Fine Mode Aerosols Research Articles

Amino acids in the water-soluble and water-insoluble fractions of the aerosols at a forested site in Japan

In order to clarify the concentration levels and sources of the water-insoluble amino acids (WIAA) in aerosols that have been received little attention in previous studies and to discuss their potential impact on nitrogen deposition, the WIAA in the coarse-mode (d = 2.0–10 μm) and fine-mode (d < 2.0 μm) aerosols were measured at a forested site in Japan together with the water-soluble free and combined amino acids. The sum of the WIAA showed more than a 6-times higher concentration than that of the water-soluble amino acids in the coarse-mode range and a similar concentration to that in the fine-mode range, which suggests a significant contribution of the WIAA to the amino acids pool in aerosols. The WIAA were predominantly partitioned into the coarse-mode range. The concentration of the coarse-mode WIAA was the highest in the summer and a strong correlation was found between the coarse-mode WIAA and nss-K+ concentrations. These results suggest that plant debris and the associated materials are important sources for the coarse-mode WIAA and the increase in the suspension of these particulate materials caused the enhancement of the WIAA in the summer. Remarkably higher concentrations of the coarse-mode WIAA than the water-soluble amino acids would be caused by these particulate materials. In the fine-mode range, on the other hand, the concentration of the WIAA was the highest in the spring and strongly correlated with the fine-mode nss-K+ concentrations. The fine-mode WIAA would be mainly derived from the biomass burning particles. The coarse-mode WIAA were estimated to significantly contribute to the dry deposition flux of the total nitrogen. The present study implies a potential impact of the WIAA in the coarse aerosols on nitrogen deposition.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval.

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.

Aerosol climatology, variability, and trends over the Indo-Gangetic Plain in CMIP6 models

We evaluate the performance of Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating aerosol optical depth (AOD) climatology, variability, and trends over the Indo-Gangetic Plain (IGP) considered for the period of space-borne MODIS satellite observations during 2003–2014. The multi-model ensemble (MME) mean of CMIP6 models, is considered when better correlations (≥0.5) of models against the MODIS-derived AOD observations using standard statistical skill metrics. Analysis suggests that the CMIP6 MME successfully reproduces the spatial distribution of AOD climatology and its seasonal variability, albeit with some discrepancies. In particular, CMIP6 MME underestimates AOD spatial distributions across the IGP region (∼ bais varies between 0.2 and 0.4 during different seasons). CMIP6 MME captures coarse-mode aerosol distribution similar to AERONET over the IGP, while it has limited skill in capturing the fine-mode aerosol distribution, which could be due to uncertainties pertinent to anthropogenic aerosol emissions. Furthermore, the CMIP6 MME captures the seasonal evolution of aerosols over the IGP and its sub-regions (i.e., eastern and western IGP regions). Notably, the CMIP6 MME successfully captures the dominance among various aerosol species and their contributions to total AOD over the IGP. The CMIP6 MME mimic the observed AOD trends (varies between 0.01 and 0.03 year−1) well across the study regions, except the western IGP, where the MME is unable to replicate declining AOD trends during the pre-monsoon and monsoon seasons compared to MODIS. By conducting a principal component analysis on AOD data, it is apparent that the CMIP6 MME captures AOD variances that are comparable to MODIS observations, albeit with some discrepancies. This finding of the present research will help in policymaking, and mitigation strategies in the region.

Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS

Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research.

A Modified Look-Up Table Based Algorithm with a Self-Posed Scheme for Fine-Mode Aerosol Microphysical Properties Inversion by Multi-Wavelength Lidar

A Modified Look-Up Table Based Algorithm with a Self-Posed Scheme for Fine-Mode Aerosol Microphysical Properties Inversion by Multi-Wavelength Lidar

Biomass Burning Aerosol Observations and Transport over Northern and Central Argentina: A Case Study

Biomass Burning Aerosol Observations and Transport over Northern and Central Argentina: A Case Study

Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data

Abstract. The properties of aerosols are highly uncertain owing to the complex changes in their composition in different regions. The radiative properties of different aerosol types differ considerably and are vital for studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. In response, our study aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. This novel algorithm incorporates an optical database, constructed using the Mie scattering model, and employs a random forest algorithm to classify different aerosol types based on the optical data from the database. The complex refractive index was used as a baseline to assess the performance of our hybrid algorithm against the traditional Gaussian kernel density clustering method for aerosol-type identification. The hybrid algorithm demonstrated impressive consistency rates of 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse (mixed, course-mode aerosol), mixed-fine (mixed, fine-mode aerosol), urban/industrial, and biomass burning aerosols, respectively. Moreover, it achieved remarkable precision, with evaluation metric indexes for micro-precision, micro-recall, micro-F1-score, and accuracy of 95 %, 89 %, 91 %, and 89 %, respectively. Lastly, a global map of aerosol types was generated using the new hybrid algorithm to characterize aerosol types across the five continents. This study, utilizing a novel approach for the classification of aerosol, will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.

