ther extend these IRI values to encompass the entire UV–VIS band in our model. This extrapolation may not accurately capture IRI variations across VIS wavelengths, potentially overemphasizing absorption.

The reduction in the model absorption bias with respect to AERONET in the mineral experiments is a consequence of two factors. First, dust aerosol absorption largely results from the varying fraction of iron oxides, given the comparatively weak absorption by the other simulated minerals at solar wavelengths. Second, hematite is often observed together with goethite (Formenti et al., 2008), another iron oxide that is less absorbing than hematite (Bedidi and Cervelle, 1993). We take into account the radiative effect of goethite by using the DB19 empirical relationships between dust IRI and the combined mass fraction of both iron oxides, thus interpreting our soil hematite fraction prescribed by Claquin et al. (1999) more generally as a less absorbing iron oxide mixture that includes goethite. Our reduced absorption bias suggests that a realistic representation of the dust absorption at UV–VIS wavelengths must be founded upon accurate prescription of the separate hematite and goethite fractions in soils along with the modeling of their individual radiative effects. These improvements may also lead to a stronger correlation between model and AERONET SSA at specific locations and months while preserving the agreement of the spatial and temporal ranges.

Measurements to evaluate the modeled aerosol mineral fractions are limited (Perlwitz et al., 2015b; Pérez García-Pando et al., 2016), which results in uncertain estimates of measurement climatologies. In contrast, modelers are on the verge of greatly improved knowledge of soil mineral content from the NASA EMIT hyperspectrometer that is currently measuring VIS and NIR surface reflectances from the International Space Station. The few hundred soil analyses that are the basis for current soil atlases like Claquin et al. (1999) and Journet et al. (2014) will be supplemented with over a billion retrievals of soil minerals, providing unprecedented knowledge of source composition with both greater regional detail and global extent (Green et al., 2020).

Despite the remaining discrepancies with respect to AERONET observations, this work represents a promising step towards a refined modeling system capable of incorporating the huge amount of information about soil composition that will soon be provided by EMIT. This could allow the NASA GISS ModelE2.1 to achieve a more realistic representation of dust as a composite of different minerals that would enable an improved assessment of the dust DRE and its impacts upon regional and global climate.

## Appendix A: Derivation of the imaginary index of free iron oxides

In the EXT scheme, we need to assign a CRI to the radiatively active component consisting solely of crystalline

<figure><img src="image_17.png" /><figcaption>**Figure A1.** Test calculation of the dependence of real ($n_{acc}$; **a** and **b**) and imaginary ($k_{acc}$; **c** and **d**) indices of an accretion-like composite particle upon the volume ($v_{iox}$; **a** and **c**) and mass ($m_{iox}$; **b** and **d**) fractions of an iron oxide-like inclusion embedded in a homogeneous host-like medium according to different mixing rules (volume-weighted mean: VM; Maxwell Garnett: MG; Bruggeman: BG) in the limit of small fractions of the inclusion (below 0.06). The real indices of the inclusion and host are, respectively, set to $n_{iox} = 2.5$ and $n_{hos} = 1.5$, the imaginary indices to $k_{iox} = 0.2$ and $k_{hos} = 0.002$ and the mass densities to $\rho_{iox} = 5 \text{ g cm}^{-3}$ and $\rho_{hos} = 2.5 \text{ g cm}^{-3}$.</figcaption></figure>

or free iron oxides. Here, we describe the inversion procedure for calculating the IRI of iron oxides consistent with the empirical relationships of DB19 that we compare to literature values corresponding to iron oxide minerals like hematite and goethite (Fig. 4). As a first step, we must assume a mixing rule to link the IRI of dust particles to that of its constituent minerals. Figure A1 shows that commonly used mixing rules such as VM (volume-weighted mean), MG (Maxwell Garnett) and BG (Bruggeman) are approximately linear for small fractions of iron oxides (as measured by DB19). (VM is exactly linear for any fraction of iron oxides.) This linearity suggests that we can derive the IRI of iron oxides by matching an analytic expression of a mixing rule (that we derive below) to the linear regression of the empirical relationships from DB19 between the particle IRI and the iron oxide fraction.

In general, the MG mixing rule (Markel, 2016) gives the effective complex permittivity $\epsilon = (n + ik)^2$, where n and k are the RRI and IRI, respectively, of a composite (in our case accretion) starting from the permittivities of a homogeneous host medium and small inclusions (iron oxides). In the linear
ther extend these IRI values to encompass the entire UV–VIS band in our model. This extrapolation may not accurately capture IRI variations across VIS wavelengths, potentially overemphasizing absorption.

The reduction in the model absorption bias with respect to AERONET in the mineral experiments is a consequence of two factors. First, dust aerosol absorption largely results from the varying fraction of iron oxides, given the comparatively weak absorption by the other simulated minerals at solar wavelengths. Second, hematite is often observed together with goethite (Formenti et al., 2008), another iron oxide that is less absorbing than hematite (Bedidi and Cervelle, 1993). We take into account the radiative effect of goethite by using the DB19 empirical relationships between dust IRI and the combined mass fraction of both iron oxides, thus interpreting our soil hematite fraction prescribed by Claquin et al. (1999) more generally as a less absorbing iron oxide mixture that includes goethite. Our reduced absorption bias suggests that a realistic representation of the dust absorption at UV–VIS wavelengths must be founded upon accurate prescription of the separate hematite and goethite fractions in soils along with the modeling of their individual radiative effects. These improvements may also lead to a stronger correlation between model and AERONET SSA at specific locations and months while preserving the agreement of the spatial and temporal ranges.

Measurements to evaluate the modeled aerosol mineral fractions are limited (Perlwitz et al., 2015b; Pérez García-Pando et al., 2016), which results in uncertain estimates of measurement climatologies. In contrast, modelers are on the verge of greatly improved knowledge of soil mineral content from the NASA EMIT hyperspectrometer that is currently measuring VIS and NIR surface reflectances from the International Space Station. The few hundred soil analyses that are the basis for current soil atlases like Claquin et al. (1999) and Journet et al. (2014) will be supplemented with over a billion retrievals of soil minerals, providing unprecedented knowledge of source composition with both greater regional detail and global extent (Green et al., 2020).

Despite the remaining discrepancies with respect to AERONET observations, this work represents a promising step towards a refined modeling system capable of incorporating the huge amount of information about soil composition that will soon be provided by EMIT. This could allow the NASA GISS ModelE2.1 to achieve a more realistic representation of dust as a composite of different minerals that would enable an improved assessment of the dust DRE and its impacts upon regional and global climate.

## Appendix A: Derivation of the imaginary index of free iron oxides

In the EXT scheme, we need to assign a CRI to the radiatively active component consisting solely of crystalline

<figure><img src="image_18.png" /><figcaption>**Figure A1.** Test calculation of the dependence of real ($n_{acc}$; **a** and **b**) and imaginary ($k_{acc}$; **c** and **d**) indices of an accretion-like composite particle upon the volume ($v_{iox}$; **a** and **c**) and mass ($m_{iox}$; **b** and **d**) fractions of an iron oxide-like inclusion embedded in a homogeneous host-like medium according to different mixing rules (volume-weighted mean: VM; Maxwell Garnett: MG; Bruggeman: BG) in the limit of small fractions of the inclusion (below 0.06). The real indices of the inclusion and host are, respectively, set to $n_{iox} = 2.5$ and $n_{hos} = 1.5$, the imaginary indices to $k_{iox} = 0.2$ and $k_{hos} = 0.002$ and the mass densities to $\rho_{iox} = 5 \text{ g cm}^{-3}$ and $\rho_{hos} = 2.5 \text{ g cm}^{-3}$.</figcaption></figure>

or free iron oxides. Here, we describe the inversion procedure for calculating the IRI of iron oxides consistent with the empirical relationships of DB19 that we compare to literature values corresponding to iron oxide minerals like hematite and goethite (Fig. 4). As a first step, we must assume a mixing rule to link the IRI of dust particles to that of its constituent minerals. Figure A1 shows that commonly used mixing rules such as VM (volume-weighted mean), MG (Maxwell Garnett) and BG (Bruggeman) are approximately linear for small fractions of iron oxides (as measured by DB19). (VM is exactly linear for any fraction of iron oxides.) This linearity suggests that we can derive the IRI of iron oxides by matching an analytic expression of a mixing rule (that we derive below) to the linear regression of the empirical relationships from DB19 between the particle IRI and the iron oxide fraction.

In general, the MG mixing rule (Markel, 2016) gives the effective complex permittivity $\epsilon = (n + ik)^2$, where n and k are the RRI and IRI, respectively, of a composite (in our case accretion) starting from the permittivities of a homogeneous host medium and small inclusions (iron oxides). In the linear
limit for small volume fractions of iron oxides ($v_{iox}$), the MG expression is identical to BG (Markel, 2016) and is given by

$$ \left\{ \begin{aligned} \epsilon_{acc} &= \epsilon_{hos} + 3\epsilon_{hos}Xv_{iox} \\ X &= \frac{\epsilon_{iox} - \epsilon_{hos}}{\epsilon_{iox} + 2\epsilon_{hos}} \end{aligned} \right. , \qquad (A1) $$

whose imaginary component is

$$ 2n_{acc}k_{acc} = 2n_{hos}k_{hos} + 3Iv_{iox}, \qquad (A2) $$

where the factor $I$ is the imaginary part of the product $\epsilon_{hos}X$. From Eq. (A2), it is evident that MG couples the RRI and IRI of the accretion, which in turn depend upon coupled RRIs and IRIs of both the host medium and iron oxides according to a nonlinear relation. Therefore, to derive an equation only for the IRIs, an independent constraint upon the RRIs is required. For simplicity, we use Eq. (2) (i.e., the VM rule) as the linear relation among the RRIs of accretion, host medium and iron oxides: as already discussed in Sect. 2.2.3, for small fractions of iron oxides, different mixing rules result in negligible differences in the accretion RRI. Substituting Eq. (2) into Eq. (A2) leads to

$$ k_{acc} = \frac{n_{hos}k_{0} + (3/2)Iv_{iox}}{n_{hos} + (n_{iox} - n_{hos})v_{iox}}, \qquad (A3) $$

where we indicate the host medium IRI with $k_0$ to highlight that here it is in general different from the IRI of the host amalgam calculated in Sect. 2.2.2 ($k_{hos}$ in Eq. 1), as it takes its value from the linear regression of the empirical relationships of DB19 (see below). Expanding Eq. (A3) to the first-order term in the volume fraction of iron oxides results in the final analytic expression of the accretion IRI:

$$ k_{acc} = k_{0} + \frac{(3/2)I - k_{0}(n_{iox} - n_{hos})}{n_{hos}}v_{iox} = k_{0} + l_{1}v_{iox}. \qquad (A4) $$

We assume now that dust particles of DB19 are two-component accretions whose IRI follows the linearized MG rule given by Eq. (A4), which we directly match to a linear regression of DB19 data to estimate the IRI of iron oxides. Given the non-trivial expression of the factor $I$ in Eq. (A4) (i.e., Eqs. A1 and A2), we apply an inversion procedure consisting of the following steps:

1. We perform a linear regression of the IRI retrievals of DB19 for all the measurement wavelengths with respect to the volume fractions of iron oxides in their dust aerosol samples, which we derive from the mass fractions using the mineral mass densities in Table 1. We thus estimate the empirical fit intercept ($l_0^*$) and slope ($l_1^*$).

2. We calculate the factor $I$ for a high number of input IRIs of iron oxides and then the analytical slope ($l_1$) in Eq. (A4) using the known RRIs of the host amalgam (Sect. 2.2.2) and iron oxides (taken from SZ15) along

with the host medium IRI as the empirical intercept: $k_0 = l_0^*$. We span 10 000 logarithmically equidistant IRI values between 0.001 and 1 for iron oxides, resulting in a relative accuracy of ~0.07%.

3. We infer the IRI of iron oxides by selecting the input value that makes the analytical and empirical slopes equal: $l_1 = l_1^*$. We also derive the uncertainty in the inferred IRI by perturbing the empirical slope based on the fit error (varying the empirical intercept has a negligible impact).

Figure 4 shows our calculated IRI of iron oxides at VIS wavelengths, which is roughly intermediate between the IRIs for pure hematite and goethite measured by Bedidi and Cervelle (1993).

This derivation contains an inconsistency compared to our assumptions elsewhere in this work. The linear regression of the IRI retrievals from DB19, along with the unconstrained fit intercept used for the host medium, differs from the polynomial representation of Fig. 3. To calculate the IRI of accretions (Sect. 2.2.3), we choose the polynomial function to reduce the systematic low bias of the linear function that results for large mass fractions of iron oxides while the host amalgam (Sect. 2.2.2) is set in the limit of vanishing iron oxides. This suggests that the candidate mixing rules of Fig. A1 are not suitable for our IRI calculations for accretions and the host mixture, which are consistent with DB19 and SZ15. Nonetheless, deriving a mixing rule that diverges from linearity for small volume fractions of iron oxides while being consistent with Maxwell's equations is outside the scope of this work. In any case, this inconsistency has little practical impact given the small radiative effect of pure iron oxides in our model calculations (Fig. 10) but could be addressed in future studies.

## Appendix B: Calculation of dust optical properties

In this section, we describe the calculation of dust optical properties within the discrete SW bands of ModelE2.1 that we use in our experiments, including the HOM control run in which we assume globally homogeneous composition along with the EXT and INT experiments in which instead we implement a dependency upon the varying mineral composition.

