Abstract. The representation of organic aerosols (OA) in global climate models fails to account for the wide range of species found in the atmosphere. Previous studies have observed that the optical parameters of OA species vary depending on the source from which they are emitted, as well as on their physical and chemical characteristics. However, accounting for all OA species in climate models is not practical. Hence, we have grouped OA species according to their optical parameters and physico-chemical characteristics. We classified OA as strongly absorbing brown carbon (S-BrC), moderately absorbing brown carbon (M-BrC), weakly absorbing brown carbon (W-BrC) and very weakly absorbing brown carbon (VW-BrC). We defined thresholds based on the imaginary refractive index (IRI) for a broad wavelength range from 300 to 550 nm. The classification demonstrates clear optical separation at 350–500 nm, with mass absorption coefficient (MAC) values spanning two orders of magnitude from VW-BrC (0.004 m2/g) to S-BrC (1 m2/g) at 400 nm. Representative species from each category were suggested as surrogates. This choice of species includes both absorbing and scattering OA and enables more accurate representation of OA in climate models and satellite retrievals, improving aerosol radiative forcing estimates.

The CERES Ocean Validation Experiment (COVE) was an instrument suite located at the Chesapeake Light Station approximately 25 km east of Virginia Beach, Virginia (36.9 degrees N, 75.7 degrees W). COVE provided surface verification for the Clouds and the Earth's Radiant Energy System (CERES) satellite measurements for 16 years. However, the large light station occupied approximately 15 % of the field of view of the upwelling longwave flux measurement (LW up arrow), so radiation from the structure artificially perturbed the measurements. Hence, we use data from multiple instruments that are not influenced by the structure to accurately obtain LW up arrow; we call this the longwave component summation technique. The instruments required for the component summation are an infrared radiation thermometer to measure sea surface temperature, a pyrgeometer to measure downwelling longwave irradiance, and an air temperature probe. We find a strong negative bias between the obstructed upwelling pyrgeometer measurements and the component summation LW up arrow in the colder months, less so in the warmer months. The bias ranged from -6% to +5% over COVE from 2004-2013. These range of biases are larger than the Baseline Surface Radiation Network (BSRN) targeted uncertainties of 2 % or 3 W m-2 (whichever is greatest), indicating that the component summation technique provides a significant correction to standard BSRN protocols when an obstruction is present. This work documents how we determine the component summation LW up arrow irradiances, demonstrates that the calculated values achieve a relative standard error of 0.6 % and are within the 2 % target uncertainty, and presents guidelines for implementing this methodology at other locations.

Brown carbon (BrC) is an absorbing organic aerosol, primarily emitted through biomass burning (BB), with a large degree of variability in observed chemical and microphysical properties. This makes model representation of the species difficult. Retrievals of BrC from measured radiance fields have the potential to constrain model schemes and improve estimates of BrC radiative effects. We used a retrieval of BrC optical depth and mass from Aerosol Robotic Network (AERONET) sites in BB regions to evaluate GISS ModelE Earth system model (ESM)'s BrC module. We approached this evaluation by comparing BrC properties defined in ModelE with underlying properties prescribed by the AERONET retrieval, and then constrained our scheme to match those assumptions. Our analysis showed that just with this initial harmonization, model bias was decreased, and performance, relative to the retrieval, was improved. This allowed for an estimate of global average BrC radiative effect, 0.03 W m-2, grounded in the measured radiance fields represented by the retrieval. Through this work, we demonstrate the necessity of harmonizing model scheme parameters with speciated retrieval's assumptions, even if such assumptions are not necessarily more physically correct, to ensure an "apples-to-apples" comparison and ultimately improve the estimate of individual aerosol direct effects within a climate model.