Complex Hygroscopic Behavior of Ambient Aerosol Particles Revealed by a Piezoelectric Technique.

Understanding the complex interactions between atmospheric aerosols and water vapor in subsaturated regions of the atmosphere is crucial for modeling and predicting aerosol-cloud-radiation-climate interactions. However, the microphysical mechanisms of these interactions for ambient aerosols remain poorly understood. For this study, size-resolved samples were collected from a high-altitude, relatively clean site situated in the Western Ghats of India during the monsoon season, in order to study background and preindustrial processes as a baseline for climate functioning within the context of the most polluted region of the world. Measurements of humidity-dependent mass-based growth factors, hygroscopicity, deliquescence behavior, and aerosol liquid water content (ALWC) were made by a novel approach using a quartz crystal microbalance based on a piezo-electric sensor. The climate-relevant fine-mode aerosols (≤2.5 μm) exhibited strong size-dependent variations in their interactions with water vapor and contributed a high fraction of ALWC. Deliquescence occurred for relatively large aerosols (diameter >180 nm) but was absent for smaller aerosols. The deliquescence relative humidity for ambient aerosols was significantly lower than that of pure inorganic salts, suggesting a strong influence of organic species. Our study establishes an improved approach for accurately measuring aerosol water uptake characteristics of ambient aerosols in the subsaturated regime, aiding in the assessment of radiative forcing effects and improving climate models.

Relationship between aerosol and cloud characteristics over Delhi in North India during the dry and wet season

The study investigates the spatio-temporal variations of aerosol and cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite at an urban megacity Delhi in the northern part of India during the dry and wet season for 15 years period from November 2004 to October 2019. This research comprises the study of crucial aerosol optical properties such as aerosol optical depth (AOD), Angstrom exponent (AE) along with the cloud properties such as water vapor (WV), cloud liquid water path (CLWP), cloud effective radius liquid (CERL), cloud fraction (CF), cloud optical depth (COD), cloud top pressure (CTP), and cloud top temperature (CTT). High AOD was observed across the Indo-Gangetic Plain (IGP), including Delhi and exhibited a range of 0.49–0.89 during the dry season and 0.73 to 0.98 during the wet season with a mean value of 0.73 ± 0.05 during the entire study period. High AE value (mean 1.40 ± 0.05) indicates dominance of fine-mode aerosols over the station, associated with high anthropogenic activities. A significantly large increasing trend in AOD was observed over the station during the wet season as compared to the dry season. The seasonal studies represent the maximum value of WV and CTP in the wet season. In addition, CF, CERL, COD and CLWP values were also high during the wet season. Furthermore, a correlation analyses between the aerosol and cloud parameters were carried out during both the seasons. From the analysis, AOD shows a positive correlation with WV, CF, COD and CLWP in both the seasons whereas a negative correlation was observed with CTP in wet season, while positive in dry season. On the other hand, a poor correlation was observed between AOD verses AE and CTT in both the seasons. These findings can contribute to advancing our understanding of aerosol-cloud interactions and inflate air quality assessments at an urban megacity like Delhi.

Dramatic improvement of aerosol pollution status over the East Asian ocean: from the establishment of Japanese environmental quality standard for PM2.5 in 2009 to its achievement in 2021