### B1 Sets of complex refractive index

Given the CRIs for the mineral components at the seven wavelengths of DB19 (Sects. 2.2.2 and 2.2.3 and Appendix A), to improve the accuracy of our optical calculations, we extrapolate the CRIs to 20 wavelengths ranging from 0.30 to 1 µm which are a subset covering the UV–VIS band (0.30–0.77 µm) of the full set of SW wavelengths at which the default dust CRI is prescribed in ModelE2.1 (Table B1). (The remaining five NIR bands cover wavelengths
limit for small volume fractions of iron oxides ($v_{iox}$), the MG expression is identical to BG (Markel, 2016) and is given by

$$ \left\{ \begin{aligned} \epsilon_{acc} &= \epsilon_{hos} + 3\epsilon_{hos}Xv_{iox} \\ X &= \frac{\epsilon_{iox} - \epsilon_{hos}}{\epsilon_{iox} + 2\epsilon_{hos}} \end{aligned} \right. , \qquad (A1) $$

whose imaginary component is

$$ 2n_{acc}k_{acc} = 2n_{hos}k_{hos} + 3Iv_{iox}, \qquad (A2) $$

where the factor $I$ is the imaginary part of the product $\epsilon_{hos}X$. From Eq. (A2), it is evident that MG couples the RRI and IRI of the accretion, which in turn depend upon coupled RRIs and IRIs of both the host medium and iron oxides according to a nonlinear relation. Therefore, to derive an equation only for the IRIs, an independent constraint upon the RRIs is required. For simplicity, we use Eq. (2) (i.e., the VM rule) as the linear relation among the RRIs of accretion, host medium and iron oxides: as already discussed in Sect. 2.2.3, for small fractions of iron oxides, different mixing rules result in negligible differences in the accretion RRI. Substituting Eq. (2) into Eq. (A2) leads to

$$ k_{acc} = \frac{n_{hos}k_{0} + (3/2)Iv_{iox}}{n_{hos} + (n_{iox} - n_{hos})v_{iox}}, \qquad (A3) $$

where we indicate the host medium IRI with $k_0$ to highlight that here it is in general different from the IRI of the host amalgam calculated in Sect. 2.2.2 ($k_{hos}$ in Eq. 1), as it takes its value from the linear regression of the empirical relationships of DB19 (see below). Expanding Eq. (A3) to the first-order term in the volume fraction of iron oxides results in the final analytic expression of the accretion IRI:

$$ k_{acc} = k_{0} + \frac{(3/2)I - k_{0}(n_{iox} - n_{hos})}{n_{hos}}v_{iox} = k_{0} + l_{1}v_{iox}. \qquad (A4) $$

We assume now that dust particles of DB19 are two-component accretions whose IRI follows the linearized MG rule given by Eq. (A4), which we directly match to a linear regression of DB19 data to estimate the IRI of iron oxides. Given the non-trivial expression of the factor $I$ in Eq. (A4) (i.e., Eqs. A1 and A2), we apply an inversion procedure consisting of the following steps:

1. We perform a linear regression of the IRI retrievals of DB19 for all the measurement wavelengths with respect to the volume fractions of iron oxides in their dust aerosol samples, which we derive from the mass fractions using the mineral mass densities in Table 1. We thus estimate the empirical fit intercept ($l_0^*$) and slope ($l_1^*$).

2. We calculate the factor $I$ for a high number of input IRIs of iron oxides and then the analytical slope ($l_1$) in Eq. (A4) using the known RRIs of the host amalgam (Sect. 2.2.2) and iron oxides (taken from SZ15) along

with the host medium IRI as the empirical intercept: $k_0 = l_0^*$. We span 10 000 logarithmically equidistant IRI values between 0.001 and 1 for iron oxides, resulting in a relative accuracy of ~0.07%.

3. We infer the IRI of iron oxides by selecting the input value that makes the analytical and empirical slopes equal: $l_1 = l_1^*$. We also derive the uncertainty in the inferred IRI by perturbing the empirical slope based on the fit error (varying the empirical intercept has a negligible impact).

Figure 4 shows our calculated IRI of iron oxides at VIS wavelengths, which is roughly intermediate between the IRIs for pure hematite and goethite measured by Bedidi and Cervelle (1993).

This derivation contains an inconsistency compared to our assumptions elsewhere in this work. The linear regression of the IRI retrievals from DB19, along with the unconstrained fit intercept used for the host medium, differs from the polynomial representation of Fig. 3. To calculate the IRI of accretions (Sect. 2.2.3), we choose the polynomial function to reduce the systematic low bias of the linear function that results for large mass fractions of iron oxides while the host amalgam (Sect. 2.2.2) is set in the limit of vanishing iron oxides. This suggests that the candidate mixing rules of Fig. A1 are not suitable for our IRI calculations for accretions and the host mixture, which are consistent with DB19 and SZ15. Nonetheless, deriving a mixing rule that diverges from linearity for small volume fractions of iron oxides while being consistent with Maxwell's equations is outside the scope of this work. In any case, this inconsistency has little practical impact given the small radiative effect of pure iron oxides in our model calculations (Fig. 10) but could be addressed in future studies.

## Appendix B: Calculation of dust optical properties

In this section, we describe the calculation of dust optical properties within the discrete SW bands of ModelE2.1 that we use in our experiments, including the HOM control run in which we assume globally homogeneous composition along with the EXT and INT experiments in which instead we implement a dependency upon the varying mineral composition.

### B1 Sets of complex refractive index

Given the CRIs for the mineral components at the seven wavelengths of DB19 (Sects. 2.2.2 and 2.2.3 and Appendix A), to improve the accuracy of our optical calculations, we extrapolate the CRIs to 20 wavelengths ranging from 0.30 to 1 µm which are a subset covering the UV–VIS band (0.30–0.77 µm) of the full set of SW wavelengths at which the default dust CRI is prescribed in ModelE2.1 (Table B1). (The remaining five NIR bands cover wavelengths
<table><caption>**Table B1.** Real and imaginary refractive indices ($n+ik$), for the subset of 20 default shortwave wavelengths ($\lambda$), used in this work for default dust (subscript “hom”) in the control run with homogeneous composition (HOM) (Sinyuk et al., 2003; Volz, 1973), free crystalline iron oxides (subscript “iox”), the host amalgam (subscript “hos”) and static accretion (subscript “acc”) in the mineral experiment with external mixing configuration (EXT) (Di Biagio et al., 2019; Scanza et al., 2015). The best-fit parameter defining the polynomial fitting function ($p_1$ in Eq. 1), which is also used in the mineral experiment with internal mixing configuration (INT), and the CRI of default dust for the full set of shortwave wavelengths up to 4.114 µm are also reported.</caption><thead><tr><th>λ (μm)</th><th>n<sub>hom</sub> + ik<sub>hom</sub></th><th>n<sub>iox</sub> + ik<sub>iox</sub></th><th>n<sub>hos</sub> + ik<sub>hos</sub></th><th>n<sub>acc</sub> + ik<sub>acc</sub></th><th>p<sub>1</sub></th></tr></thead><tbody><tr><td>0.300</td><td>1.600 + i8.700 × 10<sup>-3</sup></td><td>1.682 + i2.871 × 10<sup>-2</sup></td><td>1.531 + i1.352 × 10<sup>-3</sup></td><td>1.535 + i6.832 × 10<sup>-3</sup></td><td>3.444 × 10<sup>+1</sup></td></tr><tr><td>0.325</td><td>1.595 + i7.000 × 10<sup>-3</sup></td><td>1.814 + i1.377 × 10<sup>-1</sup></td><td>1.522 + i1.310 × 10<sup>-3</sup></td><td>1.530 + i6.470 × 10<sup>-3</sup></td><td>3.389 × 10<sup>+1</sup></td></tr><tr><td>0.350</td><td>1.590 + i5.800 × 10<sup>-3</sup></td><td>1.988 + i2.175 × 10<sup>-1</sup></td><td>1.515 + i1.247 × 10<sup>-3</sup></td><td>1.528 + i6.202 × 10<sup>-3</sup></td><td>3.405 × 10<sup>+1</sup></td></tr><tr><td>0.375</td><td>1.585 + i4.600 × 10<sup>-3</sup></td><td>2.187 + i2.708 × 10<sup>-1</sup></td><td>1.511 + i1.167 × 10<sup>-3</sup></td><td>1.529 + i5.973 × 10<sup>-3</sup></td><td>3.473 × 10<sup>+1</sup></td></tr><tr><td>0.400</td><td>1.580 + i3.600 × 10<sup>-3</sup></td><td>2.396 + i3.004 × 10<sup>-1</sup></td><td>1.508 + i1.074 × 10<sup>-3</sup></td><td>1.532 + i5.731 × 10<sup>-3</sup></td><td>3.575 × 10<sup>+1</sup></td></tr><tr><td>0.425</td><td>1.576 + i2.900 × 10<sup>-3</sup></td><td>2.601 + i3.091 × 10<sup>-1</sup></td><td>1.506 + i9.705 × 10<sup>-4</sup></td><td>1.535 + i5.430 × 10<sup>-3</sup></td><td>3.691 × 10<sup>+1</sup></td></tr><tr><td>0.450</td><td>1.572 + i2.400 × 10<sup>-3</sup></td><td>2.786 + i2.995 × 10<sup>-1</sup></td><td>1.505 + i8.607 × 10<sup>-4</sup></td><td>1.539 + i5.037 × 10<sup>-3</sup></td><td>3.802 × 10<sup>+1</sup></td></tr><tr><td>0.475</td><td>1.568 + i2.000 × 10<sup>-3</sup></td><td>2.936 + i2.744 × 10<sup>-1</sup></td><td>1.504 + i7.481 × 10<sup>-4</sup></td><td>1.542 + i4.534 × 10<sup>-3</sup></td><td>3.889 × 10<sup>+1</sup></td></tr><tr><td>0.500</td><td>1.564 + i1.800 × 10<sup>-3</sup></td><td>3.036 + i2.366 × 10<sup>-1</sup></td><td>1.503 + i6.362 × 10<sup>-4</sup></td><td>1.544 + i3.924 × 10<sup>-3</sup></td><td>3.933 × 10<sup>+1</sup></td></tr><tr><td>0.525</td><td>1.561 + i1.600 × 10<sup>-3</sup></td><td>3.071 + i1.888 × 10<sup>-1</sup></td><td>1.502 + i5.286 × 10<sup>-4</sup></td><td>1.544 + i3.238 × 10<sup>-3</sup></td><td>3.916 × 10<sup>+1</sup></td></tr><tr><td>0.550</td><td>1.558 + i1.400 × 10<sup>-3</sup></td><td>3.041 + i1.369 × 10<sup>-1</sup></td><td>1.501 + i4.330 × 10<sup>-4</sup></td><td>1.542 + i2.550 × 10<sup>-3</sup></td><td>3.817 × 10<sup>+1</sup></td></tr><tr><td>0.575</td><td>1.555 + i1.200 × 10<sup>-3</sup></td><td>2.978 + i9.386 × 10<sup>-2</sup></td><td>1.499 + i3.660 × 10<sup>-4</sup></td><td>1.539 + i1.989 × 10<sup>-3</sup></td><td>3.619 × 10<sup>+1</sup></td></tr><tr><td>0.600</td><td>1.553 + i1.100 × 10<sup>-3</sup></td><td>2.917 + i7.320 × 10<sup>-2</sup></td><td>1.498 + i3.450 × 10<sup>-4</sup></td><td>1.536 + i1.645 × 10<sup>-3</sup></td><td>3.304 × 10<sup>+1</sup></td></tr><tr><td>0.625</td><td>1.551 + i9.000 × 10<sup>-4</sup></td><td>2.877 + i7.711 × 10<sup>-2</sup></td><td>1.496 + i3.694 × 10<sup>-4</sup></td><td>1.533 + i1.480 × 10<sup>-3</sup></td><td>2.898 × 10<sup>+1</sup></td></tr><tr><td>0.650</td><td>1.550 + i8.000 × 10<sup>-4</sup></td><td>2.849 + i9.299 × 10<sup>-2</sup></td><td>1.495 + i4.149 × 10<sup>-4</sup></td><td>1.531 + i1.385 × 10<sup>-3</sup></td><td>2.488 × 10<sup>+1</sup></td></tr><tr><td>0.675</td><td>1.548 + i7.000 × 10<sup>-4</sup></td><td>2.828 + i1.078 × 10<sup>-1</sup></td><td>1.494 + i4.569 × 10<sup>-4</sup></td><td>1.530 + i1.312 × 10<sup>-3</sup></td><td>2.158 × 10<sup>+1</sup></td></tr><tr><td>0.700</td><td>1.544 + i7.000 × 10<sup>-4</sup></td><td>2.808 + i1.163 × 10<sup>-1</sup></td><td>1.493 + i4.857 × 10<sup>-4</sup></td><td>1.528 + i1.257 × 10<sup>-3</sup></td><td>1.936 × 10<sup>+1</sup></td></tr><tr><td>0.800</td><td>1.540 + i8.000 × 10<sup>-4</sup></td><td>2.744 + i1.073 × 10<sup>-1</sup></td><td>1.492 + i5.125 × 10<sup>-4</sup></td><td>1.526 + i1.195 × 10<sup>-3</sup></td><td>1.716 × 10<sup>+1</sup></td></tr><tr><td>0.900</td><td>1.535 + i1.000 × 10<sup>-3</sup></td><td>2.704 + i7.996 × 10<sup>-2</sup></td><td>1.492 + i5.086 × 10<sup>-4</sup></td><td>1.524 + i1.115 × 10<sup>-3</sup></td><td>1.587 × 10<sup>+1</sup></td></tr><tr><td>1.000</td><td>1.530 + i2.000 × 10<sup>-3</sup></td><td>2.683 + i1.016 × 10<sup>-1</sup></td><td>1.491 + i6.218 × 10<sup>-4</sup></td><td>1.520 + i7.008 × 10<sup>-4</sup></td><td>2.392 × 10<sup>+0</sup></td></tr><tr><td>1.250</td><td>1.520 + i3.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>1.500</td><td>1.510 + i5.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.000</td><td>1.500 + i8.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.507</td><td>1.468 + i9.856 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.614</td><td>1.475 + i1.139 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.829</td><td>1.481 + i4.194 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.957</td><td>1.481 + i4.194 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.043</td><td>1.481 + i2.920 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.257</td><td>1.519 + i1.759 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.471</td><td>1.475 + i1.317 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.686</td><td>1.481 + i1.075 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.900</td><td>1.481 + i6.383 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>4.114</td><td>1.481 + i4.444 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr></tbody></table>

from 0.77 to 4 µm.) We use spline interpolation to spectrally extend the CRIs of host mixture and iron oxides, together with the fitting polynomial function, and then calculate the CRI for static and dynamic accretions using Eqs. (1) and (2) (Table B1).

