Climate models misrepresent Southern Ocean clouds, causing warming biases. Using ship data, we updated a weather model to test how sea spray affects cloud ice. Adding realistic ocean particles prevents deep clouds from freezing too fast, correcting sunlight trasmissions. Yet, shallow clouds remain flawed due to missing moisture. Thus, accurate climate predictions require improving both aerosol-cloud interactions and large-scale weather dynamics.

We studied how extreme wildfire clouds are detected from space and compared this with weather radar and lightning observations. We found that satellites can reliably detect when these clouds begin, but later tend to track spreading cloud tops rather than the active storm itself. This can make events appear larger and longer-lasting than they are. Combining different types of observations is therefore essential to better understand and monitor these dangerous fire-driven storms.

Better characterization of precipitation characteristics has important implications for simulated storm behaviors and remotely-sensed retrievals of cloud intensity. We use a unique radar technique to estimate the fall speeds of media found within deep tropical clouds. These retrievals can be performed within intense storm conditions that are otherwise impractical to sample. Our observations suggest mixed media or graupel fall speeds are contextually faster than prior references.

The interaction between natural marine aerosols, clouds and radiation in the Southern Ocean is a major source of uncertainty in climate models. We evaluate the Australian climate model using aerosol observations and find it underestimates aerosol number often by over 50%. Model changes were tested to improve aerosol concentrations, but some of our changes had severe negative effects on the larger climate system, highlighting issues in aerosol-cloud interaction modelling.

In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.