# Presentation: 135458

## 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

**Notes:**
The title of our project is pretty self-explanatory.

My co-investigators include Bill Gustafson, Elaine Chapman, Jim Barnard, and Rahul Zaveri

## 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?

**Primary Scientific Questions**

**Pittsburgh**

**~270 km**

- “Urban” Scale Modeling Domain

- “Regional” Scale Modeling Domain

- MODIS Aerosol Optical Depth

**~780 km**

**G-1 flight path**

******x = ~1 km**

******x = ~9 km**

**surface**

**aloft**

**export**

**low**

**point sources**

**high**

**typical GCM cell**

**Notes:**
As we know, predictions of particulate mass, composition, size distribution, and optical properties still contain relatively large uncertainties.  Advances in the treatment of aerosols in models has been hindered by relatively few particulate measurements, and a lack of concurrent meteorological, chemical, and aerosol measurements needed to evaluate and improve models.

Therefore our primary questions include: (questions)

As indicated by the plot on the left, an urban-scale domain with a 1 km grid spacing can be use to encompass typical G-1 aircraft flight paths and resolve much of the spatial variations in particulates resulting from urban and point sources.  Local evolution of these particulate plumes will depend on transport of pollutants from upwind sources.  These urban and point source plumes are subsequently transported into the larger regional environment and impact other locations.

The MODIS satellite measurements indicates large variations in aerosol optical depth that are not likely to be resolved by GCMs.  Urban to regional scale models can be used to better understand the processes that contribute to these variations.

## 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

**Overall Approach**

- Infrastructure

- Urban-to-Regional
- Scale Modeling

- Laboratory

- Global Modeling

- Process Modeling

- Field Campaigns

- data

- planning,
- interpretation

- measurements

- parameterizations

- Berkowitz and Berg

**ASP Collaborations**

- Ghan

- ???

- process modules

- input

- boundary conditions

- parameterizations

- Doran and Barnard

**Types of ASP Projects**

**Notes:**
This schematic diagram depicts the relationships between our urban-to-regional scale modeling project and other ASP projects.

By integrating field measurements and multi-scale modeling, we will obtain a comprehensive picture of how various atmospheric processes affect the evolution of aerosols and radiative forcing.  In this way, we can provide a link between field campaign measurements and global modeling.

We anticipate collaborating with the field campaigns proposed by Carl Berkowitz and Larry Berg, and Chris Doran and Jim Barnard, and the global modeling proposed by Steve Ghan.

One of my objectives at this meeting is to establish collaborations with other ASP investigators that can provide new process modeling modules or laboratory findings that can be used to improve the treatment of aerosols in our model.

## 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*

**Research Tool: WRF-chem**

- modeling research

- developed under
- prior ASP support

- DOE Atmospheric
- Science Program

- climate modeling
- community

**Notes:**
The model we will use is the chemistry version of the Weather Research and Forecasting model, called WRF-chem.

WRF is the next-generation meteorological model being developed by several agencies and the first version of WRF-chem was developed by the NOAA Forecast Systems Laboratory.

Over the past year, we have had internal funding to integrate into WRF-chem modules that have been developed using prior ASP support.  These include the CBM-Z gas-phase mechanism developed by Rahul, the MOSAIC aerosol mechanism developed by Rahul Zaveri and Dick Easter, the FAST-J photolysis scheme adapted by Elaine Chapman and Jim Barnard, and aerosol optical properties developed by Jim Barnard and Rahul Zaveri.  Details of the treatment of direct forcing, indirect forcing  and SOA formation are described on our web site.

We have chosen WRF-chem as our modeling framework for a number of reasons.  First, meteorology and chemistry are coupled on-line so that we can simulate feedback processes associated with aerosols, chemistry, clouds, and radiation.  The model also has 2-way nesting so that we can simulate aerosol evolution over multiple scales.  The framework is useful for comparing the performance of different aerosol modules, while the emission and meteorology remain the same.  Many previous inter-model comparison studies are complicated by differences in emissions and meteorology, not just the treatment of aerosols.  And finally, WRF is a community model so that ASP module development can be quickly transferred to the larger scientific community.  And we can also take advantage of new research conducted outside of ASP.

## Aerosols in WRF-chem

*sectional size distribution; moving-center or two-moment approach for the dynamic equations for mass and number; each size bin is internally mixed*
*composition: SO**4 **, NO**3 **, NH**4 **, CL, CO**3 **, NA, CA, other inorganics, OC, EC*

**Aerosols in WRF-chem**

**particle diameter (********m)**

**0.01**

**0.1**

**1**

**10**

**100**

**0.01**

**0.1**

**1**

**10**

**100**

**particle diameter (********m)**

**mass**

**mass**

**modal approach**

**MOSAIC - sectional approach**

**Accumulation Mode**

**Coarse**
**Mode**

**Aiken**
**Mode**

- 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

**Notes:**
This slide briefly describes how we treat aerosols in WRF-chem.  In contrast with the modal representation of aerosol size distribution that was in the first version of  WRF-chem, MOSAIC employs a sectional approach.

MOSAIC differs from other aerosol mechanisms in that it has new treatments for the activity coefficients, thermodynamic solver, and dynamic integration of the coupled gas-aerosol partitioning equations. They are more numerically efficient than other methods, without sacrificing accuracy, to reduce the overall computational time required for 3-D applications. These new treatments are described in recent journal articles written by Rahul Zaveri.

