The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

摘要

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloudfeedback estimates. Depending on whether a region is dominated by cumulus(Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback issomewhat different in both spaceborne and large-eddy simulation studies.Therefore, simulating the correct amount and variation of the Cu and Sccloud distributions could be crucial to predict future cloud feedbacks. Herewe document spatial distributions and profiles of Sc and Cu clouds derivedfrom Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations(CALIPSO) and CloudSat measurements. For this purpose, we create a newdataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset(CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiformoutflow and Cu. To separate the Cu from Sc, we design an original methodbased on the cloud height, horizontal extent, vertical variability andhorizontal continuity, which is separately applied to both CALIPSO andcombined CloudSat–CALIPSO observations. First, the choice of parameters usedin the discrimination algorithm is investigated and validated in selectedCu, Sc and Sc–Cu transition case studies. Then, the global statistics arecompared against those from existing passive- and active-sensor satelliteobservations. Our results indicate that the cloud optical thickness – asused in passive-sensor observations – is not a sufficient parameter todiscriminate Cu from Sc clouds, in agreement with previous literature. Usingclustering-derived datasets shows better results although one cannotcompletely separate cloud types with such an approach. On the contrary,classifying Cu and Sc clouds and the transition between them based on theirgeometrical shape and spatial heterogeneity leads to spatial distributionsconsistent with prior knowledge of these clouds, from ground-based,ship-based and field campaigns. Furthermore, we show that our methodimproves existing Sc–Cu classifications by using additional information oncloud height and vertical cloud fraction variation. Finally, the CASCCADdatasets provide a basis to evaluate shallow convection and stratocumulusclouds on a global scale in climate models and potentially improve ourunderstanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana,2019, https://doi.org/10.5281/zenodo.2667637) is availableon the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website athttps://zenodo.org/record/2667637 (last access: 5 November 2019).