity. In this document, unless specified otherwise, daily averaged aerosol 

radiative forcing is reported. 

(9) Another subtlety is the distinction between 

a “forcing” and a “feedback.” As different parts 

of the climate system interact, it is often unclear 

Aerosol radiative 

forcing is defined 

as the net energy 

flux (downwelling 

minus upwelling) 

difference between 

an initial and a 

perturbed aerosol 

loading state.
The U.S. Climate Change Science Program Chapter 1

14

which elements are “causes” of climate change 

(forcings among them), which are responses 

to these causes, and which might be some of 

each. So, for example, the concept of aerosol 

effects on clouds is complicated by the impact 

clouds have on aerosols; the aggregate is often 

called aerosol-cloud interactions. This distinction sometimes matters, as it is more natural 

to attribute responsibility for causes than for 

responses. However, practical environmental 

considerations usually depend on the net result 

of all influences. In this report, “feedbacks” 

are taken as the consequences of changes in 

surface or atmospheric temperature, with the 

understanding that for some applications, the 

accounting may be done differently. 

In summary, aerosol radiative forcing, the 

fundamental quantity about which this report 

is written, must be qualified by specifying the 

initial and perturbed aerosol states for which 

the radiative flux difference is calculated, the 

altitude at which the quantity is assessed, the 

wavelength regime considered, the temporal 

averaging, the cloud conditions, and whether 

total or only human-induced contributions are 

considered. The definition given here, qualified 

as needed, is used throughout the report. 

Although the possibility that aerosols affect 

climate was recognized more than 40 years 

ago, the measurements needed to establish the 

magnitude of such effects, or even whether 

Figure 1.3a. (Above) Global average radiative forcing (RF) estimates and uncertainty ranges in 2005, 

relative to the pre-industrial climate. Anthropogenic carbon dioxide (CO2), methane (CH4), nitrous 

oxide (N2O), ozone, and aerosols as well as the natural solar irradiance variations are included. Typical 

geographical extent of the forcing (spatial scale) and the assessed level of scientific understanding (LOSU) 

are also given. Forcing is expressed in units of watts per square meter (W m-2). The total anthropogenic 

radiative forcing and its associated uncertainty are also given. Figure from IPCC (2007).

Figure 1.3b. (Left) Probability distribution functions (PDFs) for anthropogenic aerosol and GHG 

RFs. Dashed red curve: RF of long-lived greenhouse 

gases plus ozone; dashed blue curve: RF of aerosols (direct and cloud albedo RF); red filled curve: 

combined anthropogenic RF. The RF range is at 

the 90% confidence interval. Figure adapted from 

IPCC (2007).
15

Atmospheric Aerosol Properties and Climate Impacts

specific aerosol types warm or cool the surface, 

were lacking. Satellite instruments capable of 

at least crudely monitoring aerosol amount 

globally were first deployed in the late 1970s. 

But scientific focus on this subject grew substantially in the 1990s (e.g. Charlson et al., 

1990; 1991; 1992; Penner et al., 1992), in part 

because it was recognized that reproducing the 

observed temperature trends over the industrial 

period with climate models requires including 

net global cooling by aerosols in the calculation 

(IPCC, 1995; 1996), along with the warming 

influence of enhanced atmospheric greenhouse 

gas (GHG) concentrations – mainly carbon 

dioxide, methane, nitrous oxide, chlorofluorocarbons, and ozone. 

Improved satellite instruments, ground- and 

ship-based surface monitoring, more sophisticated chemical transport and climate models, 

and field campaigns that brought all these 

elements together with aircraft remote sensing 

and in situ sampling for focused, coordinated 

study, began to fill in some of the knowledge 

gaps. By the Fourth IPCC Assessment Report, 

the scientific community consensus held 

that in global average, the sum of direct and 

indirect top-of-atmosphere (TOA) forcing by 

anthropogenic aerosols is negative (cooling) of 

about -1.3 W m-2 (-2.2 to -0.5 W m-2). This is 

significant compared to the positive forcing by 

anthropogenic GHGs (including ozone), about 

2.9 ± 0.3 W m-2 (IPCC, 2007). However, the 

spatial distribution of the gases and aerosols 

are very different, and they do not simply exert 

compensating influences on climate. 

The IPCC aerosol forcing assessments are based 

largely on model calculations, constrained as 

much as possible by observations. At present, aerosol influences are not yet quantified 

adequately, according to Figure 1.3a, as scientific understanding is designated as “Medium 

- Low” and “Low” for the direct and indirect 

climate forcing, respectively. The IPCC AR4 

(2007) concluded that uncertainties associated 

with changes in Earth’s radiation budget due to 

anthropogenic aerosols make the largest contribution to the overall uncertainty in radiative 

forcing of climate change among the factors assessed over the industrial period (Figure 3b). 

Although AOD, aerosol properties, aerosol 

vertical distribution, and surface reflectivity 

all contribute to aerosol radiative forcing, AOD 

usually varies on regional scales more than the 

other aerosol quantities involved. Forcing efficiency (Eτ), defined as a ratio of direct aerosol 

radiative forcing to AOD at 550 nm, reports the 

sensitivity of aerosol radiative forcing to AOD, 

and is useful for isolating the influences of 

particle properties and other factors from that 

of AOD. Eτ is expected to exhibit a range of 

values globally, because it is governed mainly 

by aerosol size distribution and chemical 

composition (which determine aerosol singlescattering albedo and phase function), surface 

reflectivity, and solar irradiance, each of which 

exhibits pronounced spatial and temporal variations. To assess aerosol RF, Eτ is multiplied by 

the ambient AOD.

Figure 1.4 shows a range of Eτ, derived from 

AERONET surface sun photometer network 

measurements of aerosol loading and particle 

properties, representing different aerosol and 

surface types, and geographic locations. It 

demonstrates how aerosol direct solar radiative 

forcing (with initial state taken as the absence 

of aerosol) is determined by a combination of 

aerosol and surface properties. For example, Eτ

due to southern African biomass burning smoke 

is greater at the surface and smaller at TOA than 

South American smoke because the southern 

African smoke absorbs sunlight more strongly, 

and the magnitude of Eτ for mineral dust for 

several locations varies depending on the underlying surface reflectance. Figure 1.4 illustrates 

one further point, that the radiative forcing by 

aerosols on surface energy balance can be much 

greater than that at TOA. This is especially true 

Figure 1.4. The clear-sky forcing efficiency Eτ, defined as the diurnally averaged 

aerosol direct radiative effect (W m-2) per unit AOD at 550 nm, calculated at 

both TOA and the surface, for typical aerosol types over different geographical 

regions. The vertical black lines represent ± one standard deviation of Eτ for 

individual aerosol regimes and A is surface broadband albedo. (adapted from 

Zhou et al., 2005).

Reproducing the 

observed temperature trends 

over the industrial 

period with climate 

models requires 

including net global 

cooling by aerosols 

in the calculation.

The radiative 

forcing by aerosols 

on surface energy 

balance can be 

much greater than 

that at the top of 

the atmosphere.
The U.S. Climate Change Science Program Chapter 1

16

when the particles have SSA substantially less 

than 1, which can create differences between 

surface and TOA forcing as large as a factor of 

five (e.g., Zhou et al., 2005).

Table 1.2 presents estimates of cloud-free, instantaneous, aerosol direct RF dependence on 

AOD, and on aerosol and surface properties, 

calculated for three sites maintained by the US 

Department of Energy’s Atmospheric Radiation 

Measurement (ARM) program, where surface 

and atmospheric conditions span a significant 

range of natural environments (McComiskey et 

al., 2008a). Here aerosol RF is evaluated relative 

to an initial state that is the complete absence of 

aerosols. Note that aerosol direct RF dependence 

on individual parameters varies considerably, 

depending on the values of the other parameters, 

and in particular, that aerosol RF dependence on 

AOD actually changes sign, from net cooling to 

net warming, when aerosols reside over an exceedingly bright surface. Sensitivity values are 

given for snapshots at fixed solar zenith angles, 

relevant to measurements made, for example, by 

polar-orbiting satellites.

The lower portion of Table 1.2 presents upper 

bounds on instantaneous measurement uncertainty, assessed individually for each of AOD, 

SSA, g, and A, to produce a 1 W m-2 top-ofatmosphere, cloud-free aerosol RF accuracy. 

The values are derived from the upper portion 

of the table, and reflect the diversity of conditions captured by the three ARM sties. Aerosol 

RF sensitivity of 1 W m-2 is used as an example; 

uncertainty upper bounds are obtained from the 

partial derivative for each parameter by neglecting the uncertainties for all other parameters. 

These estimates produce an instantaneous AOD 

measurement uncertainty upper bound between 

about 0.01 and 0.02, and SSA constrained to 

about 0.02 over surfaces as bright or brighter 

than the ARM Southern Great Plains site, 

typical of mid-latitude, vegetated land. Other 

researchers, using independent data sets, have 

derived ranges of Eτ and aerosol RF sensitivity 

similar to those presented here, for a variety of 

conditions (e.g., Christopher and Jones, 2008; 

Yu et al., 2006; Zhou et al., 2005).

These uncertainty bounds provide a baseline 

against which current and expected near-future 

instantaneous measurement capabilities are 

assessed in Chapter 2. Model sensitivity is 

usually evaluated for larger-scale (even global) 

and longer-term averages. When instantaneous 

measured values from a randomly sampled 

population are averaged, the uncertainty component associated with random error diminishes 

as something like the inverse square root of the 

number of samples. As a result, the accuracy 

limits used for assessing more broadly averaged 

model results corresponding to those used for 

assessing instantaneous measurements, would 

have to be tighter, as discussed in Chapter 4.

In summary, much of the challenge in quantifying aerosol influences arises from large 

spatial and temporal heterogeneity, caused 

by the wide variety of aerosol sources, sizes 

and compositions, the spatial non-uniformity 

and intermittency of these sources, the short 

atmospheric lifetime of most aerosols, and the 

spatially and temporally non-uniform chemical 

and microphysical processing that occurs in the 

atmosphere. In regions having high concentrations of anthropogenic aerosol, for example, 

aerosol forcing is much stronger than the global 

average, and can exceed the magnitude of GHG 

warming, locally reversing the sign of the net 

forcing. It is also important to recognize that 

the global-scale aerosol TOA forcing alone 

is not an adequate metric for climate change 

(NRC, 2005). Due to aerosol absorption, 

mainly by soot, smoke, and some desert dust 

particles, the aerosol direct radiative forcing at 

the surface can be much greater than the TOA 

forcing, and in addition, the radiative heating 

of the atmosphere by absorbing particles can 

change the atmospheric temperature structure, 

affecting vertical mixing, cloud formation and 

evolution, and possibly large-scale dynamical 

systems such as the monsoons (Kim et al., 2006; 

Lau et al., 2008). By realizing aerosol’s climate 

significance and the challenge of charactering 

highly variable aerosol amount and properties, 

the US Climate Change Research Initiative 

(CCRI) identified research on atmospheric concentrations and effects of aerosols specifically 

as a top priority (NRC, 2001).

1.3. Reducing Uncertainties in AerosolClimate Forcing Estimates

Regional as well as global aerosol radiative effects on climate are estimated primarily through 

the use of climate models (e.g., Penner et al., 

1994; Schulz et al., 2006). These numerical 

models are evaluated based on their ability to 

simulate the aerosol- and cloud-related processes that affect climate for current and past 

In regions having high 

concentrations of 

anthropogenic aerosol, aerosol forcing is 

much stronger than 

the global average, 

and can exceed the 

magnitude of greenhouse gas warming.

Radiative heating of 

the atmosphere by 

absorbing particles 

can change the atmospheric temperature 

structure, affecting 

vertical mixing, cloud 

formation and evolution, and possibly 

large-scale dynamical 

systems.
17

Atmospheric Aerosol Properties and Climate Impacts

conditions. The derived accuracy serves as a 

measure of the accuracy with which the models 

might be expected to predict the dependence of 

future climate conditions on prospective human 

activities. To generate such predictions, the 

models must simulate the physical, chemical, 

and dynamical mechanisms that govern aerosol formation and evolution in the atmosphere 

(Figure 1.1), as well as the radiative processes 

that govern their direct and indirect climate 

impact (Figure 1.2), on all the relevant space 

and time scales.

Some models simulate aerosol emissions, transports, chemical processing, and sinks, using 

atmospheric and possibly also ocean dynamics generated off-line by separate numerical 

systems. These are often called Chemistry 

and Transport Models (CTMs). In contrast, 

General Circulation Models or Global Climate 

Models (GCMs) can couple aerosol behavior 

and dynamics as part of the same calculation, 

and are capable of representing interactions 

between aerosols and dynamical aspects of 

the climate system, although currently many 

Table 1.2. Top-of-atmosphere, cloud-free, instantaneous direct aerosol radiative forcing 

dependence on aerosol and surface properties. Here TWP, SGP, and NSA are the Tropical 

West Pacific island, Southern Great Plains, and North Slope Alaska observation stations 

maintained by the DOE ARM program, respectively. Instantaneous values are given at 

specific solar zenith angle. Upper and middle parts are from McComiskey et al. (2008a). 

Representative, parameter-specific measurement uncertainty upper bounds for producing 

1 W m-2 instantaneous TOA forcing accuracy are given in the lower part, based on sensitivities at three sites from the middle part of the table.

Parameters TWP SGP NSA

Aerosol properties (AOD, SSA, g), solar zenith angle (SZA), surface albedo (A), and aerosol 

direct RF at TOA (F):

AOD 0.05 0.1 0.05

SSA 0.97 0.95 0.95

g 0.8 0.6 0.7

A 0.05 0.1 0.9

SZA 30 45 70

F (W m-2) -2.2 -6.3 2.6

Sensitivity of cloud-free, instantaneous, TOA direct aerosol radiative forcing to aerosol and 

surface properties , W m-2 per unit change in property:

∂F/∂(AOD) -45 -64 51

∂F/∂(SSA) -11 -50 -60

∂F/∂g 13 23 2

∂F/∂A 8 24 6

Representative measurement uncertainty upper bounds for producing 1 W m-2 accuracy 

of aerosol RF:

AOD 0.022 0.016 0.020

SSA 0.091 0.020 0.017

g 0.077 0.043 

A 0.125 0.042 0.167
The U.S. Climate Change Science Program Chapter 1

18

of them still use prescribed aerosols to study 

climate sensitivity.

The IPCC AR4 total anthropogenic radiative 

forcing estimate, shown in Figure 1.3, is 1.6 

W m-2 from preindustrial times to the present, 

with a likely range of 0.6 to 2.4 W m-2. This 

estimate includes long-lived GHGs, ozone, and 

aerosols. The increase in global mean surface 

temperature of 0.7°C, from the transient climate 

simulations in response to this forcing, yields 

a transient climate sensitivity (defined as the 

surface temperature change per unit RF) over 

the industrial period of 0.3 to 1.1°C/(W m-2). 

Under most emission scenarios, CO2 is expected 

to double by the latter part of the 21st century. 

A climate sensitivity range of 0.3 to 1.1°C/(W 

m-2) translates into a future surface temperature 

increase attributable to CO2 forcing at the time 

of doubled CO2 of 1.2 to 4.7°C. Such a range 

is too wide to meaningfully predict the climate 

response to increased greenhouse gases (e.g., 

Caldeira et al., 2003). As Figure 1.3 shows, 

the largest contribution to overall uncertainty 

in estimating the climate response is from 

aerosol RF.

The key to reducing uncertainty in the role of 

aerosols in climate is to much better represent 

the processes that contribute to the aerosol climate effects in models. This report highlights 

three specific areas for continued, focused 

effort: (1) improving measurement quality and 

coverage, (2) achieving more effective use of 

measurements to constrain model simulations 

and to test model parameterizations, and (3) 

producing more accurate representation of 

aerosols and clouds in models. This section 

provides a brief introduction to the current 

state of aerosol measurements and model 

representations of aerosol processes, as they 

relate to assessing aerosol impacts on climate. 

More complete discussion of these topics and 

assessment of possible next steps are given in 

Chapters 2, 3, and 4.

Improving measurement quality and coverage. Aerosol mass concentration, size and 

composition distributions, and absorption properties, as functions of location and time, are the 

main aerosol-specific elements of CTMs. They 

depend on primary particle and precursor gas 

emissions, on gas-to-particle conversion processes, on transport, humidification and cloud 

processing, and removal mechanisms. Satellite 

instruments, surface-based networks (in situ

and remote sensing), and research aircraft all 

contribute quantitative measurements of aerosol 

properties and/or distributions that can be used 

to help constrain models, as well as to test and 

refine the model representations of processes 

that govern aerosol life cycles. As described 

in Chapter 2, the current situation reflects the 

significant progress that has been made over 

the past decade in satellite, airborne, groundbased and laboratory instrumentation, actual 

measurements available from each of these 

sources, remote sensing retrieval methods, and 

data validation techniques.

However, each type of measurement is limited in terms of the accuracy, and spatial and 

temporal sampling of measured quantities. 

At present, satellite passive imagers monitor 

AOD globally up to once per day, with accuracies under cloud-free, good but not necessarily 

ideal viewing conditions of about 0.05 or (0.1 to 

0.2) x AOD, whichever is larger, for vegetated 

land, somewhat better over dark water, and less 

well over bright desert (e.g., Kahn et al., 2005a; 

Remer et al., 2005). Reliable AOD retrieval 

over snow and ice from passive remote sensing 

imagers has not yet been achieved. From space, 

aerosol vertical distribution is provided mainly 

by lidars that offer sensitivity to multiple layers, even in the presence of thin cloud, but they 

require several weeks to observe just a fraction 

of a percent of the planet.

From the expansive vantage point of space, 

there is enough information to identify columnaverage ratios of coarse to fine AOD, or even 

aerosol air mass types in some circumstances, 

but not sufficient to deduce chemical composition and vertical distribution of type, nor to constrain light absorption approaching the ~0.02 

SSA sensitivity suggested in Section 1.2.

As a result, it is difficult to separate anthropogenic from natural aerosols using currently 

available satellite data alone, though attempts at 

this have been made based on retrieved particle 

size and shape information (see Chapter 2). At 

present, better quantification of anthropogenic 

aerosol depends upon integrating satellite measurements with other observations and models. 

Aircraft and ground-based in situ sampling 

can help fill in missing physical and chemical 

detail, although coverage is very limited in 

The key to reducing 

uncertainty in the 

role of aerosols in 

climate is to much 

better represent 

the processes that 

contribute to the 

aerosol climate 

effects in models.
19

Atmospheric Aerosol Properties and Climate Impacts

both space and time. Models can contribute by 

connecting observed aerosol distributions with 

likely sources and associated aerosol types. 

Surface remote-sensing monitoring networks 

offer temporal resolution of minutes to hours, 

and greater column AOD accuracy than satellite observations, but height-resolved particle 

property information has been demonstrated 

by only a few cutting-edge technologies such 

as high-spectral-resolution lidar (HSRL), and 

again, spatial coverage is extremely limited. 

Even for satellite observations, sampling is an 

issue. From the passive imagers that provide 

the greatest coverage, AOD retrievals can only 

be done under cloud-free conditions, leading 

to a “clear-sky bias,” and there are questions 

about retrieval accuracy in the vicinity of 

clouds. And retrievals of aerosol type from 

these instruments as well as from surface-based 

passive remote sensing require at least a certain 

minimum column AOD to be effective; the 

thresholds depend in part on aerosol type itself 

and on surface reflectivity, leading to an “AOD 

bias” in these data sets.

Other measurement-related issues include 

obtaining sufficiently extensive aerosol vertical distributions outside the narrow sampling 

beam of space-based, airborne, or ground-based 

lidars, retrieving layer-resolved aerosol properties, which is especially important in the many 

regions where multiple layers of different types 

are common, obtaining representative in situ

samples of large particles, since they tend to be 

under-sampled when collected by most aircraft 

inlets, and acquiring better surface measurement coverage over oceans.

Achieving more effective use of measurements 

to constrain models. Due to the limitations 

associated with each type of observational data 

record, reducing aerosol-forcing uncertainties 

requires coordinated efforts at integrating data 

from multiple platforms and techniques (Seinfeld 

et al., 1996; Kaufman et al., 2002a; Diner et al., 

2004; Anderson et al., 2005a). Initial steps have 

been taken to acquire complementary observations from multiple platforms, especially through 

intensive field campaigns, and to merge data sets, 

exploiting the strengths of each to provide better 

constraints on models (e.g., Bates et al., 2006; Yu 

et al., 2006; Kinne et al., 2006; see Chapter 2, 

Section 2.2.6). Advanced instrument concepts, 

coordinated measurement strategies, and retrieval techniques, if implemented, promise to further 

improve the contributions observations make to 

reducing aerosol forcing uncertainties.

Producing more accurate representation of 

aerosols in models. As discussed in Chapter 

3, models, in turn, have developed increasingly sophisticated representations of aerosol 

types and processes, have improved the spatial 

resolution at which simulations are performed, 

and through controlled experiments and intercomparisons of results from many models, 

have characterized model diversity and areas 

of greatest uncertainty (e.g., Textor et al., 2006; 

Kinne et al., 2006).

A brief chronology of aerosol modeling used for 

the IPCC reports illustrates these developments. 

In the IPCC First Assessment Report (1990), 

the few transient climate change simulations 

that were discussed used only increases in 

greenhouse gases. By IPCC Second Assessment Report (1995), although most GCMs still 

considered only greenhouse gases, several 

simulations included the direct effect of sulfate 

aerosols. The primary purpose was to establish 

whether the pattern of warming was altered by 

including aerosol-induced cooling in regions 

of high emissions such as the Eastern U.S. and 

eastern Asia. In these models, the sulfate aerosol distribution was derived from a sulfur cycle 

model constrained by estimated past aerosol 

emissions and an assumed future sulfur emission scenario. The aerosol forcing contribution 

was mimicked by increasing the surface albedo, 

which improved model agreement with the observed global mean temperature record for the 

final few decades of the twentieth century, but 

not for the correct reasons (see Chapter 3).

The IPCC Third Assessment Report (TAR, 

2001) report cited numerous groups that included aerosols in both 20th and 21st century 

simulations. The direct effect of sulfate aerosols 

was required to reproduce the observed global 

temperature change, given the models’ climate 

sensitivity and ocean heat uptake. Although 

most models still represented aerosol forcing 

by increasing the surface albedo, several groups 

explicitly represented sulfate aerosols in their 

atmospheric scattering calculations, with geographical distributions determined by off-line 

CTM calculations. The first model calculations 

that included any indirect effects of aerosols on 

clouds were also presented.

Due to the limitations associated with 

each type of observational data record, 

reducing aerosolforcing uncertainties 

requires coordinated 

efforts at integrating 

data from multiple 

platforms and

techniques.
The U.S. Climate Change Science Program Chapter 1

20

The most recent IPCC assessment report 

(AR4; 2007) summarized the climate change 

experiments from more than 20 modeling 

groups that this time incorporated representations of multiple aerosol species, including 

black and organic carbon, mineral dust, sea 

salt and in some cases nitrates (see Chapter 

3). In addition, many attempts were made to 

simulate indirect effects, in part because the 

better understood direct effect appeared to 

be insufficient to properly simulate observed 

temperature changes, given model sensitivity. 

As in previous assessments, the AR4 aerosol 

distributions responsible for both the direct 

and indirect effect were produced off-line, as 

opposed to being run in a coupled mode that 

would allow simulated climate changes to feed 

back on the aerosol distributions.

The fact that models now use multiple aerosol 

types and often calculate both direct and indirect 

aerosol effects does not imply that the requisite 

aerosol amounts and optical characteristics, or 

the mechanisms of aerosol-cloud interactions, 

are well represented. For example, models tend 

to have lower AOD relative to measurements, 

and are poorly constrained with regard to speciation (see Table 3.2 and Figure 3.1 in Chapter 

3). To bridge the gap between measurements 

and models in this area, robust relationships 

need to be established for different aerosol 

types, connecting the AOD and types retrieved 

from spacecraft, aircraft, and surface remote 

sensing observations, with the aerosol mass 

concentrations that are the fundamental aerosol 

quantities tracked in CTMs and GCMs.

As detailed below, continued progress with 

measurement, modeling, and at the interface 

between the two, promises to improve estimates 

of aerosol contributions to climate change, and 

to reduce the uncertainties in these quantities 

reflected in Figure 1.3.

1.4 Contents of This Report

This report assesses current understanding of 

aerosol radiative effects on climate, focusing 

on developments of aerosol measurement and 

modeling subsequent to IPCC TAR (2001). It 

reviews the present state of understanding of 

aerosol influences on Earth’s climate system, 

and in particular, the consequences for climate 

change of their direct and indirect effects. 

This report does not deal with several natural 

forcings that involve aerosols. Stratospheric 

aerosols produced by large volcanic eruptions 

exert large, short-term effects which are particularly important for characterizing climate 

system response to forcing, and the effects of 

recent eruptions (e.g. Pinatubo) are well documented (e.g., Minnis et al., 1993; McCormick 

et al., 1995; Robock et al., 2002). However 

these effects are intermittent and have only 

short-term environmental impacts (ca. 1 year). 

Galactic cosmic rays, modulated by the 11-year 

solar cycle, have been reported to correlate 

with the total cloud cover (e.g., Svensmark and 

Friis-Christensen, 1997), possibly by aiding the 

nucleation of new particles that grow into cloud 

condensation nuclei (e.g., Turco et al., 1998). 

However, the present mainstream consensus 

is that these phenomena exert little to no effect 

on cloud cover or other cloud properties (e.g., 

Lockwood and Fröhlich, 2008; Kristjánsson et 

al., 2008).

The Executive Summary reviews the key concepts involved in the study of aerosol effects 

on climate, and provides a chapter-by-chapter 

summary of conclusions from this assessment. 

Chapter 1 provides basic definitions, radiative 

forcing accuracy requirements, and background 

material on critical issues needed to motivate 

the more detailed discussion and assessment 

given in subsequent chapters.

Chapter 2 assesses the aerosol contributions to 

radiative forcing based on remote sensing and 

in situ measurements of aerosol amounts and 

properties. Current measurement capabilities 

and limitations are discussed, as well as synergy with models, in the context of the needed 

aerosol radiative forcing accuracy.

Model simulation of aerosols and their direct 

and indirect effects are examined in Chapter 

3. Representations of aerosols used for IPCC 

AR4 (2007) climate simulations are discussed, 

providing an overview of near-term modeling 

option strengths and limitations for assessing 

aerosol forcing of climate.

Finally, Chapter 4 provides an assessment of 

how current capabilities, and those within reach 

for the near future, can be brought together to 

reduce the aerosol forcing uncertainties reported in IPCC AR4 (2007).

Continued progress 

with measurement, 

modeling, and at the 

interface between 

the two, promises to 

improve estimates of 

aerosol contribution 

to climate change.
Remote Sensing and In Situ Measurements 

of Aerosol Properties, Burdens, and 

Radiative Forcing

Lead Authors: Hongbin Yu, NASA GSFC/UMBC; Patricia K. Quinn, NOAA PMEL; Graham Feingold, NOAA 

ESRL; Lorraine A. Remer, NASA GSFC; Ralph A. Kahn, 

NASA GSFC

Contributing Authors: Mian Chin, NASA GSFC; Stephen E. Schwartz, DOE BNL

CHAPTER

2

21

Atmospheric Aerosol Properties and Climate Impacts

2.1. Introduction

As discussed in Chapter 1, much of the challenge in quantifying aerosol direct radiative 

forcing (DRF) and aerosol-cloud interactions 

arises from large spatial and temporal heterogeneity of aerosol concentrations, compositions, 

and sizes, which requires an integrated approach that effectively combines measurements 

and model simulations. Measurements, both 

in situ and remote sensing, play essential roles 

in this approach by providing data with sufficient accuracy for validating and effectively 

constraining model simulations. For example, to 

achieve an accuracy of 1 W m-2 for the instantaneous, top-of-atmosphere (TOA) aerosol DRF 

under cloud free conditions, the accuracy for 

measuring aerosol optical depth (AOD) should 

be within 0.01 and 0.02 for mid-visible wavelength, and that for single-scattering albedo 

(SSA) should be constrained to about 0.02 over 

land (Chapter 1, Table 1.2). The measurement 

requirements would be much tighter in order 

to achieve the same forcing accuracy at the 

surface. Quantifying anthropogenic component 

of DRF and aerosol indirect radiative forcing 

would impose additional accuracy requirements on measurements of aerosol chemical 

composition and microphysical properties (e.g., 

size distribution) that are needed to attribute 

material to sources or source type.

Over the past decade and since the Intergovermental Panel on Climate Change (IPCC) 

Third Assessment Report (TAR) (IPCC 2001) 

in particular, a great deal of effort has gone into 

improving measurement data sets (as summarized in Yu et al., 2006; Bates et al., 2006; Kahn 

et al., 2004). Principal efforts have been:

• Development and implementation of new and 

enhanced satellite-borne sensors examining 

aerosol effects on atmospheric radiation;

• Execution of focused field experiments examining aerosol processes and properties in 

various aerosol regimes around the globe; 

• Establishment and enhancement of groundbased networks measuring aerosol properties 

and radiative forcing; and

• Development and deployment of new and 

enhanced instrumentation, importantly 

aerosol mass spectrometers examining size 

dependent composition and several methods 

for measuring aerosol SSA. 

These efforts have made it feasible to shift the 

estimates of aerosol radiative forcing increasingly from largely model-based as in IPCC 

TAR to measurement-based as in the IPCC 

Fourth Assessment Report (AR4) (IPCC 2007). 

Satellite measurements that are evaluated, 

supplemented, and constrained by groundbased remote sensing measurements and in situ

measurements from focused field campaigns, 

provide the basis for the regional- to globalscale assessments. Chemistry and transport 

models (CTMs) are used to interpolate and 

supplement the data in regions and under conditions where observational data are not available 

or to assimilate high-quality data from various 

observations to constrain and thereby improve 
The U.S. Climate Change Science Program Chapter 2

22

model simulations of aerosol impacts. These 

developments have played an important role 

in advancing the scientific understanding of 

aerosol direct and indirect radiative forcing as 

documented in the IPCC AR4 (IPCC, 2007).

The goals of this chapter are to: 

• provide an overview of current aerosol measurement capabilities and limitations; 

• describe the concept of synergies between different types of measurements and

models; 

• assess estimates of aerosol direct and indirect radiative forcing from different observational approaches; and 

• discuss outstanding issues to which measurements can contribute. 

The synthesis and assessment in this chapter 

lays groundwork needed to develop a future 

research strategy for understanding and quantifying aerosol-climate interactions.

2.2. Overview of Aerosol Measurement 

Capabilities

2.2.1. SATELLITE REMOTE SENSING

A measurement-based characterization of 

aerosols on a global scale can be realized only 

through satellite remote sensing, which is the 

only means of characterizing the large spatial 

and temporal heterogeneities of aerosol distributions. Monitoring aerosols from space has 

been performed for over two decades and is 

planned for the coming decade with enhanced 

capabilities (King et al., 1999; Foster et al., 

2007; Lee et al., 2006; Mishchenko et al., 

2007b). Table 2.1 summarizes major satellite 

measurements currently available for the tropospheric aerosol characterization and radiative 

forcing research.

Early aerosol monitoring from space relied on 

sensors that were designed for other purposes. 

The Advanced Very High Resolution Radiometer (AVHRR), intended as a cloud and surface 

monitoring instrument, provides radiance 

observations in the visible and near infrared 

wavelengths that are sensitive to aerosol properties over the ocean (Husar et al., 1997; Mishchenko et al., 1999). Originally intended for 

ozone monitoring, the ultraviolet (UV) channels 

used for the Total Ozone Mapping Spectrometer 

(TOMS) are sensitive to aerosol UV absorption 

with little surface interferences, even over land 

(Torres et al., 1998). This UV-technique makes 

TOMS suitable for monitoring biomass burning 

smoke and dust, though with limited sensitivity 

near the surface (Herman et al., 1997) and for 

retrieving aerosol single-scattering albedo from 

space (Torres et al., 2005). (A new sensor, the 

Ozone Monitoring Instrument (OMI) aboard 

Aura, has improved on such UV-technique 

advantages, providing higher spatial resolution 

and more spectral channels, see Veihelmann 

et al., 2007). Such historical sensors have 

provided multi-decadal climatology of aerosol 

optical depth that has significantly advanced 

the understanding of aerosol distributions and 

long-term variability (e.g., Geogdzhayev et al., 

2002; Torres et al., 2002; Massie et al., 2004; 

Mishchenko et al., 2007a; Mishchenko and 

Geogdzhayev, 2007; Zhao et al., 2008a).

Over the past decade, satellite aerosol retrievals 

have become increasingly sophisticated. Now, 

satellites measure the angular dependence of 

radiance and polarization at multiple wavelengths from UV through the infrared (IR) at 

fine spatial resolution. From these observations, 

retrieved aerosol products include not only optical depth at one wavelength, but also spectral 

optical depth and some information about particle size over both ocean and land, as well as 

more direct measurements of polarization and 

phase function. In addition, cloud screening 

is much more robust than before and onboard 

calibration is now widely available. Examples 

of such new and enhanced sensors include the 

MODerate resolution Imaging Spectroradiometer (MODIS, see Box 2.1), the Multi-angle Imaging SpectroRadiometer (MISR, see Box 2.2), 

Polarization and Directionality of the Earth’s 

Reflectance (POLDER, see Box 2.3), and OMI, 

among others. The accuracy for AOD measurement from these sensors is about 0.05 or 20% 

of AOD (Remer et al., 2005; Kahn et al., 2005a) 

and somewhat better over dark water, but that 

for aerosol microphysical properties, which is 

useful for distinguishing aerosol air mass types, 

is generally low. The Clouds and the Earth’s 

Radiant Energy System (CERES, see Box 2.4) 

measures broadband solar and terrestrial radiances. The CERES radiation measurements in 

combination with satellite retrievals of aerosol 

optical depth can be used to determine aerosol 

direct radiative forcing.

Satellite remote 

sensing is the only 

means of characterizing the large spatial 

and temporal heterogeneities of aerosol 

distributions.
23

Atmospheric Aerosol Properties and Climate Impacts

Table 2.1. Summary of major satellite measurements currently available for the tropospheric aerosol characterization and radiative forcing research.

Category Properties Sensor/platform Parameters Spatial coverage Temporal 

coverage

Columnintegrated

Loading

AVHRR/NOAAseries

optical depth

~daily coverage of 

global ocean 1981-present

TOMS/Nimbus, 

ADEOS1, EP

~daily coverage 

of global land and 

ocean 

1979-2001

POLDER-1, -2, 

PARASOL 1997-present

MODIS/Terra, 

Aqua

2000-present 

(Terra)

2002-present 

(Aqua)

MISR/Terra

~weekly coverage 

of global land and 

ocean, including 

bright desert and 

nadir sun-glint

2000-present

OMI/Aura

~daily coverage 

of global land and 

ocean

2005-present

Size, shape

AVHRR/NOAAseries Ångström exponent global ocean 1981-present

POLDER-1, -2, 

PARASOL

fine-mode fraction, 

Ångström exponent, 

non-spherical fraction

global land+ocean 1997-present

MODIS/Terra, 

Aqua

fine-mode fraction global land+ocean 

(better quality 

over ocean)

2000-present 

(Terra)

2002-present 

(Aqua)

Ångström exponent

effective radius

global ocean asymmetry factor

MISR/Terra

Ångström exponent, 

small, medium, large 

fractions, non-spherical fraction

global land+ocean 2000-present

Absorption

TOMS/Nimbus, 

ADEOS1, EP absorbing aerosol 

index, single-scattering 

albedo, absorbing optical depth global land+ocean

1979-2001

OMI/Aura 2005-present

MISR/Terra

single-scattering 

albedo 

(2-4 bins)

2000-present

Verticalresolved

Loading, 

size, and 

shape

GLAS/ICESat extinction/backscatter

global land+ocean, 

16-day repeating 

cycle, single-nadir 

measurement

2003-present 

(~3months/

year)

CALIOP/CALIPSO

extinction/backscatter, 

color ratio, depolarization ratio2006-present
The U.S. Climate Change Science Program Chapter 2

24

Complementary to these passive sensors, active 

remote sensing from space is also now possible 

and ongoing (see Box 2.5). Both the Geoscience Laser Altimeter System (GLAS) and 

the Cloud and Aerosol Lidar with Orthogonal 

Polarization (CALIOP) are collecting essential 

information about aerosol vertical distributions. Furthermore, the constellation of six 

afternoon-overpass spacecrafts (as illustrated in 

Figure 2.5), the so-called A-Train (Stephens et 

al., 2002) makes it possible for the first time to 

conduct near simultaneous (within 15-minutes) 

measurements of aerosols, clouds, and radiative 

fluxes in multiple dimensions with sensors in 

complementary capabilities.

The improved accuracy of aerosol products 

(mainly AOD) from these new-generation 

sensors, together with improvements in characterizing the earth’s surface and clouds, can 

help reduce the uncertainties associated with 

estimating the aerosol direct radiative forcing 

(Yu et al., 2006; and references therein). The 

retrieved aerosol microphysical properties, 

such as size, absorption, and non-spherical 

fraction can help distinguish anthropogenic 

Box 2.1: MODerate resolution Imaging Spectroradiometer

MODIS performs near global daily observations of atmospheric aerosols. Seven of 36 channels (between 0.47 and 

2.13 μm) are used to retrieve aerosol properties over cloud and surface-screened areas (Martins et al., 2002; Li et 

al., 2004). Over vegetated land, MODIS retrieves aerosol optical depth at three visible channels with high accuracy 

of ±0.05±0.2τ (Kaufman et al., 1997; Chu et al., 2002; Remer et al., 2005; Levy et al., 2007b). Most recently a deepblue algorithm (Hsu et al., 2004) has been implemented to retrieve aerosols over bright deserts on an operational 

basis, with an estimated accuracy of 20-30%. Because of the greater simplicity of the ocean surface, MODIS has the 

unique capability of retrieving not only aerosol optical depth with greater accuracy, i.e., ±0.03±0.05τ (Tanré et al., 

1997; Remer et al., 2002; 2005; 2008), but also quantitative aerosol size parameters (e.g., effective radius, fine-mode 

fraction of AOD) (Kaufman et al., 2002a; Remer et al., 2005; Kleidman et al., 2005). The fine-mode fraction has been 

used as a tool for separating anthropogenic aerosol from natural ones and estimating the anthropogenic aerosol direct 

climate forcing (Kaufman et al., 2005a). Figure 2.1 shows composites of MODIS AOD and fine-mode fraction that illustrate seasonal and geographical variations of aerosol types. Clearly seen from the figure is heavy pollution over East 

Asia in both months, biomass burning smoke over South Africa, South America, and Southeast Asia in August, heavy 

dust storms over North Atlantic in both months and over Arabian Sea in August, and a mixture of dust and pollution 

plume swept across North Pacific in April.

