Aerosol Robotic Network Stations Research Articles

Changes in Anthropogenic Aerosols during the First Wave of COVID-19 Lockdowns in the Context of Long-Term Historical Trends at 51 AERONET Stations

A quasi-consensus has steadily formed in the scientific literature on the fact that the prevention measures implemented by most countries to curb the 2020 COVID-19 pandemic have led to significant reductions in pollution levels around the world, especially in urban environments. Fewer studies have looked at how these reductions at the ground level translate into variations in the whole atmosphere. In this study, we examine the columnar values of aerosols at 51 mainland European stations of the Aerosol Robotic Network (AERONET). We show that when considered in the context of the long-term trend over the last decade, the columnar aerosol levels for 2020, at the regional level, do not appear exceptional. Both the yearly means and the number of episodes with extreme values for this period are within one standard deviation of the long-term trends. We conclude that the spatially and temporally localized reductions do not add up to statistically significant reductions at the global levels of aerosols. Furthermore, considering that pandemic lockdowns can be thought of as a simulation of a climate change mitigation scenario, we conclude that such lifestyle-based changes present a very low potential as a global climate change mitigation strategy.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Retrieval of Aerosol Optical Depth and FMF over East Asia from Directional Intensity and Polarization Measurements of PARASOL

The advantages of performing aerosol retrieval with multi-angle, multi-spectral photopolarimetric measurements over intensity-only measurements come from this technique’s sensitivity to aerosols’ microphysical properties, such as their particle size, shape, and complex refraction index. In this study, an extended LUT (Look Up Table) algorithm inherited from a previous work based on the assumption of surface reflectance spectral shape invariance is proposed and applied to PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) measurements to retrieve aerosols’ optical properties including aerosol optical depth (AOD) and aerosol fine-mode fraction (FMF). Case studies conducted over East China for different aerosol scenes are investigated. A comparison between the retrieved AOD regional distribution and the corresponding MODIS (Moderate-resolution Imaging Spectroradiometer) C6 AOD products shows similar spatial distributions in the Jing-Jin-Ji (Beijing–Tianjin–Hebei, China’s mega city cluster) region. The PARASOL AOD retrievals were compared against the AOD measurements of seven AERONET (Aerosol Robotic Network) stations in China to evaluate the performance of the retrieval algorithm. In the fine-particle-dominated regions, lower RMSEs were found at Beijing and Hefei urban stations (0.16 and 0.18, respectively) compared to those at other fine-particle-dominated AERONET stations, which can be attributed to the assumption of surface reflectance spectral shape invariance that has significant advantages in separating the contribution of surface and aerosol scattering in urban areas. For the FMF validation, an RMSE of 0.23, a correlation of 0.57, and a bias of −0.01 were found. These results show that the algorithm performs reasonably in distinguishing the contribution of fine and coarse particles.

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

Correlative study of Aerosol Optical Depth with Precipitable water over five AERONET station across the world

Ground-based measurements were taken at various locations, including Kathmandu Bode, Beijing, Gandhi College (located in South Asian zone, highly polluted area), Canberra, and UNC Gaitan (located in South Eastern zone, low polluted area), from January to December 2016. The study investigated the seasonal and inter-annual AOD that were obtained using CIMEL Sunphotometer, as a part of the Aerosol Robotic Network (AERONET). The data showed that Kathmandu (0.45±0.01), Gandhi College (0.45±0.02 and Beijing (0.85±0.03) had higher AOD values compared to other stations, with Kathmandu being a high-altitude site. The analysis found that pre-monsoon AOD were higher over Kathmandu (0.45±0.01), Gandhi College (0.45±0.02), UNC Gaitan (0.28±0.001), and Canberra (0.06±0.002). On the other hand, the monsoon AOD was higher over Beijing (0.85±0.03). The study identified various factors contributing to the higher AOD values in specific regions. Beijing, Gandhi College, and Kathmandu, located in Asian monsoon regions, experienced high aerosol loading due to economic activities, vehicles, urbanization, vegetation fire, land clearing for crop cultivation, as well as winter biomass burning, heating needs, and pollution from bricks kilns, factories, and vehicles. Canberra and UNC Gaitan had higher AOD values primarily due to biomass burning and dust. AOD values are higher for shorter wavelength and vice-versa. We conducted a correlation analysis between AOD and perceptible water at different wavelengths and observed one to one correspondence. The columnar water vapor (CWV) exhibited a high correlation with AOD in Canberra (Coefficient of determination, R2 = 0.61) at 1640 nm but had no relationship with AOD in Gandhi College (Coefficient of determination, R2 = 0) at 1640nm. There is weak negative correlation between AOD and preceptable water over Kathmandu and UNC Gaitan, but have positive correlation in Canberra, Beijing and Gandhi College

