Dust Model Research Articles

Simba-EoR: early galaxy formation in the simba simulation including a new sub-grid interstellar medium model

ABSTRACT We update the dust model present within the simba galaxy simulations with a self-consistent framework for the co-evolution of dust and molecular hydrogen populations in the interstellar medium, and use this to explore $z \ge 6$ galaxy evolution. In addition to tracking the evolution of dust and molecular hydrogen abundances, our model fully integrates these species into the simba simulation, explicitly modelling their impact on physical processes such as star formation and cooling through the inclusion of a novel two-phase sub-grid model for interstellar gas. Running two cosmological simulations down to $z \sim 6$ we find that our simba-EoR model displays a generally tighter concordance with observational data than fiducial simba. Additionally we observe that our simba-EoR models increase star formation activity at early epochs, producing larger dust-to-gas ratios consequently. Finally, we discover a significant population of hot dust at $\sim 100$ K, aligning with contemporaneous observations of high-redshift dusty galaxies, alongside the large $\sim 20$ K population typically identified.

Nebular dust attenuation with the Balmer and Paschen lines based on the MaNGA survey

Dust attenuations observed by stars and ionized gas are not necessarily the same. The lack of observational constraints on the nebular dust attenuation curve leaves a large uncertainty when correcting nebular dust attenuation with stellar continuum-based attenuation curves. Making use of the DAP catalogs of the MaNGA survey, we investigate the nebular dust attenuation of regions traced by the Balmer and Paschen lines. Based on a simple simulation, we find that star-forming regions on kpc scales favor the classic foreground screen dust model rather than the uniform mixture model. We propose a novel approach to fit the dust attenuation curve using the emission-line fluxes directly. For strong hydrogen recombination lines (e.g. and the slopes of the nebular attenuation curve can be well determined and are found to be in good agreement with the Fitzpatrick Milky Way extinction curve with an accuracy of $ 4<!PCT!>$ in terms of the correction factor. However, severe contaminations and/or systematic uncertainties prevent us from obtaining reasonable values of the slopes for weak recombination lines (e.g., the high-order Balmer lines or the Paschen lines). We discuss how the choice of emission line measurement methods affects the results. Our results demonstrate the difficulty of deriving an average nebular dust attenuation curve given the current ground-based emission-line measurements.

First Observational Evidence That Dust‐Driven Cloud Phase Changes Cool the Surface Over Summertime Arctic Sea Ice

AbstractCloud phase has important impacts on Arctic surface temperatures, and circumstantial evidence suggests that dust aerosols have strong regional impacts on Arctic cloud phase. We used 7 years of satellite observations and model and reanalysis products to control for co‐varying meteorology, and to assess how dust and other aerosols impact cloud phase and cloud radiative effects over the summertime sea ice. We focus on clouds at 3 km, where dust modeling is most accurate. There is strong indication that dust aerosols caused about 4.5% of clouds below −15°C to change phase, with smaller effects at higher temperatures. Sulfate has a smaller impact on cloud phase. Dust is associated with cloud‐mediated surface cooling of up to a 6.3 W m−2 below single‐layer clouds at ∼3 km in June. This is the first observational study to constrain likely dust‐related cloud radiative effects over the summertime Arctic sea ice.

Using land use methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover

Using land use methodology to construct ring spatial variables for modeling and mapping spatial distribution of dust in snow cover

The DEHVILS in the details: Type Ia supernova Hubble residual comparisons and mass step analysis in the near-infrared

Measurements of type Ia supernovae (SNe Ia) in the near-infrared (NIR) have been used both as an alternate path to cosmology compared to optical measurements and as a method of constraining key systematics for the larger optical studies. With the DEHVILS sample, the largest published NIR sample with consistent NIR coverage of maximum light across three NIR bands (Y, J, and H), we check three key systematics: (i) the reduction in Hubble residual scatter as compared to the optical, (ii) the measurement of a “mass step” or lack thereof and its implications, and (iii) the ability to distinguish between various dust models by analyzing slopes and correlations between Hubble residuals in the NIR and optical. We produce SN Ia simulations of the DEHVILS sample and find that it is harder to differentiate between various dust models than previously understood. Additionally, we find that fitting with the current SALT3-NIR model does not yield accurate wavelength-dependent stretch-luminosity correlations, and we propose a limited solution for this problem. From the data, we see that (i) the standard deviation of Hubble residual values from NIR bands treated as standard candles are 0.007–0.042 mag smaller than those in the optical, (ii) the NIR mass step is not constrainable with the current sample size of 47 SNe Ia from DEHVILS, and (iii) Hubble residuals in the NIR and optical are correlated in the data. We test a few variations on the number and combinations of filters and data samples, and we observe that none of our findings or conclusions are significantly impacted by these modifications.

