1. GISS GCM Developmental Efforts for NASA MAP Program Surabi Menon, Igor Sednev (LBNL) and Dorothy Koch, Susanne Bauer (NASA GISS/Columbia University) and Doug Wright, Bob Mc Graw (BNL) And Unfunded Collaborators: Tony Del Genio, Andy Lacis, Brian Cairns, Michael Mishchenko, Jim Hansen (NASA GISS) Aerosol-Cloud-Climate Interactions

2. GOALS OBJECTIVES Implement “appropriate” parameterizations to improve representation of aerosol-cloud-climate interactions in the Goddard Institute for Space Studies (GISS) SCM/GCM. Use satellite data to constrain/enhance model parameterisations and climate prediction capabilities. Establish sensitivity of climate to aerosol emissions, processes and aerosol-cloud effects.

3. GOALS OBJECTIVES Implement “appropriate” parameterizations to improve representation of aerosol-cloud-climate interactions in the Goddard Institute for Space Studies (GISS) SCM/GCM. Use satellite data to constrain/enhance model parameterisations and climate prediction capabilities. Establish sensitivity of climate to aerosol emissions, processes and aerosol-cloud effects.

4. GOAL 1 Model Development Aerosol Module 1.Implement an aerosol microphysical scheme : PCA-QMOM (McGraw, Wright -- BNL) 2.Add aerosol heterogeneous chemical effects (Bauer -- GISS) 3.Couple aerosol microphysics scheme with current GCM aerosol chemistry/transport module (Koch, Bauer, Wright) Aerosol-Cloud Module 1.Include prognostic cloud droplet and ice nucleation scheme (Menon, Sednev - LBNL) 2.Introduce two-moment bulk scheme (Sednev, Menon; and Del Genio) 3.Develop SCM version of this scheme for GCSS/CMAI type studies (Sednev, Menon; and Del Genio) Aerosol-Cloud-Climate Module 1. Radiation package for internally and externally mixed aerosols (GISS team)

5. GOAL 1 Model Development Aerosol Module 1.Implement an aerosol microphysical scheme : PCA-QMOM (McGraw, Wright -- BNL)  2.Add aerosol heterogeneous chemical effects (Bauer -- GISS)  3.Couple aerosol microphysics scheme with current GCM aerosol chemistry/transport module (Koch, Bauer, Wright) Aerosol-Cloud Module 1.Include prognostic cloud droplet and ice nucleation scheme (Menon, Sednev - LBNL) 2.Introduce two-moment bulk scheme (Sednev, Menon; and Del Genio) 3.Develop SCM version of this scheme for GCSS/CMAI type studies (Sednev, Menon; and Del Genio)  Aerosol-Cloud-Climate Module 1. Radiation package for internally and externally mixed aerosols (GISS team)

6. AEROSOL MODULE Model Development Aerosol microphysical scheme : PCA-QMOM (McGraw, Wright -- BNL) Quadrature method of moments: (McGraw,1997) represents aerosol as moments of particle size distribution (PSD). Changes to the aerosols (condensation and coagulation) are applied to the moments. Solution for the moments ==>>solve differential equations (same form as chemical reaction rates) Requires no assumption about the distribution. QMOM keeps track of the average PSD to obtain a set of equations for various moments. Eliminates the difficulties of numerical diffusion, faster than the sectional approach, requiring only the lower order moments. It is highly accurate; comparison with the sectional approach is included (McGraw and Wright 2003). For generally mixed multivariate aerosols: Use principal component analysis (PCA) (Yoon and McGraw 2004a,b). Track lowest (2 nd ) order moments of the covariance matrix, but enough to include desired compositional information, size information and shape information. The PCA MOM formulation is relatively fast because it does not require matrix inversion.

7. Bin Scheme Modal Scheme (Mass and Number) Sectional Methods are very expansive especially when particle dynamic is considered. The size distributions has to be known. Quadrature Method of Moments: Aerosols are represented by the moments of the size distribution. QMOM does not need any specific information on size distribution QMOM keeps track of the average particle size distribution to obtain a set of equations for various moments. These equations are much simpler than describing the size distribution. Lower moments contain enough information for aerosol application.

8. 0st: number 1st: radius 2nd: surface area 3rd: volume -> mass etc…

9. AEROSOL MODULE Model Development Aerosol heterogeneous chemical effects (Bauer -- GISS) Accounts for changes to aerosol properties due to interactions between particles Planned Treatment Sulfate – dust mixture Sulfate - sea salt mixture Sulfate - black carbon mixture Black carbon - dust mixture Internally mixed aerosols Secondary electron images of CaCO3 and China loess (left) before and (right) after reaction with gaseous HNO3 in the presence of water vapor. Laskin et al. 2005

