Abstract

CloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water ... Show moreCloudSat observed tropical liquid and ice water content (L/IWC, Version 4) profiles are compared with GEOS5 analyses, NCAR-CAM3, and GFDL-AM2 simulations. Both the analyses and free-running general circulation models (GCMs) underestimate IWC in the upper troposphere, with the simulated ice water paths (IWPs) being 22% (GEOS5), 9% (CAM3), and 54% (AM2) of the CloudSat retrieval. For liquid clouds, GEOS5 produces the closest match to CloudSat, with a distinct peak in LWC around 1.5-2 km. CAM3 and AM2 generate liquid clouds in a broad vertical layer in the lower and middle troposphere, resulting in slightly higher column-integrated liquid water path (LWP) than CloudSat, despite the fact that their LWC in the boundary layer is only 60%-70% of CloudSat. The data assimilation model and two GCMs produce substantial middle-level clouds, more than the CloudSat retrieval. We sort the cloud profiles by midtropospheric vertical velocity (ω500), sea surface temperature (SST), and lower tropospheric stability (LTS). The high clouds in the models are concentrated in large-scale ascending, warm SST, and low LTS regimes, consistent with the CloudSat observation. The CAM3 and AM2 model-simulated middle-level clouds are strongly correlated with ω500 but less clustered in the domains of SST and LTS. For low clouds, both CloudSat and GOES5 analyses show the preferential distribution of low clouds in regions of large-scale subsidence, relatively cold SST (SST < 27°C), and high LTS (LTS > 15 K), while CAM3 and AM2 low clouds are strongly controlled by SST and LTS and only weakly correlated with ω500. Exclusion of precipitating scenes would reduce the tropical mean CloudSat LWP and IWP by 73% and 48%, respectively, which does not fully explain the model-data discrepancies. Show less