The large uncertainty in the mineral dust direct radiative effect (DRE) hinders projections of future climate change due to
anthropogenic activity. Resolving modeled dust mineral speciation allows for spatially and temporally varying refractive indices consistent with dust aerosol composition. Here, for the first time, we quantify the range in dust
DRE at the top of the atmosphere (TOA) due to current uncertainties in the
surface soil mineralogical content using a dust mineral-resolving climate
model. We propagate observed uncertainties in soil mineral abundances from
two soil mineralogy atlases along with the optical properties of each
mineral into the DRE and compare the resultant range with other sources of
uncertainty across six climate models. The shortwave DRE responds region-specifically to the dust burden depending on the mineral speciation
and underlying shortwave surface albedo: positively when the regionally averaged annual surface albedo is larger than 0.28 and negatively
otherwise. Among all minerals examined, the shortwave TOA DRE and single
scattering albedo at the 0.44–0.63
Mineral dust emitted from erodible land surfaces has myriad impacts on the Earth system and human society by perturbing the radiation budget (Tegen and Fung, 1994; Sokolik and Toon, 1996), interacting with cloud processes (Rosenfeld et al., 2001; DeMott et al., 2003; Mahowald and Kiehl, 2003; Atkinson et al., 2013), affecting ocean and land biogeochemical cycles (Swap et al., 1992; Jickells et al., 2005; Mahowald et al., 2017), causing respiratory and cardiovascular disease (Meng and Lu, 2007), contributing to other ailments like meningitis (Pérez García-Pando et al., 2014), and modifying atmospheric chemistry (Dentener et al., 1996; Martin et al., 2003). Dust aerosol (here defined as soil particles suspended in the atmosphere) perturbs the radiative energy balance directly by scattering and absorbing shortwave and longwave radiation, known as the aerosol–radiation interaction (Boucher et al., 2013), and indirectly by changing the cloud albedo and lifetime by acting as cloud condensation nuclei (CCN) and ice nuclei (IN) (Nenes et al., 2014) and by increasing diabatic heating in the atmosphere and evaporating cloud (Hansen et al., 1997; Bollasina et al., 2008; Jacobson, 2012), known as the aerosol–cloud interaction (Boucher et al., 2013). Through interactions with radiation and cloud, dust can feed back upon meteorology in the planetary boundary layer, the large-scale circulation, and the energy, water, and carbon cycles (Miller and Tegen, 1999; Perlwitz et al., 2001; Pérez et al., 2006; Solmon et al., 2008; Lau et al., 2009; Mahowald et al., 2011; Shao et al., 2011).
At the global scale, mineral dust is estimated to warm the atmosphere and
cool the Earth's surface in the shortwave spectral range and induces opposite effects in the longwave spectral range
(Sokolik and Toon, 1996; Kok et al.,
2017). However, these estimates are currently highly uncertain. A recent
review which synthesized data on dust abundance, optical properties, and
size distribution estimated that at the top of the atmosphere (TOA) the shortwave, longwave, and net direct radiative effects (DREs) of dust range between [
Much of the DRE uncertainty can be attributed to uncertainties in the dust
aerosol composition and its evolution during transport
(Hand et al., 2004; Baker
and Croot, 2010; Shao et al., 2011). Most of the abovementioned impacts of
dust aerosols on climate are closely related to the composition of minerals
in dust particles: (1) the dust DRE in some longwave bands depends on quartz
or calcite, and across many shortwave bands dust strongly depends on the
iron-oxide content and its mixing state with other minerals (Sokolik et al., 1998; Sokolik and Toon, 1999); (2) chemical
reactions occurring on the dust particle surface depend on dust minerals
(particularly, calcite) and chemical composition (Dentener et al.,
1996; Hanisch and Crowley, 2003; Kumar et al., 2014); (3) the liquid water
uptake rate and ice nucleation ability of dust is determined by its
hygroscopicity, size, and shape and is thus related to the physio-chemical properties of the minerals (e.g., feldspar) (Karydis et al., 2011; Atkinson et al., 2013); (4) after atmospheric processing, iron-bearing minerals (e.g., hematite, goethite, illite, and hydroxide) contained in dust aerosols contribute a large fraction of the atmospheric bioavailable iron flux to remote ocean regions. This can cause dust–iron
fertilization to occur and thus influences ocean marine primary productivity
and biomass accumulation (Meskhidze
et al., 2003; Journet et al., 2008; Schroth et al., 2009); and (5) phosphorus-bearing minerals are important for marine and terrestrial biogeochemistry effects, for example, the North Pacific Ocean and Amazon rainforest (Swap et
al., 1992; Okin et al., 2004; Letelier et al., 2019). Currently, the soil
mineral compositions required by dust-speciated models are provided by either Claquin et al. (1999) (C1999 hereafter) – with additional
extrapolation to other soil types (three new soil units and soil
phosphorous) proposed by Nickovic et al. (2012) – or Journet et al. (2014) (J2014 hereafter). The mineral composition of clay-
(between 0 and 2
A technique to model dust aerosol optical properties, accounting for their physicochemical characteristics, was proposed by Sokolik and Toon (1999). The authors demonstrated, via offline radiative transfer calculations, that the DRE by mineral dust was highly dependent on the representation of its mineral-specific absorption properties. They suggested that internal mixing of iron oxides (hematite and goethite) with less absorptive minerals enhances the absorption of shortwave radiation and can reverse the sign from a negative (cooling) to positive (warming) DRE at the TOA. Later studies (Alfaro et al., 2004; Lafon et al., 2006; Balkanski et al., 2007; Formenti et al., 2014; Li and Sokolik, 2018) confirmed the importance of iron oxides to the shortwave dust DRE, particularly near dust source areas, even when they are mixed with particles that are also strongly absorbing (e.g., black carbon) (Alfaro et al., 2004). Two main types of iron-oxide minerals are found in soils: hematite and goethite (Journet et al., 2014). Iron in both minerals is generally to be found in a (III) oxidation state, but they have distinct optical properties in the shortwave spectrum: hematite exhibits a more pronounced spectral absorption and has a comparatively stronger ability to absorb shortwave radiation than goethite. Consequently, the calculated estimates of the single scattering albedo (SSA) for hematite– and goethite–clay aggregates, with the same size distribution, are significantly different (Lafon et al., 2006). Iron oxides represent 2.4 %–4.5 % of the total dust mass (Formenti et al., 2008), although a slightly larger range (0.7 %–5.8 %) of iron oxides in dust was reported in a more recent study (Di Biagio et al., 2019). Northern African samples exhibited a dominance of goethite over hematite (percentage mass content of iron oxides: 52 %–78 % versus 22 %–48 %, respectively) (Formenti et al., 2014). The partitioning of these two iron oxides is thus necessary to accurately estimate the DRE, because of the difference in their optical properties and a strong regional variation in their soil content (Lafon et al., 2006; Formenti et al., 2014; Di Biagio et al., 2019).
