The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)
<p>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 spaceborne and large-eddy simulation studies. T...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2019-11-01
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| Series: | Earth System Science Data |
| Online Access: | https://www.earth-syst-sci-data.net/11/1745/2019/essd-11-1745-2019.pdf |
| _version_ | 1828319341910687744 |
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| author | G. Cesana G. Cesana A. D. Del Genio H. Chepfer H. Chepfer |
| author_facet | G. Cesana G. Cesana A. D. Del Genio H. Chepfer H. Chepfer |
| author_sort | G. Cesana |
| collection | DOAJ |
| description | <p>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 spaceborne 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-Aerosol 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), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform
outflow and Cu. To separate the Cu from Sc, we design an original method
based on the cloud height, horizontal extent, vertical variability and
horizontal continuity, which is separately 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 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. Using
clustering-derived datasets shows better results although one cannot
completely separate cloud types with such an approach. On the contrary,
classifying Cu and Sc clouds and the transition between them based on their
geometrical shape and spatial heterogeneity leads to spatial distributions
consistent with prior knowledge of these clouds, from ground-based,
ship-based and field campaigns. Furthermore, we show that our method
improves existing Sc–Cu classifications by using additional information on
cloud height and vertical cloud fraction variation. Finally, the CASCCAD
datasets provide a basis to evaluate shallow convection and stratocumulus
clouds on a global scale in climate models and potentially improve our
understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana,
2019, <a href="https://doi.org/10.5281/zenodo.2667637">https://doi.org/10.5281/zenodo.2667637</a>) is available
on the Goddard Institute for Space Studies (GISS) website at <span class="uri">https://data.giss.nasa.gov/clouds/casccad/</span> (last access: 5 November 2019) and on the zenodo website at
<span class="uri">https://zenodo.org/record/2667637</span> (last access: 5 November 2019).</p> |
| first_indexed | 2024-04-13T17:56:03Z |
| format | Article |
| id | doaj.art-3bbdd353eeed487ab28056a4ee4d7e69 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-04-13T17:56:03Z |
| publishDate | 2019-11-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-3bbdd353eeed487ab28056a4ee4d7e692022-12-22T02:36:31ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162019-11-01111745176410.5194/essd-11-1745-2019The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)G. Cesana0G. Cesana1A. D. Del Genio2H. Chepfer3H. Chepfer4Columbia University, Center for Climate Systems Research, Earth Institute, New York, NY, USANASA Goddard Institute for Space Studies, New York, NY, USANASA Goddard Institute for Space Studies, New York, NY, USALMD/IPSL, Sorbonne Université, Paris, FranceLMD/IPSL, CNRS, École Polytechnique, Palaiseau, France<p>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 spaceborne 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-Aerosol 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), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately 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 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. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, <a href="https://doi.org/10.5281/zenodo.2667637">https://doi.org/10.5281/zenodo.2667637</a>) is available on the Goddard Institute for Space Studies (GISS) website at <span class="uri">https://data.giss.nasa.gov/clouds/casccad/</span> (last access: 5 November 2019) and on the zenodo website at <span class="uri">https://zenodo.org/record/2667637</span> (last access: 5 November 2019).</p>https://www.earth-syst-sci-data.net/11/1745/2019/essd-11-1745-2019.pdf |
| spellingShingle | G. Cesana G. Cesana A. D. Del Genio H. Chepfer H. Chepfer The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) Earth System Science Data |
| title | The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) |
| title_full | The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) |
| title_fullStr | The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) |
| title_full_unstemmed | The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) |
| title_short | The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD) |
| title_sort | cumulus and stratocumulus cloudsat calipso dataset casccad |
| url | https://www.earth-syst-sci-data.net/11/1745/2019/essd-11-1745-2019.pdf |
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