Aerosol-Cloud-Climate Interaction: A Case Study from the Indian Ocean. Sagnik Dey
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1 Aerosol-Cloud-Climate Interaction: A Case Study from the Indian Ocean Sagnik Dey Centre for Atmospheric Sciences Indian Institute of Technology Delhi sagnik@cas.iitd.ac.in
2 Content Background and Motivation Case Study Indian Ocean Challenges Ahead - Discussion
3 Aerosol concentration over India is three times higher than the global mean concentration (Dey and Di Girolamo, JGR, 2010) Aerosols in India amplify greenhouse warming (Ramanathan et al., Nature, 2007) IPCC AR4, 2007
4 Aerosol-Cloud-Climate Conundrum Climatic Effects Direct Effect Indirect Effect Semi-direct Effect
5 Semi-direct Effect Absorbing aerosols Ackerman et al., 2000
6 1. Focus on individual cloud system Examine aerosol-cloud relationship 2. Develop regime-specific parameterization for large-scale models
7 Global Initiative Global Energy and Water Cycle Experiment (GEWEX) GEWEX Cloud System Study (GCSS) Boundary layer clouds Cirrus clouds Extra-tropical layer clouds Precipitating Convective clouds Polar clouds
8 Clouds and Climate 4% increase in low clouds offsets doubling of CO 2 A.J. Slingo Sensitivity of Earth s Radiation Budget to changes in low clouds. Nature. 343, Requires reduction of measurement error of cloud fraction to 1% Ohring, G. et al Satellite Instrument Calibration for Measuring Global Climate Change. Bull. Amer. Met. Soc., 86,
9 Model-simulated Cloud Cover Source: GEWEX inter-comparison study
10 Satellite-measured Cloud Cover From Stubenrauch et al. (2009, GEWEX report) Cumulus clouds
11 Cumulus Clouds Ubiquitous in tropics important role in global energy and water budget, act as feeder system in deep convection Most poorly represented cloud type in climate models lack of observational dataset to improve model simulation
12 Scale Effect OR We need very high resolution satellite data to address this problem
13 Collection of ASTER (15-m resolution) data Mean AOD from MISR ( ) Jul 2006 Sep 2006 Gulf of Mexico Sep 2004 Dec 2004 Tropical western Atlantic (RICO) Nov 2006 Apr 2007 Indian Ocean
14 Resolution Effect Cloud Fraction 15m 1.1km 9% 83% Cloud Fraction 15m 1.1km 8% 32%
15 Case Study over Indian Ocean 1200 ASTER (15-m) images are collected for the period Nov 2006 Apr 2007
16 Potential Problems Multi-layer clouds Sun glint Only 277 ASTER scenes out of 1200 are analyzed because these scenes contain only cumulus clouds
17 70 km Analysis Dey et al., JGR, km λ = slope Cloud Fraction, Cloud Size and Top Height Distribution (ASTER) 17.6 km 17.6 km Aerosol Optical Depth (MISR) Meteorology (NCEP reanalysis data)
18 <0.1 <0.1 > > Cloud Fraction and Cloud Size Distribution vs. AOD Regime 2 Regime # Samples Regime Cloud Fraction 3.0 λ E N Mean CF # Samples λ > E 5 S AOD Dey et al., Geophys. Res. Lett., 2011
19 1. Direct cloud contamination Only bias in AOD due to direct cumulus contamination on MISR AOD retrieval (Zhao et al., GRL, 2009)
20 <0. < > D Radiative Effect If affects the relation: AOD and AE would have consistently increased with Cloud Fraction (Marshak et al., JGR, 2008) λ 2 < λ 1 λ 1 AOD Cloud Cover # Samples Cloud Fraction 3.0 λ 2.7 Mean CF # Samples 0.03 λ >0.5 AOD
21 <0.1 > Effect of Meteorology 80 ω 4 SST θ RH PW RH (%) and PW (kg m -2 ) θ and SST (K) 0.02 ω (Pa s -1 ) > > < 0. 1 AOD Dey et al., GRL, 2011
22 Relative Distributions of Aerosols and Cloud Scenario 1 Scenario 2 Surface
23 Cloud Top Height vs. AOD MISR-median CTH MISR-90th percentile CTH ASTER-median CTH ASTER-90th percentile CTH CTH (m) > AOD Dey et al., 2011 Ramanathan et al., 2007
24 Highlights A transition from indirect to semi-direct effect of aerosols on cumulus clouds Observed relationship cannot be explained by meteorology or remote sensing artifacts Regular sensor must be used for multi-year and multiseason coverage Simple parameterization of cloud properties in terms of aerosols will not hold true
25 Correcting CF bias in MISR
26 Highlights A transition from indirect to semi-direct effect of aerosols on cumulus clouds Observed relationship cannot be explained by meteorology or remote sensing artifacts Regular sensor must be used for multi-year and multiseason coverage Simple parameterization of cloud properties in terms of aerosols will not hold true
27 Parameterization of CCN Ramanathan et al., 2001
28 Include Semi-direct effect Wang and McFarquhar (2006; 2008) cloud resolving model to understand the semi-direct effect on diurnal variations of cloud micro- and macrophysics Daytime reduction of cloud cover is prominent for cumulus dominated regime
29 Multi-scale Modeling Framework Cloud Resolving Model Coupled Ocean- Atmospheric Climate Model Randall et al., 2003
30 Stereoscopic parallax Forward-viewing MISR camera MISR flight direction cloudtop height apparent cloud position
31 Stereoscopic parallax Backward-viewing MISR camera MISR flight direction cloud-top height parallax
32 Why is the problem so difficult to address? Stevens and Feingold, 2009
33 Global Efforts Aerosol-Cloud-Precipitation Climate (ACPC) Initiative - Integrated research program to investigate the interactions and feedbacks among aerosols, cloud processes, precipitation, and the climate system IGBP ileaps, IGAC ACPC WCRP GEWEX IGBP: International Geosphere- Biosphere Programme WCRP: World Climate Research Programme IGAC: International Global Atmospheric Chemistry ileaps: Integrated ecosystem-atmosphere Process Study Land
34 National Efforts CAIPEX (Cloud-Aerosol-Interaction and Precipitation Enhancement Experiment) CTCZ (Continental Tropical Convergence Zone) Campaign
35 Direct Effect TOA Extinction = scattering + absorption Surface Haywood and Boucher, 2000
36 Twomey, st Indirect Effect
37 2 nd Indirect Effect Albrecht, 1989
38 Aerosol Optical Depth GFDL-Model Observation Ganguly et al., 2009
39 Emission Inventory What do we need? (1) Aerosol vertical distribution (2) Microphysical processes (3) Mixing State Aerosol properties (1) Microphysical processes (2) Radiative processes (3) Dynamical processes Meteorology Cloud Properties Grid size Parameterization of aerosol-cloud relations 39
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