A-Train Symposium, April 19-21, 2017, Pasadena, CA Influence of Ice Cloud Microphysics on Imager-Based Estimates of Earth’s Radiation Budget   Norman G. Loeb1, Seiji Kato1, Patrick Minnis1, Ping Yang2, Sunny Sun-Mack3, Fred G. Rose3, Gang Hong3, Seung-Hee Ham3 1NASA Langley Research Center, Hampton, VA 2Texas A&M University, College Station, TX 3Science Systems and Applications, Inc., Hampton, VA A-Train Symposium, April 19-21, 2017, Pasadena, CA

Computed Imager-Based Top-of-Atmosphere (TOA) and Surface Radiative Fluxes for Ice Clouds Background: Derived from radiative transfer model calculations initialized using imager-based cloud and aerosol retrievals and meteorological assimilation data. Shapes, sizes and habits of ice cloud particles cannot be independently retrieved a priori from passive visible/infrared imager measurements. Assumptions about the scattering properties of ice clouds are needed in order to retrieve ice cloud optical properties from imager measurements. The retrieved ice cloud optical properties are then used as input to a radiative transfer model (e.g., Fu-Liou) to compute broadband radiative fluxes. A critical requirement is that the same assumptions about shape/size/habit be used in the cloud optical property retrievals and broadband radiative flux calculations. Objective: Assess how the choice of ice cloud particle model impacts computed shortwave (SW) and longwave (LW) radiative fluxes at the TOA and surface.

Methodology Consider 3 ice cloud particle models that have been (or will be) used in the CERES production system. Perform cloud retrievals using MODIS imager pixel data with CERES cloud retrieval code for each of the 3 ice cloud particle models. Use the retrieved cloud optical properties as input to Langley Fu-Liou radiative transfer code to compute broadband radiative fluxes at TOA and surface. Use spectral bulk scattering data produced for each ice cloud particle model. Compare surface and TOA fluxes determined using the different ice cloud particle models. Period considered: March 1-10, 2008.

Ice Cloud Particle Models Smooth Hexagonal Columns (Used in CERES Edition 2 & 3 Processing) Roughened Hexagonal Columns (Used in CERES Edition 4 Processing) Two Habit Model (To be used in CERES Edition 5 Processing)

Roughened Hexagonal Column

CERES Cloud Retrieval Algorithm (Applied to MODIS Imager Radiances) Cloud Mask: Cascading threshold tests provide clear or cloudy classifications with confidence flags Bands Used: 0.65, 1.24, 1.64, 3.8, 8.6, 11.03, 12.02 μm, additionally for MODIS: 1.38, 2.13, 6.7, 13.34 μm Cloud Retrieval: (1) Visible Infrared Shortwave-infrared Split-window Technique (VISST) ( Day Time, snow free) (2) Shortwave-infrared Infrared Near-infrared Technique (SINT) (Day Time, snow covered) (3) Shortwave-infrared Infrared Split-window Technique (SIST) (Night Time, all surface) The IR (11 μm), VIS (0.65, or 1.24, 1.6, 2.1 μm), and SIR (3.8 μm) radiances are primarily sensitive to changes in cloud temperature (Tc), optical depth (τ), and particle size (re for liquid and De for ice), respectively. VISST or SINT performs the iterative process for each cloud phase, with an initial guess of De = 45 μm & Tc= T (Zc= 9 km) for ice clouds (re= 8 μm & Tc= T(Z= 3 km) for liquid clouds), until the calculated radiances ( < 20 ) converge with the observations. Cloud Phase: If only one phase solution from CERES cloud retrieval & Tc is reasonable, the phase is accepted for the solution If dual phase solutions, a simple temperature check to determine the phase Otherwise, apply a set of sequential tests. Reference: P. Minnis et al., IEEE Trans. Geosci. Remote Sens., vol. 49, NO. 11, November 2011

Aqua-MODIS March 2008 Cloud Mask (Day Time) Total 1,387,278,277 THM Smooth Clear Cloud 489,733,158 35.302 % 66,753 0.005 % 220,713 0.016 % 897,257,653 64.677 % Day Time Cloud Mask Agreement = 35.302 + 64.677 (%) = 99.98 %

Aqua-MODIS March 2008 Cloud Phase (Day Time) Total 868,875,462 THM Smooth Ice Water 325,709,209 37.49 % 7,377,135 0.85 % 4,957,261 0.57 % 530,831,857 61.09 % Day Time Cloud Phase Agreement = 37.49 + 61.09 (%) = 98.58 %

Cloud Property Differences at Aqua Overpass Time (THM minus Smooth) Cloud Optical Depth Difference Effective Radius Difference (mm) Overall optical depth difference is -2.3 (-28% of Global Mean) and RMS difference is 2.8 (32% of GM). Overall effective radius difference is -3.9 mm (16% of GM) and RMS difference is 5.2 mm (16% of GM).

SW TOA Flux Difference at Aqua Overpass Time (THM(Ret)/THM(Fwd) minus Smooth(Ret)/Smooth(Fwd)) Difference (Wm-2) Difference (%) Overall regional RMS difference is ~1%. However, in some locations regional differences reach 3%. Differences tend to be positive in tropics and negative in midlatitudes.

SW TOA Flux Difference at Aqua Overpass Time (THM(Ret)/THM(Fwd) minus Smooth(Ret)/THM(Fwd)) Difference (Wm-2) Difference (%) Overall regional RMS difference is ~2%. However, in some locations regional differences reach 5%. Differences are negative everywhere. Likely due to larger optical depth in Smooth(Ret).

SW Surface Downward Flux Difference at Aqua Overpass Time (THM(Ret)/THM(Fwd) minus Smooth(Ret)/Smooth(Fwd)) Difference (Wm-2) Difference (%) Overall regional RMS difference is ~1%. Local regional differences can reach 15% (Greenland). Differences are positive everywhere. Likely associated with smaller optical thicknesses and smaller De for THM (increased transmission in water vapor and near-infrared cloud absorption bands).

SW Surface Downward Flux Difference at Aqua Overpass Time (THM(Ret)/THM(Fwd) minus Smooth(Ret)/THM(Fwd)) Difference (Wm-2) Difference (%) Overall regional RMS difference is ~1.5%. Local regional differences double in many places compared to case in which consistent retrievals and forward calculation is used.

LW Surface Downward Flux Difference at Aqua Overpass Time (THM(Ret)/THM(Fwd) minus Smooth(Ret)/Smooth(Fwd)) Difference (Wm-2) Difference (%) Overall regional RMS difference is 0.3%. However, in some locations regional differences reach 3%.

(Regions with >80% Ice Cloud Coverage; March, 2008) Computed (Rough Hex. Column) – CERES Observed SW TOA Flux Difference (Diurnally Avg’d) (Regions with >80% Ice Cloud Coverage; March, 2008) (Wm-2) RMS difference due to ice particle model assumption is likely much smaller than computed-obs RMS difference (1% vs 11%).

Conclusions Computed TOA and SFC fluxes are fairly insensitive to ice cloud particle model if consistent assumptions are made in retrievals and forward calculations. Regional RMS differences between fluxes for THM and Smooth models is 1% for both TOA and surface. Local TOA flux differences reach 3%. Local SFC flux differences can be much larger (15% over Greenland). Very little change in LW fluxes. Differences generally double if inconsistent assumptions are made about ice cloud particle model in cloud optical retrievals and forward calculations. While radiative fluxes are relatively insensitive to ice particle mode, cloud retrievals are highly dependent upon what assumptions are made about shape/habit/size, etc. Differences of ~30% in optical depth and ~15% in effective radius due to choice of ice particle model.