Sensitivity of Aerosol Indirect Effects to Representation of Autoconversion

1. Sensitivity of Aerosol Indirect Effects to Representation of Autoconversion Wei-Chun Hsieh with Peter J. Adams , and John H. Seinfeld Advisor: A. Nenes Earth and Atmospheric Science Sixth Annual Graduate Student Symposium

2. Motivation • Estimate of Indirect effect is subject to the largest uncertainty for climatic forcing assessment (IPCC, 2007) Indirect effect High albedo Reflect more sunlight More CCN Less CCN • “first” indirect effect: decrease cloud droplet size • “second” indirect effect: change precipitation and lifetime • Uncertainty in estimate of indirect effect is related to cloud • microphysical schemes, especially autoconversion parameterization (Lohmann and Feitcher, 2005)

3. Model • GISS GCM II-prime [Hansen et al., 1983] • An online aerosol simulation [Adams et al., 1999, 2001; Koch et al., 1999] • Cloud activation parameterization [Fountoukis and Nenes, 2005] • 40 x 50 horizontal resolution and nine vertical layers between the surface and the model top at 10 mb • 6 years of run for each pair of simulation • Present day (PD) and Pre-industrial (PI) aerosols • The GISS autoconversion scheme Sundqvist et al., 1989 SD qc: Cloud water mixing ratio Autoconversion rate

4. Computing Autoconversion • Using microphysical parameterization LWMR: Liquid Water Mixing Ratio; N: Cloud droplet number concentration; LWC: Liquid Water Content • Direct integration of Kinetic Collection Equation (KCE) A: Autoconversion rate x, x’: mass of two droplets, n(x): droplet size distribution, K(x,x’): collection kernel

5. Autoconversion Globalmean value How does the change of autoconversion affect indirect forcing estimate? • The annual mean, global distribution of the GCM's first-layer • autoconversion rate for present day simulation. • Autoconversion is expressed in unit of 10-10kg m-3s-1.

6. Indirect forcing Defined as changes in net short wave flux (W m-2) at Top Of Atmosphere (TOA) between present day and pre-industrial simulation. • Strong negative forcing in highly polluted areas

7. Changes in LWP • Difference of LWP between present day and preindustrial day simulations • Patterns of indirect forcing are in accord with patterns for changes in LWP

8. Zonal averaged properties

9. Sensitivity of collection kernel to forcing estimate • Turbulent Collection Kernel • Ayala kernel [Ayala et al., 2008a, b] • Zhou kernel [Zhou et al., 2001] • - combining turbulent (Zhou et al., 2001) and gravitational kernel (Long, 1974) Turbulent condition: dissipation rate = 34.71 cm2s-3, velocity fluctuation= 0.5 ms-1.

10. Autoconversion LWP (PD-PI) Indirect forcing

11. Conclusion • The predicted autoconversion rate may increase or decrease as compared to model's default parameterization, depends on autoconversion scheme used. • Considering water vapor feedback, we saw an increase in liquid water path due to the suppression of precipitation as a result of increasing aerosol concentration. • The spatial distribution of indirect forcing strongly correlates with simulated changes in LWP, the largest cooling is seen in highly polluted areas. • Effect of turbulent collection kernel on indirect forcing is smaller as compared to uncertainty from applying different microphysical schemes. • The estimated indirect effect is very sensitive to the autoconversion scheme used, ranging from -2.05 Wm-2 for KK and to -0.89 W m-2 for GRV simulation.

12. Acknowledgments • Dr. Lian-Ping Wang (KCE code) • Earth & Atmospheric Science, Georgia Institute of Technology • Aerosol-Cloud-Climate Interaction Research group • Friends & Family

13. Thank you Questions?