Aerosols are emitted into the atmosphere by natural and anthropogenic events. They scatter and absorb the incoming solar radiation playing a major role in heating and cooling the atmosphere. To determine the heating and cooling rates, remote sensing and global climate modelers use the optical and physical properties of these aerosols. Table of Aerosol Optics (TAO) database is a platform for the scientists to acquire the optical properties of several aerosol species and types for wavelengths ranging from 0.25 µm to 40 µm (673 values) including phase matrices for 203 angles and 36 relative humidity values. TAO will provide the users with choices on the source, shape, size distribution, wavelength range, etc. to accommodate the needs from various atmospheric science groups. We have generated the optical calculations such as the extinction, absorption, single scattering albedo, asymmetry parameter, phase functions etc. using Mie theory for spherical aerosol species like Black Carbon (BC), Organic Carbon (OC) etc. along with an OCBC mixture. OC varies in composition depending on the source of emission. For instance, wildfire emits highly absorbing OC such as Tarballs (type of BrC) along with low viscous OC whereas, anthropogenic emissions generate slightly absorbing OC such as Toluene derived aerosols. The number fraction of Tarballs range from 10% to 95% depending on transport distance and atmospheric aging. Anthropogenic aromatic compounds such as Toluene and Xylene constitute about 70% of the total OC. Organic aerosol being complex in composition, we have classified them based on their optical parameters such as single scattering albedo (SSA) and mass absorbing coefficient (MAC). In TAO we classified organic aerosols into three groups based on their MAC values as: Brown Carbon, Slightly Absorbing Aerosol, and Scattering Aerosol. This classification will improve the representation of organic aerosols in climate and weather models. Hence the TAO database has a potential to replace the database that was developed decades ago and are being widely used among the modeling communities. Using the measurements from latest study will improve the climate and weather data.

This effort is dedicated to construction of a relational database and an interactive web system that organizes and communicates aerosol optical and microphysical characteristics assembled in the Table of Aerosol Optics (TAO) community repository. The TAO project (https://science.larc.nasa.gov/mira-wg/topics/tao/) is an extension of historical efforts (e.g., Shettle and Fenn, 1979; d’Almeida et al., 1991; Koepke et al., 1997; Hess et al., 1998) on providing libraries of aerosol characteristics for applications in global chemical transport modeling and remote sensing. Aerosol characteristics such as size distribution, complex refractive index, shape, mixing state, extinction, absorption, single-scatter albedo, lidar ratio, etc. are provided for different aerosol types, wavelengths, be originated from laboratory measurements, in situ or remote sensing observations. Combination of aerosol characteristics, their origins, types, spectral domains, computational techniques used for single-scatter properties become quickly very complex and is expected to evolve in future. The open access and interactive principles of TAO implies increasing complexity of its database structure that requires involvement of dedicated computer science technics for its organization and management. The relational database conception, for instance, is widely used in many domains that require such data organization and naturally appropriates to TAO. The relational database consists in structuring the data in multiple tables, with so-called primary or foreign keys that relates between entity types, parameters and their value in unique or multiple connections. We therefore started development of tools for uploading of the TAO data into the format of relational database and creation of a web interface for an interactive communication with the community. This work is expected to be presented as complimentary to a more general presentation about the TAO project by G. L. Schuster and gather valuable feedbacks from modelers, in situ and remote sensing experts on the data needs, convenient exchange formats and potential applications.

There is a need to quickly convert aerosol microphysical properties into optical properties for global modeling, data assimilation, and remote sensing applications. This is generally accomplished through look-up tables (LUTs) of aerosol mass extinction coefficients (MEC), mass absorption coefficients (MAC), asymmetry parameters, normalized phase functions, etc. Unfortunately, many scientists are using outdated LUTs that are based upon measurements and computational techniques first published by Shettle and Fenn (1979) and later updated by Hess et al. (1998). Thus, the computations in common use are still largely based upon Mie theory and in situ information that has not been updated during this century.The Table of Aerosol Optics (TAO) is an open relational database (under construction) that expands upon existing LUTs by including recent measurements and new computational techniques for non-spherical particles (https://science.larc.nasa.gov/mira-wg/topics/tao/). The ‘open’ aspect of TAO is important, since the measurements and techniques of today will undoubtedly yield to different values in the future. This open architecture allows specialists to add new tables and gain exposure for their work and benefits modelers and remote sensing scientists by giving them easy access to computations that utilize the latest techniques. Quality is controlled by requiring methods to be peer-reviewed in the scientific literature.Thus far, we have computed mass extinction coefficients, mass absorption coefficients, lidar ratios, etc., at 73 wavelengths ranging from 0.25-40 µm for black carbon (BC), brown carbon (BrC), non-absorbing organic carbon, and mineral dust. For mineral dust, we use hexahedra shapes and mineral mixtures of montmorillonite, illite, hematite, and goethite. The illite volume fraction varies from 0 to 59% to capture the range of real refractive indices found in AERONET climatologies; the sum of the hematite and goethite mass fractions are ~2%. Additional mixtures will be added as appropriate.We have also computed optical properties for 22 size distributions of bare aggregated BC using the Multi-Sphere T-Matrix (MSTM) code (https://github.com/dmckwski/MSTM) at several remote sensing wavelengths. Our MSTM computations use aggregates of 20-nm spherules with particle-cluster growth. We obtained mass absorption coefficients (MACs) of 7.2-7.5 m2/g at a mid-visible wavelength (532 nm) when the BC fractal dimension was fixed at Df = 1.8 (i.e., fresh BC), consistent with values commonly recommended in literature reviews.We will present the TAO vision and example results for several aerosol types. TAO is part of the Models, In situ, and Remote sensing of Aerosols (MIRA) working group. MIRA seeks to build collaboration, consistency, and openness amongst the aerosol disciplines. We seek community feedback from aerosol scientists regarding the construction and content of TAO, especially in this early phase. Check out the MIRA webpage at https://science.larc.nasa.gov/mira-wg/ and subscribe to our mailing list at https://espo.nasa.gov/lists/listinfo/mira.Hess et al. (1998): Optical properties of aerosols and clouds: The software package OPAC, BAMS, 79, 831–844.Shettle and Fenn (1979): Tech. Rep. AFGL-TR-790214, Air Force Geophysics Laboratory, 1979.

