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.

Highlights

  • Aerosols and cloud microphysics (CMPs) control cloud properties and thereby exert a large influence on Earth’s climate

  • The cloud microphysical (CMP) processes that shape these properties are important to be represented in global climate models

  • This study conducted a sensitivity analysis with an emulated perturbed parameter ensembles (PPEs) to illuminate the impact of selected CMP processes on model output

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Summary

Introduction

Aerosols and cloud microphysics (CMPs) control cloud properties and thereby exert a large influence on Earth’s climate. Reddington et al (2017) argue that “aerosolclimate models are close to becoming an overdetermined system with many interacting sources of uncertainty but a limited range of observations to constrain them”, refering to the complexity in the representation of aerosols and their interaction with 35 clouds This is related to equifinality, meaning that model versions from different regions of the input parameter space may lead to the same results that compare well with observations. Diving into the importance of single processes on the overall CMPs, Bacer et al (2021) extracted process rates from the chemistry-climate model EMAC, which is based on the same CMPs as this study’s ECHAM-HAM They found that ice crystal sources in large-scale clouds are controlled by freezing and detrainment from convective clouds, while sinks are dominated by aggregation and accretion.

Methods
Phasing as a proxy for complexity
Validation
Results and Discussion
PPE of global mean variables
Seasonal analysis
Process costs and implications for simplification
Findings
Summary, conclusions and outlook
Full Text
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