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Constraining the effect of aerosol on cloud formation

Constraining the effect of aerosol on cloud formation. Markus D. Petters Colorado State University. Collaborators: Trude Eidhammer, Sonia Kreidenweis, Paul DeMott, Anthony Prenni, Christian Carrico. Sponsors.

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Constraining the effect of aerosol on cloud formation

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  1. Constraining the effect of aerosol on cloud formation Markus D. PettersColorado State University Collaborators: Trude Eidhammer, Sonia Kreidenweis, Paul DeMott, Anthony Prenni, Christian Carrico Sponsors

  2. Motivation: Changes in cloud albedo are linked to changes in cloud droplet number concentration Twohy et al., 2005

  3. The formation of cloud droplets in updrafts

  4. What are the aerosol chemical properties in the atmosphere? } } Size distribution parameters (number concentration, mode diameter, standard deviation) Aerosol chemical properties, e.g. (NH4)2SO4 At minimum 12 input parameters are needed to calculate cloud droplet number concentration } } Cloud dynamics (updraft velocity) Thermodynamics and mass accommodation

  5. The hygroscopicity parameter H2O + solute Kelvin term saturation ratio over droplet water activity Parameterize water activity hygroscopicity parameter Petters and Kreidenweis, 2007

  6. From measured CCN data we can infer the hygroscopicity of the aerosol

  7. Ranked hygroscopicity based on CCN measurements for atmospheric aerosols

  8. We propose to use kappa to represent chemical composition in models } Aerosol chemical properties, e.g. (NH4)2SO4 Note that kappa and surface tension cannot be measured independently inside a CCN instrument if the aerosol obey the -3/2 relationship.

  9. Question: What variations in are significant?

  10. From the previous data it seems that we can estimate  within ±30% How well does this constrain cloud droplet concentrations (I will argue ±0-30%)? A ±0-30% change in Nd may or may not be climatically significant, depending on cloud properties First define significant

  11. } Size distribution parameters (number concentration, mode diameter, standard deviation) Still 9 input parameters are needed to calculate cloud droplet number concentration } } Cloud dynamics (updraft velocity) Thermodynamics and mass accommodation } Aerosol chemical properties, e.g. (NH4)2SO4

  12. For k > 0.2 we can quickly estimate the relationship between diameter and supersaturation • Parcel model integrates over CCN activation spectrum • Cloud droplet concentration uniquely determined by A(s) • A(s), and thus cloud droplet concentration, depends uniquely on activation parameter

  13. Let us fix these for now (T = 280 K, p = 800 hPa) The same parameters but regrouped Vary these variables randomly over a physically reasonable domain (w = [1,10] m s-1, N = [10,50000] cm-3,a = [0.04, 1], g =[1.2, 2], Y = {s/a= [0.05, 0.08] J m-2, Dg = [30, 500] nm, k = [0.1, 1]} } Thermodynamics Where s/a, Dg , k are grouped into the activation parameter 

  14. How does a percent change in input Xi transmute into a percent change in droplet concentration Relative sensitivity of CDNC to Xi

  15. The Twomey effect: Change in droplet concentration due to the addition of CCN number S(Nt) = 1: 10% increase in particle concentration equals a 10% increase in droplets S(Nt) = 0.1: 10% increase in particle concentration equals a 1% increase in droplets Activated fraction = 1: the entire size distribution activated Activated fraction = 0: no particles activated

  16. Chemical effect: Change in droplet concentration due changes in particle hygroscopicity (k) • When all particles activate chemistry does NOT matter S(k) = 0 • Sensitive at low activated fraction, a 10% increase in hygroscopicity • ~10% increase in CDNC

  17. How to make sense of all the information? If I go through every parameter you will fall asleep Microphysical effects (size distribution) Dynamical effects (updraft + growth kinetics) Chemical effects (hygroscopicity + surface tension)

  18. Look at sensitivity scaling 70% (size distribution) 20% (updraft + growth) 10% (hygroscopicity + surface tension)

  19. Microphysical effects vs. total sensitivity Majority of cases >70% of the sensitivity is due to particle size distribution At high activated fraction this approaches 100%

  20. Dynamical effects vs. total sensitivity For the majority of cases <20% of the sensitivity is due to updraft and droplet growth kinetics

  21. Chemical effects vs. total sensitivity For all cases <10% is due to hygroscopicity and surface tension effects

  22. Disclaimers: Analysis only valid for • Lognormally-distributed, single mode aerosol • - giant CCN are omitted • Chemically homogenous size distribution • -  invariant with size • Adiabatic updrafts • - no turbulent mixing/entrainment, etc. • Some parameters are better constraint • - hygroscopicity vs. accommodation coefficient • But: It is difficult to imagine that these effects significantly reduce the dominant effect of size distribution.

  23. Conclusions • The hygroscopicity parameter () organizes CCN data in critical supersaturation vs. dry diameter space and in groups of similar hygroscopicity • The activation parameter () and hygroscopicity parameter () reduce the number of dimensions that need be considered in aerosol-cloud interaction simulations • Monte-Carlo type analysis ranks the sensitivity of warm cloud formation tosize distribution (70%), dynamical effects (20%), andchemical effects (10%).

  24. Chemical effects vs. dynamic effects

  25. Dynamical effect: Change in droplet concentration due changes in updraft (w)

  26. Dynamical effect: Change in droplet concentration due changes in condensation coefficient (a) Negative sensitivity: Alpha increases, droplets take up water faster, max supersaturation decreases.

  27. Size distribution: Change in droplet concentration due changes in geometric standard deviation

  28. Size distribution: Change in droplet concentration due changes in activation parameter (Y)

  29. The hygroscopicity parameter H2O + solute Kelvin term saturation ratio over droplet water activity Parameterize water activity hygroscopicity parameter Petters and Kreidenweis, 2007

  30. The hygroscopicity parameter, , relates critical supersaturation and dry particle diameter

  31. a shift in activation diameter at constant supersaturation is equivalent to a shift in  The hygroscopicity parameter, , relates critical supersaturation and dry particle diameter

  32. The hygroscopicity parameter, , can be derived from growth factor and CCN data For many (almost all) substances sub- and supersaturated hygrocopicities compare within 30%

  33. Usually the  values from HTDMA and CCN measurements are in reasonable agreement

  34. Assumption 2: ZSR Relationship water volume contributed by solute A water volume contributed by solute B Total water volume of the mixture + = The hygroscopicity parameter mixes linearly with aerosol volume fraction H2O + Solute A+B H2O + solute A H2O + solute B + Assumption 1: Volume Additivity Volume I + Volume II = Volume Mixture  mixes linearly with solute volume fraction 

  35. organic-organic mixtures organic-inorganic mixtures organic + insoluble organic mixture Mixing rule predicted hygroscopicity vs. CCN measured hygroscopicity from published data Svenningsson et al., ACP Broekhuizen et al., GRL Raymond and Pandis, JGR

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