Aerosols: What are we missing? What should we do in the future?

1. Aerosols: What are we missing? What should we do in the future? Peter J. Adams Carnegie Mellon University Chemistry-Climate Interactions Workshop February 11, 2003

2. Overview • How good are models? • What observations are needed? • How to deal with subgrid variability? • Where do we stand in modeling the indirect effect?

3. How good are models?

4. Uncertainty in Direct Forcing Estimates

5. COSAM (nmol SO4 / mol air) Barrie et al., Tellus 53B, 615-645, 2001

6. Model Capabilities • IPCC 2001 workshop compared 11 models against observations: • Sulfate: monthly average concentrations generally within a factor of two • Other species are “inferior” • BC: factor of 10 • Model-model discrepancies strong in free and upper troposphere • Models differ in terms of transport distance • Insufficient for climate studies

7. Reasons for Model Uncertainties • Sparse data • Spatial: free troposphere / remote regions • Temporal: short-term field campaigns • Measurement difficulties • Black carbon • Comparisons often use inconsistent meteorological fields • GCM aerosol models for climate studies • But GCM met fields generally do not match time period of observations • Especially problematic for short-term comparisons (i.e. field campaigns)

8. What observations are needed?

9. Future Directions: Models / Observations • New data sets • Satellite instruments: MODIS, MISR, others • Lidar • Consistent meteorological fields • GCMs with nudging capabilities • GCM / CTM combinations (e.g. GISS GCM and GEOS-CHEM) • “Correct” for meteorological differences • Detailed comparisons not “glamorous” but sorely needed • Need to move from minimal to systematic comparisons

10. Future Directions: Observations • Need more long-term data sets • Field campaigns provide process understanding but are weak at providing aerosol climatologies • AERONET as a prototype • Other ideas • Lidar networks • Size and chemically resolved data • Regular aircraft sampling (John Ogren) • Coordination

11. AERONET • ~180 sun photometers across globe • From 1993- • Standardized instruments and processing • Provides: spectral optical depth • Infers size distribution for column • Levels of data: raw, quality-assured, climatological • Available on web

12. AERONET Holben et al., JGR 106, 12067-12097, 2001

13. How to deal with subgrid variability?

14. Subgrid Variability: Direct Effect • Calculated direct forcing with and without subgrid variability in clouds and RH • Limited area model (2 x 2 km) Forcing GCM: -1.92 W m-2 LAM: -3.09 W m-2 Haywood et al., GRL 24, 143-146, 1997.

15. Challenge: Subgrid Variability • Direct Effect • water uptake is nonlinear function of RH • Indirect Effect • subgrid spectrum of updraft velocities and cooling rates • Microphysics • nucleation is often a subgrid phenomenon • Models and observations

16. Confronting Subgrid Variability • Frameworks • Brute force (probably not) • Probability distribution functions • Spatial homogenization (computational mechanics) • Data availability • Observations: aircraft / satellites • Models: Large eddy simulations

17. Probability Distribution Functions • Cloud modeling • P(w, ql, qt) w: updraft velocity ql: liquid water potential temperature qt: total specific water • Functional form of P assumed • Parameters describing P become prognostic variables

18. Larson et al., JAS 59, 3519-3539, 2002

19. Probability Distribution Functions • Applications • Diagnose aerosol variability from cloud parameters • Integrate prognostic variability into scheme • Focused studies in single column models • Use in GCMs and CTMs

20. Where do we stand in modeling the indirect effect?

21. Mechanistic vs. Empirical Models Mechanistic: number of cloud drops depends on number of particles large enough to activate Number Cloud Droplets (cm-3) Boucher & Lohmann, 1995 Particle Size Sulfate Mass (mg m-3) Empirical: number of cloud drops correlated with sulfate mass based on observations

22. Empirical Approach: Limitations I: Martin et al. [1994]: -0.68 W/m2 II: Martin et al. with background CCN: -0.40 W/m2 III: Jones et al. [1994]: -0.80 W/m2 IV: Boucher and Lohmann [1995]: -1.78 W/m2 Cloud Droplets (cm-3) Sulfate Mass (mg m-3) “It is argued that a less empirical and more physically based approach is required…” Kiehl et al., JGR 105, 1441-1457, 2000

23. Aerosol Microphysics Algorithms Modal Ni, Dpgi, si Variable si makes a difference Moment Prognostic equations for Mi

24. Aerosol Microphysics Algorithms Modal Ni, Dpgi, si Variable si makes a difference Moment Prognostic equations for Mi Sectional Mass(species, bin) Moment-Sectional Number(bin) Mass(species, bin)