## Utilize ASP-Supported Field Campaign Data

*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?*

**Utilize ASP-Supported Field Campaign Data**

**Ozone, 11 LST 29 August**

**SO****2****, 11 LST 29 August**

**SO****4****, 11 LST 29 August**

- Parrish
- power plant

- OIN+OC+EC

- G-1
- 10-11 LT

- G-1
- 10-11 LT

- 3

- 6

- 9

- 12

- 3

- 4

- 2

- 1

- 50

- 70

- 90

- 40

**Notes:**
To ensure that the model predictions are similar to observations, we will be employing the meteorological, chemical, and particulate measurements obtained during ASP-supported field campaigns.  This year, we will use data from the Houston 2000 field campaign and the field campaign conducted over the northeastern U.S. last summer.  These plots depict an example simulation result at 11 LT on 29 August 2000.  The predicted SO2 plume from the Parrish Power Plant and the ozone distribution in the vicinity of Houston is consistent with the G-1 measurements.  The middle plot shows the predicted sulfate plume mixes with other inorganics, organic carbon, and black carbon emitted over the metropolitan area of Houston.  We are currently examining the variations in the direct radiative forcing associated with these particulate plumes as they are transported into the regional environment.

During FY 2006 and 2007, we will examine aerosol evolution associated with the MIRAGE-MEX and Houston field campaigns to be conducted in 2006.  Because of the limited resources of this project, we will need to determine which field campaign has the most useful data set to evaluate our WRF-chem simulations.  

## Modeling Approach

*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 ??*

**Modeling Approach**

**Notes:**
WRF-chem will be used to perform 3 types of simulations: baseline simulations, process-oriented sensitivity simulations, and simulations with improved aerosol modules.  The baseline simulations will be designed to reproduce, as best as possible, the observed meteorological, chemical, and particulate fields observed during ASP field campaign periods so that we can determine the spatial and temporal variations in radiative forcing.

The sensitivity simulations are designed to quantify the relative contribution of various processes that contribute to the evolution of aerosols and radiative forcing.

The only model development we have proposed is to refine the treatment of aerosol optical properties by replacing the volume averaging method with a shell-core approach.  As stated previously, I would be interested in collaborating with other investigators if they are interested in testing new treatments of aerosol processes.  By comparing these simulations with the baseline simulations and observations, we will be able to quantify the improvement in predictions of particulate characteristics.

## Data Needs

*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 ****, ******, 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 (****, ******, 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*
*preliminary data very useful*

**Data Needs**

- ASP Measurements
- G-1, infrastructure,
- “super sites”

- other Measurements
- operational,
- collaborators, etc.

**Notes:**
This slide summarizes the types of data we need to evaluate the model, including those from ASP field campaigns denoted by white and other data sources denoted in gray.  We need a wide variety of meteorological, chemical and particulate observations to ensure that our predictions are not getting the right particulate characteristics for the wrong reasons.

Shortly after the field campaign periods, it would be very useful to have ASP measurements available by links at a central location.  Since our project timeline is the same as everyone else’s, we cannot wait months or years for data to be fully QA’ed.  Preliminary data is needed to complete simulations within project cycle.  

## What this Project Can Provide to ASP Investigators

*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

**What this Project Can Provide to ASP Investigators**

**Notes:**
The following are examples of what our project could provide to other ASP investigators.

WRF-chem simulations could be made to provide guidance for field campaign planning.   (questions)

Our simulations will be designed to put ASP measurements into a larger context.  For example, (questions)

Upon request, we could provide 3-D meteorological, gas, and aerosol fields.  A limited set of fields could be made available via the web; however, the ten’s of Gb of output do not permit all of the model output being made available.

WRF-chem would also be useful test-bed to improve aerosol modules.  This could include participation in  model inter-comparison studies.

Finally, through a collaboration with Steve Ghan, we hope to use high-resolution WRF-chem simulations to help develop new representations of aerosols for global climate models.

## Appendix 1: References

- 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.html
- 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.

**Appendix 1: References**

## Appendix 2: TexAQS 2000 WRF-chem Simulation

**Appendix 2: TexAQS 2000 WRF-chem Simulation**

**Ozone, 11 LST 29 August**

**SO****2****, 11 LST 29 August**

**SO****2****, 16 LST 29 August**

**SO****4****, 11 LST 29 August**

- Parrish
- power plant

- trajectory
- at 11 LT

- trajectory
- at 11 LT

- 06     12    18     00     06    12    18     00    06
- hour (LT)

- 25
- 20
- 15
- 10
- 5
- 0

- mass (g m-3)

- 10.0
- 7.5
- 5.0
- 2.5
- 0.0

- mass (g m-3)

- SO4

- OIN

- OC

- NH4

- EC

- 09     10      11      12      13     14      15      16
- time (LT)

- OIN+OC+EC

- Houston East

- Composition along Trajectory
- Starting at Parrish Power Plant
- at 09 LT 29 August

- G-1
- 10-11 LT

- G-1
- 10-11 LT

- G-1
- 10-11 LT

- Parrish
- power plant

- observed   simulated

- 3

- 6

- 9

- 12

- 3

- 6

- 9

- 3

- 4

- 2

- 1

- 50

- 70

- 90

- 40

- Conroe

- 8/29

- 8/30

- time (LT)

- 40
- 30
- 20
- 10
- 0

- mass (g m-3)

- Houston
- East

- Conroe

## Appendix 3: Radiative Forcing in  WRF-chem

*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*

**Appendix 3: Radiative Forcing in  WRF-chem**

*size and number distribution,*
*composition, aerosol water*

*refractive *
*indices*

*Mie theory*

*3-D *
***** *, ***o *, and *g*

*scattering and absorption of*
*shortwave radiation*

*aerosol number*

*prognostic cloud*
*droplet number,*
*aqueous chemistry*

*cloud albedo,*
*precipitation,*
*cloud lifetime*

*aerosol activation*

*wet removal*