Evaluation of sulfate simulations using CMAQ version 4.6: The role of cloud

1. Evaluation of sulfate simulations using CMAQ version 4.6: The role of cloud Chao Luo1, Yuhang Wang1, Stephen Mueller2, and Eladio Knipping3 1Georgia Institute of Technology 2Tennessee Valley Authority 3Electric Power Research Institute

2. Modeling framework (from EPA)

3. Modeling system configurations • MM5: version 3.6.2 with FDDA. Resolution: 36kmx36 kmx34 vertical layers. • SMOKE: version 2.2 with the input of the VISTAS emission inventory for 2002, resolution:36kmx36kmx19vertical layers. • CMAQ4.6: Standard version 4.6 with SAPRC99 gas phase chemistry, AERO4 module for aerosols, Cloud convection is computed by cloud_radm and cloud_acm, resolution: 36kmx36kmx19 vertical layers.

4. Domain and observation sites

5. US EPA Regions (used for model evaluation over the continental domain)

6. SO2 evaluation (simulated SO2 > obs.)

7. Simulated sulfate smaller than observations in all regions except in winter

8. Sulfate comparison in July, 2002 The underestimation is slightly larger in the ACM scheme than RADM.

9. Partition of SO2 and sulfate is biased towards SO2

10. SO2 oxidation pathways OH Dry Deposition Loss SO2 Sulfate Precip. Cloud Cloud H2O2, O3 Wet Deposition Deposition

11. Modeled vs. Observed Cloud Cover over Atlanta for 2002 Definitions Clear: <1/8 sky cover Scattered: 1/8 through 4/8 sky cover Broken: >4/8 through 9/10 sky cover Overcast: >9/10 sky cover Steve Mueller, TVA

12. MODIS cloud fractions are much larger than CMAQ over the continent

13. Cloud water path for July • AMSR and TMI (microwave) are more accurate than MODIS (Terra & Aqua) • Default setting overestimates precipitating cloud path; ACM overestimation is more than RADM. 10%

14. Almost all clouds in CMAQ is convective, which has a larger liquid water content. Excessive precipitation removes non-precipitating cloud. • RADM and ACM in CMAQ underestimate cloud fractions, but overestimate cloud liquid water content. • Could there be a compensating effect in that heterogeneous conversion of SO2 occurs in smaller regions with faster rates? The lifetime of SO2 is long enough that it is insensitive to where the conversion takes place if we look at monthly averages.

15. Experiments design • RADM_1: RADM cloud, limit subgrid convective precipitating cloud fraction no more than 15%. • RADM_2: RADM cloud, limit subgrid convective precipitating cloud fraction no more than 10%. • ACM_1: ACM cloud, limit subgrid convective precipitating cloud fraction no more than 15%. • ACM_2: ACM cloud, limit subgrid convective precipitating cloud fraction no more than 10%. • These are attempts of a temporary fix

16. Sulfate calculated from standard CMAQ v4.6 with RADM and ACM schemes in July 2002 ACM S = 0.49 RADM S = 0.58

17. Limiting precip cloud fractions improves the model simulations ACM_1 S = 0.90 ACM_2 S = 0.97 RADM_1 S = 0.85 RADM_2 S = 0.90

18. SO2 evaluations: effect insignificant

19. RADM sulfate budget

20. ACM sulfate budget

21. Sulfate budget summary

22. Discussion and Conclusions • Gas and aqueous-phase column contributions are about the same although aqueous-phase production is larger. • Aqueous production and wet scavenging are strongly affected by simulated cloud properties. • Both cloud_radm and cloud_acm schemes underestimate cloud fractions but overestmate cloud liquid water content over the cloudy regions. The two biases appear to have compensated for one another and the aqueous-phase conversion from SO2 to sulfate appear to be adequate in summer. • Since almost all simulated clouds are convective, both schemes have excessive scavenging of sulfate. Consequently, standard CMAQ simulations of sulfate using these schemes have low biases. The bias is larger in the ACM than RADM scheme. • We introduce a model fix by limiting the precipitating cloud fractions to 10-15%. The resulting model sulfate simulations have no significant biases. The ACM scheme performs better than RADM.