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

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

How good are models?

Uncertainty in Direct Forcing Estimates

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

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

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)

What observations are needed?

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

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

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

AERONET Holben et al., JGR 106, , 2001

How to deal with subgrid variability?

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, , 1997.

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

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

Probability Distribution Functions  Cloud modeling P(w,  l, q t ) w: updraft velocity  l : liquid water potential temperature q t : total specific water  Functional form of P assumed  Parameters describing P become prognostic variables

Larson et al., JAS 59, , 2002

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

Where do we stand in modeling the indirect effect?

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

Empirical Approach: Limitations I: Martin et al. [1994]: W/m 2 II: Martin et al. with background CCN: W/m 2 III: Jones et al. [1994]: W/m 2 IV: Boucher and Lohmann [1995]: W/m 2 “It is argued that a less empirical and more physically based approach is required…” Cloud Droplets (cm -3 ) Sulfate Mass (  g m -3 ) Kiehl et al., JGR 105, , 2000

Aerosol Microphysics Algorithms Modal N i, D pgi,  i Variable  i makes a difference Moment Prognostic equations for M i

Aerosol Microphysics Algorithms Modal N i, D pgi,  i Variable  i makes a difference Moment Prognostic equations for M i Sectional Mass(species, bin) Moment-Sectional Number(bin) Mass(species, bin)

 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  m to 10  m Two-Moment Sectional Algorithm m o 2m o … Mass M1N1M1N1 M2N2M2N2... Tzivion et al., JAS 44, 3139 – 3149, 1987 Adams et al., JGR /2001JD001010, 2002

CCN 0.2%

Microphysical Models: Uncertainties  Particulate Emissions Most sulfate aerosols results from gas-phase SO 2 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

Sensitivity Scenarios  Base Case 1985 sulfur emissions all emissions as gas-phase SO 2 nucleation based on critical concentration from binary (H 2 SO 4 -H 2 O) theory  Primary Emissions 3% of sulfur emissions as sulfate  Enhanced Nucleation critical H 2 SO 4 concentration factor of 10 lower  Pre-industrial no anthropogenic emissions (but no sea salt)

Vertical Profiles

Impact of Particulate Emissions

Continental / Marine

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)

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

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).

insoluble organic no  organic with  5 ppb HNO 3 x 2 conc. 0.1 m s m s m s m s -1 marine aerosol Cooling effect Warming effect 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. Chemical effects can be as effective in altering cloud properties as doubling the aerosol concentrations!

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. drop BC core Absence of heating Presence of heating: droplet and gas phase get heated

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 base Cloud top

Conclusions  Observations Long-term Standardized networks Vertical profiling Satellites

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

Conclusions  Models Explicit microphysics Particle number budgets  Primary emissions  Nucleation

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