Cumulus And Stratocumulus Cloudsat-CAlipso Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates (Zelinka et al., 2016). 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 (Cesana et al., 2019a) and large eddy simulation studies (i.e., Bretherton, 2015). 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, Cesana et al., 2019b).

Although both the Sc and Cu clouds form within the planetary boundary layer (PBL), they have relatively different shapes as they are controlled by different physical mechanisms. The Cu (Fig. 1, second to last column) can stretch up past the PBL into the lower free troposphere (z ∼ 3 km) while they have typically a small horizontal extent (no more than a few km, Lamer et al., 2015; Nuijens et al., 2015b). On the contrary, the Sc (Fig. 1, first column) have a relatively small vertical extent (no more than a few hundred meters) and a cloud-top height (CTH) controlled by the PBL depth but spread out over tens to hundreds of kilometers (Wood, 2012) either homogeneously or heterogeneously (open-cell). In between, these two distinct regimes, various transitioning clouds may form (Albrecht et al., 2019; Teixeira et al., 2011) and the most frequent are: broken Sc and transitioning Sc-Cu, which is composed of Cu under Sc (Albrecht et al., 2019, Rauber et al., 2007) and Cu with stratiform outflow (Lamer et al., 2015, Nuijens et al., 2015b) (Fig. 1, second to fourth column, respectively). Clouds with a cloud base and a cloud top within and outside the lower free troposphere, respectively, are classified as deep Cu (Fig. 1, last column). Bearing the above facts in mind, we design the CASCCAD DA based on cloud top height (CTH) and cloud vertical and horizontal cloud fraction (VCF and HCF, respectively), which can be applied to both the General Circulation Model (GCM)- Oriented CALIPSO Cloud Product (GOCCP) instantaneous profiles and the CloudSat-CALIPSO combined observations.

Cartoon showing the different cloud morphologies in terms of increasing sea-surface temperature and altitude

Figure 1: Cartoon representing the different cloud morphologies detected by the CASCCAD DA. The blue dashed line denotes the upper limit of the low-level clouds following GOCCP definition (3.36 km). Note that the Deep Cu type is not a low cloud, but either a mid- or high-level cloud.

The CASCCAD datasets allow us to document the geographical distribution of Sc and Cu clouds on a global scale for the first time. For example, in Fig. 2 (left columns), the CASCCAD observations depict tropical oceans being almost exclusively dominated by Cu clouds (20 to 40 %) while the oceans off the west coasts of the continents are mostly covered by Sc clouds (50 to 85 %), with transitioning clouds in between (10 to 15 %).

Plot showing maps and zonal profiles of cloud cover for different cloud types

Figure 2: Maps (left; x axis, longitude [°E]; y axis, latitude [°N]) and zonal profiles (right; x axis, latitude [°N]; y axis, height [km]) of (top to bottom) low, Sc , Cu Sc-Cu transitioning and the ratio Sc to Sc-and-Cu cloud fraction (%) for (left to right) CASCCAD-GOCCP (2007-2016) and CASCCAD-CloudSat-CALIPSO (2007-2010). Different color bars are used to better separate each type of cloud. Note that the low category accounts for all clouds present below 3.36 km regardless of their cloud top height, hence the sum of Sc, Cu and transitioning cloud fraction can be smaller than the low cloud fraction.

Additionally, our results provide a broader context to earlier findings from ground-based and field campaigns. The globally-averaged profiles of Cu cloud fraction over the tropical oceans (Fig. 2, right columns) are almost identical to that found by ground-based observations over the Barbados (Nuijens et al., 2015), in terms of shape (cloud base below 1 km and cloud top above 2 km) and frequency of occurrence (∼ 10 %). Another interesting result concerns the distribution and magnitude of Sc cloud fraction (Fig. 2, left columns). Our results indicate that the Sc clouds occur up to 85 % of the time over Sc deck areas compared to 60 % in earlier studies (e.g., Wood, 2012) and that their presence in trade-wind regions is negligible as opposed to a 20 % cloud frequency (Fig. 2, left columns).

