Investigation of thin midlevel ice clouds in the Arctic using calipso data and radiative transfer modeling

Vinay Kumar Kayetha
Wed, 22 Apr 2026 17:33:14 PDT

In this research we investigate the global occurrence and properties of optically thin midlevel ice clouds. These clouds are difficult to detect with passive radiometric techniques and are under-represented in current studies. We use the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data set to identify thin midlevel ice clouds and determine their global occurrence and distribution. For the first time, we find that the global mean occurrence of these clouds is at least 4.5%, being at least 7.3% of all the tropospheric clouds detected at a horizontal scale of 10 km. Seasonally, these clouds are found most commonly in the polar regions. These clouds occur most commonly in the Arctic in winter and least commonly in the summer. In winter these clouds can occur up to 19% of the time. The occurrence of these clouds decreases with increasing spatial scale and are most commonly found at spatial scales of 25 km or less. We found five large distinct clouds over the Arctic and investigated them for their meteorological conditions and radiative effects. These thin midlevel ice clouds are formed along the frontal zones in weakly ascending air masses. Our model simulations show that thin midlevel ice clouds have a net warming effect on the surface of 23-48 W/m². We conclude that these clouds have a significant impact on the radiation budget in Arctic winters. Our study highlights the importance of active satellite-based remote sensing in globally detecting and characterizing optically thin clouds. Our estimates of occurrence and fraction of clouds represents a lower bound, as these clouds can be obscured by optically thicker clouds. The volume of measurements provided by the satellite allowed us to identify a small but consistent set of large clouds with which we could conduct a contemporary radiative analysis. These findings can be used to improve the representation of clouds and their impacts in regional and global climate models.