GRC ’07 Highlights

1. GRC ’07 Highlights Vijay Natraj & Dan Feldman

2. 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

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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. 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

22. 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