Retrieval of aerosol properties near clouds from passive remote sensing is challenging. Sunlight scattered by clouds into nearby clear regions can effectively enhance the clear area reflectance. These cloud 3D radiative effects may lead to large biases in aerosol retrievals if uncorrected, risking the incorrect interpretation of satellite observations for aerosol–cloud interaction in a cloudy atmosphere. In earlier studies, we developed a simple two-layer model (2LM) to estimate the cloud-induced clear-sky radiance enhancements in cloud fields. In this study, we take advantage of CALIPSO lidar observations, which should not be affected by the 3D radiative effect, to study passive aerosol retrievals in cloud fields in the Amazon region, specifically those produced by the operational Dark Target algorithm applied to Aqua-MODIS. From 2 years’ worth of co-located CALIPSO/MODIS aerosol retrievals, we find a larger increase in operationally retrieved MODIS AOD from clear to cloudy regions (∼0.075 or ∼40%) than for the CALIPSO AOD (∼0.021 or ∼20%). The much larger increase in MODIS AOD is mainly due to the 3D radiative effects. After using the 2LM model to account for cloud 3D radiative effects, the clear to cloudy increase in MODIS AOD was reduced to ∼0.043 (∼23%), which is much closer to CALIPSO observations. The 3D corrected average MODIS AOD for cloudy conditions is significantly larger than AOD for clear conditions, even for cloud fraction (CF) less than 0.1, suggesting aerosols in cloudy conditions are characteristically different from aerosols in clear conditions. Furthermore, the 3D correction of AOD (i.e., τ1D−τ3D) increases linearly with CF for a large range of CF. We have also examined the impact of the 3D effect on aerosol Ångström Exponent (AE) and fine model fraction (FMF) of AOD. We found that the uncorrected average AE and FMF depend strongly on CF, ∼25% increase in AE (decrease in particle size) and ∼60% increase in FMF as CF increases from 0.05 to 0.45. The 3D correction leads to smaller average AE (bigger particle size) and FMF that are almost independent of CF. Thus, the 3D corrected aerosol properties are expected to provide more accurate information for better understanding aerosol–cloud interactions.