## B2 Optical properties for default dust

We pre-calculate offline the optical properties of default dust used by the SW radiation scheme of ModelE2.1, which are the extinction and scattering efficiencies, along with the asymmetry parameter. First, we calculate monodisperse opti-

cal properties for spherical particles using the Mie code from Mishchenko et al. (2002) as functions of the default CRI (Table B1), which is globally uniform but varies across wavelengths, and the size parameter that is defined as $x = \frac{\pi d}{\lambda}$, where d is the particle diameter and λ the wavelength. We consider a size parameter grid of 5000 values ranging from 0.02 to 1000 with a constant logarithmic step. Next, we integrate the monodisperse optical properties over each aerosol size bin for the full set of default SW wavelengths (up to 4.114 µm; Table B1). For size integration, we use a step number distribution ($f_N$) corresponding to a constant volume size
<table><caption>**Table B1.** Real and imaginary refractive indices ($n+ik$), for the subset of 20 default shortwave wavelengths ($\lambda$), used in this work for default dust (subscript “hom”) in the control run with homogeneous composition (HOM) (Sinyuk et al., 2003; Volz, 1973), free crystalline iron oxides (subscript “iox”), the host amalgam (subscript “hos”) and static accretion (subscript “acc”) in the mineral experiment with external mixing configuration (EXT) (Di Biagio et al., 2019; Scanza et al., 2015). The best-fit parameter defining the polynomial fitting function ($p_1$ in Eq. 1), which is also used in the mineral experiment with internal mixing configuration (INT), and the CRI of default dust for the full set of shortwave wavelengths up to 4.114 µm are also reported.</caption><thead><tr><th>λ (μm)</th><th>n<sub>hom</sub> + ik<sub>hom</sub></th><th>n<sub>iox</sub> + ik<sub>iox</sub></th><th>n<sub>hos</sub> + ik<sub>hos</sub></th><th>n<sub>acc</sub> + ik<sub>acc</sub></th><th>p<sub>1</sub></th></tr></thead><tbody><tr><td>0.300</td><td>1.600 + i8.700 × 10<sup>-3</sup></td><td>1.682 + i2.871 × 10<sup>-2</sup></td><td>1.531 + i1.352 × 10<sup>-3</sup></td><td>1.535 + i6.832 × 10<sup>-3</sup></td><td>3.444 × 10<sup>+1</sup></td></tr><tr><td>0.325</td><td>1.595 + i7.000 × 10<sup>-3</sup></td><td>1.814 + i1.377 × 10<sup>-1</sup></td><td>1.522 + i1.310 × 10<sup>-3</sup></td><td>1.530 + i6.470 × 10<sup>-3</sup></td><td>3.389 × 10<sup>+1</sup></td></tr><tr><td>0.350</td><td>1.590 + i5.800 × 10<sup>-3</sup></td><td>1.988 + i2.175 × 10<sup>-1</sup></td><td>1.515 + i1.247 × 10<sup>-3</sup></td><td>1.528 + i6.202 × 10<sup>-3</sup></td><td>3.405 × 10<sup>+1</sup></td></tr><tr><td>0.375</td><td>1.585 + i4.600 × 10<sup>-3</sup></td><td>2.187 + i2.708 × 10<sup>-1</sup></td><td>1.511 + i1.167 × 10<sup>-3</sup></td><td>1.529 + i5.973 × 10<sup>-3</sup></td><td>3.473 × 10<sup>+1</sup></td></tr><tr><td>0.400</td><td>1.580 + i3.600 × 10<sup>-3</sup></td><td>2.396 + i3.004 × 10<sup>-1</sup></td><td>1.508 + i1.074 × 10<sup>-3</sup></td><td>1.532 + i5.731 × 10<sup>-3</sup></td><td>3.575 × 10<sup>+1</sup></td></tr><tr><td>0.425</td><td>1.576 + i2.900 × 10<sup>-3</sup></td><td>2.601 + i3.091 × 10<sup>-1</sup></td><td>1.506 + i9.705 × 10<sup>-4</sup></td><td>1.535 + i5.430 × 10<sup>-3</sup></td><td>3.691 × 10<sup>+1</sup></td></tr><tr><td>0.450</td><td>1.572 + i2.400 × 10<sup>-3</sup></td><td>2.786 + i2.995 × 10<sup>-1</sup></td><td>1.505 + i8.607 × 10<sup>-4</sup></td><td>1.539 + i5.037 × 10<sup>-3</sup></td><td>3.802 × 10<sup>+1</sup></td></tr><tr><td>0.475</td><td>1.568 + i2.000 × 10<sup>-3</sup></td><td>2.936 + i2.744 × 10<sup>-1</sup></td><td>1.504 + i7.481 × 10<sup>-4</sup></td><td>1.542 + i4.534 × 10<sup>-3</sup></td><td>3.889 × 10<sup>+1</sup></td></tr><tr><td>0.500</td><td>1.564 + i1.800 × 10<sup>-3</sup></td><td>3.036 + i2.366 × 10<sup>-1</sup></td><td>1.503 + i6.362 × 10<sup>-4</sup></td><td>1.544 + i3.924 × 10<sup>-3</sup></td><td>3.933 × 10<sup>+1</sup></td></tr><tr><td>0.525</td><td>1.561 + i1.600 × 10<sup>-3</sup></td><td>3.071 + i1.888 × 10<sup>-1</sup></td><td>1.502 + i5.286 × 10<sup>-4</sup></td><td>1.544 + i3.238 × 10<sup>-3</sup></td><td>3.916 × 10<sup>+1</sup></td></tr><tr><td>0.550</td><td>1.558 + i1.400 × 10<sup>-3</sup></td><td>3.041 + i1.369 × 10<sup>-1</sup></td><td>1.501 + i4.330 × 10<sup>-4</sup></td><td>1.542 + i2.550 × 10<sup>-3</sup></td><td>3.817 × 10<sup>+1</sup></td></tr><tr><td>0.575</td><td>1.555 + i1.200 × 10<sup>-3</sup></td><td>2.978 + i9.386 × 10<sup>-2</sup></td><td>1.499 + i3.660 × 10<sup>-4</sup></td><td>1.539 + i1.989 × 10<sup>-3</sup></td><td>3.619 × 10<sup>+1</sup></td></tr><tr><td>0.600</td><td>1.553 + i1.100 × 10<sup>-3</sup></td><td>2.917 + i7.320 × 10<sup>-2</sup></td><td>1.498 + i3.450 × 10<sup>-4</sup></td><td>1.536 + i1.645 × 10<sup>-3</sup></td><td>3.304 × 10<sup>+1</sup></td></tr><tr><td>0.625</td><td>1.551 + i9.000 × 10<sup>-4</sup></td><td>2.877 + i7.711 × 10<sup>-2</sup></td><td>1.496 + i3.694 × 10<sup>-4</sup></td><td>1.533 + i1.480 × 10<sup>-3</sup></td><td>2.898 × 10<sup>+1</sup></td></tr><tr><td>0.650</td><td>1.550 + i8.000 × 10<sup>-4</sup></td><td>2.849 + i9.299 × 10<sup>-2</sup></td><td>1.495 + i4.149 × 10<sup>-4</sup></td><td>1.531 + i1.385 × 10<sup>-3</sup></td><td>2.488 × 10<sup>+1</sup></td></tr><tr><td>0.675</td><td>1.548 + i7.000 × 10<sup>-4</sup></td><td>2.828 + i1.078 × 10<sup>-1</sup></td><td>1.494 + i4.569 × 10<sup>-4</sup></td><td>1.530 + i1.312 × 10<sup>-3</sup></td><td>2.158 × 10<sup>+1</sup></td></tr><tr><td>0.700</td><td>1.544 + i7.000 × 10<sup>-4</sup></td><td>2.808 + i1.163 × 10<sup>-1</sup></td><td>1.493 + i4.857 × 10<sup>-4</sup></td><td>1.528 + i1.257 × 10<sup>-3</sup></td><td>1.936 × 10<sup>+1</sup></td></tr><tr><td>0.800</td><td>1.540 + i8.000 × 10<sup>-4</sup></td><td>2.744 + i1.073 × 10<sup>-1</sup></td><td>1.492 + i5.125 × 10<sup>-4</sup></td><td>1.526 + i1.195 × 10<sup>-3</sup></td><td>1.716 × 10<sup>+1</sup></td></tr><tr><td>0.900</td><td>1.535 + i1.000 × 10<sup>-3</sup></td><td>2.704 + i7.996 × 10<sup>-2</sup></td><td>1.492 + i5.086 × 10<sup>-4</sup></td><td>1.524 + i1.115 × 10<sup>-3</sup></td><td>1.587 × 10<sup>+1</sup></td></tr><tr><td>1.000</td><td>1.530 + i2.000 × 10<sup>-3</sup></td><td>2.683 + i1.016 × 10<sup>-1</sup></td><td>1.491 + i6.218 × 10<sup>-4</sup></td><td>1.520 + i7.008 × 10<sup>-4</sup></td><td>2.392 × 10<sup>+0</sup></td></tr><tr><td>1.250</td><td>1.520 + i3.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>1.500</td><td>1.510 + i5.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.000</td><td>1.500 + i8.000 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.507</td><td>1.468 + i9.856 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.614</td><td>1.475 + i1.139 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.829</td><td>1.481 + i4.194 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>2.957</td><td>1.481 + i4.194 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.043</td><td>1.481 + i2.920 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.257</td><td>1.519 + i1.759 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.471</td><td>1.475 + i1.317 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.686</td><td>1.481 + i1.075 × 10<sup>-2</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>3.900</td><td>1.481 + i6.383 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>4.114</td><td>1.481 + i4.444 × 10<sup>-3</sup></td><td></td><td></td><td></td><td></td></tr></tbody></table>

from 0.77 to 4 µm.) We use spline interpolation to spectrally extend the CRIs of host mixture and iron oxides, together with the fitting polynomial function, and then calculate the CRI for static and dynamic accretions using Eqs. (1) and (2) (Table B1).

## B2 Optical properties for default dust

We pre-calculate offline the optical properties of default dust used by the SW radiation scheme of ModelE2.1, which are the extinction and scattering efficiencies, along with the asymmetry parameter. First, we calculate monodisperse opti-

cal properties for spherical particles using the Mie code from Mishchenko et al. (2002) as functions of the default CRI (Table B1), which is globally uniform but varies across wavelengths, and the size parameter that is defined as $x = \frac{\pi d}{\lambda}$, where d is the particle diameter and λ the wavelength. We consider a size parameter grid of 5000 values ranging from 0.02 to 1000 with a constant logarithmic step. Next, we integrate the monodisperse optical properties over each aerosol size bin for the full set of default SW wavelengths (up to 4.114 µm; Table B1). For size integration, we use a step number distribution ($f_N$) corresponding to a constant volume size
<table><caption>**Table B2.** Diameter bounds ($d_A$, $d_B$) of the five transported size bins of ModelE2.1, with the clay bin split into four sub-bins to allow more accurate calculation of the dust–radiation interaction at ultraviolet and visible wavelengths. The effective diameter ($d_{eff}$) for each bin is also reported.</caption><thead><tr><th>Bin</th><th>(d<sub>A</sub>, d<sub>B</sub>) (μm)</th><th>d<sub>eff</sub> (μm)</th></tr></thead><tbody><tr><td rowspan="4">Clay</td><td>(0.1, 0.2)</td><td>0.139</td></tr><tr><td>(0.2, 0.5)</td><td>0.305</td></tr><tr><td>(0.5, 1.0)</td><td>0.693</td></tr><tr><td>(1.0, 2.0)</td><td>1.386</td></tr><tr><td>Silt-1</td><td>(2.0, 4.0)</td><td>2.773</td></tr><tr><td>Silt-2</td><td>(4.0, 8.0)</td><td>5.546</td></tr><tr><td>Silt-3</td><td>(8.0, 16.0)</td><td>11.09</td></tr><tr><td>Silt-4</td><td>(16.0, 32.0)</td><td>22.18</td></tr></tbody></table>

<table><caption>**Table B3.** Grid of real ($n_{grd}$) and imaginary ($k_{grd}$) refractive indices included in the external look-up table of size-integrated single-wavelength intensive optical properties (extinction and scattering efficiency, asymmetry parameter) used for the calculation of optical properties for mineral components.</caption><thead><tr><th>n<sub>grd</sub></th></tr></thead><tbody><tr><td>1.490–1.500–1.510–1.520–1.530–1.540–1.550–1.560</td></tr><tr><td>1.570–1.580–1.590–1.600–2.000–2.400–2.800–3.200</td></tr></tbody><thead><tr><th>k<sub>grd</sub></th></tr></thead><tbody><tr><td>0–0.0001–0.0002–0.0003–0.0004–0.0005–0.0006–0.0007–0.0008–0.0009</td></tr><tr><td>0.001–0.002–0.003–0.004–0.005–0.006–0.007–0.008–0.009</td></tr><tr><td>0.01–0.02–0.03–0.04–0.05–0.06–0.07–0.08–0.09</td></tr><tr><td>0.1–0.2–0.3–0.4–0.5–0.6–0.7–0.8–0.9–1–1.1–1.2–1.3</td></tr></tbody></table>

distribution on the logarithmic scale within each bin and zero outside (Tegen and Fung, 1994):

$$f_N(d) = \begin{cases} Cd^{-4} & \text{for } d \in (d_A, d_B) \\ 0 & \text{for } d \notin (d_A, d_B) \end{cases}, \quad (B1)$$

where C is a normalization constant and ($d_A$, $d_B$) are the prescribed diameter bounds of the size bins reported in Table B2 (Perlwitz et al., 2015a). Equation (B1) is also used to calculate the effective diameter (twice the mean radius over the cross-sectional area distribution) corresponding to each bin (Table B2). The size-integrated single-wavelength optical properties are mapped onto a 10 nm resolution wavelength grid and are then averaged over the six SW bands weighted by the solar flux (Thekaekara, 1974).

