Abstract. The composition of soil dust aerosols derives from the mineral abundances in the parent soils that vary across dust source regions. Nonetheless, Earth system models (ESMs) have traditionally represented mineral dust as a globally homogeneous species. The growing interest in modeling dust mineralogy, facilitated by the recognized sensitivity of the dust climate impacts to composition, has motivated state-of-the-art ESMs to incorporate the mineral speciation of dust along with its effect upon the dust direct radiative effect (DRE). In this work, we enable the NASA Goddard Institute for Space Studies ModelE2.1 to calculate the shortwave (SW) DRE accounting for the regionally varying soil mineralogy. Mineral\u2013radiation interaction at solar wavelengths is calculated according to two alternative coupling schemes: (1)\u00a0external mixing of three mineral components that are optically distinguished, one of which contains embedded iron oxides; (2)\u00a0a single internal mixture of all dust minerals with a dynamic fraction of iron oxides that varies regionally and temporally. We link dust absorption to the fractional mass of iron oxides based on recent chamber measurements using natural dust aerosol samples. We show that coupled mineralogy overall enhances the scattering by dust, and thus the global cooling, compared to our control run with globally uniform composition. According to the external mixing scheme, the SW DRE at the top of atmosphere (TOA) changes from \u22120.25 to -0.30Wm-2, corresponding to a change in the net DRE, including the longwave effect, from \u22120.08 to -0.12Wm-2. The cooling increase is accentuated when the internal mixing scheme is configured: the SW DRE at the TOA becomes -0.34Wm-2 with a net DRE of -0.15Wm-2. The varying composition modifies the regional distribution of single scattering albedo (SSA), whose variations in specific regions can be remarkable (above 0.03) and significantly modify the regional SW DRE. Evaluation against the AErosol RObotic NETwork (AERONET) shows that explicit representation of soil mineralogy and its regional variations reduces the low bias of model dust SSA while improving the range of variability across stations and calendar months. Despite these improvements, the moderate spatiotemporal correlation with AERONET reveals remaining modeling challenges and the need for more accurate measurements of mineral fractions in soils.