PhD Proposal: Ronghao Wang

ADVISOR: Dr. Zhibo Zhang

TITLE: Investigate the radiative effects and hyperspectral fingerprints of dust aerosols

ABSTRACT: Mineral dust is one of the most abundant aerosol species in the atmosphere and exerts a complex direct radiative effect (DRE), typically producing shortwave (SW) cooling and longwave (LW) warming. Yet the magnitude of dust radiative effects remains poorly constrained because dust optical properties are highly sensitive to particle size, composition, and mixing state. This proposal addresses three related sources of uncertainty in dust radiative effects through a combined framework of satellite observations, reanalysis, and radiative transfer modeling.

First, we investigate how polluted dust mixing modifies dust radiative effects in both the SW and LW. Preliminary analyses using satellite observations and reanalysis data identify recurrent polluted dust hotspots over West Central Africa, the Indian subcontinent, and the Capital Region of China. Building on these results, we will quantify the radiative impacts of idealized dust–pollutant mixing states using core–shell representations for sulfate-coated dust and extend the framework to irregular dust–smoke aggregates. Second, we will produce an observation-constrained assessment of dust LW DRE and investigate the controlling factors. Recent dust properties retrieved from thermal-infrared (TIR) observations, especially the dust aerosol optical depth (DAOD) at TIR, provide the key information needed to better constrain coarse-mode dust properties relevant to LW radiative transfer. By combining these retrievals with longwave radiative transfer simulations, we will quantify dust LW DRE, examine its spatiotemporal variability, and assess its implications for the surface energy budget. Preliminary analysis suggests that the daily mean dust LW DRE can reach about 56% of the corresponding SW DRE in magnitude over land and 32% over the ocean, indicating that the LW effect is non-negligible. Third, we will investigate how dust optical depth, particle size, vertical structure, and mineral composition shape the hyperspectral TIR fingerprints in satellite and ground-based observations. Using systematic high-spectral-resolution radiative transfer sensitivity experiments together with selected hyperspectral TIR measurements, we will identify the spectral regions most sensitive to individual dust properties and evaluate how these sensitivities vary across dust environments.

Together, these three tasks will deliver a physically consistent representation of polluted dust mixing, an observation-constrained assessment of dust LW DRE and its regional variability, and new hyperspectral constraints linking dust microphysical properties to their infrared signatures. This work will advance both the interpretation of dust remote sensing and the representation of dust radiative effects in climate models.