Measurement-Based Estimates of Direct Radiative Effects of Absorbing Aerosols above Low-level Clouds Nan Feng and Sundar A. Christopher Department of Atmospheric Science The University of Alabama in Huntsville the AMS 96th Annual Meeting in New Orleans, LA Eighth Symposium on Aerosol–Cloud–Climate Interactions Impacts of Aerosol-cloud Interactions on Radiation I Good morning everyone. I am very glad that I have the opportunity to present my work here. Most results are from our recent JGR paper, … … related work in our group can be seen in the poster. I’m supported by NASA Earth and Space Science Fellowship Dr. Sundar Christopher is my advisor and funded by NASA Radiation Team and CLIPSO team Feng, N., and S. A. Christopher (2015), Measurement-based estimates of direct radiative effects of absorbing aerosols above clouds, J. Geophys. Res. Atmos., 120, doi:10.1002/2015JD023252 Related poster: AMS 2016-401 Chang, I. and S.A. Christopher, Temporal Variations, Radiative Forcing, and Radiative Heating Rates of Absorbing Aerosols Above Clouds, Submitted to the Quarterly Journal of the Royal Meteorological Society.

Outlines Motivation Objective Data and Method Results Conclusion This is the outline. First I am going to give a short introduction to background and motivation of this study; The study area Maritime continent over Southeast Asia; The dataset we used, and methods include model experiments design. Results and summary with be put in the last two sections.

Motivation Study of Absorbing Aerosols above Clouds (AAC) is an emerging field (e.g. Yu et al. 2012,2013) Large inter-model discrepancies exist in the cloudy-sky Direct Radiative Effects (DRE) with global annual mean values for the TOA DRE ranging from -0.16 (cooling) to +0.34 Wm-2 (warming) (Schulz et al., 2006) Current satellite measurements along with radiative transfer calculations still in infancy for providing global picture of AAC and their TOA DRE The elevated layers of biomass burning aerosols from western African (e.g. Gabon, and Congo) are frequently observed above low level stratocumulus clouds off the African coast, which presents an excellent natural laboratory for studying the DRE of AAC. The elevated layers of absorbing smoke aerosols from western African (e.g. Gabon, and Congo) biomass burning activities have been frequently observed above low level stratocumulus clouds off the African coast, which presents an excellent natural laboratory for studying the effects of aerosols above clouds (AAC) on regional energy balance in tropical and sub-tropical environments.

Objectives To develop a new algorithm for above cloud aerosol identification based on data fusion of A-Train satellite datasets [L’Ecuyer, 2010]. To estimate the DRE for AAC based on multi-sensor observations including the MODIS and pixel-level CERES on Aqua, and OMI on Aura. feng@nsstc.uah.edu

Satellite Data: A-train CALIPSO Aerosol/cloud vertical profile 532 and 1064 nm backscatter MODIS - Cloud optical thickness - Cloud effective radius ~2,300 km swath width 0.4 – 14 µm Ozone Monitoring Instrument Aerosol index ~2,600 km swath width UV CERES 0.4-16 µm : 4 channels 3.9-13.4 µm : 8 channels EVERY 15 minutes

A-Train observations of smoke aerosol above clouds over Atlantic Ocean off the coast of southwestern Africa on August 13th, 2006 OMI-Aerosol Index MODIS-COD MODIS-AOD/RGB Aerosol Layer Low Cloud

Method: Data Fusion 𝐷𝑅𝐸= 𝐹 𝑐𝑙𝑑 − 𝐹 𝑐𝑙𝑑+𝑎𝑒𝑟 𝐹 𝑐𝑙𝑑 𝐹 𝑐𝑙𝑑+𝑎𝑒𝑟 Along with all available vertical profiles of aerosols and clouds from CALIOP, daily OMI AI, MODIS COD () and CERES SW Fluxes are collocated within 0.2 × 0.2 (latitude × longitude) bins. the AAC pixels are only identified by those grids with both AI > 0.5 and COD > 0 The instantaneous DRE is then calculated based on equation below: 𝐷𝑅𝐸= 𝐹 𝑐𝑙𝑑 − 𝐹 𝑐𝑙𝑑+𝑎𝑒𝑟 Figure here shows a small section of the MODIS, OMI, CERES and CALIOP swath from NASA A-Train satellite with approximately same overpass time, which indicates the relative size of each pixel, along with a track of CALIOP pixels. 𝐹 𝑐𝑙𝑑 Upwelling SW fluxes at the TOA for an aerosol-free cloud scene Latitude and longitude of MODIS, OMI, and CERES pixels for a small portion of the swath with examples for collocations of CERES, OMI, MODIS and CALIOP pixels. 𝐹 𝑐𝑙𝑑+𝑎𝑒𝑟 Upwelling SW fluxes at the TOA for an aerosol-polluted cloud scene