Because of the importance of physio-chemical characteristics of different dust minerals to estimating the dust DRE at shortwave bands, one focus for dust model development is on improving the representation of dust minerals (Scanza et al., 2015; Perlwitz et al., 2015a) and their coupling with radiative transfer processes using mineral-specific optical properties (Sokolik and Toon, 1999). Scanza et al. (2015) introduced eight minerals (illite, kaolinite, smectite, hematite, quartz, calcite, gypsum and feldspar) identified as climatically important by C1999 into the Community Atmosphere Model of version 4 (CAM4) and five minerals (illite, kaolinite, smectite, hematite, and a bulk remainder mineral) into version 5 (CAM5) based on C1999 (both CAM4 and CAM5 are embedded within the Community Earth System Model: CESM). Similarly, the eight minerals within CAM4 were included in the NASA Goddard Institute for Space Studies (GISS) Earth System ModelE2 (Perlwitz et al., 2015a). These previous studies exhibited the models' limited ability to match the available observations of mineral fractions and ratios. This mismatch can be primarily attributed to the inherent limitations and uncertainties in the surface soil mineralogy mapping (Perlwitz et al., 2015b; Scanza et al., 2015; Y. Zhang et al., 2015) along with uncertainties in the models' emission, transport, and deposition. Perlwitz et al. (2015a, b) and Pérez García-Pando et al. (2016) show that despite these uncertainties, reconstructing the emitted mineral aggregates from the disturbed soil mineralogy atlases based upon brittle fragmentation theory (Kok, 2011) and additional empirical constraints better reproduces size-resolved mineralogy and elemental composition observations. Scanza et al. (2015) show that CAM underestimates the observed DRE efficiency near northern Africa. This underestimate could be attributed to difficulty of DRE retrieval along with the large uncertainty in hematite in the C1999 soil mineralogy atlas, which includes a range of iron-oxide abundance (0.0 %–7.0 % by weight).
Here, for the first time, we undertake a detailed and systematic study of the sensitivity of the dust DRE resulting from current uncertainties in soil mineral composition. We compare the sensitivity of DRE to uncertainties in soil mineral composition to those from other sources, such as the range in measured complex refractive indices for dust minerals and dust burdens. In this study we focus on composition of dust and do not examine other sources of uncertainty, including the mineral vertical and size distributions, cloud processes, surface albedo (Liao and Seinfeld, 1998; Li and Sokolik, 2018), and mixing and interaction of dust with pollution aerosols (Li and Shao, 2009; Huang et al., 2010; Tobo et al., 2010). In addition to C1999, as used in previous studies (Scanza et al., 2015; Perlwitz et al., 2015a), we incorporate results using the updated J2014 soil mineralogical atlas, which separates iron oxides into hematite and goethite. We focus on the sensitivity studies within only one model (CAM5) and then compare results to four other models, CAM6, GISS ModelE2, the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH; previously known as the Non-hydrostatic multiscale model (NMMB) / Barcelona Supercomputing Center (BSC) – chemical transport model (CTM), and Geophysical Fluid Dynamics Laboratory (GFDL) (see Sect. 2.2 for model descriptions), to examine both parametric and structural uncertainty sources.
Two datasets currently exist that can be used to describe the size-resolved mineralogical composition for potential dust sources around the globe. For both datasets, the soil mineralogical composition was inferred based on the hypothesis that the surface mineralogy depends on the size distribution and physio-chemical properties (e.g., appearance color) of the soil.
The first dataset was originally created by Claquin et al. (1999), who
compiled measurements linking soil type and mineral composition from the
available literature. This dataset contains information regarding an average
relative abundance of eight minerals (mean mineralogy table, MMT) in the
clay-sized and silt-sized categories for 28 soil types that are considered
wind erodible. Illite, kaolinite, and smectite (only present in the
clay-sized category) frequently dominate over calcite and quartz among
different soil types. In the silt-sized category, the dominant minerals are quartz and/or feldspar instead of hematite, gypsum, and calcite, except for
salt flats where calcite is dominant. Also included in C1999 is the standard
deviation of the mean mineral content for the 28 soil types. This study
extends hematite to the clay-sized category by assigning the same mass fraction as it is in the silt-sized category and subtracting the same mass fraction from illite, consistent with recent studies (Balkanski
et al., 2007; Nickovic et al., 2012; Scanza et al., 2015; Perlwitz et al.,
2015a). The global atlas of arid surface mineralogy is created following Claquin et al. (1999) and Scanza et al. (2015) via the FAO/UNESCO WGB84 at
5
The other soil mineral dataset presented in Journet et al. (2014) (J2014) is
an extension of C1999. It includes four additional minerals, one
(vermiculite) in the clay-sized soil category, two (mica and goethite) in
the silt-sized category, and one (chlorite) in both categories. The mean
mineralogical content was assigned to different soil units, as classified by
FAO (FAO-UNESCO, 1974: 135 soil units; FAO,
1990: 193 soil units). The standard deviation is also provided, but only for a limited number of soil units. Compared to C1999, this more recent
compilation is not confined to the soil units that are located in arid and
semi-arid areas and benefits from a use of more extensive literature. Nevertheless, there is a number of soil units lacking mineralogical
information (the mean mineralogical content and in particular the associated
standard deviation), especially for the silt-sized soil class where the
information is scarce. The mean mineralogical content for these missing soil
units was thus characterized through assumptions rather than
observation-derived data. For iron oxides, which are relevant to the DRE of
dust, data are present for only 23 % of the reported soil units (
Model sensitivity analysis in this paper focuses on results from CESM. To assess a spread in the sensitivity of DRE to representations of dust cycles, we compare CESM to three other models (GISS ModelE2, MONARCH, and GFDL), as described in this section. We employ three versions of CAM in CESM following Scanza et al. (2015): the Bulk Aerosol Model (BAM) in the CAM4 (Neale et al., 2013) and the Modal Aerosol Model (MAM) in CAM5 (Hurrell et al., 2013) and CAM6 (Danabasoglu et al., 2020). In these CAM versions, the DRE is calculated by speciating dust into minerals (Sect. 2.2.1). We construct perturbation sensitivity analyses with CAM5 only (Sect. 2.3.1), as the DRE in CAM4 is insensitive to dust minerals (Sect. “Uncertainty due to dust minerals, burden, and imaginary complex refractive index”) and the high-resolution CAM6 model is computationally expensive (a factor of 10 times more core hours is required in CAM6 compared to CAM5, particularly considering the large number of simulations needed.