The composition of soil dust aerosols derives from the mineral abundances in the parent soils that vary across dust source regions. Nonetheless, Earth system models (ESMs) have traditionally represented mineral dust as a globally homogeneous species. The growing interest in modeling dust mineralogy, facilitated by the recognized sensitivity of the dust climate impacts to composition, has motivated state-of-the-art ESMs to incorporate the mineral speciation of dust along with its effect upon the dust direct radiative effect (DRE). In this work, we enable the NASA Goddard Institute for Space Studies ModelE2.1 to calculate the shortwave (SW) DRE accounting for the regionally varying soil mineralogy. Mineral–radiation interaction at solar wavelengths is calculated according to two alternative coupling schemes: (1) external mixing of three mineral components that are optically distinguished, one of which contains embedded iron oxides; (2) a single internal mixture of all dust minerals with a dynamic fraction of iron oxides that varies regionally and temporally. We link dust absorption to the fractional mass of iron oxides based on recent chamber measurements using natural dust aerosol samples. We show that coupled mineralogy overall enhances the scattering by dust, and thus the global cooling, compared to our control run with globally uniform composition. According to the external mixing scheme, the SW DRE at the top of atmosphere (TOA) changes from −0.25 to -0.30Wm-2, corresponding to a change in the net DRE, including the longwave effect, from −0.08 to -0.12Wm-2. The cooling increase is accentuated when the internal mixing scheme is configured: the SW DRE at the TOA becomes -0.34Wm-2 with a net DRE of -0.15Wm-2. The varying composition modifies the regional distribution of single scattering albedo (SSA), whose variations in specific regions can be remarkable (above 0.03) and significantly modify the regional SW DRE. Evaluation against the AErosol RObotic NETwork (AERONET) shows that explicit representation of soil mineralogy and its regional variations reduces the low bias of model dust SSA while improving the range of variability across stations and calendar months. Despite these improvements, the moderate spatiotemporal correlation with AERONET reveals remaining modeling challenges and the need for more accurate measurements of mineral fractions in soils.

Dust aerosol is a key component in global radiative forcing and climate modeling. We developed a mineralogy-resolved spectral optical property model for dust aerosols. The model incorporates nine mineral groups and applies the effective medium methods to obtain the spectral complex refractive indices of inhomogeneous dust particles. This study considers 5,000 sets of dust mineral composition mixtures along with the use of the bimodal particle size distribution. We calculate the bulk properties of these samples in a wide spectral range from 0.2 to 50 mu m using a comprehensive database of hexahedral dust single-scattering properties. Through sensitivity analyses, this study reveals that the illite and hematite components substantially affect the optical properties of dust. Furthermore, the effective radii of both the fine-mode and the coarse-mode dust particles have significant impacts on the bulk optical properties, albeit to varying degrees. Dust aerosol plays a crucial role in influencing Earth's climate, and understanding their behavior is important for climate modeling. In this study, we developed a detailed model to study how different minerals in dust particles affect their interactions with light. We considered nine mineral groups and used robust methods to study how these minerals scatter and absorb light across a wide range of wavelengths. By combining the data of the mineral types found in dust and their particle sizes, we created thousands of dust mineral mixtures and investigate how they interact with light in different parts of the spectrum. Our findings show that certain minerals, such as illite and hematite, have a significant impact on how dust particles interact with light. Additionally, the size of both fine and coarse dust particles also plays an important role in determining the scattering and absorption of light by these particles. Dust aerosol bulk optical properties are computed from the refractive index, mineralogical composition, and particle size distribution The optical properties of dust aerosol show significant sensitivity to hematite and effective radius of the coarse dust The new mineralogy-based property model is applicable to nonspherical dust aerosol remote sensing studies

Brown carbon (BrC) is an absorbing organic aerosol (OA), primarily emitted through biomass burning (BB), which exhibits light absorption unique to both black carbon (BC) and other organic aerosols. Despite many field and laboratory studies seeking to constrain BrC properties, the radiative forcing (RF) of BrC is still highly uncertain. To better understand its climate impact, we introduced BrC to the One-Moment Aerosol (OMA) module of the GISS ModelE Earth system model (ESM). We assessed ModelE sensitivity to primary BrC processed through a novel chemical aging scheme and to secondary BrC formed from biogenic volatile organic compounds (BVOCs). Initial results show that BrC typically contributes a top-of-the-atmosphere (TOA) radiative effect of 0.04 W m−2. Sensitivity tests indicate that explicitly simulating BrC (separating it from other OAs), including secondary BrC, and simulating chemical bleaching of BrC contribute distinguishable radiative effects and should be accounted for in BrC schemes. This addition of prognostic BrC to ModelE allows greater physical and chemical complexity in OA representation with no apparent trade-off in model performance, as the evaluation of ModelE aerosol optical depth against Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectroradiometer (MODIS) retrieval data, with and without the BrC scheme, reveals similar skill in both cases. Thus, BrC should be explicitly simulated to allow more physically based chemical composition, which is crucial for more detailed OA studies like comparisons to in situ measurement campaigns. We include a summary of best practices for BrC representation within ModelE at the end of this paper.

