Improvement of Ice Particle Spectral Relative Dispersion Parameterization in the BCC‐AGCM Model and Its Impact on Global Climate Simulation

Abstract The representation of cloud microphysical processes in climate models continues to be a major challenge leading to uncertainty in climate simulations. The shape parameter (equivalent to relative dispersion) of gamma distribution for ice particles is assumed to be 0 in the Beijing Climate Center Atmospheric General Circulation Model (BCC‐AGCM). This study diagnoses the shape parameter by linking it to the ice volume‐mean diameter and analyzes the impact of the modified scheme on the performance of climate simulations. Results show that the modified scheme performs better in simulating global cloud fraction, cloud radiative forcing, and total precipitation compared to the control configuration, thereby significantly reducing simulation biases. The underlying physical mechanisms are driven by three key factors. First, the shape parameter in the modified scheme is greater than zero, narrowing the ice particle size distribution. This reduces the autoconversion of ice to snow and sedimentation processes while enhancing deposition growth, resulting in an increase in upper‐level ice clouds. The increase in ice‐clouds increases upper atmospheric temperatures, enhances atmospheric stability, and promotes the formation of lower‐level clouds. Second, the improvement in cloud fraction significantly mitigates the underestimation of longwave and shortwave cloud radiative forcing. Additionally, the overestimation of precipitation is improved, including both convective and large‐scale precipitation, particularly from an annual mean perspective. Increased atmospheric stability reduces convective precipitation, while weakened snow sources and enhanced sinks to reduce large‐scale precipitation. The study emphasizes the importance of ice particle spectral relative dispersion and provides valuable insights for improving cloud microphysics parameterization schemes.