Dust Induced Atmospheric Absorption Improves Tropical Precipitations In Climate Models

The amount of shortwave radiation absorbed by dust has remained uncertain. We have developed a more accurate representation of dust absorption that is based on the observed dust mineralogical composition 10 and accounts for very large particles. We analyze the results from two fully-coupled climate simulations of 100 years in terms of their simulated precipitation patterns against observations. A striking benefit of the new dust optical and physical properties is that tropical precipitations over Sahel, tropical North Atlantic and West Indian Ocean are significantly improved compared to observations, without degrading precipitations elsewhere. This alleviates a persistent bias in earth system models that exhibit a summer African monsoon that does not reach far 15 enough North. We show that the improvement results from a thermodynamical and dynamical response to dust absorption is unrelated to natural variability. Aerosol absorption induces more water vapor advection from the ocean to the Sahel, thereby providing an added supply of moisture available for precipitation. This work thus provides a path towards improving precipitation patterns in these regions by more realistically accounting for both physical and optical properties of the aerosol. 20

stronger than the one exerted by aerosol-cloud interactions (Miller et al., 2014;Nenes et al., 2014). Sahel precipitation is influenced by aerosol absorption (Miller et al., 2004;Solmon et al., 2008;Yoshioka et al., 2007), and absorption depends on iron oxides (hematite and goethite) that are part of dust mineralogical composition (Claquin et al., 1999;Sokolik and Toon, 1996). Over the last 15 years, simulating tropical precipitation has been 30 notoriously difficult for climate models (Fiedler et al., 2020). Improving the representation of tropical monsoons is a prerequisite to predict future changes in tropical precipitations and attribute them to observed changes in greenhouse gases and to aerosol changes. We show here how a better representation of dust aerosols leads to an unequivocal improvement in the simulation of precipitation over key climatic tropical regions, namely Sahel, tropical North Atlantic and West Indian Ocean without degrading precipitation elsewhere around the globe, and 35 subsequently discuss the thermodynamically and dynamically-driven mechanisms at play that affect the water cycle. Miller et al. (2014) showed that the increase in Sahel precipitation in response to high dust absorption is a fairly robust result across models. The link between this additional atmospheric absorption and dust physical properties, however, remains poorly understood. Conversely, (Haywood et al., 2016) discuss how some tropical 40 precipitation biases can be reduced by changing the model's energy balance between the Northern and the Southern Hemispheres, but they did so through ad hoc hemispheric albedo changes. Here we reunite these incomplete studies by describing an end-to-end physical mechanism that ties improvement in tropical precipitation to observational support for a higher level of dust absorption based on measurements of iron oxide in dust particles, measurements of the full dust particle size distribution and detailed climate simulations with 45 interactive dust.
The abundancy and variation in iron oxides, as well as the presence of large particles control absorption (Balkanski et al., 2007;Miller et al., 2004;Ryder et al., 2018;Yoshioka et al., 2007). Although iron oxides in dust are present in minute quantities from 1 to 5% by volume (Di Biagio et al., 2019;Journet et al., 2014;Kandler et al., 2007;Liu et al., 2018;Nickovic et al., 2012;Perlwitz et al., 2015;Reid, 2003), this relatively 50 small volume largely controls mineral dust absorption (Balkanski et al., 2007;Di Biagio et al., 2019;Ryder et al., 2018). Large dust particles have been shown through recent measurements to absorb strongly (Ryder et al., 2018). Since large particles are particularly abundant over source regions, they change their atmospheric energy balance. Furthermore Sahel is one of the world regions with the highest iron oxide content in soils (Journet et al., 2014;Nickovic et al., 2012). In this study, we analyse the strong relationship between high iron oxide 55 content and increased Sahel precipitation. Specifically we determine the optical properties of airborne dust coming from African deserts based on its mineralogy and show the IPSL-CM6 precipitation fields in several key https://doi.org/10.5194/acp-2021-12 Preprint. Discussion started: 15 January 2021 c Author(s) 2021. CC BY 4.0 License. tropical areas are improved compared to observations when the effect of dust absorption is introduced in this model. We finally dissect the mechanisms that explain an increase in Sahel precipitation with dust absorption and answer the question of whether these mechanisms are thermodynamical, dynamical or occur in response to 60 an improved phasing of the Atlantic Multidecadal Variability.