Wildfires and agricultural burning generate seemingly increasing smoke aerosol emissions, impacting societal and natural ecosystems. To understand smoke's effects on climate and public health, we analyzed the spatiotemporal distribution of smoke aerosols, focusing on two major light-absorbing components, namely black carbon (BC) and brown carbon (BrC) aerosols. Using NASA's Earth Polychromatic Imaging Camera (EPIC) instrument aboard NOAA's Deep Space Climate Observatory (DSCOVR) spacecraft, we inferred BC and BrC volume fractions and particle mass concentrations based on spectral absorption provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm with 1-2 h temporal resolution and similar to 10 km spatial resolution over North America and central Africa. Our analyses of regional smoke properties reveal distinct characteristics for aerosol optical depth (AOD) at 443 nm, spectral single-scattering albedo (SSA), aerosol layer height (ALH), and BC and BrC amounts. Smoke aerosols in North America showed extremely high AOD up to 6, with elevated ALH (6-7 km) and significant BrC components up to 250 mg m-2 along the transport paths, whereas the smoke aerosols in central Africa exhibited stronger light absorption (i.e., lower SSA) and lower AOD, resulting in higher-BC mass concentrations and similar BrC mass concentrations than the cases in North America. Seasonal burning source locations in central Africa, following the seasonal shift in the Intertropical Convergence Zone and diurnal variations in smoke amounts, were also captured. A comparison of retrieved AOD443, SSA443, SSA680, and ALH with collocated AERONET and CALIOP measurements shows agreement with RMSE values of 0.2, 0.03-0.04, 0.02-0.04, and 0.8-1.3 km, respectively. An analysis of the spatiotemporal average reveals distinct geographical characteristics in smoke properties closely linked to burning types and meteorological conditions. Forest wildfires over western North America generated smoke with a small-BC volume fraction of 0.011 and a high ALH with large variability (2.2 +/- 1.2 km), whereas smoke from wildfires and agricultural burning over Mexico region shows more absorption and low ALH. Smoke from savanna fires over central Africa had the most absorption, with a high-BC volume fraction (0.015) and low ALH with a small variation (1.8 +/- 0.6 km) among the analyzed regions. Tropical forest smoke was less absorbing and had a high variance in ALH. We also quantify the estimation uncertainties related to the assumptions of BC and BrC refractive indices. The MAIAC EPIC smoke properties with BC and BrC volume and mass fractions and assessment of the layer height provide observational constraints for radiative forcing modeling and air quality and health studies.

The source of dust in the global atmosphere is an important factor to better understand the role of dust aerosols in the climate system. However, it is a difficult task to attribute the airborne dust over the remote land and ocean regions to their origins since dust from various sources are mixed during long-range transport. Recently, a multi-model experiment, namely the AeroCom-III Dust Source Attribution (DUSA), has been conducted to estimate the relative contribution of dust in various locations from different sources with tagged simulations from seven participating global models. The BASE run and a series of runs with nine tagged regions were made to estimate the contribution of dust emitted in East- and West-Africa, Middle East, Central- and East-Asia, North America, the Southern Hemisphere, and the prominent dust hot spots of the Bod & eacute;l & eacute; and Taklimakan Deserts. The models generally agree in large scale mean dust distributions, however models show large diversity in dust source attribution. The inter-model differences are significant with the global model dust diversity in 30%-50%, but the differences in regional and seasonal scales are even larger. The multi-model analysis estimates that North Africa contributes 60% of global atmospheric dust loading, followed by Middle East and Central Asia sources (24%). Southern hemispheric sources account for 10% of global dust loading, however it contributes more than 70% of dust over the Southern Hemisphere. The study provides quantitative estimates of the impact of dust emitted from different source regions on the globe and various receptor regions including remote land, ocean, and the polar regions synthesized from the seven models. As the most abundant aerosol type in the Earth's atmosphere, mineral dust plays an important role in global climate by interacting with incoming and outgoing radiation, providing liquid and ice cloud nuclei, and affecting atmospheric stability. The global dust sources are relatively well characterized by the remote sensing and modeling studies as the majority of dust is emitted from the so-called dust belt which expands from North Africa to East Asia. However, it is challenging to attribute dust sources over the remote land and ocean regions, since dust is mixed during long-range transport, where it experiences complex atmospheric processes, including horizontal and vertical-advection, wet deposition, and dry deposition. Using the multi-model simulations in the Aerocom/Dust Source Attribution experiment, the present study (a) examines the model diversity in dust source attribution and (b) estimates the contribution of dust sources to various receptor regions, including remote land/ocean and the polar regions in different altitudes, from the multi-model statistics. Beyond dust sources, many remote land, ocean, and polar regions are affected by a mixture of dust from various sources around the globe. Contributions of various dust sources are quantitatively estimated in a multi-model experiment Contributions of various sources have different horizontal and vertical distributions and seasonality Dust near source regions are dominated by dust emitted in the upwind source regions; however many remote land, ocean, and polar regions are affected by a mixture of dust from various sources around the globe