Figure 2.1. A composite of MODIS/Terra observed aerosol optical 

depth (at 550 nm, green light near the peak of human vision) and 

fine-mode fraction that shows spatial and seasonal variations of 

aerosol types. Industrial pollution and biomass burning aerosols are 

predominately small particles (shown as red), whereas mineral dust 

and sea salt consist primarily of large particles (shown as green). 

Bright red and bright green indicate heavy pollution and dust plumes, 

respectively (adapted from Chin et al., 2007; original figure from 

Yoram Kaufman and Reto Stöckli).
25

Atmospheric Aerosol Properties and Climate Impacts

aerosols from natural aerosols and hence help 

assess the anthropogenic component of aerosol 

direct radiative forcing (Kaufman et al., 2005a; 

Bellouin et al., 2005, 2008; Christopher et al., 

2006; Yu et al., 2006, 2008). However, to infer 

aerosol number concentrations and examine 

indirect aerosol radiative effects from space, 

significant efforts are needed to measure 

aerosol size distribution with much improved 

accuracy, characterize aerosol type, account 

for impacts of water uptake on aerosol optical 

depth, and determine the fraction of aerosols 

that is at the level of the clouds (Kapustin et al., 

2006; Rosenfeld, 2006). In addition, satellite 

remote sensing is not sensitive to particles much 

smaller than 0.1 micrometer in diameter, which 

comprise of a significant fraction of those that 

serve as cloud condensation nuclei.

Finally, algorithms are being developed to 

retrieve aerosol absorption or SSA from satellite observations (e.g., Kaufman et al., 2002b; 

Torres et al., 2005). The NASA Glory mission, 

scheduled to launch in 2009 and to be added to 

the A-Train, will deploy a multi-angle, multispectral polarimeter to determine the global 

distribution of aerosol and clouds. It will also 

be able to infer microphysical property information, from which aerosol type (e.g., marine, 

dust, pollution, etc.) can be inferred for improving quantification of the aerosol direct 

and indirect forcing on climate (Mishchenko 

et al., 2007b). 

In summary, major advances have been made in 

both passive and active aerosol remote sensing 

from space in the past decade, providing better coverage, spatial resolution, retrieved AOD 

accuracy, and particle property information. 

However, AOD accuracy is still much poorer 

than that from surface-based sun photometers 

(0.01 to 0.02), even over vegetated land and 

dark water where retrievals are most reliable. 

Although there is some hope of approaching 

this level of uncertainty with a new generation 

of satellite instruments, the satellite retrievals 

entail additional sensitivities to aerosol and surface scattering properties. It seems unlikely that 

satellite remote sensing could exceed the sun 

photometer accuracy without introducing some 

Box 2.2: Multi-angle Imaging SpectroRadiometer

MISR, aboard the sun-synchronous, polar orbiting satellite Terra, measures upwelling solar radiance in four visible-nearIR spectral bands and at nine view angles spread out in the forward and aft directions along the flight path (Diner et al., 

2002). It acquires global coverage about once per week. A wide range of along-track view angles makes it feasible to 

more accurately evaluate the surface contribution to the TOA radiances and hence retrieve aerosols over both ocean 

and land surfaces, including bright desert and sunglint regions (Diner et al., 1998; Martonchik et al., 1998a; 2002; Kahn 

et al., 2005a). MISR AODs are within 20% or ±0.05 of coincident AERONET measurements (Kahn et al., 2005a; Abdou 

et al., 2005). The MISR multi-angle data also sample scattering angles ranging from about 60 ̊ to 160 ̊ in midlatitudes, 

yielding information about particle size (Kahn et al., 1998; 2001; 2005a; Chen et al., 2008) and shape (Kalashnikova and 

Kahn, 2006). The aggregate of aerosol microphysical properties can be used to assess aerosol airmass type, a more 

robust characterization of MISR-retrieved particle property information than individual attributes. MISR also retrieves 

plume height in the vicinity of wildfire, volcano, and mineral dust aerosol sources, where the plumes have discernable 

spatial contrast in the multi-angle imagery (Kahn et al., 2007a). Figure 2.2 is an example that illustrates MISR’s ability 

to characterize the load, optical properties, and stereo height of near-source fire plumes. 

Figure 2.2. Oregon fire on September 4, 2003 as observed by MISR: (a) MISR nadir view of the fire plume, with five patch locations numbered and wind-vectors superposed in yellow; (b) MISR aerosol optical depth at 558 nm; and (c) MISR stereo height 

without wind correction for the same region (taken from Kahn et al., 2007a). 

Major advances have 

been made in both 

passive and active 

aerosol remote 

sensing from space 

in the past decade, 

providing better 

coverage, spatial 

resolution, retrieved 

AOD accuracy, and 

particle property 

information.
The U.S. Climate Change Science Program Chapter 2

26

as-yet-unspecified new technology. Spacebased lidars are for the first time providing 

global constraints on aerosol vertical distribution, and multi-angle imaging is supplementing this with maps of plume injection height 

in aerosol source regions. Major advances 

have also been made during the past decade in 

distinguishing aerosol types from space, and 

the data are now useful for validating aerosol 

transport model simulations of aerosol air mass 

type distributions and transports, particularly 

over dark water. But particle size, shape, and 

especially SSA information has large uncertainty; improvements will be needed to better 

distinguish anthropogenic from natural aerosols 

using space-based retrievals. The particle microphysical property detail required to assess 

aerosol radiative forcing will come largely from 

targeted in situ and surface remote sensing measurements, at least for the near-future, although 

estimates of measurement-based aerosol RF can 

be made from judicious use of the satellite data 

with relaxed requirements for characterizing 

aerosol microphysical properties.

Box 2.3: POLarization and Directionality of the Earth’s Reflectance

POLDER is a unique aerosol sensor that consists of wide field-of-view imaging spectro-radiometer capable of measuring multi-spectral, multi-directional, and polarized radiances (Deuzé et al., 2001). The observed radiances can be 

exploited to better separate the atmospheric contribution from the surface contribution over both land and ocean. 

POLDER -1 and -2 flew onboard the ADEOS (Advanced Earth Observing Satellite) from November 1996 to June 

1997 and April to October of 2003, respectively. A similar POLDER instrument flies on the PARASOL satellite that 

was launched in December 2004. 

Figure 2.3 shows global horizontal patterns of AOD and Ångström exponent over the oceans derived from the 

POLDER instrument for June 1997. The oceanic AOD map (Figure 2.3.a) reveals near-coastal plumes of high AOD, 

which decrease with distance from the coast. This pattern arises from aerosol emissions from the continents, followed by atmospheric dispersion, transformation, and removal in the downwind direction. In large-scale flow fields, 

such as the trade winds, these continental plumes persist over several thousand kilometers. The Ångström exponent 

shown in Figure 2.3.b exhibits a very different pattern from that of the aerosol optical depth; specifically, it exhibits 

high values downwind of industrialized regions and regions of biomass burning, indicative of small particles arising 

from direct emissions from combustion sources and/or gas-to-particle conversion, and low values associated with 

large particles in plumes of soil dust from deserts and in sea salt aerosols.

Figure 2.3. Global maps at 18 km resolution showing monthly average (a) AOD at 865 nm and (b) Ångström exponent of 

AOD over water surfaces only for June, 1997, derived from radiance measurements by the POLDER. Reproduced with permission of Laboratoire d’Optique Atmospherique (LOA), Lille, FR; Laboratoire des Sciences du Climat et de l’Environnement 

(LSCE), Gif sur Yvette, FR; Centre National d’etudes Spatiales (CNES), Toulouse, FR; and NAtional Space Development 

Agency (NASDA), Japan.
27

Atmospheric Aerosol Properties and Climate Impacts

2.2.2. FOCUSED FIELD CAMPAIGNS

Over the past two decades, numerous focused 

field campaigns have examined the physical, 

chemical, and optical properties and radiative 

forcing of aerosols in a variety of aerosol regimes around the world, as listed in Table 2.2. 

These campaigns, which have been designed 

with aerosol characterization as the main goal 

or as one of the major themes in more interdisciplinary studies, were conducted mainly over 

or downwind of known continental aerosol 

source regions, but in some instances in lowaerosol regimes, for contrast. During each of 

these comprehensive campaigns, aerosols were 

studied in great detail, using combinations 

of in situ and remote sensing observations of 

physical and chemical properties from various 

platforms (e.g., aircraft, ships, satellites, and 

ground-based stations) and numerical modeling. In spite of their relatively short duration, 

these field studies have acquired comprehensive 

data sets of regional aerosol properties that have 

been used to understand the properties and 

evolution of aerosols within the atmosphere 

and to improve the climatology of aerosol microphysical properties used in satellite retrieval 

algorithms and CTMs.

2.2.3. GROUND-BASED IN SITU MEASUREMENT NETWORKS

Major US-operated surface in situ and remote 

sensing networks for tropospheric aerosol 

characterization and climate forcing research 

are listed in Table 2.3. These surface in situ

stations provide information about long-term 

changes and trends in aerosol concentrations 

and properties, the influence of regional sources 

on aerosol properties, climatologies of aerosol 

radiative properties, and data for testing models 

(e.g., Quinn et al., 2000; Quinn et al., 2002; 

Delene and Ogren, 2002; Sheridan and Ogren, 

1999; Fiebig and Ogren, 2006; Bates et al., 

2006; Quinn et al., 2007) and satellite aerosol 

retrievals. The NOAA Earth System Research 

Laboratory (ESRL) aerosol monitoring network 

consists of baseline, regional, and mobile stations. These near-surface measurements include 

submicrometer and sub-10 micrometer scattering and absorption coefficients from which the 

extinction coefficient and single-scattering albedo can be derived. Additional measurements 

include particle concentration and, at selected 

sites, CCN concentration, the hygroscopic 

growth factor, and chemical composition. 

Several of the stations, which are located across 

North America and world-wide, are in regions 

where recent focused field campaigns have 

been conducted. The measurement protocols 

at the stations are similar to those used during 

the field campaigns. Hence, the station data are 

directly comparable to the field campaign data 

so that they provide a longer-term measure of 

mean aerosol properties and their variability, 

as well as a context for the shorter-duration 

measurements of the field campaigns.

The Interagency Monitoring of Protected Visual 

Environment (IMPROVE), which is operated by 

the National Park Service Air Resources Division, has stations across the US located within 

national parks (Malm et al., 1994). Although the 

primary focus of the network is air pollution, 

the measurements are also relevant to climate 

forcing research. Measurements include fine 

and coarse mode (PM2.5 and PM10) aerosol 

mass concentration; concentrations of elements, 

sulfate, nitrate, organic carbon, and elemental 

carbon; and scattering coefficients.

In addition, to these US-operated networks, 

there are other national and international surface networks that provide measurements of 

aerosol properties including, but not limited to, 

the World Meteorological Organization (WMO) 

Global Atmospheric Watch (GAW) network 

(http://www.wmo.int/pages/prog/arep/gaw/

Box 2.4: Clouds and the Earth’s Radiant Energy System

CERES measures broadband solar and terrestrial radiances at 

three channels with a large footprint (e.g., 20 km for CERES/Terra) 

(Wielicki et al., 1996). It is collocated with MODIS and MISR aboard 

Terra and with MODIS on Aqua. The observed radiances are converted to TOA irradiances or fluxes using the Angular Distribution 

Models (ADMs) that are functions of viewing angle, sun angle, and 

scene type (Loeb and Kato, 2002; Zhang et al., 2005a; Loeb et al., 

2005). Such estimates of TOA solar flux in clear-sky conditions can 

be compared to the expected flux for an aerosol-free atmosphere, 

in conjunction with measurements of aerosol optical depth from 

other sensors (e.g., MODIS and MISR) to derive the aerosol direct 

radiative forcing (Loeb and Manalo-Smith, 2005; Zhang and Christopher, 2003; Zhang et al., 2005b; Christopher et al., 2006; Patadia et 

al., 2008). The derived instantaneous value is then scaled to obtain 

a daily average. A direct use of the coarse spatial resolution CERES 

measurements would exclude aerosol distributions in partly cloudy 

CERES scenes. Several approaches that incorporate coincident, high 

spatial and spectral resolution measurements (e.g., MODIS) have 

been employed to overcome this limitation (Loeb and Manalo-Smith, 

2005; Zhang et al., 2005b). 
The U.S. Climate Change Science Program Chapter 2

28

monitoring.html), the European Monitoring and 

Evaluation Programme (EMEP) (http://www.

emep.int/), the Canadian Air and Precipitation Monitoring Network (CAPMoN) (http://

www.msc-smc.ec.gc.ca/capmon/index_e.cfm), 

and the Acid Deposition Monitoring Network 

in East Asia (EANET) (http://www.eanet.cc/

eanet.html).

2.2.4. IN S ITU AEROSOL PROF ILING

PROGRAMS

In addition to long-term ground based measurements, regular long-term aircraft in situ

measurements recently have been implemented 

at several locations. These programs provide a 

statistically significant data set of the vertical 

distribution of aerosol properties to determine 

Box 2.5: Active Remote Sensing of Aerosols

Following the success of a demonstration of lidar system aboard the U.S. Space Shuttle mission in 1994, i.e., Lidar 

In-space Technology Experiment (LITE) (Winker et al., 1996), the Geoscience Laser Altimeter System (GLAS) was 

launched in early 2003 to become the first polar orbiting satellite lidar. It provides global aerosol and cloud profiling 

for a one-month period out of every three-to-six months. It has been demonstrated that GLAS is capable of detecting and discriminating multiple layer clouds, atmospheric boundary layer aerosols, and elevated aerosol layers (e.g., 

Spinhirne et al., 2005). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), launched 

on April 28, 2006, is carrying a lidar instrument (Cloud and Aerosol Lidar with Orthogonal Polarization - CALIOP) 

that has been collecting profiles of the attenuated backscatter at visible and near-infrared wavelengths along with 

polarized backscatter in the visible channel (Winker et al., 2003). CALIOP measurements have been used to derive 

the above-cloud fraction of aerosol extinction optical depth (Chand et al., 2008), one of the important factors determining aerosol direct radiative forcing in cloudy conditions. Figure 2.4 shows an event of trans-Atlantic transport of 

Saharan dust captured by CALIPSO. Flying in formation with the Aqua, AURA, POLDER, and CloudSat satellites, the 

vertically resolved information is expected to greatly improve passive aerosol and cloud retrievals as well as allow 

the retrieval of vertical distributions of aerosol extinction, fine- and coarse-mode separately (Kaufman et al., 2003; 

Leon et al., 2003; Huneeus and Boucher, 2007). 

Figure 2.4. A dust event that originated in the Sahara desert on 17 August 2007 and was transported to the Gulf of Mexico. 

Red lines represent back trajectories indicating the transport track of the dust event. Vertical images are 532 nm attenuated 

backscatter coefficients measured by CALIOP when passing over the dust transport track. The letter “D” designates the dust 

layer, and “S” represents smoke layers from biomass burning in Africa (17–19 August) and South America (22 August). The 

track of the high-spectral-resolution-lidar (HSRL) measurement is indicated by the white line superimposed on the 28 August 

CALIPSO image. The HSRL track is coincident with the track of the 28 August CALIPSO measurement off the coast of Texas 

between 28.75°N and 29.08°N (taken from Liu et al., 2008).
29

Atmospheric Aerosol Properties and Climate Impacts

spatial and temporal variability through the 

vertical column and the influence of regional 

sources on that variability. In addition, the measurements provide data for satellite and model 

validation. As part of its long-term ground measurements, NOAA has conducted regular flights 

over Bondville, Illinois since 2006. Measurements include light scattering and absorption 

coefficients, the relative humidity dependence 

of light scattering, aerosol number concentration 

and size distribution, and chemical composition. 

The same measurements with the exception of 

number concentration, size distribution, and 

chemical composition were made by NOAA 

during regular overflights of DOE ARM’s 

Southern Great Plains (SGP) site from 2000 to 

2007 (Andrews et al., 2004) (http://www.esrl.

noaa.gov/gmd/aero/net/index.html).

In summary of sections 2.2.2, 2.2.3, and 2.2.4, 

in situ measurements of aerosol properties have 

greatly expanded over the past two decades 

as evidenced by the number of focused field 

campaigns in or downwind of aerosol source 

regions all over the globe, the continuation of 

existing and implementation of new sampling 

networks worldwide, and the implementation 

of regular aerosol profiling measurements 

from fixed locations. In addition, in situ measurement capabilities have undergone major 

advancements during this same time period. 

These advancements include the ability to 

measure aerosol chemical composition as a 

function of size at a time resolution of seconds 

to minutes (e.g., Jayne et al., 2000), the development of instruments able to measure aerosol 

absorption and extinction coefficients at high 

sensitivity and time resolution and as a function of relative humidity (e.g., Baynard et al., 

2007; Lack et al., 2006), and the deployment of 

these instruments across the globe on ships, at 

ground-based sites, and on aircraft. However, 

further advances are needed to make this newly 

developed instrumentation more affordable and 

turn-key so that it can be deployed more widely 

to characterize aerosol properties at a variety 

of sites world-wide.

2.2.5. GROUND-BASED REMOTE SENSING

MEASUREMENT NETWORKS

The Aerosol Robotic Network (AERONET) 

program is a federated ground-based remote 

sensing network of well-calibrated sun photometers and radiometers (http://aeronet.gsfc.

nasa.gov).

AERONET includes about 200 sites around the 

world, covering all major tropospheric aerosol 

regimes (Holben et al., 1998; 2001), as illustrated in Figure 2.6. Spectral measurements of sun 

and sky radiance are calibrated and screened 

for cloud-free conditions (Smirnov et al., 2000). 

AERONET stations provide direct, calibrated 

measurements of spectral AOD (normally at 

wavelengths of 440, 670, 870, and 1020 nm) 

with an accuracy of ±0.015 (Eck et al., 1999). In 

addition, inversion-based retrievals of a variety 

of effective, column-mean properties have been 

developed, including aerosol single-scattering 

albedo, size distributions, fine-mode fracFigure 2.5. A constellation of five spacecraft that overfly the Equator at about 1:30 PM, the so-called 

A-Train, carries sensors having complementary capabilities, offering unprecedented opportunities to study 

aerosols from space in multiple dimensions.

In situ measurements of aerosol 

properties have 

greatly expanded 

over the past 

two decades as 

evidenced by the 

number of focused 

field campaigns, the 

world-wide sampling networks, and 

the regular aerosol 

profiling measurements from fixed 

locations.
The U.S. Climate Change Science Program Chapter 2

30

Table 2.2. List of major intensive field experiments that are relevant to aerosol research in a variety of aerosol regimes 

around the globe conducted in the past two decades (updated from Yu et al., 2006).

Aerosol 

Regimes

Intensive Field Experiments Major References Name Location Time Period

Anthropogenic 

aerosol and 

boreal forest 

from North 

America 

and West 

Europe

TARFOX North Atlantic July 1996 Russell et al., 1999

NEAQS North Atlantic July-August 2002 Quinn and Bates, 2003

SCAR-A North America 1993 Remer et al., 1997

CLAMS East Coast of U.S. July-August 2001 Smith et al., 2005

INTEX-NA, 

ICARTT North America Summer 2004 Fehsenfeld et al., 2006

DOE AIOP northern Oklahoma May 2003 Ferrare et al., 2006

MILAGRO Mexico city, Mexico March 2006 Molina et al., 2008

TexAQS/

GoMACCS

Texas and

Gulf of Mexico August-September 2006 Jiang et al., 2008; Lu et al., 2008

ARCTAS

North-central 

Alaska to Greenland

(Arctic haze)

March-April 2008

http://www.espo.nasa.gov/arctas/

ARCTAS Northern Canada 

(smoke) June-July 2008

ACE-2 North Atlantic June-July 1997 Raes et al., 2000

MINOS Mediterranean 

region July-August 2001 Lelieveld et al., 2002

LACE98 Lindberg, Germany July-August 1998 Ansmann et al., 2002

Aerosols99 Atlantic January-February 1999 Bates et al., 2001

Brown Haze 

in South 

Asia

INDOEX Indian subcontinent 

and Indian Ocean January-April 1998 and 1999 Ramanathan et al., 2001b

ABC South and East 

Asia ongoing Ramanathan and Crutzen, 2003

Anthropogenic 

aerosol and 

desert dust 

mixture 

from East 

Asia

EAST-AIRE China March-April 2005 Li et al., 2007

INTEX-B northeastern 

Pacific April 2006 Singh et al., 2008

ACE-Asia East Asia and 

Northwest Pacific

April 2001 Huebert et al., 2003; Seinfeld et al., 

2004

TRACE-P March-April 2001 Jacob et al., 2003

PEM-West

A & B

Western Pacific off 

East Asia

September-October 1991

February-March 1994 Hoell et al., 1996; 1997

Biomass 

burning 

smoke in the 

tropics

BASE-A Brazil 1989 Kaufman et al., 1992

SCAR-B Brazil August-September 1995 Kaufman et al., 1998

LBA-SMOCC Amazon basin September-November 2002 Andreae et al., 2004

SAFARI2000 South Africa and 

South Atlantic

August -September 2000 King et al., 2003

SAFARI92 September-October 1992 Lindesay et al., 1996

TRACE-A South Atlantic September-October 1992 Fishman et al., 1996

DABEX West Africa January-February 2006 Haywood et al., 2008

Mineral 

dusts from 

North Africa 

and Arabian 

Peninsula

SAMUM Southern Morocco May-June 2006 Heintzenberg et al., 2009

SHADE West coast of 

North Africa September 2000 Tanré et al., 2003

PRIDE Puerto Rico June-July 2000 Reid et al., 2003

UAE2 Arabian Peninsula August-September 2004 Reid et al., 2008

Remote 

Oceanic 

Aerosol

ACE-1 Southern Oceans December 1995 Bates et al., 1998; Quinn and Coffman, 

1998
31

Atmospheric Aerosol Properties and Climate Impacts

Table 2.3. Summary of major US surface in situ and remote sensing networks for the tropospheric aerosol characterization and radiative forcing research. All the reported quantities are column-integrated or column-effective, 

except as indicated.

Surface Network

Measured/derived parameters Spatial 

coverage

Temporal 

coverage Loading Size, shape Absorption Chemistry

In Situ

NOAA ESRL 

aerosol monitoring

(http://www.esrl.

noaa.gov/gmd/

aero/)

nearsurface 

extinction 

coefficient, 

optical 

depth, 

CN/CCN 

number 

concentrations

Angstrom 

exponent, 

hemispheric 

backscatter 

fraction, 

asymmetry

factor, 

hygroscopic 

growth

singlescattering 

albedo, 

absorption 

coefficient

chemical 

composition in 

selected 

sites and 

periods

5 baseline 

stations, 

several 

regional 

stations, 

aircraft 

and mobile 

platforms

1976

onward

NPS/EPA IMPROVE

(http://vista.cira.

colostate.edu/ 

improve/)

near-surface mass 

concentrations and 

derived 

extinction 

coefficients 

by species

fine and 

coarse

separately

singlescattering 

albedo, 

absorption 

coefficient

ions,

ammonium sulfate, 

ammonium 

nitrate, 

organics, 

elemental 

carbon, 

fine soil

156 national 

parks and 

wilderness 

areas in the 

U.S.

1988

onward

Remote 

Sensing

NASA AERONET 

(http://aeronet.gsfc.

nasa.gov)

optical 

depth

fine-mode 

fraction, 

Angstrom 

exponents, 

asymmetry 

factor, phase 

function, 

non-spherical 

fraction

singlescattering 

albedo, 

absorption 

optical 

depth, 

refractive 

indices N/A

~200 sites 

over global 

land and 

islands

1993

onward

DOE ARM

(http://www.arm.

gov)

6 sites and 

1 mobile 

facility in 

N. America, 

Europe, 

and Asia

1989

onward

NOAA SURFRAD

(http://www.srrb.

noaa.gov/surfrad/)

N/A N/A N/A

7 sites in 

the U.S.

1995

onward

AERONET- MAN

(http://aeronet.gsfc.

nasa.gov/maritime_

aerosol_network.

html)

global 

ocean

2004-

present

(periodically)

NASA MPLNET

(http://mplnet.gsfc.

nasa.gov/)

vertical 

profiles of 

backscatter 

/extinction 

coefficient

N/A N/A N/A

~30 sites 

in major 

continents, 

usually

collocated 

with 

AERONET 

and ARM 

sites

2000 

onward
The U.S. Climate Change Science Program Chapter 2

32

tion, degree of non-sphericity, phase function, 

and asymmetry factor (Dubovik et al., 2000; 

Dubovik and King, 2000; Dubovik et al., 2002; 

O’Neill, et al., 2004). The SSA can be retrieved 

with an accuracy of ±0.03, but only for AOD 

>0.4 (Dubovik et al., 2002), which precludes 

much of the planet. These retrieved parameters 

have been validated or are undergoing validation by comparison to in situ measurements 

(e.g., Haywood et al., 2003; Magi et al., 2005; 

Leahy et al., 2007).

Recent developments associated with AERONET algorithms and data products include: 

• simultaneous retrieval of aerosol and surface 

properties using combined AERONET and 

satellite measurements (Sinyuk et al., 2007) 

with surface reflectance taken into account 

(which significantly improves AERONET 

SSA retrieval accuracy) (Eck et al., 2008); 

• the addition of ocean color and high frequency solar flux measurements; and

• the establishment of the Maritime Aerosol 

Network (MAN) component to monitor 

aerosols over the World oceans from shipsof-opportunity (Smirnov et al., 2006).

Because of consistent calibration, cloud-screening, and retrieval methods, uniformly acquired 

and processed data are available from all stations, some of which have operated for over 

10 years. These data constitute a high-quality, 

ground-based aerosol climatology and, as such, 

have been widely used for aerosol process studies as well as for evaluation and validation of 

model simulation and satellite remote sensing 

applications (e.g., Chin et al., 2002; Yu et al., 

2003, 2006; Remer et al., 2005; Kahn et al., 

2005a). In addition, AERONET retrievals of 

aerosol size distribution and refractive indices 

have been used in algorithm development for 

satellite sensors (Remer et al., 2005; Levy et al., 

2007a). A set of aerosol optical properties provided by AERONET has been used to calculate 

the aerosol direct radiative forcing (Procopio 

et al., 2004; Zhou et al., 2005), which can be 

used to evaluate both satellite remote sensing 

measurements and model simulations.

AERONET measurements are complemented 

by other ground-based aerosol networks having 

less geographical or temporal coverage, such 

as the Atmospheric Radiation Measurement 

(ARM) network (Ackerman and Stokes, 2003), 

NOAA’s national surface radiation budget 

network (SURFRAD) (Augustine et al., 2008) 

and other networks with multifilter rotating 

shadowband radiometer (MFRSR) (Harrison 

et al., 1994; Michalsky et al., 2001), and several 

lidar networks including 

• NASA Micro Pulse Lidar Network (MPLNET) (Welton et al., 2001; 2002);

• Regional East Atmospheric Lidar Mesonet 

(REALM) in North America (Hoff et al., 

2002; 2004); 

• European Aerosol Research Lidar Network 

(EARLINET) (Matthias et al., 2004); and 

• Asian Dust Network (AD-Net) (e.g., Murayama et al., 2001). 

Obtaining accurate aerosol extinction profile 

observations is pivotal to improving aerosol 

radiative forcing and atmospheric response 

calculations. The values derived from these 

lidar networks with state-of-the-art techniques (Schmid et al., 2006) are helping to 

fill this need. 

2.2.6. SYNERGY OF MEASUREMENTS AND

MODEL SIMULATIONS

Individual approaches discussed above have 

their own strengths and limitations, and are usually complementary. None of these approaches 

alone is adequate to characterize large spatial 

and temporal variations of aerosol physical and 

chemical properties and to address complex 

aerosol-climate interactions. The best strategy 

for characterizing aerosols and estimating their 

radiative forcing is to integrate measurements 

from different satellite sensors with complementary capabilities from in situ and surfacebased measurements. Similarly, while models 

are essential tools for estimating regional and 

global distributions and radiative forcing of 

aerosols at present as well as in the past and 

the future, observations are required to provide 

Figure 2.6. Geographical coverage of active AERONET sites in 2006.

AERONET includes 

about 200 sites 

around the world, 

covering all major tropospheric 

aerosol regimes. 

AERONET stations provide direct, 

calibrated measurements of spectral 

AOD with an accuracy of ±0.015.
33

Atmospheric Aerosol Properties and Climate Impacts

constraints and validation of the models. In the 

following, several synergistic approaches to 

studying aerosols and their radiative forcing 

are discussed.

Closure experiments: During intensive field 

studies, multiple platforms and instruments 

are deployed to sample regional aerosol properties through a well-coordinated experimental 

design. Often, several independent methods 

are used to measure or derive a single aerosol 

property or radiative forcing. This combination 

of methods can be used to identify inconsistencies in the methods and to quantify uncertainties in measured, derived, and calculated 

aerosol properties and radiative forcings. This 

approach, often referred to as a closure experiment, has been widely employed on both individual measurement platforms (local closure) 

and in studies involving vertical measurements 

through the atmospheric column by one or more 

platforms (column closure) (Quinn et al., 1996; 

Russell et al., 1997).

Past closure studies have revealed that the best 

agreement between methods occurs for submicrometer, spherical particles such that different 

measures of aerosol optical properties and 

optical depth agree within 10 to 15% and often 

better (e.g., Clarke et al., 1996; Collins et al., 

2000; Schmid et al., 2000; Quinn et al., 2004). 

Larger particle sizes (e.g., sea salt and dust) 

present inlet collection efficiency issues and 

non-spherical particles (e.g., dust) lead to differences in instrumental responses. In these cases, 

differences between methods for determining 

aerosol optical depth can be as great as 35% 

(e.g., Wang et al., 2003; Doherty et al., 2005). 

Closure studies on aerosol clear-sky DRF reveal 

uncertainties of about 25% for sulfate/carbonaceous aerosol and 60% for dust-containing 

aerosol (Bates et al., 2006). Future closure studies could integrate surface- and satellite-based 

radiometric measurements of AOD with in situ

optical, microphysical, and aircraft radiometric 

measurements for a wide range of situations. 

There is also a need to maintain consistency in 

comparing results and expressing uncertainties 

(Bates et al., 2006).

Constraining models with in situ measurements: In situ measurements of aerosol chemical, microphysical, and optical properties with 

known accuracy, based in part on closure 

studies, can be used to constrain regional 

CTM simulations of aerosol direct forcing, as 

described by Bates et al. (2006). A key step in 

the approach is assigning empirically derived 

optical properties to the individual chemical 

components generated by the CTM for use in a 

Radiative Transfer Model (RTM). Specifically, 

regional data from focused, short-duration field 

programs can be segregated according to aerosol type (sea salt, dust, or sulfate/carbonaceous) 

based on measured chemical composition and 

particle size. Corresponding measured optical 

properties can be carried along in the sorting 

process so that they, too, are segregated by 

aerosol type. The empirically derived aerosol 

properties for individual aerosol types, including mass scattering efficiency, single-scattering 

albedo, and asymmetry factor, and their dependences on relative humidity, can be used in 

place of assumed values in CTMs.

Short-term, focused measurements of aerosol 

properties (e.g., aerosol concentration and AOD) 

also can be used to evaluate CTM parameterizations on a regional basis, to suggest improvements to such uncertain model parameters, 

such as emission factors and scavenging coefficients (e.g., Koch et al., 2007). Improvements 

in these parameterizations using observations 

yield increasing confidence in simulations 

covering regions and periods where and when 

measurements are not available. To evaluate 

the aerosol properties generated by CTMs on 

broader scales in space and time, satellite observations and long-term in situ measurements 

are required. 

Improving model simulations with satellite measurements: Global measurements of 

aerosols from satellites (mainly AOD) with 

well-defined accuracies offer an opportunity to 

evaluate model simulations at large spatial and 

temporal scales. The satellite measurements can 

also be used to constrain aerosol model simulations and hence the assessment of aerosol DRF 

through data assimilation or objective analysis 

process (e.g., Collins et al., 2001; Yu et al., 2003; 

2004, 2006; Liu et al., 2005; Zhang et al., 2008). 

Both satellite retrievals and model simulations 

have uncertainties. The goal of data integration 

is to minimize the discrepancies between them, 

and to form an optimal estimate of aerosol distributions by combining them, typically with 

weights inversely proportional to the square of 

the errors of individual descriptions. Such integration can fill gaps in satellite retrievals and 

The best strategy 

for characterizing aerosols and 

estimating their 

radiative forcing is 

to integrate measurements from 

different satellite 

sensors with in situ 

and surface based 

measurements. 

Observations are 

required to provide 

constraints and 

validation of the 

models.
The U.S. Climate Change Science Program Chapter 2

34

Figure 2.7. Comparison of the mean concentration (μg m-3) and standard 

deviation of the modeled (STEM) aerosol chemical components with shipboard measurements during INDOEX, ACE-Asia, and ICARTT. After Bates 

et al. (2006).

generate global distributions of aerosols that are 

consistent with ground-based measurements 

(Collins et al., 2001; Yu et al., 2003, 2006; Liu 

et al., 2005). Recent efforts have also focused on 

retrieving global sources of aerosol from satellite observations using inverse modeling, which 

may be valuable for reducing large aerosol 

simulation uncertainties (Dubovik et al., 2007). 

Model refinements guided by model evaluation 

and integration practices with satellite retrievals 

can then be used to improve aerosol simulations 

of the pre- and post-satellite eras.

Current measurement-based understanding 

of aerosol characterization and radiative forcing is assessed in Section 2.3 through intercomparisons of a variety of measurement-based 

estimates and model simulations published in 

literature. This is followed by a detailed discussion of major outstanding issues in section 2.4.

2.3. Assessments of Aerosol Characterization and Climate Forcing

This section focuses on the assessment of 

measurement-based aerosol characterization 

and its use in improving estimates of the direct 

radiative forcing on regional and global scales. 

In situ measurements provide highly accurate 

aerosol chemical, microphysical, and optical 

properties on a regional basis and for the particular time period of a given field campaign. 

Remote sensing from satellites and groundbased networks provide spatial and temporal 

coverage that intensive field campaigns lack. 

Both in situ measurements and remote sensing 

have been used to determine key parameters 

for estimating aerosol direct radiative forcing 

including aerosol single scattering albedo, 

asymmetry factor, optical depth Remote sensing has also been providing simultaneous 

measurements of aerosol optical depth and 

radiative fluxes that can be combined to derive 

aerosol direct radiative forcing at the TOA with 

relaxed requirement for characterizing aerosol 

properties. Progress in using both satellite and 

surface-based measurements to study aerosolcloud interactions and aerosol indirect forcing 

is also discussed. 

2.3.1. THE USE OF MEASURED AEROSOL

PROPERTIES TO IMPROVE MODELS

The wide variety of aerosol data sets from 

intensive field campaigns provides a rigorous 

“testbed” for model simulations of aerosol 

properties and distributions and estimates of 

DRF. As described in Section 2.2.6, in situ

measurements can be used to constrain regional 

CTM simulations of aerosol properties, DRF, 

anthropogenic component of DRF, and to evaluate CTM parameterizations. In addition, in situ

measurements can be used to develop simplifying parameterizations for use by CTMs.

Several factors contribute to the uncertainty 

of CTM calculations of size-distributed aerosol composition including emissions, aerosol 

removal by wet deposition, processes involved 

in the formation of secondary aerosols and 

the chemical and microphysical evolution of 

aerosols, vertical transport, and meteorological fields including the timing and amount of 

precipitation, formation of clouds, and relative 

humidity. In situ measurements made during 

focused field campaigns provide a point of 

comparison for the CTM-generated aerosol 

distributions at the surface and at discrete points 

above the surface. Such comparisons are essential for identifying areas where the models 

need improvement.
35

Atmospheric Aerosol Properties and Climate Impacts

Figure 2.8. Location of aerosol chemical composition measurements with aerosol mass spectrometers. Colors for the labels indicate 

the type of sampling location: urban areas (blue), <100 miles downwind of major cites (black), and rural/remote areas >100 miles 

downwind (pink). Pie charts show the average mass concentration and chemical composition: organics (green), sulfate (red), nitrate 

(blue), ammonium (orange), and chloride (purple), of non-refractory PM1. Adapted from Zhang et al. (2007).

Figure 2.7 shows a comparison of submicrometer and supermicrometer aerosol chemical 

components measured during INDOEX, ACEAsia, and ICARTT onboard a ship and the 

same values calculated with the STEM Model 

(e.g., Carmichael et al., 2002, 2003; Tang et 

al., 2003, 2004; Bates et al., 2004; Streets et 

al., 2006b). To permit direct comparison of 

the measured and modeled values, the model 

was driven by analyzed meteorological data 

and sampled at the times and locations of the 

shipboard measurements every 30 min along 

the cruise track. The best agreement was found 

for submicrometer sulfate and BC. The agreement was best for sulfate; this is attributed to 

greater accuracy in emissions, chemical conversion, and removal for this component. Underestimation of dust and sea salt is most likely 

due to errors in model-calculated emissions. 

Large discrepancies between the modeled and 

measured values occurred for submicrometer 

particulate organic matter (POM) (INDOEX), 

and for particles in the supermicrometer size 

range such as dust (ACE-Asia), and sea salt (all 

regions). The model underestimated the total 

mass of the supermicrometer aerosol by about 

a factor of 3.