10. Aerosol-Cloud Module CCN activation and condensation growth of cloud droplets affect CDNC and mixing ratio of cloud water at early stages of cloud lifetime. (A. Kotzick, 1999) hygroscopichydrophobic Images from Phil. Durkee, Presentation At Uinv. of Oslo http://terra.nasa.gov/FactSheets/Clouds/

11. Aerosol-Cloud MODULE Images from Rosenfeld. Based on Rudich et al. 2002, GRL

12. SCM Development Std SCM (Based on old GCM) Fixed Cloud droplet number, Ice Nuclei Allows for ice or water but not both within the same time step No TKE at all levels

13. SCM Development Std SCM Fixed Cloud droplet number, Ice Nuclei Allows for ice or water but not both within the same time step No TKE at all levels GCM Turbulence Scheme Accounts for TKE at all levels BULK

14. SCM Development Std SCM Fixed Cloud droplet number, Ice Nuclei Allows for ice or water but not both within the same time step No TKE at all levels GCM Turbulence Scheme Accounts for TKE at all levels Bin Microphysics Explicit representation of cloud microphysics (Khain and Sednev 1996) Use prescribed aerosol DSD Meyer’s scheme for ice nucl. (u,v,T,q, radiation) BULKBIN Advection

15. Final Setup Std SCM Fixed Cloud droplet number, Ice Nuclei Allows for ice or water but not both within the same time step No TKE at all levels GCM Turbulence Scheme Accounts for TKE at all levels Bin Microphysics Explicit representation of cloud microphysics (Khain and Sednev 1996) BULK BIN Use as benchmark Compare bulk scheme with bin scheme two-moment scheme Couple box module version of PCA-QMOM scheme to SCM 1 2 Setup being used for ARM case studies

16. DOE ARM Case Study MPACE - Arctic Stratus Use GISS SCM to simulate clouds observed during the MPACE campaign. Time period: Oct 5 - Oct 22 (two week episode) Use the initialisation and forcing data set from LLNL (Xie et al.) Use cloud microphysical and radar measurements from MPACE campaign Participate in SCM/LES intercomparison efforts led by Klein et al. (Cloud Parameterization and modeling Working Group) Can the SCM capture cloud morphology/structure observed during the two weeks? Case Study A: Oct 5 - Oct 8 --Multi-layer midlevel clouds Case Study B: Oct 8 - Oct 12 -- single level low clouds

17. Specific Framework Activities A.CMAI Framework New ways to represent cloud and precipitation processes in the NASA GISS GCM Use a two-moment bulk scheme: Based on Chen and Liu (2004) or version adapted for MM5 and being implemented in CAM (Morrison et al. 2005) Number conc. (N) and mixing ratio (q) of 4 classes: cloud drop, cloud ice, rain, snow Represent interactions between them : Melting, freezing, collection, sedimentation, autoconversion, accretion, evaporation, condensation, deposition, sublimation Need to treat dispersion?? Diagnose from predicted moments or prognose independently

18. Specific Framework Activities B.Common Datasets and Analysis Tools Integrate the DOE ARM MPACE and TWP-ICE case study with DIME Focus is on mixed-phase stratus and deep convective systems (Currently being analysed within the Cloud Parameterisation and Modeling Working Group) C. Common Models Efforts are being made to use these data sets and the GISS LES model (Fridlind/Ackerman) to develop parameterizations for the GISS GCM (Scale-analysis features) Part of DOE ARM project : Use statistics from LES (Fridlind, Ackerman) and observations. Examine sensitivity to: dynamics and cloud microphysics

19. Specific Framework Activities B.Common Datasets and Analysis Tools Integrate the DOE ARM MPACE and TWP-ICE case study with DIME Focus is on mixed-phase stratus and deep convective systems (Currently being analysed within the Cloud Parameterisation and Modeling Working Group) C. Common Models Efforts are being made to use these data sets and the GISS LES model (Fridlind/Ackerman) to develop parameterizations for the GISS GCM (Scale-analysis features) NEED MORE EFFORT HERE!!!!!!!!! Implement new physical processes in ModelE with help from CMAI liaisons at GMAO units Droplet activation depends on local SS, vertical velocity and thermodynamics (T,P, wv mixing ratio) --- hard to compute at grid scale, and on aerosol characteristics PDF of updraft velocity for continental and maritime air masses

20. Specific Framework Activities D.Metrics Develop useful set of metrics when accounting for aerosol-cloud effects: Changes to cloud water distribution -- liquid water path, precipitation, cloud cover (vertical as well) Precipitation rates for warm and cold clouds Detrainment of convective condensate (Amount and vertical distribution) Radiative fluxes (Surface and TOA) Cloud droplet radius (effective and volume) Drop size distribution for hydrometeors and aerosols How do these vary with and without aerosol-cloud interactions, role of large-scale dynamics ? What details of aerosol-cloud processes are most critical in representing realistic climate ? Provide details of changes to climate due to aerosol-induced cloud changes and the sensitivity of the change to various effects (aerosol-cloud treatment)

21. You are done