Transcript GRC ’07 Highlights

GRC ’07 Highlights
Vijay Natraj & Dan Feldman
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 > ability to
precipitate it out => slowing of hydrological cycle
New Observations and Model Approaches for
Addressing Key Cloud-Precipitation-Climate Questions
• How do aerosols affect the hydrological cycle?
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Arctic warming in summer but cooling in winter
Long-range transport of SO2 into Arctic
H2SO4 coating observed on aerosol
Dehydration-greenhouse feedback
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
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:
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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
properties needs more work
to
characterize
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
Multiscale Modeling of Cloud Systems
• World’s first GCRM
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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
Multiscale Modeling of Cloud Systems
• GCRMs and MMFs make it possible for cloud
observers and GCM developers to compare
apples with apples
• When something doesn’t 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
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
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 submicron 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
Aerosol Measurements from Multiple Instruments and
Platforms: What Questions can be Answered by
Combining Different Techniques?
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Problem 3: Aerosol remote sensing in the vicinity of
clouds
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Wen et al., IEEE Geosci. Rem. Sens. Lett.
Study of the aerosol-cloud boundary essential for:
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Field study of suborbital AOD data near cloud edges
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Understanding appropriate cloud screening methods in aerosol
remote sensing
Investigating aerosol indirect effect on climate
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
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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
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
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
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
Aerosol Indirect Effects: The Importance of
Cloud Physics and Feedbacks
• Aerosols can influence Earth’s 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 system’s feedbacks
• Twomey’s hypothesis (first indirect effect):
– ↑ # aerosol particles → ↑ conc of cloud droplets Nd
• For given LWC, greater Nd => smaller droplets
• ↑ Nd => ↑ total surface area => clouds reflect more solar radiation
Aerosol Indirect Effects: The Importance of
Cloud Physics and Feedbacks
• Albrecht’s 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
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
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
appropriate simplifications
as
possible,
with
– Dynamics: updraft velocity, thermodynamics
– Particle
characteristics:
size,
concentration,
chemical
composition
– Cloud processes: droplet formation, drizzle formation, chemistry
inside cloud droplets
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
Is Arctic Sea-Ice Melting Stimulated by AerosolCloud-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
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
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