POM makes up a large and variable fraction 

of aerosol mass throughout the anthropogenically influenced northern hemisphere, and yet 

models have severe problems in properly 

representing this type of aerosol. Much of this 

discrepancy follows from the models inability 

to represent the formation of secondary organic 

aerosols (SOA) from the precursor volatile organic compounds (VOC). Figure 2.8 shows 

a summary of the results from aerosol mass 

spectrometer measurements at 30 sites over 

North America, Europe, and Asia. Based on 

aircraft measurements of urban-influenced air 

over New England, de Gouw et al. (2005) found 

that POM was highly correlated with secondary 

anthropogenic gas phase species suggesting 

that the POM was derived from secondary 

anthropogenic sources and that the formation 

took one day or more.

Figure 2.9 shows scatterplots of submicrometer 

POM versus acetylene (a gas phase primary 

emitted VOC species) and isopropyl nitrate (a 

secondary gas phase organic species formed by 

atmospheric reactions). The increase in submicrometer POM with increasing photochemical 

age could not be explained by the removal of 

Particulate organic 

matter makes up a 

large and variable 

fraction of aerosol 

mass throughout 

the northern 

hemisphere, and yet 

models have severe 

problems in properly 

representing this 

type of aerosol.
The U.S. Climate Change Science Program Chapter 2

36

VOC alone, which are its traditionally recognized precursors. This result suggests that 

other species must have contributed and/or that 

the mechanism for POM formation is more efficient than assumed by models. Similar results 

were obtained from the 2006 MILAGRO field 

campaign conducted in Mexico City (Kleinman 

et al., 2008), and comparisons of GCM results 

with several long-term monitoring stations also 

showed that the model underestimated organic 

aerosol concentrations (Koch et al., 2007). Recent laboratory work suggests that isoprene 

may be a major SOA source missing from previous atmospheric models (Kroll et al., 2006; 

Henze and Seinfeld, 2006), but underestimating 

sources from certain economic sectors may also 

play a role (Koch et al., 2007). Models also have 

difficulty in representing the vertical distribution of organic aerosols, underpredicting their 

occurrence in the free troposphere (FT) (Heald 

et al., 2005). While organic aerosol presents 

models with some of their greatest challenges, 

even the distribution of well-characterized sulfate aerosol is not always estimated correctly in 

models (Shindell et al., 2008a).

Comparisons of DRF and its anthropogenic 

component calculated with assumed optical 

properties and values constrained by in situ

measurements can help identify areas of uncertainty in model parameterizations. In a study 

described by Bates et al. (2006), two different 

CTMs (MOZART and STEM) were used to 

calculate dry mass 

concentrations of 

the dominant aerosol 

species (sulfate, organic carbon, black 

carbon, sea salt, and 

dust). In situ measurements were used 

to calculate the corresponding optical 

properties for each 

aerosol type for use 

in a radiative transfer model. Aerosol 

DRF and its anthropogenic component 

estimated using the 

empirically derived 

and a priori optical 

properties were then 

compared. The DRF 

and its anthropogenic component were calculated as the net downward solar flux difference 

between the model state with aerosol and of 

the model state with no aerosol. It was found 

that the constrained optical properties derived 

from measurements increased the calculated 

AOD (34 ± 8%), TOA DRF (32 ± 12%), and 

anthropogenic component of TOA DRF (37 ± 

7%) relative to runs using the a priori values. 

These increases were due to larger values of the 

constrained mass extinction efficiencies relative 

to the a priori values. In addition, differences 

in AOD due to using the aerosol loadings from 

MOZART versus those from STEM were much 

greater than differences resulting from the a 

priori vs. constrained RTM runs.

In situ observations also can be used to generate simplified parameterizations for CTMs and 

RTMs thereby lending an empirical foundation 

to uncertain parameters currently in use by 

models. CTMs generate concentration fields 

of individual aerosol chemical components 

that are then used as input to radiative transfer 

models (RTMs) for the calculation of DRF. 

Currently, these calculations are performed 

with a variety of simplifying assumptions 

concerning the RH dependence of light scattering by the aerosol. Chemical components 

often are treated as externally mixed each with 

a unique RH dependence of light scattering. 

However, both model and measurement studies reveal that POM, internally mixed with 

Figure 2.9. Scatterplots of the submicrometer POM measured during NEAQS versus a) acetylene and 

b) iso-propyl nitrate. The colors of the data points in a) denote the photochemical age as determined by 

the ratios of compounds of known OH reactivity. The gray area in a) shows the range of ratios between 

submicrometer POM and acetylene observed by Kirchstetter et al. (1999) in tunnel studies. Adapted from 

de Guow et al. (2005).
37

Atmospheric Aerosol Properties and Climate Impacts

water-soluble salts, can reduce the hygroscopic 

response of the aerosol, which decreases its 

water content and ability to scatter light at 

elevated relative humidity (e.g., Saxena et al., 

1995; Carrico et al., 2005). The complexity of 

the POM composition and its impact on aerosol 

optical properties requires the development of 

simplifying parameterizations that allow for 

the incorporation of information derived from 

field measurements into calculations of DRF 

(Quinn et al., 2005). Measurements made during INDOEX, ACE-Asia, and ICARTT revealed 

a substantial decrease in fσsp(RH) with increasing mass fraction of POM in the accumulation 

mode. Based on these data, a parameterization 

was developed that quantitatively describes the 

relationship between POM mass fraction and 

fσsp(RH) for accumulation mode sulfate-POM 

mixtures (Quinn et al., 2005). This simplified 

parameterization may be used as input to RTMs 

to derive values of fσsp(RH) based on CTM estimates of the POM mass fraction. Alternatively, 

the relationship may be used to assess values of 

fσsp(RH) currently being used in RTMs.

2.3.2. INTERCOMPARISONS OF SATELLITE

MEASUREMENTS AND MODEL SIMULATION OF AEROSOL OPTICAL DEPTH

As aerosol DRF is highly dependent on the 

amount of aerosol present, it is of first-order 

importance to improve the spatial characterization of AOD on a global scale. This requires an 

evaluation of the various remote sensing AOD 

data sets and comparison with model-based 

AOD estimates. The latter comparison is particularly important if models are to be used in 

projections of future climate states that would 

result from assumed future emissions. Both 

remote sensing and model simulation have 

uncertainties and satellite-model integration 

is needed to obtain an optimum description of 

aerosol distribution.

Figure 2.10 shows an intercomparison of annual average AOD at 550 nm from two recent 

satellite aerosol sensors (MODIS and MISR), 

five model simulations (GOCART, GISS, 

SPRINTARS, LMDZ-LOA, LMDZ-INCA) 

and three satellite-model integrations (MO_GO, 

MI_GO, MO_MI_GO). These model-satellite 

integrations are conducted by using an optimum interpolation approach (Yu et al., 2003) to 

constrain GOCART simulated AOD with that 

from MODIS, MISR, or MODIS over ocean and 

MISR over land, denoted as MO_GO, MI_GO, 

and MO_MI_GO, respectively. MODIS values 

of AOD are from Terra Collection 4 retrievals 

and MISR AOD is based on early post launch 

retrievals. MODIS and MISR retrievals give a 

comparable average AOD on the global scale, 

with MISR greater than MODIS by 0.01~0.02 

depending on the season. However, differences 

between MODIS and MISR are much larger 

when land and ocean are examined separately: 

AOD from MODIS is 0.02-0.07 higher over land 

but 0.03-0.04 lower over ocean than the AOD 

from MISR. Several major causes for the systematic MODIS-MISR differences have been 

identified, including instrument calibration 

and sampling differences, different assumptions about ocean surface boundary conditions 

made in the individual retrieval algorithms, 

missing particle property or mixture options in 

the look-up tables, and cloud screening (Kahn 

Figure 2.10. Comparison of annual mean aerosol optical depth (AOD) at 550 nm between satellite retrievals (MODIS, MISR), model simulations (GOCART, SPRINTARS, GISS, LMDZ-INCA, LMDZ-LOA), 

and satellite-model integrations (MO_GO, MI_GO, MO_MI_GO) averaged over land, ocean, and globe 

(all limited to 60°S-60°N region) (figure generated from Table 6 in Yu et al., 2006).

As aerosol direct 

radiative forcing is 

highly dependent 

on the amount of 

aerosol present, it 

is of first-order importance to improve 

the spatial characterization of aerosol 

optical depth on a 

global scale.
The U.S. Climate Change Science Program Chapter 2

38

et al., 2007b). The MODIS-MISR AOD differences are being reduced by continuous efforts 

on improving satellite retrieval algorithms and 

radiance calibration. The new MODIS aerosol retrieval algorithms in Collection 5 have 

resulted in a reduction of 0.07 for global land 

mean AOD (Levy et al., 2007b), and improved 

radiance calibration for MISR removed ~40% 

of AOD bias over dark water scenes (Kahn et 

al., 2005b).

The annual and global average AOD from the 

five models is 0.19±0.02 (mean ± standard 

deviation) over land and 0.13±0.05 over ocean, 

respectively. Clearly, the model-based mean 

AOD is smaller than both MODIS- and MISRderived values (except the GISS model). A 

similar conclusion has been drawn from more 

extensive comparisons involving more models 

and satellites (Kinne et al., 2006). On regional 

scales, satellite-model differences are much 

larger. These differences could be attributed in 

part to cloud contamination (Kaufman et al., 

2005b; Zhang et al., 2005c) and 3D cloud effects 

in satellite retrievals (Kaufman et al., 2005b; 

Wen et al., 2006) or to models missing important aerosol sources/sinks or physical processes 

(Koren et al., 2007b). Integrated satellite-model 

products are generally in-between the satellite 

retrievals and the model simulations, and agree 

better with AERONET measurements (e.g., Yu 

et al., 2003).

As in comparisons between models and in situ

measurements (Bates et al., 2006), there appears to be a relationship between uncertainties 

in the representation of dust in models and the 

uncertainty in AOD, and its global distribution. 

For example, the GISS model generates more 

dust than the other models (Fig. 2.11), resulting 

in a closer agreement with MODIS and MISR 

in the global mean (Fig. 2.10). However, the 

distribution of AOD between land and ocean 

is quite different from MODIS- and MISRderived values. 

Figure 2.11 shows larger model differences 

in the simulated percentage contributions of 

individual components to the total aerosol optical depth on a global scale, and hence in the 

simulated aerosol single-scattering properties 

(e.g., single-scattering albedo, and phase function), as documented in Kinne et al. (2006). 

This, combined with the differences in aerosol 

loading (as characterized by AOD) determines 

the model diversity in simulated aerosol direct 

radiative forcing, as discussed later. However, 

current satellite remote sensing capability is 

not sufficient to constrain model simulations 

of aerosol components. 

2.3.3. SATELLITE BASED ESTIMATES OF AEROSOL DIRECT RADIATIVE FORCING

Table 2.4 summarizes approaches to estimating 

the aerosol direct radiative forcing, including a 

brief description of methods, identifies major 

sources of uncertainty, and provides references. 

These estimates fall into three broad categories, 

namely (A) satellite-based, (B) satellite-model 

integrated, and (C) model-based. As satellite 

aerosol measurements are generally limited 

to cloud-free conditions, the discussion here 

focuses on assessments of clear-sky aerosol 

direct radiative forcing, a net (downwelling 

minus upwelling) solar flux difference between 

with aerosol (natural + anthropogenic) and in 

the absence of aerosol. 

Global distributions. Figure 2.12 shows global 

distributions of aerosol optical depth at 550 nm 

(left panel) and diurnally averaged clear-sky 

TOA DRF (right panel) for March-April-May 

(MAM) based on the different approaches. The 

DRF at the surface follows the same pattern 

as that at the TOA but is significantly larger 

in magnitude because of aerosol absorption. 

It appears that different approaches agree on 

large-scale patterns of aerosol optical depth and 

the direct radiative forcing. In this season, the 

aerosol impacts in the Northern Hemisphere are 

much larger than those in the Southern Hemisphere. Dust outbreaks and biomass burning 

elevate the optical depth to more than 0.3 over 

Figure 2.11. Percentage contributions of individual aerosol components (SU – 

sulfate, BC – black carbon, POM – particulate organic matter, DU – dust, SS – sea 

salt) to the total aerosol optical depth (at 550 nm) on a global scale simulated by 

the five models (data taken from Kinne et al., 2006).
39

Atmospheric Aerosol Properties and Climate Impacts

Table 2.4. Summary of approaches to estimating the aerosol direct radiative forcing in three categories: (A) satellite retrievals; (B) satellite-model integrations; and (C) model simulations. (adapted from Yu et al., 2006).

Category Product Brief Descriptions Identified Sources of 

Uncertainty Major References

A.

Satellite

retrievals

MODIS

Using MODIS retrievals of a 

linked set of AOD, ω0, and phase 

function consistently in conjunction with a radiative transfer 

model (RTM) to calculate TOA 

fluxes that best match the observed radiances.

Radiance calibration, cloudaerosol discrimination, 

instantaneous-to-diurnal scaling, RTM parameterizations

Remer and Kaufman, 2006

MODIS_A

Splitting MODIS AOD over 

ocean into mineral dust, sea salt, 

and biomass-burning and pollution; using AERONET measurements to derive the size distribution and single-scattering albedo 

for individual components.

Satellite AOD and FMF retrievals, overestimate due to 

summing up the compositional 

direct forcing, use of a single 

AERONET site to characterize a large region 

Bellouin et al., 2005

CERES_A

Using CERES fluxes in combination with standard MODIS 

aerosol. Calibration of CERES radiances, large CERES footprint, 

satellite AOD retrieval, 

radiance-to-flux conversion 

(ADM), instantaneous-todiurnal scaling, narrow-tobroadband conversion

Loeb and Manalo-Smith, 2005; 

Loeb and Kato, 2002

CERES_B

Using CERES fluxes in combination with NOAA NESDIS aerosol 

from MODIS radiances.

CERES_C

Using CERES fluxes in combination with MODIS (ocean) and 

MISR (non-desert land) aerosol 

with new angular models for 

aerosols.

Zhang et al., 2005a,b; Zhang 

and Christopher, 2003; 

Christopher et al., 2006; 

Patadia et al., 2008

POLDER

Using POLDER AOD in combination with prescribed aerosol 

models (similar to MODIS).

Similar to MODIS Boucher and Tanré, 2000;

Bellouin et al., 2003

B.

Satellitemodel 

integrations

MODIS_G Using GOCART simulations to fill 

AOD gaps in satellite retrievals.

Propagation of uncertainties 

associated with both satellite 

retrievals and model simulations (but the model-satellite 

integration approach does 

result in improved AOD quality for MO_GO, and MO_MI_

GO)

*Aerosol single-scattering 

albedo and asymmetry factor 

are taken from GOCART 

simulations;

*Yu et al., 2003, 2004, 2006

MISR_G

MO_GO Integration of MODIS and GOCART AOD.

MO_MI_

GO

Integration of GOCART AOD 

with retrievals from MODIS 

(Ocean) and MISR (Land).

SeaWiFS Using SeaWiFS AOD and assumed aerosol models.

Similar to MODIS_G and 

MISR_G, too weak aerosol 

absorption

Chou et al., 2002

C.

Model

simulations

GOCART

Offline RT calculations using 

monthly average aerosols with a 

time step of 30 min (without the 

presence of clouds). 

Emissions, parameterizations of a variety of sub-grid 

aerosol processes (e.g., wet 

and dry deposition, cloud 

convection, aqueous-phase 

oxidation), assumptions on 

aerosol size, absorption, mixture, and humidification of 

particles, meteorology fields, 

not fully evaluated surface 

albedo schemes, RT parameterizations

Chin et al., 2002; Yu et al., 

2004

SPRINTARS Online RT calculations every 3 

hrs (cloud fraction=0). Takemura et al., 2002, 2005

GISS Online model simulations and 

weighted by clear-sky fraction.

Koch and Hansen, 2005;

Koch et al., 2006

LMDZINCAOnline RT calculations every 2 

hrs (cloud fraction = 0).

Balkanski et al., 2007; Schulz

et al., 2006; Kinne et al., 

2006

LMDZ-LOA Online RT calculations every 2 

hrs (cloud fraction=0). Reddy et al., 2005a, b 
The U.S. Climate Change Science Program Chapter 2

40

Figure 2.12. Geographical patterns of seasonally (MAM) averaged aerosol optical depth at 550 nm (left 

panel) and the diurnally averaged clear-sky aerosol direct radiative (solar spectrum) forcing (W m-2) at 

the TOA (right panel) derived from satellite (Terra) retrievals (MODIS, Remer et al., 2005; Remer and 

Kaufman, 2006; MISR, Kahn et al., 2005a; and CERES_A, Loeb and Manalo-Smith, 2005), GOCART 

simulations (Chin et al., 2002; Yu et al., 2004), and GOCART-MODIS-MISR integrations (MO_MI_GO, 

Yu et al., 2006) (taken from Yu et al., 2006).

large parts of North Africa and the tropical 

Atlantic. In the tropical Atlantic, TOA cooling as large as -10 W m-2 extends westward to 

Central America. In eastern China, the optical 

depth is as high as 0.6-0.8, resulting from the 

combined effects of industrial activities and biomass burning in the south, and dust outbreaks 

in the north. The Asian impacts also extend to 

the North Pacific, producing a TOA cooling of 

more than -10 W m-2. Other areas having large 

aerosol impacts include Western Europe, midlatitude North Atlantic, and much of South Asia 

and the Indian Ocean. Over the “roaring forties” 

in the Southern Hemisphere, high winds generate a large amount of sea salt. Elevated optical 

depth, along with high solar zenith angle and 

hence large backscattering to space, results in 

a band of TOA cooling of more than -4 W m-2.

However, there is also some question as to 

whether thin cirrus (e.g., Zhang et al., 2005c) 

and unaccounted-for whitecaps contribute to 

the apparent enhancement in AOD retrieved 

by satellite. Some differences exist between 

different approaches. For example, the early 
41

Atmospheric Aerosol Properties and Climate Impacts

post-launch MISR retrieved optical depths over 

the southern hemisphere oceans are higher than 

MODIS retrievals and GOCART simulations. 

Over the “roaring forties”, the MODIS derived 

TOA solar flux perturbations are larger than the 

estimates from other approaches.

Global mean. Figure 2.13 summarizes the 

measurement- and model-based estimates of 

clear-sky annual-averaged DRF at both the TOA 

and surface from 60°S to 60°N. Seasonal DRF 

values for individual estimates are summarized 

in Table 2.5 and Table 2.6 for ocean and land, 

respectively. Mean, median and standard error 

ε (ε=σ/(n-1)1/2), where σ is standard deviation 

and n is the number of methods) are calculated 

for measurement- and model-based estimates 

separately. Note that although the standard 

deviation or standard error reported here is not 

a fully rigorous measure of a true experimental 

uncertainty, it is indicative of the uncertainty 

because independent approaches with independent sources of errors are used (see Table 2.4; 

in the modeling community, this is called the 

“diversity”, see Chapter 3).

• Ocean: For the TOA DRF, a majority of 

measurement-based and satellite-model 

integration-based estimates agree with 

each other within about 10%. On annual 

average, the measurement-based estimates 

give the DRF of -5.5±0.2 W m-2 (mean±ε) at 

the TOA and -8.7±0.7 W m-2 at the surface. 

This suggests that the ocean surface cooling is about 60% larger than the cooling 

at the TOA. Model simulations give wide 

ranges of DRF estimates at both the TOA 

and surface. The ensemble of five models 

gives the annual average DRF (mean ± 

ε) of -3.2±0.6 W m-2 and -4.9±0.8 W m-2

at the TOA and surface, respectively. On 

average, the surface cooling is about 37% 

larger than the TOA cooling, smaller than 

the measurement-based estimate of surface 

and TOA difference of 60%. However, the 

‘measurement-based’ estimate of surface

DRF is actually a calculated value, using 

poorly constrained particle properties.

• Land: It remains challenging to use satellite measurements alone for characterizing 

complex aerosol properties over land surfaces with high accuracy. As such, DRF 

estimates over land have to rely largely on 

model simulations and satellite-model integrations. On a global and annual average, 

the satellite-model integrated approaches 

derive a mean DRF of -4.9 W m-2 at the TOA 

and -11.9 W m-2 at the surface respectively. 

The surface cooling is more than a factor 

of 2 larger than the TOA cooling because 

of aerosol absorption. Note that the TOA 

DRF of -4.9 W m-2 agrees quite well with 

the most recent satellite-based estimate of 

-5.1±1.1 W m-2 over non-desert land based 

on coincident measurements of MISR AOD 

and CERES solar flux (Patadia et al., 2008). 

For comparisons, an ensemble of five model 

simulations derives a DRF (mean ± ε) over 

land of -3.0±0.6 W m-2 at the TOA and 

-7.6±0.9 W m-2 at the surface, respectively. 

Seasonal variations of DRF over land, as derived from both measurements and models, 

are larger than those over ocean.

The above analyses show that, on a global 

average, the measurement-based estimates of 

DRF are 55-80% greater than the model-based 

estimates. The differences are even larger on 

regional scales. Such measurement-model 

differences are a combination of differences 

in aerosol amount (optical depth), single-scattering properties, surface albedo, and radiative 

transfer schemes (Yu et al., 2006). As discussed 

earlier, MODIS retrieved optical depths tend 

to be overestimated by about 10-15% due to 

the contamination of thin cirrus and clouds in 

Figure 2.13. Summary of 

observation- and modelbased (denoted as OBS and 

MOD, respectively) estimates of clear-sky, annual 

average DRF at the TOA 

and at the surface. The box 

and vertical bar represent 

median and standard error, 

respectively. (taken from Yu 

et al., 2006).
The U.S. Climate Change Science Program Chapter 2

42

Table 2.5. Summary of seasonal and annual average clear-sky DRF (W m-2) at the TOA and the surface (SFC) 

over global OCEAN derived with different methods and data. Sources of data: MODIS (Remer & Kaufman, 

2006), MODIS_A (Bellouin et al., 2005), POLDER (Boucher and Tanré, 2000; Bellouin et al., 2003), CERES_A 

and CERES_B (Loeb and Manalo-Smith, 2005), CERES_C (Zhang et al., 2005b), MODIS_G, MISR_G, MO_GO, 

MO_MI_GO (Yu et al., 2004; 2006), SeaWiFS (Chou et al., 2002), GOCART (Chin et al., 2002; Yu et al., 2004), 

SPRINTARS (Takemura et al., 2002), GISS (Koch and Hansen, 2005; Koch et al., 2006), LMDZ-INCA (Kinne et 

al., 2006; Schulz et al., 2006), LMDZ-LOA (Reddy et al., 2005a, b). Mean, median, standard deviation (σ), and 

standard error (ε) are calculated for observations (Obs) and model simulations (Mod) separately. The last row is 

the ratio of model median to observational median. (taken from Yu et al., 2006)

Products

DJF MAM JJA SON ANN

TOA SFC TOA SFC TOA SFC TOA SFC TOA SFC

MODIS -5.9 -5.8 -6.0 -5.8 -5.9

MODIS_A* -6.0 -8.2 -6.4 -8.9 -6.5 -9.3 -6.4 -8.9 -6.4 -8.9

CERES_A -5.2 -6.1 -5.4 -5.1 -5.5

CERES_B -3.8 -4.3 -3.5 -3.6 -3.8

CERES_C -5.3 -5.4 -5.2 -5.3

MODIS_G -5.5 -9.1 -5.7 -10.4 -6.0 -10.6 -5.5 -9.8 -5.7 -10.0

MISR_G** -6.4 -10.3 -6.5 -11.4 -7.0 -11.9 -6.3 -10.9 -6.5 -11.1

MO_GO -4.9 -7.8 -5.1 -9.3 -5.4 -9.4 -5.0 -8.7 -5.1 -8.8

MO_MI_GO -4.9 -7.9 -5.1 -9.2 -5.5 -9.5 -5.0 -8.6 -5.1 -8.7

POLDER

-5.7 -5.7 -5.8 -5.6 -5.7

-5.2*** -7.7***

SeaWiFS -6.0 -6.6 -5.2 -5.8 -4.9 -5.6 -5.3 -5.7 -5.4 -5.9

Obs. Mean -5.4 -8.3 -5.6 -9.2 -5.6 -9.4 -5.4 -8.8 -5.5 -8.7

Obs. Median -5.5 -8.1 -5.7 -9.3 -5.5 -9.5 -5.4 -8.8 -5.5 -8.8

Obs. σ 0.72 1.26 0.64 1.89 0.91 2.10 0.79 1.74 0.70 1.65

Obs. ε 0.23 0.56 0.20 0.85 0.29 0.94 0.26 0.78 0.21 0.67

GOCART -3.6 -5.7 -4.0 -7.2 -4.7 -8.0 -4.0 -6.8 -4.1 -6.9

SPRINTARS -1.5 -2.5 -1.5 -2.5 -1.9 -3.3 -1.5 -2.5 -1.6 -2.7

GISS -3.3 -4.1 -3.5 -4.6 -3.5 -4.9 -3.8 -5.4 -3.5 -4.8

LMDZ -INCA -4.6 -5.6 -4.7 -5.9 -5.0 -6.3 -4.8 -5.5 -4.7 -5.8

LMDZ -LOA -2.2 -4.1 -2.2 -3.7 -2.5 -4.4 -2.2 -4.1 -2.3 -4.1

Mod. Mean -3.0 -4.4 -3.2 -4.8 -3.5 -5.4 -3.3 -4.9 -3.2 -4.9

Mod. Median -3.3 -4.1 -3.5 -4.6 -3.5 -4.9 -3.8 -5.4 -3.5 -4.8

Mod. σ 1.21 1.32 1.31 1.84 1.35 1.82 1.36 1.63 1.28 1.6

Mod. ε 0.61 0.66 0.66 0.92 0.67 0.91 0.68 0.81 0.64 0.80

Mod./Obs. 0.60 0.51 0.61 0.50 0.64 0.52 0.70 0.61 0.64 0.55

* High bias may result from adding the DRF of individual components to derive the total DRF (Bellouin et al., 2005).

** High bias most likely results from an overall overestimate of 20% in early post-launch MISR optical depth retrievals (Kahn et al., 2005).

*** Bellouin et al. (2003) use AERONET retrieval of aerosol absorption as a constraint to the method in Boucher and Tanré (2000), 

deriving aerosol direct radiative forcing both at the TOA and the surface.
43

Atmospheric Aerosol Properties and Climate Impacts

general (Kaufman et al., 2005b). Such overestimation of optical depth would result in a 

comparable overestimate of the aerosol direct 

radiative forcing. Other satellite AOD data may 

have similar contamination, which however has 

not yet been quantified. On the other hand, the 

observations may be measuring enhanced AOD 

and DRF due to processes not well represented 

in the models including humidification and enhancement of aerosols in the vicinity of clouds 

(Koren et al., 2007b).

From the perspective of model simulations, 

uncertainties associated with parameterizations of various aerosol processes and meteorological fields, as documented under the 

AEROCOM and Global Modeling Initiative 

(GMI) frameworks (Kinne et al., 2006; Textor 

et al., 2006; Liu et al., 2007), contribute to the 

large measurement-model and model-model 

discrepancies. Factors determining the AOD 

should be major reasons for the DRF discrepancy and the constraint of model AOD with 

well evaluated and bias reduced satellite AOD 

through a data assimilation approach can reduce 

the DRF discrepancy significantly. Other factors (such as model parameterization of surface 

reflectance, and model-satellite differences in 

single-scattering albedo and asymmetry factor 

due to satellite sampling bias toward cloud-free 

conditions) should also contribute, as evidenced 

by the existence of a large discrepancy in the radiative efficiency (Yu et al., 2006). Significant 

effort will be needed in the future to conduct 

comprehensive assessments. 

Table 2.6. Summary of seasonal and annual average clear-sky DRF (W m-2) at the TOA and the surface (SFC) over 

global LAND derived with different methods and data. Sources of data: MODIS_G, MISR_G, MO_GO, MO_MI_GO 

(Yu et al., 2004, 2006), GOCART (Chin et al., 2002; Yu et al., 2004), SPRINTARS (Takemura et al., 2002), GISS 

(Koch and Hansen, 2005; Koch et al., 2006), LMDZ-INCA (Balkanski et al., 2007; Kinne et al., 2006; Schulz et al., 

2006), LMDZ-LOA (Reddy et al., 2005a, b). Mean, median, standard deviation (σ), and standard error (ε) are calculated for observations (Obs) and model simulations (Mod) separately. The last row is the ratio of model median to 

observational median. (taken from Yu et al., 2006)

Products

DJF MAM JJA SON ANN

TOA SFC TOA SFC TOA SFC TOA SFC TOA SFC

MODIS_G -4.1 -9.1 -5.8 -14.9 -6.6 -17.4 -5.4 -12.8 -5.5 -13.5

MISR_G -3.9 -8.7 -5.1 -13.0 -5.8 -14.6 -4.6 -10.7 -4.9 -11.8

MO_GO -3.5 -7.5 -5.1 -12.9 -5.8 -14.9 -4.8 -10.9 -4.8 -11.6

MO_MI_GO -3.4 -7.4 -4.7 -11.8 -5.3 -13.5 -4.3 -9.7 -4.4 -10.6

Obs. Mean -3.7 -8.2 -5.2 -13.2 -5.9 -15.1 -4.8 -11.0 -4.9 -11.9

Obs. Median -3.7 -8.1 -5.1 -13.0 -5.8 -14.8 -4.7 -10.8 -4.9 -11.7

Obs. σ 0.33 0.85 0.46 1.29 0.54 1.65 0.46 1.29 0.45 1.20

Obs. ε 0.17 0.49 0.26 0.74 0.31 0.85 0.27 0.75 0.26 0.70

GOCART -2.9 -6.1 -4.4 -10.9 -4.8 -12.3 -4.3 -9.3 -4.1 -9.7

SPRINTARS -1.4 -4.0 -1.5 -4.6 -2.0 -6.7 -1.7 -5.2 -1.7 -5.1

GISS -1.6 -3.9 -3.2 -7.9 -3.6 -9.3 -2.5 -6.6 -2.8 -7.2

LMDZ-INCA -3.0 -5.8 -4.0 -9.2 -6.0 -13.5 -4.3 -8.2 -4.3 -9.2

LMDZ-LOA -1.3 -5.4 -1.8 -6.4 -2.7 -8.9 -2.1 -6.7 -2.0 -6.9

Mod. Mean -2.0 -5.0 -3.0 -7.8 -3.8 -10.1 -3.0 -7.2 -3.0 -7.6

Mod. Median -1.6 -5.4 -3.2 -7.9 -3.6 -9.3 -2.5 -6.7 -2.8 -7.2

Mod. σ 0.84 1.03 1.29 2.44 1.61 2.74 1.24 1.58 1.19 1.86

Mod. ε 0.42 0.51 0.65 1.22 0.80 1.37 0.62 0.79 0.59 0.93

Mod./Obs. 0.43 0.67 0.63 0.61 0.62 0.63 0.53 0.62 0.58 0.62

On a global average, 

the measurementbased estimates of 

aerosol direct radiative forcing are 

55-80% greater than 

the model-based 

estimates. The differences are even larger 

on regional scales.
The U.S. Climate Change Science Program Chapter 2

44

2.3.4. SATELLITE BASED ESTIMATES OF ANTHROPOGENIC COMPONENT OF AEROSOL DIRECT RADIATIVE FORCING

Satellite instruments do not measure the 

aerosol chemical composition needed to discriminate anthropogenic from natural aerosol 

components. Because anthropogenic aerosols 

are predominantly sub-micron, the fine-mode 

fraction derived from POLDER, MODIS, or 

MISR might be used as a tool for deriving anthropogenic aerosol optical depth. This could 

provide a feasible way to conduct measurementbased estimates of anthropogenic component 

of aerosol direct radiative forcing (Kaufman et 

al., 2002a). Such method derives anthropogenic 

AOD from satellite measurements by empirically correcting contributions of natural sources 

(dust and maritime aerosol) to the sub-micron 

AOD (Kaufman et al., 2005a). The MODISbased estimate of anthropogenic AOD is about 

0.033 over oceans, consistent with model assessments of 0.030~0.036 even though the total 

AOD from MODIS is 25-40% higher than the 

models (Kaufman et al., 2005a). This accounts 

for 21±7% of the MODIS-observed total aerosol optical depth, compared with about 33% of 

anthropogenic contributions estimated by the 

models. The anthropogenic fraction of AOD 

should be much larger over land (i.e., 47±9% 

from a composite of several models) (Bellouin 

et al., 2005), comparable to the 40% estimated 

by Yu et al. (2006). Similarly, the non-spherical 

fraction from MISR or POLDER can be used 

to separate dust from spherical aerosol (Kahn 

et al., 2001; Kalashnikova and Kahn, 2006), 

providing another constraint for distinguishing 

anthropogenic from natural aerosols.

There have been several estimates of anthropogenic component of DRF in recent years. Table 

2.7 lists such estimates of anthropogenic component of TOA DRF that are from model simulations (Schulz et al., 2006) and constrained to 

some degree by satellite observations (Kaufman 

et al., 2005a; Bellouin et al., 2005, 2008; Chung 

et al., 2005; Christopher et al., 2006; Matsui and 

Pielke, 2006; Yu et al., 2006; Quaas et al., 2008; 

Zhao et al., 2008b). The satellite-based clear-sky 

DRF by anthropogenic aerosols is estimated to 

be -1.1 ± 0.37 W m-2 over ocean, about a factor 

of 2 stronger than model simulated -0.6 W m-2. 

Similar DRF estimates are rare over land, but 

a few studies do suggest that the anthropogenic 

DRF over land is much more negative than that 

over ocean (Yu et al., 2006; Bellouin et al., 2005, 

2008). On global average, the measurementbased estimate of anthropogenic DRF ranges 

from -0.9 to -1.9 W m-2, again stronger than the 

model-based estimate of -0.8 W m-2. Similar to 

DRF estimates for total aerosols, satellite-based 

estimates of anthropogenic component of DRF 

are rare over land. 

On global average, anthropogenic aerosols are 

generally more absorptive than natural aerosols. 

As such the anthropogenic component of DRF 

is much more negative at the surface than at 

TOA. Several observation-constrained studies 

estimate that the global average, clear-sky, anthropogenic component of DRF at the surface 

ranges from -4.2 to -5.1 W m-2 (Yu et al., 2004; 

Bellouin et al., 2005; Chung et al., 2005; Matsui 

and Pielke, 2006), which is about a factor of 2 

larger in magnitude than the model estimates 

(e.g., Reddy et al., 2005b). 

Uncertainties in estimates of the anthropogenic 

component of aerosol DRF are greater than for 

the total aerosol, particularly over land. An 

uncertainty analysis (Yu et al., 2006) partitions the uncertainty for the global average 

anthropogenic DRF between land and ocean 

more or less evenly. Five parameters, namely 

fine-mode fraction (ff) and anthropogenic 

fraction of fine-mode fraction (faf) over both 

land and ocean, and τ over ocean, contribute 

nearly 80% of the overall uncertainty in the 

anthropogenic DRF estimate, with individual 

shares ranging from 13-20% (Yu et al., 2006). 

These uncertainties presumably represent a 

lower bound because the sources of error are 

assumed to be independent. Uncertainties associated with several parameters are also not 

well defined. Nevertheless, such uncertainty 

analysis is useful for guiding future research 

and documenting advances in understanding.

2.3.5. AEROSOL-CLOUD INTERACTIONS

AND INDIRECT FORCING

Satellite views of the Earth show a planet 

whose albedo is dominated by dark oceans and 

vegetated surfaces, white clouds, and bright 

deserts. The bright white clouds overlying 

darker oceans or vegetated surface demonstrate 

the significant effect that clouds have on the 

Earth’s radiative balance. Low clouds reflect 

Uncertainties in 

estimates of the 

anthropogenic component of aerosol 

direct radiative 

forcing are greater 

than for the total 

aerosol, particularly 

over land.
45

Atmospheric Aerosol Properties and Climate Impacts

incoming sunlight back to space, acting to cool 

the planet, whereas high clouds can trap outgoing terrestrial radiation and act to warm the 

planet. In the Arctic, low clouds have also been 

shown to warm the surface (Garrett and Zhao, 

2006). Changes in cloud cover, in cloud vertical 

development, and cloud optical properties will 

have strong radiative and therefore, climatic 

impacts. Furthermore, factors that change cloud 

development will also change precipitation 

processes. These changes may alter amounts, 

locations and intensities of local and regional 

rain and snowfall, creating droughts, floods and 

severe weather.

Cloud droplets form on a subset of aerosol 

particles called cloud condensation nuclei 

(CCN). In general, an increase in aerosol leads 

to an increase in CCN and an increase in drop 

concentration. Thus, for the same amount of 

liquid water in a cloud, more available CCN will 

result in a greater number but smaller size of 

droplets (Twomey, 1977). A cloud with smaller 

but more numerous droplets will be brighter and 

reflect more sunlight to space, thus exerting a 

cooling effect. This is the first aerosol indirect 

radiative effect, or “albedo effect”. The effectiveness of a particle as a CCN depends on its 

size and composition so that the degree to which 

clouds become brighter for a given aerosol perturbation, and therefore the extent of cooling, 

depends on the aerosol size distribution and its 

size-dependent composition. In addition, aerosol perturbations to cloud microphysics may 

involve feedbacks; for example, smaller drops 

are less likely to collide and coalesce; this will 

inhibit growth, suppressing precipitation, and 

possibly increasing cloud lifetime (Albrecht et 

al., 1989). In this case clouds may exert an even 

stronger cooling effect.

A distinctly different aerosol effect on clouds 

exists in thin Arctic clouds (LWP < 25 g m-2) 

having low emissivity. Aerosol has been shown 

Table 2.7. Estimates of anthropogenic components of aerosol optical depth (τant) and clear-sky DRF at the TOA from 

model simulations (Schulz et al., 2006) and approaches constrained by satellite observations (Kaufman et al., 2005a; 

Bellouin et al., 2005, 2008; Chung et al., 2005; Yu et al., 2006; Christopher et al., 2006; Matsui and Pielke, 2006; Quaas 

et al., 2008; Zhao et al., 2008b).