## B3 Optical properties for mineral components

The optical properties for mineral components are calculated during runtime only in the UV–VIS band of ModelE2.1 (0.30–0.77 µm). After defining the CRI for each mineral component (Table B1), either globally uniform for free iron oxides, host mixture and static accretion (EXT) or spatially and temporally varying for dynamic accretion (INT),

we pick intensive optical properties (extinction and scattering efficiency, asymmetry parameter) from an external look-up table that we pre-calculate using a grid of RRIs and IRIs specifically selected to cover typical dust values with high resolution (Table B3). The look-up intensive properties are already integrated over the eight size bins of ModelE2.1 (Table B2) for the subset of 20 default shortwave wavelengths through the same procedure described in Appendix B2. We then derive the optical properties for the actual CRI through bilinear interpolation of the look-up properties corresponding to the four closest grid CRIs. Finally, we average the single-wavelength optical properties over the UV–VIS band of ModelE2.1 following the same method used for default dust (Appendix B2).

**Code availability.** The version of ModelE used in this work, including the model source code and necessary configuration and input files, is available at Zenodo (Obiso et al., 2024, https://doi.org/10.5281/zenodo.10808381).

The Mie code for calculating the look-up tables of dust optical properties used in this work is publicly available at https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html (Goddard Institute for Space Studies, 2002).

**Data availability.** The analysis scripts for generating the figures of this work, along with the required input data and the instructions for the user, are available at Zenodo (Obiso et al., 2024, https://doi.org/10.5281/zenodo.10808381).

The output files of the three model experiments of this work are also available at https://portal.nccs.nasa.gov/datashare/giss-publish/pub/Obiso_ACP_2024 (NASA Center for Climate Simulation, 2024) as stand-alone files, for users who are only interested in quickly accessing the model output without downloading the full analysis package.

The AERONET Version 3 inversion data product can be downloaded from https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_inversions.html (Goddard Space Flight Center, 2024).

**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/acp-24-5337-2024-supplement.

**Author contributions.** VO and RLM, in collaboration with CPGP and MGA, designed the strategy for the coupling between minerals and radiation in ModelE2.1 and defined the methods, analysis and objectives of the experiments. VO implemented the coupling schemes in the model, ran the simulations, performed the analyses and wrote the manuscript. RLM supervised all the phases of the project. MGA helped to define the filtering conditions for selecting dusty scenes and generated the AERONET climatologies used for model evaluation, while GLS offered guidance about the AERONET product. CDB and PF gave advice on the interpretation of the retrieved imaginary indices and measured mineral fractions from natural dust aerosol samples. JPP, SEB and KT offered technical support for ModelE2.1 and contributed to identify-
<table><caption>**Table B2.** Diameter bounds ($d_A$, $d_B$) of the five transported size bins of ModelE2.1, with the clay bin split into four sub-bins to allow more accurate calculation of the dust–radiation interaction at ultraviolet and visible wavelengths. The effective diameter ($d_{eff}$) for each bin is also reported.</caption><thead><tr><th>Bin</th><th>(d<sub>A</sub>, d<sub>B</sub>) (μm)</th><th>d<sub>eff</sub> (μm)</th></tr></thead><tbody><tr><td rowspan="4">Clay</td><td>(0.1, 0.2)</td><td>0.139</td></tr><tr><td>(0.2, 0.5)</td><td>0.305</td></tr><tr><td>(0.5, 1.0)</td><td>0.693</td></tr><tr><td>(1.0, 2.0)</td><td>1.386</td></tr><tr><td>Silt-1</td><td>(2.0, 4.0)</td><td>2.773</td></tr><tr><td>Silt-2</td><td>(4.0, 8.0)</td><td>5.546</td></tr><tr><td>Silt-3</td><td>(8.0, 16.0)</td><td>11.09</td></tr><tr><td>Silt-4</td><td>(16.0, 32.0)</td><td>22.18</td></tr></tbody></table>

<table><caption>**Table B3.** Grid of real ($n_{grd}$) and imaginary ($k_{grd}$) refractive indices included in the external look-up table of size-integrated single-wavelength intensive optical properties (extinction and scattering efficiency, asymmetry parameter) used for the calculation of optical properties for mineral components.</caption><thead><tr><th>n<sub>grd</sub></th></tr></thead><tbody><tr><td>1.490–1.500–1.510–1.520–1.530–1.540–1.550–1.560</td></tr><tr><td>1.570–1.580–1.590–1.600–2.000–2.400–2.800–3.200</td></tr></tbody><thead><tr><th>k<sub>grd</sub></th></tr></thead><tbody><tr><td>0–0.0001–0.0002–0.0003–0.0004–0.0005–0.0006–0.0007–0.0008–0.0009</td></tr><tr><td>0.001–0.002–0.003–0.004–0.005–0.006–0.007–0.008–0.009</td></tr><tr><td>0.01–0.02–0.03–0.04–0.05–0.06–0.07–0.08–0.09</td></tr><tr><td>0.1–0.2–0.3–0.4–0.5–0.6–0.7–0.8–0.9–1–1.1–1.2–1.3</td></tr></tbody></table>

distribution on the logarithmic scale within each bin and zero outside (Tegen and Fung, 1994):

$$f_N(d) = \begin{cases} Cd^{-4} & \text{for } d \in (d_A, d_B) \\ 0 & \text{for } d \notin (d_A, d_B) \end{cases}, \quad (B1)$$

where C is a normalization constant and ($d_A$, $d_B$) are the prescribed diameter bounds of the size bins reported in Table B2 (Perlwitz et al., 2015a). Equation (B1) is also used to calculate the effective diameter (twice the mean radius over the cross-sectional area distribution) corresponding to each bin (Table B2). The size-integrated single-wavelength optical properties are mapped onto a 10 nm resolution wavelength grid and are then averaged over the six SW bands weighted by the solar flux (Thekaekara, 1974).

## B3 Optical properties for mineral components

The optical properties for mineral components are calculated during runtime only in the UV–VIS band of ModelE2.1 (0.30–0.77 µm). After defining the CRI for each mineral component (Table B1), either globally uniform for free iron oxides, host mixture and static accretion (EXT) or spatially and temporally varying for dynamic accretion (INT),

we pick intensive optical properties (extinction and scattering efficiency, asymmetry parameter) from an external look-up table that we pre-calculate using a grid of RRIs and IRIs specifically selected to cover typical dust values with high resolution (Table B3). The look-up intensive properties are already integrated over the eight size bins of ModelE2.1 (Table B2) for the subset of 20 default shortwave wavelengths through the same procedure described in Appendix B2. We then derive the optical properties for the actual CRI through bilinear interpolation of the look-up properties corresponding to the four closest grid CRIs. Finally, we average the single-wavelength optical properties over the UV–VIS band of ModelE2.1 following the same method used for default dust (Appendix B2).

**Code availability.** The version of ModelE used in this work, including the model source code and necessary configuration and input files, is available at Zenodo (Obiso et al., 2024, https://doi.org/10.5281/zenodo.10808381).

The Mie code for calculating the look-up tables of dust optical properties used in this work is publicly available at https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html (Goddard Institute for Space Studies, 2002).

**Data availability.** The analysis scripts for generating the figures of this work, along with the required input data and the instructions for the user, are available at Zenodo (Obiso et al., 2024, https://doi.org/10.5281/zenodo.10808381).

The output files of the three model experiments of this work are also available at https://portal.ncics.nasa.gov/datashare/giss-publish/pub/Obiso_ACP_2024 (NASA Center for Climate Simulation, 2024) as stand-alone files, for users who are only interested in quickly accessing the model output without downloading the full analysis package.

The AERONET Version 3 inversion data product can be downloaded from https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_inversions.html (Goddard Space Flight Center, 2024).

**Supplement.** The supplement related to this article is available online at: https://doi.org/10.5194/acp-24-5337-2024-supplement.

**Author contributions.** VO and RLM, in collaboration with CPGP and MGA, designed the strategy for the coupling between minerals and radiation in ModelE2.1 and defined the methods, analysis and objectives of the experiments. VO implemented the coupling schemes in the model, ran the simulations, performed the analyses and wrote the manuscript. RLM supervised all the phases of the project. MGA helped to define the filtering conditions for selecting dusty scenes and generated the AERONET climatologies used for model evaluation, while GLS offered guidance about the AERONET product. CDB and PF gave advice on the interpretation of the retrieved imaginary indices and measured mineral fractions from natural dust aerosol samples. JPP, SEB and KT offered technical support for ModelE2.1 and contributed to identify-
ing the best implementation strategies. All the co-authors reviewed the manuscript and provided comments and suggestions that improved several aspects of the project.

**Competing interests.** At least one of the co-authors is a member of the editorial board of *Atmospheric Chemistry and Physics*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** We thank the editor and the two anonymous reviewers, whose comments improved the manuscript.

Computational resources supporting this work were provided by the NASA High-End Computing (HEC) program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.

We thank Reto A. Ruedy for his valuable technical support with ModelE and the supercomputer infrastructure.

We are grateful to Michael I. Mishchenko, whose vast legacy includes publicly available codes for calculating the scattering of radiation by aerosols.

We also thank the AERONET scientists and staff for establishing the stations, maintaining the instruments and making the data publicly available.

Claudia Di Biagio and Paola Formenti acknowledge the AERIS data center (https://www.aeris-data.fr, last access: 19 February 2024) of the DATA TERRA French national research infrastructure for distributing and curating the data produced by the CESAM chamber through the hosting of the EUROCHAMP data center (https://data.eurochamp.org, last access: 19 February 2024).

**Financial support.** Vincenzo Obiso was supported by the NASA Postdoctoral Program at the NASA Goddard Institute for Space Studies, administered by Oak Ridge Associated Universities under contract with NASA. Additional support was provided by the NASA Modeling, Analysis and Prediction Program (NNG14HH42I) and the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures Instrument (EVI-4) mission, both of which also supported Ron L. Miller.

Carlos Pérez García-Pando, María Gonçalves Ageitos and Vincenzo Obiso acknowledge funding by the European Research Council under the Horizon 2020 research and innovation program through the ERC Consolidator Grant FRAGMENT (grant agreement no. 773051), the AXA Research Fund through the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC), the European Union's Horizon 2020 research and innovation program under grant agreement no. 821205 (FORCeS) and national grants HEAVY PID2022-140365OB-I00 and BIOTA PID2022-139362OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.

Jan P. Perlwitz has been funded by NASA grants nos. 80NSSC19K0056 (Modeling, Analysis and Prediction Program), 80NSSC19K0984 (Atmospheric Composition Modeling and Analysis Program) and 80NSSC22K1663 (Goddard Institute for Space Studies Model Development).

Gregory L. Schuster is supported by the National Aeronautics and Space Administration through the Science Mission Directorate's Earth Science Division.

The laboratory experiments to retrieve the dust refractive indices in Di Biagio et al. (2017, 2019) that feed this work were supported by the French national program LEFE/INSU (Les Enveloppes Fluides et l'Environnement/Institut National des Sciences de l'Univers) and by the OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through dedicated research funding to the RED-DUST project. They were acquired with the CESAM simulation chamber, a national facility of the CNRS-INSU (Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique) and part of the French ACTRIS research infrastructure. The CESAM simulation chamber also received funding from the European Union's Horizon 2020 research and innovation program through the EUROCHAMP-2020 Infrastructure Activity under grant agreement no. 730997. Claudia Di Biagio was supported by the Centre National des Etudes Spatiales (CNES) and by the CNRS via the Labex L-IPSL, which is funded by the ANR (grant no. ANR-10-LABX-0018).

