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?– 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:– 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

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– 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 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

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

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 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

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 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

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