While the radiative influence of clouds on Arctic sea ice is known, the influence of sea ice cover on Arctic clouds is challenging to detect, separate from atmospheric circulation, and attribute to human activities. Providing observational constraints on the two‐way relationship between sea ice cover and Arctic clouds is important for predicting the rate of future sea ice loss. Here we use 8 years of CALIPSO (Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations) spaceborne lidar observations from 2008 to 2015 to analyze Arctic cloud profiles over sea ice and over open water. Using a novel surface mask to restrict our analysis to where sea ice concentration varies, we isolate the influence of sea ice cover on Arctic Ocean clouds. The study focuses on clouds containing liquid water because liquid‐containing clouds are the most important cloud type for radiative fluxes and therefore for sea ice melt and growth. Summer is the only season with no observed cloud response to sea ice cover variability: liquid cloud profiles are nearly identical over sea ice and over open water. These results suggest that shortwave summer cloud feedbacks do not slow long‐term summer sea ice loss. In contrast, more liquid clouds are observed over open water than over sea ice in the winter, spring, and fall in the 8 year mean and in each individual year. Observed fall sea ice loss cannot be explained by natural variability alone, which suggests that observed increases in fall Arctic cloud cover over newly open water are linked to human activities.
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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