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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

Authors

Schimmel,  Willi
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Barrientos-Velasco,  Carola
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Witthuhn,  Jona
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Radenz,  Martin
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Barja González,  Boris
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Kalesse-Los,  Heike
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Schimmel, W., Barrientos-Velasco, C., Witthuhn, J., Radenz, M., Barja González, B., Kalesse-Los, H. (2023): Improved cloud phase retrievals based on remote-sensing observations have the potential to decrease the Southern Ocean shortwave cloud radiation bias, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-3856


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020689
Abstract
An accurate determination of location and amount of liquid water in clouds is crucial for precipitation formation, cloud lifetime, and cloud radiative effects. Most remote-sensing retrievals, such as Cloudnet use lidar measurements to infer the location of liquid cloud droplets from measurements. However, lidar observations are of very limited use for optically thick or multilayer mixed-phase clouds (MPC) where they usually underestimate the presence of liquid water due to full signal attenuation, leading to large biases in simulated radiative fluxes. At the same time, general circulation models largely overestimate the downwelling shortwave radiation at the bottom of the atmosphere especially in the Southern Ocean regions. We argue that, in order to reduce this shortwave radiation bias in models, we first need better observational-based retrievals for supercooled-liquid detection that can be used for model validation. For this purpose, the machine-learning-based retrieval VOODOO is used to capture the extent of liquid layers over the complete vertical range of the clouds.To conceptualize the latter, a case study from the DACAPO-PESO campaign in Punta Arenas, Chili (53.13° S, 70.88° W) was investigated in detail by performing a radiative closures study. The shortwave cloud radiative effects of multilayer non-precipitating stratiform MPC - with liquid water layers detected by Cloudnet and VOODOO - was determined using a 1-D radiative transfer simulator and validated with downwelling pyranometer observations. The shortwave radiation bias was reduced by a factor of two suggesting that improved liquid-layer detection helps to decrease the shortwave radiation bias in radiative transfer simulations.