Manganese (Mn) is a key cofactor in enzymes responsible for lignin decay (mainly Mn peroxidase), regulating the rate of litter degradation and carbon (C) turnover in temperate and boreal forest biomes.While soil Mn is mainly derived from bedrock, atmospheric Mn could also contribute to soil Mn cycling, especially within the surficial horizon, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g. industrialization and land-use change due to agriculture) transport, and deposition into the terrestrial and marine ecosystem, remains uncertain. Here, we use compiled emission datasets for each identified source to model and quantify the atmospheric Mn cycle with observational constraints. We estimated global emissions of atmospheric Mn in aerosols (<10 µm in aerodynamic diameter) to be 1500 Gg Mn yr-1. Approximately 32% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened soil Mn “pseudo” turnover times in surficial soils about 1-m depth (ranging from 1,000 to over 10,000,000 years) by 1-2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn-to-N ratio of the atmospheric deposition in non-desert dominated regions (between 5×10-5 and 0.02) across industrialized areas, but still lower than soil Mn-to-N ratio by 1-3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, illuminating the role of Mn deposition in these ecosystems.

Aircraft measurement campaigns have revealed that super coarse dust (diameter > 10 um) surprisingly accounts for approximately a quarter of aerosols by mass in the atmosphere. However, most global aerosol models either underestimate or do not include super coarse dust abundance. To address this problem, we use brittle fragmentation theory to develop a parameterization for the emitted dust size distribution that includes emission of super coarse dust. We implement this parameterization in the Community Earth System Model (CESM) and find that it brings the model in good agreement with measurements of super coarse dust close to dust source regions. However, the model still underestimates super coarse dust in dust outflow regions. Thus, we conclude that model underestimation of super coarse atmospheric dust is in part due to the underestimation of super coarse dust emission and likely in part due to errors in deposition processes.

A key challenge in accurate simulations of desert dust emission is the parameterization of the threshold wind speed above which dust emission occurs. However, the existing parameterizations yield a unrealistically low dust emission threshold in some climate models such as the Community Earth System Model (CESM), leading to higher simulated dust source activation frequencies than observed and requiring global tuning constants to scale down dust emissions. Here we develop a more realistic parameterization for the dust emission threshold in CESM. In particular, we account for the dissipation of surface wind momentum by surface roughness elements such as vegetation, rocks, and pebbles, which reduce the wind momentum exerted on the bare soil surface. We achieve this by implementing a dynamic wind drag partition model by considering the roughness of the time-varying vegetation as quantified by the leaf area index (LAI), as well as the time-invariant rocks and pebbles using satellite-derived aeolian roughness length. Furthermore, we account for the effect of soil size on dust emission threshold by replacing the currently used globally constant soil median diameter with a spatially varying soil texture map. Results show that with the new parameterization dust emissions decrease by 20–80% over source regions such as Africa, Middle East, and Asia, thereby reducing the need for the global tuning constant. Simulated dust emissions match better in both spatiotemporal variability and emission frequency when compared against satellite observed dust activation frequency data. Our results suggest that including more physical dust emission parameterizations into climate models can lessen bias and improve simulation results, possibly eliminate the use of empirical source functions, and reduce the need for tuning constants. This development could improve assessments of dust impacts on the Earth system.