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Georgia Institute of Technology • School of Earth and Atmospheric Sciences

AEROSOL-CLOUD-CLIMATE INTERACTIONS IN NASA GMI Athanasios Nenes NASA-GMI Workshop November, 2003. Georgia Institute of Technology • School of Earth and Atmospheric Sciences 311 Ferst Drive, N.W. • Atlanta, GA 30332-0340 404-894-3893 • www.eas.gatech.edu. 1.

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Georgia Institute of Technology • School of Earth and Atmospheric Sciences

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  1. AEROSOL-CLOUD-CLIMATE INTERACTIONS IN NASA GMI Athanasios Nenes NASA-GMI Workshop November, 2003 Georgia Institute of Technology • School of Earth and Atmospheric Sciences 311 Ferst Drive, N.W. • Atlanta, GA 30332-0340 404-894-3893 • www.eas.gatech.edu 1

  2. Aerosol-Cloud interactions in GMI • “Input” variables • Cloud liquid water content. • Aerosol size distribution (mass based: inferred distribution) and chemistry. • Wind fields. • Static stability/turbulence (for vertical velocity). • “Output” variables • Droplet number • Droplet size distribution characteristics • Cloud optical properties • Cloud coverage • Subgrid statistics

  3. Aerosol-Cloud interactions in GMI Objective 1 (“short-term”) Incorporation ofexistingaerosol-cloud interaction modules Currently used (empirical and mechanistic) aerosol-cloud interaction modules will be incorporated within the GMI. This will allow comparison of GMI-based indirect forcing estimates with values published in the literature (a type of “reality-check”). Objective 2 (“short-term”) Development of newaerosol-cloud interaction modules for the GMI A variety of aerosol modules are/will be incorporated within the GMI (mass-based, modal, sectional). Consistent aerosol-cloud interaction schemes must be developed for each description based on the “population-splitting” methodology of Nenes and Seinfeld (JGR, 2003) for each type of aerosol module available in GMI.

  4. Aerosol-Cloud interactions in GMI • Objective 3 (“mid-term”) • Indirect forcing assessments • The lack of feedbacks from clouds allows assessments of the “first” indirect (or “Twomey”) aerosol effect only. • The suite of aerosol and aerosol-cloud interaction modules will allow a comprehensive assessment of the sensitivity to aerosol physics and parameterization. • Explicitly test sensitivity of indirect forcing estimates to: • Different size distribution representatons (e.g., modal vs. sectional) • Aerosol mixing state • Presence of a variety of “chemical” effects (organics, condensable gases and • kinetically-limited aerosol) • Size-dependant composition

  5. Aerosol-Cloud interactions in GMI • Objective 4 (“long-term”) • Testing of subgrid variability schemes • Subgrid variability schemes for aerosol and dynamics developed during the NASA-IDS CACTUS will be tested within the GMI using assimilated meteorology fields and comparison with remote sensing products. • Objective 5 (“long-term”) • Couple aerosol-interaction modules with other GMI components and processes • In-cloud chemistry (cloud droplet distribution characteristics) • Wet deposition (cloud droplet distribution characteristics). • Below & above cloud photochemistry (cloud optical properties).

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