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.