Method: Radiative Transfer Model Atmosphere compositions: TROPICAL Solar geometry: SZA – CALIPOSO LEVEL 2 PRODUCT Surface types: ALBEDO – CALIPOSO LEVEL 2 PRODUCT Clouds conditions : Clear sky/Cloudy Cloud optical thickness – CALIPSO observations Cloud Layer height – CALIPSO observations Cloud particle size – water cloud 10m, ice cloud 20 m Aerosols optical properties Aerosol Layer Height – CALIPSO observations Radiative Transfer Equation MODEL OUTPUTS Wavelength-dependent aerosols optical depth (green), single scattering albedo (red), and asymmetric factor (blue). SW/LW Fluxes at TOA (Up, Down, Net) SW/LW Fluxes at Surface (Up, Down, Net)

Results: CALIPSO AAC AOD + OMI AI + MODIS COD Radiative transfer calculation Variations of TOA shortwave flux for AAC show an increasing (decreasing) trend along with increase of AOD for low (high) COD values. Fsw determined by both aerosol and cloud properties Linear relationship btw above-cloud AOD and AI for various COD bins (Yu et al., 2012) AI magnitude for AAC events depends on aerosol, cloud properties, and vertical distributions of layers. To derive the OMI AI baseline values for aerosol-free cloud conditions AI < 0.5 ~AAC AOD <0.015 (Alfaro-Contreras et al., 2015)

Model and observations MODIS COD + OMI AI + CERES SW Flux Results: a general decrease in SW flux is seen as a function of AI for AAC with high COD values (12 <  < 32); the SW Flux can be seen to increase with AI for AAC with low COD values (0 <  < 8) the COD values of 8-12 appear to be the critical COD below (above) which aerosol scattering (absorbing) effect dominates Similar results have been shown in a recent study for dust and smoke aerosols above clouds based on the radiative transfer calculations (Jethva et al. 2013) The DRE can be calculated based on observed variation in SW Flux with AI among various COD bins. The close agreements between measurement and model based Fcld estimations Comparison of Fcld btw Model and observations MODIS COD + OMI AI + CERES SW Flux

Results: Depending on the range of COD and AI values, DRE of AACs can be calculated based on the estimated Fsw from aerosol-free cloud scene (AI<0.5) The greatest concentrations of AAC with strongest positive (warming) effects (> 35 Wm-2) lie just west of the Southern Africa coast between 0-15E. This finding is consistent with previous studies using both satellite observations and RT model calculations [de Graaf et al., 2012, 2014; Meyer et al., 2013]. The lower Fcld for aerosol-free cloud scene in this study can cause a higher DRE of AAC than that from RT model calculations DRE as a function of AI and COD The regionally-averaged DRE of AAC

Uncertainties of DRE estimations during AAC events The simulated Aerosol Index is found to be sensitive to pre-assumed cloud, aerosol properties and layer heights of clouds. Two or three times larger AI magnitude can be seen in the higher COD range over 20 than same amount of aerosols over lower COD values between 0 and 4 the above cloud AI index is associated with aerosol-cloud height difference in the boundary layer under 5km. The increase of aerosol-cloud layer height distance can apparently cause close to 30% increase in AI.

Uncertainties of ARE estimations during AAC events Varied 0.55m AOD values from 0.1 to 3.0 to build the quantitative correlations between AI and above cloud SW DRE based on various solar zenith angles. Higher SZA (>30) can cause distinct lower SW DRE of specific AAC events 10% ~ 40% difference in SW anisotropic factors between AAC and pure cloud over ocean for VZA btw 0 and 60. For TOA SW flux: 100 Wm-2 to 700 Wm-2 for AAC cases in this study, the inaccurate ADM can cause about 10 - 20% overestimations of TOA Fcld+aero.

Conclusions A-train satellite data sets (OMI,CALIPSO, MODIS) can be used for aerosols above clouds identification algorithm development and verification Critical COD values exist to determine the signs of DRE due to AAC DREs of AAC are more sensitive to lower COD values than higher COD values. In-situ measurements are necessary to refine satellite algorithms and reduce uncertainties in radiative transfer calculations In-situ measurements such as near future field experiments including ORACLES, CLARIFY-2016, and LASIC will be important, which can provide more detailed aerosol properties and vertical distribution information are needed. feng@nsstc.uah.edu