Results
We present in this study results from a fully-coupled climate simulation of 100 years (from which we analyse the last 30) and compare simulated precipitations with observations. Iron oxide content is based on the observations of dust mineralogical composition over the Sahel region (Di Biagio et al., 2019;Lafon et al., 2006) and 65 displayed for soils in Fig. 1. We infer the refractive index of the mineral dust using an optical model. Figure 2 illustrates the influence of the iron oxide content and the size of a particle on its radiative absorption. In this Figure, the aerosol absorption increases with the aerosol co-single-scattering-albedo (coSSA) along the x-axis.
The coSSA is defined as: (1) 70 the solid blue line illustrates the absorption calculated when only particles of dust of less than 10 m are considered. Furthermore, the shift from the solid blue to the solid orange line indicates dust absorption increase when particles larger than 10 m are taken into account. Hence, looking at the graph, particles of diameter less than 10 m with an iron oxide content of 5.0% absorb the same amount of radiation than particles with 3.0% iron oxide for which we consider also the diameters greater than 10 m. With these mineralogical compositions 75 we obtain a coSSA of 0.09. Considering dust particles with iron oxide volume content of 1.5%, thought to be the global median value as discussed in Balkanski et al. (2007), the coSSA is 0.032 and it almost doubles to 0.058 when large particles are also considered. Hence, large particles, which are particularly abundant over the Sahel region, increase substantially the aerosol absorption. This has yet to be taken into account in many models since they do not generally include particles sizes above 10 m. 80 We now discuss the changes in aerosol direct radiative effect for dust containing 3.0% iron oxide which corresponds to the amount of iron oxides measured over Sahel (Fig. 1). Figure 3 illustrates the June, July, August and September (JJAS) mean radiative perturbation due to the presence of dust. Over the bright surfaces of the Sahel region, the top-of-atmosphere shortwave radiative perturbation is positive, i.e., an atmosphere with dust is less reflective than an atmosphere without dust. At the surface, the radiative effect from dust is strongly 85 negative (-18 W.m -2 over the Sahel) as the dominant term is the reduction of shortwave radiation due to either https://doi.org/10.5194/acp-2021-12 Preprint. Discussion started: 15 January 2021 c Author(s) 2021. CC BY 4.0 License. back-scatter or absorption of radiation by dust. The difference between TOA and surface determines the dust JJAS mean atmospheric absorption due to dust (+26 W m -2 over the Sahel). Since dust is highly variable in time, particularly strong dust episodes are characterized by atmospheric absorption that reaches several hundred watts per square meter (Pérez et al., 2006). Note that, in comparison, greenhouse gases contribute to a globally-90 averaged radiative forcing of only 3 W m -2 (Myhre et al., 2013) relatively constant on short timescales .
To evaluate the impact of this change of radiative forcing on precipitation, we calculate the difference in 30-year (1985-2014) precipitation averaged over June, July, August and September (JJAS) between the simulation with dust and the one with no dust (Figs 4 & 5). These figures show an increase in precipitation between 6 and 20°N latitude over Africa. A general feature of most ESMs is to have a summer African monsoon that does not reach 95 far enough North compared to observations such as Tropical Rainfall Measuring Mission (Roehrig et al., 2013) (TRMM). Figure 5 shows that precipitation averaged from 10°W to 10°E increases from 0.5 to 1.5 mm day -1 over the summer months (JJAS). Accounting for dust absorption hence shifts northward the extent of the African summer monsoon.
