GRC

1
GRC 07 Highlights

• Vijay Natraj Dan Feldman

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New Observations and Model Approaches for
Addressing Key Cloud-Precipitation-Climate
Questions

• H2O feedback or -?
• Observations from AIRS,MSU,ERBE/CERES
• Satellite data sources reveal feedback
• Is the hydrological cycle slowing down?
• Yes changes in radiative heating by clouds is an
  important factor in the answer
• Processes determining vertical structure of
  clouds loom as important
• Models predict H2O accumulates at a rate gt
  ability to precipitate it out gt slowing of
  hydrological cycle

3
New Observations and Model Approaches for
Addressing Key Cloud-Precipitation-Climate
Questions

• How do aerosols affect the hydrological cycle?
• Arctic warming in summer but cooling in winter
• Long-range transport of SO2 into Arctic
• H2SO4 coating observed on aerosol
• Dehydration-greenhouse feedback

4
Cloud Occurrence, Cloud Overlap and Cloud
Microphysics from the First Year of CloudSat and
CALIPSO

• 2006-06 to 2007-03
• CloudSat/CALIPSO Cloud Cover 0.66
• MODIS Clouds Cover 0.63
• Ubiquitous low clouds over southern ocean
• Continents stand out as minima in low cloud cover
• Thickest clouds in western Pacific ( 4 km)
• Large fraction of multilayer clouds ( 40-45)
  over tropics

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Cloud Occurrence, Cloud Overlap and Cloud
Microphysics from the First Year of CloudSat and
CALIPSO

• Multilayer clouds mostly cirrus over
  stratocumulus (high-based over low-based)
• In general
• Atmospheric column contains multiple cloud layers
• Composed of two phases of H2O
• Size distributions that are at least bimodal
• Occur at night more than half the time
• Going beyond occurrence to characterize
  properties needs more work

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Multiscale Modeling of Cloud Systems

• Cloud Feedbacks remain largest source of
  uncertainty IPCC, 2007
• (Charney et al., 1979 said same thing!)
• Problem is multiple scales
• Cloud-scale processes relatively well understood
• Translation to global scales requires very
  powerful computer
• Hence cloud parameterizations
• No GCM has physical parameterization of
  convection

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Multiscale Modeling of Cloud Systems

• Worlds first GCRM
• 3.5 km cell size
• Top at 40 km
• 54 layers
• 15-second time step
• 10 simulated days per day on half of Earth
  simulator (2560 CPUs)
• Multiscale Modeling Framework (MMF)
• Hundreds of times more expensive than GCM
• Hundreds of times less expensive than GCRM

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Multiscale Modeling of Cloud Systems

• GCRMs and MMFs make it possible for cloud
  observers and GCM developers to compare apples
  with apples
• When something doesnt work, we can look inside
  to see how simulation compares with observations
• Focused efforts under way
• To develop improved parameterizations for CRMs
• To develop radically improved second generation
  MMF

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Aerosol Measurements from Multiple Instruments
and Platforms What Questions can be Answered by
Combining Different Techniques?

• Problem 1 Measurements of aerosol radiative
  forcing of climate
• Redemann et al., JGR, 2006
• Ames Airborne Tracking Sunphotometer (AATS) and
  Solar Spectral Flux Radiometer (SSFR)
• Plots of net spectral irradiance as function of
  AOD
• Slope gives aerosol radiative forcing efficiency
• Visible wavelength range -45.8 Wm-2 /- 13.1
  Wm-2
• Spread probably due to wide range of aerosol
  types

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Aerosol Measurements from Multiple Instruments
and Platforms What Questions can be Answered by
Combining Different Techniques?

• Problem 2 Measurements of anthropogenic fraction
  of aerosol radiative forcing of climate
• Anderson et al., JGR, 110, 2005
• Natural and anthropogenic aerosols distinguished
  using fine mode fraction (FMF) of optical depth
• Combination of airborne aerosol in-situ
  measurements (I) and airborne sunphotometry (SP)
  to establish relationship b/w sub-micron fraction
  (SMF) of AOD and Angstrom exponent (A)
• MODIS FMF has systematic high-bias of 0.2
  compared to SMF from I/SP
• Definition differences b/w SMF and FMF
• Detector problems
• Assumption of spherical shape for dust
• A might be better retrieval product
• Rigorous validation with existing sun photometer
  measurements

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Aerosol Measurements from Multiple Instruments
and Platforms What Questions can be Answered by
Combining Different Techniques?

• Problem 3 Aerosol remote sensing in the vicinity
  of clouds
• Wen et al., IEEE Geosci. Rem. Sens. Lett.
• Study of the aerosol-cloud boundary essential
  for
• Understanding appropriate cloud screening methods
  in aerosol remote sensing
• Investigating aerosol indirect effect on climate
• Field study of suborbital AOD data near cloud
  edges
• In 75 of the cases there was an increase of
  5-25 in AOD in the closest 2 km near the clouds
• MODIS-observed mid-visible reflectances in the
  vicinity
• Also show an increase with decreasing distance to
  cloud edge
• May be because of 3-D effects, or increased
  aerosol concentration or size near clouds as
  indicated by suborbital observations

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Passive Polarimetric Remote Sensing of Aerosols