**Review statement.** This paper was edited by Manvendra Krishna Dubey and reviewed by two anonymous referees.

## References

Adebiyi, A. A. and Kok, J. F.: Climate models miss most of the coarse dust in the atmosphere, Sci. Adv., 6, eaaz9507, https://doi.org/10.1126/sciadv.aaz9507, 2020.

Ahn, H.-J., Park, S.-U., and Chang, L.-S.: Effect of direct radiative forcing of Asian dust on the meteorological fields in East Asia during an Asian dust event period, J. Appl. Meteorol. Climatol., 46, 1655–1681, https://doi.org/10.1175/JAM2551.1, 2007.

Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007.

Bauer, S. E. and Koch, D.: Impact of heterogeneous sulfate formation at mineral dust surfaces on aerosol loads and radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res.-Atmos., 110, D17202, https://doi.org/10.1029/2005JD005870, 2005.

Bauer, S. E., Tsigaridis, K., Faluvegi, G., Kelley, M., Lo, K. K., Miller, R. L., Nazarenko, L., Schmidt, G. A., and Wu, J.: Historical (1850–2014) aerosol evolution and role on climate forcing using the GISS ModelE2.1 contribution to CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS001978, https://doi.org/10.1029/2019MS001978, 2020.

Bedidi, A. and Cervelle, B.: Light scattering by spherical particles with hematite and goethitelike optical properties: Effect of water
ing the best implementation strategies. All the co-authors reviewed the manuscript and provided comments and suggestions that improved several aspects of the project.

**Competing interests.** At least one of the co-authors is a member of the editorial board of *Atmospheric Chemistry and Physics*. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

**Disclaimer.** Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

**Acknowledgements.** We thank the editor and the two anonymous reviewers, whose comments improved the manuscript.

Computational resources supporting this work were provided by the NASA High-End Computing (HEC) program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.

We thank Reto A. Ruedy for his valuable technical support with ModelE and the supercomputer infrastructure.

We are grateful to Michael I. Mishchenko, whose vast legacy includes publicly available codes for calculating the scattering of radiation by aerosols.

We also thank the AERONET scientists and staff for establishing the stations, maintaining the instruments and making the data publicly available.

Claudia Di Biagio and Paola Formenti acknowledge the AERIS data center (https://www.aeris-data.fr, last access: 19 February 2024) of the DATA TERRA French national research infrastructure for distributing and curating the data produced by the CESAM chamber through the hosting of the EUROCHAMP data center (https://data.eurochamp.org, last access: 19 February 2024).

**Financial support.** Vincenzo Obiso was supported by the NASA Postdoctoral Program at the NASA Goddard Institute for Space Studies, administered by Oak Ridge Associated Universities under contract with NASA. Additional support was provided by the NASA Modeling, Analysis and Prediction Program (NNG14HH42I) and the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures Instrument (EVI-4) mission, both of which also supported Ron L. Miller.

Carlos Pérez García-Pando, María Gonçalves Ageitos and Vincenzo Obiso acknowledge funding by the European Research Council under the Horizon 2020 research and innovation program through the ERC Consolidator Grant FRAGMENT (grant agreement no. 773051), the AXA Research Fund through the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center (BSC), the European Union's Horizon 2020 research and innovation program under grant agreement no. 821205 (FORCeS) and national grants HEAVY PID2022-140365OB-I00 and BIOTA PID2022-139362OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU.

Jan P. Perlwitz has been funded by NASA grants nos. 80NSSC19K0056 (Modeling, Analysis and Prediction Program), 80NSSC19K0984 (Atmospheric Composition Modeling and Analysis Program) and 80NSSC22K1663 (Goddard Institute for Space Studies Model Development).

Gregory L. Schuster is supported by the National Aeronautics and Space Administration through the Science Mission Directorate's Earth Science Division.

The laboratory experiments to retrieve the dust refractive indices in Di Biagio et al. (2017, 2019) that feed this work were supported by the French national program LEFE/INSU (Les Enveloppes Fluides et l'Environnement/Institut National des Sciences de l'Univers) and by the OSU-EFLUVE (Observatoire des Sciences de l'Univers-Enveloppes Fluides de la Ville à l'Exobiologie) through dedicated research funding to the RED-DUST project. They were acquired with the CESAM simulation chamber, a national facility of the CNRS-INSU (Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique) and part of the French ACTRIS research infrastructure. The CESAM simulation chamber also received funding from the European Union's Horizon 2020 research and innovation program through the EUROCHAMP-2020 Infrastructure Activity under grant agreement no. 730997. Claudia Di Biagio was supported by the Centre National des Etudes Spatiales (CNES) and by the CNRS via the Labex L-IPSL, which is funded by the ANR (grant no. ANR-10-LABX-0018).

**Review statement.** This paper was edited by Manvendra Krishna Dubey and reviewed by two anonymous referees.

## References

Adebiyi, A. A. and Kok, J. F.: Climate models miss most of the coarse dust in the atmosphere, Sci. Adv., 6, eaaz9507, https://doi.org/10.1126/sciadv.aaz9507, 2020.

Ahn, H.-J., Park, S.-U., and Chang, L.-S.: Effect of direct radiative forcing of Asian dust on the meteorological fields in East Asia during an Asian dust event period, J. Appl. Meteorol. Climatol., 46, 1655–1681, https://doi.org/10.1175/JAM2551.1, 2007.

Balkanski, Y., Schulz, M., Claquin, T., and Guibert, S.: Reevaluation of Mineral aerosol radiative forcings suggests a better agreement with satellite and AERONET data, Atmos. Chem. Phys., 7, 81–95, https://doi.org/10.5194/acp-7-81-2007, 2007.

Bauer, S. E. and Koch, D.: Impact of heterogeneous sulfate formation at mineral dust surfaces on aerosol loads and radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res.-Atmos., 110, D17202, https://doi.org/10.1029/2005JD005870, 2005.

Bauer, S. E., Tsigaridis, K., Faluvegi, G., Kelley, M., Lo, K. K., Miller, R. L., Nazarenko, L., Schmidt, G. A., and Wu, J.: Historical (1850–2014) aerosol evolution and role on climate forcing using the GISS ModelE2.1 contribution to CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS001978, https://doi.org/10.1029/2019MS001978, 2020.

Bedidi, A. and Cervelle, B.: Light scattering by spherical particles with hematite and goethitelike optical properties: Effect of water
impregnation, J. Geophys. Res.-Solid Earth, 98, 11941–11952, https://doi.org/10.1029/93JB00188, 1993.

Cakmur, R. V., Miller, R. L., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Tegen, I., and Zender, C. S.: Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations, J. Geophys. Res.-Atmos., 111, D06207, https://doi.org/10.1029/2005JD005791, 2006.

Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, J. Geophys. Res.-Atmos., 104, 22243–22256, https://doi.org/10.1029/1999JD900416, 1999.

Colarco, P. R., Nowotnick, E. P., Randles, C. A., Yi, B., Yang, P., Kim, K.-M., Smith, J. A., and Bardeen, C. G.: Impact of radiatively interactive dust aerosols in the NASA GEOS-5 climate model: Sensitivity to dust particle shape and refractive index, J. Geophys. Res.-Atmos., 119, 753–786, https://doi.org/10.1002/2013JD020046, 2014.

Derimian, Y., Karnieli, A., Kaufman, Y. J., Andreae, M. O., Andreae, T. W., Dubovik, O., Maenhaut, W., and Koren, I.: The role of iron and black carbon in aerosol light absorption, Atmos. Chem. Phys., 8, 3623–3637, https://doi.org/10.5194/acp-8-3623-2008, 2008.

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazanau, M., Pangui, E., Journet, E., Nowak, S., Caquineau, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing, Atmos. Chem. Phys., 17, 1901–1929, https://doi.org/10.5194/acp-17-1901-2017, 2017.

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazanau, M., Pangui, E., Journet, E., Nowak, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content, Atmos. Chem. Phys., 19, 15503–15531, https://doi.org/10.5194/acp-19-15503-2019, 2019.

Di Biagio, C., Balkanski, Y., Albani, S., Boucher, O., and Formenti, P.: Direct radiative effect by mineral dust aerosols constrained by new microphysical and spectral optical data, Geophys. Res. Lett., 47, e2019GL086186, https://doi.org/10.1029/2019GL086186, 2020.

Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002.

Formenti, P., Rajot, J. L., Desboeufs, K., Caquineau, S., Chevaillier, S., Nava, S., Gaudichet, A., Journet, E., Triquet, S., Alfaro, S., Chiari, M., Haywood, J., Coe, H., and Highwood, E.: Regional variability of the composition of mineral dust from western Africa: Results from the AMMA SOP0/DABEX and DODO field campaigns, J. Geophys. Res.-Atmos., 113, D00C13, https://doi.org/10.1029/2008JD009903, 2008.

Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S. J.: Sources and distributions of aerosols simulated with the GOCART model, J. Geophys. Res. Atmos.,

106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.

Ginoux, P., Garbuzov, D., and Hsu, N. C.: Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data, J. Geophys. Res.-Atmos., 115, D05204, https://doi.org/10.1029/2009JD012398, 2010.

Go, S., Lyapustin, A., Schuster, G. L., Choi, M., Ginoux, P., Chin, M., Kalashnikova, O., Dubovik, O., Kim, J., da Silva, A., Holben, B., and Reid, J. S.: Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations, Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, 2022.

Goddard Institute for Space Studies: Lorenz-Mie FORTRAN programs based on Mishchenko et al. (2002), Goddard Institute for Space Studies [code], https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html (last access: 12 March 2024), 2002.

Goddard Space Flight Center: AERONET V3 inversion data product, Goddard Space Flight Center [data set], https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_inversions.html (last access: 12 March 2024), 2024.

Gonçalves Ageitos, M., Obiso, V., Miller, R. L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, N. M., Green, R. O., Thompson, D. R., and Pérez García-Pando, C.: Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts, Atmos. Chem. Phys., 23, 8623–8657, https://doi.org/10.5194/acp-23-8623-2023, 2023.

Green, R. O., Mahowald, N., Ung, C., Thompson, D. R., Bator, L., Bennet, M., Bernas, M., Blackway, N., Bradley, C., Cha, J., Clark, P., Clark, R., Cloud, D., Diaz, E., Ben-Dor, E., Duren, R., Eastwood, M. L., Ehlmann, B. L., Fuentes, L., Ginoux, P., Gross, J., He, Y., Kalashnikova, O., Kert, W., Keymeulen, D., Klimesh, M., Ku, D., Kwong-Fu, H., Liggett, E., Li, L., Lundeen, S., Makowski, M. D., Mazer, A. S., Miller, R., Mouroulis, P., Oaida, B., Okin, G. S., Ortega, A., Oyake, A., Nguyen, H., Pace, T., Painter, T. H., Pempejian, J., Pérez García-Pando, C., Pham, T., Phillips, B., Pollock, R., Purcell, R., Realmuto, V., Schoolcraft, J., Sen, A., Shin, S., Shaw, L., Soriano, M., Swayze, G., Thingvold, E., Vaid, A., and Zan, J.: The Earth Surface Mineral Dust Source Investigation: An Earth science imaging spectroscopy mission, in: 2020 IEEE Aerospace Conference, 1–15, https://doi.org/10.1109/AERO47225.2020.9172731, 2020.

Hand, V. L., Capes, G., Vaughan, D. J., Formenti, P., Haywood, J. M., and Coe, H.: Evidence of internal mixing of African dust and biomass burning particles by individual particle analysis using electron beam techniques, J. Geophys. Res.-Atmos., 115, D13301, https://doi.org/10.1029/2009JD012938, 2010.

Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527–610, https://doi.org/10.1007/BF00168069, 1974.

Heinold, B., Helmert, J., Hellmuth, O., Wolke, R., Ansmann, A., Marticorena, B., Laurent, B., and Tegen, I.: Regional modeling of Saharan dust events using LM-MUSCAT: Model description and case studies, J. Geophys. Res.-Atmos., 112, D11204, https://doi.org/10.1029/2006JD007443, 2007.
impregnation, J. Geophys. Res.-Solid Earth, 98, 11941–11952, https://doi.org/10.1029/93JB00188, 1993.

Cakmur, R. V., Miller, R. L., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Tegen, I., and Zender, C. S.: Constraining the magnitude of the global dust cycle by minimizing the difference between a model and observations, J. Geophys. Res.-Atmos., 111, D06207, https://doi.org/10.1029/2005JD005791, 2006.

Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, J. Geophys. Res.-Atmos., 104, 22243–22256, https://doi.org/10.1029/1999JD900416, 1999.

Colarco, P. R., Nowotnick, E. P., Randles, C. A., Yi, B., Yang, P., Kim, K.-M., Smith, J. A., and Bardeen, C. G.: Impact of radiatively interactive dust aerosols in the NASA GEOS-5 climate model: Sensitivity to dust particle shape and refractive index, J. Geophys. Res.-Atmos., 119, 753–786, https://doi.org/10.1002/2013JD020046, 2014.

Derimian, Y., Karnieli, A., Kaufman, Y. J., Andreae, M. O., Andreae, T. W., Dubovik, O., Maenhaut, W., and Koren, I.: The role of iron and black carbon in aerosol light absorption, Atmos. Chem. Phys., 8, 3623–3637, https://doi.org/10.5194/acp-8-3623-2008, 2008.

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazanau, M., Pangui, E., Journet, E., Nowak, S., Caquineau, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing, Atmos. Chem. Phys., 17, 1901–1929, https://doi.org/10.5194/acp-17-1901-2017, 2017.

Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazanau, M., Pangui, E., Journet, E., Nowak, S., Andreae, M. O., Kandler, K., Saeed, T., Piketh, S., Seibert, D., Williams, E., and Doussin, J.-F.: Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content, Atmos. Chem. Phys., 19, 15503–15531, https://doi.org/10.5194/acp-19-15503-2019, 2019.

Di Biagio, C., Balkanski, Y., Albani, S., Boucher, O., and Formenti, P.: Direct radiative effect by mineral dust aerosols constrained by new microphysical and spectral optical data, Geophys. Res. Lett., 47, e2019GL086186, https://doi.org/10.1029/2019GL086186, 2020.

Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002.

Formenti, P., Rajot, J. L., Desboeufs, K., Caquineau, S., Chevaillier, S., Nava, S., Gaudichet, A., Journet, E., Triquet, S., Alfaro, S., Chiari, M., Haywood, J., Coe, H., and Highwood, E.: Regional variability of the composition of mineral dust from western Africa: Results from the AMMA SOP0/DABEX and DODO field campaigns, J. Geophys. Res.-Atmos., 113, D00C13, https://doi.org/10.1029/2008JD009903, 2008.

Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S. J.: Sources and distributions of aerosols simulated with the GOCART model, J. Geophys. Res. Atmos.,

106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.

Ginoux, P., Garbuzov, D., and Hsu, N. C.: Identification of anthropogenic and natural dust sources using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue level 2 data, J. Geophys. Res.-Atmos., 115, D05204, https://doi.org/10.1029/2009JD012398, 2010.

Go, S., Lyapustin, A., Schuster, G. L., Choi, M., Ginoux, P., Chin, M., Kalashnikova, O., Dubovik, O., Kim, J., da Silva, A., Holben, B., and Reid, J. S.: Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations, Atmos. Chem. Phys., 22, 1395–1423, https://doi.org/10.5194/acp-22-1395-2022, 2022.

Goddard Institute for Space Studies: Lorenz-Mie FORTRAN programs based on Mishchenko et al. (2002), Goddard Institute for Space Studies [code], https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html (last access: 12 March 2024), 2002.

Goddard Space Flight Center: AERONET V3 inversion data product, Goddard Space Flight Center [data set], https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_inversions.html (last access: 12 March 2024), 2024.

Gonçalves Ageitos, M., Obiso, V., Miller, R. L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, N. M., Green, R. O., Thompson, D. R., and Pérez García-Pando, C.: Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts, Atmos. Chem. Phys., 23, 8623–8657, https://doi.org/10.5194/acp-23-8623-2023, 2023.

Green, R. O., Mahowald, N., Ung, C., Thompson, D. R., Bator, L., Bennet, M., Bernas, M., Blackway, N., Bradley, C., Cha, J., Clark, P., Clark, R., Cloud, D., Diaz, E., Ben-Dor, E., Duren, R., Eastwood, M. L., Ehlmann, B. L., Fuentes, L., Ginoux, P., Gross, J., He, Y., Kalashnikova, O., Kert, W., Keymeulen, D., Klimesh, M., Ku, D., Kwong-Fu, H., Liggett, E., Li, L., Lundeen, S., Makowski, M. D., Mazer, A. S., Miller, R., Mouroulis, P., Oaida, B., Okin, G. S., Ortega, A., Oyake, A., Nguyen, H., Pace, T., Painter, T. H., Pempejian, J., Pérez García-Pando, C., Pham, T., Phillips, B., Pollock, R., Purcell, R., Realmuto, V., Schoolcraft, J., Sen, A., Shin, S., Shaw, L., Soriano, M., Swayze, G., Thingvold, E., Vaid, A., and Zan, J.: The Earth Surface Mineral Dust Source Investigation: An Earth science imaging spectroscopy mission, in: 2020 IEEE Aerospace Conference, 1–15, https://doi.org/10.1109/AERO47225.2020.9172731, 2020.

Hand, V. L., Capes, G., Vaughan, D. J., Formenti, P., Haywood, J. M., and Coe, H.: Evidence of internal mixing of African dust and biomass burning particles by individual particle analysis using electron beam techniques, J. Geophys. Res.-Atmos., 115, D13301, https://doi.org/10.1029/2009JD012938, 2010.

Hansen, J. E. and Travis, L. D.: Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527–610, https://doi.org/10.1007/BF00168069, 1974.

Heinold, B., Helmert, J., Hellmuth, O., Wolke, R., Ansmann, A., Marticorena, B., Laurent, B., and Tegen, I.: Regional modeling of Saharan dust events using LM-MUSCAT: Model description and case studies, J. Geophys. Res.-Atmos., 112, D11204, https://doi.org/10.1029/2006JD007443, 2007.
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and Clouds: The software package OPAC, B. Am. Meteorol. Soc., 79, 831–844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998.

Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.

Holben, B. N., Eck, T. F., Slutsker, I., Smirnov, A., Sinyuk, A., Schafer, J., Giles, D., and Dubovik, O.: AERONET’s Version 2.0 quality assurance criteria, in: Proc. SPIE 6408, Remote Sens. Atmos. Clouds, p. 64080Q, https://doi.org/10.1117/12.706524, 2006.

Huang, Y., Kok, J. F., Kandler, K., Lindqvist, H., Nousiainen, T., Sakai, T., Adebiyi, A., and Jokinen, O.: Climate models and remote sensing retrievals neglect substantial desert dust asphericity, Geophys. Res. Lett., 47, e2019GL086592, https://doi.org/10.1029/2019GL086592, 2020.

Huang, Y., Kok, J. F., Saito, M., and Muñoz, O.: Single-scattering properties of ellipsoidal dust aerosols constrained by measured dust shape distributions, Atmos. Phys., 23, 2557–2577, https://doi.org/10.5194/acp-23-2557-2023, 2023.

Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmos. Chem. Phys., 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.

Kalashnikova, O. V. and Sokolik, I. N.: Modeling the radiative properties of nonspherical soil-derived mineral aerosols, J. Quant. Spectrosc. Ra., 87, 137–166, https://doi.org/10.1016/j.jqsrt.2003.12.026, 2004.

Kandler, K., Schütz, L., Deutscher, C., Ebert, M., Hofmann, H., Jäckel, S., Jaenicke, R., Knippertz, P., Lieke, K., Massling, A., Petzold, A., Schladitz, A., Weinzierl, B., Wiedensohler, A., Zorn, S., and Weinbruch, S.: Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006, Tellus B, 61, 32–50, https://doi.org/10.1111/j.1600-0889.2008.00385.x, 2009.

Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Syst., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.

Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon, J. Geophys. Res.-Atmos., 109, D21208, https://doi.org/10.1029/2004JD004999, 2004.

Klose, M., Jorba, O., Gonçalves Ageitos, M., Escribano, J., Dawson, M. L., Obiso, V., Di Tomaso, E., Basart, S., Montané Pinto, G., Macchia, F., Ginoux, P., Guerschman, J., Prigent, C., Huang,

Y., Kok, J. F., Miller, R. L., and Pérez García-Pando, C.: Mineral dust cycle in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) Version 2.0, Geosci. Model Dev., 14, 6403–6444, https://doi.org/10.5194/gmd-14-6403-2021, 2021.

Koch, D., Jacob, D., Tegen, I., Rind, D., and Chin, M.: Tropospheric sulfur simulation and sulfate direct radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res.-Atmos., 104, 23799–23822, https://doi.org/10.1029/1999JD900248, 1999.

Kok, J. F.: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, https://doi.org/10.1073/pnas.1014798108, 2011.

Kok, J. F., Ridley, D. A., Zhou, Q., Miller, R. L., Zhao, C., Heald, C. L., Ward, D. S., Albani, S., and Haustein, K.: Smaller desert dust cooling effect estimated from analysis of dust size and abundance, Nat. Geosci., 10, 274–278, https://doi.org/10.1038/ngeo2912, 2017.

Kok, J. F., Adebiyi, A. A., Albani, S., Balkanski, Y., Checa-Garcia, R., Chin, M., Colarco, P. R., Hamilton, D. S., Huang, Y., Ito, A., Klose, M., Leung, D. M., Li, L., Mahowald, N. M., Miller, R. L., Obiso, V., Pérez García-Pando, C., Rocha-Lima, A., Wan, J. S., and Whicker, C. A.: Improved representation of the global dust cycle using observational constraints on dust properties and abundance, Atmos. Chem. Phys., 21, 8127–8167, https://doi.org/10.5194/acp-21-8127-2021, 2021.

Li, J., Carlson, B. E., Dubovik, O., and Lacis, A. A.: Recent trends in aerosol optical properties derived from AERONET measurements, Atmos. Chem. Phys., 14, 12271–12289, https://doi.org/10.5194/acp-14-12271-2014, 2014.

Li, L., Mahowald, N. M., Miller, R. L., Pérez García-Pando, C., Klose, M., Hamilton, D. S., Gonçalves Ageitos, M., Ginoux, P., Balkanski, Y., Green, R. O., Kalashnikova, O., Kok, J. F., Obiso, V., Paynter, D., and Thompson, D. R.: Quantifying the range of the dust direct radiative effect due to source mineralogy uncertainty, Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, 2021.

Liu, Y. and Daum, P. H.: Relationship of refractive index to mass density and self-consistency of mixing rules for multicomponent mixtures like ambient aerosols, J. Aerosol Sci., 39, 974–986, https://doi.org/10.1016/j.jaerosci.2008.06.006, 2008.

Longtin, D. R., Shettle, E. P., Hummel, J. R., and Pryce, J. D.: A wind dependent desert aerosol model: Radiative properties, Tech. Rep. AFGL-TR-88-0112, Air Force Geophysics Laboratory, Hanscom AFB, MA, USA, 114 pp., https://apps.dtic.mil/sti/pdfs/ADA201951.pdf (last access: 3 March 2024), 1988.

Markel, V. A.: Introduction to the Maxwell Garnett approximation: tutorial, J. Opt. Soc. Am. A, 33, 1244–1256, https://doi.org/10.1364/JOSAA.33.001244, 2016.

Meloni, D., Di Sarra, A., Di Iorio, T., and Fiocco, G.: Influence of the vertical profile of Saharan dust on the visible direct radiative forcing, J. Quant. Spectrosc. Ra, 93, 397–413, https://doi.org/10.1016/j.jqsrt.2004.08.035, 2005.

Meng, Z., Yang, P., Kattawar, G. W., Bi, L., Liou, K. N., and Laszlo, I.: Single-scattering properties of tri-axial ellipsoidal mineral dust aerosols: A database for application to radiative transfer calculations, J. Aerosol Sci., 41, 501–512, https://doi.org/10.1016/j.jaerosci.2010.02.008, 2010.
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and Clouds: The software package OPAC, B. Am. Meteorol. Soc., 79, 831–844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998.

Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.

Holben, B. N., Eck, T. F., Slutsker, I., Smirnov, A., Sinyuk, A., Schafer, J., Giles, D., and Dubovik, O.: AERONET’s Version 2.0 quality assurance criteria, in: Proc. SPIE 6408, Remote Sens. Atmos. Clouds, p. 64080Q, https://doi.org/10.1117/12.706524, 2006.

Huang, Y., Kok, J. F., Kandler, K., Lindqvist, H., Nousiainen, T., Sakai, T., Adebiyi, A., and Jokinen, O.: Climate models and remote sensing retrievals neglect substantial desert dust asphericity, Geophys. Res. Lett., 47, e2019GL086592, https://doi.org/10.1029/2019GL086592, 2020.

Huang, Y., Kok, J. F., Saito, M., and Muñoz, O.: Single-scattering properties of ellipsoidal dust aerosols constrained by measured dust shape distributions, Atmos. Phys., 23, 2557–2577, https://doi.org/10.5194/acp-23-2557-2023, 2023.

Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmos. Chem. Phys., 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.

Kalashnikova, O. V. and Sokolik, I. N.: Modeling the radiative properties of nonspherical soil-derived mineral aerosols, J. Quant. Spectrosc. Ra., 87, 137–166, https://doi.org/10.1016/j.jqsrt.2003.12.026, 2004.

Kandler, K., Schütz, L., Deutscher, C., Ebert, M., Hofmann, H., Jäckel, S., Jaenicke, R., Knippertz, P., Lieke, K., Massling, A., Petzold, A., Schladitz, A., Weinzierl, B., Wiedensohler, A., Zorn, S., and Weinbruch, S.: Size distribution, mass concentration, chemical and mineralogical composition and derived optical parameters of the boundary layer aerosol at Tinfou, Morocco, during SAMUM 2006, Tellus B, 61, 32–50, https://doi.org/10.1111/j.1600-0889.2008.00385.x, 2009.

Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Syst., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.

Kirchstetter, T. W., Novakov, T., and Hobbs, P. V.: Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon, J. Geophys. Res.-Atmos., 109, D21208, https://doi.org/10.1029/2004JD004999, 2004.

Klose, M., Jorba, O., Gonçalves Ageitos, M., Escribano, J., Dawson, M. L., Obiso, V., Di Tomaso, E., Basart, S., Montané Pinto, G., Macchia, F., Ginoux, P., Guerschman, J., Prigent, C., Huang,

Y., Kok, J. F., Miller, R. L., and Pérez García-Pando, C.: Mineral dust cycle in the Multiscale Online Nonhydrostatic Atmosphere Chemistry model (MONARCH) Version 2.0, Geosci. Model Dev., 14, 6403–6444, https://doi.org/10.5194/gmd-14-6403-2021, 2021.

