1. Parameterization of cloud droplet formation and autoconversion in large-scale models Wei-Chun Hsieh Advisor: Athanasios Nenes 10,Nov 2006 EAS Graduate Student Symposium Photo source: CSTRIPE imagery

2. How does aerosol affect climate? Aerosol act as Cloud Condensation Nuclei (CCN). Anthropogenic emissions increase their levels  Decreases cloud droplet size  more reflection of sunlight  cloud precipitation decreases Both effects are called “aerosol indirect climatic effect”. The “second” aerosol indirect effect affect cloud lifetime and the hydrological cycle. This is potentially a very important (but uncertain) component of climate change. More CCN Less CCN

3. Evidence from Satellite: ship tracks Rosenfeld, Kaufman, and Koren, ACPD, 2005 In the region of shiptracks, droplet size is VERY small and clouds are thicker (i.e., they don’t drizzle as much). Outside of the shiptrack region, droplet are very big (enough to drizzle and form rain). West coast (California) in the visible. Effective radius (μm) West coast (California)

4. Estimate of aerosol indirect effect subject to large uncertainty And this is just for the “first” indirect effect! No estimate with any degree of certainty for“second” indirect effect.

5. Linking aerosols to cloud & rain formation n Autoconversion is the process which describe collision and coalescence of cloud drops in warm liquid clouds, initializing precipitation and is the dominate process of the drizzle formation in stratiform clouds. larger/cloud droplets aerosols droplets nucleation, activation diffusional growth collision- coalescence drizzle formation rain drops rain Size Classification Processes

6. Autoconversion schemes I LcLc PKPK L P K : Autoconversion rate L: Liquid water content L c : critical liquid water content, represents when precipitation starts. q c : cloud water mixing ratio E MC refers to an average collection efficiency H: Heaviside function Threshold process cloud droplet number concentration mean volume drop diameter standard deviation of the cloud-drop size distribution L: Liquid water content (MC) (CP)

7. Autoconversion schemes II P: Autoconversion rate Two parameters  4 and  6 are related to cloud spectrum dispersion relative dispersion  (defined as the standard deviation of cloud drop distribution divide by the mean drop size) (R4) (R6)

8. time/ height S max air parcel Activation parameterization Fountoukis and Nenes activation parameterization (2005), which predicts the equilibrium cloud droplet number concentration based on parcel maximum supersaturation(S max ). Köhler theory: for those CCN (Cloud Condensation Nuclei) with critical supersaturation(S c ) less than S max can be activated to cloud droplets. compute the droplet size distribution at the point of S max Parcel supersaturation S

9. New Framework Our framework computes the evolution of the droplet size distribution as a function of height in the cloud; P at each point in the cloud are calculated and then integrated over the whole depth to obtain total P. droplet ascend in an updraft and evolve within a Lagrangian parcel. Growth beyond the point of smax in a cloud is represented by the diffusional growth of the droplet size distribution as it ascends in the cloud. At each point in the cloud, autoconversion is calculated using existing parameterizations. height autoconversion air parcel continually goes up

10. Evaluation of parameterization: Comparison between parcel model and parameterization supersaturation (S) LWMR Liquid Water Mixing Ratio

11. Evaluation of new framework Comparison of autoconversion rates calculated from  Parcel model  Parameterization  In-situ field measurements data In-situ liquid water content, droplet number concentration, droplet spectrum NOT measured precipitation

12. Autoconversion rates from parcel model, parameterization and in-situ data Autoconversion rates increase with increase of LWMR R6 predicts lower autoconversion rates Difference between autoconversion schemes can be up to 2 order of magnitude

13. The predicted autoconversion rates by parcel model and paramterization agree well with observed ones. This good agreement indicates that the predicted cloud droplet number concentration by parcel model and parameterization is close to observed values, since the MC scheme depends on droplet number and liquid water content only. Comparison of autoconversion rates calculated from parcel model, parameterization, in-situ data Observation is from CSTRIPE (marine stratocumulus)

14. . Underestimation of autoconversion rates by parcel model, parameterization. This underestimation mainly due to underestimation of droplet spectrum by parcel model and parameterization

15. Summary A parameterization framework that links cloud activation with collision-coalescence and drizzle formation is developed for usage in global models. The calculated autoconversion rates from parcel model and parameterization agree well with those calculated from in-situ observations of droplet size distribution in cumulus and stratocumulus clouds. The developed parameterization framework reasonably represents the evolution of cloud droplets in updraft regions and is capable for different cloud types.

16. Future plans Implementation of autoconversion parameterization into GISS GCM GCM runs of precipitation patterns based on implemented parameterization Evaluate the GCM precipitation with satellite retrieved precipitation data from TRMM (Tropical Rainfall Measuring Mission) Simulations of aerosol indirect forcing Evaluate the GCM cloud microphysics properties with satellite data

17. Acknowledgments DOE, Department of Energy Athanasios Nenes Nicholas Meshkhidze Rafaella Sotiropoulou Christos Fountoukis Akua Asa-Awuku Luz Padro Jeessy Medina Donifan Barahona

18. Thank you Questions?