Abstract The severe aerosol pollution in East Asia has been a focus of much research. In Japan, the environmental quality standard (EQS) for PM2.5 was established in 2009 (daily average, 35 μg/m3; annual average, 15 μg/m3), and its achievement rate was below 50% during the early 2010s. Then, the PM2.5 concentration gradually decreased, the achievement rate improved, and the EQS for PM2.5 was finally achieved (100%) in fiscal year (FY) 2021. Because transboundary aerosol pollution is an important factor in Japanese air quality, here we analyzed the long-term dataset of the satellite-measured fine-mode aerosol optical depth (AODf) over the East Asian ocean to reveal the changes in the transboundary aerosol over East Asia. Overall, a decrease in AODf was seen over the entire East Asian ocean during the period analyzed. A gradual declining trend in AODf was measured (−4% to −5%/year over the adjacent ocean around Japan) and corresponded well to the trend in PM2.5 concentration observed in Japan (−5.3%/year) during FY2010–FY2021. Due to the domestic contribution in Japan, the negative trend was slightly greater for Japanese PM2.5 concentration than for AODf over the adjacent ocean around Japan, and we concluded that the main reason for the dramatic air quality improvement in PM2.5 in Japan was driven by the improvement of transboundary aerosol pollution over East Asia. In addition, the 12-year analysis period (FY2010 to FY2021) was divided into three parts: stagnation (FY2010 to FY2014), in which PM2.5 and AODf remained the same as they were in FY2010; improvement (FY2015 to FY2018), in which PM2.5 and AODf declined dramatically; and achievement (FY2019 to FY2021), in which PM2.5 and AODf declined further.

Unveiling global land fine- and coarse-mode aerosol dynamics from 2005 to 2020 using enhanced satellite-based monthly inversion data

Accurate fine-mode and coarse-mode aerosol knowledge is crucial for understanding their impacts on the climate and Earth's ecosystems. However, current satellite-based Fine-Mode Aerosol Optical Depth (FAOD) and Coarse-Mode Aerosol Optical Depth (CAOD) methods have drawbacks including inaccuracies, low spatial coverage, and limited temporal duration. To overcome these issues, we developed new global-scale FAOD and CAOD from 2005 to 2020 using a novel deep learning model capable of the synergistic retrieval of two aerosol sizes. After validation with the aerosol robotic network (AERONET) and sky radiometer network (SKYNET), the new monthly FAOD and CAOD showed significant improvements in accuracy and spatial coverage. From 2005 to 2020, the new data showed that China had the greatest decrease in FAOD and CAOD. In contrast, India and South Latin America had a significant increase in FAOD versus North Africa in CAOD. FAOD in the regions of Argentina, Paraguay, and Uruguay in South America have shown an upward trend. The results reveal that FAOD and CAOD display distinct patterns of change in the same regions, particularly on the west coast of the United States where FAOD is increasing, while CAOD is decreasing. Aside from the year 2020 due to the global COVID-19 pandemic, the analysis showed that although China has seen at least an +85% increase in energy consumption and urban expansion in 2019 compared to 2005 due to the needs of development and construction, the implementation of China's air pollution control policies has led to a significant decrease in FAOD (−46%) and CAOD (−65%) after 2013. This research enriches our comprehension of global fine and coarse aerosol patterns, additional investigations are needed to determine the potential global implications of these changes.

Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS

A comprehensive study on the regional and spatial distributions of aerosol columnar optical and physical characteristics utilizing high-quality Aerosol Robotic Network (AERONET) datasets over Asia (Central, South, South-East, and East Asia), along with spatiotemporal collocated validation of two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated aerosol optical depth (AOD) and Ångström exponent (AE) on annual and seasonal scales, is conducted for the first time. AOD is the highest over South Asia in each season, followed by South-East, East, and Central Asia. Annual regional average AOD at 0.50 μm over South Asia is 0.61. Combined influence of both fine mode anthropogenic aerosol emissions from fossil fuel combustion and biomass burning, and coarse mode dust aerosols from seasonal transport lead to higher AOD and total volume concentration (TVC), and exhibit significant spatiotemporal variations. Coarse volume fraction (CVF) and effective radius (Reff) are higher over Central Asia due to the dominance of coarse dust aerosols. East and South-East Asia are dominated by fine mode aerosols and result in higher fine volume concentration (FVC), AE, fine mode fraction (FMF), and lower Reff. Compared to the other regions of the globe (except Africa), AOD is higher over Asia, with higher spatiotemporal variabilities in AOD, AE, FMF, and TVC. Over Asia, the agreement between AERONET and CAMS AOD is better than that of AERONET and MERRA-2 AOD. For high AOD conditions, underestimation in model AODs is higher, and lower fraction of model AODs satisfy the Global Climate Observing System (GCOS) requirement over all regions, and these are more pronounced over Asia. Biases in model AODs are higher over Asia compared to the other regions of the globe, and lower over North America, Europe, and Australia. Higher bias in model AEs compared to AODs over all the regions shows substantial challenges in simulating spectral AOD and appropriate contributions of fine and coarse mode aerosols. These findings over a global aerosol hotspot region, Asia, along with other regions of the globe are crucial for accurate simulation and fine-tuning of aerosol characteristics by regional and global models, and for reducing uncertainties in the assessment of radiative and climate impact of aerosols.