Koch, D., Jacob, D., Tegen, I., Rind, D., and Chin, M.: Tropospheric sulfur simulation and sulfate direct radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res.-Atmos., 104, 23799–23822, https://doi.org/10.1029/1999JD900248, 1999.

Kok, J. F.: A scaling theory for the size distribution of emitted dust aerosols suggests climate models underestimate the size of the global dust cycle, P. Natl. Acad. Sci. USA, 108, 1016–1021, https://doi.org/10.1073/pnas.1014798108, 2011.

Kok, J. F., Ridley, D. A., Zhou, Q., Miller, R. L., Zhao, C., Heald, C. L., Ward, D. S., Albani, S., and Haustein, K.: Smaller desert dust cooling effect estimated from analysis of dust size and abundance, Nat. Geosci., 10, 274–278, https://doi.org/10.1038/ngeo2912, 2017.

Kok, J. F., Adebiyi, A. A., Albani, S., Balkanski, Y., Checa-Garcia, R., Chin, M., Colarco, P. R., Hamilton, D. S., Huang, Y., Ito, A., Klose, M., Leung, D. M., Li, L., Mahowald, N. M., Miller, R. L., Obiso, V., Pérez García-Pando, C., Rocha-Lima, A., Wan, J. S., and Whicker, C. A.: Improved representation of the global dust cycle using observational constraints on dust properties and abundance, Atmos. Chem. Phys., 21, 8127–8167, https://doi.org/10.5194/acp-21-8127-2021, 2021.

Li, J., Carlson, B. E., Dubovik, O., and Lacis, A. A.: Recent trends in aerosol optical properties derived from AERONET measurements, Atmos. Chem. Phys., 14, 12271–12289, https://doi.org/10.5194/acp-14-12271-2014, 2014.

Li, L., Mahowald, N. M., Miller, R. L., Pérez García-Pando, C., Klose, M., Hamilton, D. S., Gonçalves Ageitos, M., Ginoux, P., Balkanski, Y., Green, R. O., Kalashnikova, O., Kok, J. F., Obiso, V., Paynter, D., and Thompson, D. R.: Quantifying the range of the dust direct radiative effect due to source mineralogy uncertainty, Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, 2021.

Liu, Y. and Daum, P. H.: Relationship of refractive index to mass density and self-consistency of mixing rules for multicomponent mixtures like ambient aerosols, J. Aerosol Sci., 39, 974–986, https://doi.org/10.1016/j.jaerosci.2008.06.006, 2008.

Longtin, D. R., Shettle, E. P., Hummel, J. R., and Pryce, J. D.: A wind dependent desert aerosol model: Radiative properties, Tech. Rep. AFGL-TR-88-0112, Air Force Geophysics Laboratory, Hanscom AFB, MA, USA, 114 pp., https://apps.dtic.mil/sti/pdfs/ADA201951.pdf (last access: 3 March 2024), 1988.

Markel, V. A.: Introduction to the Maxwell Garnett approximation: tutorial, J. Opt. Soc. Am. A, 33, 1244–1256, https://doi.org/10.1364/JOSAA.33.001244, 2016.

Meloni, D., Di Sarra, A., Di Iorio, T., and Fiocco, G.: Influence of the vertical profile of Saharan dust on the visible direct radiative forcing, J. Quant. Spectrosc. Ra, 93, 397–413, https://doi.org/10.1016/j.jqsrt.2004.08.035, 2005.

Meng, Z., Yang, P., Kattawar, G. W., Bi, L., Liou, K. N., and Laszlo, I.: Single-scattering properties of tri-axial ellipsoidal mineral dust aerosols: A database for application to radiative transfer calculations, J. Aerosol Sci., 41, 501–512, https://doi.org/10.1016/j.jaerosci.2010.02.008, 2010.
Miller, R. L., Tegen, I., and Perlwitz, J.: Surface radiative forcing by soil dust aerosols and the hydrologic cycle, J. Geophys. Res.-Atmos., 109, D04203, https://doi.org/10.1029/2003JD004085, 2004.

Miller, R. L., Cakmur, R. V., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Prigent, C., Ruedy, R., Schmidt, G. A., and Tegen, I.: Mineral dust aerosols in the NASA Goddard Institute for Space Sciences ModelE atmospheric general circulation model, J. Geophys. Res.-Atmos., 111, D06208, https://doi.org/10.1029/2005JD005796, 2006.

Miller, R. L., Knippertz, P., Pérez García-Pando, C., Perlwitz, J. P., and Tegen, I.: Impact of Dust Radiative Forcing upon Climate, in: Mineral Dust: a Key Player in the Earth System, edited by: Knippertz, P. and Stuut, J.-B. W., Chap. 13, 327–357, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-8978-3_13, 2014.

Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Kelley, M., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: CMIP6 historical simulations (1850–2014) with GISS-E2.1, J. Adv. Model. Earth Syst., 13, e2019MS002034, https://doi.org/10.1029/2019MS002034, 2021.

Mishchenko, M. I., Travis, L. D., and Lacis, A. A.: Scattering, Absorption, and Emission of Light by Small Particles, 462 pp., Cambridge University Press, ISBN 9780521782524, 2002.

Moosmüller, H., Engelbrecht, J. P., Skiba, M., Frey, G., Chakrabarty, R. K., and Arnott, W. P.: Single scattering albedo of fine mineral dust aerosols controlled by iron concentration, J. Geophys. Res.-Atmos., 117, D11210, https://doi.org/10.1029/2011JD016909, 2012.

NASA Center for Climate Simulation: Model output files, as a stand-alone product, from Obiso et al. (2024), NASA Center for Climate Simulation [data set], https://portal.nccs.nasa.gov/datashare/giss-publish/pub/Obiso_ACP_2024 (last access: 12 March 2024), 2024.

Nazarenko, L. S., Tausnev, N., Russell, G. L., Rind, D., Miller, R. L., Schmidt, G. A., Bauer, S. E., Kelley, M., Ruedy, R., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Romanou, A., Shindell, D. T., Sun, S., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: Future climate change under SSP emission scenarios with GISS-E2.1, J. Adv. Model. Earth Syst., 14, e2021MS002871, https://doi.org/10.1029/2021MS002871, 2022.

Noh, Y. M., Lee, K., Kim, K., Shin, S.-K., Müller, D., and Shin, D. H.: Influence of the vertical absorption profile of mixed Asian dust plumes on aerosol direct radiative forcing over East Asia, Atmos. Environ., 138, 191–204, https://doi.org/10.1016/j.atmosenv.2016.04.044, 2016.

Obiso, V., Gonçalves Ageitos, M., Pérez García-Pando, C., Perlwitz, J. P., Schuster, G. L., Bauer, S. E., Di Biagio, C., Formenti, P., Tsigaridis, K., and Miller, R. L.: Observationally constrained regional variations of shortwave absorption by iron oxides emphasize the cooling effect of dust, Version v1, Zenodo [data set/code], https://doi.org/10.5281/zenodo.10808381, 2024.

Panta, A., Kandler, K., Alastuey, A., González-Flórez, C., González-Romero, A., Klose, M., Querol, X., Reche, C., Yus-Díez, J., and Pérez García-Pando, C.: Insights into the single-particle composition, size, mixing state, and aspect ratio of freshly emitted mineral dust from field measurements in the Moroccan Sahara using electron microscopy, Atmos. Chem. Phys., 23, 3861–3885, https://doi.org/10.5194/acp-23-3861-2023, 2023.

Patterson, E. M., Gillette, D. A., and Stockton, B. H.: Complex index of refraction between 300 and 700 nm for Saharan aerosols, J. Geophys. Res., 82, 3153–3160, https://doi.org/10.1029/JC082i021p03153, 1977.

Pausata, F. S. R., Messori, G., and Zhang, Q.: Impacts of dust reduction on the northward expansion of the African monsoon during the Green Sahara period, Earth Planet. Sci. Lett., 434, 298–307, https://doi.org/10.1016/j.epsl.2015.11.049, 2016.

Pérez, C., Nickovic, S., Pejanovic, G., Baldasano, J. M., and Özsoy, E.: Interactive dust-radiation modeling: A step to improve weather forecasts, J. Geophys. Res.-Atmos., 111, D16206, https://doi.org/10.1029/2005JD006717, 2006.

Pérez García-Pando, C., Miller, R. L., Perlwitz, J. P., Rodríguez, S., and Prospero, J. M.: Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory, Geophys. Res. Lett., 43, 10520–10529, https://doi.org/10.1002/2016GL069873, 2016.

Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 1: Representing key processes, Atmos. Chem. Phys., 15, 11593–11627, https://doi.org/10.5194/acp-15-11593-2015, 2015a.

Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations, Atmos. Chem. Phys., 15, 11629–11652, https://doi.org/10.5194/acp-15-11629-2015, 2015b.

Querry, M. R.: Optical constants, Tech. Rep. CRDC-CR-85034, University of Missouri, Kansas City, MO, USA, 413 pp., https://apps.dtic.mil/sti/pdfs/ADA158623.pdf (last access: 3 March 2024), 1985.

Ridley, D. A., Heald, C. L., Kok, J. F., and Zhao, C.: An observationally constrained estimate of global dust aerosol optical depth, Atmos. Chem. Phys., 16, 15097–15117, https://doi.org/10.5194/acp-16-15097-2016, 2016.

Ryder, C. L., Highwood, E. J., Lai, T. M., Sodemann, H., and Marsham, J. H.: Impact of atmospheric transport on the evolution of microphysical and optical properties of Saharan dust, Geophys. Res. Lett., 40, 2433–2438, https://doi.org/10.1002/grl.50482, 2013a.

Ryder, C. L., Highwood, E. J., Rosenberg, P. D., Trembath, J., Brooke, J. K., Bart, M., Dean, A., Crosier, J., Dorsey, J., Brindley, H., Banks, J., Marsham, J. H., McQuaid, J. B., Sodemann, H., and Washington, R.: Optical properties of Saharan dust aerosol and contribution from the coarse mode as measured during the
Miller, R. L., Tegen, I., and Perlwitz, J.: Surface radiative forcing by soil dust aerosols and the hydrologic cycle, J. Geophys. Res.-Atmos., 109, D04203, https://doi.org/10.1029/2003JD004085, 2004.

Miller, R. L., Cakmur, R. V., Perlwitz, J., Geogdzhayev, I. V., Ginoux, P., Koch, D., Kohfeld, K. E., Prigent, C., Ruedy, R., Schmidt, G. A., and Tegen, I.: Mineral dust aerosols in the NASA Goddard Institute for Space Sciences ModelE atmospheric general circulation model, J. Geophys. Res.-Atmos., 111, D06208, https://doi.org/10.1029/2005JD005796, 2006.

Miller, R. L., Knippertz, P., Pérez García-Pando, C., Perlwitz, J. P., and Tegen, I.: Impact of Dust Radiative Forcing upon Climate, in: Mineral Dust: a Key Player in the Earth System, edited by: Knippertz, P. and Stuut, J.-B. W., Chap. 13, 327–357, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-8978-3_13, 2014.

Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Kelley, M., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: CMIP6 historical simulations (1850–2014) with GISS-E2.1, J. Adv. Model. Earth Syst., 13, e2019MS002034, https://doi.org/10.1029/2019MS002034, 2021.

Mishchenko, M. I., Travis, L. D., and Lacis, A. A.: Scattering, Absorption, and Emission of Light by Small Particles, 462 pp., Cambridge University Press, ISBN 9780521782524, 2002.

Moosmüller, H., Engelbrecht, J. P., Skiba, M., Frey, G., Chakrabarty, R. K., and Arnott, W. P.: Single scattering albedo of fine mineral dust aerosols controlled by iron concentration, J. Geophys. Res.-Atmos., 117, D11210, https://doi.org/10.1029/2011JD016909, 2012.

NASA Center for Climate Simulation: Model output files, as a stand-alone product, from Obiso et al. (2024), NASA Center for Climate Simulation [data set], https://portal.nccs.nasa.gov/datashare/giss-publish/pub/Obiso_ACP_2024 (last access: 12 March 2024), 2024.

Nazarenko, L. S., Tausnev, N., Russell, G. L., Rind, D., Miller, R. L., Schmidt, G. A., Bauer, S. E., Kelley, M., Ruedy, R., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Romanou, A., Shindell, D. T., Sun, S., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: Future climate change under SSP emission scenarios with GISS-E2.1, J. Adv. Model. Earth Syst., 14, e2021MS002871, https://doi.org/10.1029/2021MS002871, 2022.

Noh, Y. M., Lee, K., Kim, K., Shin, S.-K., Müller, D., and Shin, D. H.: Influence of the vertical absorption profile of mixed Asian dust plumes on aerosol direct radiative forcing over East Asia, Atmos. Environ., 138, 191–204, https://doi.org/10.1016/j.atmosenv.2016.04.044, 2016.

Obiso, V., Gonçalves Ageitos, M., Pérez García-Pando, C., Perlwitz, J. P., Schuster, G. L., Bauer, S. E., Di Biagio, C., Formenti, P., Tsigaridis, K., and Miller, R. L.: Observationally constrained regional variations of shortwave absorption by iron oxides emphasize the cooling effect of dust, Version v1, Zenodo [data set/code], https://doi.org/10.5281/zenodo.10808381, 2024.

