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 |
| Summary: | <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> |
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| ISSN: | 1866-3508 1866-3516 |