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Improved cloud phase retrievals based on remote-sensing observations have the potential to decrease the Southern Ocean shortwave cloud radiation bias
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  • Willi Schimmel,
  • Carola Barrientos Velasco,
  • Jonas Witthuhn,
  • Martin Radenz,
  • Boris Barja González,
  • Heike Kalesse-Los
Willi Schimmel
Leibniz Institute for Tropospheric Research (TROPOS)

Corresponding Author:schimmel@tropos.de

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Carola Barrientos Velasco
Leibniz Institute for Tropospheric Research (TROPOS)
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Jonas Witthuhn
Leipzig Institute for Meteorology (LIM), University of Leipzig
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Martin Radenz
Leibniz Institute for Tropospheric Research
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Boris Barja González
Atmospheric Research Laboratory, University of Magallanes
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Heike Kalesse-Los
University of Leipzig
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Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. Lidar-based retrievals are limited in optically thick or multilayer clouds, leading to positive biases in simulated shortwave radiative fluxes. At the same time, general circulation models also tend to overestimate the downwelling shortwave radiation at the surface especially in the Southern Ocean regions. To reduce this SW radiation bias in models, we first need better observational-based retrievals for liquid detection which can later be used for model validation. To address this, a machine-learning-based liquid-layer detection method called VOODOO was employed in a proof-of-concept study using a single column radiative transfer model to compare shortwave cloud radiative effects of liquid-containing clouds detected by Cloudnet and VOODOO to ground-based and satellite observations. Results showed a reduction in shortwave radiation bias, indicating that liquid-layer detection with machine-learning retrievals can improve radiative transfer simulations.
18 Apr 2023Submitted to ESS Open Archive
18 Apr 2023Published in ESS Open Archive