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Aerosol Indirect Effect: The elusive component of climate change

Aerosol Indirect Effect: The elusive component of climate change. Athanasios Nenes School of Earth and Atmospheric Sciences School of Chemical and Biomolecular Engineering Georgia Institute of Technology Georgia Air Quality & Climate Summit, May 4, 2006. photo: S.Lance. Higher A. l. bedo.

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Aerosol Indirect Effect: The elusive component of climate change

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  1. Aerosol Indirect Effect: The elusive component of climate change Athanasios NenesSchool of Earth and Atmospheric SciencesSchool of Chemical and Biomolecular Engineering Georgia Institute of TechnologyGeorgia Air Quality & Climate Summit, May 4, 2006 photo: S.Lance

  2. Higher A l bedo CCN Polluted Environment (more CCN) Humans affect clouds and hydrological cycle? Yes! By changing the concentration of Cloud Condensation Nuclei (CCN) in the atmosphere. This phenomenon is known as the “indirect climatic effect of aerosols”. Lower A l bedo CCN Clean Environment (few CCN) Increasing particles tends to cool climate (potentially alot). Climate models are the only tools for assessing the anthropogenic indirect effect, but predictions are subject to very large uncertainty.

  3. Observational evidence of indirect effect “Ship tracks”: linear features of high cloud reflectivity embedded in marine stratus clouds, resulting from aerosols emitted by ships. Ship plume incorporated into cloud Ship Track M.Kulmala: “Nucleation and Atmospheric Aerosols, 1996”

  4. Anthropogenic indirect effect: important & elusive IPCC (2001) How important is the indirect effect in Georgia?

  5. Problems with Computer Models • Cloud formation happens at smaller spatial scales than global climate models can resolve, and must be parameterized. • Aerosol-cloud interactions are complex; many processes are poorly represented, constrained and/or understood. • Climate models provide limited information about clouds and aerosols. • Quantifying the aerosol indirect effect requires a relationship between aerosol and cloud droplet number concentration. Empirical relationships often used.We need to “get away” from this.

  6. Solution: Introduce as much physics as possible Dynamics • Updraft Velocity • Large Scale Thermodynamics Particle characteristics • Size • Concentration • Chemical Composition Cloud Processes • Cloud droplet formation • Drizzle formation • Rainwater formation • Chemistry inside cloud droplets collision/coalescence drop growth activation particles “Population” links: Aerosol - CCN – Droplets – Drizzle All the links need to be incorporated in global models.

  7. Cloud drop formation in GCMs: using first principles • Advantages • Explicit representation of aerosol chemistry and size distribution. • Explicitly calculate droplet number and size distribution based on first principles. • Chemically complex and externally mixed aerosol can be treated. t drop growth Smax activation aerosol S • Implications • Much slower than empirical relationships. • Need for subgrid cloud dynamics (updraft velocity). These quantities are not explicitly resolved by GCMs and must be parameterized. • Detailed information on aerosol size distribution and chemical composition is needed.

  8. Nenes and Seinfeld (2003) cloud formation relationship Input: P,T, vertical wind, particle characteristics. Output: Droplet number & size distribution, Smax How: Solve an algebraic equation. • Features: • 103 times faster than full numerical cloud model. • can be implemented in Global Climate Model. • can treat complex chemical composition. • in-situ validation for a wide range of airmass and cloud types (Meskhidze et al., JGR; Fountoukis et al., in review)

  9. Aerosol Indirect Effect: How do we estimate it? • We use a global climate model (GCM) • simulation with current day emissions • simulation without anthropogenic emissions • (“preindustrial” scenario) • compute cloud droplet number, optical depth • and change in radiation from the aerosol-cloud • interactions (“indirect forcing”) • compare annual average forcing to greenhouse • gas warming (~ 2.5 W m-2) • Net forcing (greenhouse + indirect) is used as a • proxy for climate change.

  10. NASA Global Modeling Initiative (GMI) http://gmi.gsfc.nasa.gov/gmi.html Modeling the Indirect Effect Global Model #1 • 3-D chemistry-transport model (CTM) • Multiple “packages” for e.g., chemistry & aerosol • Metrological inputs from GCMs (GEOS-4 FVGCM & GISS-II’) or data assimilation systems (NASA DAO) • Any vertical resolution; horizontal resolutions of 1°x1.25°, 2°x2.5°, or 4°x5° • Multi-year assessment simulations

  11. Modeling the Indirect Effect • Global Model #2 • NASA GISS II’ GCM (fully-coupled climate model) • 4’5’ horizontal resolution • 9 vertical layers (27-959 mbar) • Aerosol Microphysics • The TwO-Moment Aerosol Sectional (TOMAS) microphysics model (Adams and Seinfeld, JGR, 2002) is applied in the simulations. • Two moments of the size distribution (mass and number) are tracked and conserved for each size bin in the microphysical processes of coagulation, condensation/evaporation and nucleation. • A bulk microphysics version is also available & used.

