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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2020-14
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-14
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 06 Mar 2020

Submitted as: methods for assessment of models | 06 Mar 2020

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This preprint is currently under review for the journal GMD.

Multi-layer Cloud Conditions in Trade Wind Shallow Cumulus – Confronting Models with Airborne Observations

Marek Jacob1, Pavlos Kollias1,2, Felix Ament3, Vera Schemann1, and Susanne Crewell1 Marek Jacob et al.
  • 1Institute for Geophysics and Meteorology, University of Cologne, Albertus-Magnus-Platz, 50923 Cologne, Germany
  • 2School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000, USA
  • 3Meteorological Institute, Universität Hamburg, Bundesstrasse 55, 20146 Hamburg, Germany

Abstract. Airborne remote sensing observations over the tropical Atlantic Ocean upstream of Barbados are used to characterize trade wind shallow cumulus clouds and to benchmark two cloud-resolving ICON (ICOsahedral Nonhydrostatic) model simulations at kilo- and hectometer scales. The clouds were observed by an airborne nadir pointing backscatter lidar, a cloud radar, and a microwave radiometer in the tropical dry winter season during daytime. For the model benchmark, forward operators convert the model data into the observational space for considering instrument specific cloud detection thresholds. The forward simulations reveal the different detection limits of the lidar and radar observations, i.e., most clouds with cloud liquid water content greater than 10−7 kg/kg are detectable by the lidar, whereas the radar is primarily sensitive to the rain-category hydrometeors in the models and can detect even low amounts of rain.

The observations reveal two prominent modes of cumulus cloud top heights separating the clouds into two layers. The lower mode relates to boundary layer convection with tops closely above the lifted condensation level, which is at about 700 m above sea level. The upper mode is driven by shallow moist convection, also contains shallow outflow anvils, and is closely related to the trade inversion at about 2.3 km above sea level. The two cumulus modes are reflected differently by the lidar and the radar observations and under different liquid water path (LWP) conditions. The storm-resolving model (SRM) at kilometer scale reproduces the cloud modes barely and shows the most cloud tops slightly above the observed lower mode. The large-eddy model (LEM) at hectometer scale reproduces better the observed cloudiness distribution with a clear bimodal separation. We hypothesize that slight differences in the autoconversion parametrizations could have caused the different cloud development in the models. Neither model seems to account for in-cloud drizzle particles that do not precipitate down to the surface but generate a stronger radar signal even in scenes with low LWP. Our findings suggest that even if the SRM is a step forward for better cloud representation in climate research, the LEM can better reproduce the observed shallow cumulus convection and should therefore in principle represent cloud radiative effects and water cycle better.

Marek Jacob et al.

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Latest update: 28 Mar 2020
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Short summary
Shallow clouds over the oceans are an undetermined but climatologically important topic for numerical atmosphere simulations. We provide a method how airborne remote sensing observations over the tropical Atlantic can be used to characterize those clouds and to benchmark two cloud-resolving models. E.g., forward operators allow applying instrument-specific thresholds for cloud detection to model data. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Shallow clouds over the oceans are an undetermined but climatologically important topic for...
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