Articles | Volume 11, issue 4
https://doi.org/10.5194/essd-11-1745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-1745-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
25 Nov 2019
Data description paper |
| 25 Nov 2019
| 25 Nov 2019
The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)
Columbia University, Center for Climate Systems Research, Earth
Institute, New York, NY, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Anthony D. Del Genio
NASA Goddard Institute for Space Studies, New York, NY, USA
Hélène Chepfer
LMD/IPSL, Sorbonne Université, Paris, France
LMD/IPSL, CNRS, École Polytechnique, Palaiseau, France
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- Multi-objective observational constraint of tropical Atlantic and Pacific low-cloud variability narrows uncertainty in cloud feedback M. Wu et al. 10.1038/s41467-024-53985-w
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- Evaluation of Cloud Mask and Cloud Top Height from Fengyun-4A with MODIS Cloud Retrievals over the Tibetan Plateau W. Xu & D. Lyu 10.3390/rs13081418
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- Exploring the Cloud Top Phase Partitioning in Different Cloud Types Using Active and Passive Satellite Sensors O. Bruno et al. 10.1029/2020GL089863
Latest update: 04 Feb 2025
Short summary
Low clouds (cloud top below 3 km) drive most of the uncertainty in future climate projections. Here we create a new dataset, the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies the different types of low clouds – stratocumulus and cumulus – based on their morphology. CASCCAD provides a basis to evaluate climate models and potentially improve our understanding of the cloud response to climate warming, as well as reduce the uncertainty in future climate projection.
Low clouds (cloud top below 3 km) drive most of the uncertainty in future climate projections....
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