Diagnosing aerosol-meteorological interactions on snow within Earth system models: A proof-of-concept study over High Mountain Asia

Snowmelt in the Third Pole, or High Mountain Asia (HMA), serves as a vital water source for 30 % of the world's population and is strongly influenced by interactions between aerosols and meteorology. However, understanding these interactions remains uncertain due to their complexity and limitations in existing approaches using model sensitivity and process-denial experiments. In addition, these interactions are insufficiently represented in current climate models. Equally ambiguous is the impact of these interactions on snow processes in the context of climate change. Here we use network theory, a graphical approach that maps the relationships between variables as interconnected nodes, to identify key variables that influence snowmelt processes. We focus on the late snowmelt season (May–July) using daily data (from 2003–2019) from satellite observations and reanalyses. We combine statistical and machine learning methods to highlight the underappreciated relevance of coupled processes between aerosols and meteorology on snow, as well as the inconsistent representation of aerosol–meteorology interactions on snow within major reanalyses. These inconsistencies reflect fundamental differences in model design. In particular, we identify underrepresented dust interactions with near-surface temperature and large-scale circulation and gaps in cloud cover interactions, especially in the least coupled reanalysis. Carbonaceous aerosols and large-scale circulation emerge as main drivers of aerosol–meteorology in snow interactions, highlighting their relevance in Earth system models (ESMs) for the accurate assessment of water availability in developing economies. These insights point to the degree of complexity of these interactions and their relative strength of representation across ESMs. The proposed framework can be extended to help diagnose other complex Earth system processes and complement conventional feedback separation methods. This has broader implications for the future development of coupled models to improve Earth system predictability.