Aerosol Indirect Effects Steven Ghan Pacific Northwest National Laboratory

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Aerosol Indirect EffectsSteven GhanPacific
Northwest National Laboratory

• Definitions and mechanisms
• Evidence
• Modeling
• What have we learned?
• How has ARM contributed?
• Current ARM projects
• Goals
• A strategy of attack
• An invitation

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Definitions and Mechanisms

• Indirect effect influence of aerosol on cloud
  droplet or crystal number and hence cloud optical
  depth
• First indirect effect influence through impact
  on effective radius, with no change in water
  content of cloud
• Second indirect effect influence on cloud
  optical depth through influence of droplet number
  on mean droplet size and hence initiation of
  precipitation
• Semi-direct effect influence of aerosol
  absorption of sunlight on cloud liquid water and
  hence cloud optical depth
• First dispersion effect influence on cloud
  optical depth through influence of aerosol on
  dispersion of droplet size distribution, with no
  change in water content of cloud
• Second dispersion effect influence on cloud
  optical depth through influence of aerosol on
  dispersion and hence initiation of precipitation
• Glaciation indirect effect influence of aerosol
  on conversion of haze and droplets to ice
  crystals, and hence on cloud optical depth and
  initiation of precipitation

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A little math
Cloud optical depth
Cloud water mixing ratio
Effective radius
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Evidence from Satellite Ship Tracks
Rosenfeld, Kaufman, and Koren, Switching cloud
cover and dynamical regimes from open to closed
Benard cells in response to the suppression of
precipitation by aerosols, Atmos. Chem. Phys.
Disc., submitted, 2005.
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More Evidence from Satellite
Breon, Tanre, and Generoso, Aerosol effect on
cloud droplet size monitored from
satellite,Science, 2002
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Evidence from MODIS
Rosenfeld, Kaufman, and Koren, Switching cloud
cover and dynamical regimes from open to closed
Benard cells in response to the suppression of
precipitation by aerosols, Atmos. Chem. Phys.
Disc., submitted, 2005.
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Evidence from ARM Data
Kim, Schwartz, Miller, and Min, Effective radius
of cloud droplets by ground-based remote sensing
Relationship to aerosol, JGR 2003
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More Evidence from ARM Data
Feingold, Eberhard, Veron, and Previdi, First
measurements of the Twomey indirect effect using
ground-based remote sensors. GRL, 2003.
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Evidence from In Situ Measurements
Conant, VanReken, Rissman, Varutbangkul, Jonsson,
Nenes, Jimenez, Delia, Bahreini, Roberts, Flagan,
and Seinfeld Aerosol--cloud drop concentration
closure in warm cumulus. JGR 2004.
Meskhidze, Nenes, Conant, and Seinfeld
Evaluation of a new cloud droplet activation
parameterization with in situ data from
CRYSTAL-FACE and CSTRIPE. JGR 2005.
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Modeling

• Prognostic droplet number is now widely used in
  global models used to estimate indirect effects.
• Much of the uncertainty in global estimates of
  indirect effects arises from uncertainty
  associated with turbulence, entrainment, and
  subgrid variability in cloud microphysics, which
  are critical challenges for all cloud
  parameterizations.
• Additional uncertainty comes from the
  concentrations and physical properties of the
  aerosol.
• Cloud-resolving models with explicit microphysics
  have been used effectively to explore indirect
  effects.
• Cloud-resolving models are beginning to use bulk
  microphysics with prognostic droplet number.
  These can be embedded in GCMs.
• Prognostic crystal number is now being applied to
  both global and cloud-resolving models.

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What Have We Learned?

• Indirect effects seem to be large enough to be
  climatically important.
• The second indirect effect could be as large as
  the first.
• Increasing aerosols might enhance precipitation
  from convective clouds.
• Droplet dispersion seems to be correlated with
  droplet number.
• Glaciation is likely to reduce cloud optical
  depth and locally enhance precipitation.
• Many feedbacks and dependencies need to be sorted
  out.
• The uncertainty in estimates is not well known.
• The models are not yet well constrained by
  measurements.

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Issues

• Most of observed variability in cloud albedo is
  due to variability in water path, independent of
  droplet/crystal number.
• Indirect effects are particularly difficult to
  detect at the SGP. Clouds are seldom adiabatic
  and vary widely in depth.
• Uncertainty in boundary conditions is a challenge
  for cloud and single-column modeling.
• It is difficult to measure CCN and IN
  concentration near cloud base with remote
  sensing.
• Multiple equilibrium states (polluted-nonprecipita
  ting, clean-precipitating) for the same aerosol
  source may exist.

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How Has ARM Contributed?

• First physically-based prediction of droplet
  number in global models.
• First physically-based parameterization of
  droplet nucleation.
• Application of the physically-based treatment to
  estimate first second indirect effects.
• First estimate of the first dispersion effect.
• Development of the first physically-based
  treatment of autoconversion.
• First spatially-resolved estimate of uncertainty
  of first indirect effect.
• First ground-based estimates of the first
  indirect effect.
• Development of the first ground-based methods to
  retrieve hygroscopic growth and CCN profiles.
• First detection of longwave indirect effect in
  Arctic.

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Current ARM and ASP Indirect Effects Projects
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Current ARM Indirect Effects Projects (Cold and
Mixed-Phase Clouds)
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Indirect Effects Goals for ARM

• Provide the aerosol, updraft and cloud
  measurements needed to directly measure indirect
  effects and to achieve closure for CCN,
  nucleation, and rain initiation.
• Provide the forcing needed to drive and evaluate
  indirect effects simulations by cloud-resolving
  and single column models.
• Use error propagation to quantify uncertainty in
  indirect effects simulated by
• cloud-resolving models
• single-column models
• global climate models.
• Reduce uncertainty in simulated indirect effects
  in global models by 50.

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A Strategy of Attack

• For past (M-PACE, MASRAD) and future (CLASIC,
  ISDAC) IOPs
• Measure winds, T, RH, aerosol size distribution
  and composition, CCN(S), IN, Nd, Ni, LWC, IWC,
  droplet size dist, crystal size dist.
• Perform closure experiments for CCN, IN, droplet
  nucleation, crystal nucleation, and rain
  initiation.
• Evaluate turbulence, Nd, Ni, LWC, and IWC
  simulated by cloud-resolving and single-column
  models given prescribed aerosol and boundary
  conditions.
• Compare cloud-resolving and single-column
  simulations.
• Use error propagation to quantify contributions
  to uncertainty.
• Refine models, re-evaluate, and apply indirect
  effects packages to global models.
• Evaluate simulation of indirect effects processes
  in global models.

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An Invitation

• Members of all ARM working groups could bring
  valuable expertise to this problem
• Cloud Parameterization and Modeling
• Cloud Properties
• Aerosol
• Clouds with Low Optical Water Depths
• Instantaneous Radiative Flux
• An indirect effects breakout session is scheduled
  for this afternoon.
• An indirect effects team will be formed during
  the session.
• Will it be a separate working group or a
  cross-cutting effort?
• Please attend and help us get organized and
  mobilized!