Modelling the impact of host galaxy dust on type Ia supernova distance measurements

ABSTRACT Type Ia Supernovae (SNe Ia) are a critical tool in measuring the accelerating expansion of the universe. Recent efforts to improve these standard candles have focused on incorporating the effects of dust on distance measurements with SNe Ia. In this paper, we use the state-of-the-art Dark Energy Survey 5 year sample to evaluate two different families of dust models: empirical extinction models derived from SNe Ia data and physical attenuation models from the spectra of galaxies. In this work, we use realistic simulations of SNe Ia to forward-model different models of dust and compare summary statistics in order to test different assumptions and impacts on SNe Ia data. Among the SNe Ia-derived models, we find that a logistic function of the total-to-selective extinction $R_V$ best recreates the correlations between supernova distance measurements and host galaxy properties, though an additional 0.02 mag of grey scatter is needed to fully explain the scatter in SNIa brightness in all cases. These empirically derived extinction distributions are highly incompatible with the physical attenuation models from galactic spectral measurements. From these results, we conclude that SNe Ia must either preferentially select extreme ends of galactic dust distributions, or that the characterization of dust along the SNe Ia line-of-sight is incompatible with that of galactic dust distributions.

Numerical simulation of multi-factor influence mechanism in “Barrier closure” dust removal technology

A novel “Barrier closure” method is introduced in this study to address the challenges of sealing dust sources in open workplaces, rapid dust diffusion, and difficulty in controlling dust pollution. The coupling model of airflow and dust is applied to analyze the influence of various factors on dust diffusion. The results show that before using the new technology, the dust concentration at the sidewalk exceeded 350 mg/m3 due to the movement of the dusty airflow. After using the new technology, the airflow flows to the sidewalk under the blocking action of the baffle, resulting in higher mechanical energy of the airflow on the sidewalk side than the coal wall side. Thus, inhibits the movement of the dusty airflow to the sidewalk area. In the case of downwind and upwind coal mining, the dust concentration in the sidewalk area is controlled below 50 mg/m3, which is 94.2 % and 55.6 % reduction, respectively, compared to the absence of the new technology. When the wind-regulation baffle is 3.6m from the rear drum of the coal cutter, the dust concentration in the working area is controlled within 20.0 mg/m3, and the dust removal efficiency reaches 94.2 %. When the inlet wind velocity is 2.0 m/s, the dust concentration along the sidewalk is less than 100 mg/m3, the dust concentration in the operation area is less than 20.0 mg/m3, and the dust removal efficiency is 94.5 %. This study provides a novel approach for establishing clean working environments.

Study on the influence of combined utilization of air-fog curtain on fully mechanized face

To address the ambiguous regulations for dust control and removal in the joint use of air curtain and fog curtain, this study developed a multi-field coupling model of airflow, dust, and droplets. By incorporating the spraying method into both traditional ventilation technology and air curtain technology, this study comparatively analyzed the dust control and removal efficiencies of these two methods under various ventilation conditions in tunnels. The spray experiment and simulated result indicated a maximum relative error of 19.9%. The simulation results demonstrated that the use of traditional ventilation technology resulted in significant dust contamination in the rear and top regions of the tunnel. Although the dust pollution was ameliorated when the pressure-suction ratio was 1.25, the dust control effect remained unsatisfactory, with the pollution distance extending 37.0m. After implementing air curtain technology, the airflow generated a spiral air curtain due to the wall attachment effect, effectively confining the majority of dust to the heading face area. When the axial flow ratio was 45%, the dust concentration at exceeding 13.0m from the heading face dropped to blow 1.0mg/m3, significantly reducing health risks for operators.

