English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties aretherefore important to represent in global climate models. Historically, parameterizations in these models have grown more detailed andcomplex. However, a simpler formulation of CMP processes may leave the model results mostly unchanged while enabling an easier interpretation ofmodel results and helping to increase process understanding. This study employs sensitivity analysis of an emulated perturbed parameter ensemble ofthe global aerosol–climate model ECHAM-HAM to illuminate the impact of selected CMP cloud ice processes on model output. The response to theperturbation of a process serves as a proxy for the effect of a simplification. Autoconversion of ice crystals is found to be the dominant CMPprocess in influencing key variables such as the ice water path and cloud radiative effects, while riming of cloud droplets on snow has the mostinfluence on the liquid phase. Accretion of ice and snow and self-collection of ice crystals have a negligible influence on model output and aretherefore identified as suitable candidates for future simplifications. In turn, the dominating role of autoconversion suggests that this processhas the greatest need to be represented correctly. A seasonal and spatially resolved analysis employing a spherical harmonics expansion of the datacorroborates the results. This study introduces a new application for the combination of statistical emulation and sensitivity analysis to evaluatethe sensitivity of a complex numerical model to a specific parameterized process. It paves the way for simplifications of CMP processes leading tomore interpretable climate model results.

Assessing the potential for simplification in global climate model cloud microphysics