Addressing Complexity in Global Aerosol Climate Model Cloud Microphysics

Abstract

In climate modeling, our task is to represent an immensely complex system which we wish to understand and learn about. Model development inherently faces tradeoffs between epistemic values like tractability, interpretability, validation potential, and specificity (Beucler et al., 2021;Larsen et al., 2016;Undorf et al., 2022), which manifest themselves in the ever present question of where to draw the line for details. Understanding nature requires a simplification of the mechanisms at play, but wanting to be precise calls for more details that increase complexity (Tapiador et al., 2019). For the term "model complexity" we here follow the "loose definition" mentioned in García-Callejas and Araújo (2016) that a model becomes more complex the more difficult to comprehend its computations are and the more processes/computations there are (Baartman et al. (2020) andRandall et al. (2003)).Climate modeling has followed the mirror view (Parker, 2022), meaning that it wants to mirror the Earth system in the model representation. Different modeling approaches would be following for example, the predictive or