Understanding the multi-wavelength thermal dust polarisation from the Orion molecular cloud in light of the radiative torque paradigm

Dust grains play a key role in various astrophysical processes and serve as indicators of interstellar medium structures, density, and mass. Understanding their physical properties and chemical composition is a crucial goal in astrophysics. Dust polarisation is a valuable tool for studying these properties. The radiative torque (RAT) paradigm, which includes radiative torque alignment (RAT-A) and radiative torque disruption (RAT-D), is essential to interpreting the dust polarisation data and constraining the fundamental properties of dust grains. However, it has been used primarily to interpret observations at a single wavelength. In this study, we analyse the thermal dust polarisation spectrum obtained from observations with SOFIA/HAWC+ and JCMT/POL-2 in the Orion molecular cloud 1 (OMC-1) region and compare the observational data with our numerical results using the RAT paradigm. In general, we show that the dense gas exhibits a positive spectral slope, whereas the warm regions show a negative one. We demonstrate that a one-layer dust (one-phase) model can only reproduce the observed spectra at certain locations and cannot match those with prominent V-shaped spectra (for which the degree of polarisation initially decreases with wavelength from 54 to ~300µm and then increases at longer wavelengths). To address this, we improved our model by incorporating two dust components (warm and cold) along the line of sight, resulting in a two-phase model. This improved model successfully reproduces the V-shaped spectra. The best model corresponds to a mixture composition of silicate and carbonaceous grains in the cold medium. Finally, by assuming the plausible model of grain alignment, we were able to infer the inclination angle of the magnetic fields in OMC-1. This approach is an important step towards a better understanding the physics of grain alignment and constraining 3D magnetic fields using dust polarisation spectra.

Redshift-dependent galaxy formation efficiency at z = 5 − 13 in the FirstLight Simulations

Context. Some models of the formation of first galaxies predict low masses and faint objects at extremely high redshifts, z ≃ 9 − 15. However, the first observations of this epoch indicate a higher-than-expected number of bright (sometimes massive) galaxies. Aims. Numerical simulations can help to elucidate the mild evolution of the bright end of the UV luminosity function and they can provide the link between the evolution of bright galaxies and variations of the galaxy formation efficiency across different redshifts. Methods. We use the FirstLight database of 377 zoom-in cosmological simulations of a volume- and mass-complete sample of galaxies. Mock luminosities are estimated by a dust model constrained by observations of the β–MUV relation at z = 6 − 9. Results. FirstLight contains a high number of bright galaxies, MUV ≤ −20, consistent with current data at z = 6 − 13. The evolution of the UV cosmic density is driven by the evolution of the galaxy efficiency and the relation between MUV and halo mass. The efficiency of galaxy formation increases significantly with mass and redshift. At a fixed mass, galactic halos at extremely high redshifts convert gas into stars at a higher rate than at lower redshifts. The high gas densities in these galaxies enable high efficiencies. Our simulations predict higher number densities of massive galaxies, M* ≃ 109 M⊙, than other models with constant efficiency. Conclusions. Cosmological simulations of galaxy formation with detailed models of star formation and feedback can reproduce the different regimes of galaxy formation across cosmic history.

Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system

This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.

Probing cold gas with Mg ii and Ly α radiative transfer

ABSTRACT The Mg ii resonance doublet at 2796 Å and 2803 Å is an increasingly important tool to study cold, $T \sim 10^{4}\,$ K, gas – an observational-driven development requiring theoretical support. We develop a new Monte Carlo radiative transfer code to systematically study the joined Mg ii and Ly α escape through homogeneous and ‘clumpy’ multiphase gas with dust in arbitrary three-dimensional geometries. Our main findings are (i) the Mg ii spectrum differs from Ly α due to the large difference in column densities, even though the atomic physics of the two lines are similar. (ii) The Mg ii escape fraction is generally higher than that of Ly α because of lower dust optical depths and path lengths – but large variations due to differences in dust models and the clumpiness of the cold medium exist. (iii) Clumpy media possess a ‘critical covering factor’ above which Mg ii radiative transfer matches a homogeneous medium. The critical covering factors for Mg ii and Ly α differ, allowing constraints on the cold gas structure. (iv) The Mg ii doublet ratio $R_{\rm MgII}$ varies for strong outflows/inflows ($\gtrsim 700 \,\mathrm{km\, s}^{-1}$), in particular, $R_{\rm MgII}\lt 1$ being an unambiguous tracer for powerful galactic winds. (v) Scattering of stellar continuum photons can decrease $R_{\rm MgII}$ from two to one, allowing constraints on the scattering medium. Notably, we introduce a novel probe of the cold gas column density – the halo doublet ratio – which we show to be a powerful indicator of ionizing photon escape. We discuss our results in the context of interpreting and modelling observations as well as their implications for other resonant doublets.

