Journal of Geophysical Research Atmospheres · 2017 · 89 citations · 32 references
Natural SciencesEarth SciencesMeteorologyAtmospheric ScienceClimate ScienceClimatologyAerosol-cloud InteractionsClimate ModelingCloud Optical PropertiesCloud PropertiesCloud Radiative EffectsLidar Remote SensingAirborne LidarAtmospheric OpacityCloud Cover ObservationCloud Longwave Radiative EffectCloud Radiative Effects In Climate ModelsLidar SensingLongwave Cloud Radiative EffectsShortwave Cloud Radiative Effects
Abstract The spaceborne lidar CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation) directly measures atmospheric opacity. In 8 years of CALIPSO observations, we find that 69% of vertical profiles penetrate through the complete atmosphere. The remaining 31% do not reach the surface, due to opaque clouds. The global mean altitude of full attenuation of the lidar beam (z_opaque) is 3.2 km, but there are large regional variations in this altitude. Of relevance to cloud‐climate studies, the annual zonal mean longwave cloud radiative effect and annual zonal mean z_opaque weighted by opaque cloud cover are highly correlated (0.94). The annual zonal mean shortwave cloud radiative effect and annual zonal mean opaque cloud cover are also correlated (−0.95). The new diagnostics introduced here are implemented within a simulator framework to enable scale‐aware and definition‐aware evaluation of the LMDZ5B global climate model. The evaluation shows that the model overestimates opaque cloud cover (31% obs. versus 38% model) and z_opaque (3.2 km obs. versus 5.1 km model). In contrast, the model underestimates thin cloud cover (35% obs. versus 14% model). Further assessment shows that reasonable agreement between modeled and observed longwave cloud radiative effects results from compensating errors between insufficient warming by thin clouds and excessive warming due to overestimating both z_opaque and opaque cloud cover. This work shows the power of spaceborne lidar observations to directly constrain cloud‐radiation interactions in both observations and models.
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