Improvements of the Double-Moment Bulk Cloud Microphysics Scheme in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)

Abstract

Abstract This study revises the collisional growth, heterogeneous ice nucleation, and homogeneous ice nucleation processes in a double-moment bulk cloud microphysics scheme implemented in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). The revised cloud microphysical processes are tested by 10-day global simulations with a horizontal resolution of 14 km. It is found that both the aggregation of cloud ice with smaller diameters and the graupel production by riming are overestimated in the current schemes. A new method that numerically integrates the collection kernel solves this issue, and consequently, the lifetime of cloud ice is reasonably extended in reference to satellite observations. In addition, the results indicate that a reduction in graupel modulates the convective intensity, particularly in intense rainfall systems. The revision of both heterogeneous and homogeneous ice nucleation significantly increases the production rate of cloud ice number concentration. With these revisions, the new version of the cloud microphysics scheme successfully improves outgoing longwave radiation, particularly over the intertropical convergence zone, in reference to satellite observations. Therefore, the revisions are beneficial for both long-term climate simulations and representing the structure of severe storms. Significance Statement Very high-resolution global atmospheric models have been developed to simultaneously address global climate and regional weather. In general, cloud microphysics schemes used in such global models are introduced from regional weather forecasting models to realistically represent mesoscale cloud systems. However, a cloud microphysics scheme that was originally developed with the aim of weather forecasting can cause unexpected errors in global climate simulations because such a cloud microphysics scheme is not designed for interdisciplinary usage across spatiotemporal scales. This study focuses on systematic model biases in evaluating the terminal velocity of ice cloud particles and proposes a method to accurately calculate the growth rate of ice cloud particles. Improvements in ice cloud modeling successfully reduce model biases in the global energy budget. In addition, the internal structure of intense rainfall systems is modified using the new cloud model. Therefore, improvements in ice cloud modeling could further increase the reliability of weather forecasting, seasonal prediction, and climate projection.