Published on January 2, 2008
Slide1: ﴀ ﴀ 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 AEROSOL-CLOUD-CLIMATE INTERACTIONS Athanasios Nenes Georgia Power Workshop April 14, 2003 Slide2: Aerosol indirect effect: the elusive component of climate Slide3: Why is the indirect effect poorly understood? Indirect forcing uncertainties arise because: Aerosol-cloud interactions take place at smaller spatial scales than climate models can resolve, and must be parameterized. Aerosol-cloud interactions are complex; many aspects are unknown or poorly understood. Climate models provide limited information about clouds, and aerosols. Central problem of indirect effect: Determine the relationship between aerosol and cloud radiative properties, using the limited information available by climate models. This problem has historically been reduced to finding the relationship between aerosol concentration and cloud droplet number concentration. Two approaches have been adopted. Slide4: First approach: empirical Most studies utilize this approach. Large predictive uncertainty, without “chances” of improving. Slide5: Source of uncertainty: Droplet formation is complex. S t Cloud supersaturation is affected by changes in the CCN population Activation is a highly nonlinear process. Empirical relations cannot capture this accurately enough. Slide6: Second approach: from first principles. Cloud droplet number balance in each grid box of the model: Activation is the direct aerosol-cloud microphysical link. Two types of information are necessary for its calculation: Aerosol chemistry and size distribution (CCN) Representation of subgrid dynamics in cloud-forming regions. Embedding a numerical activation model is too slow; must use a parameterization. Existing parameterizations are derived assuming idealized cloud dynamics, aerosol composition and size distribution. Are they good enough? (Hint: No). Slide7: Fitting ambient size distributions to prescribed functional form introduces biases which can be important for indirect effect. Prescribed size distribution bias Slide8: Unaccounted “chemical” effects on droplet activation Slightly soluble compounds (Shulman et al., 1996): They add solute to the drop as it grows; this facilitates their ability to activate. Examples: organics (succinic acid), CaSO4. Soluble gases (Kulmala et al., 1993): They add solute to the drop as it grows; this facilitates their ability to activate. Examples: HNO3, HCl, NH3. A(g) A(aq) A(g) A(aq) A(g) A(aq) Slide9: Unaccounted “chemical” effects on droplet activation Surface-active soluble compounds (Facchini et al., 1999): They decrease surface tension of droplets; this facilitates their ability to activate. Examples: organics (succinic acid, humic substances). The departure from pure water values can be very large! Surface tension change is different for each CCN. C(mol l -1 ) 1e-4 1e-3 1e-2 1e-1 Surface tension (dyne/cm) 50 55 60 65 70 75 Droplet concentration range at activation Surface tension data from cloud and fog water samples. Pure water Charlson et al., Science, 2001 Slide10: Unaccounted “chemical” effects on droplet activation Film-forming compounds (e.g., Feingold & Chuang, 2002): They can slow down droplet growth. Once the film breaks, rapid growth is resumed: Examples: hydrophobic organics. Such substances do not alter droplet thermodynamics; they affect the kinetics of droplet growth. If present, such substances can strongly affect droplet number. Slide11: Cloud droplet formation dynamics with different chemical effects log10(size) log10(concentration) log10(size) Soluble CCN vs. CCN with films movie movie Slide12: Chemical effects: assessment of their importance. 10% soluble organics (no surface tension change) Updraft Velocity (m s-1) Nd /Nd, basecase Basecase concentration 2 10% film-forming organic 10% surface-active organics 0.80 1.00 1.20 1.40 1.60 1.80 2.00 2.20 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Nenes et al., GRL, 2002 Slide13: Chemical effects: summary, implications for parameterizations Chemical effects are seen to be important for many conditions, and even be more effective than doubling aerosol concentrations. Chemical effects can be synergistic. One effect can be important for low updrafts (e.g. soluble gas effects) and another at higher updrafts (e.g. surface tension effects). This would lead to a systematic increase in droplet number for almost any cloud type. Lack of including them in activation parameterizations can lead to important uncertainties in indirect forcing. What does all this mean for current aerosol activation parameterizations? They are not adequate. We need to develop a new parameterization. Slide14: New parameterization: underlying ideas Use sectional representation of aerosol chemistry and size distribution. Each section can: have its own chemical composition i-th section characterized by (i-1, i) boundaries piecewise linear profiles between boundaries Multiple populations with their own distributions can co-exist and compete for water vapor. Modified Köhler theory for computing CCN properties. Slide15: New parameterization: underlying ideas Properties calculated from energy, mass balances. Adiabatic parcel framework used: Lagrangian framework of reference Parcel properties are uniform Constant updraft velocity Parcel pressure is equal to ambient S Smax t Derive expression for the condensational growth of CCN; include within the supersaturation balance for the parcel, and solve for the maximum. Challenge: to derive an expression of the condensation rate at Smax. Solution: “Population splitting”. Slide16: Each population has an analytical expression of the condensation rate. Supersaturation d(CCN)/dS Smax Spart Population 2: Size close to critical diameter; recently activated Population 1: Size very different from critical diameter New parameterization: “Population Splitting” Spart can be analytically derived or obtained from simulations. Slide17: New parameterization: Formulation Input: P,T, updraft velocity (cooling rate), RH, aerosol characteristics. Output: Droplet number, Smax How: Solve the algebraic equation for Smax (numerically) Water vapor condensation from kinetically “limited” CCN Water vapor condensation from CCN that “instantaneously” activate Slide18: Performance of new parameterization (200 test cases) Nenes and Seinfeld, in press Slide19: Performance of existing parameterization (Ghan et al., 2000) Nenes and Seinfeld, in press Slide20: New parameterization: marine aerosol with surfactants 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.1 1 10 Updraft Velocity (m/s) Activation Fraction Numerical Simulation (s.t. effects present) Parameterization (s.t. effects present) Parameterization (s.t. effects absent) Nenes and Seinfeld, in press Slide21: New parameterization: assessment. A powerful activation parameterization has been developed for aerosol of: “arbitrary” (sectional) size distribution, multiple populations of aerosol (e.g. sulfate+seasalt) present, complex chemical and size-dependant composition (surfactants, slightly soluble substances present). Furthermore, it: is fast (103-104 times faster than full numerical parcel model). uses minimal amount of empirical information. exhibits increased robustness and accuracy. To be included in the future: incorporate other activation effects (e.g., films). Experiments will be done to provide an appropriate data set. parcel-scale entrainment/mixing for diabatic activation. collision-coalescence to parameterize aerosol-cloud lifetime effects. Slide22: New parameterization: implementation in global model PDF of updrafts in cloud-forming regions Number of activated droplets for updraft w (new parameterization) Currently being implemented into the GISS/TOMAS model (Adams and Seinfeld, JGR, 2002). Slide23: GENERAL SUMMARY The indirect effect of atmospheric aerosols is one of the most important and challenging aspects of climate prediction science. A large source of uncertainty can be due to incomplete consideration of chemical composition. These “chemical” effects are included in a new parameterization of aerosol-cloud interactions. Parameterizations have been developed, and included within a comprehensive climate model (GISS/TOMAS). Acknowledgments: NASA, ONR
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