A framework to infer volume water fraction, soluble fraction and dry size distributions of fine-mode aerosol from multi-angle, multi-spectral polarimetry retrievals of column-averaged ambient aerosol properties is presented. The method is applied to observations of the Research Scanning Polarimeter (RSP) obtained during two NASA aircraft campaigns, namely the Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) and the Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex). All aerosol retrievals are statistically evaluated using in situ data. Volume water fraction is inferred from the retrieved ambient real part of the refractive index, assuming a dry refractive index of 1.54 and by applying a volume mixing rule to obtain the effective ambient refractive index. The uncertainties in inferred volume water fraction resulting from this simplified model are discussed and estimated to be lower than 0.2 and decreasing with increasing volume water fraction. The daily mean retrieved volume water fractions correlate well with the in situ values with a mean absolute difference of 0.09. Polarimeter-retrieved ambient effective radius for daily data is shown to increase as a function of volume water fraction as expected. Furthermore, the effective variance of the size distributions also increases with increasing effective radius, which we show is consistent with an external mixture of soluble and insoluble aerosol. The relative variations of effective radius and variance over an observation period are then used to estimate the soluble fraction of the aerosol. Daily results of soluble fraction correlate well with in situ-observed sulfate mass fraction with a correlation coefficient of 0.79. Subsequently, inferred water and soluble fractions are used to derive dry fine-mode size distributions from their ambient counterparts. While dry effective radii obtained in situ and from RSP show similar ranges, in situ values are generally substantially smaller during the ACTIVATE deployments, which may be due to biases in RSP retrievals or in the in situ observations, or both. Both RSP and in situ observations indicate the dominance of aerosol with low hygroscopicity during the ACTIVATE and CAMP2Ex campaigns. Furthermore, RSP indicates a high degree of external mixing of particles with low and high hygroscopicity. These retrievals of fine-mode water volume fraction and soluble fraction may be used for the evaluation of water uptake in atmospheric models. Furthermore, the framework allows us to estimate the variation in the concentration of fine-mode aerosol larger than a specific dry radius limit, which can be used as a proxy for the variation in cloud condensation nucleus concentrations. This framework may be applied to multi-angle, multi-spectral satellite data expected to be available in the near future.

Optical properties are computed for fractal-like aggregate black carbon (BC) aerosols coated with different substances. Two models are used for these aerosols: (i) the coated aggregate model (model I), where coating is added to the voids and surroundings of BC fractal-like aggregates; and (ii) the closed-cell aggregate model (model II), where coating is added concentrically to each monomer of BC fractal-like aggregates. Our results favor choosing the coated aggregate model (model I) to simulate scattering and absorption by coated BC aerosols because this model is morphologically more realistic, and because this model yields mass absorption cross section (MAC) and backscattering linear depolarization ratio (LDR) values that are consistent with field measurements. Moreover the corresponding computed degree of linear polarization (DoLP) and LDR values are very sensitive to changes in the coating volume fraction (fvol) and the coating refractive index (m). With the same absorbing BC core, the MAC value increases steadily with increasing fvol and m values. For example, using a density of 1.8 g/cm(3) and BC refractive index 1.95 + i0.79, the calculated MAC values for uncoated BC aggregates range from 6.2 to 6.8 m(2)/g at 0.55 mu m. When coating material is applied to the BC aggregates, the calculated MAC values for model I particles increase to between 9.8 and 13.2 m(2)/g (depending on m) when fvol = 87.5%. The backscattering LDR values also tend to increase with the increasing m values for the shapes and sizes considered in our study. For model I particles, the backscattering LDR values span a wide range of 4.2-27.8% at a wavelength of 0.35 mu m at fvol = 87.5% when m increases from 1.33 to 1.55. Our results are relevant to analyses of polarimetric and lidar observations of smoke particles, especially when these particles undergo hygroscopic growth. (C) 2022 Published by Elsevier Ltd.

