Synthesis of Field Observations and MultiScale Modeling of Aerosol Evolution and its Impact on Radia

1
Synthesis of Field Observations and Multi-Scale
Modeling of Aerosol Evolution and its Impact on
Radiation Forcing from Urban to Regional Scales

• Principal Investigator Jerome D. Fast
• Co-Investigators William I. Gustafson Jr.,
  Elaine G. Chapman, James C. Barnard, Rahul A.
  Zaveri
• Pacific Northwest National Laboratory, Richland,
  Washington
• ASP Science Meeting, 25-27 January 2005,
  Charleston, South Carolina

2
Primary Scientific Questions

• What are the uncertainties associated with urban
  to regional-scale predictions of anthropogenic
  particulates as they are transported from urban
  sources and mixed into the regional environment
  with precursor trace gases, natural particulates,
  and particulates from other anthropogenic
  sources? How do those uncertainties affect
  estimates of direct and indirect forcing?
• Is the failure to resolve urban to regional-scale
  variations in aerosol forcing significant in
  terms of global climate modeling? Which key
  processes resolved by urban regional models
  need to be better represented in global models?

Urban Scale Modeling Domain
Regional Scale Modeling Domain
MODIS Aerosol Optical Depth
Dx 1 km
Dx 9 km
G-1 flight path
export
point sources
Pittsburgh
aloft
surface
low
high
270 km
780 km
typical GCM cell
3
Overall Approach

• Provide a link between field campaign
  measurements and global modeling by synthesizing
  measurements and multi-scale modeling to obtain
  comprehensive picture of how various atmospheric
  processes affect the evolution of aerosols and
  radiative forcing

ASP Collaborations
Types of ASP Projects
Infrastructure
measurements
Berkowitz and Berg
Field Campaigns
Doran and Barnard
planning, interpretation
data
parameterizations
process modules
Process Modeling
Urban-to-Regional Scale Modeling
Laboratory
???
input
boundary conditions
parameterizations
Global Modeling
Ghan
4
Research Tool WRF-chem

• The Weather Research and Forecasting (WRF) model
  is the next-generation meteorological model being
  developed collaboratively among several agencies
• NOAA / FSL developed the first version of
  WRF-chem
• PNNL internal funding has been used to adapt
  WRF-chem to include
• CBM-Z gas-phase chemistry mechanism
  (investigators Rahul Zaveri)
• MOSAIC aerosol mechanism (investigators Rahul
  Zaveri, Richard Easter)
• FAST-J photolysis scheme (investigators Elaine
  Chapman, James Barnard)
• aerosol optical property modules (investigators
  James Barnard, Rahul Zaveri)
• see http//www.pnl.gov/atmos_sciences/JDF/wrf-chem
  .html for direct and indirect forcing, SOA
  formation
• Why WRF-chem?
• on-line coupling of meteorology and chemistry
  so that aerosol-chemistry-cloud-radiation
  feedback processes can be simulated
• 2-way nesting to simulate aerosols over
  urban-regional-continental scales
• framework useful for module inter-comparison
  studies
• community model for research and operational
  applications

developed under prior ASP support
climate modeling community
DOE Atmospheric Science Program
modeling research
5
Aerosols in WRF-chem

• Size distribution and composition
• sectional size distribution moving-center or
  two-moment approach for the dynamic equations for
  mass and number each size bin is internally
  mixed
• composition SO4 , NO3 , NH4 , CL, CO3 , NA, CA,
  other inorganics, OC, EC

modal approach
MOSAIC - sectional approach
Accumulation Mode
Coarse Mode
Aiken Mode
mass
mass
0.01
0.1
1
10
100
0.01
0.1
1
10
100
particle diameter (mm)
particle diameter (mm)

• MOSAIC has 3 unique components (Zaveri et al.
  2005a,b,c)
• MTEM - Multi-component Taylor Expansion Model
  mixing rule for activity coefficients of
  electrolytes in multi-component aqueous solutions
• MESA - Multi-component Equilibrium Solver for
  Aerosols thermodynamic equilibrium solver for
  solid, liquid, or mixed phase aerosols
• ASTEEM - Adaptive Step Time-split Explicit Euler
  Method dynamic integration of the coupled
  gas-aerosol partitioning differential equations

numerically efficient (reduces the of levels of
iterations and of iterations) without
sacrificing accuracy have been compared with
other techniques
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Utilize ASP-Supported Field Campaign Data

• Current field campaigns (FY 2005)
• Houston 2000 (through an internal project to
  develop and evaluate WRF-chem)
• NEAQS 2004
• Up-coming field campaigns (FY 2006-2007)
• MIRAGE-MEX 2006
• Houston 2006
• others with data more useful for this project?