We now examine whether precipitations are better represented when the effect absorbing dust on atmospheric 100 heating is accounted for. The accuracy with which the model captures the African monsoon was analyzed by comparing the incursion of the precipitations into the African continent with the TRMM observations for the same period of 15-years (2000-2014) over the summer months (JJAS). Figure 6 introduces the change in water budget due to the effect of absorbing dust over an airshed of the size of the Sahel (10°N-20°N; 15°W-35°E). We derive the amount of water advected into this region with the same units than precipitation or evaporation (mm 105 day -1 , see Methods). The effect of dust on precipitation is to increase the advection of moisture entering Sahel from the southern edge at 10°N. Figure S1 shows that aerosol absorption induces more water vapor advection from the ocean to the Sahel, thereby providing an added supply of moisture that is available for precipitation.
When sufficient water vapor is advected towards the African continent, aerosol absorption changes the regional energy balance and enhances vertical exchanges through the air column (Miller et al., 2014;Solmon et al., 110 2008). The largest change in water flux into the Sahel airshed is through the southern border of the region at 10°N with a flux reduction of 89% due to dust absorption, from -0.41 mm day -1 (i.e. exiting the Sahel box) to only -0.05 mm day -1 . The change in flux on the western side of the airshed is significantly smaller and amounts to 0.1 mm day -1 . By comparison, the increases in precipitation and evaporation over the Sahel region are 0.40 and 0.09 mm day -1 , respectively, for the months JJAS averaged over the 30-year period . 115

Discussion
We now compare the distribution of the surface precipitation between the two model simulations with and without dust with observations from the Global Precipitation Climatology Project (GPCP) for the months when African monsoon sets in from June to September. The precipitation statistics based upon the model/measurements comparison show significant improvements over Sahel, North Africa and the North 120 Atlantic. We quantify these improvements in Table 1 through a comparison of the bias, the root mean square error (RMSE) and the spatial correlation between precipitation fields. The largest changes occur over the Sahel where precipitation increases by 21% over the period and the negative bias and of the RMSE are reduced by 34 and 29%, respectively. The spatial correlation of the precipitation is also improved from 0.951 to 0.965. Other regions where all of the mentioned statistics are improved are the North Atlantic and the West Indian Ocean. 125 Over Northern Africa, the strong dust absorption causes an excess precipitation but there are improvements in the RMSE and the spatial correlation.
The Sahel region is prone to substantial atmospheric dust absorption as it is an important mineral dust source region. Being an active source region, the lower troposphere above Sahel experiences high dust loads and a large mass fraction comprises large-size particles of diameters above 10 m (larger particles being more absorbing 130 than smaller ones). Compounding these effects, soils from Sahel have a higher iron oxide content than other soils from North Africa (Di Biagio et al., 2019;Formenti et al., 2014;Kandler et al., 2007;Liu et al., 2018). The high atmospheric absorption that exists over the Sahel and illustrated by Fig. 3, induces water moisture advection from the North Atlantic in regions off the West Coast of Africa during the period of the African summer monsoon (Fig. S1). We present below the terms of the energy budget that play a role over the Sahel have been 135 discussed by Miller et al. (2014). We also explain the main elements that lead to upward movement of the moist air above the Sahel region that then generates precipitation. The action of mineral dust aerosol on precipitation is felt through the modifications of the diabatic heating and how the aerosol affects evaporation.
The terms in the energy balance that influence evaporation are the dimming caused by the aerosol layer of -18 W.m -2 over the Sahel (see Fig. 3) which needs to be compensated by the decrease of net latent heat flux that 140 leaves the surface. Following Miller et al.(2014), this surface flux reduction can be expressed as: where represents the change upward LW radiation flux, and the two other terms, and are the changes in turbulent fluxes of latent and sensible heat due to the presence of dust. Over the oceans, the change in latent heat flux dominates and the evaporation is reduced hence diminishing precipitation over these regions (see 145 last column of Table 1). Figure S2 indicates the vertical amount of transported Moist Static Energy (MSE). Two regions can be distinguished, one over the Sahel where the transport is upward as a result of the absorbed energy by dust, and the surrounding regions with a downward motion. These are the main perturbations that accompany the precipitation anomaly caused by the presence of dust. Other variables that increase are: evaporation, lowlevel cloud cover, liquid water path. As noticed by Miller et al. (2004), and reinforced by the results presented 150 here, evaporation over Sahel increases with dust absorption (see Fig. S3), while precipitation increases over the same region. Part of the evaporation is supplied by the moisture brought through precipitation over the same region. One mechanism for supplying the moisture is the advection that is evidenced in Fig. S1 and in the water budget presented on Fig. 6.
