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Discussion papers
https://doi.org/10.5194/essd-2019-73
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-73
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 22 May 2019

Submitted as: data description paper | 22 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Grégory Cesana1,2, Anthony D. Del Genio2, and Hélène Chepfer3,4 Grégory Cesana et al.
  • 1Columbia University, Center for Climate Systems Research, Earth Institute, New York, NY, USA
  • 2NASA Goddard Institute for Space Studies, New York, NY, USA
  • 3LMD/IPSL, Sorbonne Université, Paris, France
  • 4LMD/IPSL, CNRS, École Polytechnique, Palaiseau, France

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both space-borne and large eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CAlipso Dataset (CASCCAD). To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent and vertical variability, which is applied to both CALIPSO and combined CloudSat-CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and and Sc-Cu transition case studies. Then, the global statistics are compared against those from existing passive and active-sensor satellite observations. Our results indicate that the cloud optical thickness –as used in passive-sensor observations– is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. On the contrary, classifying Cu and Sc clouds based on their geometrical shape leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, shipbased and field campaigns. Furthermore, we show that our method improves existing Sc-Cu classification by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection (Cu) and boundary layer (Sc) clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, DOI:https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ and on zenodo website at https://zenodo.org/record/2667637 .

Grégory Cesana et al.
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Status: final response (author comments only)
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Grégory Cesana et al.
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