Revision of the paper Complex refractive indices and single scattering albedo of global dust aerosols in the shortwave spectrum and relationship to iron content and size

Abstract

I suggest adding a sentence reflecting how the new RI results compare to older datasets – i.e. the real part is similar but the imaginary part falls at the low end of the published range. This is an important finding. As suggested by the reviewer we added this comment to the abstract. P2 l46 – ‘an intrinsic property of matter’ – although this is true, I suggest rewording, such as, ‘k was found to be independent of size’ since several other studies have found k to be size‒dependent. We corrected the text as suggested. L85 – insert ‘geographic’ before ‘differences persist: : :’ We corrected the text as suggested. L106‒108 – It would be worth saying why models still assume the same dust composition globally – e.g. due to computational cost of additional tracers? And/or lack of a globally consistent information dataset? Following the reviewer suggestion we modified the text as below: “In spite of this sensitivity, present climate models adopt a globally‒constant spectral complex refractive index (and SSA) for dust, and hence still implicitly assume the same dust mineralogical composition at the global scale. This is mainly due to the lack of a globally consistent dataset providing information of the geographical variability of the dust scattering and absorption properties (e.g., Sunset et al., 2018).” L207 – ‘Ogreen’ typo We corrected the typo as suggested. L257 – RI range for n and k – which values were used? Different values for different experiments? Where/how is this range applied? As explained in Di Biagio et al. (2017) the optical to geometrical diameter conversion was performed as following: optical calculations were computed over the spectral range of the WELAS and at the GRIMM operating wavelength using Mie theory for spherical particles by fixing n at 1.47, 1.50, and 1.53, and by varying k in steps of 0.001 between 0.001 and 0.005. Dg was then set at the mean ±1 standard deviation of the values obtained for the different n and k. This calculation procedure is now better described in Table 1 where all the details of instrument artefact corrections are provided. L268 – ‘cut at 10 microns’ ‒ this contradicts p5 l178 which says 5 microns/8 microns (50/100% efficiency). There is no contradiction since the 10 μm and the 8 μm represent respectively the 100% cutoff for the SW optical instruments and the OPCs sampling lines due to their geometry. As explained in the text, the particle losses along sampling lines for OPCs were corrected to retrieve the “real” size distribution of the dust aerosols in CESAM, dN/dlogDCESAM. Successively the losses along sampling lines for SW instruments were accounted for to derive from dN/dlogDCESAM the size distribution sensed by SW optical instruments that is denoted in the text as dN/dlogDSWoptics. L278‒279‒ It would be useful to mention the cut‒off diameters again here as deff,coarse does not represent the full size range. We clarified this point in the text as suggested by the reviewer. L280 – it would be useful to say why modes were fitted to the size distribution, for non‒experts. We modified the sentence as below: “The dust size distribution, (dN/dlogD)SWoptics, measured at each 10‒min time step for each sample was fitted with a sum of five lognormal functions to smooth data inhomogeneities linked to the different instrument’s operating principles and artefacts”. L283 – how were modes fitted? (I think this is provided later in the paper but it should be mentioned here). We added this sentence in the text to specify how the fitting was performed: “Fitting was performed using the Levenberg‒Marquardt algorithm”. L301 – what about other uncertainties to the size distribution, such as shape assumptions and/or Mie‒regime singularities? Concerning the uncertainties in the spherical assumption, we extended the discussion in Sect. 3.3 and we remind to it at the end of Sect. 2.2. Concerning the uncertainties in Mie regime singularities, basically they come from the fact that if optical calculations are performed at too low size resolution then it is not possible to resolve Mie singularities. We tried to test the impact of that on our results by performing Mie calculations at higher diameter resolution (dlogD=0.01 μm) than used in original calculations (dlogD=0.06 μm). We found that increasing the resolution of the calculation modifies less than 1% on average our results for the optical to geometrical diameter correction. So in our case the impact of Mie singularities is almost negligible. We discussed these two points in the main text. “Other sources of uncertainties are linked to the spherical assumption to perform the optical to geometrical diameter conversion (discussed in Sect. 3.3) as well as those due to Mie resonance oscillations of the calculated scattering intensities. Concerning Mie resonances, a sensitivity study was performed varying the size resolution of our calculations (high/low diameter resolution in the calculations to have a better/worse reproduction of Mie resonance oscillations) and show that Mie resonances impact the optical to geometrical correction by less than 1%.” L342‒3 – and also when comparing the RI data? The refractive index dataset is shown not to be sensitive to changes in the size distribution, based on the results of our analysis. Therefore differences in temporal sampling for the different samples should not affect the comparison of data for the refractive index. Nonetheless, and in order to keep generality, we rewrote the final sentence of the section as: “This difference in time sampling should be kept in mind when comparing data for these two samples to the rest of the dataset.”