To complete the analysis of the possible mechanisms that explain this improvement in Sahel precipitation, we 155 examined whether a different phasing of the Atlantic Multidecadal Variability (Enfield et al., 2001) (AMV), a basin-wide low-frequency variations of the sea surface temperature over the North Atlantic, could be responsible for better reproducing observed precipitation fields. Figure S4 compares the observed and simulated AMV Index (Enfield et al., 2001;Trenberth and Shea, 2006). The AMV index is more in phase in the simulation without dust than in the simulation with dust. We conclude from this analysis that the precipitation improvements brought 160 about by dust absorption, which are also evident for earlier portions of the 100-years long simulations, are not due to a better phasing of natural variability in the dust simulation. Instead they correspond to dynamical effects that respond to a thermodynamically driven forcing.

Conclusion
This study was designed to realistically represent dust absorption over the Sahel region and describe the 165 mechanisms by which dust stimulates summer precipitation (JJAS) over the region. Our modelling includes two aspects not accounted for prior to it, first accounting for the high iron oxide content of the region in the optical properties, and secondly taking into consideration the extra-absorption from very large particles, with diameters greater than 10 m, generally not represented in climate models. The striking benefit to estimate more precisely dust absorption and take into account very large particles is that, at least in the IPSL-CM6 model 1 considered 170 here, tropical precipitations are significantly improved compared to observations. This important result came almost serendipitously, as we set to check if the comparison between simulated and observed precipitation over 30 years showed improvement. In key regions of tropical precipitations, namely: Sahel, tropical North Atlantic and West Indian Ocean the precipitation in the IPSL-CM6 climate model are significantly improved without degrading precipitations elsewhere. We believe that this is not restricted to this climate model as other models 175 participating in the CMIP exercises have the same bias over Sahel, which is to have to little advection of water vapor northward into the region during the Northern Hemisphere summer months. We thus provide a path towards improving precipitation patterns in these regions by more realistically accounting for both physical (size-based) and optical (absorption) properties of the aerosol. Our results also offer a strong physical basis for a stabilizing feedback loop involving dust emission, atmospheric absorption, Sahel precipitation, and vegetation, 180 as hypothesized by Carslaw et al. (2013), which could create multiannual or multidecadal oscillations at these latitudes that could also interact with natural models of variability in the Atlantic region. Future studies should therefore account for the role of water recycling from semi-arid vegetation (Yu et al., 2017) which plays a potentially important role in this loop.

IPSL-CM6 description
The climate model used here is the low resolution model from the Institut Pierre-Simon Laplace Climate Modelling Centre described by Boucher et al. (2020). The horizontal resolution is 2.5° in longitude and 1.28° in latitude with a discretization of the vertical into 79 layers that extends to about 80 km. For the ocean model, 190 NEMO, that includes sea-ice and biogeochemistry, the horizontal resolution is 1° and the model is discretized using 75 vertical levels. The aerosols are run interactively in the simulations presented here.

