the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Feb 2024
Quantifying the dust direct radiative effect in the Southwestern United States: findings from multiyear measurements
Abstract. Mineral aerosols (i.e., dust) can affect climate and weather by absorbing and scattering shortwave (SW) and longwave (LW) radiation in the Earth’s atmosphere (the direct radiative effect). It is thought that the dust direct radiative effect is sufficiently strong that the presence of dust can significantly alter surface temperatures, static stability of the atmosphere, and the top of the atmosphere energy balance. Yet despite its importance, understanding of this parameter is so poor that, for example, the sign of the net direct radiative effect at top of the atmosphere is unconstrained, and thus it is unknown if changes in dust over time cool or warm Earth’s climate. Here we develop and apply new methods to estimate the SW direct effect via observations of aerosols and radiation made over a three-year period in a desert region of the southwestern United States. We generate region-specific dust optical properties via a novel data set of soil mineralogy from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) instrument, which are then used to model the SW and LW dust direct radiative effect. From the observations and model output we find that the net dust direct radiative effect, on average, is −6 ± 1 and −2 ± 1 W m−2 at the surface and top of the atmosphere, respectively. Our results suggest that the magnitude of the SW component is about twice that in the LW, underscoring the importance of quantifying the iron oxide content of dust since these minerals strongly affect dust SW absorbtivity.
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RC1: 'Comment on acp-2024-1', Anonymous Referee #1, 26 Mar 2024
General Comments
The authors present an analysis of the radiative effects of dust from the Salton Sea area of southern California, making use of a complementary set of surface observations (radiometric and meteorological), satellite radiation retrievals, and aircraft retrievals of surface soil mineralogy, in conjunction with radiative transfer simulations. The authors make further comparisons of their dust radiative forcing estimates with estimates by other authors for dust over other parts of the globe, including North Africa, the Mediterranean, the Arabian Peninsula, and China. It is an extensive and thorough analysis of the multiple datasets and simulations that have been used, and I have only minor comments.
One general point to make is that many of the measurements are daytime-only, and that the simulations during the nighttime period, while these are considered in Sections 4.3 and 5.3 using in-situ and reanalysis data, are relatively less constrained than during the daytime period. AERONET data are not available during the night, nor would the Roundshot camera images be available either, thereby reducing the capability to identify and measure dust storms during this period of the diurnal cycle. Is there a risk of this biasing the results towards the daytime period?
Something that seems a bit missing in Sections 2.4 and 4.1.2 (for example) is an impression both of the average aerosol optical depth and also its distribution (e.g. its maximum value) over the Salton Sea area. I see that the average value is briefly mentioned much later on in Section 4.3. Quantifying the AOD distribution would help to put the dust loadings over the Salton Sea into context, compared to other dust regions such as the Sahara where the dust loadings may often be more substantial. I see that this possibility is referred to in the middle paragraph of the conclusions.
Specific comments
Figures 12-14: in essence these figures are just tables. However I am not going to criticize this, since actually the surface and atmosphere background colors help to provide a more intuitive understanding of what the DRE values indicate.
Lines 478-480: what are the specific µ ranges? I presume they are similar to the 0-45° range specified on line 501, generally indicating periods during the middle-of-the-day.
Table A1: it may be worth mentioning in the caption what the UTC time is in relation to the local time.
Citation: https://doi.org/10.5194/acp-2024-1-RC1 -
RC2: 'Comment on acp-2024-1', Zhibo Zhang, 02 Apr 2024
Summary
This manuscript presents a study of the direct radiative effect (DRE) of dust aerosols in the southeastern California region using the combined ground and satellite observations, and radiative transfer simulations. In the first step, the instantaneous dust DRE was derived using two methods: 1) the observation based through regression of the measured SW and LW fluxes with respect to measured dust optical thickness; 2) radiative transfer simulation of dust DRE based on observed or derived dust particle properties. In particular, an interesting method is developed to diagnose the dust mineralogy and therefore complex refractive index (CRI) from the AVVRIS-C soil mineralogy retrievals and a dust emission model. After the comparison of the two methods for the instantaneous DRE, the radiative transfer simulations were extended to compute the diurnal DRE. The results are analyzed and discussed in the light of previous studies.
Overall, this is a nice study of the dust DRE based on the comprehensive observations and model simulations, even though it is limited to a specific spot. I enjoy reading it. It fits well with the scope of ACP and is generally well written and clearly presented. On the other hand, I have a few questions and concerns about the methodology used in this study as listed below. I also think some parts of the manuscript need to be clarified and revised significantly before the manuscript can be accepted for publication. Overall, I would recommend a major revision.
- I like the method to diagnose the dust particle mineralogy and refractive index in section 3.1. I think it is interesting, useful and applicable to other measurements like EMIT. But I have a few questions and suggestions.