Mineral composition is also calculated using an updated version of the NASA ModelE2.1 (Schmidt et al., 2014) (ModelE2 hereafter; Sect. 2.2.2) as described in Perlwitz et al. (2015a, b) and Pérez García-Pando et al. (2016). Since the relation of the DRE to simulated minerals in this model is still under development, we apply a statistical relationship between simulated minerals and shortwave dust DRE in CAM5 to predict the shortwave DRE (Sect. 2.3.4) based on simulated minerals in GISS ModelE2. The MONARCH (Sect. 2.2.3) and GFDL models (Sect. 2.2.4) do not include dust mineral speciation, so we use the DRE related to aerosol optical depth (AOD) for bulk dust (DOD) (Sect. 2.3.4).
Dust mineral speciation (illite, kaolinite, montmorillonite, hematite, quartz, calcite, feldspar, and gypsum) was incorporated for CAM4 (Scanza et al., 2015) and CAM5 (Scanza et al., 2015; Hamilton et al., 2019) using C1999. Here we add a new mineral tracer for goethite to CAM5 to use J2014 and adopt the incorporated CAM5 mineral species when using C1999. Recently, a new CAM6 model for CESM2 was released which was updated to an improved two-moment prognostic cloud microphysics, MG2 (Gettelman and Morrison, 2015), from MG (Morrison and Gettelman, 2008) used in CAM5. For this study, we incorporate the mineral speciation of CAM5, closely related to the Department of Energy model, the Energy Exascale Earth System Model (E3SM) (Liu et al., 2016; Lauritzen et al., 2018; Caldwell et al., 2019), into the CAM6 model. Each mineral was separately emitted, transported, and deposited in the model. Aerosols including dust in both CAM5 and CAM6 are subdivided into interstitial (within the clear air) and cloud-borne (within clouds) particles for a better representation of advection and deposition processes, as documented in Liu et al. (2012). In the atmosphere each mineral interacts with the shortwave and longwave radiation.
The dust emission, transport, and deposition are simulated by the Dust
Entrainment And Deposition model (DEAD, Zender et al.,
2003) which has been implemented in the land and atmosphere components of
the CESM and described in detail previously (Zender et al., 2003; Mahowald et al., 2006; Albani et al., 2014). The emission of dust
occurs within non-vegetated, dry soil regions and is initiated once a friction velocity threshold has been exceeded. The friction velocity
threshold is parameterized as a function of the soil state (e.g., soil moisture, snow cover, surface crust, vegetation cover) and near-surface meteorology (e.g., air density, horizontal wind speed). Vegetation tends to protect the soil from wind erosion by reducing the energy transfer of wind
momentum to the soil surface. This effect of vegetation on dust emissions is
represented via a linear dependence on the leaf area index (LAI)
(Mahowald et al., 2006). No dust emission
occurs within grid cells with the LAI exceeding 0.3 m
The default dust model utilizes a prescribed soil erodibility source
function (Ginoux et al., 2001) which associates dust emissions
with topographical depressions where abundant erodible sediment accumulates (Ginoux et al., 2001; Zender
et al., 2003; Mahowald et al., 2006). In this study, we use an updated
physical dust emission scheme developed by
Kok et al. (2014a), based on the brittle
fragmentation theory (Kok, 2011) which has been shown to improve
model–observation comparisons without the source function (Kok et al., 2014b). The emitted size
distribution of either bulk dust (sum of all minerals or non-speciated dust)
or minerals is assumed to be independent of the soil properties of the
source location and wind speeds (Albani
et al., 2014; Perlwitz et al., 2015a; Scanza et al., 2015) and currently
only considers the likely climatologically most relevant diameter range from 0.01 to 10
MAM mode size parameters in CAM5 and CAM6 by default. We reverted the coarse mode parameters in CAM6 to those in CAM5 in our CAM6 simulation.
Dust mineral species carried within each mode in CAM5 and CAM6 are internally mixed with each other and with other non-dust species (e.g., sea salt, sulfate, black carbon, primary and/or secondary organic matter) in the same mode under the homogenous assumption (the same proportions of each components in any individual aerosol particle) but externally mixed between the different modes (Liu et al., 2012, 2016). In comparison, all aerosol species are externally mixed in CAM4, but the optical properties for dust species (SSA, the extinction coefficient, and the asymmetry factor) are calculated offline using the MIEV0 software (Wiscombe, 1980) with a spherical shape assumption and prescribed aerosol size distribution independent of locations.
The radiative flux at each vertical model layer, at 19 (band centre range: 0.22–4.36
Real
CAM6 and CAM5(4) are configured with default horizontal resolutions
(longitude by latitude: 1.25
The TOA dust DRE under all-sky conditions, unless otherwise stated, is
calculated following Eq. (1) as the instantaneous difference of net fluxes
(
NASA GISS ModelE2 has a horizontal resolution of 2.5
Prognostic calculation of dust mineral emissions (Perlwitz et al., 2015a, b;
Pérez García-Pando et al., 2016) is done based upon the fractional
mass abundance of eight minerals within the soil, as derived from
measurements of wet-sieved soils by C1999. For particle diameters
Each mineral is transported separately within five size bins ranging from
clay to silt diameters (0.10–2.0, 2.0–4.0, 4.0–8.0, and 16–32
The MONARCH model developed at the BSC (e.g., Pérez et al., 2011; Badia et al., 2017) contains advanced chemistry and aerosol packages and is coupled online with the NMMB, which allows for running either global or high-resolution
(convection-permitting) regional simulations (Janjic et al., 2001;
Janjic and Gall, 2012). The dust module of MONARCH (Haustein
et al., 2012; Klose et al., 2021; Pérez et al., 2011) includes
different parameterizations of dust emission, including those from Marticorena and Bergametti (1995), Ginoux et al. (2001), Shao (2001, 2004), Shao et al. (2011), Kok et al. (2014a), and
Klose et al. (2014). The model simulations
performed for this study utilize the dust emission scheme from Ginoux et al. (2001) with some modifications described in Klose et al. (2021). The
model includes eight dust size transport bins ranging up to 20
The radiation scheme is RRTMG (Iacono et al., 2001, 2008). In the longwave, we assume refractive indices from the Optical Properties of Aerosols and Clouds (OPAC) dataset (Hess et al., 1998) and spherical particle shape. In the shortwave, we assume tri-axial ellipsoids as described by Kok et al. (2017), who used the dust single-scattering database of Meng et al. (2010) and size-dependent refractive indices based on a globally averaged mineralogical composition. The radiation flux is diagnosed twice, one with all aerosol species and the other one solely without dust aerosol to determine the DRE for bulk dust. While MONARCH does not calculate mineral speciation of dust, we include its DOD as a measure of uncertainty in comparison to DREs related to uncertainty in the soil mineral composition.