Comparative Analysis of Aerosol Vertical Characteristics over the North China Plain Based on Multi-Source Observation Data

In this paper, multi-source observation, such as aircraft, ground-based remote sensing, and satellite-retrieved data, has been utilized to compare and analyze the vertical characteristics of aerosol optical properties and the planetary boundary layer height (HPBL) over the North China Plain (NCP) region during May–June 2016. Aircraft observations show the vertical profiles of aerosol absorption coefficients (σabs), scattering coefficients (σsca), and extinction coefficients (σext) gradually decrease with altitude, with their maximum values near HPBL. The vertical profiles of σext depended most on the vertical distribution of measured σsca, indicating a significant contribution of scattering aerosols. In addition, the prominent characteristic of the inverse relationship between σext and moisture profile could serve as a reference for predicting air quality in the NCP region. The lower layer pollution during the field experiment was likely caused by the accumulation of fine-mode aerosols, characterized by the vertical distribution of the Ångström exponent and the Aerosol Robotic Network (AERONET) products. Typically, HPBL derived from aircraft and surface Micro Pulse Lidar (MPL) was approximate, while the predicted HPBL by meteorological data indicates an underestimation of ~192 m. Aerosol optical depth (AOD) calculated from aircraft and ground-based remote sensing (such as MPL and AERONET) experienced a strong correlation, and both of them exhibited a similar tendency. However, the AOD retrieved from satellites was significantly larger than that from aircraft and ground-based remote sensing. Overall, the inversion algorithm, cloud identification algorithm, representativeness of the space, and time of the observation may lead to an overestimation or underestimation of AOD under certain circumstances. This study may serve as a re-evaluation of AOD retrieved from multi-source observations and provide a reference to uncover the actual atmospheric environment in the NCP regions.

MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm

Abstract. For over 40 years, the Geostationary Operational Environmental Satellite (GOES) system has provided frequent snapshots of the Western Hemisphere. The advanced baseline imagers (ABIs) on the GOES-16, GOES-17, and GOES-18 platforms are the first GOES-series imagers that meet the precision requirements for high-quality, aerosol-related research. We present MAGARA, a Multi-Angle Geostationary Aerosol Retrieval Algorithm, that leverages multi-angle ABI imagery to exploit the differences in autocorrelation timescales between surface reflectance, aerosol type, and aerosol loading. MAGARA retrieves pixel-level (up to 1 km) aerosol loading and fine-mode fraction at up to the cadence of the measurements (10 min), fine- and coarse-mode aerosol particle properties at a daily cadence, and surface properties by combining the multi-angle radiances with robust surface characterization inherent to temporally tiled algorithms. We present three case studies, and because GOES-17 was not making observations for one case, we present this as a unique demonstration of the multi-angle algorithm using only a single ABI sensor. We also compare MAGARA retrievals of fine-mode (FM) aerosol optical depth (AOD), coarse-mode (CM) AOD, and single-scattering albedo (SSA) statistically, with coincident AErosol RObotic NETwork (AERONET) spectral deconvolution algorithm (SDA) and inversion retrievals for the same period, and against bias-corrected NOAA GOES-16 and GOES-17 retrieved 550 nm AOD. For MAGARA vs. coincident AERONET over-land 500 nm fine-mode fraction and AOD&gt;0.3, MAE=0.031, RMSE=0.100, and r=0.902, indicating good sensitivity to fine-mode fraction over land, especially for smoky regions. For bias-corrected MAGARA vs. coincident AERONET spectral single-scattering albedo with MAGARA AOD&gt;0.5 (n=116), MAE=0.010, RMSE=0.015, and the correlation is 0.87. MAGARA performs best in regions where surface reflectance varies over long timescales with minimal clouds. This represents a large portion of the western half of the United States, much of north-central Africa and the Middle East, some of central Asia, and much of Australia. For these regions, aerosol type and aerosol loading on timescales as short as 10 min could allow for novel research into aerosol–cloud interactions, improvements to air-quality modeling and forecasting, and tighter constraints on direct aerosol radiative forcing.

