Evaluation of the VISTAS 2002 CMAQ/CAMx Annual Simulations

T. W. Tesche & Dennis McNally -- Alpine Geophysics, LLCRalph Morris -- ENVIRON

Gail Tonnesen -- UC RiversidePatricia Brewer -- VISTAS Technical CoordinatorJames Boylan – Georgia Dept of Natural Resources

Models-3 CMAS Conference18-20 October 2004

Chapel Hill, NC

Outline

• VISTAS objectives

• Model set-up for initial Phase II runs

• Highlights of CMAQ/CAMx evaluations– Operational, Comparative, Diagnostic,

Mechanistic

• Some findings from diagnostic studies

• Suggestions

VISTAS AQ Modeling Objectives

• Phase I: – Evaluate suite of models for episodic and annual

simulation of Regional Haze & PM2.5 on 36/12 km US grid• Phase II:

– Select and evaluate preferred model(s) for 2002 annual period via detailed model performance and sensitivity evaluations

– Evaluate emission control strategies for regional haze, particularly for VISTAS region.

– Support VISTAS states responsible for upcoming PM2.5 attainment demonstrations.

Model Set-up for Initial 2002 Annual Run

• 36/12 km grid, 19 layers• CMAQ v4.3 and CAMx v4.0• MM5 (Pleim-Xiu_ACM8 36/12 km)• 2002 Emissions for VISTAS states (WRAP and CENRAP

updates; NEI 1999 V2 for rest of U.S.)

• CMAQ (CB4, SORGAM); CAMx (CB4, SOAP)• BCs from 2001 Seasonal GEOS-CHEM• Models run in 4 quarters with 15 day spin-up• VISTAS Phase II Modeling Protocol followed

• For reports, results, presentations….

http://pah.cert.ucr.edu/vistas/vistas2/reports

Operational Evaluation• Focus on

– Visibility-related PM species– Identify needed improvements before final 2002

basecase simulations begin (next week…!)

• Use suite of 15 metrics and graphical tools

• Evaluate by month and monitoring network

• Multiple evaluation teams– ENVIRON, UCR, Alpine, VISTAS-TAWG, GA-DNR

Monitors in VISTAS 12 km MPE Domain

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IMPROVE

CASTNET

SEARCH

STN

NADP

AQS

Yorkville, Yorkville, GAGA

SulfateSulfate Fractional Bias and Error: CMAQ Fractional Bias and Error: CMAQ

(note scale: 0-100%)(note scale: 0-100%)

IMPROVE Data for VISTAS States: 12 km grid

Fractional Bias in 24- hr Avg Sulfate, %

-100.0

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Fractional Error in 24- hr Avg Sulfate, %

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NitrateNitrate Fractional Bias and Error: CMAQ Fractional Bias and Error: CMAQ (note scale: 0-200%)(note scale: 0-200%)

Fractional Error in VISTAS IMPROVE 24- hr Avg Nitrate, %

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IMPROVE Data for VISTAS States: 12 km grid

Fractional Bias in VISTAS IMPROVE 24- hr Avg Nitrate, %

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All Four Networks: 12 Months (2002)

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Average Concentration (g/m3)

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Sulfate

Nitrate

Ammonium

Organics

EC

Soils

PM2.5

PM10

CM

(+) Goal

(-) Goal

(+) Criteria

(-) Criteria

Data for VISTAS States: 12 km grid

Bias as Function of Concentration: CMAQ

• Good: SO4 and EC

• Good-Fair: PM2.5 and PM10

• Fair: NH4

• Fair-Poor OC and CM

• Poor NO3 and Soils

Operational Evaluation Summary for CMAQ & CAMx

Comparative Evaluation

• Inter-compare CMAQ V4.3 and CAMx V.4 • Use identical SMOKE/MM5 inputs & VISTAS

evaluation protocol• Examine reasons for similar and divergent

behavior– Gas phase and aerosol species– Wet and dry deposition patterns

• Conduct sensitivity experiments to elucidate similar and divergent behavior in CMAQ and CAMx

CMAQCMAQ/CAMx/CAMx Fractional Error: 12 Fractional Error: 12 kmkm

CMAQCMAQ/CAMx/CAMx Fractional Bias: 12 Fractional Bias: 12 kmkm

EC/CM “Flip-Flop”

In general: CMAQ and CAMx respond consistently for most gas-phase and PM species

Winter: Large over-predictions of NO3 and CM

Summer: Large under-predictions of NO3 (but concentrations are quite small)

All Seasons: Soils over-predicted; OC under-predicted (understated primary OC emissions?)

