National Center for Atmospheric Research, Boulder, CO 8030

1
Development and Evaluation of Global-Through-Urban
WRF/Chem Gas-Phase Mechanism, Gas-Aerosol
Coupling, and Aerosol-Cloud Interactions

• Yang Zhang, Xin-Yu Wen, and Ying Pan
• North Carolina State University, Raleigh, NC
  27695
• Prakash Karamchandani and Christian Seigneur
• Atmospheric and Environmental Research, Inc., San
  Ramon, CA 94583
• David G. Streets and Qiang Zhang
• Argonne National Laboratory, Argonne, IL 60439
• William C. Skamarock
• National Center for Atmospheric Research,
  Boulder, CO 80307
• the 7th Annual CMAS Conference, October 6-8,
  2008, Chapel Hill, NC

2
Presentation Outline

• Background and Motivation
• Model Development Highlights
• Incorporation of CB05 and Its Global Extension
  (CB05_GE)
• Coupling of CB05/CB05_GE with MADRID
• Incorporation of An Aerosol Activation/CCN Module
• Development of Global Through Urban Nesting
  Capabilities
• Summary and Potential Applications

3
Modeling Climate Change (CC)-Air Quality (AQ)
Interactions Background and Motivation
Downscale BCs/ICs
General Circulation Model
Mesoscale Climate Model
Offline models
Fine to Coarse Grid Feedbacks
Online models
Population/Economic Growth Energy and Land Use
Unified models
Large scale Weather
Radiation, CCN, Cloud
Radiation, CCN, Cloud
Mesoscale Weather
Biogenic emissions
Anthro. emissions
Global Air Quality Model
Mesoscale Air Quality Model
Neighborhood Scale Human Health Effect
Downscale BCs/ICs
Fine to Coarse Grid Feedbacks
Air Quality Management Global Warming Mitigation
Economy/ Policy Analysis
Climate Policy Analysis
4
Development of Global-through-Urban Weather
Research and Forecasting Model with Chemistry
(GU-WRF/Chem)

• Objectives
• Develop a unified GU-WRF/Chem for integrated
  modeling at all scales
• Apply GU-WRF/Chem to replicate and examine
  feedbacks and to reduce uncertainties in
  climate-chemistry modeling at regional and global
  scales

• Overall Approach

Globalize WRF/Chem
Improve science
Apply/Evaluate the model
Quantify CC-AQ feedbacks

• Key Model Development
• Compile an adequate global emission inventory
• Link global WRF with chemistry/aerosol modules in
  mesoscale WRF/Chem
• Develop appropriate model treatments for upper
  troposphere and stratosphere
• Incorporate CB05/CB05_GE into GU-WRF/Chem
• Couple CB05/CB05_GE with aerosol modules and
  aqueous chemistry
• Improve SOA and incorporate a more accurate
  aerosol activation module
• Nest from global to urban domains with mass
  conservation/consistency

5
Incorporation of CB05 and CB05_GE into GU-WRF/Chem

• A Total of 120 New Reactions in CB05_GE
• 5 stratospheric reactions (O2, N2O, O1D)
• 78 reactions for 25 halogen species (48 for 14 Cl
  and 30 for 11 Br species)
• 4 mercury reactions (Hg(0) and Hg(II))
• 13 heterogeneous reactions on aerosol/cloud and
  20 reactions on PSCs
• H2O, CH4, O2 and H2 are treated as
  chemically-reactive species

• Box Model Test

O3
Hg(II)
Hg(0)
Arctic
Upper Troposphere
6
July Monthly Mean Mixing Ratios of New Species
from GU-WRF/Chem-CB05GE (D01 and D03)
Cl
N2O
CLONO2
Hg(0)
7
Absolute Changes in July Monthly Mean Mixing
Ratios of ALD2 and O3 (CB05_GE - CB05)
ALD2
O3
0.015 km
-1.8 to 0.04
-0.3 to 0.6
25 km
-41.7 to -5
-9.0 to 0.0
8
Observed vs. Simulated Column Predictions in July
2001 (GU-WRF/Chem-CB05-MADRID)
CB05-MADRID
Observation
MOPITTCO
TOMS/ SBUV TOR
MODIS AOD
9
Simulated Surface CCN Using Different Aerosol
Activation Modules in July 2001
Abdul_Razzak Ghan
Fountoukis and Nenes
Correlation
CCN (S0.02)
CCN (S0.1)
CCN6
CCN (S1)
10
Observed vs. Simulated Column CCN and Total Cloud
Fractions
Abdul_Razzak Ghan
Nenes and Seinfeld
MODIS
Column CCN (S1)
Total Cloud Fraction
11
GU-WRF/Chem Configurations for Nested Simulations