Data Sources

Ocean Land Global Estimated 

uncertainty or 

model diversity for 

DRF τant

DRF

(W m-2) τant

DRF

(W m-2) τant

DRF

(W m-2)

Kaufman et al. (2005a) 0.033 -1.4 30%

Bellouin et al. (2005) 0.028 -0.8 0.13 0.062 -1.9 15%

Chung et al. (2005) -1.1

Yu et al. (2006) 0.031 -1.1 0.088 -1.8 0.048 -1.3

47% (ocean), 84% 

(land), and 62% 

(global)

Christopher et al. (2006) -1.4 65%

Matsui and Pielke (2006) -1.6 30°S-30°N oceans

Quaas et al. (2008) -0.7 -1.8 -0.9 45%

Bellouin et al. (2008) 0.021 -0.6 0.107 -3.3 0.043 -1.3

Update to Bellouin 

et al. (2005) with 

MODIS Collection

5 data

Zhao et al. (2008b) -1.25 35%

Schulz et al. (2006) 0.022 -0.59 0.065 -1.14 0.036 -0.77

30-40%; same emissions prescribed for 

all models 
The U.S. Climate Change Science Program Chapter 2

46

to increase the longwave emissivity in these 

clouds, thereby warming the surface (Lubin and 

Vogelman, 2006; Garrett and Zhao, 2006). 

Some aerosol particles, particularly black carbon and dust, also act as ice nuclei (IN) and in 

so doing, modify the microphysical properties 

of mixed-phase and ice-clouds. An increase in 

IN will generate more ice crystals, which grow 

at the expense of water droplets due to the difference in vapor pressure over ice and water 

surfaces. The efficient growth of ice particles 

may increase the precipitation efficiency. In 

deep convective, polluted clouds there is a delay 

in the onset of freezing because droplets are 

smaller. These clouds may eventually precipitate, but only after higher altitudes are reached 

that result in taller cloud tops, more lightning 

and greater chance of severe weather (Rosenfeld 

and Lensky, 1998; Andreae et al., 2004). The 

present state of knowledge of the nature and 

abundance of IN, and ice formation in clouds 

is extremely poor. There is some observational 

evidence of aerosol influences on ice processes, 

but a clear link between aerosol, IN concentrations, ice crystal concentrations and growth 

to precipitation has not been established. This 

report therefore only peripherally addresses 

ice processes. More information can be found 

in a review by the WMO/IUGG International 

Aerosol-Precipitation Scientific Assessment 

(Levin and Cotton, 2008).

In addition to their roles as CCN and IN, aerosols also absorb and scatter light, and therefore 

they can change atmospheric conditions (temperature, stability, and surface fluxes) that 

influence cloud development and properties 

(Hansen et al., 1997; Ackerman et al., 2000). 

Thus, aerosols affect clouds through changing 

cloud droplet size distributions, cloud particle 

phase, and by changing the atmospheric environment of the cloud.

2.3.5A. REMOTE SENSING OF AEROSOLCLOUD INTERACTIONS AND INDIRECT

FORCING

The AVHRR satellite instruments have observed relationships between columnar aerosol 

loading, retrieved cloud microphysics, and 

cloud brightness over the Amazon Basin that 

are consistent with the theories explained above 

(Kaufman and Nakajima, 1993; Kaufman and 

Fraser, 1997; Feingold et al., 2001), but do 

not necessarily prove a causal relationship. 

Other studies have linked cloud and aerosol 

microphysical parameters or cloud albedo and 

droplet size using satellite data applied over the 

entire global oceans (Wetzel and Stowe, 1999; 

Nakajima et al., 2001; Han et al., 1998). Using 

these correlations with estimates of aerosol 

increase from the pre-industrial era, estimates 

of anthropogenic aerosol indirect radiative 

forcing fall into the range of -0.7 to -1.7 W m-2

(Nakajima et al., 2001).

Introduction of the more modern instruments 

(POLDER and MODIS) has allowed more 

detailed observations of relationships between 

aerosol and cloud parameters. Cloud cover can 

both decrease and increase with increasing 

aerosol loading (Koren et al., 2004; Kaufman et 

al., 2005c; Koren et al., 2005; Sekiguchi et al., 

2003; Matheson et al., 2005; Yu et al., 2007). The 

same is true of LWP (Han et al., 2002; Matsui 

et al., 2006). Aerosol absorption appears to be 

an important factor in determining how cloud 

cover will respond to increased aerosol loading 

(Kaufman and Koren, 2006; Jiang and Feingold, 

2006; Koren et al., 2008). Different responses of 

cloud cover to increased aerosol could also be 

correlated with atmospheric thermodynamic and 

moisture structure (Yu et al., 2007). Observations in the MODIS data show that aerosol loading correlates with enhanced convection and 

greater production of ice anvils in the summer 

Atlantic Ocean (Koren et al., 2005), which conflicts with previous results that used AVHRR 

and could not isolate convective systems from 

shallow clouds (Sekiguchi et al., 2003). 

In recent years, surface-based remote sensing 

has also been applied to address aerosol effects 

on cloud microphysics. This method offers some 

interesting insights, and is complementary to the 

global satellite view. Surface remote sensing can 

only be applied at a limited number of locations, 

and therefore lacks the global satellite view. 

However, these surface stations yield high temporal resolution data and because they sample 

aerosol below, rather than adjacent to clouds they 

do not suffer from “cloud contamination”. With 

the appropriate instrumentation (lidar) they can 

measure the local aerosol entering the clouds, 

rather than a column-integrated aerosol optical 

depth. Under well-mixed conditions, surface in 

situ aerosol measurements can be used. Surface 

remote-sensing studies are discussed in more 

The present state 

of knowledge of 

the nature and 

abundance of ice 

nuclei and ice formation in clouds 

is extremely poor.
47

Atmospheric Aerosol Properties and Climate Impacts

detail below, although the main science issues 

are common to satellite remote sensing.

Feingold et al. (2003) used data collected at 

the ARM Southern Great Plains (SGP) site to 

allow simultaneous retrieval of aerosol and 

cloud properties. A combination of a Doppler 

cloud radar and a microwave radiometer was 

used to retrieve cloud drop effective radius re

profiles in non-precipitating (radar reflectivity 

Z < -17 dBZ), ice-free clouds. Simultaneously, 

sub-cloud aerosol extinction profiles were 

measured with a lidar to quantify the response 

of drop sizes to changes in aerosol properties. 

Cloud data were binned according to liquid 

water path (LWP) as measured with a microwave radiometer, consistent with Twomey’s 

(1977) conceptual view of the aerosol impact on 

cloud microphysics. With high temporal/spatial 

resolution data (on the order of 20’s or 100’s of 

meters), realizations of aerosol-cloud interactions at the large eddy scale were obtained, and 

quantified in terms of the relative decrease in 

re in response to a relative increase in aerosol 

extinction (dln re/dln extinction), as shown in 

Figure 2.14. Examining the dependence in this 

way reduces reliance on absolute measures of 

cloud and aerosol parameters and minimizes 

sensitivity to measurement error, provided errors are unbiased. This formulation permitted 

these responses to be related to cloud microphysical theory. Restricting the examination 

to updrafts only (as determined from the radar 

Doppler signal) permitted examination of the 

role of updraft in determining the response of 

re to changes in aerosol (via changes in drop 

number concentration Nd). Analysis of data 

from 7 days showed that turbulence intensifies 

the aerosol impact on cloud microphysics. 

In addition to radar/microwave radiometer 

retrievals of aerosol and cloud properties, 

measurements of cloud optical depth by surface based radiometers such as the MFRSR 

(Michalsky et al., 2001) have been used in 

combination with measurements of cloud LWP 

by microwave radiometer to measure an average value of re during daylight when the solar 

elevation angle is sufficiently high (Min and 

Harrison, 1996). Using this retrieval, Kim et 

al. (2003) performed analyses of the re response 

to changes in aerosol at the same continental site, using a surface measurement of the 

aerosol light scattering coefficient instead of 

using extinction near cloud base as a proxy for 

CCN. Variance in LWP was shown to explain 

most of the variance in cloud optical depth, 

exacerbating detection of an aerosol effect. 

Although a decrease in re was observed with 

increasing scattering coefficient, the relation 

was not strong, indicative of other influences 

on re and/or decoupling between the surface and 

cloud layer. A similar study was conducted by 

Garrett et al. (2004) at a location in the Arctic. 

They suggested that summertime Arctic clouds 

are more sensitive to aerosol perturbations than 

clouds at lower latitudes. The advantage of the 

MFRSR/microwave radiometer combination is 

that it derives re from cloud optical depth and 

LWP and it is not as sensitive to large drops as 

the radar is. A limitation is that it can be applied 

only to clouds with extensive horizontal cover 

during daylight hours.

More recent data analyses by Feingold et al. 

(2006), Kim et al. (2008) and McComiskey et al. 

(2008b) at a variety of locations, and modeling 

work (Feingold, 2003) have investigated (i) the 

use of different proxies for cloud condensation 

nuclei, such as the light scattering coefficient 

and aerosol index; (ii) sensitivity of cloud 

microphysical/optical properties to controlling factors such as aerosol size distribution, 

entrainment, LWP, and updraft velocity; (iii) the 

effect of optical- as opposed to radar-retrievals 

of drop size; and (iv) spatial heterogeneity. 

These studies have reinforced the importance 

of LWP and vertical velocity as controlling 

parameters. They have also begun to reconcile 

the reasons for the large discrepancies between 

various approaches, and platforms (satellite, aircraft in situ, and surface-based remote sensing). 

These investigations are important because satFigure 2.14. Scatter plots 

showing mean cloud drop 

effective radius (re) vs. 

aerosol extinction coefficient (unit: km-1) for 

various liquid water path 

(LWP) bands on April 3, 

1998 at ARM SGP site 

(adapted from Feingold et 

al., 2003).
The U.S. Climate Change Science Program Chapter 2

48

ellite measurements that use a similar approach 

are being employed in GCMs to represent the 

albedo indirect effect (Quaas and Boucher, 

2005). In fact, the weakest albedo indirect effect 

in IPCC (2007) derives from satellite measurements that have very weak responses of re to 

changes in aerosol. The relationship between 

these aerosol-cloud microphysical responses 

and cloud radiative forcing has been examined 

by McComiskey and Feingold (2008). They 

showed that for plane-parallel clouds, a typical 

uncertainty in the logarithmic gradient of a 

re-aerosol relationship of 0.05 results in a local 

forcing error of -3 to -10 W m-2, depending on 

the aerosol perturbation. This sensitivity reinforces the importance of adequate quantification of aerosol effects on cloud microphysics 

to assessment of the radiative forcing, i.e., the 

indirect effect. Quantification of these effects 

from remote sensors is exacerbated by measurement errors. For example, LWP is measured to 

an accuracy of 25 g m-2 at best, and since it is 

the thinnest clouds (i.e., low LWP) that are most 

susceptible (from a radiative forcing perspective) to changes in aerosol, this measurement 

uncertainty represents a significant uncertainty 

in whether the observed response is related to 

aerosol, or to differences in LWP. The accuracy 

and spatial resolution of satellite-based LWP 

measurements is much poorer and this represents a significant challenge. In some cases 

important measurements are simply absent, e.g., 

updraft is not measured from satellite-based 

remote sensors.

Finally, cloud radar data from CloudSat, along 

with the A-train aerosol data, is providing great 

opportunity for inferring aerosol effects on 

precipitation (e.g., Stephens and Haynes, 2007). 

The aerosol effect on precipitation is far more 

complex than the albedo effect because the instantaneous view provided by satellites makes 

it difficult to establish causal relationships.

2.3.5B. IN SITU STUDIES OF AEROSOLCLOUD INTERACTIONS

In situ observations of aerosol effects on cloud 

microphysics date back to the 1950s and 1960s 

(Gunn and Phillips, 1957; Squires, 1958; Warner, 1968; Warner and Twomey, 1967; Radke et 

al., 1989; Leaitch et al., 1992; Brenguier et al., 

2000; to name a few). These studies showed 

that high concentrations of CCN from anthropogenic sources, such as industrial pollution 

or the burning of sugarcane, can increase 

cloud droplet number concentration Nd, thus 

increasing cloud microphysical stability and 

potentially reducing precipitation efficiency. 

As in the case of remote sensing studies, the 

causal link between aerosol perturbations and 

cloud microphysical responses (e.g., re or Nd) 

is much better established than the relationship 

between aerosol and changes in cloud fraction, 

LWC, and precipitation (see also Levin and 

Cotton, 2008).

In situ cloud measurements are usually regarded as “ground truth” for satellite retrievals 

but in fact there is considerable uncertainty in 

measured parameters such liquid water content 

(LWC), and size distribution, which forms the 

basis of other calculations such as drop concentration, re and extinction. It is not uncommon 

to see discrepancies in LWC on the order of 

50% between different instruments, and cloud 

drop size distributions are difficult to measure, 

particularly for droplets < 10 μm where Mie 

scattering oscillations generate ambiguities 

in drop size. Measurement uncertainty in re

from in situ probes is assessed, for horizontally 

homogeneous clouds, to be on the order of 15-

20%, compared to 10% for MODIS and 15-20% 

for other spectral measurements (Feingold et al., 

2006). As with remote measurements it is prudent to consider relative (as opposed to absolute) 

changes in cloud microphysics related to relative changes in aerosol. An added consideration 

is that in situ measurements typically represent 

a very small sample of the atmosphere akin to 

a thin pencil line through a large volume. For 

an aircraft flying at 100 m s-1 and sampling at 1 

Hz, the sample volume is on the order of 10 cm3. 

The larger spatial sampling of remote sensing 

has the advantage of being more representative 

but it removes small-scale (i.e., sub samplingvolume) variability, and therefore may obscure 

important cloud processes.

Measurements at a wide variety of locations 

around the world have shown that increases 

in aerosol concentration lead to increases in 

Nd. However the rate of this increase is highly 

variable and always sub-linear, as exemplified 

by the compilation of data in Ramanathan et 

al. (2001a). This is because, as discussed previously, Nd is a function of numerous parameters 

in addition to aerosol number concentration, 

including size distribution, updraft velocThe aerosol effect 

on precipitation is 

far more complex 

than the albedo 

effect because 

the instantaneous 

view provided by 

satellites makes it 

difficult to establish causal relationships.
49

Atmospheric Aerosol Properties and Climate Impacts

ity (Leaitch et al., 1996), and composition. 

In stratocumulus clouds, characterized by 

relatively low vertical velocity (and low supersaturation) only a small fraction of particles 

can be activated whereas in vigorous cumulus 

clouds that have high updraft velocities, a much 

larger fraction of aerosol particles is activated. 

Thus the ratio of Nd to aerosol particle number 

concentration is highly variable.

In recent years there has been a concerted effort 

to reconcile measured Nd concentrations with 

those calculated based on observed aerosol size 

and composition, as well as updraft velocity. 

These so-called “closure experiments” have 

demonstrated that on average, agreement in Nd

between these approaches is on the order of 20% 

(e.g., Conant et al., 2004). This provides confidence in theoretical understanding of droplet 

activation, however, measurement accuracy is 

not high enough to constrain the aerosol composition effects that have magnitudes < 20%.

One exception to the rule that more aerosol 

particles result in larger Nd is the case of giant 

CCN (sizes on the order of a few microns), 

which, in concentrations on the order of 1 cm-3

(i.e., ~ 1% of the total concentration) can lead to 

significant suppression in cloud supersaturation 

and reductions in Nd (O’Dowd et al., 1999). The 

measurement of these large particles is difficult 

and hence the importance of this effect is hard 

to assess. These same giant CCN, at concentrations as low as 1/liter, can significantly affect 

the initiation of precipitation in moderately 

polluted clouds (Johnson, 1982) and in so doing 

alter cloud albedo (Feingold et al., 1999).

The most direct link between the remote sensing of aerosol-cloud interactions discussed in 

section 2.3.5.1 and in situ observations is via 

observations of relationships between drop 

concentration Nd and CCN concentration. 

Theory shows that if re-CCN relationships are 

calculated at constant LWP or LWC, their logarithmic slope is -1/3 that of the Nd-CCN logarithmic slope (i.e., dlnre/dlnCCN = -1/3 dlnNd/

dlnCCN). In general, Nd-CCN slopes measured 

in situ tend to be stronger than equivalent 

slopes obtained from remote sensing – particularly in the case of satellite remote sensing 

(McComiskey and Feingold 2008). There are a 

number of reasons for this: (i) in situ measurements focus on smaller spatial scales and are 

more likely to observe the droplet activation 

process as opposed to remote sensing that 

incorporates larger spatial scales and includes 

other processes such as drop coalescence that 

reduce Nd, and therefore the slope of the NdCCN relationship (McComiskey et al., 2008b). 

(ii) Satellite remote sensing studies typically do 

not sort their data by LWP, and this has been 

shown to reduce the magnitude of the re-CCN 

response (Feingold, 2003).

In conclusion, observational estimates of aerosol indirect radiative forcings are still in their 

infancy. Effects on cloud microphysics that 

result in cloud brightening have to be considered along with effects on cloud lifetime, cover, 

vertical development and ice production. For 

in situ measurements, aerosol effects on cloud 

microphysics are reasonably consistent (within 

~ 20%) with theory but measurement uncertainties in remote sensing of aerosol effects on 

clouds, as well as complexity associated with 

three-dimensional radiative transfer, result in 

considerable uncertainty in radiative forcing. 

The higher order indirect effects are poorly 

understood and even the sign of the microphysical response and forcing may not always 

be the same. Aerosol type and specifically the 

absorption properties of the aerosol may cause 

different cloud responses. Early estimates of 

observationally based aerosol indirect forcing 

range from -0.7 to -1.7 W m-2 (Nakajima et al., 

2001) and -0.6 to -1.2 W m-2 (Sekiguchi et al., 

2003), depending on the estimate for aerosol 

increase from pre-industrial times and whether 

aerosol effects on cloud fraction are also included in the estimate.

2.4. Outstanding Issues

Despite substantial progress, as summarized in 

section 2.2 and 2.3, most measurement-based 

studies so far have concentrated on influences 

produced by the sum of natural and anthropogenic aerosols on solar radiation under clear sky 

conditions. Important issues remain: 

• Because accurate measurements of aerosol 

absorption are lacking and land surface reflection values are uncertain, DRF estimates 

over land and at the ocean surface are less 

well constrained than the estimate of TOA 

DRF over ocean. 

• Current estimates of the anthropogenic component of aerosol direct radiative forcing have 

large uncertainties, especially over land. 

For in situ measurements, aerosol effects 

on cloud microphysics are reasonably 

consistent with 

theory but measurement uncertainties 

in remote sensing of 

aerosol effects on 

clouds result in considerable uncertainty 

in radiative forcing.
The U.S. Climate Change Science Program Chapter 2

50

• Because there are very few measurements 

of aerosol absorption vertical distribution, 

mainly from aircraft during field campaigns, 

estimates of direct radiative forcing of 

above-cloud aerosols and profiles of atmospheric radiative heating induced by aerosol 

absorption are poorly constrained. 

• There is a need to quantify aerosol impacts 

on thermal infrared radiation, especially for 

dust. 

• The diurnal cycle of aerosol direct radiative 

forcing cannot be adequately characterized 

with currently available, sun-synchronous, 

polar orbiting satellite measurements. 

• Measuring aerosol, cloud, and ambient meteorology contributions to indirect radiative 

forcing remains a major challenge. 

• Long-term aerosol trends and their relationship to observed surface solar radiation 

changes are not well understood. 

The current status and prospects for these areas 

are briefly discussed below.

Measuring aerosol absorption and singlescattering albedo: Currently, the accuracy 

of both in situ and remote sensing aerosol 

SSA measurements is generally ±0.03 at best, 

which implies that the inferred accuracy of 

clear sky aerosol DRF would be larger than 

1 W m-2 (see Chapter 1). Recently developed 

photoacoustic (Arnott et al., 1997) and cavity 

ring down extinction cell (Strawa et al., 2002) 

techniques for measuring aerosol absorption 

produce SSA with improved accuracy over 

previous methods. However, these methods 

are still experimental, and must be deployed 

on aircraft. Aerosol absorption retrievals from 

satellites using the UV-technique have large 

uncertainties associated with its sensitivity 

to the height of the aerosol layer(s) (Torres et 

al., 2005), and it is unclear how the UV results 

can be extended to visible wavelengths. Views 

in and out of sunglint can be used to retrieve 

total aerosol extinction and scattering, respectively, thus constraining aerosol absorption over 

oceans (Kaufman et al., 2002b). However, this 

technique requires retrievals of aerosol scattering properties, including the real part of the 

refractive index, well beyond what has so far 

been demonstrated from space. In summary, 

there is a need to pursue a better understanding 

of the uncertainty in SSA from both in situ measurements and remote sensing retrievals and, 

with this knowledge, to synthesize different 

data sets to yield a characterization of aerosol 

absorption with well-defined uncertainty (Leahy et al., 2007). Laboratory studies of aerosol 

absorption of specific known composition are 

also needed to interpret in situ measurements 

and remote sensing retrievals and to provide 

updated database of particle absorbing properties for models.

Estimating the aerosol direct radiative forcing over land: Land surface reflection is large, 

heterogeneous, and anisotropic, which complicates aerosol retrievals and DRF determination 

from satellites. Currently, the aerosol retrievals 

over land have relatively lower accuracy than 

those over ocean (Section 2.2.5) and satellite 

data are rarely used alone for estimating DRF 

over land (Section 2.3). Several issues need to 

be addressed, such as developing appropriate 

angular models for aerosols over land (Patadia 

et al., 2008) and improving land surface reflectance characterization. MODIS and MISR 

measure land surface reflection wavelength 

dependence and angular distribution at high 

resolution (Moody et al., 2005; Martonchik et 

al., 1998b; 2002). This offers a promising opportunity for inferring the aerosol direct radiative 

forcing over land from satellite measurements 

of radiative fluxes (e.g., CERES) and from critical reflectance techniques (Fraser and Kaufman, 1985; Kaufman, 1987). The aerosol direct 

radiative forcing over land depends strongly on 

aerosol absorption and improved measurements 

of aerosol absorption are required. 

Distinguishing anthropogenic from natural 

aerosols: Current estimates of anthropogenic 

components of AOD and direct radiative forcing have larger uncertainties than total aerosol 

optical depth and direct radiative forcing, particularly over land (see Section 2.3.4), because 

of relatively large uncertainties in the retrieved 

aerosol microphysical properties (see Section 

2.2). Future measurements should focus on 

improved retrievals of such aerosol properties 

as size distribution, particle shape, and absorption, along with algorithm refinement for better 

aerosol optical depth retrievals. Coordinated in 

situ measurements offer a promising avenue for 

validating and refining satellite identification 

of anthropogenic aerosols (Anderson et al., 

2005a, 2005b). For satellite-based aerosol type 

characterization, it is sometimes assumed that 

There is a need 

to pursue a better 

understanding of 

the uncertainty in 

single scattering 

albedo from both 

in situ measurements and remote 

sensing retrievals.
51

Atmospheric Aerosol Properties and Climate Impacts

all biomass-burning aerosol is anthropogenic 

and all dust aerosol is natural (Kaufman et al., 

2005a). The better determination of anthropogenic aerosols requires a quantification of 

biomass burning ignited by lightning (natural 

origin) and mineral dust due to human induced 

changes of land cover/land use and climate 

(anthropogenic origin). Improved emissions 

inventories and better integration of satellite 

observations with models seem likely to reduce 

the uncertainties in aerosol source attribution. 

Profiling the vertical distributions of aerosols: Current aerosol profile data are far from 

adequate for quantifying the aerosol radiative 

forcing and atmospheric response to the forcing. The data have limited spatial and temporal 

coverage, even for current spaceborne lidar 

measurements. Retrieving aerosol extinction 

profile from lidar measured attenuated backscatter is subject to large uncertainties resulting 

from aerosol type characterization. Current 

space-borne Lidar measurements are also not 

sensitive to aerosol absorption. Because of lack 

of aerosol vertical distribution observations, 

the estimates of DRF in cloudy conditions 

and dust DRF in the thermal infrared remain 

highly uncertain (Schulz et al., 2006; Sokolik 

et al., 2001; Lubin et al., 2002). It also remains 

challenging to constrain the aerosol-induced 

atmospheric heating rate increment that is 

essential for assessing atmospheric responses 

to the aerosol radiative forcing (e.g., Yu et al., 

2002; Feingold et al., 2005; Lau et al., 2006). 

Progress in the foreseeable future is likely to 

come from (1) better use of existing, global, 

space-based backscatter lidar data to constrain 

model simulations, and (2) deployment of new 

instruments, such as high-spectral-resolution 

lidar (HSRL), capable of retrieving both extinction and backscatter from space. The HSRL lidar system will be deployed on the EarthCARE 

satellite mission tentatively scheduled for 2013 

(http://asimov/esrin.esi.it/esaLP/ASESMYNW9SC_Lpearthcare_1.html). 

Characterizing the diurnal cycle of aerosol 

direct radiative forcing: The diurnal variability of aerosol can be large, depending 

on location and aerosol type (Smirnov et al., 

2002), especially in wildfire situations, and in 

places where boundary layer aerosols hydrate 

or otherwise change significantly during the 

day. This cannot be captured by currently available, sun-synchronous, polar orbiting satellites. 

Geostationary satellites provide adequate time 

resolution (Christopher and Zhang, 2002; Wang 

et al., 2003), but lack the information required 

to characterize aerosol types. Aerosol type 

information from low earth orbit satellites can 

help improve accuracy of geostationary satellite 

aerosol retrievals (Costa et al., 2004a, 2004b). 

For estimating the diurnal cycle of aerosol DRF, 

additional efforts are needed to adequately characterize the anisotropy of surface reflection (Yu 

et al., 2004) and daytime variation of clouds. 

Studying aerosol-cloud interactions and 

indirect radiative forcing: Remote sensing 

estimates of aerosol indirect forcing are still 

rare and uncertain. Improvements are needed 

for both aerosol characterization and measurements of cloud properties, precipitation, water 

vapor, and temperature profiles. Basic processes still need to be understood on regional and 

global scales. Remote sensing observations of 

aerosol-cloud interactions and aerosol indirect 

forcing are for the most part based on simple 

correlations among variables, from which 

cause-and-effects cannot be deduced. One difficulty in inferring aerosol effects on clouds 

from the observed relationships is separating 

aerosol from meteorological effects, as aerosol loading itself is often correlated with the 

meteorology. In addition, there are systematic 

errors and biases in satellite aerosol retrievals for partly cloud-filled scenes. Stratifying 

aerosol and cloud data by liquid water content, 

a key step in quantifying the albedo (or first) 

indirect effect, is usually missing. Future work 

will need to combine satellite observations with 

in situ validation and modeling interpretation. 

A methodology for integrating observations 

(in situ and remote) and models at the range 

of relevant temporal/spatial scales is crucial to 

improve understanding of aerosol indirect effects and aerosol-cloud interactions. 

Quantifying long-term trends of aerosols 

at regional scales: Because secular changes 

are subtle, and are superposed on seasonal and 

other natural variability, this requires the construction of consistent, multi-decadal records of 

climate-quality data. To be meaningful, aerosol 

trend analysis must be performed on a regional 

basis. Long-term trends of aerosol optical depth 

have been studied using measurements from 

surface remote sensing stations (e.g., Hoyt and 

Remote sensing 

estimates of aerosol 

indirect forcing are 

still rare and uncertain. Improvements 

are needed for both 

aerosol characterization and measurements of cloud 

properties, precipitation, water vapor, and 

temperature profiles.

To be meaningful, 

aerosol trend analysis 

must be performed 

on a regional basis.
The U.S. Climate Change Science Program Chapter 2

52

Frohlich, 1983; Augustine et al., 2008; Luo et 

al., 2001) and historic satellite sensors (Massie 

et al., 2004; Mishchenko et al., 2007a; Mishchenko and Geogdzhayev, 2007; Zhao et al., 

2008a). An emerging multi-year climatology 

of high quality AOD data from modern satellite sensors (e.g., Remer et al., 2008; Kahn et 

al., 2005a) has been used to examine the interannual variations of aerosol (e.g., Koren et al., 

2007a, Mishchenko and Geogdzhayev, 2007) 

and contribute significantly to the study of 

aerosol trends. Current observational capability 

needs to be continued to avoid any data gaps. 

A synergy of aerosol products from historical, 

modern and future sensors is needed to construct as long a record as possible. Such a data 

synergy can build upon understanding and reconciliation of AOD differences among different 

sensors or platforms (Jeong et al., 2005). This 

requires overlapping data records for multiple 

sensors. A close examination of relevant issues 

associated with individual sensors is urgently 

needed, including sensor calibration, algorithm 

assumptions, cloud screening, data sampling 

and aggregation, among others. 

Linking aerosol long-term trends with 

changes of surface solar radiation: Analysis of the long-term surface solar radiation 

record suggests significant trends during past 

decades (e.g., Stanhill and Cohen, 2001; Wild 

et al., 2005; Pinker et al., 2005; Alpert et al., 

2005). Although a significant and widespread 

decline in surface total solar radiation (the 

sum of direct and diffuse irradiance) occurred up to 1990 (so-called solar dimming), 

a sustained increase has been observed during 

the subsequent decade. Speculation suggests 

that such trends result from decadal changes 

of aerosols and the interplay of aerosol direct 

and indirect radiative forcing (Stanhill and 

Cohen, 2001; Wild et al., 2005; Streets et al., 

2006a; Norris and Wild, 2007; Ruckstuhl et 

al., 2008). However, reliable observations of 

aerosol trends are required test these ideas. In 

addition to aerosol optical depth, changes in 

aerosol composition must also be quantified, 

to account for changing industrial practices, 

environmental regulations, and biomass burning emissions (Novakov et al., 2003; Streets et 

al., 2004; Streets and Aunan et al., 2005). Such 

compositional changes will affect the aerosol 

SSA and size distribution, which in turn will 

affect the surface solar radiation (e.g., Qian et 

al., 2007). However such data are currently rare 

and subject to large uncertainties. Finally, a 

better understanding of aerosol-radiation-cloud 

interactions and trends in cloudiness, cloud 

albedo, and surface albedo is badly needed 

to attribute the observed radiation changes to 

aerosol changes with less ambiguity.

2.5. Concluding Remarks

Since the concept of aerosol-radiation-climate 

interactions was first proposed around 1970, 

substantial progress has been made in determining the mechanisms and magnitudes of 

these interactions, particularly in the last ten 

years. Such progress has greatly benefited 

from significant improvements in aerosol 

measurements and increasing sophistication 

of model simulations. As a result, knowledge 

of aerosol properties and their interaction with 

solar radiation on regional and global scales is 

much improved. Such progress plays a unique 

role in the definitive assessment of the global 

anthropogenic radiative forcing, as “virtually 

certainly positive” in IPCC AR4 (Haywood 

and Schulz, 2007). 

In situ measurements of aerosols: New in situ

instruments such as aerosol mass spectrometers, photoacoustic techniques, and cavity ring 

down cells provide high accuracy and fast time 

resolution measurements of aerosol chemical 

and optical properties. Numerous focused field 

campaigns and the emerging ground-based 

aerosol networks are improving regional aerosol 

chemical, microphysical, and radiative property 

characterization. Aerosol closure studies of 

different measurements indicate that measurements of submicrometer, spherical sulfate and 

carbonaceous particles have a much better 

accuracy than that for dust-dominated aerosol. 

The accumulated comprehensive data sets of 

regional aerosol properties provide a rigorous 

“test bed” and strong constraint for satellite 

retrievals and model simulations of aerosols 

and their direct radiative forcing. 

Remote sensing measurements of aerosols: 

Surface networks, covering various aerosol 

regimes around the globe, have been measuring aerosol optical depth with an accuracy of 

0.01~0.02, which is adequate for achieving the 

accuracy of 1 W m-2 for cloud-free TOA DRF. 

On the other hand, aerosol microphysical properties retrieved from these networks, especially 

A better understanding of 

aerosol-radiationcloud interactions

and trends in 

cloudiness, cloud 

albedo, and 

surface albedo is 

badly needed to 

attribute the observed radiation 

changes to aerosol changes with 

less ambiguity.
53

Atmospheric Aerosol Properties and Climate Impacts

SSA, have relatively large uncertainties and 

are only available in very limited conditions. 

Current satellite sensors can measure AOD 

with an accuracy of about 0.05 or 15 to 20% 

in most cases. The implementation of multiwavelength, multi-angle, and polarization 

measuring capabilities has also made it possible 

to measure particle properties (size, shape, and 

absorption) that are essential for characterizing 

aerosol type and estimating anthropogenic 

component of aerosols. However, these microphysical measurements are more uncertain than 

AOD measurements.

Observational estimates of clear-sky aerosol 

direct radiative forcing: Closure studies based 

on focused field experiments reveal DRF uncertainties of about 25% for sulfate/carbonaceous 

aerosol and 60% for dust at regional scales. The 

high-accuracy of MODIS, MISR and POLDER 

aerosol products and broadband flux measurements from CERES make it feasible to obtain 

observational constraints for aerosol TOA DRF 

at a global scale, with relaxed requirements for 

measuring particle microphysical properties. 

Major conclusions from the assessment are:

• A number of satellite-based approaches 

consistently estimate the clear-sky diurnally 

averaged TOA DRF (on solar radiation) to 

be about -5.5±0.2 W m-2 (mean ± standard 

error from various methods) over global 

ocean. At the ocean surface, the diurnally 

averaged DRF is estimated to be -8.7±0.7 

W m-2. These values are calculated for the 

difference between today’s measured total 

aerosol (natural plus anthropogenic) and the 

absence of all aerosol.

• Overall, in comparison to that over ocean, 

the DRF estimates over land are more poorly 

constrained by observations and have larger 

uncertainties. A few satellite retrieval and 

satellite-model integration yield the overland clear-sky diurnally averaged DRF of 

-4.9±0.7 W m-2 and -11.8±1.9 W m-2 at the 

TOA and surface, respectively. These values 

over land are calculated for the difference between total aerosol and the complete absence 

of all aerosol.

• Use of satellite measurements of aerosol microphysical properties yields that on a global 

ocean average, about 20% of AOD is contributed by human activities and the clear-sky 

TOA DRF by anthropogenic aerosols is 

-1.1±0.4 W m-2. Similar DRF estimates are 

rare over land, but a few measurement-model 

integrated studies do suggest much more 

negative DRF over land than over ocean. 

• These satellite-based DRF estimates are 

much greater than the model-based estimates, with differences much larger at 

regional scales than at a global scale.

Measurements of aerosol-cloud interactions 

and indirect radiative forcing: In situ measurement of cloud properties and aerosol effects 

on cloud microphysics suggest that theoretical 

understanding of the activation process for water cloud is reasonably well-understood. Remote 

sensing of aerosol effects on droplet size associated with the albedo effect tends to underestimate the magnitude of the response compared 

to in situ measurements. Recent efforts trace 

this to a combination of lack of stratification of 

data by cloud water, the relatively large spatial 

scale over which measurements are averaged 

(which includes variability in cloud fields, and 

processes that obscure the aerosol-cloud processes), as well as measurement uncertainties 

(particularly in broken cloud fields). It remains 

a major challenge to infer aerosol number concentrations from satellite measurements. The 

present state of knowledge of the nature and 

abundance of IN, and ice formation in clouds 

is extremely poor. 

Despite the substantial progress in recent decades, several important issues remain, such 

as measurements of aerosol size distribution, 

particle shape, absorption, and vertical profiles, 

and the detection of aerosol long-term trend 

and establishment of its connection with the 

observed trends of solar radiation reaching the 

surface, as discussed in section 2.4. Furthering the understanding of aerosol impacts on 

climate requires a coordinated research strategy 

to improve the measurement accuracy and use 

the measurements to validate and effectively 

constrain model simulations. Concepts of future research in measurements are discussed 

in Chapter 4 “Way Forward”.

The high-accuracy 

of satellite measurements makes 

it feasible to obtain 

observational constraints for aerosol 

top-of-atmosphere 

direct radiative forcing at a global scale.

Furthering the understanding of aerosol 

impacts on climate 

requires a coordinated research strategy to improve the 

measurement accuracy and to constrain/

validate models with 

measurements.
The U.S. Climate Change Science Program Chapter 2

54

Sampling the Arcic Haze. Pollution and smoke aerosols can travel long distances, from mid-latitudes to the Arctic, causing 

“Arctic Haze”. Photo taken from the NASA DC-8 aircraft during the ARCTAS field experiment over Alaska in April 2008.

Credit: Mian Chin, NASA.
Modeling the Effects of Aerosols on 

Climate

Lead Authors: David Rind, NASA GISS; Mian Chin, 

NASA GSFC; Graham Feingold, NOAA ESRL; David G. 

Streets, DOE ANL

Contributing Authors: Ralph A. Kahn, NASA GSFC; 

Stephen E. Schwartz, DOE BNL; Hongbin Yu, NASA 

GSFC/UMBC

CHAPTER

3

55

Atmospheric Aerosol Properties and Climate Impacts

3.1. Introduction

The IPCC Fourth Assessment Report (AR4) 

(IPCC, 2007) concludes that man’s influence 

on the warming climate is in the category of 

“very likely”. This conclusion is based on, 

among other things, the ability of models to 

simulate the global and, to some extent, regional variations of temperature over the past 

50 to 100 years. When anthropogenic effects 

are included, the simulations can reproduce the 

observed warming (primarily for the past 50 

years); when they are not, the models do not 

get very much warming at all. In fact, all of 

the models runs for the IPCC AR4 assessment 

(more than 20) produce this distinctive result, 

driven by the greenhouse gas increases that 

have been observed to occur.

These results were produced in models whose 

average global warming associated with a 

doubled CO2 forcing of 4 W m-2 was about 3°C. 

This translates into a climate sensitivity (surface temperature change per forcing) of about 

0.75°C/(W m-2). The determination of climate 

sensitivity is crucial to projecting the future 

impact of increased greenhouse gases, and the 

credibility of this projected value relies on the 

ability of these models to simulate the observed 

temperature changes over the past century. 