Comparative Evaluation Summary

Diagnostic Evaluation• Examine PM and gas-phase species by network

• Evaluate effects of grid resolution, model response by sub-region, and range of time scales

• Examine differences in CMAQ/CAMx response

• Synthesize CMAQ/CAMx model evaluation results to elucidate possible sources of model bias and error (e.g. formulation, inputs, …)

CMAQ NO3 Fractional Bias: 12 km

Seasonal & Annual Average AerosolSeasonal & Annual Average AerosolBias and Error: CMAQBias and Error: CMAQ

Bias in Seasonal/Annual CMAQ Predictions, %

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SulfateNitrateOCECPM2.5CM

IMPROVE Data for VISTAS States: 12 km grid

Error in Seasonal/Annual CMAQ Predictions,%

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SulfateNitrateOCECPM2.5CM

Spatial Mean Nitrate: VISTAS vs. MANE-VUSpatial Mean Nitrate: VISTAS vs. MANE-VU

VISTAS: Jan ‘02 MANE-VU Jan ‘02

VISTAS: May ‘02 MANE-VU May ‘02

CMAQ

Spatial Mean Sulfate: VISTAS vs. MRPOSpatial Mean Sulfate: VISTAS vs. MRPO

VISTAS: Jan ‘02 MRPO Jan ‘02

VISTAS: May ‘02 MRPO May ‘02

CMAQ

Spatial Mean EC Dry DepositionCMAQ-Jan ’02 CAMx-Jan ’02

CMAQ-Jul ’02 CAMx-Jul ‘02

CMAQ dep CMAQ dep >> CAMx depCAMx dep for ECfor EC

Spatial Mean CM Dry Deposition

CMAQ-Jan ’02 CAMx-Jan ’02

CMAQ-Jul ’02 CAMx-Jul ‘02

CMAQ dep CMAQ dep <<<< CAMxCAMx depdep for ECfor EC

SEARCH Hourly Sulfate at Yorkville, GA: Jan ‘02

SEARCH Hourly Nitrate at Yorkville, GA: Jan ‘02

Yorkville NO3, Temp & Mixing Ratio Time Series (Jan ’02)

NO3

Mixing Ratio

Temperature

SEARCH Hourly Nitrogen Species at Yorkville, GA: Jan ‘02

NO

HNO3NOy

NO2

Bias in Hourly VISTAS Domain-Wide MM5 Fields: Jan ‘02

- CMAQ and CAMx consistent for most species across all domains and time scales.

- EC/CM bias ‘flip-flop’ due to different dry deposition algorithms in CMAQ/CAMx

- OC bias differences in CMAQ/CAMx, in part, attributed to- Different SOA chemistry formulations- Different environmental chamber data sets and

parameterizations.

Diagnostic Evaluation Summary

Mechanistic Evaluation: CB4 vs SAPRC99 for Jan ’02 & Jul ’01

Episodes• Very Similar Base Case Performance for SO4, NO3 and OC:

– Differences between 36 and 12 km grid larger than differences between CB4 and SAPRC

– SAPRC exhibits slightly improved performance for ozone compared to CB4

• Generally Similar Response to 30% Controls, except:– SO4 sensitivity to NOx controls

• SAPRC approximately twice as sensitive • Tied to H2O2 and O3 sensitivity to NOx controls

– O3 sensitivity to VOC • SAPRC more sensitive than CB4

Three Suggestions• Devote greater emphasis to the diagnostic

component of MPE (consider range of time and space scales, super-site data sets)

• Utilize the extensive 2002 aircraft data base for aloft model evaluation (probe ‘regional transport’ issue)

• Employ corroborative models to explore key uncertainties in– Input data base development

– Base case model performance

– Reliability of model response to emission controls