Panta, A., Kandler, K., Alastuey, A., González-Flórez, C., González-Romero, A., Klose, M., Querol, X., Reche, C., Yus-Díez, J., and Pérez García-Pando, C.: Insights into the single-particle composition, size, mixing state, and aspect ratio of freshly emitted mineral dust from field measurements in the Moroccan Sahara using electron microscopy, Atmos. Chem. Phys., 23, 3861–3885, https://doi.org/10.5194/acp-23-3861-2023, 2023.

Patterson, E. M., Gillette, D. A., and Stockton, B. H.: Complex index of refraction between 300 and 700 nm for Saharan aerosols, J. Geophys. Res., 82, 3153–3160, https://doi.org/10.1029/JC082i021p03153, 1977.

Pausata, F. S. R., Messori, G., and Zhang, Q.: Impacts of dust reduction on the northward expansion of the African monsoon during the Green Sahara period, Earth Planet. Sci. Lett., 434, 298–307, https://doi.org/10.1016/j.epsl.2015.11.049, 2016.

Pérez, C., Nickovic, S., Pejanovic, G., Baldasano, J. M., and Özsoy, E.: Interactive dust-radiation modeling: A step to improve weather forecasts, J. Geophys. Res.-Atmos., 111, D16206, https://doi.org/10.1029/2005JD006717, 2006.

Pérez García-Pando, C., Miller, R. L., Perlwitz, J. P., Rodríguez, S., and Prospero, J. M.: Predicting the mineral composition of dust aerosols: Insights from elemental composition measured at the Izaña Observatory, Geophys. Res. Lett., 43, 10520–10529, https://doi.org/10.1002/2016GL069873, 2016.

Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 1: Representing key processes, Atmos. Chem. Phys., 15, 11593–11627, https://doi.org/10.5194/acp-15-11593-2015, 2015a.

Perlwitz, J. P., Pérez García-Pando, C., and Miller, R. L.: Predicting the mineral composition of dust aerosols – Part 2: Model evaluation and identification of key processes with observations, Atmos. Chem. Phys., 15, 11629–11652, https://doi.org/10.5194/acp-15-11629-2015, 2015b.

Querry, M. R.: Optical constants, Tech. Rep. CRDC-CR-85034, University of Missouri, Kansas City, MO, USA, 413 pp., https://apps.dtic.mil/sti/pdfs/ADA158623.pdf (last access: 3 March 2024), 1985.

Ridley, D. A., Heald, C. L., Kok, J. F., and Zhao, C.: An observationally constrained estimate of global dust aerosol optical depth, Atmos. Chem. Phys., 16, 15097–15117, https://doi.org/10.5194/acp-16-15097-2016, 2016.

Ryder, C. L., Highwood, E. J., Lai, T. M., Sodemann, H., and Marsham, J. H.: Impact of atmospheric transport on the evolution of microphysical and optical properties of Saharan dust, Geophys. Res. Lett., 40, 2433–2438, https://doi.org/10.1002/grl.50482, 2013a.

Ryder, C. L., Highwood, E. J., Rosenberg, P. D., Trembath, J., Brooke, J. K., Bart, M., Dean, A., Crosier, J., Dorsey, J., Brindley, H., Banks, J., Marsham, J. H., McQuaid, J. B., Sodemann, H., and Washington, R.: Optical properties of Saharan dust aerosol and contribution from the coarse mode as measured during the
Fennec 2011 aircraft campaign, Atmos. Chem. Phys., 13, 303–325, https://doi.org/10.5194/acp-13-303-2013, 2013b.

Ryder, C. L., Highwood, E. J., Walser, A., Seibert, P., Philipp, A., and Weinzierl, B.: Coarse and giant particles are ubiquitous in Saharan dust export regions and are radiatively significant over the Sahara, Atmos. Chem. Phys., 19, 15353–15376, https://doi.org/10.5194/acp-19-15353-2019, 2019.

Samset, B. H., Stjern, C. W., Andrews, E., Kahn, R. A., Myhre, G., Schulz, M., and Schuster, G. L.: Aerosol absorption: Progress towards global and regional constraints, Curr. Clim. Change Rep., 4, 65–83, https://doi.org/10.1007/s40641-018-0091-4, 2018.

Scanza, R. A., Mahowald, N., Ghan, S., Zender, C. S., Kok, J. F., Liu, X., Zhang, Y., and Albani, S.: Modeling dust as component minerals in the Community Atmosphere Model: development of framework and impact on radiative forcing, Atmos. Chem. Phys., 15, 537–561, https://doi.org/10.5194/acp-15-537-2015, 2015.

Scheuvens, D. and Kandler, K.: On Composition, Morphology, and Size Distribution of Airborne Mineral Dust, in: Mineral Dust: A Key Player in the Earth System, edited by: Knippertz, P. and Stuut, J.-B. W., Chap. 2, 15–49, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-8978-3_2, 2014.

Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V., Chen, Y.-H., Cheng, Y., Clune, T. L., Del Genio, A., de Fainchtein, R., Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A. A., LeGrande, A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S., Miller, R. L., Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J., Putman, W. M., Rind, D., Romanou, A., Sato, M., Shindell, D. T., Sun, S., Syed, R. A., Tausnev, N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao, M.-S., and Zhang, J.: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive, J. Adv. Model. Earth Syst., 6, 141–184, https://doi.org/10.1002/2013MS000265, 2014.

Schuster, G. L., Dubovik, O., and Arola, A.: Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species, Atmos. Chem. Phys., 16, 1565–1585, https://doi.org/10.5194/acp-16-1565-2016, 2016.

Sinyuk, A., Torres, O., and Dubovik, O.: Combined use of satellite and surface observations to infer the imaginary part of refractive index of Saharan dust, Geophys. Res. Lett., 30, 1081, https://doi.org/10.1029/2002GL016189, 2003.

Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020.

Sokolik, I. N. and Toon, O. B.: Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths, J. Geophys. Res.-Atmos., 104, 9423–9444, https://doi.org/10.1029/1998JD200048, 1999.

Strong, J. D. O., Vecchi, G. A., and Ginoux, P.: The response of the tropical Atlantic and west African climate to Saharan dust in a fully coupled GCM, J. Climate, 28, 7071–7092, https://doi.org/10.1175/JCLI-D-14-00797.1, 2015.

Tegen, I. and Fung, I.: Modeling of mineral dust in the atmosphere: Sources, transport, and optical thickness, J. Geophys. Res.-Atmos., 99, 22897–22914, https://doi.org/10.1029/94JD01928, 1994.

Thekaekara, M. P.: Solar Energy Monitor in Space (SEMIS), Symposium on Solar Radiation Measurements and Instrumentation, Smithsonian Institution, Radiation Biology Laboratory, Rockville, MD, USA, 13–15 November 1973, https://ntrs.nasa.gov/api/citations/19760011136/downloads/19760011136.pdf (last access: 3 March 2024), 1974.

Usher, C. R., Michel, A. E., and Grassian, V. H.: Reactions on mineral dust, Chem. Rev., 103, 4883–4940, https://doi.org/10.1021/cr020657y, 2003.

van der Does, M., Korte, L. F., Munday, C. I., Brummer, G.-J. A., and Stuut, J.-B. W.: Particle size traces modern Saharan dust transport and deposition across the equatorial North Atlantic, Atmos. Chem. Phys., 16, 13697–13710, https://doi.org/10.5194/acp-16-13697-2016, 2016.

Volz, F. E.: Infrared optical constants of ammonium sulfate, Sahara dust, volcanic pumice and flyash, Appl. Opt., 12, 564–568, https://doi.org/10.1364/AO.12.000564, 1973.

Yoshioka, M., Mahowald, N. M., Conley, A. J., Collins, W. D., Fillmore, D. W., Zender, C. S., and Coleman, D. B.: Impact of desert dust radiative forcing on Sahel precipitation: Relative importance of dust compared to sea surface temperature variations, vegetation changes, and greenhouse gas warming, J. Climate, 20, 1445–1467, https://doi.org/10.1175/JCLI4056.1, 2007.

Zhang, X. L., Wu, G. J., Zhang, C. L., Xu, T. L., and Zhou, Q. Q.: What is the real role of iron oxides in the optical properties of dust aerosols?, Atmos. Chem. Phys., 15, 12159–12177, https://doi.org/10.5194/acp-15-12159-2015, 2015.
Fennec 2011 aircraft campaign, Atmos. Chem. Phys., 13, 303–325, https://doi.org/10.5194/acp-13-303-2013, 2013b.

Ryder, C. L., Highwood, E. J., Walser, A., Seibert, P., Philipp, A., and Weinzierl, B.: Coarse and giant particles are ubiquitous in Saharan dust export regions and are radiatively significant over the Sahara, Atmos. Chem. Phys., 19, 15353–15376, https://doi.org/10.5194/acp-19-15353-2019, 2019.

Samset, B. H., Stjern, C. W., Andrews, E., Kahn, R. A., Myhre, G., Schulz, M., and Schuster, G. L.: Aerosol absorption: Progress towards global and regional constraints, Curr. Clim. Change Rep., 4, 65–83, https://doi.org/10.1007/s40641-018-0091-4, 2018.

Scanza, R. A., Mahowald, N., Ghan, S., Zender, C. S., Kok, J. F., Liu, X., Zhang, Y., and Albani, S.: Modeling dust as component minerals in the Community Atmosphere Model: development of framework and impact on radiative forcing, Atmos. Chem. Phys., 15, 537–561, https://doi.org/10.5194/acp-15-537-2015, 2015.

Scheuvens, D. and Kandler, K.: On Composition, Morphology, and Size Distribution of Airborne Mineral Dust, in: Mineral Dust: A Key Player in the Earth System, edited by: Knippertz, P. and Stuut, J.-B. W., Chap. 2, 15–49, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-8978-3_2, 2014.

Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., Bleck, R., Canuto, V., Chen, Y.-H., Cheng, Y., Clune, T. L., Del Genio, A., de Fainchtein, R., Faluvegi, G., Hansen, J. E., Healy, R. J., Kiang, N. Y., Koch, D., Lacis, A. A., LeGrande, A. N., Lerner, J., Lo, K. K., Matthews, E. E., Menon, S., Miller, R. L., Oinas, V., Oloso, A. O., Perlwitz, J. P., Puma, M. J., Putman, W. M., Rind, D., Romanou, A., Sato, M., Shindell, D. T., Sun, S., Syed, R. A., Tausnev, N., Tsigaridis, K., Unger, N., Voulgarakis, A., Yao, M.-S., and Zhang, J.: Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive, J. Adv. Model. Earth Syst., 6, 141–184, https://doi.org/10.1002/2013MS000265, 2014.

Schuster, G. L., Dubovik, O., and Arola, A.: Remote sensing of soot carbon – Part 1: Distinguishing different absorbing aerosol species, Atmos. Chem. Phys., 16, 1565–1585, https://doi.org/10.5194/acp-16-1565-2016, 2016.

Sinyuk, A., Torres, O., and Dubovik, O.: Combined use of satellite and surface observations to infer the imaginary part of refractive index of Saharan dust, Geophys. Res. Lett., 30, 1081, https://doi.org/10.1029/2002GL016189, 2003.

Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020.

Sokolik, I. N. and Toon, O. B.: Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to IR wavelengths, J. Geophys. Res.-Atmos., 104, 9423–9444, https://doi.org/10.1029/1998JD200048, 1999.

Strong, J. D. O., Vecchi, G. A., and Ginoux, P.: The response of the tropical Atlantic and west African climate to Saharan dust in a fully coupled GCM, J. Climate, 28, 7071–7092, https://doi.org/10.1175/JCLI-D-14-00797.1, 2015.

Tegen, I. and Fung, I.: Modeling of mineral dust in the atmosphere: Sources, transport, and optical thickness, J. Geophys. Res.-Atmos., 99, 22897–22914, https://doi.org/10.1029/94JD01928, 1994.

Thekaekara, M. P.: Solar Energy Monitor in Space (SEMIS), Symposium on Solar Radiation Measurements and Instrumentation, Smithsonian Institution, Radiation Biology Laboratory, Rockville, MD, USA, 13–15 November 1973, https://ntrs.nasa.gov/api/citations/19760011136/downloads/19760011136.pdf (last access: 3 March 2024), 1974.

Usher, C. R., Michel, A. E., and Grassian, V. H.: Reactions on mineral dust, Chem. Rev., 103, 4883–4940, https://doi.org/10.1021/cr020657y, 2003.

van der Does, M., Korte, L. F., Munday, C. I., Brummer, G.-J. A., and Stuut, J.-B. W.: Particle size traces modern Saharan dust transport and deposition across the equatorial North Atlantic, Atmos. Chem. Phys., 16, 13697–13710, https://doi.org/10.5194/acp-16-13697-2016, 2016.

Volz, F. E.: Infrared optical constants of ammonium sulfate, Sahara dust, volcanic pumice and flyash, Appl. Opt., 12, 564–568, https://doi.org/10.1364/AO.12.000564, 1973.

Yoshioka, M., Mahowald, N. M., Conley, A. J., Collins, W. D., Fillmore, D. W., Zender, C. S., and Coleman, D. B.: Impact of desert dust radiative forcing on Sahel precipitation: Relative importance of dust compared to sea surface temperature variations, vegetation changes, and greenhouse gas warming, J. Climate, 20, 1445–1467, https://doi.org/10.1175/JCLI4056.1, 2007.

Zhang, X. L., Wu, G. J., Zhang, C. L., Xu, T. L., and Zhou, Q. Q.: What is the real role of iron oxides in the optical properties of dust aerosols?, Atmos. Chem. Phys., 15, 12159–12177, https://doi.org/10.5194/acp-15-12159-2015, 2015.