However, in producing the observed temperature trend in the past, the models made use of 

very uncertain aerosol forcing. The greenhouse 

gas change by itself produces warming in 

models that exceeds that observed by some 

40% on average (IPCC, 2007). Cooling associated with aerosols reduces this warming to the 

observed level. Different climate models use 

differing aerosol forcings, both direct (aerosol 

scattering and absorption of short and longwave radiation) and indirect (aerosol effect on 

cloud cover reflectivity and lifetime), whose 

magnitudes vary markedly from one model to 

the next. Kiehl (2007) using nine of the IPCC 

(2007) AR4 climate models found that they 

had a factor of three forcing differences in the 

aerosol contribution for the 20th century. The 

differing aerosol forcing is the prime reason 

why models whose climate sensitivity varies 

by almost a factor of three can produce the 

observed trend. It was thus concluded that 

the uncertainty in IPCC (2007) anthropogenic 

climate simulations for the past century should 

really be much greater than stated (Schwartz et 

al., 2007; Kerr, 2007), since, in general, models 

with low/high sensitivity to greenhouse warming used weaker/stronger aerosol cooling to 

obtain the same temperature response (Kiehl, 

2007). Had the situation been reversed and 

the low/high sensitivity models used strong/

weak aerosol forcing, there would have been 

a greater divergence in model simulations of 

the past century.

Therefore, the fact that a model has accurately 

reproduced the global temperature change in 

the past does not imply that its future forecast 

is accurate. This state of affairs will remain 

until a firmer estimate of radiative forcing (RF) 

by aerosols, in addition to that by greenhouse 

gases, is available.

The uncertainty in 

anthropogenic climate 

simulation for the 

past century should 

be much greater than 

stated, since models 

with low/high sensitivity to greenhouse 

warming used weaker/stronger aerosol 

cooling to obtain the 

same temperature 

response.
The U.S. Climate Change Science Program Chapter 3

56

Two different approaches are used to assess 

the aerosol effect on climate. “Forward modeling” studies incorporate different aerosol 

types and attempt to explicitly calculate the 

aerosol RF. From this approach, IPCC (2007) 

concluded that the best estimate of the global 

aerosol direct RF (compared with preindustrial 

times) is -0.5 (-0.9 to -0.1) W m-2 (see Figure 

1.3, Chapter 1). The RF due to the cloud albedo or brightness effect (also referred to as 

first indirect or Twomey effect) is estimated 

to be -0.7 (-1.8 to -0.3) W m-2. No estimate 

was specified for the effect associated with 

cloud lifetime. The total negative RF due to 

aerosols according to IPCC (2007) estimates 

(see Figure 1.3 in Chapter 1) is then -1.3 (-2.2 

to -0.5) W m-2. In comparison, the positive 

radiative forcing (RF) from greenhouse gases 

(including tropospheric ozone) is estimated 

to be +2.9 ± 0.3 W m-2; hence tropospheric 

aerosols reduce the influence from greenhouse 

gases by about 45% (15-85%). This approach 

however inherits large uncertainties in aerosol 

amount, composition, and physical and optical 

properties in modeling of atmospheric aerosols. 

The consequences of these uncertainties are 

discussed in the next section.

The other method of calculating aerosol forcing 

is called the “inverse approach” – it is assumed 

that the observed climate change is primarily 

the result of the known climate forcing contributions. If one further assumes a particular 

climate sensitivity (or a range of sensitivities), 

one can determine what the total forcing had 

to be to produce the observed temperature 

change. The aerosol forcing is then deduced as 

a residual after subtraction of the greenhouse 

gas forcing along with other known forcings 

from the total value. Studies of this nature 

come up with aerosol forcing ranges of -0.6 

to -1.7 W m-2 (Knutti et al., 2002, 2003; IPCC 

AR4 Chap.9); -0.4 to -1.6 W m-2 (Gregory 

et al., 2002); and -0.4 to -1.4 W m-2 (Stott et 

al., 2006). This approach however provides a 

bracket of the possible range of aerosol forcing 

without the assessment of current knowledge of 

the complexity of atmospheric aerosols.

This chapter reviews the current state of 

aerosol RF in the global models and assesses 

the uncertainties in these calculations. First 

representation of aerosols in the forward 

global chemistry and transport models and the 

diversity of the model simulated aerosol fields 

are discussed; then calculation of the aerosol 

direct and indirect effects in the climate models 

is reviewed; finally the impacts of aerosols on 

climate model simulations and their implications are assessed.

3.2. Modeling of Atmospheric Aerosols

The global aerosol modeling capability has 

developed rapidly in the past decade. In the late 

1990s, there were only a few global models that 

were able to simulate one or two aerosol components, but now there are a few dozen global 

models that simulate a comprehensive suite 

of aerosols in the atmosphere. As introduced 

in Chapter 1, aerosols consist of a variety of 

species including dust, sea salt, sulfate, nitrate, 

and carbonaceous aerosols (black and organic 

carbon) produced from natural and man-made 

sources with a wide range of physical and 

optical properties. Because of the complexity 

of the processes and composition, and highly 

inhomogeneous distribution of aerosols, accurately modeling atmospheric aerosols and their 

effects remains a challenge. Models have to take 

into account not only the aerosol and precursor 

emissions, but also the chemical transformation, 

transport, and removal processes (e.g. dry and 

wet depositions) to simulate the aerosol mass 

concentrations. Furthermore, aerosol particle 

size can grow in the atmosphere because the 

ambient water vapor can condense on the 

aerosol particles. This “swelling” process, 

called hygroscopic growth, is most commonly 

parameterized in the models as a function of 

relative humidity.

3.2.1. ESTIMATES OF EMISSIONS

Aerosols have various sources from both 

natural and anthropogenic processes. Natural 

emissions include wind-blown mineral dust, 

aerosol and precursor gases from volcanic 

eruptions, natural wild fires, vegetation, and 

oceans. Anthropogenic sources include emissions from fossil fuel and biofuel combustion, 

industrial processes, agriculture practices, and 

human-induced biomass burning.

Following earlier attempts to quantify manmade primary emissions of aerosols (Turco et 

al., 1983; Penner et al., 1993) systematic work 

was undertaken in the late 1990s to calculate 

Because of the 

complexity of the 

processes and 

composition, and 

highly inhomogeneous distribution of aerosols, 

accurately modeling atmospheric 

aerosols and their 

effects remains a 

challenge.
57

Atmospheric Aerosol Properties and Climate Impacts

emissions of black carbon (BC) and organic 

carbon (OC), using fuel-use data and measured 

emission factors (Liousse et al., 1996; Cooke 

and Wilson, 1996; Cooke et al., 1999). The 

work was extended in greater detail and with 

improved attention to source-specific emission 

factors in Bond et al. (2004), which provides 

global inventories of BC and OC for the year 

1996, with regional and source-category discrimination that includes contributions from 

industrial, transportation, residential solid-fuel 

combustion, vegetation and open biomass burning (forest fires, agricultural waste burning, 

etc.), and diesel vehicles.

Emissions from natural sources—which include wind-blown mineral dust, wildfires, 

sea salt, and volcanic eruptions—are less well 

quantified, mainly because of the difficulties 

of measuring emission rates in the field and 

the unpredictable nature of the events. Often, 

emissions must be inferred from ambient observations at some distance from the actual source. 

As an example, it was concluded (Lewis and 

Schwartz, 2004) that available information on 

size-dependent sea salt production rates could 

only provide order-of-magnitude estimates. The 

natural emissions in general can vary dramatically over space and time.

Aerosols can be produced from trace gases in 

the atmospheric via chemical reactions, and 

those aerosols are called secondary aerosols, as 

distinct from primary aerosols that are directly 

emitted to the atmosphere as aerosol particles. 

For example, most sulfate and nitrate aerosols 

are secondary aerosols that are formed from 

their precursor gases, sulfur dioxide (SO2) and 

nitrogen oxides (NO and NO2, collectively 

called NOx), respectively. Those sources have 

been studied for many years and are relatively 

well known. By contrast, the sources of secondary organic aerosols (SOA) are poorly understood, including emissions of their precursor 

gases (called volatile organic compounds, VOC) 

from both natural and anthropogenic sources 

and the atmospheric production processes.

Globally, sea salt and mineral dust dominate 

the total aerosol mass emissions because of the 

large source areas and/or large particle sizes. 

However, sea salt and dust also have shorter 

atmospheric lifetimes because of their large 

particle size, and are radiatively less active 

than aerosols with small particle size, such as 

sulfate, nitrate, BC, and particulate organic 

matter (POM, which includes both carbon and 

non-carbon mass in the organic aerosol, see 

Glossary), most of which are anthropogenic 

in origin.

Because the anthropogenic aerosol RF is usually evaluated (e.g., by the IPCC) as the anthropogenic perturbation since the pre-industrial 

period, it is necessary to estimate the historical 

emission trends, especially the emissions in the 

pre-industrial era. Compared to estimates of 

present-day emissions, estimates of historical 

emission have much larger uncertainties. Information for past years on the source types 

and strengths and even locations are difficult 

to obtain, so historical inventories from preindustrial times to the present have to be based 

on limited knowledge and data. Several studies 

on historical emission inventories of BC and 

OC (e.g., Novakov et al., 2003; Ito and Penner 

2005; Bond et al., 2007; Fernandes et al., 2007; 

Junker and Liousse, 2008), SO2 (Stern, 2005), 

and various species (van Aardenne et al., 

2001; Dentener et al., 2006) are available in 

the literature; there are some similarities and 

some differences among them, but the emission estimates for early times do not have the 

rigor of the studies for present-day emissions. 

One major conclusion from all these studies is 

that the growth of primary aerosol emissions in 

the 20th century was not nearly as rapid as the 

growth in CO2 emissions. This is because in 

the late 19th and early 20th centuries, particle 

emissions such as BC and POM were relatively 

high due to the heavy use of biofuels and the 

lack of particulate controls on coal-burning 

facilities; however, as economic development 

continued, traditional biofuel use remained 

fairly constant and particulate emissions from 

coal burning were reduced by the application of 

technological controls (Bond et al., 2007). Thus, 

particle emissions in the 20th century did not 

grow as fast as CO2 emissions, as the latter are 

roughly proportional to total fuel use—oil and 

gas included. Another challenge is estimating 

historical biomass burning emissions. A recent 

study suggested about a 40% increase in carbon 

emissions from biomass burning from the beginning to the end of last century (Mouillot et 

al., 2006), but it is difficult to verify.

Aerosols have 

various sources 

from both natural 

and anthropogenic 

processes, including 

dust, volcanic 

eruptions, fires, fossil 

fuel and biofuel 

combustion, and 

agricultural practices.
The U.S. Climate Change Science Program Chapter 3

58

where M is the aerosol mass loading per unit 

area (g m-2), MEE is the mass extinction 

efficiency or specific extinction in unit of

m2 g-1, which is

(3.2)

where Qext is the extinction coefficient (a function of particle size distribution and refractive 

index), reff is the aerosol particle effective 

radius, ρ is the aerosol particle density, and 

f is the ratio of ambient aerosol mass (wet) 

to dry aerosol mass M. Here, M is the result 

from model-simulated atmospheric processes 

and MEE embodies the aerosol physical (including microphysical) and optical properties. 

Since Qext varies with radiation wavelength, 

so do MEE and AOD. AOD is the quantity 

that is most commonly obtained from remote 

sensing measurements and is frequently used 

for model evaluation (see Chapter 2). AOD 

is also a key parameter determining aerosol 

radiative effects. 

Here the results from the recent multipleglobal-model studies by the AeroCom project 

are summarized, as they represent the current 

assessment of model-simulated atmospheric 

aerosol loading, optical properties, and RF for 

the present-day. AeroCom aims to document 

differences in global aerosol models and comAs an example, Table 3.1 shows estimated anthropogenic emissions of sulfur, BC and POM 

in the present day (year 2000) and pre-industrial 

time (1750) compiled by Dentener et al., 2006. 

These estimates have been used in the Aerosol 

Comparisons between Observations and Models (AeroCom) project (Experiment B, which 

uses the year 2000 emission; and Experiment 

PRE, which uses pre-industrial emissions), for 

simulating atmospheric aerosols and anthropogenic aerosol RF. The AeroCom results are 

discussed in Sections 3.2.2 and 3.3.

3.2.2. AEROSOL MASS LOADING AND OPTICAL DEPTH

In the global models, aerosols are usually simulated in the successive steps of sources (emission and chemical formation), transport (from 

source location to other area), and removal 

processes (dry deposition, in which particles 

fall onto the surface, and wet deposition by rain) 

that control the aerosol lifetime. Collectively, 

emission, transport, and removal determine the 

amount (mass) of aerosols in the atmosphere.

Aerosol optical depth (AOD), which is a measure of solar or thermal radiation being attenuated by aerosol particles via scattering or 

absorption, can be related to the atmospheric 

aerosol mass loading as follows:

(3.1)

MEE = 3Qext

4πρreff

⋅ f

AOD = MEE · M

Table 3.1. Anthropogenic emissions of aerosols and precursors for 2000 and 1750. Adapted 

from Dentener et al., 2006.

Source Species*

Emission#

2000

(Tg/yr)

Emission

1750

(Tg/yr)

Biomass burning

BC

POM

S

 3.1

34.7

 4.1

1.03

12.8

1.46

Biofuel

BC

POM

S

 1.6

 9.1

 9.6

0.39

1.56

0.12

Fossil fuel

BC

POM

S

 3.0

 3.2

98.9

# Data source for 2000 emission: biomass burning – Global Fire Emission Dataset (GFED); biofuel BC and 

POM – Speciated Pollutant Emission Wizard (SPEW); biofuel sulfur – International Institute for Applied 

System Analysis (IIASA); fossil fuel BC and POM – SPEW; fossil fuel sulfur – Emission Database for Global 

Atmospheric Research (EDGAR) and IIASA. Fossil fuel emission of sulfur (S) is the sum of emission from 

industry, power plants, and transportation listed in Dentener et al., 2006.

* S=sulfur, including SO2 and particulate sulfate. Most emitted as SO2, and 2.5% emitted as sulfate.
59

Atmospheric Aerosol Properties and Climate Impacts

pare the model output to observations. Sixteen 

global models participated in the AeroCom 

Experiment A (AeroCom-A), for which every 

model used their own configuration, including their own choice of estimating emissions 

(Kinne et al., 2006; Textor et al., 2006). Five 

major aerosol types: sulfate, BC, POM, dust, 

and sea salt, were included in the experiments, 

although some models had additional aerosol 

species. Of those major aerosol types, dust and 

sea-salt are predominantly natural in origin, 

whereas sulfate, BC, and POM have major 

anthropogenic sources.

Table 3.2 summarizes the model results from 

the AeroCom-A for several key parameters: 

Table 3.2. Summary of statistics of AeroCom Experiment A results from 16 global models. Data from Textor et al. 

(2006) and Kinne et al. (2006), and AeroCom website (http://nansen.ipsl.jussieu.fr/AEROCOM/data.html).

Quantity Mean Median Range Stddev

/mean*

Sources (Tg yr-1)

Sulfate 179 186 98-232 22%

Black carbon 11.9 11.3 7.8-19.4 23%

Organic matter 96.6 96.0 53-138 26%

Dust 1840 1640 672-4040 49%

Sea salt 16600 6280 2180-121000 199%

Removal rate (day-1)

Sulfate 0.25 0.24 0.19-0.39 18%

Black carbon 0.15 0.15 0.066-0.19 21%

Organic matter 0.16 0.16 0.09-0.23 24%

Dust 0.31 0.25 0.14-0.79 62%

Sea salt 5.07 2.50 0.95-35.0 188%

Lifetime (day)

Sulfate 4.12 4.13 2.6-5.4 18%

Black carbon 7.12 6.54 5.3-15 33%

Organic matter 6.54 6.16 4.3-11 27%

Dust 4.14 4.04 1.3-7.0 43%

Sea salt 0.48 0.41 0.03-1.1 58%

Mass loading (Tg)

Sulfate 1.99 1.98 0.92-2.70 25%

Black carbon 0.24 0.21 0.046-0.51 42%

Organic matter 1.70 1.76 0.46-2.56 27%

Dust 19.2 20.5 4.5-29.5 40%

Sea salt 7.52 6.37 2.5-13.2 54%

MEE at 550 nm (m2 g-1)

Sulfate 11.3 9.5 4.2-28.3 56%

Black carbon 9.4 9.2 5.3-18.9 36%

Organic matter 5.7 5.7 3.7-9.1 26%

Dust 0.99 0.95 0.46-2.05 45%

Sea salt 3.0 3.1 0.97-7.5 55%

AOD at 550 nm

Sulfate 0.035 0.034 0.015-0.051 33%

Black carbon 0.004 0.004 0.002-0.009 46%

Organic matter 0.018 0.019 0.006-0.030 36%

Dust 0.032 0.033 0.012-0.054 44%

Sea salt 0.033 0.030 0.02-0.067 42%

Total AOT at 550 nm 0.124 0.127 0.065-0.151 18%

* Stddev/mean was used as the term “diversity” in Textor et al., 2006.
The U.S. Climate Change Science Program Chapter 3

60

Sources (emission and chemical transformation), mass loading, lifetime, removal rates, and 

MEE and AOD at a commonly used, mid-visible, wavelength of 550 nanometer (nm). These 

are the globally averaged values for the year 

2000. Major features and conclusions are:

• Globally, aerosol source (in mass) is dominated by sea salt, followed by dust, sulfate, 

POM, and BC. Over the non-desert land 

area, human activity is the major source of 

sulfate, black carbon, and organic aerosols.

• Aerosols are removed from the atmosphere 

by wet and dry deposition. Although sea 

salt dominates the emissions, it is quickly 

removed from the atmosphere because of 

its large particle size and near-surface distributions, thus having the shortest lifetime. 

The median lifetime of sea salt from the 

AeroCom-A models is less than half a day, 

whereas dust and sulfate have similar lifetimes of 4 days and BC and POM 6-7 days.

• Globally, small-particle-sized sulfate, BC, 

and POM make up a little over 10% of total 

aerosol mass in the atmosphere. However, 

they are mainly from anthropogenic activity, 

so the highest concentrations are in the most 

populated regions, where their effects on 

climate and air quality are major concerns. 

• Sulfate and BC have their highest MEE at 

mid-visible wavelengths, whereas dust is 

lowest among the aerosol types modeled. 

That means for the same amount of aerosol 

mass, sulfate and BC are more effective at 

attenuating (scattering or absorbing) solar 

radiation than dust. This is why the sulfate 

AOD is about the same as dust AOD even 

though the atmospheric amount of sulfate 

mass is 10 times less than that of the dust.

• There are large differences, or diversities, 

among the models for all the parameters 

listed in Table 3.2. The largest model diversity, shown as the % standard deviation from 

the all-model-mean and the range (minimum 

and maximum values) in Table 3.2, is in sea 

salt emission and removal; this is mainly associated with the differences in particle size 

range and source parameterizations in each 

model. The diversity of sea salt atmospheric 

loading however is much smaller than that of 

sources or sinks, because the largest particles 

have the shortest lifetimes even though they 

comprise the largest fraction of emitted and 

deposited mass.

• Among the key parameters compared in 

Table 3.2, the models agree best for simulated total AOD – the % of standard deviation from the model mean is 18%, with the 

extreme values just a factor of 2 apart. The 

median value of the multi-model simulated 

global annual mean total AOD, 0.127, is 

also in agreement with the global mean 

values from recent satellite measurements. 

However, despite the general agreement in 

total AOD, there are significant diversities 

at the individual component level for aerosol 

optical thickness, mass loading, and mass 

extinction efficiency. This indicates that uncertainties in assessing aerosol climate forcing are still large, and they depend not only 

on total AOD but also on aerosol absorption 

and scattering direction (called asymmetry 

factor; see next page and Glossary), both of 

which are determined by aerosol physical 

Figure 3.1. Global annual averaged AOD (upper panel) and aerosol mass loading (lower panel) with their components simulated by 15 models in AeroComA (excluding one model which only reported mass). SU=sulfate, BC=black 

carbon, POM=particulate organic carbon, DU=dust, SS=sea salt. Model abbreviations: LO=LOA (Lille, Fra), LS=LSCE (Paris, Fra), UL=ULAQ (L’Aquila, 

Ita), SP=SPRINTARS (Kyushu, Jap), CT=ARQM (Toronto, Can), MI=MIRAGE 

(Richland, USA), EH=ECHAM5 (MPI-Hamburg, Ger), NF=CCM-Match (NCARBoulder, USA), OT=Oslo-CTM (Oslo, Nor), OG=OLSO-GCM (Oslo, Nor) [prescribed background for DU and SS], IM=IMPACT (Michigan, USA), GM=GFDLMozart (Princeton, NJ, USA), GO=GOCART (NASA-GSFC, Washington DC, 

USA), GI=GISS (NASA-GISS, New York, USA), TM=TM5 (Utrecht, Net). Also 

shown in the upper panel are the averaged observation data from AERONET 

(Ae) and the satellite composite (S*). See Kinne et al. (2006) for details. Figure 

produced from data in Kinne et al. (2006).

Globally, sulfate, 

BC, and POM 

make up a little 

over 10% of total 

aerosol mass in 

the atmosphere. 

However, they derive mainly from 

anthropogenic 

activity with the 

highest concentrations in the most 

populated regions.
61

Atmospheric Aerosol Properties and Climate Impacts

and optical properties. In addition, even 

with large differences in mass loading and 

MEE among different models, these terms 

could compensate for each other (eq. 3.1) 

to produce similar AOD. This is illustrated 

in Figure 3.1. For example, model LO and 

LS have quite different mass loading (44 

and 74 mg m-2, respectively), especially for 

dust and sea salt amount, but they produce 

nearly identical total AOD (0.127 and 0.128, 

respectively).

• Because of the large spatial and temporal 

variations of aerosol distributions, regional 

and seasonal diversities are even larger than 

the diversity for global annual means.

To further isolate the impact of the differences in emissions on the diversity of simulated 

aerosol mass loading, identical emissions for 

aerosols and their precursor were used in the 

AeroCom Experiment B exercise in which 

12 of the 16 AeroCom-A models participated 

(Textor et al., 2007). The comparison of the results and diversity between AeroCom-A and -B 

for the same models showed that using harmonized emissions does not significantly reduce 

model diversity for the simulated global mass 

and AOD fields, indicating that the differences 

in atmospheric processes, such as transport, 

removal, chemistry, and aerosol microphysics, play more important roles than emission 

in creating diversity among the models. This 

outcome is somewhat different from another 

recent study, in which the differences in calculated clear-sky aerosol RF between two models 

(a regional model STEM and a global model 

MOZART) were attributed mostly to the differences in emissions (Bates et al., 2006), although the conclusion was based on only two 

model simulations for a few focused regions. 

It is highly recommended from the outcome 

of AeroCom-A and -B that, although more detailed evaluation for each individual process 

is needed, multi-model ensemble results, e.g., 

median values of multi-model output variables, 

should be used to estimate aerosol RF, due to 

their greater robustness, relative to individual 

models, when compared to observations (Textor et al., 2006, 2007; Schulz et al., 2006).

3.3. Calculating Aerosol Direct Radiative Forcing

The three parameters that define the aerosol 

direct RF are the AOD, the single scattering 

albedo (SSA), and the asymmetry factor (g), 

all of which are wavelength dependent. AOD 

is indicative of how much aerosol exists in the 

column, SSA is the fraction of radiation being 

scattered versus the total attenuation (scattered 

and absorbed), and the g relates to the direction 

of scattering that is related to the size of the 

particles (see Chapter 1). An indication of the 

particle size is provided by another parameter, 

the Ångström exponent (Å), which is a measure 

of differences of AOD at different wavelengths. 

For typical tropospheric aerosols, Å tends to be 

inversely dependent on particle size; larger values of Å are generally associated with smaller 

aerosols particles. These parameters are further 

related; for example, for a given composition, 

the ability of a particle to scatter radiation decreases more rapidly with decreasing size than 

does its ability to absorb, so at a given wavelength varying Å can change SSA. Note that 

AOD, SSA, g, Å, and all the other parameters 

in eq. 3.1 and 3.2 vary with space and time due 

to variations of both aerosol composition and 

relative humidity, which influence these characteristics.

In the recent AeroCom project, aerosol direct 

RF for the solar spectral wavelengths (or shortwave) was assessed based on the 9 models that 

participated in both Experiment B and PRE in 

which identical, prescribed emissions for present (year 2000) and pre-industrial time (year 

1750) listed in Table 3.1 were used across the 

models (Schulz et al., 2006). The anthropogenic direct RF was obtained by subtracting AeroCom-PRE from AeroCom-B simulated results. 

Because dust and sea salt are predominantly 

from natural sources, they were not included in 

the anthropogenic RF assessment although the 

land use practice can contribute to dust emissions as “anthropogenic”. Other aerosols that 

were not considered in the AeroCom forcing 

assessment were natural sulfate (e.g. from volcanoes or ocean) and POM (e.g. from biogenic 

hydrocarbon oxidation), as well as nitrate. The 

aerosol direct forcing in the AeroCom assessment thus comprises three major anthropogenic 

aerosol components sulfate, BC, and POM.

The IPCC AR4 (IPCC, 2007) assessed anthropogenic aerosol RF based on the model results 

published after the IPCC TAR in 2001, including those from the AeroCom study discussed 

above. These results (adopted from IPCC 

There are large 

differences in 

simulated aerosol 

fields among the 

models.
The U.S. Climate Change Science Program Chapter 3

62

AR4) are shown in Table 3.3 for sulfate and 

Table 3.4 for carbonaceous aerosols (BC and 

POM), respectively. All values listed in Table 

3.3 and 3.4 refer to anthropogenic perturbation, i.e., excluding the natural fraction of these 

aerosols. In addition to the mass burden, MEE, 

and AOD, Table 3.3 and 3.4 also list the “normalized forcing”, also known as “forcing efficiency”, one for the forcing per unit AOD, and 

the other the forcing per gram of aerosol mass 

(dry). For some models, aerosols are externally 

mixed, that is, each aerosol particle contains 

Table 3.3. Sulfate mass loading, MEE and AOD at 550 nm, shortwave radiative forcing at the top of the atmosphere, 

and normalized forcing with respect to AOD and mass. All values refer to anthropogenic perturbation. Adapted 

from IPCC AR4 (2007) and Schulz et al. (2006).

Model Mass load 

(mg m-2)

MEE

(m2 g-1)

AOD at

550 nm

TOA Forcing 

(W m-2)

Forcing/AOD

(W m-2)

Forcing/mass

(W g-1)

Published since IPCC 2001

A CCM3 2.23 -0.56 -251

B GEOSCHEM 1.53 11.8 0.018 -0.33 -18 -216

C GISS 3.30 6.7 0.022 -0.65 -30 -197

D GISS 3.27 -0.96 -294

E GISS* 2.12 -0.57 -269

F SPRINTARS 1.55 9.7 0.015 -0.21 -135

G LMD 2.76 -0.42 -152

H LOA 3.03 9.9 0.03 -0.41 -14 -135

I GATORG 3.06 -0.32 -105

J PNNL 5.50 7.6 0.042 -0.44 -10 -80

K UIO-CTM 1.79 10.6 0.019 -0.37 -19 -207

L UIO-GCM 2.28 -0.29 -127

AeroCom: Identical emissions used for year 2000 and 1750

M UMI 2.64 7.6 0.02 -0.58 -29 -220

N UIO-CTM 1.70 11.2 0.019 -0.36 -19 -212

O LOA 3.64 9.6 0.035 -0.49 -14 -135

P LSCE 3.01 7.6 0.023 -0.42 -18 -140

Q ECHAM5-HAM 2.47 6.5 0.016 -0.46 -29 -186

R GISS** 1.34 4.5 0.006 -0.19 -32 -142

S UIO-GCM 1.72 7.0 0.012 -0.25 -21 -145

T SPRINTARS 1.19 10.9 0.013 -0.16 -12 -134

U ULAQ 1.62 12.3 0.02 -0.22 -11 -136

Average A-L 2.70 9.4 0.024 -0.46 -18 -181

Average M-U 2.15 8.6 0.018 -0.35 -21 -161

Minimum A-U 1.19 4.5 0.006 -0.96 -32 -294

Maximum A-U 5.50 12.3 0.042 -0.16 -10 -80

Std dev A-L 1.09 1.9 0.010 0.202 7 68

Std dev M-U 0.83 2.6 0.008 0.149 8 35

%Stddev/avg A-L 40% 20% 41% 44% 38% 38%

%Stddev/avg M-U 39% 30% 45% 43% 37% 22%

Model abbreviations: CCM3=Community Climate Model; GEOSCHEM=Goddard Earth Observing System-Chemistry; GISS=Goddard 

Institute for Space Studies; SPRINTARS=Spectral Radiation-Transport Model for Aerosol Species; LMD=Laboratoire de Meteorologie Dynamique; LOA=Laboratoire d’Optique Atmospherique; GATORG=Gas, Aerosol Transport and General circulation model; PNNL=Pacific 

Northwest National Laboratory; UIO-CTM=Univeristy of Oslo CTM; UIO-GCM=University of Oslo GCM; UMI=University of Michigan; 

LSCE=Laboratoire des Sciences du Climat et de l’Environment; ECHAMS5-HAM=European Centre Hamburg with Hamburg Aerosol Module; 

ULAQ=University of lL’Aquila.
63

Atmospheric Aerosol Properties and Climate Impacts

only one aerosol type such as sulfate, whereas 

other models allow aerosols to mix internally 

to different degrees, that is, each aerosol particle can have more than one component, such 

as black carbon coated with sulfate. For models 

with internal mixing of aerosols, the component values for AOD, MEE, and forcing were 

extracted (Schulz et al., 2006).

Considerable variation exists among these models for all quantities in Table 3.3 and 3.4. The 

RF for all the components varies by a factor of 

Table 3.4. Particulate organic matter (POM) and black carbon (BC) mass loading, AOD at 550 nm, shortwave radiative forcing 

at the top of the atmosphere, and normalized forcing with respect to AOD and mass. All values refer to anthropogenic perturbation. Based on IPCC AR4 (2007) and Schulz et al. (2006).

POM BC

Model

Mass 

load

(mg m-2)

MEE

(m2 g-1)

AOD at

550 nm

TOA 

Forcing 

(W m-2)

Forcing/ 

AOD

(W m-2)

Forcing/

mass

(W g-1)

Mass 

load 

(mg m-2)

MEE

(m2 g-1)

AOD at

550 nm 

x1000

TOA 

Forcing 

(W m-2)

Forcing/ 

AOD

(W m-2)

Forcing/

mass

(W g-1)

Published since IPCC 2001

A SPRINTARS -0.24 -107 0.36

B LOA 2.33 6.9 0.016 -0.25 -16 -140 0.37 0.55

C GISS 1.86 9.1 0.017 -0.26 -15 -161 0.29 0.61

D GISS 1.86 8.1 0.015 -0.30 -20 -75 0.29 0.35

E GISS* 2.39 -0.18 -92 0.39 0.50

F GISS 2.49 -0.23 -101 0.43 0.53

G SPRINTARS 2.67 10.9 0.029 -0.27 -9 -23 0.53 0.42

H GATORG 2.56 -0.06 -112 0.39 0.55

I MOZGN 3.03 5.9 0.018 -0.34 -19

J CCM 0.33 0.34

K UIO-GCM 0.30 0.19

AeroCom: Identical emissions for year 2000 & 1750

L UMI 1.16 5.2 0.0060 -0.23 -38 -198 0.19 6.8 1.29 0.25 194 1316

M UIO-CTM 1.12 5.2 0.0058 -0.16 -28 -143 0.19 7.1 1.34 0.22 164 1158

N LOA 1.41 6.0 0.0085 -0.16 -19 -113 0.25 7.9 1.98 0.32 162 1280

O LSCE 1.50 5.3 0.0079 -0.17 -22 -113 0.25 4.4 1.11 0.30 270 1200

P ECHAM5-

HAM 1.00 7.7 0.0077 -0.10 -13 -100 0.16 7.7 1.23 0.20 163 1250

Q GISS** 1.22 4.9 0.0060 -0.14 -23 -115 0.24 7.6 1.83 0.22 120 917

R UIO-GCM 0.88 5.2 0.0046 -0.06 -13 -68 0.19 10.3 1.95 0.36 185 1895

S SPRINTARS 1.84 10.9 0.0200 -0.10 -5 -54 0.37 9.5 3.50 0.32 91 865

T ULAQ 1.71 4.4 0.0075 -0.09 -12 -53 0.38 7.6 2.90 0.08 28 211

Average A-K 2.40 8.2 0.019 -0.24 -16 -102 0.37 0.44 1242

Average L-T 1.32 6.1 0.008 -0.13 -19 -106 0.25 7.7 1.90 0.25 153 1121

Minimum A-T 0.88 4.4 0.005 -0.34 -38 -198 0.16 4.4 1.11 0.08 28 211

Maximum A-T 3.03 10.9 0.029 -0.06 -5 -23 0.53 10.3 3.50 0.61 270 2103

Std dev A-K 0.39 1.7 0.006 0.09 4 41 0.08 0.06 384

Std dev L-T 0.32 2.0 0.005 0.05 10 46 0.08 1.6 0.82 0.09 68 450

%Stddev/avg A-K 16% 21% 30% 36% 26% 41% 22% 23% 31%

%Stddev/avg L-T 25% 33% 56% 39% 52% 43% 32% 21% 43% 34% 45% 40%
The U.S. Climate Change Science Program Chapter 3

64

6 or more: Sulfate from 0.16 to 0.96 W m-2, 

POM from -0.06 to -0.34 W m-2, and BC from 

+0.08 to +0.61 W m-2, with the standard deviation in the range of 30 to 40% of the ensemble 

mean. It should be noted that although BC has 

the lowest mass loading and AOD, it is the only 

aerosol species that absorbs strongly, causing 

positive forcing to warm the atmosphere, in 

contrast to other aerosols that impose negative 

forcing to cool the atmosphere. As a result, the 

net anthropogenic aerosol forcing as a whole 

becomes less negative when BC is included. 

The global average anthropogenic aerosol direct RF at the top of the atmosphere (TOA) 

from the models, together with observationbased estimates (see Chapter 2), is presented in 

Figure 3.2. Note the wide range for forcing in 

Figure 3.2. The comparison with observationbased estimates shows that the model estimated forcing is in general lower, partially because 

the forcing value from the model is the difference between present-day and pre-industrial 

time, whereas the observation-derived quantity 

is the difference between an atmosphere with 

and without anthropogenic aerosols, so the 

“background” value that is subtracted from the 

total forcing is higher in the models. 

The discussion so far has dealt with global average values. The geographic distributions of 

multi-model aerosol direct RF has been evaluated among the AeroCom models, which are 

shown in Figure 3.3 for total and anthropogenic 

AOD at 550 nm and anthropogenic aerosol RF 

at TOA, within the atmospheric column, and 

at the surface. Globally, anthropogenic AOD is 

about 25% of total AOD (Figure 3.3a and b) but 

is more concentrated over polluted regions in 

Asia, Europe, and North America and biomass 

burning regions in tropical southern Africa and 

South America. At TOA, anthropogenic aerosol 

causes negative forcing over mid-latitude continents and oceans with the most negative values 

(-1 to -2 W m-2) over polluted regions (Figure 

3.3c). Although anthropogenic aerosol has a 

cooling effect at the surface with surface forcing values down to -10 W m-2 over China, India, 

and tropical Africa (Figure 3.3e), it warms the 

atmospheric column with the largest effects 

again over the polluted and biomass burning regions. This heating effect will change the atmospheric circulation and can affect the weather 

and precipitation (e.g., Kim et al., 2006).

Basic conclusions from forward modeling of 

aerosol direct RF are:

• The most recent estimate of all-sky shortwave aerosol direct RF at TOA from anthropogenic sulfate, BC, and POM (mostly from 

fossil fuel/biofuel combustion and biomass 

burning) is -0.22 ± 0.18 W m-2 averaged 

globally, exerting a net cooling effect. This 

value would represent the low-end of the 

forcing magnitude, since some potentially 

significant anthropogenic aerosols, such as 

nitrate and dust from human activities are 

not included because of their highly uncertain sources and processes. IPCC AR4 had 

adjusted the total anthropogenic aerosol 

direct RF to -0.5 ± 0.4 W m-2 by adding 

estimated anthropogenic nitrate and dust 

forcing values based on limited modeling 

studies and by considering the observationbased estimates (see Chapter 2).

• Both sulfate and POM negative forcing 

whereas BC causes positive forcing because 

of its highly absorbing nature. Although BC 

comprises only a small fraction of anthropogenic aerosol mass load and AOD, its forcing 

efficiency (with respect to either AOD or 

mass) is an order of magnitude stronger than 

sulfate and POM, so its positive shortwave 

forcing largely offsets the negative forcing 

Figure 3.2. Aerosol direct radiative forcing in various climate and aerosol models. 

Observed values are shown in the top section. From IPCC (2007).

Although black 

carbon has the 

lowest mass loading 

and optical depth, 

it is the only 

aerosol species that 

absorbs strongly, 

causing positive 

forcing to warm 

the atmosphere, 

in contrast to 

other aerosols that 

impose negative 

forcing to cool the 

atmosphere.
65

Atmospheric Aerosol Properties and Climate Impacts

from sulfate and POM. This points out the 

importance of improving the model ability to 

simulate each individual aerosol components 

more accurately, especially black carbon. 

Separately, it is estimated from recent model 

studies that anthropogenic sulfate, POM, and 

BC forcings at TOA are -0.4, -0.18, +0.35 W 

m-2, respectively. The anthropogenic nitrate 

and dust forcings are estimated at -0.1 W m-2

for each, with uncertainties exceeds 100% 

(IPCC AR4, 2007). 

• In contrast to long-lived greenhouse gases, 

anthropogenic aerosol RF exhibits significant 

regional and seasonal variations. The forcing 

magnitude is the largest over the industrial 

and biomass burning source regions, where 

the magnitude of the negative aerosol forcing 

can be of the same magnitude or even stronger 

than that of positive greenhouse gas forcing. 

