Overview of the Climate Impact on Regional Air Quality (CIRAQ) Project Ellen J. Cooter *, Alice...
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Transcript of Overview of the Climate Impact on Regional Air Quality (CIRAQ) Project Ellen J. Cooter *, Alice...
Overview of the Climate Impact on Regional Air Quality (CIRAQ) Project
Ellen J. Cooter*, Alice Gilliland*, William Benjey*, Robert Gilliam* and Jenise Swall*
U.S. EPA, National Exposure Research Laboratory, Atmospheric Modeling Division,
Research Triangle Park, NC
2004 Models-3 ConferenceOctober 18-20, 2004
*On assignment from NOAA Air Resources Laboratory
• Objective: Examine potential climate change impacts on O3 and PM using the regional scale Community Multiscale Air Quality (CMAQ) model linked with global scale climate and chemical transport models
• Supports U.S. Climate Change Science Program (CCSP) research goals and synthesis products
• AMD PIs include:
Ellen Cooter Project managementClimate assessment, landscape/vegetation change
Alice GillilandCMAQ modeling and linkages with global CTMsBill Benjey Air quality emissions, future emission scenariosRobert Gilliam Regional climate model evaluation
• Collaborators include: Ruby Leung Pacific Northwest National Laboratory (MM5
RCM) Dan Loughlin EPA NRMRL (future emission scenarios) Daniel Jacob Harvard University (GISS, GEOS-CHEM)
Loretta Mickley Peter Adams Carnegie Mellon University (global CTM) Ron Neilson USDA-FS, Pacific Northwest Research Station
Climate Impacts on Regional Air Quality (CIRAQ)
CIRAQ Information Flow and Responsibilities
EPA /NRMRLEPA / NOAA(ASMD)
Agency Key
EPA / NCEADOE / PNNLUSDA /FS
CCSP
Synthesis Report 4.5
Air Quality Scenarios
CCSP
Base Program
3. Atmospheric Composition
CCSPSynthesis Report 4.6
Socioeconomic Impacts ofClimate Variability
Vegetation Change
Anthropogenic Emissions
MM5/RCM Meteorology(GCM Downscaling)
Biogenic emissionsAir Quality
(Ozone and PM)
GCM
(Harvard via STAR)
CIRAQ Project Timeline
• FY03-05 Understanding the global to regional climate linkage to
support regional scale air quality simulations
• FY04-07Understanding the impact of climate change on regional
air quality (CIRAQ Phase 1)
- Develop 5-yr current and future (fixed technology and landuse) emissions scenarios
- Perform 5-yr current and future (2050) CMAQ simulations
• FY06-09 Understanding the impact of climate and emissions
changes on regional air quality (CIRAQ Phase 2)
Downscaled Meteorology(linking global and regional scale climate)
• GCM (Harvard University) GISS version II’ 6hrly output saved for 10 present-day and 10 future
years. Used as boundary and initial conditions to MM5
• Downscaling with MM5 (DOE/PNNL) MM5 run in regional climate mode
• 23 layers, MRF planetary boundary layer parameterization, Grell cumulus cloud parameterization, RRTM radiation scheme and mixed phase microphysics
36km x36km horizontal resolution spanning continental US, northern Mexico and southern Canada
Exectute CMAQ Model
Place new RCM file to beprocessed in project work
directory
Modify Input file to reflect runrequirements and name/location
of new RCM file
Update MCIP namelist filewith new run information
Collect resultant MCIPoutput, rename and store
according to date
Option to compress RCMoutput to conserve disk usage
and archive.
* Perform Quality Check onMCIP output
1) Compare specified variables withRCM data for consistency throughMCIP2) Examine range and hourly changeof specified (Input File) variables ateach time period and grid point.3) Extract surface datacorresponding to NWS obs sites4) Update CMAQ database with thedetails of the QA/QC checks
Generate GrADS output ofspecified variables for postanalysis (Temp, PBL Hgt,winds, precip, etc).