Dust modeling
Dust emission fluxes are calculated in two steps: in a first step, we derive the horizontal flux of dust that is mobilized based upon three criteria: a threshold velocity that depends on the nature of the upper soil, the wind 195 speed at 10-meters and an erodibility factor that takes into account the effect of soil moisture. These erodibility factors were tuned following the procedure described in Balkanski et al. (2004). Total emissions and loads compare well with the constraints given by Ridley et al. (2016) and by Kok et al. (2017). The dust particle size distribution are emitted with a constant shape following the Brittle theory described by Kok (2011). The size distribution is represented by one or several modes represented by a log-normal distributions with a mass median 200 diameter which varies in response to the sink processes of the dust cycle. Simulations are done either with one mode centered at 2.5 m with a width of 2.0 that represents the accumulation and coarse mode (Denjean et al., 2016;Schulz et al., 1998). Accounting for large particles of more than 10.0 m follows a treatment of the size distribution with four modes (Di Biagio et al., 2020). The four-mode distribution has mass median diameters of 1.0, 2.5, 7.0 and 22.0 m, respectively. The mineral composition which is described below is chosen to have the 205 same dust absorption on all simulations.
Dust absorption is influenced mainly by the iron oxide embedded in the dust aggregates (Di Biagio et al., 2019;Lafon et al., 2006;Ryder et al., 2018). Measurements of iron oxides on soils from around the world have been reported for two particle size class the clays with diameters of less than 2 microns and the silts with diameters between 2 and 64 m. The iron oxide varies drastically depending on the soil types and most measurements 210 indicate a weight content of 1 to 7% equivalent to 0.5 to 3.5% (Di Biagio et al., 2019;Engelbrecht et al., 2016;Journet et al., 2014;Lafon et al., 2006;Moosmüller et al., 2012) by volume. To determine the amount of iron oxides over Sahel, we used the high-resolution database published by Nickovic et al. (2012)  resolution of the database, the mineral content of hematite could be retrieved for 6,026,016 points. For the clay 215 fraction (diameter ≤ 2 m), 50% of these points had and hematite content of more than 2% by weight (equivalent to 1% by volume since density of hematite is twice that of all other minerals except goethite); 30% (respectively 17%) of the points had an hematite content of more than 3% (resp. 4%) by weight. For the silt fraction (diameter > 2 m), 49% of these points had and goethite content of more than 2% by weight (equivalent to 1% by volume since density of hematite is twice that of all other minerals except goethite); 30% (respectively 12%) of the 220 points had an hematite content of more than 4% (resp. 5%) by weight. Assuming that hematite and goethite contents are the same for these soils and accounting for the density of hematite (5300 kg m -3 ) and goethite (3800 kg m -3 ), we estimate that iron-oxides (hematite+goethite) represent 5.3% by weight and 3.0% by volume of mineral dust that has a density of 2650 kg m -3 . Hence, in the simulation, we took the optical properties of mineral dust with a volume of 3.0% made of iron oxides. 225 The absorption of dust size distribution is determined as follows: we consider dust as the mixture of six minerals kaolinite (kaol), illite (lili), montmorillonite (montmo), quartz (qua), calcite (calci) and hematite (hema). The difference in optical properties between goethite and hematite are not considered in this paper as we focus on the mechanisms by which dust absorption causes an increase of precipitation over the Sahel and not into having a very precise calculation of this absorption. We vary the VOLUME content of hematite with the following 230 values: 0.9, 1.5, 2.7, 3.0, 4.0, 5.0 and 10%.
A description of the Maxwell-Bruggeman approximation used here to compute the refractive index of dust can be found in Balkanski et al. (2007). The first step of the computation is for each of these hematite content compute the refractive index of the mixtures: kaol-hema, illi-hema, montmo-illi, qua-hema and calci-hema (for example for 3% hematite, all mixtures are composed of 97-3%). The second step is to compute the refractive 235 index for the two most abundant constituents of the mixture as clays associated with hematite that is illi-hema and kaol-hema. The third step takes the resulting mixture illi-kaol-hema and mixes it with the third most abundant mineral montmorillonite. The resulting mixture illi-kaol-montmo-hema is then mixed with the fourth most abundant mineral quartz. And finally the mixture illi-kaol-montmo-quartz is mixed with calcite, the least abundant of those minerals. We refer the reader to Table 1 that explains the abundancies of the different 240 assemblages and minerals. Figure 2 illustrates how co-albedo (1.-SSA), SSA (single scattering albedo) varies with increasing iron oxide content and the effect of considering large particles (diameter > 10 m). For a co-albedo of 0.09, we can see from these curves that the same absorption from the whole size distribution including large particles (orange solid line) requires only 3.0% volume content of iron oxide whereas for particles of less than 10 m in diameter 245 the have to include a volume of 5.0% of iron oxide. This is also observed in field measurements (see Fig. 8 from Ryder et al. ( 2013)).
