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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-2019-216
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-216
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 04 Sep 2019

Submitted as: methods for assessment of models | 04 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

An evaluation of clouds and radiation in a Large-Scale Atmospheric Model using a Cloud Vertical Structure classification

Dongmin Lee1,2, Lazaros Oreopoulos2, and Nayeong Cho3,2 Dongmin Lee et al.
  • 1Morgan State University
  • 2NASA Goddard Space Flight Center
  • 3University Space Research Association

Abstract. We revisit Cloud Vertical Structure (CVS) classes we have previously employed to classify the planet’s cloudiness. The CVS classification reflects simple combinations of simultaneous cloud occurrence in the three standard layers traditionally used to separate low, middle, and high clouds and was applied to a dataset derived from active lidar and cloud radar observations. This classification is now introduced in an Atmospheric Global Climate Model (AGCM), specifically NASA’s GEOS-5, in order to evaluate the realism of its cloudiness and of the radiative effects associated with the various CVS classes. Determination of CVS and associated radiation in the model is possible thanks to the implementation of a subcolumn cloud generator which is paired with the model’s radiative transfer algorithm. We assess GEOS-5 cloudiness in terms of the statistics and geographical distributions of the CVS classes, as well as features of their associated Cloud Radiative Effect (CRE). We decompose the model’s CVS-specific CRE errors into component errors stemming from biases in the frequency of occurrence of the CVSs, and biases in their internal radiative characteristics. Our framework sheds additional light into the verisimilitude of cloudiness in large scale models and can be used to complement cloud evaluations that take advantage of satellite simulator implementations.

Dongmin Lee et al.
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Short summary
We apply a cloud classification method based on cloud vertical structure (CVS) from active sensors to evaluate the cloudiness of NASA’s GEOS-5 model. We assess the model’s CVS classes to observations. We also evaluate simulated cloud radiative effect and its contributions and apply an analysis framework whereby the source of the model radiative effect errors is traced back to either errors in the nature of the simulated CVS classes or in the frequency at which they are produced by the model.
We apply a cloud classification method based on cloud vertical structure (CVS) from active...
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