SO4, 11 LST 29 August
SO2, 11 LST 29 August
Ozone, 11 LST 29 August
OINOCEC
3
4
6
G-1 10-11 LT
G-1 10-11 LT
2
9
12
1
50
90
40
3
70
Parrish power plant
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Modeling Approach

• 1) Baseline Simulations
• reproduce, as best as possible, the observed
  meteorological, chemical, and particulate fields
  observed during ASP field campaign periods
• urban-regional-continental scales, multi-day to
  multi-week periods
• determine spatial and temporal variations in
  radiative forcing
• 2) Process-Oriented Sensitivity Simulations
  examples include
• turn-off direct forcing, indirect forcing, or
  secondary aerosol formation
• vary emission rates, composition, size
  distribution
• vary spatial resolution
• 3) Simulations with Improved Aerosol Modules
• treatment of aerosol optical properties
• contributions from ASP investigators ??

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Data Needs

• Meteorology
• vertical profiles of wind, temperature, and
  humidity boundary layer depth
• cloud optical depth, cloud droplet number,
  droplet distribution, CCN
• radiation direct, diffuse, at multiple
  wavelengths to obtain t, wo, g broadband fluxes
• operational meteorology, surface radiation
  measurements at regional scales
• Trace gases
• concentrations of trace gases relevant to aerosol
  evolution
• photolysis rates (influenced by aerosols)
• operational surface trace gases at regional
  scales
• gridded emission rates of trace gases
• Aerosols
• mass, number, size distribution, composition at
  multiple surface sites and aloft
• vertical variations of light scattering and
  absorption (t, wo , g) spectral fluxes
• Lidar aerosol backscatter and extinction
• operational surface PM2.5, PM10, composition at
  regional scales
• satellite aerosol optical depth, smoke
• gridded emission rates of particulate composition
  and size distribution
• Data access
• central location for ASP field campaign data ftp
  or web site links

ASP Measurements G-1, infrastructure, super
sites
other Measurements operational, collaborators,
etc.
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What this Project Can Provide to ASP Investigators

• guidance for field campaign planning
• Based on scientific objectives, where should
  measurement sites be located?
• How often will meteorological conditions be
  conducive for measurement strategy?
• put ASP measurements into a larger context
• What is the fate of particulates measured by ASP?
• What are the large-scale influences on local ASP
  measurements?
• provide output from WRF-chem to ASP investigators
• 3-D meteorological, gas, and aerosol fields
• input for Lagrangian box model studies
• use WRF-chem as a testbed to improve
  representation of aerosol processes
• use WRF-chem simulations to develop new
  representations of aerosols for global climate
  models