- First, there is no discussion about the uncertainty of this method. While I understand that a direct validation is very difficult (perhaps comparing with lab measurements?), I still think there should be an estimation of the uncertainties. For example, I believe AVIRIS soil mineralogy fraction retrievals should have documented uncertainties, right? How does that uncertainty affect the derived dust mineralogy and CRI? It is also interesting and revealing to compare the derived dust CRI with previous studies, especially the database by Di Biagio et al. (2017,2019) For example, the Di Biagio et al. (2019) provides a range of dust SW CRI in their Figure 8. How does the dust CRI in this study (Figure 7) compare to the range of their results?
- Second, a couple of features of the dust CRI in Figure 7 seem abnormal to me. In the LW, the imaginary part of the CRI has a broad peak between 6 and 10 µm. This absorption peak seems to broad compared to previous studies. For example, most of CRI samples in Di Biagio et al. (2019) has very little absorption at 7-8µm (See Figure 10). But in this study, the imaginary CRI at 7-8 µm is larger than 1. What mineral is responsible for this strong absorption? In addition, the real and imaginary parts of the CRI are not independent but satisfy the the Kramers–Kronig relationship (Bohren and Huffmann, 1983). However, this does not seem to be the case in Figure 7. Can you explain what is the relationship, if any, between the real and imaginary parts of the CRI in your computation?
- My second major concern is with the linear model to decompose the net SW flux as a linear function of dust aerosol optical depth (DAOD), precipitable water and other factors. I don’t understand why a linear model is used here; why not use approximate radiative transfer model like two-stream approximation? For a dust layer on top of reflecting surface the total reflectance can be approximated as $R_d + \frac{R_sT_d^2}{1-R_dRs}$ where $R_d$ and $T_d$ are the reflectance and transmittance of the dust layer and $R_s$ is surface reflectance. It is not too difficult to add the effect of perceptible water and solar zenith angle to this framework. I suspect that the linear model is simply an approximation when the DAOD is small. In my personal view, I don’t think the linear model is intuitive that can shed any light on the underlying physics. I would recommend some discussion based on physical model, e.g., two stream approximation. If linear model is used then there should be some explanation of it theoretical foundation and connection to the radiative transfer.
- The discussion of the comparison between the observation-based and model-based DRE results in section 4.2 is not very satisfying or convincing. In my view, the results from the two method are significantly large. For example, the observational based instantaneous DREs at TOA, surface and atmosphere are -9, -15 and 6 W/m^2, respectively. In comparison the results from the model-based computation are -3, -16 and 13 W/m^2. The differences of DRE at TOA and within atmosphere are approaching the limit of uncertainty (I would suggest a statistical test to see the significance of the mean value difference between model based and observation based results). But these significant differences are not really discussed in section 4.2. There is no hypothesis of potential reason or speculation of causes. To me this is a waste of opportunity and seems diminish the purpose of developing two different approaches for computing DRE. I hope to see some mode detailed comparison and insightful analysis of the differences in the revised manuscript.
- My last major concern is on the computation of the LW DRE. Based on the information provided in section 3.1.1, the scattering properties of dust, in particular extinction (Figure 8c) in the LW is extrapolated from SW to LW based on the SW DAOD and assumed dust particle size. As shown in a few previous studies, such extrapolation is highly sensitive to assumption of dust particle size (e.g., see Figure 7 of Song et al. 2018 and Figure 13 of Zheng et al. 2022). This uncertainty should be clearly pointed out and if possible quantify. For example, in addition to the dust PSD used in the current study, the authors can use a coarser dust PSD, for example, FENNEC-SAL PSD from Ryder et al. (2013) to test the sensitivity of LW DRE to dust particle size assumption.
- In the observation based method, “The atmospheric components of the direct radiative effect and forcing efficiency are interpreted as the differences between their respective TOA and surface values”. Note that this method is only method under the 1-D radiative transfer assumption where for a atmosphere column Absorption=1-Reflection-Tansmission. In reality, i.e., 3-D radiative transfer there should be an additional term H that corresponds to the net horizontal flux transfer. For homogeneous dust layer and surface, this 1-D radiative transfer assumption is probably fine but it could face larger error when dust plume is inhomogeneous or there is significant surface inhomogeneity. Nevertheless, this uncertainty should be pointed out and discussed in the revised manuscript.
- “$fs$” in Eq 2 should be $f_s$ with subscript.
Citation: https://doi.org/10.5194/acp-2024-1-RC2 - I like the method to diagnose the dust particle mineralogy and refractive index in section 3.1. I think it is interesting, useful and applicable to other measurements like EMIT. But I have a few questions and suggestions.
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