The model is run from 2007 to 2011 at a horizontal resolution of
1.0
The latest GFDL global climate model includes the fourth version of the coupled Climate Model (CM4) and Earth System Model (ESM4), with detailed descriptions provided by Held et al. (2019) and Dunne et al. (2020), respectively. In CM4 dust emission depends only on wind speeds with prescribed dust sources (Ginoux et al., 2001), while in ESM4 it depends also on soil water and ice, snow cover, leaf and stem area indices, and land use type, which are all dynamically calculated, except for land use (Evans et al., 2016). The dust size distribution at emission follows the brittle fragmentation theory of Kok (2011). The simulations are performed from 2010 to 2015 with observed sea surface temperature and sea ice (i.e., AMIP simulation; Taylor et al., 2000). Dust DRE is not calculated within this model, but the modeled DOD is used to assess the effect of cross-model differences.
A set of sensitivity studies, based primarily on CAM5, is conducted to characterize the range in DRE due to uncertainties in the soil mineralogical composition. To determine the uncertainty in soil mineralogy, we use two different approaches to estimate the mineral content of soils: the first is based on C1999 and the second is based on J2014. We consider the set of climatically important minerals identified in the soil compilations of C1999 and J2014, although other minerals may be important, especially in specific regions. However, optical analyses of aerosolized soil samples show that shortwave absorption varies most strongly with iron oxides like hematite and goethite (Moosmüller et al., 2012; Di Biagio et al., 2019), suggesting that other radiatively active minerals are mainly present in small concentrations.
We select simulations with soil mineralogy derived from the MMT of C1999 as the baseline (see Sect. 3.1 for the resultant hematite aerosol mass percentage). In addition to the mean, the MMT provides uncertainty ranges for each mineral and for each soil type, for which we calculate the 95 % confidence interval of the mineral fraction (Fig. 2). Hematite mass abundance is low, but in general, it has the largest relative uncertainty. Atlases containing the high- and low-bound minerals (high-bound mineral: upper limit of the 95% confidence interval of the abundance of a mineral in the corresponding category and soil type; the low-bound mineral is similarly defined) such as hematite, illite, and smectite are similarly created following C1999 using soil type to prescribe mineral fractions. When perturbing the amount of one mineral, we conserve emitted dust mass through an identical and opposite change in soil abundance of the dominant mineral (referred to as the offsetting mineral) within the same clay- or silt-sized category. Another criterion to select the offsetting mineral is that it should have a minimized impact on the simulated instantaneous TOA fluxes. For example, illite and kaolinite occupy the same clay-sized soil category (mass fraction: 0.39) in the calcaric soil type. In this case, we choose kaolinite as the offsetting mineral, because the DRE is less sensitive (measured by the relative change in the DRE over the relative change in the high-bound kaolinite aerosol content with respect to the base value) to this mineral than to illite in test simulations. Similarly to Scanza et al. (2015), we employ a nearest-neighbor algorithm to estimate mineral fractions of land mass not specified by the MMT of C1999 in avoid of “zero” dust emissions in these regions. The spatial distribution of uncertainties in the soil mineral abundance based on which we estimate the propagated error in the DRE calculation is discussed in Sect. 2.3.2.
Mean mineral percentage (C1999: colored filled dots; J2014: colored triangle) and associated uncertainty (error bars) in the clay-
In addition to C1999, we consider three scenarios based on J2014. One uses
the mean mineral fraction from J2014. The other two use low and high bounds
on iron oxides. We consider these bounds to be the average hematite and goethite mass fractions
Table 2 summarizes the experiments undertaken in this study. In the
simulations with unperturbed mineralogy (C1999 or J2014), emissions are
tuned following Albani et al. (2014) to yield a global mean
DOD of
List of experiments for the sensitivity test using CAMs
(CAM4, CAM5, and CAM6), ModelE2, MONARCH, and GFDL with speciated (indicated
by C1999 and J2014) and bulk dust. All the model results were processed onto
2.5
Comparison of simulated (the baseline case; see text for
details) dust surface concentration and deposition with observations. Also shown is the correlation in the log space (
Comparison of seasonally resolved DOD from the baseline simulation (blue) over 15 regions with that (brown) obtained in Ridley et al. (2016), who bias-corrected satellite-based retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Radiometer (MISR) using AERONET measurements and a model ensemble (see Ridley et al., 2016, for details). The shading area shows an example that the model greatly overestimated DOD compared to observations over some of the subregions. Error bars represent the standard deviation. For definition of the 15 regions see Fig. 1 of Ridley et al. (2016).
Dust optical properties are based upon Mie theory which idealizes particles as spheres. In contrast, AOD retrieved from sun photometers accounts for dust asphericity (Dubovik et al., 2002). To match modeled dust mass extinction efficiency with observations, we augment DOD globally by
To compare the uncertainty in the DRE from mineralogy to the other factors
whose uncertainties have been well quantified, we perturb the DOD and the
imaginary complex refractive index of the mineral. We do not compare the
resultant DRE uncertainty due to other error sources (see Appendix A), such as mixing and chemical reaction of dust with pollution aerosols (e.g., H
After undertaking the first set of sensitivity runs, it was found that the
calibration of DOD inadvertently double counted the mineral mass, resulting
in dust emissions that were too low to obtain a DOD of
Here we discuss the sensitivity studies with CAM5 using a range of surface mineralogical maps based on the uncertainty in mineralogical composition by soil type (Fig. 2). Following the methodology described in the previous section and Scanza et al. (2015), multiple soil atlases are created and remapped onto CAM5 and CAM6 longitude and latitude grids based on C1999 and J2014 (shown in Fig. S2 for the distribution of minerals in J2014 and in Fig. S3 for the difference between J2014 and C1999) and corresponding soil uncertainties (e.g., Fig. 5). By subtracting the base value from the high-bound mass fraction for each mineral, we obtain the atlas of high-branch uncertainty for minerals such as illite, smectite, hematite, and goethite plus hematite in terms of absolute change (Fig. 5a, b, c, d; also shown is the relative change in Fig. 5e, f, g, h, respectively).