**Exectute MCIP program
Inspect QA/QC logs for baddata
User input required
Automated
External programs
* Approximate time for QC to run 1 month is 10 min** Approximate time for MCIP to run 1 month is 210 min
Smoke Emissions
The Regional Climate Model Data Management and Quality Control Tool
(Lead, Gilliam)
RCM Evaluation/Analysis
The Goal:To understand climatological biases that could impact
CMAQ model performance
The Challenge:RCM scenarios characterize time periods under
representative climatological conditions and will not necessarily reproduce day-to-day and exact year-to-
year observations.
The Solution:Base evaluation on temporal and spatial characteristics of model
output means, extremes and variability.
MM5/RCMObs
MM5/RCM/MCIP EvaluationTime Series Analysis
(Leads, Gilliland and Swall)
• Meteorological conditions include annual, diurnal, and interannual cycles, in addition to stochastic variability
• These cyclical components can be isolated using time series analysis techniques (e.g., filtering techniques, Fourier analysis, etc.)
• Amplitude of these cycles and the extent of variability can be compared for observational data and model output
• Understanding these cyclical patterns allows for better detection of climate change signals and investigation of these changes
MM5/RCM/MCIP EvaluationSpatial Analysis
(Lead, Cooter)
Goal: Develop methods to compare spatial patterns of gridded
meteorological (or other) means and extremes across datasets.
Method:• Cluster analysis
– Wards (means)– Average linkage and k-means (extremes)
Analysis:• Visual – difference mappings• Quantitative – frequency analysis
Developed and tested using 10 years of NCEP and NCEP/AMIP reanalysis data
Question:
Do average summer season 700mb transport patterns look similar?
NCEP Reanalysis R-1
(black arrows)
NCEP/AMIP Reanalysis R-2
(red arrows)
(R2 – R1)
Question:
Are the relative frequencies of average summer season 700mb transport patterns similar?
NCEP Reanalysis R-1 NCEP/AMIP Reanalysis R-2
Location and distribution of patterns 1 and 4 are similar.
Location and distribution of patterns 2, 3 and 5 are different
Pattern NumberPattern Number
Rel
ativ
e P
atte
rn F
requ
ency
Rel
ativ
e P
atte
rn F
requ
ency
Question:
•How many unique (extreme) patterns can be identified?•Ex, NCEP R-1 has 7 patterns; NCEP/AMIP R-2 has 7 patterns
•Are the patterns similar across datasets? •Ex. Early summer drought pattern
•Do the patterns occur in a similar fashion across datasets?•Ex., R-1 pattern found 112 times in R-1, R-1 pattern found 99 times in R-2•Ex, R-2 pattern found 272 times in R-2, R-2 pattern found 198 times in R-1
NCEP R1 NCEP/AMIP R2 R2-R1
SMOKE Emission Modeling(Lead, Benjey)
BiogenicModel
MobileModel
AreaModel
PointModel
Land Cover and
Land Use Data
RCM/MCIPScenarios
EmissionsInventories
HourlyEmissions
FactorComputation
Hourly LayerFractions
MergeProcessing
CMAQ
MM5/RCM-Driven Isoprene Emissions
CMAQ Air Quality Simulations(Lead, Gilliland)
Plan
• O3, PM2.5, PM10, sulfate and nitrate deposition, …
• U.S. continental domain, 36 km horizontal resolution
• Linkages to global scale chemical transport simulations through boundary conditions Two global CTMs (Harvard and Carnegie-Mellon) Both driven by GISS II’ GCM
Challenge• Global CTM chemical mechanism matched to SAPRC (AMD)• Temporal and spatial scale issues (Univ. of Houston)
CMAQ simulations are expected to begin during FY05
DisclaimerPortions of the research presented here were
performed under the Memorandum of Understanding between the U.S.
Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s National
Oceanic and Atmospheric Administration (NOAA) and under agreement number
DW13921548.
Although this work was reviewed by EPA and NOAA and approved for publication, it may not necessarily reflect official Agency policy.