GRC ’07 Highlights

GRC 07 HighlightsVijay Natraj & Dan Feldman

New Observations and Model Approaches for Addressing Key Cloud-Precipitation-Climate QuestionsH2O feedback: + or -? :Observations from AIRS,MSU,ERBE/CERESSatellite data sources reveal + feedbackIs the hydrological cycle slowing down?Yes; changes in radiative heating by clouds is an important factor in the answerProcesses determining vertical structure of clouds loom as importantModels 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 QuestionsHow do aerosols affect the hydrological cycle?Arctic warming in summer but cooling in winterLong-range transport of SO2 into ArcticH2SO4 coating observed on aerosolDehydration-greenhouse feedback

Cloud Occurrence, Cloud Overlap and Cloud Microphysics from the First Year of CloudSat and CALIPSO2006-06 to 2007-03:CloudSat/CALIPSO Cloud Cover: 0.66MODIS Clouds Cover: 0.63Ubiquitous low clouds over southern oceanContinents stand out as minima in low cloud coverThickest 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 CALIPSOMultilayer clouds mostly cirrus over stratocumulus (high-based over low-based)In general:Atmospheric column contains multiple cloud layersComposed of two phases of H2OSize distributions that are at least bimodalOccur at night more than half the timeGoing beyond occurrence to characterize properties needs more work

Multiscale Modeling of Cloud SystemsCloud Feedbacks remain largest source of uncertainty IPCC, 2007 (Charney et al., 1979 said same thing!)Problem is multiple scalesCloud-scale processes relatively well understoodTranslation to global scales requires very powerful computerHence cloud parameterizationsNo GCM has physical parameterization of convection

Multiscale Modeling of Cloud SystemsWorlds first GCRM3.5 km cell sizeTop at 40 km54 layers15-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 GCMHundreds of times less expensive than GCRM

Multiscale Modeling of Cloud SystemsGCRMs and MMFs make it possible for cloud observers and GCM developers to compare apples with applesWhen something doesnt work, we can look inside to see how simulation compares with observationsFocused efforts under wayTo develop improved parameterizations for CRMsTo 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 climateRedemann et al., JGR, 2006Ames Airborne Tracking Sunphotometer (AATS) and Solar Spectral Flux Radiometer (SSFR)Plots of net spectral irradiance as function of AODSlope gives aerosol radiative forcing efficiencyVisible wavelength range: -45.8 Wm-2 +/- 13.1 Wm-2Spread 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 climateAnderson et al., JGR, 110, 2005Natural and anthropogenic aerosols distinguished using fine mode fraction (FMF) of optical depthCombination 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/SPDefinition differences b/w SMF and FMFDetector problemsAssumption of spherical shape for dustA might be better retrieval productRigorous 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 cloudsWen et al., IEEE Geosci. Rem. Sens. Lett.Study of the aerosol-cloud boundary essential for:Understanding appropriate cloud screening methods in aerosol remote sensingInvestigating aerosol indirect effect on climateField study of suborbital AOD data near cloud edgesIn ~75% of the cases there was an increase of 5-25% in AOD in the closest 2 km near the cloudsMODIS-observed mid-visible reflectances in the vicinityAlso show an increase with decreasing distance to cloud edgeMay be because of 3-D effects, or increased aerosol concentration or size near clouds as indicated by suborbital observations

Passive Polarimetric Remote Sensing of AerosolsAccurate determination of aerosol optical depth and microphysical properties necessary to evaluate aerosol radiative forcingPolarimetry useful because:It contains more information about microphysicsRelative (rather than absolute) radiometric calibration necessary to give highly accurate aerosol retrievalsPolarized radiances have contributions from surface and atmosphereEffects of surface need to be understood

Passive Polarimetric Remote Sensing of AerosolsOcean reflectance low away from sun glintL-M algorithm used to retrieve aerosolPolarization of land surfaces generated at surface interfaceRefractive index of natural targets varies little within typical spectral domainsSurface polarized reflectance spectrally greyMeasurement at 2250 nm (where aerosol load is low) used to characterize and correct for surface effectsShorter wavelengths used to retrieve aerosol load and microphysical properties

Predicting Chemical Weather: Improvements Through Advanced Methods to Integrate Models and MeasurementsChemical Transport Models (CTMs) poorly constrained primarily due to uncertain emission estimates Improvements in analysis capability require integration of models and measurementsExtension of formal data assimilation techniques to aerosols needed to help reduce uncertaintiesAerosol 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 FeedbacksAerosols can influence Earths radiation budget by:Direct interaction with sunlight: direct effectAltering 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 feedbacksTwomeys hypothesis (first indirect effect): # aerosol particles conc of cloud droplets NdFor given LWC, greater Nd => smaller droplets Nd => total surface area => clouds reflect more solar radiation

Aerosol Indirect Effects: The Importance of Cloud Physics and FeedbacksAlbrechts hypothesis (second indirect effect): Nd precipitation (coalescence efficiency of cloud droplets strongly with droplet size) cloud thickness, LWC, coverage more reflective cloudsModel estimates of the two major AIEsPincus and Baker (1994)1st and 2nd AIEs comparableGCMs (Lohmann and Feichter, 2005)1st AIE: -0.5 to -1.9 Wm-22nd AIE: -0.3 to -1.4 Wm-2Relatively limited investigation of factors controlling relative importance of the two AIEs

Aerosol Indirect Effects: The Importance of Cloud Physics and FeedbacksRelative strength of 2nd AIE largely determined by balance between:Moistening/cooling due to suppression of precipitationDrying/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 changeUncertainty originates from complex and multi-scale nature of aerosol-cloud interactionsForces climate models to use empirical approachesIncorporate as much physics as possible, with appropriate simplificationsDynamics: updraft velocity, thermodynamicsParticle characteristics: size, concentration, chemical compositionCloud processes: droplet formation, drizzle formation, chemistry inside cloud droplets

How can In-Situ Observations Constrain and Improve Modeling of Aerosol Indirect Effects?ChallengesRepresenting cumulative effect of organics on cloud formation in simple and realistic wayUse 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 worldThinning of Arctic sea-ice: Lindsay and Zhang, J. Climate, 2005Ice-albedo feedback traditionally thought to be causeGarrett and Zhao, Nature, 2006: Ice-infrared feedback primarily responsibleBetween winter and early spring, Arctic characterized by widespread pollution called Arctic hazePolluted air transport from mid-latitude Eurasia and N AmericaBecause of low precipitation, pollution accumulatesIncreased surface warming from aerosol modifications to cloud LW emissivity

Observational Constraints on Climate-Carbon Cycle Feedbacks11 coupled climate-carbon models used to simulate 21st century climate and CO2 under similar scenariosAll agree that CO2 global warmingHowever, they disagree in the magnitudeCO2 increase alone will tend to enhance carbon storage by both land and oceanClimate change alone will tend to release land and ocean carbon to atmosphere

Observational Constraints on Climate-Carbon Cycle FeedbacksMagnitude 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 uncertaintiesMajor uncertainties in land-use emissions