The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Grégory Cesana,Hélène Chepfer,Anthony D Del Genio

doi:10.5194/essd-11-1745-2019

Abstract

Abstract. 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, https://doi.org/10.5281/zenodo.2667637) is available on 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 at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Highlights

Whether clouds will amplify or dampen global warming, referred to as cloud feedbacks, continues to be a dominant source of uncertainty in future climate projections, for which low clouds over the tropics and at midlatitudes contribute up to 50 % in recent generations of the Coupled Model Intercomparison Project (CMIP) models (Zelinka et al, 2016)
We investigate the sensitivity of the discrimination algorithm (DA) to some of the criteria presented in Sect. 3 using GOCCP observations: the horizontal cloud fraction (HCF), cloud top height (CTH) and vertical profiles of cloud fraction (VCF) thresholds and the horizontal continuity test
We compare the results of the standard DA applied separately to GOCCP and RLGeoProf against the 2BCCL cloud types – for the same case studies – and utilize the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance to provide a broader context of the cloud scene

Summary

Introduction

Whether clouds will amplify or dampen global warming, referred to as cloud feedbacks, continues to be a dominant source of uncertainty in future climate projections, for which low clouds over the tropics and at midlatitudes contribute up to 50 % in recent generations of the Coupled Model Intercomparison Project (CMIP) models (Zelinka et al, 2016). Sc clouds cap the planetary boundary layer (PBL) over cool oceans in conditions with a strong cloud top inversion, mostly off the western coasts of continents (e.g., Klein and Hartmann, 1993) They are typically hundreds of meters thick with a large horizontal extent, which can be either homogeneous (in decks) or heterogenous (open and closed cells), and a stable cloud top height. As the ocean warms up further west in the trade-wind regions, the latent heat flux increases and the convection becomes surface driven, favoring breaking up of Sc and the subsequent formation of Cu clouds (Albrecht et al, 2019; Wyant et al, 1997) These clouds are horizontally limited and scattered – i.e., with a modest cloud cover – and their tops can rise above the PBL. Since these low clouds are governed by distinct processes, they may respond differently to climate warming (e.g., Bretherton, 2015), and there is no fundamental reason to expect the two cloud types to exhibit the same feedback

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Conclusion