Advancing understanding and predictability of aerosol effective radiative forcing due to structural uncertainty in an Earth system model

Earth system models struggle to accurately simulate aerosol’s interactions with weather and climate. This is largely attributed to structural uncertainty including insufficient process representation and model resolution due to limited computer power, and model tuning has become a low-cost remedy for improving performance. With unprecedented computational capability, improved understanding, modern software, and novel machine learning algorithms, high-resolution Earth system modeling with accurate and yet expensive process representations has become possible. In this study, we quantify the impacts of longstanding structural uncertainty on aerosol effective radiative forcing (ERF) by incorporating much more sophisticated process representations in U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM). The ERF associated with aerosol-cloud interactions is further decomposed into the Twomey effect, liquid water path (LWP) adjustment, and cloud fraction adjustment using a satellite-based radiative kernel so that the impacts of each new process representation on aerosol ERF can be evaluated against observations. We find that while increasing model resolution to kilometer scale changes aerosol ERF by 30%, model physics representations (aerosol mixing assumption, condensational growth, secondary organic and sulfate aerosol formation, aerosol optics, aerosol activation, emission, giant aerosol, chemistry, warm rain process, and aerosol-turbulence coupling) contributes to a factor-of-two variation in aerosol ERF. As opposed to model tuning, this approach improves understanding and increases confidence in simulations as they are traceable to physics. Furthermore, even though the model’s total aerosol ERF or the Twomey effect alone can be brought to agree well with satellite estimate, significant biases in LWP and cloud fraction adjustments remain, highlighting the importance of improving aerosol interactions with cloud macrophysics in the model.