Abstract
Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. We use high-resolution (20–100-m horizontal resolution) large-eddy simulations to produce realistic 3D cloud fields covering three dominant regimes of low-latitude clouds: shallow cumulus, marine stratocumulus, and deep convective cumulonimbus. A Monte Carlo code is used to run 3D and ICA broadband radiative transfer calculations; we calculate the top-of-atmosphere (TOA) reflected flux and surface irradiance biases as functions of solar zenith angle for these three cloud regimes. Finally, we use satellite observations of cloud water path (CWP) climatology, and the robust correlation between CWP and TOA flux bias in our LES sample, to roughly estimate the impact of neglecting 3D cloud radiative effects on a global scale. We find that the flux bias is largest at small zenith angles and for deeper clouds, while the albedo bias is most prominent for large zenith angles. In the tropics, the annual-mean shortwave radiative flux bias is estimated to be 3.1 ± 1.6 W m−2, reaching as much as 6.5 W m−2 locally.
Significance Statement
Clouds cool Earth by reflecting sunlight back to space. The amount of reflection is determined by their location, details of their 3D structure, and the droplets or ice crystals they are composed of. Global models cannot simulate the 3D structure of clouds because computational power is limited, so they approximate that clouds only scatter sunlight in a 1D vertical column. In this study, we use local models to directly simulate how clouds scatter sunlight in 3D and compare with a 1D approximation. We find the largest bias for overhead sun and for deeper clouds. Using satellite observations of bulk cloud properties, we estimate the tropical annual-mean bias introduced by the 1D approximation to be 3.1 ± 1.6 W m−2.
Zhang’s current affiliation: Department of Earth and Planetary Sciences, The Johns Hopkins University, Baltimore, Maryland.
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