• Period 1-31 Jul., 2001
• Vertical resolution 27 layers (1000-50 mb)
• Emissions CAM4, MOZART4, and RETRO
• Mechanisms CBMZ/MOSAIC/CMU Aq. Chem.

• Domain
• Global 4 5, 45 (lat) 72 (lon)
• Trans-Pacific 0.8 1, 55 (lat) 240 (lon)
• Continental U.S. 0.27 0.33, 105 (lat) 210
  (lon)

D01 Global D02 Trans-Pacific D03 CONUS
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July Monthly Mean Near-Surface O3Global vs.
Trans-Pacific vs. CONUS
D01, GU-WRF/Chem
D03, GU-WRF/Chem
Trans-Pacific Domain Monthly Mean
O3 concentration
ppmv
D02, GU-WRF/Chem
108 108 km2, MM5/CMAQ
13
July Monthly Mean Near-Surface PM2.5Global vs.
Trans-Pacific vs. CONUS
D01, GU-WRF/Chem
D03, GU-WRF/Chem
Trans-Pacific Domain Monthly Mean
Pm2.5 aerosol dry mass
ug m-3
D02, GU-WRF/Chem
108 108 km2, MM5/CMAQ
14
Evaluation of Near-Surface O3 and PM2.5 over CONUS
Sim. vs. Obs. Overlay
Max-8h O3
24h avg PM2.5
Statistics (NMB,)
15
July Monthly Mean Tropospheric O3 Residual
(TOR)Obs. vs. Sim. Over Global and CONUS (D01
and D03)
D03
D01
Obs
Obs
Sim
Sim
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Summary and Potential Applications of GU-WRF/Chem

• Summary
• GU-WRF/Chem provides consistent boundary
  conditions and physical/chemical mechanisms to
  initiate nested regional/urban simulations.
• Initial application demonstrates promising skills
  for surface, vertical, and column meteorological
  and chemical variables.
• Potential Applications and Extensions
• Impact of intercontinental transport on air
  quality management
• Asian pollution export and its impact on US air
  quality control
• Use of feedbacks to guide integrated emission
  control strategies for CC/AQ
• Isolate feedbacks of species and quantify air
  quality/health/climate benefits
• Impacts of global CC/AQ on human health and
  implications on control policies
• The effects of CO2 and fuel-use on air pollution
  and associated mortality
• Impacts of global CC/AQ on water resources and
  ecosystems
• The effects of Hg in air and water quality
• Interactions among atmosphere, ocean, and land
• The roles of biogeochemical cycles in climate
  change and resource management

17
Acknowledgments

• Project sponsor EPA STAR R83337601
• Mark Richardson, Caltech, for sharing global WRF
• Ken Schere, Golam Sarwar, and Shawn Roselle, U.S.
  EPA, for providing CB05 and CB05Cltx, Shaocai Yu,
  U.S. NOAA/EPA, for providing Fortran code for
  statistical calculation
• Athanasios Nenes, Georgia Tech, for providing
  aerosol activation code
• Andreas Richter, the University of Bremen,
  Germany, for providing GOME NO2 data Hilary E.
  Snell, AER Inc., for processing MOPITT CO and
  GOME NO2 Jack Fishman and John K. Creilson, NASA
  Langley Research Center, for providing TOR data
• Carey Jang, Jonathan Pleim, and Sharon Phillips,
  U.S. EPA, for helpful discussions on coupled
  meteorology and air quality models
• Xiao-Ming Hu and Kai Wang, NCSU, for help in
  post-processing results