• Accurate determination of aerosol optical depth
  and microphysical properties necessary to
  evaluate aerosol radiative forcing
• Polarimetry useful because
• It contains more information about microphysics
• Relative (rather than absolute) radiometric
  calibration necessary to give highly accurate
  aerosol retrievals
• Polarized radiances have contributions from
  surface and atmosphere
• Effects of surface need to be understood

13
Passive Polarimetric Remote Sensing of Aerosols

• Ocean reflectance low away from sun glint
• L-M algorithm used to retrieve aerosol
• Polarization of land surfaces generated at
  surface interface
• Refractive index of natural targets varies little
  within typical spectral domains
• Surface polarized reflectance spectrally grey
• Measurement at 2250 nm (where aerosol load is
  low) used to characterize and correct for surface
  effects
• Shorter wavelengths used to retrieve aerosol load
  and microphysical properties

14
Predicting Chemical Weather Improvements Through
Advanced Methods to Integrate Models and
Measurements

• Chemical Transport Models (CTMs) poorly
  constrained primarily due to uncertain emission
  estimates
• Improvements in analysis capability require
  integration of models and measurements
• Extension of formal data assimilation techniques
  to aerosols needed to help reduce uncertainties
• Aerosol radiative effects substantially different
  when using observations as opposed to
  parameterizations (Bates et al., ACP, 2006)
• Intensive field experiments (e.g. ICARTT) provide
  our best efforts to comprehensively observe a
  region

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Aerosol Indirect Effects The Importance of Cloud
Physics and Feedbacks

• Aerosols can influence Earths radiation budget
  by
• Direct interaction with sunlight direct effect
• Altering cloud radiative properties indirect
  effect (AIE)
• Useful to divide AIE into two types
• Primary or quasi-instantaneous effects (e.g.
  Twomey effect, dispersion effect)
• Effects that require understanding of the
  systems feedbacks
• Twomeys hypothesis (first indirect effect)
• ? aerosol particles ? ? conc of cloud droplets
  Nd
• For given LWC, greater Nd gt smaller droplets
• ? Nd gt ? total surface area gt clouds reflect
  more solar radiation

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Aerosol Indirect Effects The Importance of Cloud
Physics and Feedbacks

• Albrechts hypothesis (second indirect effect)
• ? Nd ? ? precipitation (coalescence efficiency of
  cloud droplets ? strongly with droplet size) ? ?
  cloud thickness, LWC, coverage ? more reflective
  clouds
• Model estimates of the two major AIEs
• Pincus and Baker (1994)
• 1st and 2nd AIEs comparable
• GCMs (Lohmann and Feichter, 2005)
• 1st AIE -0.5 to -1.9 Wm-2
• 2nd AIE -0.3 to -1.4 Wm-2
• Relatively limited investigation of factors
  controlling relative importance of the two AIEs

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Aerosol Indirect Effects The Importance of Cloud
Physics and Feedbacks

• Relative strength of 2nd AIE largely determined
  by balance between
• Moistening/cooling due to suppression of
  precipitation
• Drying/warming due to enhanced entrainment of
  overlying air

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How can In-Situ Observations Constrain and
Improve Modeling of Aerosol Indirect Effects?

• AIE one of the most uncertain components of
  climate change
• Uncertainty originates from complex and
  multi-scale nature of aerosol-cloud interactions
• Forces climate models to use empirical approaches
• Incorporate as much physics as possible, with
  appropriate simplifications
• Dynamics updraft velocity, thermodynamics
• Particle characteristics size, concentration,
  chemical composition
• Cloud processes droplet formation, drizzle
  formation, chemistry inside cloud droplets

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How can In-Situ Observations Constrain and
Improve Modeling of Aerosol Indirect Effects?

• Challenges
• Representing cumulative effect of organics on
  cloud formation in simple and realistic way
• Use in-situ observations to constrain
  state-of-the-art droplet parameterizations in GCMs

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Is Arctic Sea-Ice Melting Stimulated by
Aerosol-Cloud-Radiative Interactions?

• Arctic warming at a rate 2 x rest of the world
• Thinning of Arctic sea-ice Lindsay and Zhang, J.
  Climate, 2005
• Ice-albedo feedback traditionally thought to be
  cause
• Garrett and Zhao, Nature, 2006 Ice-infrared
  feedback primarily responsible
• Between winter and early spring, Arctic
  characterized by widespread pollution called
  Arctic haze
• Polluted air transport from mid-latitude Eurasia
  and N America
• Because of low precipitation, pollution
  accumulates
• Increased surface warming from aerosol
  modifications to cloud LW emissivity

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Observational Constraints on Climate-Carbon Cycle
Feedbacks

• 11 coupled climate-carbon models used to simulate
  21st century climate and CO2 under similar
  scenarios
• All agree that ? CO2 ? global warming
• However, they disagree in the magnitude
• CO2 increase alone will tend to enhance carbon
  storage by both land and ocean
• Climate change alone will tend to release land
  and ocean carbon to atmosphere

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Observational Constraints on Climate-Carbon Cycle
Feedbacks

• Magnitude of increase in anthropogenic CO2
  emissions remaining in the atmosphere uncertain
  (8-52 ppmv extra CO2/K of global warming)
• Observations can be used to constrain models to
  reduce uncertainties
• Major uncertainties in land-use emissions