Cloud Object Analysis and Modeling of Cloud-Aerosol Interactions and Cloud Feedbacks with the Combined CERES and CALIPSO Data Kuan-Man Xu NASA Langley Research Center (LaRC) Co-Is: David Winker (LaRC) Ping Yang (Texas A&M) Tom Zhao (NOAA)

Objectives Theme 1: cloud object analysis of integrated cloud, aerosol, and radiation data sets and Theme 2: model integration and improvement in the area of radiation parameterization and chemical reaction and aerosol microphysics Goal: to quantitatively estimate cloud feedbacks and the aerosol direct and indirect forcings under different aerosol environments (separating aerosol effect from dynamic effect observationally)

Observational Approach Cloud object analysis with CERES/CALIPSO data for thin anvil clouds and polar clouds –A cloud object is a contiguous patch of cloudy regions with a single dominant cloud-system type, –The shape and size of a cloud object is determined by the satellite footprint data and by the footprint selection criteria for a given cloud-system type More accurate cloud and aerosol measurements from CALIPSO Merging x-y view (CERES) and y-z view (CALIPSO) of cloud systems Database object.larc.nasa.gov/

Modeling approach (LaRC CRM) Simplified but more realistic third-order turbulence closure (Cheng and Xu 2006) –Predicting all first-, second- and three third-order moments (w” 3,  l ”3,q w ”3 ), a total of 12 moments –Double Gaussian pdf, representing all subgrid variabilities Double-moment microphysics scheme that predicts the mixing ratio and number concentrations of hydrometeor species (Morrison et al. 2005) –Detailed treatment of droplet activation Total aerosol number concentration Aerosol composition and aerosol size distribution PDF of w”, grid-scale w’, radiative cooling (Q R ) –Detailed treatment of ice nucleation Aerosol chemical and microphysics model (ACMM) Improved treatment of optical properties of ice clouds

Linking cloud dynamics, turbulence, aerosol and radiative processes with cloud microphysics Double moment microphysics: droplet activiation Turbulence closure: PDF(w”) Aerosols: composition, size distribution, etc. Cloud dynamics: Nonhydrostatic (u’, v’, w’,  l ’,q w ’) Radiative processes Cheng and Xu (2006) Krueger (1988) Toon et al. (1988) Zhao et al. (1996, 1997) Fu and Liou (1993) Morrison et al. (2005) Abdul-Razzak et al. (1998) Abdul-Razzak and Ghan (2000)

Test of Turbulence Closures (1-D): Cumulus from BOMEX (Cheng and Xu 2006) f (%)

Test of Turbulence Closures: Cumulus from BOMEX (2-D simulation with different vertical resolutions) (%) (a) dz = 40 m (b) fv-GCM resolution

Test of Two-moment Microphysics: MPACE Period B 2-D CRM simulation

Test of Two-moment Microphysics: MPACE Clean vs. Polluted Clouds ( Period A ) (a) Clean(b) Polluted Droplet activation follows that of Abdul-Razzak et al. (1998) and Abdul-Razzak and Ghan (2000) Probability distribution of droplet effective radius

Aerosol Chemistry and Microphysics Model (ACMM) (Dr. Tom Zhao; Co-I) ACMM is a stand-along box model and include the processes of nucleation, condensation/evaporation, coagulation, and deposition. It has the options to turn on and off certain microphysical process. The model is flexible in choosing the number of types of particles (including sea salt, sulfate, organic aerosols, etc.), the number of compositions, and the number of the bins for the consideration of computational efficiency. It is practical for coupling with multi-dimensional models (such as CRM). The aerosol model can keep track of the different particles as they evolve (see the example shown below).

The Evolution of the Size Distributions of a Mixture of Particle Types over 36-hour Period

Parameterization of cloud optical properties in GCMs and CRMs (Prof. Ping Yang, Co-I) Two Steps Model diagnostic variables Cloud bulk radiative properties effective radius Step 1 T (k) What we know from model What we want to know Step 2 T (K)

Potential problems with the parameterization of the optical properties of ice clouds in NCAR-CAM3.0 All ice crystals are assumed to be solid hexagonal columns Hexagonal column droxtalplate Aggregate Bullet rosette Hollow column In reality, an ice cloud is a mixture of ice crystals with various habits. Moreover, the distribution of the habits is a function of the particle size.

Potential problems with the parameterization of the optical properties of ice clouds in NCAR-CAM3.0 Only 10 PSDs were used to derive the parameters for the scheme –8 from Heymsfield and Platt (1984) –2 from Heymsfield (1975) Thousands of in-situ PSDs have been obtained in the major field campaigns(e.g., FIRE-I, FIRE-II, ARM, CRYSTAL-FACE, etc. ) over the past decade

Development of a new optical property parameterization of ice clouds for NCAR CAM 3.0 Maximum Dimension(  m) Particle Density (# m -3  m -1 ) 60  m 1000  m 2500  m 100% droxtal 15% bullet rosettes 50% solid columns 35% plates 45% hollow columns 45% solid columns 10% aggregates 97% bullet rosette 3% aggregates Particle size distribution Habit distribution Data: Yang et al. (Appl. Opt. 2005), Baum et al. (J.A.M. 2005) Newest ice crystal single-scattering property database, More than 1000 PSDs, newest habit distribution

Preliminary results With the same cloud temperature, the old parameterization scheme tends to overestimate the cloud effective radius, underestimate the cloud reflectivity, and overestimate the cloud transmissivity. The impacts on climate need to be further investigated.

Specific “framework” activities Implementation and improvement of two-moment cloud microphysics parameterization (Morrison et al. 2005) Cloud object data (deep convection, boundary-layer cloud objects and other types) for testing CRM improvement and GCM parameterizations (to be linked to CERES webpage and GCSS DIME webpage) Parameterization of optical properties of ice clouds can be directly transferred to other CRMs and GCMs Transfer knowledge learned from CRM to SCM regarding microphysics and aerosol-chemistry models Contribute to GCSS activities (WG1, WG4 and WG 5) Contribute to the multi-scale modeling framework effort at NSF STC and DOE ARM, as well as NSF/NOAA CPT

Specific “support” elements of the framework Implementation and improvement of two-moment cloud microphysics parameterization (Morrison et al. 2005) Improvement of ice nucleation processes Testing the parameterization of optical properties of ice clouds in GEOS-5 or GISS Model E