Evidence for Spatially Distinct Galactic Dust Populations

We present an implementation of a Bayesian mixture model using Hamiltonian Monte Carlo techniques to search for the spatial separation of Galactic dust populations. Utilizing intensity measurements from the Planck High Frequency Instrument, we apply this model to high-latitude Galactic dust emission. Our analysis reveals a strong preference for a spatially varying two-population dust model over a one-population dust model, when the latter must capture the total variance in the sky. Each dust population is well characterized by a single-component spectral energy distribution (SED) and accommodates small variations. These populations could signify two distinct components or may originate from a one-component model with different temperatures resulting in different SED scalings. While no spatial information is built into the likelihood, our investigation unveils large-scale spatially coherent structures with high significance, pointing to a physical origin for the observed spatial variation. These results are robust to our choice of likelihood and input data. Furthermore, this spatially varying two-population model is the most favored from Bayesian evidence calculations. Incorporating IRAS 100 μm to constrain the Wein side of the blackbody function, we find the dust populations differ at the 2.5σ level in the spectral index (β d ) versus temperature (T d ) plane. The presence of multiple dust populations has implications for component separation techniques frequently employed in the recovery of the cosmic microwave background.

Bayesian analysis of the molecular emission and dust continuum of protoplanetary disks

Context. The MIRI instrument on board the James Webb Space Telescope probes the chemistry and dust mineralogy of the inner regions of protoplanetary disks. The observed spectra are unprecedented in their detail and reveal a rich chemistry with strong diversity between objects. This complicates interpretations that are mainly based on manual continuum subtraction and 0D slab models. Aims. We investigate the physical conditions under which the gas emits in protoplanetary disks. Based on MIRI spectra, we apply a full Bayesian analysis that provides the posterior distributions of dust and molecular properties, such as column densities and emission temperatures. Methods. To do so, we introduced the Dust Continuum Kit with Line emission from Gas (DuCKLinG), a Python-based model simultaneously describing the molecular line emission and the dust continuum of protoplanetary disks without large computational cost. The model describes the dust continuum emission by dust models with precomputed dust opacities. The molecular emission is based on LTE slab models but from extended radial ranges with gradients in column densities and emission temperatures. We compare the model to observations using Bayesian analysis with linear regression techniques to reduce the dimension of the parameter space. We benchmarked this model to a complex thermo-chemical ProDiMo model of AATau and fit the MIRI spectrum of GW Lup. The latter allowed for a comparison to the previous results obtained with single slab models and hand-fitted continuum. Results. We successfully decrease the computational time of the fitting method by a factor of 80 by eliminating linear parameters, such as the emission areas, from the Bayesian run. This approach does not significantly change the retrieved molecular parameters, and only the calculated errors on the optically thin dust masses slightly decrease. For an AA Tau ProDiMo mock observation, we find that the retrieved molecular conditions from DuCKLinG (column densities from 3 × 1018 cm−2 to 4 × 1020 cm−2, radial range from 0.2 au to 1.2 au, and temperature range from about 200 K to 400 K) fall within the true values from ProDiMo (column densities between 4 × 1017 cm-2 to 5 × 1020 cm−2, radial extent 0.1 au to 6.6 au, and temperature range from about 120 to 1000 K). The smaller DuCKLinG ranges can be explained by the relative flux contributions of the different parts of ProDiMo. The parameter posterior of GW Lup reinforces previously found results. The previously determined column densities fall within the retrieved ranges in this study for all examined molecules (CO2, H2O, HCN, and C2H2). Similar overlap is found for the temperatures with only the temperature range of HCN (from 570−60+60 to 750−70+90 K) not including the previously found value (875 K). This discrepancy may be due to the simultaneous fitting of all molecules compared to the step-by-step fitting of the previous study. There is statistically significant evidence for radial temperature and column density gradients for H2O and CO2 compared to the constant temperature and column density assumed in the 0D slab models. Additionally, HCN and C2H2 emit from a small region with near constant conditions. Due to the small selected wavelength range 13.6–16.3 µm, the dust properties are not well constrained for GW Lup. DuCKL inG can become an important tool to analyse the molecular emission and dust mineralogy of large samples based on JWST /MIRI spectra in an automated way.