Changes in soil concentration (fractional amount) of illite (ill), smectite (sme), hematite (hem), and goethite (goe) in the clay
category. In
The amount of soil variability for other minerals tends to be smaller than
for iron-oxide and hydroxide elements in terms of relative change (e.g., Fig. 5e, f
compared to Fig. 5g, h). In addition, as shown later (e.g., Sect. 3.2.2), the iron-oxide and hydroxide minerals are more important for the DRE than the other minerals are, such that we focus our discussion here on
iron-bearing minerals. Our calculation shows that in C1999 hematite, illite,
and smectite in clay range between 0.27 %–0.86 %, 9.0 %–15 %, and
6.8 %–13 %, respectively, by mass with base values of 0.56 %, 12 %, and 10 %. In comparison, the globally mean hematite in J2014 is smaller
(
Hematite and goethite are the most common iron oxides present in soils. In-lab analysis shows goethite being less absorptive than hematite (Formenti et al., 2014). Thus, partitioning these iron oxides at emission is relevant to accurately represent the dust DRE in the shortwave spectrum. C1999, however, only considers iron oxides to be in the form of hematite, while J2014 distinguishes two different iron-oxide species, hematite (present in the clay size) and goethite (both in clay and silt size fractions), consistent with other measurements (Lafon et al., 2006; Formenti et al., 2008, 2014). Both datasets agree on the scarce mass abundance of iron oxides in the clay- and silt-sized categories as compared to other minerals (note our extension of hematite to the clay-sized category in C1999). The combined iron-oxide (hematite and goethite) abundance in J2014 represents a much larger soil fraction than in C1999 (Fig. 5), particularly in the global average. We found that J2014 shows the dominance of the iron-oxide content by goethite over hematite, regardless of source region. Hematite in J2014 presents strong regional differences as in C1999 with mass fractions predominantly below 1.5 %, but in some arid regions, for instance northern Africa, reaching up to 5.0 % (Journet et al., 2014).
C1999 exhibits a large uncertainty in the soil abundance of hematite in the
soils of Australia, central and southern Africa, western India, the south-eastern part of North America, and eastern Brazil (Fig. 5c). Particularly
for areas considered to be sand dunes within the Sahel, the high-bound hematite in the clay-sized category is
Illite dominates the clay-sized category. Most regions in C1999 show over 25 % illite by mass in the clay-sized soils and both atlases report up to
50 % clay-sized illite over some Sahara sand dunes. The region-to-region
variation for illite is less pronounced than for low-abundance minerals
(e.g., feldspars, hematite, and calcite). In comparison to hematite, the soil content uncertainty in illite in terms of the relative change is small (
Spatially, we quantify the contribution of each uncertain parameter
described in Sect. 2.3.1 to the total dust DRE uncertainty by accounting
for the deviation in DRE from the perturbed case to the baseline case at
target grid boxes. Specifically, the dust DRE due to uncertainties in soil
mineralogy (e.g., hematite) is obtained following Eq. (2):
Loeb and Su (2010) applied the root-mean sum of the squares of the
uncertainties associated with each perturbing experiment (e.g., DOD), to get the total DRE uncertainty in the global average. This method was also used by Yoshioka et al. (2007) to estimate the errors for differences between two groups of data. Here, we utilize a
similar method and apply it to the grid-cell level to get the total DRE uncertainty (Eq. 3 for C1999 and Eqs. 4 and 5 to account for
difference between the two soil datasets) due to parameters we considered
(minerals, dust burden, and imaginary complex refractive index for each mineral):
Our adopted method, firstly, indicates an assumption that any difference between the experiment and base on the DRE calculation belongs to a part of the overall uncertainty and thus should be accounted for at the grid-cell level (Eqs. 3, 4, and 5), and secondly, effectively assumes that the perturbed parameters are independent. As in Loeb and Su (2010), we separate cases with a stronger warming from those with the opposite effect, splitting uncertainty into low and high branches but at the grid-cell level. These branches show the maximum range of DRE uncertainty that we can achieve through any combination of our perturbed experiments, assuming that these perturbations are independent.
We do not quantify the global mean uncertainty by simply averaging the value we obtained at all grid boxes, because there is no simple relationship between local and global uncertainty. Local uncertainty correlates across neighboring grid boxes, and this correlation probably varies spatially. Therefore, a simple average of the local deviation would very likely lead to bias in the global mean estimate toward regions with large correlation. Instead, we characterize global average uncertainty of the DRE based on the global mean of different cases as in Loeb and Su (2010).
In addition to the total DRE uncertainty due to all parameters considered, to quantify the contribution of uncertainty in the soil distribution of iron oxides to the total uncertainty, we repeat the above calculation but single out the effect of iron oxides.
In order to understand the relative importance of uncertainties in mineral amounts to other uncertainties in dust DRE, we require estimates of the DRE from other model estimates, using up-to-date dust optics and size distributions, but there are limited models available that simulate mineral distributions. At present, the relation of dust mineral composition to AOD and DRE in ModelE2 is under development. Instead, we predict the shortwave dust DRE assuming that the relationship between the DRE and the monthly column hematite mass in CAM5 also holds in ModelE2. This relationship is founded by applying a least squares regression to each grid cell based on the monthly DRE and atmospheric column hematite mass in a CAM5 case with the high-bound hematite in the clay-sized category. We select the CAM5 high-bound case, because it simulated a similar global hematite loading to that in ModelE2. The regression model only includes hematite because the shortwave DRE is most sensitive to it. This is supported by various laboratory experiments of dust samples (Moosmüller et al., 2012; Di Biagio et al., 2019) and will be discussed further in Sect. “Uncertainty due to dust minerals, burden, and imaginary complex refractive index”.
As a test of the regression model, the DRE derived solely from hematite mass
in CAM5 shows good agreement and self-consistency with the actual DRE (Fig. S4a, b). The predicted DRE aligns well with the actual value: the global mean
difference is
Similarly, the shortwave dust DRE in GFDL is predicted based on its
simulated bulk DOD (i.e., without mineral speciation) using the least squares regression derived from CAM5. To make the models more comparable, we increase the dust amounts in the GFDL model by a factor of 1.5, so that the DODs are both
DOD and dust burdens (Tg) in CAM4, CAM5 with C1999 and
J2014, CAM6 with C1999 with hematite coming solely from the clay-sized
category, and ModelE2 with C1999, GFDL, and MONARCH. Note differences in the global mean dust SSA calculation between CAMs and MONARCH: in CAM, the
global mean dust SSA was derived from the simulated SSA for total aerosols
at the 0.44–0.63
N/A – no data.