. L402 – ‘contrasting’ ! ‘contradictory’? We corrected the text as suggested. L447 – Muller et al (2011) report observations at Capo Verde, not transported across the Atlantic. The reviewer is right, so we reformulated the sentence as below “Conversely, the values of Deff,coarse behind the SW instruments inlets are mostly in agreement with those reported for dust transported at Capo Verde and across the Atlantic ocean (⁓3 μm, Maring et al., 2003; Müller et al., 2011; Denjean et al., 2016b)”. L460‒464 – And also the fact that the size distribution above the 50% transmission efficiency (5 microns?) is not well represented, should be mentioned. This point is now specified in the paper. The new text is: “The overall shape of the dust size distribution sensed by the SW instruments is comparable to that measured during atmospheric long‒range transport, even if the fraction of particles above 3.9 μm diameter, which is at the 50% cutoff of the transmission efficiency for the SW optical instruments, is significantly under‒represented compared to observations. (i.e., Betzer et al., 1988; Formenti et al., 2001; Maring et al., 2003; Ryder et al., 2013b; Jeong et al., 2014; Denjean et al., 2016b). » Figure 4 and discussion in lines 451‒464 – the authors should consider that some of the observational data they show from other campaigns was also restricted by maximum size measured or by inlet transmission efficiencies (e.g. NAMMA, PRIDE). Information on some of these restrictions are provided in Ryder et al. (2018), table 1. As such, some of these datasets likely underestimate the coarse mode size distribution. Transported dust size distributions are also available for the AER‒D campaign in the same paper which would add to the data already shown in Figure 5 and did not suffer from inlet restrictions. We added data from AER‒D (Ryder et al., 2018) and from SALTRACE (Weinzierl et al., 2017) in Figure 4. We also added a brief discussion on the inlet restriction for field data at the end of Sect. 4.1.1: “It should be keep in mind that often also field data are affected by inlet restrictions so that they cannot measure the whole coarse dust fraction (see Table 1 in Ryder et al., 2018). The lowest cutoff for field data shown in Fig. 4 are for the NAMMA and PRIDE datasets and correspond to upper size limits at 5 and 10 μm in diameter, respectively. Being these values above our cutoff of 3.9 μm, it means that the comparison with our size dataset is meaningful within the range of our measurements. To note that only the data from AER‒D did not suffer from significant inlet restrictions thus leading to the observation of giant dust particles up to tens of microns in the Saharan Air Layer off the coasts of Western Africa.“ L466 – it would be useful to add a line on the importance of iron oxides vs elemental iron for the benefit of non‒specialists. We included this sentence in the main text: “Elemental iron include the iron in the form of iron oxides and hydroxides, hematite and goethite (the so‒called free iron, mostly controlling SW absorption) and the iron incorporated in the crystal structure of silicates and alluminosilicates (illite, smectite), conversely not considerably contributing to SW absorption (Karickhoff and Bailey, 1973; Lafon et al., 2004).”. L469 – ‘Australia’ – should this be Namib‒2? (values are not consistent with those in the table). The reviewer is right and we corrected the text accordingly. L469 – ‘Iron oxides account for 11 and 62% of the iron mass’ – where do these values come from? They are not shown in table 3? The fraction of the iron mass that is in the form of iron oxides was determined by XANES (X‒ray absorption near‒edge structure) measurements as described in more detail in Caponi et al. (2017). Briefly, the XANES spectra of the dust sample was deconvoluted using the spectra of five standards of Fe(III)‒bearing minerals previously measured with the same technique. The linear deconvolution provided the proportionality factors representing the mass fraction of elemental iron to be assigned to the ith standard mineral. In particular, the values of the proportionality factors for hematite and goethite represent the mass fractions of elemental iron that can be attributed to these two species. The mass of the Fe oxides is the sum of the mass fractions of hematite and go