• There is a large spread of model-calculated 

aerosol RF even in the global annual averaged values. The AeroCom study shows that 

the model diversity at some locations (mostly 

East Asia and African biomass burning regions) can reach ±3 W m-2, which is an order 

of magnitude above the global averaged forcing value of -0.22 W m-2. The large diversity 

reflects the low level of current understanding of aerosol radiative forcing, which is 

compounded by uncertainties in emissions, 

transport, transformation, removal, particle 

size, and optical and microphysical (including hygroscopic) properties.

Figure 3.3. Aerosol optical thickness and anthropogenic 

shortwave all-sky radiative forcing from the AeroCom 

study (Schulz et al., 2006). Shown in the figure: total AOD 

(a) and anthropogenic AOD (b) at 550 nm, and radiative 

forcing at TOA (c), atmospheric column (d), and surface 

(e). Figures from the AeroCom image catalog (http://

nansen.ipsl.jussieu.fr/AEROCOM/data.html).

(a) Mean AOD 550 nm

(b) Anthropogenic AOD 550 nm

(c) Anthro. aerosol TOA forcing (W m-2)

(d) Anthro. aerosol atmospheric forcing (W m-2)

(e) Anthro. Aerosol surface forcing (W m-2)

In contrast to longlived greenhouse 

gases, anthropogenic 

aerosol radiative 

forcing exhibits significant regional and 

seasonal variations.
The U.S. Climate Change Science Program Chapter 3

66

• In spite of the relatively small value of forcing at TOA, the magnitudes of anthropogenic forcing at the surface and within the 

atmospheric column are considerably larger: 

-1 to -2 W m-2 at the surface and +0.8 to +2 

W m-2 in the atmosphere. Anthropogenic 

aerosols thus cool the surface but heat the 

atmosphere, on average. Regionally, the 

atmospheric heating can reach annually 

averaged values exceeding 5 W m-2 (Figure 

3.3d). These regional effects and the negative 

surface forcing are expected to exert an important effect on climate through alteration 

of the hydrological cycle.

3.4. Calculating Aerosol Indirect Forcing

3.4.1. AEROSOL EFFECTS ON CLOUDS

A subset of the aerosol particles can act as 

cloud condensation nuclei (CCN) and/or ice 

nuclei (IN). Increases in aerosol particle concentrations, therefore, may increase the ambient 

concentrations of CCN and IN, affecting cloud 

properties. For a fixed cloud liquid water content, a CCN increase will lead to more cloud 

droplets so that the cloud droplet size will 

decrease. That effect leads to brighter clouds, 

the enhanced albedo then being referred to as 

the “cloud albedo effect” (Twomey, 1977), also 

known as the first indirect effect. If the droplet 

size is smaller, it may take longer to rainout, 

leading to an increase in cloud lifetime, hence 

the “cloud lifetime” effect (Albrecht, 1989), 

also called the second indirect effect. Approximately one-third of the models used for 

the IPCC 20th century climate change simulations incorporated an aerosol indirect effect, 

generally (though not exclusively) considered 

only with sulfates.

Shown in Figure 3.4 are results from published 

model studies indicating the different RF values 

from the cloud albedo effect. The cloud albedo 

effect ranges from -0.22 to -1.85 W m-2; the 

lowest estimates are from simulations that 

constrained representation of aerosol effects 

on clouds with satellite measurements of drop 

size vs. aerosol index. In view of the difficulty 

of quantifying this effect remotely (discussed 

later), it is not clear whether this constraint provides an improved estimate. The estimate in the 

IPCC AR4 ranges from +0.4 to -1.1 W m-2, with 

a “best-guess” estimate of 0.7 W m-2.

The representation of cloud effects in GCMs 

is considered below. However, it is becoming 

increasingly clear from studies based on high 

resolution simulations of aerosol-cloud interactions that there is a great deal of complexity that 

is unresolved in climate models. This point is 

examined again in section 3.4.4. 

Most models did not incorporate the “cloud lifetime effect”. Hansen et al. (2005) compared this 

latter influence (in the form of time-averaged 

cloud area or cloud cover increase) with the 

cloud albedo effect. In contrast to the discussion in IPCC (2007), they argue that the cloud 

cover effect is more likely to be the dominant 

one, as suggested both by cloud-resolving 

model studies (Ackerman et al., 2004) and 

satellite observations (Kaufman et al., 2005c). 

The cloud albedo effect may be partly offset by 

reduced cloud thickness accompanying aerosol 

pollutants, producing a meteorological (cloud) 

Fig. 3.4. Radiative forcing from the cloud albedo effect (1st aerosol indirect effect) 

in the global climate models used in IPCC 2007 (IPCC Fig. 2.14). For additional 

model designations and references, see IPCC 2007, chapter 2. Species included in 

the lower panel are sulfate, sea salt, organic and black carbon, dust and nitrates; 

in the top panel, only sulfate, sea salt and organic carbon are included.

Anthropogenic 

aerosols cool the 

surface but heat the 

atmosphere.
67

Atmospheric Aerosol Properties and Climate Impacts

rather than aerosol effect (see the discussion in 

Lohmann and Feichter, 2005). (The distinction 

between meteorological feedback and aerosol 

forcing can become quite opaque; as noted 

earlier, the term feedback is restricted here to 

those processes that are responding to a change 

in temperature.) Nevertheless, both aerosol 

indirect effects were utilized in Hansen et al. 

(2005), with the second indirect effect calculated by relating cloud cover to the aerosol number 

concentration, which in turn is a function of 

sulfate, nitrate, black carbon and organic carbon concentration. Only the low altitude cloud 

influence was modeled, principally because 

there are greater aerosol concentrations at low 

levels, and because low clouds currently exert 

greater cloud RF. The aerosol influence on high 

altitude clouds, associated with IN changes, is 

a relatively unexplored area for models and as 

well for process-level understanding.

Hansen et al. (2005) used coefficients to normalize the cooling from aerosol indirect effects 

to between -0.75 and -1 W m-2, based on comparisons of modeled and observed changes in 

the diurnal temperature range as well as some 

satellite observations. The response of the 

GISS model to the direct and two indirect effects is shown in Figure 3.5. As parameterized, 

the cloud lifetime effect produced somewhat 

greater negative RF (cooling), but this was the 

result of the coefficients chosen. Geographically, it appears that the “cloud cover” effect 

produced slightly more cooling in the Southern Hemisphere than did the “cloud albedo” 

response, with the reverse being true in the 

Northern Hemisphere (differences on the order 

of a few tenths °C).

3.4.2. MODEL EXPERIMENTS

There are many different factors that can explain the large divergence of aerosol indirect 

effects in models (Fig. 3.4). To explore this in 

more depth, Penner et al. (2006) used three 

general circulation models to analyze the differences between models for the first indirect 

effect, as well as a combined first plus second 

indirect effect. The models all had different 

cloud and/or convection parameterizations. 

In the first experiment, the monthly average 

aerosol mass and size distribution of, effectively, sulfate aerosol were prescribed, and 

all models followed the same prescription 

for parameterizing the cloud droplet number 

concentration (CDNC) as a function of aerosol 

concentration. In that sense, the only difference among the models was their separate 

cloud formation and radiation schemes. The 

different models all produced similar droplet 

Fig. 3.5. Anthropogenic impact on cloud cover, planetary albedo, radiative flux at the surface (while holding sea surface 

temperatures and sea ice fixed) and surface air temperature change from the direct aerosol forcing (top row), the 1st indirect 

effect (second row) and the second indirect effect (third row). The temperature change is calculated from years 81-120 of 

a coupled atmosphere simulation with the GISS model. From Hansen et al. (2005).

There are many 

different factors 

that can explain the 

large divergence of 

indirect effects in 

models.
The U.S. Climate Change Science Program Chapter 3

68

Fig. 3.6. Global average present-day short wave cloud forcing at TOA 

(top) and change in whole sky net outgoing shortwave radiation (bottom) 

between the present-day and pre-industrial simulations for each model 

in each experiment. Adapted from Penner et al. (2006).

effective radii, and therefore shortwave cloud 

forcing, and change in net outgoing whole sky 

radiation between pre-industrial times and the 

present. Hence the first indirect effect was 

not a strong function of the cloud or radiation 

scheme. The results for this and the following 

experiments are presented in Figure 3.6, where 

the experimental results are shown sequentially 

from left to right for the whole sky effect, and 

in Table 3.5 for the clear-sky and cloud forcing 

response as well.

The change in cloud forcing is the difference 

between whole sky and clear sky outgoing radiation in the present day minus pre-industrial 

simulation. The large differences seen between 

experiments 5 and 6 are due to the inclusion of 

the clear sky component of aerosol scattering and 

absorption (the direct effect) in experiment 6.

In the second experiment, the aerosol mass and 

size distribution were again prescribed, but 

now each model used its own formulation for 

relating aerosols to droplets. In this case one of 

the models produced larger effective radii and 

therefore a much smaller first indirect aerosol 

effect (Figure 3.6, Table 3.5). However, even 

in the two models where the effective radius 

change and net global forcing were similar, 

the spatial patterns of cloud forcing differ, 

especially over the biomass burning regions of 

Africa and South America.

The third experiment allowed the models to 

relate the change in droplet size to change in 

precipitation efficiency (i.e., they were now also 

allowing the second indirect effect - smaller 

droplets being less efficient rain producers – as 

well as the first). The models utilized the same 

relationship for autoconversion of cloud droplets to precipitation. Changing the precipitation 

efficiency results in all models producing an 

increase in cloud liquid water path, although 

the effect on cloud fraction was smaller than 

in the previous experiments. The net result was 

to increase the negative radiative forcing in all 

three models, albeit with different magnitudes: 

for two of the models the net impact on outgoing 

shortwave radiative increased by about 20%, 

whereas in the third model (which had the much 

smaller first indirect effect), it was magnified 

by a factor of three.

In the fourth experiment, the models were now 

each allowed to use their own formulation to 

relate aerosols to precipitation efficiency. This 

introduced some additional changes in the 

whole sky shortwave forcing (Figure 3.6). 

In the fifth experiment, models were allowed to 

produce their own aerosol concentrations, but 

were given common sources. This produced 

the largest changes in the RF in several of the 

models. Within any one model, therefore, the 

change in aerosol concentration has the largest 

effect on droplet concentrations and effective 

radii. This experiment too resulted in large 

changes in RF.

In the last experiment, the aerosol direct effect 

was included, based on the full range of aerosols 

used in each model. While the impact on the 

whole-sky forcing was not large, the addition 

of aerosol scattering and absorption primarily affected the change in clear sky radiation 

(Table 3.5).

The results of this study emphasize that in addition to questions concerning cloud physics, the 

differences in aerosol concentrations among the 
69

Atmospheric Aerosol Properties and Climate Impacts

models play a strong role in inducing differences 

in the indirect effect(s), as well as the direct one.

Observational constraints on climate model 

simulations of the indirect effect with satellite 

data (e.g., MODIS) have been performed previously in a number of studies (e.g., Storelvmo et 

al., 2006, Lohmann et al., 2006, Quaas et al., 

2006, Menon et al., 2008). These have been 

somewhat limited since the satellite retrieved 

data used do not have the vertical profiles 

needed to resolve aerosol and cloud fields (e.g., 

cloud droplet number and liquid water content); 

the temporal resolution of simultaneous aerosol 

and cloud product retrievals are usually not 

available at a frequency of more than one a 

day; and higher level clouds often obscure low 

clouds and aerosols. Thus, the indirect effect, 

especially the second indirect effect, remains, 

to a large extent, unconstrained by satellite 

observations. However, improved measurements of aerosol vertical distribution from the 

newer generation of sensors on the A-train 

platform may provide a better understanding 

of changes to cloud properties from aerosols. 

Simulating the top-of-atmosphere reflectance 

for comparison to satellite measured values 

could be another way to compare model with 

observations, which would eliminate the inconsistent assumptions of aerosol optical properties and surface reflectance encountered when 

compared the model calculated and satellite 

retrieved AOD values.

3.4.3. ADDITIONAL AEROSOL INFLUENCES

Various observations have empirically related 

aerosols injected from biomass burning or 

industrial processes to reductions in rainfall 

(e.g., Warner, 1968; Eagan et al., 1974; Andreae et al., 2004; Rosenfeld, 2000). There are 

several potential mechanisms associated with 

this response.

In addition to the two indirect aerosol effects 

noted above, a process denoted as the “semidirect” effect involves the absorption of solar 

radiation by aerosols such as black carbon and 

dust. The absorption increases the temperature, thus lowering the relative humidity and 

producing evaporation, hence a reduction in 

cloud liquid water. The impact of this process 

depends strongly on what the effective aerosol 

absorption actually is; the more absorbing the 

aerosol, the larger the potential positive forcing 

Table 3.5. Differences in present day and pre-industrial outgoing solar radiation (W m-2) in the different experiments. Adapted from Penner et al. (2006).

MODEL EXP 1 EXP 2 EXP 3 EXP 4 EXP 5 EXP 6

Whole-sky

CAM-Oslo -0.648 -0.726 -0.833 -0.580 -0.365 -0.518

LMD-Z -0.682 -0.597 -0.722 -1.194 -1.479 -1.553

CCSR -0.739 -0.218 -0.733 -0.350 -1.386 -1.386

Clear-sky

CAM-Oslo -0.063 -0.066 -0.026 0.014 -0.054 -0.575

LMD-Z -0.054 0.019 0.030 -0.066 -0.126 -1.034

CCSR 0.018 -0.007 -0.045 -0.008 0.018 -1.160

Cloud-forcing

CAM-Oslo -0.548 -0.660 -0.807 -0.595 -0.311 0.056

LMD-Z -0.628 -0.616 -0.752 -1.128 -1.353 -0.518

CCSR -0.757 -0.212 -0.728 -0.345 -1.404 -0.200

EXP1: tests cloud formation and radiation schemes 

EXP2: tests formulation for relating aerosols to droplets 

EXP3: tests inclusion of droplet size influence on precipitation efficiency 

EXP4: tests formulation of droplet size influence on precipitation efficiency 

EXP5: tests model aerosol formulation from common sources

EXP6: added the direct aerosol effect 
The U.S. Climate Change Science Program Chapter 3

70

on climate (by reducing low level clouds and allowing more solar radiation to reach the surface). 

This effect is responsible for shifting the critical 

value of SSA (separating aerosol cooling from 

aerosol warming) from 0.86 with fixed clouds to 

0.91 with varying clouds (Hansen et al., 1997). 

Reduction in cloud cover and liquid water is one 

way aerosols could reduce rainfall.

More generally, aerosols can alter the location 

of solar radiation absorption within the system, 

and this aspect alone can alter climate and precipitation even without producing any change 

in net radiation at the top of the atmosphere (the 

usual metric for climate impact). By decreasing 

solar absorption at the surface, aerosols (from 

both the direct and indirect effects) reduce the 

energy available for evapotranspiration, potentially resulting in a decrease in precipitation. 

This effect has been suggested as the reason 

for the decrease in pan evaporation over the last 

50 years (Roderick and Farquhar, 2002). The 

decline in solar radiation at the surface appears 

to have ended in the 1990s (Wild et al., 2005), 

perhaps because of reduced aerosol emissions 

in industrial areas (Kruger and Grasl, 2002), 

although this issue is still not settled. 

Energy absorption by aerosols above the 

boundary layer can also inhibit precipitation 

by warming the air at altitude relative to the 

surface, i.e., increasing atmospheric stability. 

The increased stability can then inhibit convection, affecting both rainfall and atmospheric 

circulation (Ramanathan et al., 2001a; Chung 

and Zhang, 2004). To the extent that aerosols 

decrease droplet size and reduce precipitation 

efficiency, this effect by itself could result in 

lowered rainfall values locally. 

In their latest simulations, Hansen et al. (2007) 

did find that the indirect aerosol effect reduced 

tropical precipitation; however, the effect is 

similar regardless of which of the two indirect 

effects is used, and also similar to the direct effect. So it is likely that the reduction of tropical 

precipitation is because of aerosol induced cooling at the surface and the consequent reduced 

evapotranspiration. Similar conclusions were 

reached by Yu et al. (2002) and Feingold et al. 

(2005). In this case, the effect is a feedback and 

not a forcing. 

The local precipitation change, through its impacts on dynamics and soil moisture, can have 

large positive feedbacks. Harvey (2004) concluded from assessing the response to aerosols 

in eight coupled models that the aerosol impact 

on precipitation was larger than on temperature. 

He also found that the precipitation impact differed substantially among the models, with little 

correlation among them.

Recent GCM simulations have further examined the aerosol effects on hydrological cycle. 

Ramanathan et al. (2005) showed from fully 

coupled ocean–atmosphere GCM experiments 

that the “solar dimming” effect at the surface, 

i.e., the reduction of solar radiation reaching the 

surface, due to the inclusion of absorbing aerosol forcing causes a reduction in surface evaporation, a decrease in meridional sea surface 

temperature (SST) gradient and an increase in 

atmospheric stability, and a reduction in rainfall 

over South Asia. Lau and Kim (2006) examined 

the direct effects of aerosol on the monsoon 

water cycle variability from GCM simulations 

with prescribed realistic global aerosol forcing 

and proposed the “elevated heat pump” effect, 

suggesting that atmospheric heating by absorbing aerosols (dust and black carbon), through 

water cycle feedback, may lead to a strengthening of the South Asia monsoon. These model 

results are not necessarily at odds with each 

other, but rather illustrate the complexity of the 

aerosol–monsoon interactions that are associated with different mechanisms, whose relative 

importance in affecting the monsoon may be 

strongly dependent on spatial and temporal 

scales and the timing of the monsoon. These 

results may be model dependent and should be 

further examined.

3.4.4. HIGH RESOLUTION MODELING

Largely by its nature, the representation of 

the interaction between aerosol and clouds in 

GCMs is poorly resolved. This stems in large 

part from the fact that GCMs do not resolve 

convection on their large grids (order of several 

hundred km), that their treatment of cloud microphysics is rather crude, and that as discussed 

previously, their representation of aerosol needs 

improvement. Superparametrization efforts 

(where standard cloud parameterizations in the 

GCM are replaced by resolving clouds in each 

grid column of the GCM via a cloud resolving 

model, e.g., Grabowski, 2004) could lead the 

way for the development of more realistic cloud 

fields and thus improved treatments of aerosolcloud interactions in large-scale models. HowAerosols can alter 

the location of 

solar radiation 

absorption within 

the system, and this 

aspect alone can 

alter climate and 

precipitation.
71

Atmospheric Aerosol Properties and Climate Impacts

ever, these are just being incorporated in models 

that resolve both cloud and aerosols. Detailed 

cloud parcel models have been developed to 

focus on the droplet activation problem (that 

asks under what conditions droplets actually 

start forming) and questions associated with 

the first indirect effect. The coupling of aerosol 

and cloud modules to dynamical models that 

resolve the large turbulent eddies associated 

with vertical motion and clouds [large eddy 

simulations (LES) models, with grid sizes of 

~100 m and domains ~10 km] has proven to be 

a powerful tool for representing the details of 

aerosol-cloud interactions together with feedbacks (e.g., Feingold et al., 1994; Kogan et al., 

1994; Stevens et al., 1996; Feingold et al., 1999; 

Ackerman et al., 2004). This section explores 

some of the complexity in the aerosol indirect 

effects revealed by such studies to illustrate how 

difficult parameterizing these effects properly 

in GCMs could really be. 

3.4.4A. THE FIRST INDIRECT EFFECT

The relationship between aerosol and drop concentrations (or drop sizes) is a key piece of the 

first indirect effect puzzle. (It should not, however, be equated to the first indirect effect which 

concerns itself with the resultant RF). A huge 

body of measurement and modeling work points 

to the fact that drop concentrations increase 

with increasing aerosol. The main unresolved 

questions relate to the degree of this effect, 

and the relative importance of aerosol size distribution, composition and updraft velocity in 

determining drop concentrations (for a review, 

see McFiggans et al., 2006). Studies indicate 

that the aerosol number concentration and size 

distribution are the most important aerosol factors. Updraft velocity (unresolved by GCMs) is 

particularly important under conditions of high 

aerosol particle number concentration. 

Although it is likely that composition has 

some effect on drop number concentrations, 

composition is generally regarded as relatively 

unimportant compared to the other parameters 

(Fitzgerald, 1975; Feingold, 2003; Ervens et al., 

2005; Dusek et al., 2006). Therefore, it has been 

stated that the significant complexity in aerosol composition can be modeled, for the most 

part, using fairly simple parameterizations that 

reflect the soluble and insoluble fractions (e.g., 

Rissler et al., 2004). However, composition cannot be simply dismissed. Furthermore, chemical 

interactions also cannot be overlooked. A large 

uncertainty remains concerning the impact 

of organic species on cloud droplet growth 

kinetics, thus cloud droplet formation. Cloud 

drop size is affected by wet scavenging, which 

depends on aerosol composition especially for 

freshly emitted aerosol. And future changes 

in composition will presumably arise due to 

biofuels/biomass burning and a reduction in 

sulfate emissions, which emphasizes the need 

to include composition changes in models when 

assessing the first indirect effect. The simple 

soluble/insoluble fraction model may become 

less applicable than is currently the case. 

The updraft velocity, and its change as climate 

warms, may be the most difficult aspect to 

simulate in GCMs because of the small scales 

involved. In GCMs it is calculated in the 

dynamics as a grid box average, and parameterized on the small scale indirectly because 

it is a key part of convection and the spatial 

distribution of condensate, as well as droplet 

activation. Numerous solutions to this problem have been sought, including estimation of 

vertical velocity based on predicted turbulent 

kinetic energy from boundary layer models 

(Lohmann et al., 1999; Larson et al., 2001) and 

PDF representations of subgrid quantities, such 

as vertical velocity and the vertically-integrated 

cloud liquid water (‘liquid water path’, or LWP) 

(Pincus and Klein, 2000; Golaz et al., 2002a,b; 

Larson et al., 2005). Embedding cloud-resolving 

models within GCMs is also being actively 

pursued (Grabowski et al., 1999; Randall et al., 

2003). Numerous other details come into play; 

for example, the treatment of cloud droplet activation in GCM frameworks is often based on 

the assumption of adiabatic conditions, which 

may overestimate the sensitivity of cloud to 

changes in CCN (Sotiropoulou et al., 2006, 

2007). This points to the need for improved 

theoretical understanding followed by new 

parameterizations.

3.4.4B. OTHER INDIRECT EFFECTS

The second indirect effect is often referred to as 

the “cloud lifetime effect”, based on the premise 

that non-precipitating clouds will live longer. 

In GCMs the “lifetime effect” is equivalent to 

changing the representation of precipitation production and can be parameterized as an increase 

in cloud area or cloud cover (e.g., Hansen et al., 

2005). The second indirect effect hypothesis 

states that the more numerous and smaller drops 

associated with aerosol perturbations, suppress 
The U.S. Climate Change Science Program Chapter 3

72

collision-induced rain, and result in a longer 

cloud lifetime. Observational evidence for the 

suppression of rain in warm clouds exists in 

the form of isolated studies (e.g. Warner, 1968) 

but to date there is no statistically robust proof 

of surface rain suppression (Levin and Cotton, 

2008). Results from ship-track studies show 

that cloud water may increase or decrease in the 

tracks (Coakley and Walsh, 2002) and satellite 

studies suggest similar results for warm boundary layer clouds (Han et al., 2002). Ackerman 

et al. (2004) used LES to show that in stratocumulus, cloud water may increase or decrease 

in response to increasing aerosol depending on 

the relative humidity of the air overlaying the 

cloud. Wang et al. (2003) showed that all else 

being equal, polluted stratocumulus clouds tend 

to have lower water contents than clean clouds 

because the small droplets associated with polluted clouds evaporate more readily and induce 

an evaporation-entrainment feedback that dilutes the cloud. This result was confirmed by 

Xue and Feingold (2006) and Jiang and Feingold 

(2006) for shallow cumulus, where pollution 

particles were shown to decrease cloud fraction. 

Furthermore, Xue et al. (2008) suggested that 

there may exist two regimes: the first, a precipitating regime at low aerosol concentrations 

where an increase in aerosol will suppress precipitation and increase cloud cover (Albrecht, 

1989); and a second, non precipitating regime 

where the enhanced evaporation associated 

with smaller drops will decrease cloud water 

and cloud fraction.

The possibility of bistable aerosol states was 

proposed earlier by Baker and Charlson (1990) 

based on consideration of aerosol sources and 

sinks. They used a simple numerical model to 

suggest that the marine boundary layer prefers 

two aerosol states: a clean, oceanic regime 

characterized by a weak aerosol source and less 

reflective clouds; and a polluted, continental 

regime characterized by more reflective clouds. 

On the other hand, study by Ackerman et al. 

(1994) did not support such a bistable system 

using a somewhat more sophisticated model. 

Further observations are needed to clarify the 

nature of cloud/aerosol interactions under a 

variety of conditions.

Finally, the question of possible effects of aerosol on cloud lifetime was examined by Jiang et 

al. (2006), who tracked hundreds of cumulus 

clouds generated by LES from their formative 

stages until they dissipated. They showed that 

in the model there was no effect of aerosol 

on cloud lifetime, and that cloud lifetime was 

dominated by dynamical variability.

It could be argued that the representation 

of these complex feedbacks in GCMs is not 

warranted until a better understanding of the 

processes is at hand. Moreover, until GCMs are 

able to represent cloud scales, it is questionable 

what can be obtained by adding microphysical 

complexity to poorly resolved clouds. A better 

representation of aerosol-cloud interactions 

in GCMs therefore depends on the ability to 

improve representation of aerosols and clouds, 

as well as their interaction, in the hydrologic 

cycle. This issue is discussed further in the 

next chapter.

3.5. Aerosol in the Climate Models

3.5.1. AEROSOL IN THE IPCC AR4 CLIMATE

MODEL SIMULATIONS

To assess the atmospheric and climate response 

to aerosol forcing, e.g., changes in surface temperate, precipitation, or atmospheric circulation, 

aerosols, together with greenhouse gases should 

be an integrated part of climate model simulation under the past, present, and future conditions. Table 3.6 lists the forcing species that 

were included in 25 climate modeling groups 

used in the IPCC AR4 (2007) assessment. All 

the models included long-lived greenhouse 

gases, most models included sulfate direct forcing, but only a fraction of those climate models 

considered other aerosol types. In other words, 

aerosol RF was not adequately accounted for 

in the climate simulations for the IPCC AR4. 

Put still differently, the current aerosol modeling capability has not been fully incorporated 

into the climate model simulations. As pointed 

out in Section 3.4, fewer than one-third of the 

models incorporated an aerosol indirect effect, 

and most considered only sulfates. 

The following discussion compares two of 

the IPCC AR4 climate models that include all 

major forcing agencies in their climate simulation: the model from the NASA Goddard Institute for Space Studies (GISS) and from the 

NOAA Geophysical Fluid Dynamics Laboratory (GFDL). The purpose in presenting these 

comparisons is to help elucidate how modelers 

go about assessing their aerosol components, 

A better 

representation 

of aerosol-cloud 

interactions in 

climate models 

depends on the 

ability to improve 

representation of 

aerosols and clouds, 

as well as their 

interaction, in the 

hydrologic cycle.
73

Atmospheric Aerosol Properties and Climate Impacts

and the difficulties that entail. A particular 

concern is how aerosol forcings were obtained 

in the climate model experiments for IPCC 

AR4. Comparisons with observations have 

already led to some improvements that can be 

implemented in climate models for subsequent 

climate change experiments (e.g., Koch et al., 

2006, for GISS model). This aspect is discussed 

further in chapter 4.

3.5.1A. THE GISS MODEL

There have been many different configurations 

of aerosol simulations in the GISS model over 

the years, with different emissions, physics 

packages, etc., as is apparent from the multiple 

GISS entries in the preceding figures and 

tables. There were also three different GISS 

GCM submissions to IPCC AR4, which varied 

in their model physics and ocean formulation. 

Table 3.6. Forcings used in IPCC AR4 simulations of 20th century climate change. This table is adapted from SAP 

1.1 Table 5.2 (compiled using information provided by the participating modeling centers, see http://www-pcmdi.

llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php) plus additional information from that website. 

Eleven different forcings are listed: well-mixed greenhouse gases (G), tropospheric and stratospheric ozone (O), 

sulfate aerosol direct (SD) and indirect effects (S), black carbon (BC) and organic carbon aerosols (OC), mineral 

dust (MD), sea salt (SS), land use/land cover (LU), solar irradiance (SO), and volcanic aerosols (V). Check mark 

denotes inclusion of a specific forcing. As used here, “inclusion” means specification of a time-varying forcing, with 

changes on interannual and longer timescales. 

MODEL COUNTRY G O SD SI BC OC MD SS LU SO V

1 BCC-CM1 China √ √ √

2 BCCR-BCM2.0 Norway √ √ √ √

3 CCSM3 USA √ √ √ √ √ √ √

4 CGCM3.1(T47) Canada √ √

5 CGCM3.1(T63) Canada √ √

6 CNRM-CM3 France √ √ √ √

7 CSIRO-Mk3.0 Australia √ √

8 CSIRO-Mk3.5 Australia √ √

9 ECHAM5/MPI-OM Germany √ √ √ √

10 ECHO-G Germany/

Korea √ √ √ √ √ √

11 FGOALS-g1.0 China √ √

12 GFDL-CM2.0 USA √ √ √ √ √ √ √ √

13 GFDL-CM2.1 USA √ √ √ √ √ √ √ √

14 GISS-AOM USA √ √ √

15 GISS-EH USA √ √ √ √ √ √ √ √ √ √ √

16 GISS-ER USA √ √ √ √ √ √ √ √ √ √ √

17 INGV-SXG Italy √ √ √

18 INM-CM3.0 Russia √ √ √

19 IPSL-CM4 France √ √ √

20 MIROC3.2(hires) Japan √ √ √ √ √ √ √ √ √ √

21 MIROC3.2(medres) Japan √ √ √ √ √ √ √ √ √ √

22 MRI-CGCM2.3.2 Japan √ √ √ √

23 PCM USA √ √ √ √ √

24 UKMO-HadCM3 UK √ √ √ √

25 UKMO-HadGEM1 UK √ √ √ √ √ √ √ √ √
The U.S. Climate Change Science Program Chapter 3

74

(Note that the aerosols in these three GISS versions are different from those in the AeroCom 

simulations described in section 3.2 and 3.3.) 

The GCM results discussed below all relate 

to the simulations known as GISS model ER 

(Schmidt et al., 2006, see Table 3.6).

Although the detailed description and model 

evaluation have been presented in Liu et al. 

(2006), below are the general characteristics of 

aerosols in the GISS ER:

Aerosol fields: The aerosol fields used in the 

GISS ER is a prescribed “climatology” which 

is obtained from chemistry transport model 

simulations with monthly averaged mass concentrations representing conditions up to1990. 

Aerosol species included are sulfate, nitrate, 

BC, POM, dust, and sea salt. Dry size effective 

radii are specified for each of the aerosol types, 

and laboratory-measured phase functions are 

employed for all solar and thermal wavelengths. 

For hygroscopic aerosols (sulfate, nitrate, POM, 

and sea salt), formulas are used for the particle 

growth of each aerosol as a function of relative 

humidity, including the change in density and 

optical parameters. With these specifications, 

the AOD, single scattering albedo, and phase 

function of the various aerosols are calculated. 

While the aerosol distribution is prescribed as 

monthly mean values, the relative humidity 

component of the extinction is updated each 

hour. The global averaged AOD at 550 nm is 

about 0.15.

Global distribution: When comparing with 

AOD from observations by multiple satellite 

sensors of MODIS, MISR, POLDER, and 

AVHRR and surface based sunphotometer 

network AERONET (see Chapter 2 for detailed 

information about data), qualitative agreement 

is apparent, with generally higher burdens in 

Northern Hemisphere summer, and seasonal 

variations of smoke over southern Africa and 

South America, as well as wind blown dust over 

northern African and the Persian Gulf. Aerosol 

optical depth in both model and observations 

is smaller away from land. There are, however, 

considerable discrepancies between the model 

and observations. Overall, the GISS GCM has 

reduced aerosol optical depths compared with 

the satellite data (a global, clear-sky average of 

about 80% compared with MODIS and MISR 

data), although it is in better agreement with 

AERONET ground-based measurements in 

some locations (note that the input aerosol 

values were calibrated with AERONET data). 

The model values over the Sahel in Northern 

Hemisphere winter and the Amazon in Southern Hemisphere winter are excessive, indicative 

of errors in the biomass burning distributions, at 

least partially associated with an older biomass 

burning source used (the source used here was 

from Liousse et al., 1996).

Seasonal variation: A comparison of the seasonal distribution of the global AOD between 

the GISS model and satellite data indicates that 

the model seasonal variation is in qualitative 

agreement with observations for many of the 

locations that represent major aerosol regimes, 

although there are noticeable differences. For 

example, in some locations the seasonal variations are different from or even opposite to the 

observations.

Particle size parameter: The Ångström exponent (Å), which is determined by the contrast 

between the AOD at two or more different 

wavelengths and is related to aerosol particle 

size (discussed in section 3.3). This parameter is 

important because the particle size distribution 

affects the efficiency of scattering of both short 

and long wave radiation, as discussed earlier. Å

from the GISS model is biased low compared 

with AERONET, MODIS, and POLDER data, 

although there are technical differences in 

determining the Å. This low bias suggests that 

the aerosol particle size in the GISS model 

is probably too large. The average effective 

radius in the GISS model appears to be 0.3-0.4 

μm, whereas the observational data indicates 

a value more in the range of 0.2-0.3 μm (Liu 

et al., 2006).

Single scattering albedo: The model-calculated 

SSA (at 550 nm) appears to be generally higher 

than the AERONET data at worldwide locations 

(not enough absorption), but lower than AERONET data in Northern Africa, the Persian Gulf, 

and the Amazon (too much absorption). This 

discrepancy reflects the difficulties in modeling 

BC, which is the dominant absorbing aerosol, 

and aerosol sizes. Global averaged SSA at 550 

nm from the GISS model is at about 0.95.

Aerosol direct RF: The GISS model calculated 

anthropogenic aerosol direct shortwave RF 
75

Atmospheric Aerosol Properties and Climate Impacts

is -0.56 W m-2 at TOA and -2.87 W m-2 at the 

surface. The TOA forcing (upper left, Figure 

3.7) indicates that, as expected, the model has 

larger negative values in polluted regions and 

positive forcing at the highest latitudes. At the 

surface (lower left, Figure 3.7) GISS model 

values exceed -4 W m-2 over large regions. Note 

there is also a longwave RF of aerosols (right 

column), although they are much weaker than 

the shortwave RF.

There are several concerns for climate change 

simulations related to the aerosol trend in the 

GISS model. One is that the aerosol fields in 

the GISS AR4 climate simulation (version ER) 

are kept fixed after 1990. In fact, the observed 

trend shows a reduction in tropospheric aerosol 

optical thickness from 1990 through the present, at least over the oceans (Mishchenko and 

Geogdzhayev, 2007). Hansen et al. (2007) suggested that the deficient warming in the GISS 

model over Eurasia post-1990 was due to the 

lack of this trend. Indeed, a possible conclusion 

from the Penner et al. (2002) study was that the 

GISS model overestimated the AOD (presumably associated with anthropogenic aerosols) 

poleward of 30°N. However, when an alternate 

experiment reduced the aerosol optical depths, 

the polar warming became excessive (Hansen 

et al., 2007). The other concern is that the GISS 

model may underestimate the organic and sea 

salt AOD, and overestimate the influence of 

black carbon aerosols in the biomass burning 

regions (deduced from Penner et al., 2002; Liu 

et al., 2006). To the extent that is true, it would 

indicate the GISS model underestimates the 

aerosol direct cooling effect in a substantial portion of the tropics, outside of biomass burning 

areas. Clarifying those issues requires numerous modeling experiments and various types 

of observations.

3.5.1B. THE GFDL MODEL

A comprehensive description and evaluation 

of the GFDL aerosol simulation are given in 

Ginoux et al. (2006). Below are the general 

characteristics:

Aerosol fields: The aerosols used in the GFDL 

climate experiments are obtained from simulations performed with the MOZART 2 model 

(Model for Ozone and Related chemical Tracers) (Horowitz et al., 2003; Horozwitz, 2006). 

The exceptions were dust, which was generated 

with a separate simulation of MOZART 2, using sources from Ginoux et al. (2001) and wind 

fields from NCEP/NCAR reanalysis data; and 

sea salt, whose monthly mean concentrations 

were obtained from a previous study by Haywood et al. (1999). It includes most of the same 

aerosol species as in the GISS model (although 

it does not include nitrates), and, as in the GISS 

model, relates the dry aerosol to wet aerosol 

optical depth via the model’s relative humidity 

for sulfate (but not for organic carbon); for sea 

salt, a constant relative humidity of 80% was 

used. Although the parameterizations come 

from different sources, both models maintain a 

Fig. 3.7. Direct radiative 

forcing by anthropogenic 

aerosols in the GISS model (including sulfates, BC, 

OC and nitrates). Short 

wave forcing at TOA and 

surface are shown in the 

top left and bottom left 

panels. The corresponding thermal forcing is indicated in the right hand 

panels. Figure provided by 

A. Lacis, GISS.
The U.S. Climate Change Science Program Chapter 3

76

very large growth in sulfate particle size when 

the relative humidity exceeds 90%.

Global distributions: Overall, the GFDL global 

mean aerosol mass loading is within 30% of 

that of other studies (Chin et al., 2002; Tie et 

al., 2005; Reddy et al., 2005a), except for sea 

salt, which is 2 to 5 times smaller. However, 

the sulfate AOD (0.1) is 2.5 times that of other 

studies, whereas the organic carbon value is 

considerably smaller (on the order of 1/2). 

Both of these differences are influenced by 

the relationship with relative humidity. In the 

GFDL model, sulfate is allowed to grow up to 

100% relative humidity, but organic carbon 

does not increase in size as relative humidity 

increases. Comparison of AOD with AVHRR 

and MODIS data for the time period 1996-2000 

shows that the global mean value over the ocean 

(0.15) agrees with AVHRR data (0.14) but there 

are significant differences regionally, with the 

model overestimating the value in the northern 

mid latitude oceans and underestimating it in 

the southern ocean. Comparison with MODIS 

also shows good agreement globally (0.15), but 

in this case indicates large disagreements over 

land, with the model producing excessive AOD 

over industrialized countries and underestimating the effect over biomass burning regions. 