Once dust is emitted, the uncertainties in the soil mineral abundance (see
Sect. 2.3.2) propagate into the uncertainties in the simulated atmospheric
dust aerosol mineralogical composition. Table 4 lists the base global mean
atmospheric dust mass fractions for hematite (1.7 %), illite (27 %), and
smectite (18 %) and their uncertainty ranges (1.1 %–2.2 %, 22 %–32 %, and 13 %–23 %, respectively: absolute changes in low and high bounds with respect to the base) in CAM5 using C1999. The uncertainty range in hematite
in the clay soil fraction (0.27 %–0.86 %) results in approximately a 35 %
relative change in its simulated atmospheric burden with respect to the
base; this value is 18 % for illite and 26 % for smectite (Table 4). The brittle fragmentation theory applied to the fully disaggregated soil
particles puts clay-sized soil particles
Simulated mineral mass fraction and fractional absolute and relative changes (in percentage, %) in mineral mass fraction from the mean to the high bound in the global average.
N/A – no data.
Perturbing hematite in the silt- and clay-sized categories requires an
opposite and compensating change in the abundance of the remaining minerals
in the same soil-sized category (Sect. 2.3.1), which are often dominated
by phyllosilicates (e.g., illite, kaolinite, and smectite) (Claquin et al., 1999). As iron oxides are, in general, a small fraction of total dust mass, this change represents a tiny fraction for the
offsetting mineral, generally less than
We show spatial distributions of the relative change in simulated mass fraction due to uncertainty in iron oxides in both atlases and kaolinite in C1999 in Fig. 6 (for other minerals, see Fig. S8), and the column mean mineral mass percentage simulated in CAM5 and CAM6 in Fig. S9. Northern Africa (in particular the Sahel), Australia, followed by the Middle East, are
important sources of hematite (Claquin et al., 1999). In agreement with the
location of the maximum hematite fraction observed in soils within C1999,
large mean column hematite fractions are found in the interior of Australia
and to its north (Fig. S9k) and in the dust plume that extends from northern Africa to South America. The high hematite content in dust particles from the Middle East agrees with Krueger et al. (2004). The comparison of
iron oxides with other minerals in the global average (e.g., the smaller absolute uncertainty in hematite change comparable to other minerals and comparable relative change between hematite and kaolinite) is somewhat true
regionally (Fig. S8). For example, over northern Africa and the dust plume in downwind regions, uncertainty in the soil abundance of hematite in the
clay-sized category in C1999 leads to a relative change of
Relative change (in percentage) of simulated mass fraction
for hematite (hem) C1999 (
In addition to the variation in soil mineral distribution, the uncertainty
in the monthly mean mineral composition of dust aerosol is sensitive to the
seasonal cycle and the interannual variability in dust emissions
(Smith et al., 2017) as well as the model
version used. Figure S10c, d show the coefficient of variation (CV: calculated as the ratio of the standard deviation of the monthly means to the mean
across all experiments, including results from GISS ModelE2) for iron
oxides. The global mean CV is less than 1.0. In the regions that are
downwind of the major dust sources, except the Patagonian Desert and
Australian deserts, variability in the iron oxide(s) amount (CV
The choice of the soil mineralogy dataset and model employed has a strong impact on the derived dust DRE (Table 5 and Fig. 7). CAM5 with C1999
simulates a global mean TOA DRE of
Global mean SSA at the 0.44–0.63
Shortwave TOA DRE (W m
Regionally, the mean shortwave dust DRE for the base simulation shows
warming over northern Africa and cooling downwind (Fig. 7a), similar to previous studies (Miller and Tegen,
1998; Yoshioka et al., 2007) and other model versions used in this study
(e.g., CAM6 in Fig. 7b). We find that in the baseline where the annual mean surface albedo exceeds
Comparing the shortwave DRE from CAM5 simulations with different mineral
atlases, C1999 and J2014 (Fig. 7d), shows a slight difference in the DRE
amplitude at the global annual mean scale (
Previous studies (Sokolik and Toon,
1999; Lafon et al., 2006) have shown that hematite and goethite have
distinct optical properties at the shortwave bands. Considering both
hematite and goethite in mineral dust produced a more flat spectral SSA,
owing to the less pronounced dependence of the imaginary refractive index of
goethite on the short wavelengths (Formenti et al., 2014). If
we assume that goethite is less absorbing than hematite, we obtain a global
mean shortwave dust DRE of
In this section, we characterize the shortwave DRE uncertainty due to dust minerals, dust burdens, the imaginary refractive index of the minerals, and radiative parameterization, while other uncertainty sources are discussed in Appendix A. We evaluate the importance of iron oxides for the shortwave DRE variation relative to other minerals, dust burden, and the surface albedo. The shortwave DREs from multiple models are compared and included in the shortwave DRE estimate based on the methodology described in Sect. 2.3.3. Scanza et al. (2015) showed a model–observation comparison of the clear-sky shortwave DRE efficiency calculated with earlier versions of mineralogy CAM4 and CAM5 as well as the released versions of both models. With updated mineralogy in CAM5 as well as ported mineralogy in CAM6, we revisit the model–observation comparison in this section by also including the uncertainty in iron oxides derived from the soil abundance in C1999 and J2014.
The sensitivity studies undertaken with CAM5 (Table 2) show that the
uncertainty of hematite causes the largest change in the global mean
shortwave dust DRE (Table 5 and Fig. 8a) and SSA at the 0.44–0.63
Global mean shortwave DRE by dust
High-branch uncertainty in TOA shortwave DRE W m
Sensitivity parameter (unitless) of the shortwave DRE to
simulated minerals (hematite, smectite, and illite), DOD, and the prescribed
imaginary complex refractive indices within the known uncertainty in CAM5.
The sensitivity is measured by the ratio of the relative change in shortwave DRE to that of the parameter considered. Bars: values associated with higher
(in color) and lower bounds (dash with opposite signs to real values) of minerals, DOD, and imaginary complex refractive index.
The response of shortwave DRE to increasing DOD to the high bound (0.03
Figure 10 displays the sensitivity of the shortwave dust DRE at the TOA to
DOD, imaginary indices, and the mineral content in soil in CAM5 with C1999.
The sensitivity in Fig. 10 is calculated as the ratio of the relative change
in the DRE to the relative change in each driver, both with respect to base simulation values. The shortwave dust DRE is most sensitive to changes in
hematite in the silt-sized category. In contrast, perturbations to other
minerals, including illite and smectite, within their 95 % intervals,
induce a relatively small influence on the shortwave dust DRE in terms of
the globally averaged value owing to negligible resultant changes in the SSA
(Fig. 8b). The cancelling of opposite regional effects (Fig. 9c) by
perturbing DOD over regions with low (annual mean
The spatial distribution of the estimated uncertainty due to all effects
combined is illustrated in Fig. 11 based on the method described in Eqs. (3)
to (5) of Sect. 2.3.3. For low-branch uncertainty, we only show in Fig. 11 the global mean value (inlet numbers) because of the reginal similarity of the uncertainty associated with the two branches in amplitude. Globally, we obtain a total range of [
High-branch shortwave DRE uncertainty estimated considering all parameters
In CAM4, which employs an external aerosol mixing assumption, there is a
lack of sensitivity in the shortwave dust DRE to any mineral (Fig. S14).
