Evaluating the Role of Aerosol Mixing State in Cloud Droplet Nucleation using a New Activation Parameterization

An important source contributing to uncertainty in simulations with global climate models arises from the influence of aerosols on cloud properties. These so-called aerosol indirect effects arise from a single coupling in the model, representing how aerosols activate and serve as cloud condensation nuclei and ultimately cloud droplets. While it is possible to build explicit numerical models which describe this process in detail, these class of tools are untenable for use in global climate models due to their complexity. Instead, physically- or empirically-based parameterizations of activation are used in their place to efficiently approximate cloud droplet nucleation as a function of a few meteorological and aerosol physical/chemical properties. As global climate models are outfitted with more complex, size- and mixing state-resolving aerosol models, activation parameterizations are increasingly called upon to handle aerosol populations against which their performance has not been explicitly benchmarked.

Here, a simple scheme is proposed to evaluate the performance of activation parameterizations against a spectrum of mixing states, and two schemes commonly used in global models are studied using this framework. It is shown that each scheme exhibits systematic biases when a complex mixing state is present. To help resolve these issues, a new scheme is derived using Polynomial Chaos Expansion to build meta-models representing a full complexity parcel model. The meta-models are shown to accurately handle activation in both single-mode and mixture cases. In addition, a global sensitivity analysis is applied to benchmark the performance of the meta-models and the activation parameterizations against a detailed parcel model, and it is shown that the meta-models tend to more accurately attribute variability in activation dynamics to each input parameter and their interactions with others when compared to the physically-based parameterizations. A variety of experiments with different quadrature rules and orders are tested to optimize the performance of the chaos expansion meta-models in terms of accuracy and computational complexity.

In addition, a chaos expansion meta-model is implemented into a new global aerosol-climate model built by coupling the MIT Aerosol Model and the NCAR Community Earth System Model (CESM). The CESM already implements a physically-based activation parameterization, and estimates of anthropogenic aerosol indirect effects using both parameterizations are compared.