Overall, the global averaged AOD at 550 nm is 

0.17, which is higher than the maximum values 

in the AeroCom-A experiments (Table 3.2) and 

exceeds the observed value too (Ae and S* in 

Figure 3.1).

Composition: Comparison of GFDL modeled 

species with in situ data over North America, 

Europe, and over oceans has revealed that the 

sulfate is overestimated in spring and summer and underestimated in winter in many 

regions, including Europe and North America. 

Organic and black carbon aerosols are also 

overestimated in polluted regions by a factor 

of two, whereas organic carbon aerosols are 

elsewhere underestimated by factors of 2 to 3. 

Dust concentrations at the surface agree with 

observations to within a factor of 2 in most 

places where significant dust exists, although 

over the southwest U.S. it is a factor of 10 too 

large. Surface concentrations of sea salt are 

underestimated by more than a factor of 2. 

Over the oceans, the excessive sulfate AOD 

compensates for the low sea salt values except 

in the southern oceans.

Size and single-scattering albedo: No specific comparison was given for particle size 

or single-scattering albedo, but the excessive 

sulfate would likely produce too high a value 

of reflectivity relative to absorption except in 

some polluted regions where black carbon (an 

absorbing aerosol) is also overestimated.

As in the case of the GISS model, there are several concerns with the GFDL model. The good 

global-average agreement masks an excessive 

aerosol loading over the Northern Hemisphere 

(in particular, over the northeast U.S. and Europe) and an underestimate over biomass burning regions and the southern oceans. Several 

model improvements are needed, including 

better parameterization of hygroscopic growth 

at high relative humidity for sulfate and organic 

carbon; better sea salt simulations; correcting 

an error in extinction coefficients; and improved biomass burning emissions inventory 

(Ginoux et al., 2006).

3.5.1C. COMPARISONS BETWEEN GISS AND

GFDL MODEL

Both GISS and GFDL models were used in the 

IPCC AR4 climate simulations for climate sensitivity that included aerosol forcing. It would 

be constructive, therefore, to compare the similarities and differences of aerosols in these two 

models and to understand what their impacts are 

in climate change simulations. Figure 3.8 shows 

the percentage AOD from different aerosol 

components in the two models.

Sulfate: The sulfate AOD from the GISS model 

is within the range of that from all other models 

(Table 3.3), but that from the GFDL model exceeds the maximum value by a factor of 2.5. An 

assessment in SAP 3.2 (CCSP 2008; Shindell et 

al., 2008b) also concludes that GFDL had excessive sulfate AOD compared with other models. 

The sulfate AOD from GFDL is nearly a factor of 

4 large than that from GISS, although the sulfate 

burden differs only by about 50% between the 

two models. Clearly, this implies a large difference in sulfate MEE between the two models.

BC and POM: Compared to observations, the 

GISS model appears to overestimate the influence of BC and POM in the biomass burning 

regions and underestimate it elsewhere, whereas 

the GFDL model is somewhat the reverse: it 

overestimates it in polluted regions, and un-
77

Atmospheric Aerosol Properties and Climate Impacts

derestimates it in biomass burning areas. The 

global comparison shown in Table 3.4 indicates 

the GISS model has values similar to those from 

other models, which might be the result of such 

compensating errors. The GISS and GFDL 

models have relatively similar global-average 

black carbon contributions, and the same appears true for POM.

Sea salt: The GISS model has a much larger sea 

salt contribution than does GFDL (or indeed 

other models).

Global and regional distributions: Overall, the 

global averaged AOD is 0.15 from the GISS 

model and 0.17 from GFDL. However, as shown 

in Figure 3.8, the contribution to this AOD from 

different aerosol components shows greater 

disparity. For example, over the Southern Ocean 

where the primary influence is due to sea salt in 

the GISS model, but in the GFDL it is sulfate. 

The lack of satellite observations of the component contributions and the limited available 

in situ measurements make the model improvements at aerosol composition level difficult.

Climate simulations: With such large differences in aerosol composition and distribution 

between the GISS and GFDL models, one 

might expect that the model simulated surface 

temperature might be quite different. Indeed, 

the GFDL model was able to reproduce the 

observed temperature change during the 20th 

century without the use of an indirect aerosol 

effect, whereas the GISS model required a 

substantial indirect aerosol contribution (more 

than half of the total aerosol forcing; Hansen 

et al., 2007). It is likely that the reason for this 

difference was the excessive direct effect in 

the GFDL model caused by its overestimation 

of the sulfate optical depth. The GISS model 

direct aerosol effect (see Section 3.6) is close to 

that derived from observations (Chapter 2); this 

suggests that for models with climate sensitivity close to 0.75°C/(W m-2) (as in the GISS and 

GFDL models), an indirect effect is needed.

3.5.2. ADDITIONAL CONSIDERATIONS

Long wave aerosol forcing: So far only the 

aerosol RF in the shortwave (solar) spectrum 

has been discussed. Figure 3.7 (right column) 

shows that compared to the shortwave forcing, 

the values of anthropogenic aerosol long wave 

(thermal) forcing in the GISS model are on the 

Fig. 3.8. Percentage of aerosol optical depth in the GISS (left, based on Liu et 

al., 2006, provided by A. Lacis, GISS) and GFDL (right, from Ginoux et al., 2006) 

models associated with the different components: Sulfate (1st row), BC (2nd 

row), OC (3rd row), sea-salt (4th row), dust (5th row), and nitrate (last row. 

Nitrate not available in GFDL model). Numbers on the GISS panels are global 

average, but on the GFDL panels are maximum and minimum.
The U.S. Climate Change Science Program Chapter 3

78

order of 10%. Like the shortwave forcing, these 

values will also be affected by the particular 

aerosol characteristics used in the simulation.

Aerosol vertical distribution: Vertical distribution is particularly important for absorbing 

aerosols, such as BC and dust in calculating 

the RF, particularly when longwave forcing is 

considered (e.g. Figure 3.7) because the energy 

they reradiate depends on the temperature (and 

hence altitude), which affects the calculated 

forcing values. Several model inter-comparison 

studies have shown that the largest difference 

among model simulated aerosol distributions is 

the vertical profile (e.g. Lohmann et al., 2001; 

Penner et al., 2002; Textor et al., 2006), due 

to the significant diversities in atmospheric 

processes in the models (e.g., Table 3.2). In 

addition, the vertical distribution also varies 

with space and time, as illustrated in Figure 

3.9 from the GISS ER simulations for January 

and July showing the most probable altitude of 

aerosol vertical locations. In general, aerosols 

in the northern hemisphere are located at lower 

altitudes in January than in July, and vice versa 

for the southern hemisphere.

Mixing state: Most climate model simulations 

incorporating different aerosol types have been 

made using external mixtures, i.e., the evaluation of the aerosols and their radiative properties 

are calculated separately for each aerosol type 

(assuming no mixing between different components within individual particles). Observations 

indicate that aerosols commonly consist of 

internally mixed particles, and these “internal 

mixtures” can have very different radiative 

impacts. For example, the GISS-1 (internal 

mixture) and GISS-2 (external mixture) model 

results shows very different magnitude and sign 

of aerosol forcing from slightly positive (implying slight warming) to strong negative (implying significant cooling) TOA forcing (Figure 

3.2), due to changes in both radiative properties of the mixtures, and in aerosol amount. 

The more sophisticated aerosol mixtures from 

detailed microphysics calculations now being 

used/developed by different modeling groups 

may well end up producing very different direct 

(and indirect) forcing values.

Cloudy sky vs. clear sky: The satellite or 

AERONET observations are all for clear sky 

only because aerosol cannot be measured in 

the remote sensing technique when clouds are 

present. However, almost all the model results 

are for all-sky because of difficulty in extracting cloud-free scenes from the GCMs. So the 

AOD comparisons discussed earlier are not 

completely consistent. Because AOD can be 

significantly amplified when relative humidity 

is high, such as near or inside clouds, all-sky 

AOD values are expected to be higher than 

clear sky AOD values. On the other hand, the 

aerosol RF at TOA is significantly lower for 

all-sky than for clear sky conditions; the IPCC 

AR4 and AeroCom RF study (Schulz et al., 

2006) have shown that on average the aerosol 

RF value for all-sky is about 1/3 of that for clear 

sky although with large diversity (63%). These 

aspects illustrate the complexity of the system 

and the difficulty of representing aerosol radiative influences in climate models whose 

cloud and aerosol distributions are somewhat 

problematic. And of course aerosols in cloudy 

regions can affect the clouds themselves, as 

discussed in Section 3.4.

3.6. Impacts of Aerosols on Climate 

Model Simulations

3.6.1. SURFACE TEMPERATURE CHANGE

It was noted in the introduction that aerosol 

cooling is essential in order for models to produce the observed global temperature rise over 

the last century, at least models with climate 

sensitivities in the range of 3°C for doubled CO2 

(or ~0.75°C/(W m-2)). The implications of this 

are discussed here in somewhat more detail.

Hansen et al. (2007) show that in the GISS 

model, well-mixed greenhouse gases produce 

Fig. 3.9. Most probable 

aerosol altitude (in pressure, hPa) from the GISS 

model in January (top) and 

July (bottom). Figure from 

A. Lacis, GISS.
79

Atmospheric Aerosol Properties and Climate Impacts

a warming of close to 1°C between 1880 and 

the present (Table 3.7). The direct effect of tropospheric aerosols as calculated in that model 

produces cooling of close to -0.3°C between 

those same years, while the indirect effect 

(represented in that study as cloud cover change) 

produces an additional cooling of similar magnitude (note that the general model result quoted 

in IPCC AR4 is that the indirect RF is twice that 

of the direct effect).

The time dependence of the total aerosol 

forcing used as well as the individual species 

components is shown in Figure 3.10. The resultant warming, 0.53 (±0.04) °C including these 

and other forcings (Table 3.7), is less than the 

observed value of 0.6-0.7°C from 1880-2003. 

Hansen et al. (2007) further show that a reduction in sulfate optical thickness and the direct 

aerosol effect by 50%, which also reduced 

the aerosol indirect effect by 18%, produces a 

negative aerosol forcing from 1880 to 2003 of 

–0.91 W m-2 (down from –1.37 W m-2 with this 

revised forcing). The model now warms 0.75°C 

over that time. Hansen et al. (2007) defend this 

change by noting that sulfate aerosol removal 

over North America and western Europe during 

the 1990s led to a cleaner atmosphere. Note that 

the comparisons shown in the previous section 

suggest that the GISS model already underestimates aerosol optical depths; it is thus trends 

that are the issue here.

The magnitude of the indirect effect used by 

Hansen et al. (2005) is roughly calibrated to 

reproduce the observed change in diurnal 

temperature cycle and is consistent with some 

satellite observations. However, as Anderson et 

al. (2003) note, the forward calculation of aerosol negative forcing covers a much larger range 

than is normally used in GCMs; the values 

chosen, as in this case, are consistent with the 

inverse reasoning estimates of what is needed 

to produce the observed warming, and hence 

generally consistent with current model climate 

sensitivities. The authors justify this approach 

by claiming that paleoclimate data indicate 

a climate sensitivity of close to 0.75 (±0.25)

°C/(W m-2), and therefore something close to 

this magnitude of negative forcing is reasonable. Even this stated range leaves significant 

uncertainty in climate sensitivity and the magnitude of the aerosol negative forcing. Furthermore, IPCC (2007) concluded that paleoclimate 

data are not capable of narrowing the range of

climate sensitivity, nominally 0.375 to 1.13 

Table 3.7. Climate forcings (1880-2003) used to drive GISS climate simulations, along with the surface air temperature changes obtained for several periods. Instantaneous (Fi), adjusted (Fa), fixed SST (Fs) and effective (Fe) 

forcings are defined in Hansen et al., 2005. From Hansen et al., 2007.

Forcing agent Forcing W m-2 (1880 – 2003) ΔT surface °C (year to 2003)

Fi Fa Fs Fe 1880 1900 1950 1979

Well-mixed GHGs 2.62 2.50 2.65 2.72 0.96 0.93 0.74 0.43

Stratospheric H2O 0.06 0.05 0.03 0.01 0.05 0.00

Ozone 0.44 0.28 0.26 0.23 0.08 0.05 0.00 -0.01

Land Use -0.09 -0.09 -0.05 -0.07 -0.04 -0.02

Snow albedo 0.05 0.05 0.14 0.14 0.03 0.00 0.02 -0.01

Solar Irradiance 0.23 0.24 0.23 0.22 0.07 0.07 0.01 0.02

Stratospheric aerosols 0.00 0.00 0.00 0.00 -0.08 -0.03 -0.06 0.04

Trop. aerosol direct forcing -0.41 -0.38 -0.52 -0.60 -0.28 -0.23 -0.18 -0.10

Trop. aerosol indirect forcing -0.87 -0.77 -0.27 -0.29 -0.14 -0.05

Sum of above 1.86 1.90 0.49 0.44 0.40 0.30

All forcings at once 1.77 1.75 0.53 0.61 0.44 0.29
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°C/(W m-2), because of uncertainties in paleoclimate forcing and response; so from this 

perspective the total aerosol forcing is even 

less constrained than the GISS estimate. Hansen et al. (2007) acknowledge that “an equally 

good match to observations probably could be 

obtained from a model with larger sensitivity 

and smaller net forcing, or a model with smaller 

sensitivity and larger forcing”. 

The GFDL model results for global mean ocean 

temperature change (down to 3 km depth) 

for the time period 1860 to 2000 is shown in 

Figure 3.11, along with the different contributing factors (Delworth et al., 2005). This is the 

same GFDL model whose aerosol distribution 

was discussed previously. The aerosol forcing 

produces a cooling on the order of 50% that 

of greenhouse warming (generally similar to 

that calculated by the GISS model, Table 3.7). 

Note that this was achieved without any aerosol 

indirect effect.

The general model response noted by IPCC, 

as discussed in the introduction, was that the 

total aerosol forcing of -1.3 W m-2 reduced the 

greenhouse forcing of near 3 W m-2 by about 

45%, in the neighborhood of the GFDL and 

GISS forcings. Since the average model sensitivity was close to 0.75 °C/(W m-2), similar to 

the sensitivities of these models, the necessary 

negative forcing is therefore similar. The agreement cannot therefore be used to validate the 

actual aerosol effect until climate sensitivity 

itself is better known.

Is there some way to distinguish between greenhouse gas and aerosol forcing that would allow 

the observational record to indicate how much 

of each was really occurring? This question of 

attribution has been the subject of numerous 

papers, and the full scope of the discussion 

is beyond the range of this report. It might be 

briefly noted that Zhang et al. (2006) using results from several climate models and including 

both spatial and temporal patterns, found that 

the climate responses to greenhouse gases and 

sulfate aerosols are correlated, and separation is 

possible only occasionally, especially at global 

scales. This conclusion appears to be both 

model and method-dependent: using time-space 

distinctions as opposed to trend detection may 

work differently in different models (Gillett 

et al., 2002a). Using multiple models helps 

primarily by providing larger-ensemble sizes 

for statistics (Gillett et al., 2002b). However, 

even separating between the effects of different 

aerosol types is difficult. Jones et al. (2005) concluded that currently the pattern of temperature 

change due to black carbon is indistinguishable 

Fig. 3.10. Time dependence of aerosol optical 

thickness (left) and climate 

forcing (right). Note that as 

specified, the aerosol trends 

are all ‘flat’ from 1990 to 

2000. From Hansen et al. 

(2007).

Fig. 3.11. Change in global mean ocean temperature (left axis) and ocean heat content (right axis) 

for the top 3000 m due to different forcings in the 

GFDL model. WMGG includes all greenhouse gases 

and ozone; NATURAL includes solar and volcanic 

aerosols (events shown as green triangles on the 

bottom axis). Observed ocean heat content changes 

are shown as well. From Delworth et al., 2005.
81

Atmospheric Aerosol Properties and Climate Impacts

from the sulfate aerosol pattern. In contrast, 

Hansen et al. (2005) found that absorbing aerosols produce a different global response than 

other forcings, and so may be distinguishable. 

Overall, the similarity in response to all these 

very different forcings is undoubtedly due to the 

importance of climate feedbacks in amplifying 

the forcing, whatever its nature.

Distinctions in the climate response do appear 

to arise in the vertical, where absorbing aerosols 

produce warming that is exhibited throughout 

the troposphere and into the stratosphere, 

whereas reflective aerosols cool the troposphere 

but warm the stratosphere (Hansen et al., 2005; 

IPCC, 2007). Delworth et al. (2005) noted that 

in the ocean, the cooling effect of aerosols 

extended to greater depths, due to the thermal 

instability associated with cooling the ocean 

surface. Hence the temperature response at levels both above and below the surface may provide an additional constraint on the magnitudes 

of each of these forcings, as may the difference 

between Northern and Southern Hemisphere 

changes (IPCC, 2007 Chapter 9). The profile 

of atmospheric temperature response will be 

useful to the extent that the vertical profile of 

aerosol absorption, an important parameter to 

measure, is known.

3.6.2. IMPLICATIONS FOR CLIMATE MODEL

SIMULATIONS

The comparisons in Sections 3.2 and 3.3 suggest 

that there are large differences in model calculated aerosol distributions, mainly because of 

the large uncertainties in modeling the aerosol 

atmospheric processes in addition to the uncertainties in emissions. The fact that the total 

optical depth is in better agreement between 

models than the individual components means 

that even with similar optical depths, the aerosol 

direct forcing effect can be quite different, as 

shown in the AeroCom studies. Because the 

diversity among models and discrepancy between models and observations are much larger 

at the regional level than in global average, the 

assessment of climate response (e.g. surface 

temperature change) to aerosol forcing would 

be more accurate for global average than for regional or hemispheric differentiation. However, 

since aerosol forcing is much more pronounced 

on regional than on global scales because of 

the highly variable aerosol distributions, it is 

insufficient or even misleading to just get the 

global average right.

The indirect effect is strongly influenced by 

the aerosol concentrations, size, type, mixing 

state, microphysical processes, and vertical 

profile. As shown in previous sections, very 

large differences exist in those quantities even 

among the models having similar AOD. Moreover, modeling aerosol indirect forcing presents 

more challenges than direct forcing because 

there is so far no rigorous observational data, 

especially on a global scale, that one can use 

to test the model simulations. As seen in the 

comparisons of the GISS and GFDL model 

climate simulations for IPCC AR4, aerosol 

indirect forcing was so poorly constrained that 

it was completely ignored by one model (GFDL) 

but used by another (GISS) at a magnitude that 

is more than half of the direct forcing, in order 

to reproduce the observed surface temperature 

trends. A majority of the climate models used 

in IPCC AR4 do not consider indirect effects; 

the ones that did were mostly limited to highly 

simplified sulfate indirect effects (Table 3.6). 

Improvements must be made to at least the 

degree that the aerosol indirect forcing can 

no longer be used to mask the deficiencies in 

estimating the climate response to greenhouse 

gas and aerosol direct RF.

3.7. OUTSTANDING ISSUES

Clearly there are still large gaps in assessing 

the aerosol impacts on climate through modeling. Major outstanding issues and prospects 

of improving model simulations are discussed 

below.

Aerosol composition: Many global models are 

now able to simulate major aerosol types such 

as sulfate, black carbon, and POM, dust, and sea 

salt, but only a small fraction of these models 

simulate nitrate aerosols or consider anthropogenic secondary organic aerosols. And it is difficult to quantify the dust emission from human 

activities. As a result, the IPCC AR4 estimation 

of the nitrate and anthropogenic dust TOA 

forcing was left with very large uncertainty. 

The next generation of global models should 

therefore have a more comprehensive suite of 

aerosol compositions with better-constrained 

anthropogenic sources.

Aerosol absorption: One of the most critical 

parameters in aerosol direct RF and aerosol 

impact on hydrological cycles is the aerosol 

absorption. Most of the absorption is from BC 

despite its small contribution to total aerosol 

The fact that the 

total optical depth is 

in better agreement 

between models 

than the individual 

components means 

that even with similar optical depths, 

the aerosol direct 

forcing effect can be 

quite different among 

models... Moreover, 

modeling aerosol 

indirect forcing presents more challenges.
The U.S. Climate Change Science Program Chapter 3

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load and AOD; dust too absorbs in both the 

short and long-wave spectral ranges, whereas 

POM absorbs in the near UV. The aerosol 

absorption or SSA, will have to be much better 

represented in the models through improving 

the estimates of carbonaceous and dust aerosol 

sources, their atmospheric distributions, and 

optical properties.

Aerosol indirect effects: The activation of 

aerosol particles into CCN depends not only 

on particle size but chemical composition, with 

the relative importance of size and composition 

unclear. In current aerosol-climate modeling, 

aerosol size distribution is generally prescribed 

and simulations of aerosol composition have 

large uncertainties. Therefore the model estimated “albedo effect” has large uncertainties. 

How aerosol would influence cloud lifetime/

cover is still in debate. The influence of aerosols 

on other aspects of the climate system, such as 

precipitation, is even more uncertain, as are the 

physical processes involved. Processes that determine aerosol size distributions, hygroscopic 

growth, mixing state, as well as CCN concentrations, however, are inadequately represented 

in most of the global models. It will also be 

difficult to improve the estimate of indirect effects until the models can produce more realistic 

cloud characteristics.

Aerosol impacts on surface radiation and atmospheric heating: Although these effects are 

well acknowledged to play roles in modulating 

atmospheric circulation and water cycle, few 

coherent or comprehensive modeling studies 

have focused on them, as compared to the efforts 

that have gone to assessing aerosol RF at TOA. 

They have not yet been addressed in the previous 

IPCC reports. Here, of particular importance is 

to improve the accuracy of aerosol absorption.

Long-term trends of aerosol: To assess the 

aerosol effects on climate change the long-term 

variations of aerosol amount and composition and how they are related to the emission 

trends in different regions have to be specified. 

Simulations of historical aerosol trends can 

be problematic since historical emissions of 

aerosols have shown large uncertainties—as 

information is difficult to obtain on past source 

types, strengths, and even locations. The IPCC 

AR4 simulations used several alternative aerosol emission histories, especially for BC and 

POM aerosols.

Climate modeling: Current aerosol simulation 

capabilities from CTMs have not been fully 

implemented in most models used in IPCC 

AR4 climate simulations. Instead, a majority 

employed simplified approaches to account 

for aerosol effects, to the extent that aerosol 

representations in the GCMs, and the resulting 

forcing estimates, are inadequate. The oversimplification occurs in part because the modeling 

complexity and computing resource would be 

significantly increased if the full suite of aerosols were fully coupled in the climate models.

Observational constraints: Model improvement has been hindered by a lack of comprehensive datasets that could provide multiple 

constraints for the key parameters simulated in 

the model. The extensive AOD coverage from 

satellite observations and AERONET measurements has helped a great deal in validating 

model-simulated AOD over the past decade, but 

further progress has been slow. Large model 

diversities in aerosol composition, size, vertical distribution, and mixing state are difficult 

to constrain, because of lack of reliable measurements with adequate spatial and temporal 

coverage (see Chapter 2).

Aerosol radiative forcing: Because of the 

large spatial and temporal differences in aerosol 

sources, types, emission trends, compositions, 

and atmospheric concentrations, anthropogenic 

aerosol RF has profound regional and seasonal 

variations. So it is an insufficient measure of 

aerosol RF scientific understanding, however 

useful, for models (or observation-derived products) to converge only on globally and annually 

averaged TOA RF values and accuracy. More 

emphasis should be placed on regional and 

seasonal comparisons, and on climate effects 

in addition to direct RF at TOA.

3.8 Conclusions

From forward modeling studies, as discussed 

in the IPCC (2007), the direct effect of aerosols since pre-industrial times has resulted in 

a negative RF of about -0.5 ± 0.4 W m-2. The 

RF due to cloud albedo or brightness effect 

is estimated to be -0.7 (-1.8 to -0.3) W m-2. 

Forcing of similar magnitude has been used in 
83

Atmospheric Aerosol Properties and Climate Impacts

some modeling studies for the effect associated 

with cloud lifetime, in lieu of the cloud brightness influence. The total negative RF due to 

aerosols according to IPCC (2007) estimates 

is therefore -1.3 (-2.2 to -0.5) W m-2. With the 

inverse approach, in which aerosols provide 

forcing necessary to produce the observed temperature change, values range from -1.7 to -0.4 

W m-2 (IPCC, 2007). These results represent a 

substantial advance over previous assessments 

(e.g., IPCC TAR), as the forward model estimated and inverse approach required aerosol 

TOA forcing values are converging. However, 

large uncertainty ranges preclude using the 

forcing and temperature records to more accurately determine climate sensitivity.

There are now a few dozen models that simulate 

a comprehensive suite of aerosols. This is done 

primarily in the CTMs. Model inter-comparison 

studies have shown that models have merged 

at matching the global annual averaged AOD 

observed by satellite instruments, but they differ greatly in the relative amount of individual 

components, in vertical distributions, and in 

optical properties. Because of the great spatial 

and temporal variations of aerosol distributions, 

regional and seasonal diversities are much larger 

than that of the global annual mean. Different 

emissions and differences in atmospheric processes, such as transport, removal, chemistry, 

and aerosol microphysics, are chiefly responsible for the spread among the models. The 

varying component contributions then lead 

to differences in aerosol direct RF, as aerosol 

scattering and absorption properties depend 

on aerosol size and type. They also impact the 

calculated indirect RF, whose variations are 

further amplified by the wide range of cloud 

and convective parameterizations in models. 

Currently, the largest aerosol RF uncertainties 

are associated with the aerosol indirect effect.

Most climate models used for the IPCC AR4 

simulations employed simplified approaches, 

with aerosols specified from stand-alone CTM 

simulations. Despite the uncertainties in aerosol 

RF and widely varying model climate sensitivity, the IPCC AR4 models were generally able 

to reproduce the observed temperature record 

for the past century. This is because models 

with lower/higher climate sensitivity generally 

used less/more negative aerosol forcing to offset 

the greenhouse gas warming. An equally good 

match to observed surface temperature change 

in the past could be obtained from a model with 

larger climate sensitivity and smaller net forcing, 

or a model with smaller sensitivity and larger 

forcing (Hansen et al., 2007). Obviously, both 

greenhouse gases and aerosol effects have to be 

much better quantified in future assessments.

Progress in better quantifying aerosol impacts on climate will only be made when the 

capabilities of both aerosol observations and 

representation of aerosol processes in models 

are improved. The primary concerns and issues 

discussed in this chapter include:

• Better representation of aerosol composition 

and absorption in the global models

• Improved theoretical understanding of 

subgrid-scale processes crucial to aerosolcloud interactions and lifetime

• Improved aerosol microphysics and cloud 

parameterizations

• Better understanding of aerosol effects on 

surface radiation and hydrological cycles

• More focused analysis on regional and seasonal variations of aerosols

• More reliable simulations of aerosol historic 

long-term trends

• More sophisticated climate model simulations with coupled aerosol and cloud processes

• Enhanced satellite observations of aerosol 

type, SSA, vertical distributions, and aerosol 

radiative effect at TOA; more coordinated 

field experiments to provide constraints 

on aerosol chemical, physical, and optical 

properties.

A discussion of the “way forward” toward 

better constraints on aerosol radiative forcing, 

and hence climate sensitivity, is provided in the 

next chapter.

Progress in better 

quantifying aerosol 

impacts on climate 

will only be made 

when the capabilities 

of both aerosol observations and representation of aerosol 

processes in models 

are improved.
The U.S. Climate Change Science Program Chapter 3

84

Aerosols scatter incident sunlight in all directions, reducing the visibility of distant objects, and also decreasing the amount of solar energy 

reaching the surface, which exerts a cooling influence on the climate. Figure taken in Korea. Credit: Stephen E. Schwartz, DOE.
The Way Forward

Authors: David Rind, NASA GISS; Ralph A. Kahn, 

NASA GSFC; Mian Chin, NASA GSFC; Stephen E. 

Schwartz, DOE BNL; Lorraine A. Remer, NASA 

GSFC; Graham Feingold, NOAA ESRL; Hongbin 

Yu, NASA GSFC/UMBC; Patricia K. Quinn, NOAA 

PMEL; Rangasayi Halthore, NASA HQ/NRL

CHAPTER

4

85

Atmospheric Aerosol Properties and Climate Impacts

4.1. Major Research Needs

This review has emphasized that despite the 

increase in understanding aerosol forcing of the 

climate system, many important uncertainties 

remain. By way of perspective, concerted effort has been directed toward this issue for only 

about the past 20 years. In view of the variety 

of aerosol types and emissions, uncertain microphysical properties, great temporal and spatial 

variability, and the added complexity of aerosolcloud interactions, it is easy to understand why 

more work is required to define anthropogenic 

aerosol forcing with confidence comparable to 

that for other climate forcing agents. 

When comparing surface temperature changes 

calculated by climate models with those 

observed, the IPCC AR4 noted “broad consistency” between the modeled and observed 

temperature record over the industrial period. 

However, understanding of the degree to which 

anthropogenic aerosols offset the better-established greenhouse gas forcing is still inadequate. This limits confidence in the predicted 

magnitude of climate response to future changes 

in greenhouse gases and aerosols.

This chapter briefly summarizes the major 

research needs that have been highlighted in 

previous chapters, recognizing that achieving 

them will not necessarily be easy or straightforward. Although some important accomplishments will likely be possible in the next decade, 

others may, realistically, take considerably 

longer. Several important points should be 

kept in mind:

1. The uncertainty in assessing anthropogenic 

and aerosol impacts on climate must be 

much reduced from its current level to 

allow meaningful projections of future 

climate. Using statistical methods, IPCC 

AR4 concluded that the present-day globalaverage anthropogenic RF is 2.9 ± 0.3 W 

m-2 for long-lived greenhouse gases plus 

ozone, -1.3 (-2.2 to -0.5) W m-2 for aerosol 

direct plus aerosol-cloud-albedo, and +1.6 

(0.6 to 2.4) W m-2 for total anthropogenic 

forcing (Figure 1.3 in Chapter 1). As shown 

in Chapter 1, the current estimate of total 

anthropogenic RF yields the transient climate sensitivity range of 0.3 – 1.1°C/(W 

m-2). This translates to a possible surface 

temperature increase from 1.2°C to 4.7°C at 

the time of (equivalent) doubled CO2 forcing, which will likely occur toward the latter part of this century. Such a range is too 

wide to meaningfully predict the climate 

response to increased greenhouse gases.

The large uncertainty in total anthropogenic forcing arises primarily from current 

uncertainty in the current understanding of 

aerosol RF, as illustrated in Figure 1.3. One 

objective should be to reduce the uncertainty in global average RF by anthropogenic 

aerosols over the industrial period to ±0.3 

W m-2, equal to the current uncertainty in 

The uncertainty 

in assessing 

anthropogenic 

aerosol impacts on 

climate must be 

much reduced from 

its current level to 

allow meaningful 

projections of 

future climate.
The U.S. Climate Change Science Program Chapter 4

86

RF by anthropogenic greenhouse gases 

over this period. Then, taking the total 

anthropogenic forcing as the IPCC central 

value, 1.6 W m-2, the range in transient 

climate sensitivity would be reduced to 

0.37 – 0.54°C/(W m-2), and the corresponding increase in global mean surface temperature change at the time of doubled CO2

forcing would be between 1.5°C and 2.2°C. 

This range is small enough to make more 

meaningful global predictions pertinent 

to planning for mitigation and adaptation.

2. Evaluation of aerosol effects on climate 

must take into account high spatial and 

temporal variation of aerosol amounts 

and properties. Determining the global 

mean aerosol TOA RF is necessary but 

far from sufficient, because of the large 

spatial and temporal variation of aerosol 

distributions and composition that is in 

contrast to the much more uniformly 

distributed longer-lived greenhouse gases 

such as CO2 and methane. Therefore, 

aerosol RF at local to regional scales can 

be much stronger than its global average.

3. Understanding of the aerosol effects on 

global water cycle requires much improvement. Besides the radiative forcing, aerosols have other important climate effects. 

Their heating the atmosphere and cooling 

the surface affect atmospheric circulations 

and water cycle. The level of scientific understanding of these effects is much lower 

than that for aerosol direct RF; concerted 

research effort is required to move forward.

The approach taken for assessing aerosol 

forcing of the climate system includes both 

measurement and modeling components. As 

discussed in Chapters 2 and 3, improved observations, with some assistance from models, are 

already helping produce measurement-based 

estimates of the current aerosol direct effect 

on climate. Global models are now converging 

on key parameters such as AOD, and thanks 

to satellite and other atmospheric measurements, are moving toward better assessments 

of present-day aerosol RF. However, given the 

relatively short history of satellite observations 

and the nature of future climate prediction, the 

assessment of anthropogenic aerosol climate 

impact for past and future times will inevitably 

depend on models. Models are also required 

to apportion observed aerosols between natural and anthropogenic sources. Therefore, improving model predictions of aerosol climate 

forcing is the key to progress. To do so, it is 

essential to advance the current measurement 

capabilities that will allow much better validation of the models and fundamental improvement of model components.

The accuracy of regional to global-scale AOD 

measured by satellites is currently poorer than 

needed to substantially reduce uncertainty in 

direct radiative forcing by aerosols, but the 

required capability is within reach, based on 

the accuracy of current local surface-based 

measurement techniques. Problems remain 

in converting total aerosol forcing to forcing 

by anthropogenic aerosols. The accuracy of 

aerosol vertical distributions as measured by 

Lidar from space is approaching that required 

to be useful for evaluating chemical transport 

models, and is within reach of that required to 

reduce uncertainties in aerosol direct radiative 

forcing. 

Measurement accuracy for remotely sensed 

aerosol optical and physical properties (e.g., 

SSA, g, size) is poorer than needed to significantly reduce uncertainty in aerosol direct 

radiative forcing and to effect satisfactory 

translation between AOD retrieved from radiation-based remote-sensing measurements and 

AOD calculated from CTMs based on aerosol 

mass concentrations (the fundamental quantities tracked in the model) and optical properties. Combinations of remote-sensing and targeted in situ measurement with modeling are 

required for near-term progress in this area.

Measurements for aerosol indirect effect remain a major challenge. Sensitivity of remotesensing measurement to particle size, composition, concentration, vertical distribution, and 

horizontal distribution in the vicinity of clouds 

is poor. Combinations of detailed in situ and 

laboratory measurements and cloud-resolved 

modeling, along with spatial extrapolation using remote-sensing measurements and largerscale modeling, are required for near-term 

progress in this area.

Evaluation of 

aerosol effects on 

climate must take 

into account high 

spatial and temporal 

variation of aerosol 

amounts and 

properties.

Understanding of 

the aerosol effects 

on the global water 

cycle requires much 

improvement.
87

Atmospheric Aerosol Properties and Climate Impacts

The next sections address the priorities and recommend approach to moving forward.

4.2. Priorities

4.2.1. MEASUREMENTS

Maintain current and enhance the future 

satellite aerosol monitoring capabilities.

Satellites have been providing global aerosol 

observations since the late 1970s, with much 

improved accuracy measurements since late 

1990s, but some of them, such as the NASA 

EOS satellites (Terra, Aqua, Aura), are reaching 

or exceeding their design lives. Timely followon missions to at least maintain these capabilities are important. Assessment of aerosol climate impacts requires a long-term data record 

having consistent accuracy and high quality, 

suitable for detecting changes in aerosol amount 

and type over decadal time scales. Future satellite sensors should have the capability of acquiring information on aerosol size distribution, 

absorption, vertical distribution, and type with 

sufficiently high accuracy and adequate spatial 

coverage and resolution to permit quantification 

of forcing to required accuracy. The separation 

of anthropogenic from natural aerosols, perhaps 

based on size and shape, is essential for assessing human impacts. A brief summary of current 

capabilities and future needs of major aerosol 

measurement requirements from space is provided in Table 4.1. (More detailed discussion is 

in Chapter 2.)

Maintain, enhance, and expand the surface 

observation networks. Long-term surfacebased networks such as the NASA AERONET 

network, the NOAA ESRL and the DOE ARM 

sites have for more than a decade been providing essential information on aerosol properties 

Table 4.1. Summary of current status and future needs of major aerosol measurements from space for characterization of tropospheric aerosol and determination of aerosol climate forcing.

Satellite instrument Time Period AOD Size or 

Shape1 Absorption2 Vertical 

Profile

Global 

Coverage

Historic / Current

AVHRR Since 1981 √ √ Ocean only

TOMS 1979-2001 √ √ √

POLDER Since 1997 √ √ √

MODIS Since 2000 √ √ √

MISR Since 2000 √ √ √ √

OMI Since 2004 √ √ √

GLAS Since 20033 √ √

CALIOP Since 2006 √ √

Scheduled to Launch

VIIRS (on NPP/NPOESS) 2009- √ √

OMPS (on NPP) 2009- √ √ √

APS (on Glory) 2009- √ √ √

HSRL (on EarthCARE) 2013- √

Future Needs

Next generation instruments (polarimeter, lidar, etc.) with much improved detection accuracy and coverage for 

AOD and absorption, enhanced capability for measuring vertical profiles, aerosol types and properties, augmented 

capacity with measurements of aerosol, clouds, and precipitation.

1 Size is inferred from the spectral variation of AOD, expressed as the Ångström exponent. 

2 Determination of absorption from MISR is conditional and not always available.

3 Aerosol detection by GLAS is limited to only a few months each year because of laser power problems.
The U.S. Climate Change Science Program Chapter 4

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that is vital for satellite validation, model evaluation, and climate change assessment from 

trend analysis. Observation should be enhanced 

with additional, routine measurements of sizeresolved composition, more lidar profiling of 

vertical features, and improved measurements 

of aerosol absorption with state-of-art techniques. This, along with climate-quality data 

records constructed from satellites, would help 

establish connections between aerosol trends 

and the observed trends in radiation (e.g., dimming or brightening). 