Perturbating hematite produces a small change in SSA within 1 % (relative change, not shown) and hence a small change in the shortwave dust DRE (Fig. S14). Because of this, previous results using CAM4 were also insensitive to
changes in hematite aerosol burden (Scanza et al., 2015). Results from this
study are consistent with Sokolik and Toon (1999), who
demonstrated that to have SSA lower than 0.9 at 0.50
There are limited calculations of the dust DRE efficiency estimated from
satellite retrievals that can be used for comparison with model results.
Figure 12 compares the TOA DRE efficiency of dust under clear-sky conditions
(W m
Comparison of clear-sky shortwave (SW) and longwave (LW)
dust DRE efficiency (unit: W m
The predicted absorbing AOD (AAOD) at the band centered at 0.55
The climatologically mean AOD, AAOD, and SSA at 0.55
A fundamental question for this study is what the most important determinants in altering the shortwave DRE for different regions are. Analysis
of soil samples taken from locations representative of the Sahara and Sahel
deserts suggests that a linear correlation exists between SSA and the iron content in fine-sized dust particles (
SSA at the 0.44–0.63
First, we consider the relationship of the derived dust SSA at the 0.44–0.63
Figure 13 illustrates a strong regional variability of the derived dust SSA at
the 0.44–0.63
As in Fig. 13 but for shortwave DRE versus DOD and surface albedo.
Figure 14 shows response of the variability of shortwave DRE to that of DOD
and the surface albedo globally and over the examined sub-regions. Over all
sub regions, the variability of shortwave DRE is statistically significantly
(
Previous studies have highlighted how the variability in the DRE is due to different model representation of the sensitivity of DRE to dust optical properties, surface albedo, and aerosol–cloud interactions (e.g., Huneeus et al., 2011; Shindell et al., 2013; Kok et al., 2017). We estimate in this section the multi-model spread in the shortwave DRE using both soil mineral distributions based on all our simulations (Table 2) at each grid cell.
The shortwave DRE from ModelE2 is not directly calculated based on the model
run but derived here a posteriori via regression (see Sect. 2.3.4). Globally, the predicted shortwave DRE (
Dust DRE from MONARCH is calculated by the model and reported here. In the global average, MONARCH simulates a stronger cooling (
We estimate the DRE uncertainty to be [
As in Fig. 11 but for the shortwave DRE uncertainty
estimated based on a combination of five models (CAM5, CAM6, ModelE2, GFDL,
MONARCH). Panel
The band error in the model radiation parameterization in the model is an
important uncertainty source for the DRE estimate
(Jones et al., 2017). We assess this
uncertainty with a line-by-line calculation using CAM (e.g., Jones et al., 2017) for a 1 d (22 March 2005) simulation over northern Africa. According to the line-by-line calculation, the shortwave bands implemented in CESM introduce negative bias (
CAM5 simulated differences in the longwave dust DRE. Unlike the shortwave
DRE, the longwave DRE uncertainties mainly arise from the uncertainties in
the mineral complex refractive indices, size distribution, and vertical
distribution (effectively, dust acts similarly to a greenhouse gas) of dust
aerosol rather than mineralogy. The sensitivity tests in our model show that the longwave DRE is insensitive to the change in dust mineral contents in either the clay- or silt-sized category (Fig. 16). The global mean longwave DREs
calculated by different CAM versions are
Longwave DRE
Our calculation suggests weak impacts on the longwave dust DRE by uncertainty in the soil distribution of minerals such as quartz and feldspar
(Fig. S18), which may be a result of the longwave bands and the averaged
absorption properties of the eight minerals used in CAM5. Quartz dominates
absorption at several longwave bands (e.g., 9.2
Previous studies have suggested that omitting longwave dust scattering
results in an underestimate of the longwave DRE by between
Our baseline simulation shows a net dust warming of
Comparison of global mean shortwave (SW), longwave (LW),
and net (NET) DRE at the TOA obtained by Kok et al. (2017)
We estimate the range of the net dust DRE to be between [
The inclusion of multiple-models results into the abovementioned estimate
yields the largest net DRE range of [
Iron oxides including hematite and goethite are the most important mineral
absorbers at solar wavelengths (Sokolik
and Toon, 1999; Claquin et al., 1999; Lafon et al., 2006; Balkanski et al.,
2007; Formenti et al., 2014; Journet et al., 2014; Scanza et al., 2015; Li
and Sokolik, 2018). Here, for the first time we performed comprehensive studies to address uncertainty in dust DRE arising from the abundance of
iron oxides in soil mineralogy atlases, C1999 and J2014. We estimated this uncertainty in DRE by using dust mineralogy-speciated climate models and
focusing in particular on iron oxides with their known uncertainties in
C1999 and J2014. Detailed sensitivity studies were performed using a
perturbation analysis methodology on the eight different minerals and
associated imaginary refractive indices along with DOD. Uncertainties in
iron-oxide content represent
While hematite is a more absorbing iron oxide than goethite, our results
show that uncertainty in goethite in J2014 produces a larger uncertainty in
the shortwave DRE estimate, even larger than the uncertainty caused by the
hematite differences between C1999 and J2014. Given the volume averaging
method used in the model to compute bulk aerosol optical properties, despite
J2014 being the latest soil atlas, its introduction does not improve CAM5 predictions of the observed DRE efficiency at the TOA over northern Africa and downwind regions. While C1999 assumed that iron oxides are all in the form
of hematite, our tests highlight the importance of distinguishing goethite
from hematite for the shortwave DRE estimate. Otherwise, the model tends to
underestimate dust warming at the TOA by
Sensitivity studies in CAM5, which represents internally mixed aerosol species within each mode, demonstrated that the shortwave dust DRE at the
TOA is highly sensitive to estimates of the iron-oxide atmospheric burden; iron oxides along with other minerals considered in this study have a
negligible influence on the longwave DRE. As a consequence, the large
uncertainty in the amount of hematite present in soils leads to an
uncertainty up to 0.32 W m
The use of the volume averaging method to compute the bulk dust optical
properties (e.g., complex refractive index) based on the dust mineral species probably overestimates the shortwave absorption (X. L. Zhang et al., 2015; Li and Sokolik, 2018), leading to an artificial warming in CAM5 and CAM6. Our model
very likely underestimates a large fraction of the coarse dust particles (diameter
Considering that improving modeled mineralogical composition of dust is important to other disciplines or research subjects, such as biogeochemistry and dust–cloud interactions, a new soil atlas (Green et al., 2020) with more accurate hematite soil distribution is required. New measurement methods are expected to produce such an atlas. Incorporating this information will improve a model's ability to quantify and understand the DRE by mineral dust and its role in the Earth system.