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Appendix 1 References

• PNNLs version of WRF-chem
• Fast, J.D., W.I. Gustafson Jr., R.C. Easter, E.G.
  Chapman, J.C. Barnard, R.A. Zaveri, and G.A.
  Grell, 2004 A new fully-coupled
  meteorology-chemistry-aerosol model and initial
  results for Houston, Texas. Fall AGU Meeting, San
  Francisco, CA, A24A-04.
• Fast, J.D., J.C. Barnard, E.G. Chapman, R.C.
  Easter, W.I. Gustafson Jr., R.A. Zaveri, and G.A.
  Grell, 2004 Comparison of aerosol measurements
  during TexAQS 2000 and predictions from a
  fully-coupled meteorology-chemistry-aerosol
  model. 23rd Annual AAAR Conference, Atlanta GA,
  2D3.
• Fast, J.D., R.C. Easter, W.I. Gustafson Jr., E.G.
  Chapman, J.C. Barnard, R.A. Zaveri, and G.A.
  Grell, 2004 Evaluation of new trace gas and
  aerosol modules in WRF-chem using measurements
  from TexAQS 2000. First Joint WRF/MM5 Workshop,
  Boulder, CO, 4.2.
• http//www.pnl.gov/atmos_sciences/JDF/wrf-chem.htm
  l
• MOSAIC
• Zaveri, R.A., R.C. Easter, and A.S. Wexler, 2005
  MTEM A new method for multicomponent activity
  coefficients of electorlytes in aqueous
  atmospheric aerosols. In Press, J. Geophys. Res.
• Zaveri, R.A., R.C. Easter, and L.K. Peters, 2005
  MESA A computational efficient multicomponent
  equilibrium solver for aerosol-phase solid/liquid
  partitioning. Submitted to J. Geophys. Res.
• Zaveri, R.A., R.C. Easter, J.D. Fast, and L.K.
  Peters, 2005 MOSAIC Model for simulating
  aerosol interactions and chemistry. In
  preparation, to be submitted to J. Geophys. Res.
• CBM-Z
• Zaveri, R.A., and L.K. Peters, 1999 A new lumped
  structure photochemical mechanism for large-scale
  applications. J. Geophys. Res., 104, 30387-30415.
• FAST-J
• Barnard, J.C., E.G. Chapman, J.D. Fast, J.R.
  Schmelzer, J.R. Schlusser, and R.E. Shetter,
  2004 An evaluation of the FAST-J photolysis
  model for predicting nitrogen dioxide photolysis
  rates under clear and cloudy conditions. Atmos.
  Environ., 38, 3393-3403.
• Aerosol optical properties
• Ghan, S., N. Laulainen, R. Easter, R. Wagener, S.
  Nemesure, E. Chapman, Y. Zhang, and R. Leung,
  2001 Evaluation of aerosol direct radiative
  forcing in MIRAGE. J. Geophys. Res., 106,
  5295-5316.
• PEGASUS Applications (offline version of CBM-Z,
  MOSAIC, and FAST-J)
• Jiang G., and J.D. Fast, 2004 Modeling the
  effects of VOC and NOx emission sources on ozone
  formation in Houston during the TexAQS 2000 field
  campaign. Atmos. Environ., 38, 5071-5885.
• Fast, J.D., and W.E. Heilman, 2004 The effect of
  lake temperatures and emissions on ozone exposure
  in the western Great Lakes region. J. Appl.
  Meteor., 42, 1197-1217.
• Fast, J.D., R.A. Zaveri, X. Bian, E.G. Chapman,
  and R.C. Easter, 2002 The effect of
  regional-scale transport on oxidants in the
  vicinity of Philadelphia during the 1999 NE-OPS
  field campaign. J. Geophys. Res., 107,
  doi/10.1029/2001JD000980.

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Appendix 2 TexAQS 2000 WRF-chem Simulation
SO4, 11 LST 29 August
SO2, 11 LST 29 August
Ozone, 11 LST 29 August
OINOCEC
Conroe
3
4
6
G-1 10-11 LT
G-1 10-11 LT
2
trajectory at 11 LT
9
12
1
50
90
40
3
Houston East
70
Parrish power plant
SO2, 16 LST 29 August
25 20 15 10 5 0
Houston East
Composition along Trajectory Starting at Parrish
Power Plant at 09 LT 29 August
trajectory at 11 LT
mass (mg m-3)
10.0 7.5 5.0 2.5 0.0
3
6
observed simulated
9
G-1 10-11 LT
SO4
06 12 18 00 06 12 18 00
06 hour (LT)
mass (mg m-3)
40 30 20 10 0
OIN
Conroe
OC
NH4
EC
09 10 11 12 13 14 15
16 time (LT)
mass (mg m-3)
Parrish power plant
8/29
8/30
time (LT)
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Appendix 3 Radiative Forcing in WRF-chem

• Direct Effect (complete)
• investigators James Barnard and Rahul Zaveri
• In-Direct Effect (to be completed this FY using
  internal funding)
• investigators Steven Ghan and Richard Easter
• Secondary Organic Aerosols (to be completed this
  FY using internal funding)
• implement MADE /SORGAM version compatible with
  CBM-Z and MOSAIC
• investigator Rahul Zaveri

size and number distribution, composition,
aerosol water
scattering and absorption of shortwave radiation
refractive indices
3-D ?? , ?o , and g
Mie theory
cloud albedo, precipitation, cloud lifetime
prognostic cloud droplet number, aqueous chemistry
aerosol activation
aerosol number
wet removal