Aerosol optical and radiative characteristics under contrasting pollution conditions in a typical urban valley in Northwestern China

Aerosol optical and radiative properties play a key role in climate change. Precisely pinpointing the optical and radiative properties of various types of aerosols during diverse pollution events in urban settings remains a challenge. In this study, aerosol optical and radiative properties under contrasting pollution conditions were investigated, including dusty, haze, fireworks, and clean days, in a typical urban valley in Northwestern China, based on observations from the Sun-Sky photometers and simulations from libRadtran. The results show that on dusty days aerosols have high absorption and low backscattering, while on haze days they are characterized by fine, absorptive organic aerosol particles with pronounced forward scattering in the ultraviolet and visible spectra. On fireworks days during Chinese New Year (CNY), fine-mode aerosols from fireworks dominate, with the highest scattering and the lowest absorption under the four pollution conditions, and the particle peak radius growth responds rapidly to changes in relative humidity. Aerosols generally cause Earth's surface cooling and atmospheric warming across various pollution conditions. Notably, dusty days, clean days, and haze days all exhibit a lower positive aerosol radiative forcing (ARF) in the ultraviolet (UV) spectrum at the top of the atmosphere (TOA) due to lower single-scattering albedo (SSA). In contrast, CNY uniquely displays negative UV_ARF at TOA, attributed to high-SSA non-absorptive particles. Aerosols on dusty days have higher positive radiative forcing than on haze days, emphasizing the absorptive impact of mixed dust aerosols. These findings provide valuable insights into the behavior of aerosols under various pollution conditions in a typical urban valley, contributing to a better understanding of the environmental effects of various aerosols in arid urban regions.

Aerosol classification by application of machine learning spectral clustering algorithm

Precise understanding of aerosol classification is crucial for accurately quantifying the effects of aerosols on the Earth’s energy budget, improving remote sensing retrieval algorithms, formulating climate change-related policies, and more. In this study, we used aerosol measurements from the quality assured AERosol Robotic NETwork (AERONET) and utilized a multivariate spectral clustering algorithm, a machine learning tool, to classify global aerosols. The spectral clustering algorithm is a variant of the clustering algorithm that employs eigenvalues and eigenvectors of the data matrix to project the data into a lower-dimensional space of a similar cluster. To accomplish this, we considered five aerosol optical parameters: fine-mode Aerosol Optical Depth, Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scattering Albedo, and Refractive Index from 150 AERONET sites distributed in six continents (Africa, Asia, Australia, Europe, North and South America) during 1993 to 2022. Using the clustering analysis, we identified four primary aerosol types: dust, urban, biomass burning, and mixed aerosols. Among the continents, the African and Asian sites exhibited the highest contribution of dust aerosols, as the region has significant global dust sources. Conversely, Australia, Europe, North, and South America are predominantly influenced by fine-mode aerosols, given their considerable distance from major dust source regions.

Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models

Abstract. The University of Maryland, Baltimore County (UMBC) Hyper-Angular Rainbow Polarimeter (HARP2) will be on board NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in January 2024. In this study we systematically evaluate the retrievability and uncertainty of aerosol and ocean parameters from HARP2 multi-angle polarimeter (MAP) measurements. To reduce the computational demand of MAP-based retrievals and maximize data processing throughput, we developed improved neural network (NN) forward models for spaceborne HARP2 measurements over a coupled atmosphere and ocean system within the FastMAPOL retrieval algorithm. To this end, a cascading retrieval scheme is implemented in FastMAPOL, which leverages a series of NN models of varying size, speed, and accuracy to optimize performance. Two sets of NN models are used for reflectance and polarization, respectively. A full day of global synthetic HARP2 data was generated and used to test various retrieval parameters including aerosol microphysical and optical properties, aerosol layer height, ocean surface wind speed, and ocean chlorophyll a concentration. To assess retrieval quality, pixel-wise retrieval uncertainties were derived from error propagation and evaluated against the difference between the retrieval parameters and truth based on a Monte Carlo method. We found that the fine-mode aerosol properties can be retrieved well from the HARP2 data, though the coarse-mode aerosol properties are more uncertain. Larger uncertainties are associated with a reduced number of available viewing angles, which typically occur near the scan edge of the HARP2 instrument. Results of the performance assessment demonstrate that the algorithm is a viable approach for operational application to HARP2 data after the PACE launch.

