Earth's Mineral Dust Radiative Effect: Sensitivity to Mineralogy, Iron Oxide Optical Properties, and Particle Mixing

• Introduction

Mineral dust is a major aerosol constituent in Earth’s atmosphere. Dust plays a role in modulating the radiation budget through scattering and absorption of solar radiation. The direct radiative effect (DRE) of dust remains a source of uncertainty to which dust mineralogy is a contributing factor. The abundance and optical properties of iron-oxide bearing minerals (hematite and goethite), modulates shortwave absorption and their concentrations vary across source regions. Most global models treat dust as compositionally homogeneous with globally fixed optical properties. Here we systematically evaluate how choices in soil mineralogy datasets, mineral complex refractive indices (CRI), and particle mixing assumptions propagate to dust optical properties and radiative effects, using an experimental suite within the MONARCH–RRTMG modeling framework.

• Methods

We use the MONARCH atmospheric model coupled with the Rapid Radiative Transfer Model for GCMs (RRTMG) to perform 18 global year-long simulations, systematically varying three factors (Figure 1):

• Soil mineralogy atlases: CQ99 (Claquin et al., 1999), JN14 (Journet et al., 2014), and EMIT, the new global soil mineralogy dataset from the NASA Earth Surface Mineral Dust Source Investigation (Green et al., 2020), which provides spatially resolved estimates of surface mineral composition from orbital spectroscopy.
• Iron oxide CRI datasets: DB19 (Di Biagio et al., 2019) and SZ15 (Scanza et al., 2015), representing the range of currently available laboratory-derived optical constants for iron-bearing minerals.
• Particle mineralogy representations: bulk homogeneous dust (BLK), size-resolved but temporally fixed mineralogy (BIN), and a fully dynamic tracer-based representation (MIX) in which each mineral evolves independently through emission, transport, and deposition.

Simulated single scattering albedo (SSA) and DRE at the top of the atmosphere (TOA) and surface are evaluated against AERONET Level 2.0 inversion climatology at dust-dominated stations across six source and downwind regions.

Figure 1 – Experimental Suite

• Results

The choice of CRI dataset is the dominant control on the absolute magnitude of simulated SSA. SZ15 produces stronger shortwave absorption than DB19 across all regions and seasons. DB19 simulations typically underestimate absorption and SZ15 overestimates it. Soil atlas choice introduces spatially structured SSA differences. CQ99 yields consistently more scattering dust because it represents iron oxides as a single undifferentiated category, while JN14 and EMIT distinguish hematite and goethite as separate minerals with distinct optical properties and size dependencies, leading to enhanced absorption. The EMIT atlas, derived from orbital remote sensing rather than compiled soil surveys, produces source-region patterns that diverge from both CQ99 and JN14 (Figure 2). The mineral mixing representation has a smaller global-mean impact but generates pronounced regional and seasonal differences. MIX experiments show enhanced SSA variability in regions where iron oxide gradients are large enough for dynamic tracer evolution to diverge from fixed-mineralogy assumptions. BIN representation adds little variability beyond BLK at the global scale; in the JN14 atlas, size-dependent mineralogy partially offsets particle size effects on absorption (Figure 3).

Figure 2 – Annual Mean SSA of the EMIT MIX DB19 Experiment

For the DRE, CRI choice again dominates overall magnitude: SZ15 produces weaker net global cooling than DB19 and can locally generate warming at TOA in iron-rich source regions. MIX DRE spreads can exceed BLK–BIN differences. A systematic inversion in atlas-ordering between TOA and surface DRE is also identified, reflecting the competing effects of absorption and scattering on the vertical partitioning of radiative forcing.

Figure 3 – Regional Variability of Annual Mean SSA Values in North West Africa and Sahel

• Conclusions

The three modeling factors influence dust absorption through distinct pathways: CRI uncertainty dominates DRE magnitude; atlas choice controls spatial distribution; mixing representation governs regional and seasonal variability with limited impact on global means. These findings motivate observational priorities applicable beyond Earth — better characterization of iron oxide speciation and refractive indices across the solar spectrum is the highest-leverage target for reducing radiative forcing uncertainty in dust-laden planetary atmospheres. A computationally efficient representation of dust as three components (hematite, goethite, and a bulk remainder) would capture the dominant SSA variability without the full cost of explicit multi-mineral transport.

• References

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