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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. Motivation.

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Sensitivity of Aerosol Indirect Effects to Representation of Autoconversion

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Sensitivity of aerosol indirect effects to representation of autoconversion

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


Motivation

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)


Model

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


Computing autoconversion

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


Autoconversion

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.


Indirect forcing

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


Changes in lwp

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


Zonal averaged properties

Zonal averaged properties


Sensitivity of aerosol indirect effects to representation of autoconversion

  • 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.


Sensitivity of aerosol indirect effects to representation of autoconversion

Autoconversion

LWP (PD-PI)

Indirect forcing


Conclusion

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.


Acknowledgments

Acknowledgments

  • Dr. Lian-Ping Wang (KCE code)

  • Earth & Atmospheric Science, Georgia Institute of Technology

  • Aerosol-Cloud-Climate Interaction Research group

  • Friends & Family


Thank you

Thank you

Questions?


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