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Published October 23, 2020 | Submitted
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Top-of-atmosphere albedo bias from neglecting three-dimensional radiative transfer through clouds

Abstract

Clouds cover on average nearly 70% of Earth's surface and are important for the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and 3D structure. Earth system models are unable to perform 3D radiative transfer calculations and thus partially neglect the effect of cloud morphology on albedo. We show how the resulting radiative flux bias depends on cloud morphology and solar zenith angle. Using large-eddy simulations to produce 3D cloud fields, a Monte Carlo code for 3D radiative transfer, and observations of cloud climatology, we estimate the effect of this flux bias on global climate. The flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is largest (and negative) for large zenith angles. Globally, the radiative flux bias is estimated to be 1.6 W m⁻² and locally can be on the order of 5 W m⁻².

Additional Information

Published Online: Fri, 16 Oct 2020. C.E.S. acknowledges support from NSF Graduate Research Fellowship under Grant No. DGE-1745301. I.L. is supported by a fellowship from the Resnick Sustainability Institute at Caltech. This research was additionally supported by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program and by Mountain Philanthropies. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. All code or data used in this paper are freely available online. The LES were run using the PyCLES code (https://climate-dynamics.org/software/#pycles). The radiative transfer computations were done using the libRadtran code (http://www.libradtran.org). Post-processed LES 3D fields used as input files for libRadtran computations are available in Singer et al. (2020). The ISCCP data were downloaded from the GEWEX database (https://climserv.ipsl.polytechnique.fr/gewexca/).

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Created:
August 19, 2023
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October 20, 2023