Galaxies with grains: unraveling dust evolution and extinction curves with hydrodynamical simulations

Dust in galaxies is an important tracer of galaxy properties and their evolution over time. The physical origin of the grain size distribution, the dust chemical composition, and, hence, the associated ultraviolet-to-optical extinctions in diverse galaxies remains elusive. To address this issue, we introduce a model for dust evolution in the RAMSES code for simulations of galaxies with a resolved multiphase interstellar medium. Dust is modelled as a fluid transported with the gas component, and is decomposed into two sizes, 5 nm and 0.1 μm, and two chemical compositions for carbonaceous and silicate grains. This dust model includes the growth of dust by accretion of elements from the gas phase and by the release of dust in stellar ejecta, the destruction by thermal sputtering, supernovae, and astration, and the exchange of dust mass between the two main populations of grain sizes by coagulation and shattering. Using a suite of isolated disc simulations with different masses and metallicities, the simulations can explore the role of these processes in shaping the key properties of dust in galaxies. The simulated Milky Way analogue reproduces the dust-to-metal mass ratio, depletion factors, size distribution and extinction curves of the Milky Way. Galaxies with lower metallicities reproduce the observed decrease in the dust-to-metal mass ratio with metallicity at around a few 0.1 Z⊙. This break in the dust-to-metal ratio corresponds to a galactic gas metallicity threshold that marks the transition from an ejecta-dominated to an accretion-dominated grain growth, and that is different for silicate and carbonaceous grains, with ≃0.1 Z⊙ and ≃0.5 Z⊙ respectively. This leads to more Magellanic Cloud-like extinction curves, i.e. with steeper slopes in the ultraviolet and a weaker bump feature at 2175 Å, in galaxies with lower masses and lower metallicities. Steeper slopes in these galaxies are caused by the combination of the higher efficiency of gas accretion by silicate relative to carbonaceous grains and by the low rates of coagulation that preserves the amount of small silicate grains. Weak bumps are due to the overall inefficient accretion growth of carbonaceous dust at low metallicity, whose growth is mostly supported by the release of large grains in SN ejecta. We also show that the formation of CO molecules is a key component to limit the ability of carbonaceous dust to grow, in particular in low-metallicity gas-rich galaxies.

A HAZOP of dust explosion testing and explosibility modelling using artificial neural networks

AbstractThis work presents artificial neural network (ANN) models to determine explosion severity parameters (e.g., maximum explosion pressure and maximum rate of pressure rise) of given dust samples. ANN‐based models for explosibility parameters are presented for carbon black, zinc, urea, and oat grain flour dust samples based on data generated in a 20‐L explosion chamber. The optimal hyper‐parameters of the models have been explored using the Broyden–Fletcher–Goldfarb–Shanno, stochastic gradient descent, and Adam solvers. A hazard and operability study has also been conducted for each of the following to diagnose issues at different stages in developing the ANN‐based dust explosibility models: dust testing, model selection, parameter learning, and evaluation. Using different guidewords, the deviation of numerous factors from their design intent, causes, consequences, and specific safeguards have been provided for enabling optimal performance. This can be helpful in understanding, evaluating, and analyzing dust explosions for safer operation of industrial activities handling combustible dusts.