Quantitative estimations of atmospheric aerosol absorption are rather uncertain due to the lack of reliable information about the global distribution. Because the information about aerosol properties is commonly provided by single-viewing photometric satellite sensors that are not sensitive to aerosol absorption. Consequently, the uncertainty in aerosol radiative forcing remains one of the largest in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC AR5 and AR6). Here, we use multi-angular polarimeters (MAP) to provide constraints on emission of absorbing aerosol species and estimate global aerosol absorption optical depth (AAOD) and its climate effect. Our estimate of modern-era mid-visible AAOD is 0.0070 that is higher than IPCC by a factor of 1.3-1.8. The black carbon instantaneous direct radiative forcing (BC DRF) is +0.33 W/m2 [+0.17, +0.54]. The MAP constraint narrows the 95% confidence interval of BC DRF by a factor of 2 and boosts confidence in its spatial distribution. Chen et al. demonstrate the use of multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing.

EDITORIAL article Front. Remote Sens., 11 August 2022Sec. Satellite Missions https://doi.org/10.3389/frsen.2022.963660

Abstract. Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).

The study presents a climatology of aerosol composition concentrations obtained by a recently developed algorithm approach, namely the Generalized Retrieval of Atmosphere and Surface Properties (GRASP)/Component. It is applied to the whole archive of observations from the POLarization and Directionality of the Earth's Reflectances (POLDER-3). The conceptual specifics of the GRASP/Component approach is in the direct retrieval of aerosol speciation (component fraction) without intermediate retrievals of aerosol optical characteristics. Although a global validation of the derived aerosol component product is challenging, the results obtained are in line with general knowledge about aerosol types in different regions. In addition, we compare the GRASP-derived black carbon (BC) and dust components with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) product. Quite a reasonable general agreement was found between the spatial and temporal distribution of the species provided by GRASP and MERRA-2. The differences, however, appeared in regions known for strong biomass burning and dust emissions; the reasons for the discrepancies are discussed. The other derived components, such as concentrations of absorbing (BC, brown carbon (BrC), iron-oxide content in mineral dust) and scattering (ammonium sulfate and nitrate, organic carbon, non-absorbing dust) aerosols, represent scarce but imperative information for validation and potential adjustment of chemical transport models. The aerosol optical properties (e.g., aerosol optical depth (AOD), Ångström exponent (AE), single-scattering albedo (SSA), fine- and coarse-mode aerosol optical depth (AODF AND AODC)) derived from GRASP/Component were found to agree well with the Aerosol Robotic Network (AERONET) ground reference data, and were fully consistent with the previous GRASP Optimized, High Precision (HP) and Models retrieval versions applied to POLDER-3 data. Thus, the presented extensive climatology product provides an opportunity for understanding variabilities and trends in global and regional distributions of aerosol species. The climatology of the aerosol components obtained in addition to the aerosol optical properties provides additional valuable, qualitatively new insight about aerosol distributions and, therefore, demonstrates advantages of multi-angular polarimetric (MAP) satellite observations as the next frontier for aerosol inversion from advanced satellite observations. The extensive satellite-based aerosol component dataset is expected to be useful for improving global aerosol emissions and component-resolved radiative forcing estimations. The GRASP/Component products are publicly available (https://www.grasp-open.com/products/, last access: 15 March 2022) and the dataset used in the current study is registered under https://doi.org/10.5281/zenodo.6395384 (Li et al., 2022b).

For the 2020 winter and summer deployments of ACTIVATE and the 2019 CAMP 2 Ex campaign, daily averages of water fraction, dry effective radius and variance and number concentrations (for particles with D > 100 µm) are shown in Table S1.Numbers in between brackets are standard deviations.Geometric averages and standard deviations are given for the number