Execute a continuing series of coordinated 

field campaigns. These would aim to: (1) 

broaden the database of detailed particle optical, physical, and chemical (including cloudnucleating) properties for major aerosol types, 

(2) refine and validate satellite and surfacebased remote-sensing retrieval algorithms, (3) 

make comprehensive, coordinated, multi-platform measurements characterizing aerosols, 

radiation fields, cloud properties and related 

aerosol-cloud interactions, to serve as testbeds 

for modeling experiments at several scales, and 

(4) deepen the links between the aerosol (and 

cloud) measuring and modeling communities. 

New and improved instrument capabilities will 

be needed to provide more accurate measurements of aerosol absorption and scattering 

properties across the solar spectrum.

Initiate and carry out a systematic program 

of simultaneous measurement of aerosol, 

clouds, and precipitation variables. Measurements of aerosol properties must go hand 

in hand with measurements of cloud properties, 

and also with measurements of precipitation 

and meteorological variables, whether this will 

be from aircraft, ground-based remote sensing 

or satellite. Assessing aerosol effects on climate has focused on the interactions of aerosol 

with Earth’s radiation balance (i.e., radiative 

forcing), but in the near future, focus will shift 

to include aerosol effects on precipitation patterns, atmospheric circulation, and weather.

Fully exploit the existing information in 

satellite observations of AOD and particle 

type. An immense amount of data has been 

collected. Table 4.1 lists the most widely used 

aerosol property data sets retrieved from satellite sensors. A synthesis of data from multiple 

sensors would in many cases be a more effective resource for aerosol characterizing than 

data from individual sensors alone. However, 

techniques for achieving such synthesis are 

still in their infancy, and multi-sensor products 

have only begun to be developed. The full information content of existing data, even with 

individual sensors, has not been realized. There 

is a need to: (1) refine retrieval algorithms and 

extract greater information about aerosols from 

the joint data sets, (2) quantify data quality, (3) 

generate uniform (and as appropriate, merged), 

climate-quality data records. These must be applied to: (4) initialize, constrain, and validate 

models, (5) conduct detailed process studies, 

and (6) perform statistical trend analysis. 

Measure the formation, evolution, and 

properties of aerosols under controlled laboratory conditions. Laboratory studies are essential to determine chemical transformation 

rates for aerosol particle formation. They can 

also provide information, in a controlled environment, for particle hygroscopic growth, light 

scattering and absorption properties, and particle activation for aerosols of specific, known 

composition. Such measurements will allow 

development of suitable mixing rules and 

evaluation of the parameterizations that rely on 

such mixing rules.

Improve measurement-based techniques for

distinguishing anthropogenic from natural 

aerosols. Current satellite-based estimates of 

anthropogenic aerosol fraction rely on retrievals of aerosol type. These estimates suffer from 

limited information content of the data under 

many circumstances. More needs to be done to 

combine satellite aerosol type and vertical distribution retrievals with supporting information 

from: (1) back-trajectory and inverse modeling, (2) at least qualitative time-series of plume 

evolution from geosynchronous satellite imaging, and (3) surface monitoring and particularly 

targeted aircraft in situ measurements. Different definitions of “anthropogenic” aerosols will 

require reconciliation. The anthropogenic fraction of today’s aerosol, estimated from current 

measurements, will not produce the same aerosol radiative forcing defined as the perturbation 

of the total aerosol from pre-industrial times. 

Consistently defined perturbation states are required before measurement-based and modelbased aerosol radiative forcing estimates can 

be meaningfully compared.
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Atmospheric Aerosol Properties and Climate Impacts

4.2.2. MODELING

Improve the accuracy and capability of 

model simulation of aerosols (including 

components and atmospheric processes) 

and aerosol direct radiative forcing. Spatial 

and temporal distributions of aerosol mass concentrations are affected primarily by sources, 

removal mechanisms, atmospheric transport, 

and chemical transformations; calculations of 

aerosol direct RF require additional information about aerosol optical properties. Coordinated studies are needed to understand the 

importance of individual processes, especially 

vertical mixing and removal by convection/

precipitation. Observational strategies must 

be developed to constrain and validate the key 

parameters describing: (a) aerosol composition, (b) mass concentration, (c) vertical distribution, (d) size distribution, (e) hygroscopic 

growth, (f) aerosol absorption, (g) asymmetry 

parameter and (h) aerosol optical depth. As 

many models now include major aerosol types 

such as sulfate, BC, primary POM, dust, and 

sea salt, progress is needed on simulating nitrate and secondary organic aerosols. In addition, aerosol microphysical processes should 

be much better represented in the models. In 

practice, improving the capability of aerosol 

composition modeling will require improved 

remote sensing and in situ observations to 

discriminate among aerosol components. Improvement in modeling radiative forcing could 

be aided by data assimilation methods, in 

which the observed aerosol distributions that 

are input to the model, and the modeled shortterm response, could be compared directly with 

RF observations.

Advance the ability to model aerosol-cloudprecipitation interaction in climate models, 

particularly the simulation of clouds. The 

interaction between aerosols and clouds is 

probably the biggest uncertainty of all climate 

forcing/feedback processes. The processes 

involved are complex, and accurate simulation will require sub-grid calculations or improved aerosol and cloud parameterizations on 

global-model scales. Among the key elements 

required are: (a) cloud nucleating properties 

for different aerosol types and size distributions, (b) CCN concentrations as functions of 

supersaturation and any kinetic influences, (c) 

algorithms to simulate aerosol influences on 

cloud brightness, that include cloud fraction, 

cloud liquid water content, and precipitation 

efficiency, and (d) cloud drop concentration for 

known (measured) updraft, humidity, and temperature conditions. Improved aerosol-cloud 

interaction modeling must be built upon more 

realistic simulation of clouds and cloud process in GCMs. Cloud-resolving models offer 

one approach to tackling these questions, aided 

by the continual improvement in computing 

capability that makes possible simulations at 

the higher resolutions appropriate to these processes. Realizing the latter approach, however, 

may be a long-term goal.

Incorporate improved representation of 

aerosol processes in coupled aerosol-climate 

system models to assess the climate change. 

Coupling aerosol processes in the GCMs would 

represent a major step in climate simulation beyond the IPCC AR4. This would enable aerosols to interact with the meteorological variables such as clouds and precipitation. Climate 

change simulations need to be run for hundreds 

of years with coupled atmosphere-ocean models. Inclusion of aerosol physics and chemistry, and increasing the model resolution, will 

put large demands on computing power and 

resources. Some simplification may be necessary, especially considering that other required 

model improvements, such as finer resolution 

and carbon cycle models, also increase computing time. The near-term step is to include 

simple representations of aerosols directly in 

climate models, incorporating the major aerosol types, basic chemistry, and parameterized 

cloud droplet activation schemes. Such models 

exist today, and are ready to be applied to longterm simulations, making it possible to calculate first-order aerosol climate feedbacks. The 

next generation of models will include aerosol 

processes that allow for more realistic interactions, such as aerosol and cloud microphysical 

processes; however, the complexity included 

should be commensurate with that for other 

relevant components of the simulation, such as 

clouds and convection. Fully coupled aerosolchemistry-physics-climate models will likely 

be a model-development focus for at least the 

next decade. This should eventually lead to increasingly sophisticated model simulations of 

aerosol effects on climate, and better assessments of climate sensitivity. 
The U.S. Climate Change Science Program Chapter 4

90

4.2.3. EMISSIONS

Develop and evaluate emissions inventories 

of aerosol particles and precursor gases. A 

systematic determination of emissions of primary particles and of aerosol precursor gases 

is needed as input to modeling the geographical and temporal distribution of the amount 

and radiative forcing of aerosols. The required 

description of emissions includes the location, 

timing, activity, and amount. For particles, the 

emissions should be characterized by size distributed composition, not simply by mass emissions, because of the effects these properties 

have on direct and indirect forcings. Natural 

emissions from biogenic and volcanic sources 

should be systematically assessed. Satellite 

fire data are now being used to help constrain 

biomass-burning emissions, which include 

new information on aerosol injection height. 

Dust emission from human activities, such as 

from farming practices and land-use changes, 

likewise needs to be quantified. Characterization of aerosol trends and radiative forcing also 

requires historical emission data. For assessing 

anthropogenic impacts on future climate, projections of future anthropogenic fuel use and 

changes in wildfire, desert dust, biogenic, and 

other sources are needed, and methods used to 

obtain them carefully evaluated and possibly 

refined. Some such efforts are being pursued in 

conjunction with the IPCC.

4.3. Concluding Remarks

Narrowing the gap between the current understanding of the contribution of anthropogenic aerosols to radiative forcing and that of 

the long lived greenhouse gases will require 

progress in all aspects of aerosol-climate science. Development of new space-based, field, 

and laboratory instruments will be needed, and 

in parallel, more realistic simulations of aerosol, cloud, and atmospheric processes must be 

incorporated into models. Most importantly, 

greater synergy among different types of measurements, different types of models, and especially between measurements and models, 

is critical. Aerosol-climate science must expand to encompass not only radiative effects 

on climate, but also aerosol effects on cloud 

processes, precipitation, and weather. New initiatives will strive to more effectively include 

experimentalists, remote sensing scientists and 

modelers as equal partners, and the traditionally defined communities of aerosol scientists, 

cloud scientists, radiation scientists increasingly will find common ground in addressing 

the challenges ahead.

Narrowing the gap 

between the current 

understanding of the 

contribution of anthropogenic aerosols 

to radiative forcing 

and that of the long 

lived greenhouse 

gases will require 

progress in all 

aspects of aerosolclimate science.

Greater synergy 

among different 

types of measurements, different 

types of models, and 

especially between 

measurements and 

models, is critical.

Intense heat generated by the wildfires below causes 

smoke to rise in violent updrafts; updrafts and water 

vapor emanating from the fires also combine to 

produce towering pyrocumulus clouds. Photo taken 

from the NASA P-3B aircraft during the ARCTAS 

field experiment in July 2008 over Canada. Credit: 

Cameron McNaughton, University of Hawaii.
Atmospheric Aerosol Properties and Climate Impacts

91

GLOSSARY AND ACRONYMS

GLOSSARY

(Note: Terms in italic in each paragraph are defined 

elsewhere in this glossary.)

Absorption

the process in which incident radiant energy is retained by a substance.

Absorption coefficient

fraction of incident radiant energy removed by absorption per length of travel of radiation through the 

substance. 

Active remote sensing

a remote sensing system that transmits its own energy 

source, then measures the properties of the returned 

signal. Contrasted with passive remote sensing.

Adiabatic equilibrium

a vertical distribution of temperature and pressure in 

an atmosphere in hydrostatic equilibrium such that 

an air parcel displaced adiabatically will continue to 

possess the same temperature and pressure as its surroundings, so that no restoring force acts on a parcel 

displaced vertically.

Aerosol

a colloidal suspension of liquid or solid particles (in air).

Aerosol asymmetry factor (also called asymmetry 

parameter, g)

the mean cosine of the scattering angle, found by integration over the complete scattering phase function 

of aerosol; g = 1 denotes completely forward scattering and g = 0 denotes symmetric scattering. For 

spherical particles, the asymmetry parameter is related to particle size in a systematic way: the larger the 

particle size, the more the scattering in the forward 

hemisphere.

Aerosol direct radiative effect

change in radiative flux due to aerosol scattering and 

absorption with the presence of aerosol relative to the 

absence of aerosol.

Aerosol hemispheric backscatter fraction (b)

the fraction of the scattered intensity that is redirected 

into the backward hemisphere relative to the incident 

light; can be determined from measurements made 

with an integrating nephelometer. The larger the particle size, the smaller the b.

Aerosol indirect effects

processes referring to the influence of aerosol on 

cloud droplet concentration or radiative properties. 

Effects include the effect of aerosols on cloud droplet 

size and therefore its brightness (also known as the 

“cloud albedo effect”, “first aerosol indirect effect”, 

or ”Twomey effect”); and the effect of cloud droplet size on precipitation efficiency and possibly cloud 

lifetime (also known as the “second aerosol indirect 

effect” or “Albrecht effect”).

Aerosol mass extinction (scattering, absorption) 

efficiency

the aerosol extinction (scattering, absorption) coefficient per aerosol mass concentration, with a commonly 

used unit of m2 g-1.

Aerosol optical depth

the (wavelength dependent) negative logarithm of the 

fraction of radiation (or light) that is extinguished (or 

scattered or absorbed) by aerosol particles on a vertical path, typically from the surface (or some specified 

altitude) to the top of the atmosphere. Alternatively 

and equivalently: The (dimensionless) line integral of 

the absorption coefficient (due to aerosol particles), or 

of the scattering coefficient (due to aerosol particles), 

or of the sum of the two (extinction coefficient due to 

aerosol particles), along such a vertical path. Indicative 

of the amount of aerosol in the column, and specifically relates to the magnitude of interaction between 

the aerosols and shortwave or longwave radiation.

Aerosol phase function

the angular distribution of radiation scattered by aerosol particle or by particles comprising an aerosol. In 

practice, the phase function is parameterized with 

asymmetry factor (or asymmetry parameter). Aerosol phase function is related to aerosol hemispheric 

backscatter fraction (b) and aerosol particle size: the 

larger the particle size, the more the forward scattering (i.e. larger g and smaller b).

Aerosol radiative forcing

the net energy flux (downwelling minus upwelling) 

difference between an initial and a perturbed aerosol 

loading state, at a specified level in the atmosphere. 

(Other quantities, such as solar radiation, are assumed 

to be the same.) This difference is defined such that 

a negative aerosol forcing implies that the change in 

aerosols relative to the initial state exerts a cooling in-
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92

fluence, whereas a positive forcing would mean the change 

in aerosols exerts a warming influence. The aerosol radiative 

forcing must be qualified by specifying the initial and perturbed aerosol states for which the radiative flux difference is 

calculated, the altitude at which the quantity is assessed, the 

wavelength regime considered, the temporal averaging, the 

cloud conditions, and whether total or only human-induced 

contributions are considered (see Chapter 1, Section 1.2).

Aerosol radiative forcing efficiency

aerosol direct radiative forcing per aerosol optical depth

(usually at 550 nm). It is governed mainly by aerosol size 

distribution and chemical composition (determining the 

aerosol single-scattering albedo and phase function), surface reflectivity, and solar irradiance.

Aerosol semi-direct effect

the processes by which aerosols change the local temperature and moisture (e.g., by direct radiative heating and 

changing the heat releases from surface) and thus the local 

relative humidity, which leads to changes in cloud liquid 

water and perhaps cloud cover. 

Aerosol single-scattering albedo (SSA)

a ratio of the scattering coefficient to the extinction coefficient of an aerosol particle or of the particulate matter of 

an aerosol. More absorbing aerosols and smaller particles 

have lower SSA. 

Aerosol size distribution

probability distribution function of the number concentration, surface area, or volume of the particles comprising 

an aerosol, per interval (or logarithmic interval) of radius, 

diameter, or volume. 

Albedo

the ratio of reflected flux density to incident flux density, 

referenced to some surface; might be Earth surface, top of 

the atmosphere. 

Angström exponent (Å)

exponent that expresses the spectral dependence of aerosol

optical depth (τ) (or scattering coefficient, absorption coefficient, etc.) with the wavelength of light (λ) as inverse power 

law: τ∝λ

-Å. The Ångström exponent is inversely related to 

the average size of aerosol particles: the smaller the particles, the larger the exponent.

Anisotropic

not having the same properties in all directions.

Atmospheric boundary layer (abbreviated ABL; also 

called planetary boundary layer—PBL)

the bottom layer of the troposphere that is in contact with 

the surface of the earth. It is often turbulent and is capped 

by a statically stable layer of air or temperature inversion. 

The ABL depth (i.e., the inversion height) is variable in time 

and space, ranging from tens of meters in strongly statically 

stable situations, to several kilometers in convective conditions over deserts.

Bidirectional reflectance distribution function (BRDF)

a relationship describing the reflected radiance from a given 

region as a function of both incident and viewing directions. 

It is equal to the reflected radiance divided by the incident 

irradiance from a single direction.

Clear-sky radiative forcing

radiative forcing (of gases or aerosols) in the absence of 

clouds. Distinguished from total-sky or all-sky radiative 

forcing, which include both cloud-free and cloudy regions.

Climate sensitivity

the change in global mean near-surface temperature per unit 

of radiative forcing; when unqualified typically refers to 

equilibrium sensitivity; transient sensitivity denotes time dependent change in response to a specified temporal profile.

Cloud albedo

the fraction of solar radiation incident at the top of cloud 

that is reflected by clouds in the atmosphere or some subset 

of the atmosphere.

Cloud condensation nuclei (abbreviated CCN)

aerosol particles that can serve as seed particles of atmospheric cloud droplets, that is, particles on which water 

condenses (activates) at supersaturations typical of atmospheric cloud formation (fraction of one percent to a few 

percent, depending on cloud type); may be specified as 

function of supersaturation.

Cloud resolving model

a numerical model that resolves cloud-scale (and mesoscale) 

circulations in three (or sometimes two) spatial dimensions. 

Usually run with horizontal resolution of 5 km or less.

Coalescence

the merging of two or more droplets of precipitation (or 

aerosol particles; also denoted coagulation) into a single 

droplet or particle.

Condensation

in general, the physical process (phase transition) by which a 

vapor becomes a liquid or solid; the opposite of evaporation.

Condensation nucleus (abbreviated CN)

an aerosol particle forming a center for condensation under 

extremely high supersaturations (up to 400% for water, but 

below that required to activate small ions).
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93

Data assimilation

the combining of diverse data, possibly sampled at different 

times and intervals and different locations, into a unified 

and physically consistent description of a physical system, 

such as the state of the atmosphere.

Diffuse radiation

radiation that comes from some continuous range of directions. This includes radiation that has been scattered at least 

once, and emission from nonpoint sources.

Dry deposition

the process by which atmospheric gases and particles are 

transferred to the surface as a result of random turbulent air, 

impaction, and /or gravitational settling.

Earth Observing System (abbreviated EOS)

a major NASA initiative to develop and deploy state-of-theart remote sensing instruments for global studies of the land 

surface, biosphere, solid earth, atmosphere, oceans, and 

cryosphere. The first EOS satellite, Terra, was launched in 

December 1999. Other EOS satellites include Aqua, Aura, 

ICESat, among others.

Emission of radiation

the generation and sending out of radiant energy. The emission of radiation by natural emitters is accompanied by a 

loss of energy and is considered separately from the processes of absorption or scattering.

Emission of gases or particles

the introduction of gaseous or particulate matter into the 

atmosphere by natural or human activities, e.g., bubble 

bursting of whitecaps, agriculture or wild fires, volcanic 

eruptions, and industrial processes.

Equilibrium vapor pressure

the pressure of a vapor in equilibrium with its condensed 

phase (liquid or solid).

Evaporation (also called vaporization)

physical process (phase transition) by which a liquid is transformed to the gaseous state; the opposite of condensation.

External mixture (referring to an aerosol; contrasted with 

internal mixture)

an aerosol in which different particles (or in some usages, 

different particles in the same size range) exhibit different 

compositions.

Extinction (sometimes called attenuation)

the process of removal of radiant energy from an incident 

beam by the processes of absorption and/or scattering and 

consisting of the totality of this removal.

Extinction coefficient

fraction of incident radiant energy removed by extinction 

per length of travel of radiation through the substance. 

General circulation model (abbreviated GCM)

a time-dependent numerical model of the entire global atmosphere or ocean or both. The acronym GCM is often applied to Global Climate Model.

Geostationary satellite

a satellite to be placed into a circular orbit in a plane aligned 

with Earth’s equator, and at an altitude of approximately 

36,000 km such that the orbital period of the satellite is 

exactly equal to Earth’s period of rotation (approximately 

24 hours). The satellite appears stationary with respect to a 

fixed point on the rotating Earth. 

Hygroscopicity

the relative ability of a substance (as an aerosol) to adsorb 

water vapor from its surroundings and ultimately dissolve. 

Frequently reported as ratio of some property of particle 

or of particulate phase of an aerosol (e.g., diameter, mean 

diameter) as function of relative humidity to that at low 

relative humidity. 

Ice nucleus (abbreviated IN)

any particle that serves as a nucleus leading to the formation of ice crystals without regard to the particular physical 

processes involved in the nucleation.

In situ

a method of obtaining information about properties of an 

object (e.g., aerosol, cloud) through direct contact with that 

object, as opposed to remote sensing.

Internal mixture (referring to an aerosol; contrasted with 

external mixture)

an aerosol consisting of a mixture of two or more substances, for which all particles exhibit the same composition (or 

in some usage, the requirement of identical composition is 

limited to all particles in a given size range). Typically an 

internal mixture has a higher absorption coefficient than an 

external mixture.

Irradiance (also called radiant flux density)

a radiometric term for the rate at which radiant energy in a 

radiation field is transferred across a unit area of a surface 

(real or imaginary) in a hemisphere of directions. In general, irradiance depends on the orientation of the surface. 

The radiant energy may be confined to a narrow range of 

frequencies (spectral or monochromatic irradiance) or integrated over a broad range of frequencies. 
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94

Large eddy simulation (LES)

A three dimensional numerical simulation of turbulent flow 

in which large eddies (with scales on the order of hundreds 

of meters) are resolved and the effects of the subgrid-scale 

eddies are parameterized. The typical model grid-size is < 

100 m and modeling domains are on the order of 10 km. Because they resolve cloud-scale dynamics, large eddy simulations are powerful tools for studying the effects of aerosol on 

cloud microphysics and dynamics. 

Lidar (light detection and ranging)

a technique for detecting and characterizing objects by 

transmitting pulses of laser light and analyzing the portion 

of the signal that is reflected and returned to the sensor.

Liquid water path

line integral of the mass concentration of the liquid water 

droplets in the atmosphere along a specified path, typically 

along the path above a point on the Earth surface to the top 

of the atmosphere.

Longwave radiation (also known as terrestrial radiation or 

thermal infrared radiation)

electromagnetic radiation at wavelengths greater than 4 

μm, typically for temperatures characteristic of Earth’s 

surface or atmosphere. In practice, radiation originating by 

emission from Earth and its atmosphere, including clouds; 

contrasted with shortwave radiation.

Low Earth orbit (LEO)

an orbit (of satellite) typically between 300 and 2000 kilometers above Earth.

Mass spectrometer

instrument that fragments and ionizes a chemical substance 

or mixture by and characterizes composition by amounts of 

ions as function of molecular weight. 

Nucleation

the process of initiation of a new phase in a supercooled 

(for liquid) or supersaturated (for solution or vapor) environment; the initiation of a phase change of a substance to a 

lower thermodynamic energy state (vapor to liquid condensation, vapor to solid deposition, liquid to solid freezing).

Optical depth

the optical thickness measured vertically above some given 

altitude. Optical depth is dimensionless and may be applied 

to Rayleigh scattering optical depth, aerosol extinction (or 

scattering, or absorption) optical depth.

Optical thickness

line integral of extinction (or scattering or absorption) coefficient along a path. Dimensionless. 

Passive remote sensing

a remote sensing system that relies on the emission (transmission) of natural levels of radiation from (through) the 

target. Contrasted with active remote sensing.

Phase function

probability distribution function of the angular distribution 

of the intensity of radiation scattered (by a molecule, gas, 

particle or aerosol) relative to the direction of the incident 

beam. See also Aerosol phase function.

Polarization

a state in which rays of light exhibit different properties in 

different directions as measured azimuthially about the direction of propagation of the radiation, especially the state 

in which all the electromagnetic vibration takes place in a 

single plane (plane polarization).

Polarimeter

instrument that measures the polarization of incoming light 

often used in the characterization of light scattered by atmospheric aerosols.

Primary trace atmospheric gases or particles

substances which are directly emitted into the atmosphere 

from Earth surface, vegetation or natural or human activity, 

e.g., bubble bursting of whitecaps, fires, and industrial processes; contrasted with secondary substances.

Radar (radio detection and ranging)

similar to lidar, but using radiation in microwave range.

Radiance

a radiometric term for the rate at which radiant energy in a 

set of directions confined to a small unit solid angle around 

a particular direction is transferred across unit area of a surface (real or imaginary) projected onto this direction, per 

unit solid angle of incident direction.

Radiative forcing

the net energy flux (downwelling minus upwelling) difference between an initial and a perturbed state of atmospheric 

constituents, such as carbon dioxide or aerosols, at a specified level in the atmosphere; applies also to perturbation 

in reflected radiation at Earth’s surface due to change in 

albedo. See also Aerosol radiative forcing.

Radiative heating

the process by which temperature of an object (or volume of space that encompasses a gas or aerosol) increases in response to an excess of absorbed radiation 

over emitted radiation.
Atmospheric Aerosol Properties and Climate Impacts

95

Radiometer

instrument that measures the intensity of radiant energy 

radiated by an object at a given wavelength; may or may 

not resolve by wavelength.

Refractive index (of a medium)

the real part is a measure for how much the speed of 

light (or other waves such as sound waves) is reduced 

inside the medium relative to speed of light in vacuum, 

and the imaginary part is a measure of the amount of 

absorption when the electromagnetic wave propagates 

through the medium.

Relative humidity

the ratio of the vapor pressure of water to its saturation vapor pressure at the same temperature.

Remote sensing: a method of obtaining information about 

properties of an object (e.g., aerosol, cloud) without coming 

into physical contact with that object; opposed to in situ.

Saturation

the condition in which the vapor pressure (of a liquid 

substance; for atmospheric application, water) is equal 

to the equilibrium vapor pressure of the substance over 

a plane surface of the pure liquid substance, sometimes 

similarly for ice; similarly for a solute in contact with 

a solution.

Scattering

in a broad sense, the process by which matter is excited 

to radiate by an external source of electromagnetic radiation. By this definition, reflection, refraction, and even 

diffraction of electromagnetic waves are subsumed under scattering. Often the term scattered radiation is applied to that radiation observed in directions other than 

that of the source and may also be applied to acoustic 

and other waves.

Scattering coefficient

fraction of incident radiant energy removed by scattering

per length of travel of radiation through the substance.

Secondary trace atmospheric gases or particles

formed in the atmosphere by chemical reaction, new particle formation, etc.; contrasted with primary substances, 

which are directly emitted into the atmosphere.

Secondary organic aerosols (SOA)

organic aerosol particles formed in the atmosphere by 

chemical reactions from gas-phase precursors.

Shortwave radiation

radiation in the visible and near-visible portions of the 

electromagnetic spectrum (roughly 0.3 to 4.0 μm in 

wavelength) which range encompasses the great majority of solar radiation and little longwave (terrestrial 

thermal) radiation; contrasted with longwave (terrestrial) radiation.

Single scattering albedo (SSA)

the ratio of light scattering to total light extinction (sum 

of scattering and absorption); for aerosols, generally restricted to scattering and extinction by the aerosol particles. 

More absorbing aerosols have lower SSA; a value of unity 

indicates that the particles are not absorbing.

Solar zenith angle

angle between the vector of Sun and the zenith.

Spectrometer

instrument that measures light received in terms of the intensity at constituent wavelengths, used for example to determine chemical makeup, temperature profiles, and other 

properties of atmosphere. See also Mass spectrometer.

Stratosphere

the region of the atmosphere extending from the top of the 

troposphere, at heights of roughly 10-17 km, to the base of 

the mesosphere, at a height of roughly 50 km.

Sunglint

a phenomenon that occurs when the sun reflects off the surface of the ocean at the same angle that a satellite sensor is 

viewing the surface.

Supersaturation

the condition existing in a given portion of the atmosphere 

(or other space) when the relative humidity is greater than 

100%, that is, when it contains more water vapor than is 

needed to produce saturation with respect to a plane surface of pure water or pure ice.

Surface albedo

the ratio, often expressed as a percentage, of the amount of 

electromagnetic radiation reflected by Earth’s surface to the 

amount incident upon it. In general, surface albedo depends 

on wavelength and the directionality of the incident radiation; hence whether incident radiation is direct or diffuse, 

cf., bidirectional reflectance distribution function (BRDF). 

Value varies with wavelength and with the surface composition. For example, the surface albedo of snow and ice 

vary from 80% to 90% in the mid-visible, and that of bare 

ground from 10% to 20%.
The U.S. Climate Change Science Program Glossary and Acronyms

96

Troposphere

the portion of the atmosphere from the earth’s surface to the 

tropopause; that is, the lowest 10-20 kilometers of the atmosphere, depending on latitude and season; most weather 

occurs in troposphere.

Transient climate response

The time-dependent surface temperature response to a 

gradually evolving forcing. 

Wet scavenging or wet deposition

removal of trace substances from the air by either rain or 

snow. May refer to in-cloud scavenging, uptake of trace 

substances into cloud water followed by precipitation, 

or to below-cloud scavenging, uptake of material below 

cloud by falling precipitation and subsequent delivery to 

Earth’s surface.

Whitecap

a patch of white water formed at the crest of a wave as it 

breaks, due to air being mixed into the water.

Major reference: Glossary of Meteorology, 2nd edition, American Meteorological Society.

ACRONYMS

A Surface albedo (broadband)

Å Ångström exponent

ABC Asian Brown Cloud

ACE Aerosol Characterization Experiment

AD-Net Asian Dust Network

ADEOS Advanced Earth Observation Satellite 

ADM Angular Dependence Models 

AeroCom Aerosol Comparisons between Observations and Models

AERONET Aerosol Robotic Network

AI Aerosol Index

AIOP Aerosol Intensive Operative Period

ANL Argonne National Laboratory (DOE)

AOD (τ) Aerosol Optical Depth

AOT Aerosol Optical Thickness

APS Aerosol Polarimetry Sensor

AR4 Forth Assessment Report, IPCC

ARCTAS Arctic Research of the Composition of 

the Troposphere from Aircraft and Satellites

ARM Atmospheric Radiation Measurements

AVHRR Advanced Very High Resolution 

Radiometer 

A-Train Constellation of six afternoon overpass

satellites

BASE-A Biomass Burning Airborne and Spaceborne Experiment Amazon and Brazil

BC Black Carbon

BNL Brookhaven National Laboratory (DOE) 

BRDF Bidirectional Reflectance Distribution 

Function

CALIOP Cloud and Aerosol Lidar with Orthogonal

Polarization 

CALIPSO Cloud Aerosol Infrared Pathfinder Satellite 

Observations

CAPMoN Canadian Air and Precipitation Monitoring 

Network

CCN Cloud Condensation Nuclei

CCRI Climate Change Research Initiative

CCSP Climate Change Science, Program

CDNC Cloud Droplet Number Concentration

CERES Clouds and the Earth’s Radiant Energy 

System

CLAMS Chesapeake Lighthouse and Aircraft 

Measurements for Satellite campaign

CTM Chemistry and Transport Model

DABEX Dust And Biomass-burning Experiment

DOE Department of Energy

DRF Direct Radiative Forcing (aerosol)

EANET Acid Deposition Monitoring Network in 

East Asia

EARLINET European Aerosol Research Lidar Network

EarthCARE Earth Clouds, Aerosols, and Radiation 

Explorer
Atmospheric Aerosol Properties and Climate Impacts

97

EAST-AIRE East Asian Studies of Tropospheric 

Aerosols: An International Regional 

Experiment

EMEP European Monitoring and Evaluation 

Programme

EOS Earth Observing System

EP Earth Pathfinder

EPA Environmental Protection Agency

ERBE Earth Radiation Budget Experiment

ESRL Earth System Research Laboratory 

(NOAA)

Eτ Aerosol Forcing Efficiency (RF

normalized by AOD) 

FAR IPCC First Assessment Report (1990)

FT Free Troposphere

g Particle scattering asymmetry factor

GAW Global Atmospheric Watch

GCM General Circulation Model, Global Climate 

Model

GEOS Goddard Earth Observing System

GFDL Geophysical Fluid Dynamics Laboratory 

(NOAA)

GHGs Greenhouse Gases

GISS Goddard Institute for Space Studies 

(NASA)

GLAS Geoscience Laser Altimeter System 

GMI Global Modeling Initiative

GOCART Goddard Chemistry Aerosol Radiation 

and Transport (model)

GOES Geostationary Operational Environmental 

Satellite

GoMACCS Gulf of Mexico Atmospheric Composition 

and Climate Study

GSFC Goddard Space Flight Center (NASA)

HSRL High-Spectral-Resolution Lidar

ICARTT International Consortium for Atmospheric 

Research on Transport and Transformation

ICESat Ice, Cloud, and Land Elevation Satellite

IMPROVE Interagency Monitoring of Protected 

Visual Environment

INCA Interactions between Chemistry and 

Aerosol (LMDz model) 

INDOEX Indian Ocean Experiment 

INTEX-NA Intercontinental Transport Experiment - 

North America

INTEX-B Intercontinental Transport Experiment - 

Phase B

IPCC Intergovermental Panel on Climate 

Change

IR Infrared radiation

LBA Large-Scale Biosphere-Atmosphere 

Experiment in Amazon

LES Large Eddy Simulation

LITE Lidar In-space Technology Experiment

LMDZ Laboratoire de Météorologie Dynamique

with Zoom, France 

LOA Laboratoire d’ Optique Atmosphérique, 

France

LOSU Level of Scientific Understanding

LSCE Laboratoire des Sciences du Climat et de 

l’Environnement, France

LWC Liquid Water Content 

LWP Liquid Water Path

MAN Maritime Aerosol Network

MEE Mass Extinction Efficiency

MILAGRO Megacity Initiative: Local and Global 

Research Observations

MFRSR Multifilter Rotating Shadowband

Radiometer

MINOS Mediterranean Intensive Oxidant Study

MISR Multi-angle Imaging SpectroRadiometer 

MODIS Moderate Resolution Imaging Spectroradiometer

MOZART Model for Ozone and Related chemical 

Tracers

MPLNET Micro Pulse Lidar Network 

NASA National Aeronautics and Space

Administration

NASDA NAtional Space Development Agency, 

Japan

NEAQS New England Air Quality Study

NOAA National Oceanography and Atmosphere 

Administration

NPOESS National Polar-orbiting Operational 

Environmental Satellite System

NPP NPOESS Preparatory Project

NPS National Park Services

NRC National Research Council

OC Organic Carbon

OMI Ozone Monitoring Instrument

PARASOL Polarization and Anisotropy of Reflectance 

for Atmospheric Science, coupled with 

Observations from a Lidar

PDF Probability Distribution Function

PEM-West Western Pacific Exploratory Mission

PM Particulate Matter (aerosols)

PMEL Pacific Marine Environmental Laboratory 

(NOAA)

POLDER Polarization and Directionality of the 

Earth’s Reflectance

POM Particulate Organic Matter

PRIDE Pueto Rico Dust Experiment

REALM Regional East Atmospheric Lidar Mesonet

RF Radiative Forcing, aerosol

RH Relative Humidity 

RTM Radiative Transfer Model 

SAFARI South Africa Regional Science,

Experiment
The U.S. Climate Change Science Program Glossary and Acronyms

98

SAMUM Saharan Mineral Dust Experiment

SAP Synthesis and Assessment Product (CCSP)

SAR IPCC Second Assessment Report (1995)

SCAR-A Smoke, Clouds, and Radiation - America

SCAR-B Smoke, Clouds, and Radiation - Brazil

SeaWiFS Sea-viewing Wide Field-of-view Sensor

SGP Southern Great Plain, ARM site in 

Oklahoma

SHADE Saharan Dust Experiment

SMOCC Smoke, Aerosols, Clouds, Rainfall and 

Climate

SOA Secondary Organic Aerosol

SPRINTARS Spectral Radiation-Transport Model for 

Aerosol Species

SSA Single-Scattering Albedo

SST Sea Surface Temperature

STEM Sulfate Transport and Deposition Model

SURFRAD NOAA’s national surface radiation 

budget network

SZA Solar Zenith Angle

TAR Third Assessment Report, IPCC

TARFOX Tropospheric Aerosol Radiative Forcing 

Observational Experiment 

TCR Transient Climate sensitivity Range

TexAQS Texas Air Quality Study

TOA Top of the Atmosphere 

TOMS Total Ozone Mapping Spectrometer

TRACE-A Transport and Chemical Evolution over 

the Atlantic

TRACE-P Transport and Chemical Evolution over 

the Pacific

UAE2 United Arab Emirates Unified Aerosol 

Experiment

UMBC University of Maryland at Baltimore 

County

UV Ultraviolet radiation

VOC Volatile Organic Compounds 

WMO World Meteorological Organization

Assessing the environmental impact of cloud fields becomes even more complicated when the contributions of aerosol particles in 

and around the cloud particles are also considered. Image from MODIS. Credit: NASA.
Atmospheric Aerosol Properties and Climate Impacts

99

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Agricultural practices also affect air quality, such as leaving 

bare soil exposed to wind erosion, and burning agricultural 

waste. Photo taken from the NASA DC-8 aircraft during 

ARCTAS-CARB field experiment in June 2008 over California.

Credit: Mian Chin, NASA.
Photography and Image Credits

Cover/Title Page/Table of Contents:

Image 1: Fire in the savanna grasslands of Kruger National Park, South Africa, during the 

international Southern African Fire-Atmosphere Research Initiative (SAFARI) Experiment, 

September 1992. Due to extensive and frequent burning of the savanna grass, Africa is the 

“fire center” of the world. Credit: Joel S. Levine, NASA.

Image 2: Urban pollution in Hong Kong, May 2007. The persistent pollution haze significantly reduces the visibility. Credit: Mian Chin, NASA.

Image 3: Dust storms of northwest Africa captured by Sea-viewing Wide Field-of-view Sensor (SeaWiFS) on February 28, 2000. Credit: SeaWiFS Project at NASA Goddard Space 

Flight Center.

Image 4: Breaking ocean waves – a source of sea salt aerosols. Credit: Mian Chin, NASA.

Image 5: Clouds at sunset. Clouds and aerosols scatter the sun’s rays very effectively 

when the sun is low in the sky, creating the bright colors of sunrise and sunset. Credit: 

Mian Chin, NASA.

Image 6: Ship tracks appear when clouds are formed or modified by aerosols released in 

exhaust from ship smokestacks. Image from MODIS. Credit: NASA.

For other images in this report, please see the captions/credits located with each image.
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