Finally, by documenting the geographical distribution of Sc and Cu clouds, these datasets make it possible to evaluate the shallow convection (Cu type) and boundary layer (Sc type) clouds in state-of-the art climate models, which are typically generated by distinct parametrizations.

Download Data

Datasets contain either 2D or 3D data, with the latter including "3D" in the filename. All files are in netCDF format. A description of dataset structure and variables follows below.

  • 2D Map Data: (see Fig. 2, left columns)
    • Structure:
      • Annual mean over a 2.5°×2.5° grid (lon = 144, lat = 72, time = 1)
    • Variables:
      • low: cloud below 3.36 km regardless of its cloud top height
      • thin (CALIPSO-only): cloud below 3.36 km regardless of its cloud top height only when surface echo is detected (optical depth < 3 to 5)
      • cuall: all Cu clouds regardless of the cloud top height
      • cuh: Cu clouds with a cloud top height in the high-levels (z > 6.72 km)
      • cum: Cu clouds with a cloud top height in the mid-levels (3.36 < z < 6.72 km)
      • cul: Shallow Cu clouds with a cloud top height in the low-levels (z < 3.36 km), excluding Cu under Sc and Cu with stratiform outflow (sc2cu)
      • cu: Cu clouds with a cloud top height in the low-levels (z < 3.36 km), including Cu under Sc and Cu with stratiform outflow (sc2cu)
      • transi: transitioning Sc-Cu clouds, either broken Sc (scb) or Sc-Cu (sc2cu) clouds.
      • allsc: all Sc clouds, including broken Sc (scb)
      • sc: Sc clouds, excluding broken Sc (scb)
      • scb: Broken Sc clouds
      • sc2cu: Sc-Cu clouds, which can be either Cu under Sc or Cu with stratiform outflow
  • 3D Data: (see Fig. 2, right columns)
    • Structure:
      • Annual mean over a 2.5°×2.5° grid with 480-m equidistant levels from 0 to 19.2 km (lon = 144, lat = 72, alt = 40, time = 1)
    • Variables
      • low3D: cloud below 3.36 km regardless of its cloud top height
      • thin3D (CALIPSO-only): cloud below 3.36 km regardless of its cloud top height only when surface echo is detected (optical depth < 3 to 5)
      • cuall3D: all Cu clouds regardless of the cloud top height
      • cuh3D: Cu clouds with a cloud top height in the high-levels (z > 6.72 km)
      • cum3D: Cu clouds with a cloud top height in the mid-levels (3.36 < z < 6.72 km)
      • cul3D: Shallow Cu clouds with a cloud top height in the low-levels (z < 3.36 km), excluding Cu under Sc and Cu with stratiform outflow (sc2cu)
      • cu3D: Cu clouds with a cloud top height in the low-levels (z < 3.36 km), including Cu under Sc and Cu with stratiform outflow (sc2cu)
      • transi3D: transitioning Sc-Cu clouds, either broken Sc (scb) or Sc-Cu (sc2cu) clouds.
      • allsc3D: all Sc clouds, including broken Sc (scb)
      • sc3D: Sc clouds, excluding broken Sc (scb)

Contact

Please address all inquiries about these data to Dr. Gregory Cesana.

Citation

When referencing the CASCCAD dataset, please cite both this webpage and the journal article:

Cesana, G., Del Genio, A. D. and Chepfer, H., 2019: The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), Earth Syst. Sci. Data, 1745-1764, doi:10.5194/essd-11-1745-2019.

Acknowledgements

This dataset was developed under a CloudSat-CALIPSO RTOP at the NASA Goddard Institute for Space Studies. The authors would like to thank NASA and CNES for giving access to CALIPSO and CloudSat observations, and Climserv for giving access to CALIPSO-GOCCP observations and for providing computing resources.

References

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