Dust model sensitivity to dust source mask, sandblasting efficiency, air density, and land use: Implications for model improvement

This study compares dust storm simulations using two commonly adopted methods for representing four important dust emission parameters. Compared with a dynamic dust source mask based on land use and vegetation cover, a static mask based solely on land use overestimates dust concentration and optical depth by a factor of 2, besides generating spurious emissions. The results reinforce that seasonal variations in vegetation cover can significantly affect dust emissions. For sandblasting efficiency, a clay-dependent semiempirical expression produces 12 times more dust than does a physics-based expression. Simulations using model-predicted versus constant air density differ by only 8%. However, this difference (often overlooked) could range between 12% and 22% for annual simulations over global dust source regions. Simulations with updated versus old land use data, using the same dust source mask, differ twofold, indicating the significant impact of land use change on regional dust emission in central Arizona. The difference between simulations within each of the four pairs is generally larger than the uncertainty due to meteorology. The simulations align better with observation when using the dynamic dust source mask, the physics-based sandblasting efficiency, and the up-to-date land use data. Given the high sensitivity of dust to surface conditions, the results discussed have implications for improving the dust cycle in weather and climate models and for interpreting model intercomparisons.

Effect of Accumulated Dust Conductivity on Leakage Current of Photovoltaic Modules

Photovoltaic (PV) modules are often situated in hot and windy environments, such as deserts, where dust accumulation poses a significant problem. The build-up of dust can result in an increase in PV module leakage current, making the modules more vulnerable to potential-induced degradation (PID), ultimately leading to a reduction in the efficiency of PV power generation. In this study, we investigate the impact of dust accumulation on the surface of PV modules on leakage current. A dust model is developed based on the Arrhenius relation, taking into account the impact of temperature and density on dust conductivity. The equation for leakage current due to dust accumulation is derived based on the clean module leakage current equation. We undertake a simulation of natural conditions in a laboratory setting to analyze the impact of dust on the leakage current of photovoltaic modules. The results show the following: At high temperatures, the leakage current will significantly increase due to the elevated conductivity of the dust. The conductivity increased by 27.1%, 48.9%, 64.3%, and 118% for the four groups of dusty PV modules, respectively. Leakage current prediction has a better accuracy when dust is equated to series conductance. Dust can reduce the activation energy of PV modules by up to 3.48%.

Alaskan Glacial Dust Is an Important Iron Source to Surface Waters of the Gulf of Alaska

AbstractThis work evaluates glacial dust as a source of sediment, and associated iron (Fe), to the Fe‐limited Gulf of Alaska (GoA). A reanalysis of GoA sediment data, using rare earth elements and thorium as provenance tracers, suggests a flux to the ocean surface of Copper River (AK) glacial dust, and associated Fe, that is comparable to the flux of dust from Asia, at least 1,000 km from the narrow mountain valley glacial dust source area. This work suggests dust from Asia may not be the largest source of Fe to the GoA. Dust models fail to accurately simulate this glacial dust transport because their coarse resolution underestimates wind speeds, and the dust flux. This work suggests that glacial dust fluxes may have been important in the geologic past (e.g., the last glacial maximum) from locations where there was more extensive coverage by glaciers than at present.

A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust

Abstract. A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer in terms of diameter) and coarse-mode (supermicrometer in terms of diameter) components of atmospheric pure dust is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure dust provided by the first step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of the European Aerosol Research Lidar Network (EARLINET). Accordingly, the fine-mode component of pure dust is extracted as the residual between the LIVAS total pure dust and the coarse-mode component of pure dust. Intermediate steps involve the implementation of regionally dependent lidar-derived lidar ratio values and AErosol RObotic NETwork (AERONET)-based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) at 532 nm. The final products consist of the fine mode and coarse mode of atmospheric pure dust, quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are established primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit path and secondly in averaged profiles of seasonal–temporal resolution, 1° × 1° spatial resolution, and the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70° S and 70° N and covering more than 15 years of Earth observations (June 2006–December 2021). The quality of the CALIPSO-based fine-mode and coarse-mode dust products is assessed through the use of AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and the AERosol properties – Dust (AER-D) campaign airborne in situ particle size distributions (PSDs) as reference datasets during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, and investigation of the role of airborne dust in radiation and air quality.