Diagnosing Aerosol-Meteorological Interactions on Snow within the Earth System: A Proof-of-Concept Study over High Mountain Asia

Abstract. Snowmelt in the Third Pole, particularly in High Mountain Asia (HMA), 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 to identify key variables that influence non-linear processes within snowmelt using daily data for the late snowmelt season (May–July). We combine statistical and machine learning methods using observational and model data, 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, reflective of differences in model design. In particular, dust interactions with near-surface temperature and large-scale circulation are underrepresented, as well as gaps in cloud cover interactions especially in the least coupled reanalysis. Carbonaceous aerosols and large-scale circulation emerge as main drivers of aerosol-meteorology onto snow interactions, highlighting their relevance in Earth system models (ESMs) for the accurate assessment of water availability in developing economies. These diagnoses point to the degree of complexity of these interactions and their relative strength of representation across ESMs. The proposed framework can thus be extended to diagnose other complex Earth system processes, providing a pathway for improving Earth system predictability and reducing climate change uncertainties.