We ran 100-year simulation of the fully-coupled IPSLCM6 model with all interactive components of the aerosol including dust for the 1915-2014 period, as well as another 100-year simulation without the dust. We analyzed the last 30 years of the coupled simulations  for the summer period that includes the month of June 250 (June-July-August-September) referred to as JJAS in the rest of the text. We checked for all variables the consistency of the results compared to the previous 30-year period from 1955 to 1984.

Computation of the Direct Radiative Perturbation (DRP) from dust
We compare the fluxes both at top-of-atmosphere and at the surface for the simulation with the dust aerosol and the case when dust aerosol concentrations are set to zero in the model. The refractive indices are described for 255 the shortwave in the work from Di Biagio et al. (2019) and Balkanski et al. (2007) and for the longwave from Di Biagio et al. (2020). The solar radiation code in the LMDZ GCM consists of an improved version of the parameterizations of Fouquart and Bonnel (1980). The radiative transfer module includes a six-band (0.185-4.0 μm) scheme in the SW and the Rapid Radiative Transfer Model for Global Circulation Models radiative scheme in sixteen bands between 3.33 and 1,000 μm (Hogan and Bozzo, 2016). The model accounts for the diurnal cycle 260 of solar radiation and allows fractional cloudiness to form in a grid box. The reflectivity and transmissivity of a layer are computed using the delta Eddington approximation (Joseph et al., 1976) in the case of a maximum random overlap (Morcrette and Fouquart, 1986)  Few models have published the radiative effect of dust over the Sahel, hence to compare this effect to other publications we present in Table S1 global results and discuss how the net atmospheric absorption compares in each model. Most models published predict a net absorption of radiation by dust except for the result from 270 Yoshioka et al. (Yoshioka et al., 2007) that we discuss below. If we take the three following studies Woodward (2001) and Miller et al. (2004Miller et al. ( , 2014 they have in common that the absorption in the shortwave dominates what is absorbed in the longwave. Compared to this study, where LW absorption represents -0.41 W.m -2 , the LW absorption of these 3 papers are within the range [-0.23 to -0.03 W.m -2 ], i.e., the net surface LW radiation term is greater than the one at top-of-atmosphere. 275 In the study of Yoshioka et al. (2007) the top-of-atmosphere LW radiation effect is within the other models but the surface LW effect amounts to +1.13 W.m -2 , nearly twice the effect of this study. The consequence is that the atmospheric cooling caused by the LW effect (-0.81 W.m -2 ) more than compensates for the SW heating (+0.67 W.m -2 ) and results in a net cooling of the atmospheric column.

Deriving the Water Budget along the Sahel airshed (10°N-20°N; 15°W-35°E) 280
Following Sheen et al. (Sheen et al., 2017), we seek to determine the total flux of moisture depth across each of the airshed boundaries. Hence we compute the integrated moisture flux through the following integral: where 〈 〉 represents the monthly mean of the qu product, g is the acceleration due to gravity, w is the density of water and p s is the surface pressure. The units for the flux are kg.m -1 s -1 . The integrated fluxes across each side 285 of the airshed are first averaged and then scaled by the length along the flux trajectory (Trenberth, 1999), and then divided by the airshed area to obtain units of mm per day that can be compared to the precipitation and the evaporation fluxes over the airshed.
Deriving the vertical advection response of moist static energy over North Africa (Fig. S2) Following the method of Hill et al. (Hill et al., 2017), the following term allows to estimate the vertical advection 290 of Moist Static Energy (MSE): Where MSE, the moist static energy is derived as:     The difference has a positive (resp. negative) sign when water enters (resp. exits) the Sahel box.