Assessing the potential for simplification in global climate model cloud microphysics
<p>Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are therefore important to represent in global climate models. Historically, parameterizations in these models have grown more detailed and comp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-04-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/4737/2022/acp-22-4737-2022.pdf |
| Summary: | <p>Cloud properties and their evolution influence Earth's radiative balance. The cloud microphysical (CMP) processes that shape these properties are
therefore important to represent 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 of 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
perturbation of a process serves as a proxy for the effect of a simplification. Autoconversion of ice crystals is found to be the dominant CMP
process in influencing key variables such as the ice water path and cloud radiative effects, while riming of cloud droplets on snow has the most
influence on 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 autoconversion 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 application for the combination of statistical emulation and sensitivity analysis to evaluate
the sensitivity of a complex numerical model to a specific parameterized process. It paves the way for simplifications of CMP processes leading to
more interpretable climate model results.</p> |
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| ISSN: | 1680-7316 1680-7324 |