In this Appendix, we compare the mineral speciation uncertainties to some of the other major sources of dust DRE uncertainty. Our perturbation analysis has not explicitly accounted for all elements that are relevant to this estimate in CAM, which are discussed here.
Size distribution is known as an important parameter that strongly affects
the dust DRE (Mahowald et al., 2014). The base
shortwave DRE obtained in CAM5 based upon C1999 relies heavily on the
aerosol size distribution employed in CAM5. The representation of the size
distribution is an issue that remains as yet unsolved (Li et al., 2021a).
A single larger dust particle typically has a higher absorption efficiency
and lower scattering efficiency in the shortwave spectrum range. Therefore,
even for the size-independent mineralogical composition, although the
complex refractive index of each mineral does not depend on size
(Sokolik et al., 1993; Sokolik and Toon,
1999), the SSA decreases steadily as the fraction of large-sized dust
increases. Recent observations show significantly abundant coarse and even
“giant” (diameter
A major source of hematite is the Sahel, whose emission is sensitive to the model dynamics and dust generation scheme, even though here the model wind is nudged towards MERRA. Even though the dust scheme used by CAM (Kok et al., 2014a) shows some improvements compared to DEAD in the model–observation comparison (Kok et al., 2014b), there are still large uncertainties in representing surface soil conditions of dust source areas in global models. Despite the insensitivity of dust mass extinction efficiency to mineralogy, a new generation scheme that yields a different emission pattern could change the mass fraction of iron oxides of dust aerosol across the globe. This could modify the shortwave DRE, even with the same globally mean DOD.
Apart from the emission, many aspects of modeling dust transport (dry and wet deposition, dust–cloud interaction, and mixing states with other aerosols such as sulfate, black carbon, and sea salt) remain subject to large uncertainties. Most of them are related to uncertainties in parameterizations of the dust cycle as well as the simulated meteorology propagating in part from the reanalysis products, to which that dust mobilization is sensitive. Most models, therefore, could not perfectly reproduce the observational dust distributions (Ginoux et al., 2001; Mahowald et al., 2005; Huneeus et al., 2011; Albani et al., 2014). This is true also because of the limited spatial coverage and temporal frequency of observational datasets and their sampling bias with few measurements over remote regions. For instance, both CAM4 and CAM5 match dust deposition observations within a factor of 10 (Fig. 3). At sites such as Colle del Lys and Colle Gnifetti in Europe, the baseline simulation in CAM5 greatly overestimated the surface deposition, while over the South Pacific the model greatly underestimated the deposition. Although a notable difference exists in the dust spatial distribution among the multiple models used in this study, it is possible that the simulated spatial distributions of dust minerals do not bracket the full range of observations in dust plume extents or burdens, leaving out a part of uncertainty.
The ageing process (e.g., heterogenous chemistry) of individual dust particles acts to alter their chemical composition. For example, high-level calcite-containing dust from, e.g., parts of China and Saudi Arabia have been found to react with nitric acid and form a nitrate salt (Krueger et al., 2004). The salt compounds cause increased adsorption of water vapour from the atmosphere and thus growth of the particle size. As a result, compared to non-aged particles, aged dust is more efficiently removed by the wet and dry deposition, leading to a reduced dust burden and lifetime (Abdelkader et al., 2017). Growth of particle size by deliquescence also changes the optical properties. The importance of the atmospheric processing on changing physical–chemical properties dust aerosol depends on its mineralogy and transport path, which determine the species (e.g., secondary acids, ammonium) that accumulate on the dust surface (Sullivan et al., 2007). In contrast to the Asia dust case (Krueger et al., 2004), optical properties and chemical composition of transported dust in Mediterranean from the Saharan show negligible changes, despite mixing with pollution particles (Denjean et al., 2016). These processes, unfortunately, are still not well established.
Other relevant uncertainties for the DRE estimate that are not explicitly considered here include (1) the altitude of the dust plume (Granados-Muñoz et al., 2019), especially its location with respect to clouds (Huang et al., 2009); (2) representation of surface albedo; (3) mixing assumptions, two extreme states of which shown in CAM4 and CAM5, when in reality, the mixing state of dust minerals along with other species is somewhere in between; (4) nano-sized iron oxides that are commonly associated with clay minerals but are not well represented in the CAM model; (5) hygroscopicity for each mineral which is assumed to be identical here; and (6) the efficiency of transmitting fine-mode aerosols to coarse-model aerosols through particle coagulation.
Data are available in the Cornell eCommons repository (
The supplement related to this article is available online at:
LL and NMM designed the study with discussions with RLM, CPGP, PG, MK, MGA, DSH, OK, VO, and DP. LL developed mineralogy CAM6, performed CAM simulations, analyzed multiple model results, and wrote the manuscript with comments from NMM, RLM, CPGP, DSH, MGA, MK, PG, YB, JFK, ROG, DRT, and VO; MK performed MONARCH simulations and analysed DOD and SSA in MONARCH; RLM performed ModelE2 simulations; PG performed GFDL simulations; DP performed line-by-line calculations; MGA, CPGP, and YB provided Journet soil atlases; JFK performed the mass extinction efficient calculation for non-spherical and spherical dust.
The authors declare that they have no conflict of interest.
A portion of this work was funded by the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission. We are grateful for high-performance computing resources provided by NCAR's Computational and Information Systems Laboratory.
A portion of this work was funded by the Earth Surface Mineral Dust Source Investigation (EMIT), a NASA Earth Ventures-Instrument (EVI-4) Mission. Longlei Li, Natalie M. Mahowald, and Douglas S. Hamilton were supported by the Atkinson Centre for a Sustainable Future. Jasper F. Kok received support from NSF grant 1552519. Martina Klose received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement no. 789630 (DUST.ES). Carlos Pérez García-Pando and Maria Gonçalves Ageitos received support from the European Research Council (grant no. 773051, FRAGMENT), EU H2020 project FORCES (grant no. 821205), the AXA Research Fund, the Spanish Ministry of Science, Innovation and Universities (RYC-2015-18690 and NUTRIENT: CGL2017-88911-R), and PRACE and RES for awarding access to MareNostrum at the Barcelona Supercomputing Center to run MONARCH. Ron L. Miller received for support from the NASA Modeling, Analysis and Prediction Program (NNG14HH42I).
This paper was edited by Joshua Fu and reviewed by three anonymous referees.