25. M1 N1 M2 N2 ... ... mo 2mo … Mass Two-Moment Sectional Algorithm • Two moments of the size distribution (mass and number) are tracked for each size bin. • The average size of particles in a given section is not constant with time • Two-moment method conserves both mass and number precisely • Prevents numerical diffusion present in single-moment methods • Excellent size resolution: 30 sections from .01 mm to 10 mm Tzivion et al., JAS 44, 3139 – 3149, 1987 Adams et al., JGR 10.1029/2001JD001010, 2002

26. CCN 0.2%

27. Microphysical Models: Uncertainties • Particulate Emissions • Most sulfate aerosols results from gas-phase SO2 emissions • Particulate sulfate: <5% of anthropogenic sulfur emissions • Nucleation of new aerosol particles • Important uncertainties in mechanism and rate • Both processes contribute significant numbers of small particles • insignificant contribution to sulfate mass • important contribution to aerosol number concentrations and size distributions • Must quantify sensitivity to these uncertainties

28. Sensitivity Scenarios • Base Case • 1985 sulfur emissions • all emissions as gas-phase SO2 • nucleation based on critical concentration from binary (H2SO4-H2O) theory • Primary Emissions • 3% of sulfur emissions as sulfate • Enhanced Nucleation • critical H2SO4 concentration factor of 10 lower • Pre-industrial • no anthropogenic emissions (but no sea salt)

33. Vertical Profiles

34. Impact of Particulate Emissions

35. Continental / Marine

36. Implications • More aerosol models are including explicit microphysics to predict CCN concentration • Such models are sensitive to inputs that influence aerosol number • Nucleation / Primary particles • Physical insight into factors controlling CCN • Needs • Size-resolved emission inventories • Better understanding of nucleation • Aerosol number budgets (e.g. sea-salt)

37. Parameterizations: prescribed size distribution bias Fitting ambient size distributions to prescribed functional form introduces biases which can be important for indirect effect. This aerosol is “shifted” to larger sizes. This will bias droplet number Predictions. source: Roberts et al., in press

38. Current parameterizations: other weaknesses • Lack of explicit treatment of mass transfer limitations in droplet growth; this has been shown to be important for polluted conditions (Nenes et al., 2001). • Empirical correlations are used in many. They are derived from numerical simulations and can introduce biases when used outside their region of applicability. • They lack important chemical effects that can influence cloud droplet formation. Such effects are the presence of : • slightly soluble species in the aerosol (Shulman et al., 1996) • water soluble gas-phase species (Kulmala et al., 1993) • surface tension changes from surface-active species in the aerosol (Facchini et al., 1999). • changes in water vapor accommodation coefficient from the presence of film-forming compounds (Feingold & Chuang, 2002).

39. Chemical effects: assessment of their importance. Calculate the maximum change in cloud properties when a chemical effect is present. Numerical cloud parcel model used for the calculations. marine aerosol 0.1 m s-1 0.3 m s-1 1.0 m s-1 3.0 m s-1 Cooling effect insoluble organic no Ds organic with Ds 5 ppb HNO3 x 2 conc. Warming effect Chemical effects can be as effective in altering cloud properties as doubling the aerosol concentrations!

40. New effect: Black Carbon heating Black carbon exists in polluted aerosol; it absorbs visible sunlight and heats the surrounding air. This can leads to decreased cloud coverage, and climatic warming. If black carbon is included in cloud droplets, the heat released can increase the droplet temperature enough to affect the droplet equilibrium. This is a new effect. BC core drop Absence of heating Presence of heating: droplet and gas phase get heated

41. Black Carbon heating: potential effect on drizzle BC can effectively decrease the probability for drizzle formation. A heating mechanism can lead to climatic cooling! This effect can be parameterized (not shown). Is it important? We don’t know yet. 500 parcel average Cloud top Cloud base

42. Conclusions • Observations • Long-term • Standardized networks • Vertical profiling • Satellites

43. Conclusions • Observations • Long-term • Standardized networks • Vertical profiling • Satellites • Comparisons • Systematic and critical • Assimilated / nudged meteorologies • Correct for meteorology

44. Conclusions • Models • Explicit microphysics • Particle number budgets • Primary emissions • Nucleation

45. Conclusions • Models • Explicit microphysics • Particle number budgets • Primary emissions • Nucleation • Activation: “chemical effects” • Other processes? • E.g. Could black carbon lead to cooling? • Subgrid parameterizations