Columnar optical-radiative properties and components of aerosols in the Arctic summer from long-term AERONET measurements

Aerosols as an external factor have an important role in the amplification of Arctic warming, yet the geography of this harsh region has led to a paucity of observations, which has limited our understanding of the Arctic climate. We synthesized the latest decade (2010–2021) of data on the microphysical–optical–radiative properties of aerosols and their multi-component evolution during the Arctic summer, taking into consideration the important role of wildfire burning. Our results are based on continuous observations from eight AERONET sites across the Arctic region, together with a meteorological reanalysis dataset and satellite observations of fires, and utilize a back-trajectory model to track the source of the aerosols. The summer climatological characteristics within the Arctic Circle showed that the aerosols are mainly fine-mode aerosols (fraction >0.95) with a radius of 0.15–0.20 μm, a slight extinction ability (aerosol optical depth ∼ 0.11) with strong scattering (single scattering albedo ∼0.95) and dominant forward scattering (asymmetry factor ∼ 0.68). These optical properties result in significant cooling at the Earth's surface (∼−13 W m−2) and a weak cooling effect at the top of the atmosphere (∼−5 W m−2). Further, we found that Arctic region is severely impacted by wildfire burning events in July and August, which primarily occur in central and eastern Siberia and followed in subpolar North America. The plumes from wildfire transport aerosols to the Arctic atmosphere with the westerly circulation, leading to an increase in fine-mode aerosols containing large amounts of organic carbon, with fraction as high as 97–98 %. Absorptive carbonaceous aerosols also increase synergistically, which could convert the instantaneous direct aerosol radiative effect into a heating effect on the Earth–atmosphere system. This study provides insights into the complex sources of aerosol loading in the Arctic atmosphere in summer and emphasizes the important impacts of the increasingly frequent occurrence of wildfire burning events in recent years.

Trace elements in PM2.5aerosols in East Asian outflow in the spring of 2018: emission, transport, and source apportionment

Abstract. Trace metals in aerosol particles impact Earth's radiative budget, human health, and ocean biogeochemistry. Semi-continuous measurements of the elemental composition of fine-mode (PM2.5) aerosols were conducted using an automated X-ray fluorescence analyzer on a remote island of Japan during the spring of 2018. Temporal variations in mass concentrations of geochemically important elements for this period, such as Pb, Cu, Si, Fe, and Mn, and their relationships with the emission tracers, carbon monoxide (CO) and black carbon (BC), were reported. The Integrated Massively Parallel Atmospheric Chemical Transport (IMPACT) model was used to evaluate the source apportionment of these components and was evaluated in terms of emissions and wet removal processes. Pb and Cu originated mainly from anthropogenic sources (98 % and 93 % on average, respectively) over the East Asian continent. Positive correlations of Pb and Cu with BC and CO and the similarity of their concentration-weighted trajectories indicated that the emission sources of these metals share the region where the large CO (and BC) emission sources are located and that CO can be regarded as a tracer of continental anthropogenic emissions. The air masses with minimized impacts of the wet removal during transport were extracted to elucidate the “top-down” emission ratio of Pb and Cu to CO, which were, for the first time, evaluated as 152.7 and 63.1 µg g−1, respectively, during the spring of 2018 in the East Asian outflow. The analysis of the tagged tracer simulations by the IMPACT model confirmed that BC and Si could be used as tracers for anthropogenic and dust emissions, respectively, during the observation period. The source apportionment of Fe and Mn in PM2.5 aerosols was conducted using Si and BC tracers, which revealed that the anthropogenic contribution was 17 % and 44 % on average, respectively. Based on the air mass origins of Fe and Mn, their anthropogenic fraction varied from 2 % to 29 % and 9 % to 68 %, respectively, during the high-PM2.5-concentration periods. However, despite the non-dominant anthropogenic contributions of Fe, they could adversely affect human health and ocean biogeochemistry, owing to their higher water solubility. The modeled BC, Pb, Cu, and Fe were evaluated by separately diagnosing their emission and transport. Ratios of modeled to observed concentrations for these components were analyzed in terms of the accumulated precipitation along the transport from the East Asian continent. The current model simulations were found to overestimate the emissions (based on the Community Emissions Data System, CEDS v2021-02-05) of BC by 44 % and underestimate Cu by 45 %, anthropogenic Fe by 28 % in East Asia, and the wet deposition rates for BC and Pb. Overall, Cu in East Asia exhibited a different nature from BC and Pb in terms of emission sources and wet removal.