Impact of tropical convection on upper tropospheric cirrus in high resolution DYAMOND simulations

The high-resolution DYAMOND simulations resolve much of the cloud relevant dynamics and cause a large improvement in the structure and diurnal cycle of clouds and precipitation. Nevertheless, from DYAMOND simulations we know that cloud properties can vary significantly even in high-resolution simulations. We focus on evaluating and if possible constraining ice cloud processes in the tropics, an area that should particularly benefit from the increased resolution because deep convection is resolved and controls the tropical upper tropospheric water budget. We analyse not only the horizontal distribution of IWP but also the cloud phase and cloud vertical structure as they are crucial to Earth’s radiation budget.

When comparing the high-resolution global simulations performed within the DYAMOND project among each other and with passive remote sensing data and ERA5 reanalysis we find that the horizontal distribution of ice water path (IWP) varies significantly. In order to understand better those differences, we analysed the connection between the simulated vertical velocity and the total IWP, and the water path of the individual hydrometeors. While the PDF of tropical vertical velocity simulated by the different models is quite similar, the total ice water path connected with those vertical velocities varies strongly. In most models, high vertical velocities are connected with significantly higher IWP than liquid water path (LWP) except in the ICON simulations which simulates similarly large increases in IWP and LWP. Most models simulate large increases in larger ice hydrometeors for large vertical velocities while FV3 simulates also large increases in ice water connected with deep convection. Differences in cloud phase e.g. when comparing NICAM and ICON simulations are connected with different vertical distributions of the condensate with NICAM IWC reaching higher atmospheric levels than the ICON IWC. We attempt to constrain the vertical distribution using active remote sensing data.