  12. Modeling Framework • Emissions • IPCC scenarios (current day, preindustrial) • Tracers • Model includes 30 size bins from 10 nm to 10 m. • For each size bin, model tracks: • Aerosol number • Sulfate mass • Sea-salt mass • Gas-phase species: H2O2, SO2, DMS and H2SO4 • Cloud microphysical parameters: • Droplet number • Effective radius • Optical depth

  13. Modeling Framework In-cloud updraft velocity • Cloud-base updraft velocity is necessary to calculate • droplet number. GCMs cannot resolve this and must be • parameterized as well. Approaches considered: • Prescribed (marine: 0.3-0.5 ms-1; continental: 0.5-1 ms-1). • Diagnosed from large-scale TKE resolved in the GCM. Spectral width determined from scaling arguments or observations. • We also use an alternative proposed by Lance et al., JGR, (2004). This uses a combination of empirical aerosol-cloud droplet correlations and a parcel model to infer a “basecase” updraft velocity.

  14. North American pollution plumes Long-range transport European and Asian pollution plumes Biogenic emissions (cm-3) Cloud Droplet Number: Major features

  15. Cloud droplet number Change from Preindustrial to Present • Conditions • GISS II’ GCM • Water vapor accommodation coefficient = 0.042 CDNC (cm-3) CDNC (%)

  16. Annual average indirect forcing Global annual average ~-1 Wm-2 Georgia annual average ~-4 Wm-2

  17. Aerosol Indirect Forcing: Estimating its uncertainty • How sensitive are estimates of forcing to the: • aerosol microphysics used? • GCM wind fields? • cloud updraft velocity? • errors from application of cloud droplet • formation theory? • poorly constrained thermokinetic parameters? Interested in global distributions but focus on Georgia

  18. Aerosol Indirect Effect: Sensitivity to accommodation coefficient • Conditions • GISS II’ GCM • Accommodation coefficient range (1.0 - 0.01) CDNC (a change) CDNC (Present-Preind) Global Georgia ~ 40 cm-3 ~ 100 cm-3 ~ 750 cm-3 ~ 250 cm-3

  19. Aerosol Indirect Effect: Uncertainty from errors in theory. Forcing autoconversion Global Georgia ~ 0.5 W m-2 ~ 20 % ~ 0.5-0.8 W m-2 ~ 25-35 %

  20. Indirect Effect: Sensitivity to met.field Global annual average: -0.75 Wm-2 to -1.08 Wm-2 GISS-II’ Georgia annual average: -4.0 Wm-2 to -3.5 Wm-2 DAO GEOS-4

  21. SUMMARY • Accurate parameterizations are being developed that address key aerosol-cloud interactions at their appropriate scale, and are linked together consistently. • Complex compositional effects and aerosol heterogeneity can for the first time be treated in GCMs. • By linking parameterizations at their appropriate scale, future integration of additional interactions is possible. • A major issue is constraining chemical composition information (especially for the organics) to quantities available from GCM simulations. In-situ observations are key for this.

  22. SUMMARY • Global Indirect forcing: • Annual average: -0.96 W m-2 (50% uncertainty) • Georgia Indirect forcing: • Annual average: -4.0 W m-2 (50% uncertainty) • What does this mean? • On a global scale, warming from greenhouse gases is stronger (consistent with global change phenomena). • In Georgia, indirect effect is equal or stronger than greenhouse gas warming (consistent with minor changes in local temperature). This will change in the future with improved air quality and increased CO2

  23. ACKNOWLEDGMENTS People Nicholas Meskhidze, Gatech Rafaella Sotiropoulou, Gatech Christos Fountoukis, Gatech Jeessy Medina, Gatech John Seinfeld, Caltech Peter Adams, Carnegie Mellon Robert Griffin, UNH Laura Cottrell, UNH Funding NSF CAREER NASA NOAA Blanchard-Milliken Fellowship GA Tech Startup For more information and PDF reprints, go to http://nenes.eas.gatech.edu photo: S.Lance

  24. THANK YOU !

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