Assessing the potential for simplification in global climate model cloud microphysics

Abstract

Abstract. Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to be represented in global climate models. Historically, parameterizations in these models have grown more detailed and complex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation of model results and helping to increase process understanding. This study employs sensitivity analysis on an emulated perturbed parameter ensemble of the global aerosol-climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to the phasing of a process thereby serves as a proxy for the effect of a simplification. Aggregation of ice crystals is found to be the dominant CMP process in influencing key variables such as the ice water path or cloud radiative effects, while riming of cloud droplets on snow influences mostly the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and are therefore identified as suitable candidates for future simplifications. In turn, the dominating role of aggregation suggests that this process has the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the data corroborates the results. This study introduces a new framework to evaluate a processes' impact in a complex numerical model, and paves the way for simplifications of CMP processes leading to more interpretable climate models.

Ice Aggregation in Low‐Level Mixed‐Phase Clouds at a High Arctic Site: Enhanced by Dendritic Growth and Absent Close to the Melting Level

“…Avramov and Harrington (2010) further suggested, based on a model sensitivity experiment, that the phase partitioning of Arctic low‐level MPCs is strongly sensitive to the assumptions on mass‐size, and size‐fall speed relations of ice particles, and thus on the ice habits included in the model. It can then be expected that in addition to cloud lifetime, phase‐partitioning, and organization, precipitation further affects the radiative characteristics of the MPC (Avramov & Harrington, 2010; Eirund et al., 2019; Harrington & Olsson, 2001; Proske et al., 2021; Solomon et al., 2015; Tan & Storelvmo, 2019). Tan and Storelvmo (2019) have shown that in the Community Earth System Model (CESM) larger ice particles in Arctic MPCs lead to a stronger cloud‐phase feedback, that in turn increases the magnitude of Arctic amplification.…”

A Lagrangian Perspective of Microphysical Impact on Ice Cloud Evolution and Radiative Heating

“…The trajectories indicate then that structural differences in ice microphysics are sufficient to generate such a spread in q i . The two‐moment trajectories differ more from one another than the one‐moment ones, perhaps as the addition of uncertain parameters in more sophisticated schemes expands the space of possible cloud states (e.g., Morrison et al., 2020; Proske et al., 2021). Median N i varies by a factor of six between sets of trajectories, and the N i distributions exhibit quite different forms.…”

Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)

“…Clouds are an integral part of the Earth's radiation budget and global water cycle (Liou, 1986;Luo and Rossow, 2004;Bony et al, 2015;Zhou et al, 2016). Since cloud microphys-ical processes occur at scales that are much smaller than the resolution of commonly used atmospheric models, it remains a significant challenge for atmospheric models to represent cloud-related processes, especially ice-phase cloud microphysical processes (Mitchell et al, 2008;Spichtinger and Gierens, 2009;Wang and Penner, 2010;Erfani and Mitchell, 2016;Paukert et al, 2019;Morrison et al, 2020;Proske et al, 2022). Because it is impossible for commonly used atmospheric models (excluding the ideal model with the recently developed Lagrangian-particle-based scheme) to individually describe cloud particles (e.g., cloud droplets or ice crystals), only the macrostatistical features of cloud particles are represented in cloud microphysics schemes.…”