Aerosol Types and Their Climatology over the Dust Belt Region

Aerosols, both natural and anthropogenic, are an important but complex component of the Earth’s climate system. Their net impact on climate is about equal in magnitude to that of greenhouse gases but can vary significantly by region and type. Understanding and quantifying these aerosol effects is critical for accurate climate modeling and for developing strategies to mitigate climate change. In this paper, we utilize AERONET (Aerosol Robotic NETwork) data from 10 stations situated in the dust belt region to characterize aerosol properties essential for climate change assessment. Aerosol optical depth (AOD) data at 500 nm and Ångström exponent (α) data at the pair of wavelengths of 440 and 870 nm (α440-870) in the study region are analyzed to discriminate among different types of aerosols. The annual and monthly variabilities in AODs are analyzed to see the aerosols trend in the study region. In addition, the AOD and α440-870 data are utilized in order to determine different aerosol types during the period of study. Furthermore, the correlation coefficient between AODs and various meteorological parameters (temperature, wind speed, wind direction, relative humidity, and visibility) is analyzed. The results of the study indicate that Tamanrasset (2.49%), KAUST (1.29%), Solar Village (1.67%), and Dalanzadgad (0.64%) indicate an increasing trend, while Cairo (−0.38%), Masdar (−2.31%), Dushanbe (−1.18%), and Lahore (−0.10%) indicate a decreasing trend in AODs during the study period. Similarly, the results of characterizing aerosol types show that the highest percentage of desert dust aerosols (68%), mixed aerosols (86%), and biomass burning aerosols (15%) are found over Tamanrasset, Lahore, and Dalanzadgad AERONET stations. The study revealed a strong correlation between AODs and visibility, a moderate correlation with temperature, and a low correlation with other meteorological parameters (wind speed, wind direction, and relative humidity) in the study region. The results of the study are very encouraging and enhance our confidence in using historical AERONET data to improve our understanding of atmospheric aerosols’ characteristics.

Evaluation and Comparison of Multi-Satellite Aerosol Optical Depth Products over East Asia Ocean.

The atmosphere over the ocean is an important research field that involves multiple aspects such as climate change, atmospheric pollution, weather forecasting, and marine ecosystems. It is of great significance for global sustainable development. Satellites provide a wide range of measurements of marine aerosol optical properties and are very important to the study of aerosol characteristics over the ocean. In this study, aerosol optical depth (AOD) data from seventeen AERONET (Aerosol Robotic Network) stations were used as benchmark data to comprehensively evaluate the data accuracy of six aerosol optical thickness products from 2013 to 2020, including MODIS (Moderate-resolution Imaging Spectrometer), VIIRS (Visible Infrared Imaging Radiometer Suite), MISR (Multi-Angle Imaging Spectrometer), OMAERO (OMI/Aura Multi-wavelength algorithm), OMAERUV (OMI/Aura Near UV algorithm), and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) in the East Asian Ocean. In the East Asia Sea, VIIRS AOD products generally have a higher correlation coefficient (R), expected error within ratio (EE within), lower root mean square error (RMSE), and median bias (MB) than MODIS AOD products. The retrieval accuracy of AOD data from VIIRS is the highest in spring. MISR showed a higher EE than other products in the East Asian Ocean but also exhibited systematic underestimation. In most cases, the OMAERUV AOD product data are of better quality than OMAERO, and OMAERO overestimates AOD throughout the year. The CALIPSO AOD product showed an apparent underestimation of the AOD in different seasons (EE Below = 58.98%), but when the AOD range is small (0 < AOD < 0.1), the CALIPSO data accuracy is higher compared with other satellite products under small AOD range. In the South China Sea, VIIRS has higher data accuracy than MISR, while in the Bohai-Yellow Sea, East China Sea, Sea of Japan, and the western Pacific Ocean, MISR has the best data accuracy. MODIS and VIIRS show similar trends in R, EE within, MB, and RMSE under the influence of AOD, Angstrom exponent (AE), and precipitable water. The study on the temporal and spatial distribution of AOD in the East Asian Ocean shows that the annual variation of AOD is different in different sea areas, and the ocean in the coastal area is greatly affected by land-based pollution. In contrast, the AOD values in the offshore areas are lower, and the aerosol type is mainly clean marine type aerosol. These findings can help researchers in the East Asian Ocean choose the most accurate and reliable satellite AOD data product to better study atmospheric aerosols' impact and trends.

Operational AOD Retrieval at Subkilometer Resolution Using OceanSat‐2 OCM Over Land: SAER Algorithm, Uncertainties, Validation &amp; Inter‐Sensor Comparison

AbstractThe ocean color monitor (OCM) sensor onboard OceanSat‐2 is providing data in visible and near Infrared (NIR) bands. Due to the limited spectral coverage of OCM, the widely used dark‐target and deep‐blue aerosol algorithms cannot be adapted. Here, a new aerosol optical depth (AOD) retrieval algorithm for OCM (or similar sensors) over land, termed Space Applications Center AErosol Retrieval (SAER) is described. It utilizes two blue bands for the AOD inversion and, Red and NIR band to characterize the surface in visible bands without assuming red and NIR bands transparent to aerosols. Unlike the dark‐target algorithm, the SAER algorithm can retrieve AOD over bright arid and urban areas too. The uncertainty analysis of SAER suggests a theoretically expected error (EE) envelope of ±(0.06 + 0.26 × AOD) for typical retrieval conditions. OCM AOD over land is retrieved operationally for the first time over Indian and neighboring countries' landmass at the finest spatial resolution of 0.007°. The SAER algorithm is validated against in‐situ AOD measurements during the years 2016–2018 at 21 Aerosol Robotic Network stations located in south‐Asia. Overall validation using 1900 match‐up points shows correlations exceeding 0.8 with 74% of retrievals within EE. The retrievals over cropland, grassland, and mixed land cover types show high (low) correlation (bias), while over bright urban areas somewhat low (high) correlation (bias) is observed. Excluding monsoon season, OCM AOD retrievals show good performance over the year. The performance of MODerate Resolution Imaging Spectroradiometer dark‐target and OCM AOD, against common in‐situ, is close to each other. The study shows that OCM AOD can be used for air quality monitoring/modeling at high spatial resolution.

Footprint of the 2020 COVID-19 Lockdown on Column-Integrated Aerosol Parameters in Spain

The lockdown adopted in Spain to combat the global pandemic due to the coronavirus disease (COVID-19) led to a significant reduction in the emission of aerosols produced by road traffic and industry. This study aims to detect changes in column aerosols in Spain due to the COVID-19 lockdown. High-quality AErosol RObotic NETwork (AERONET) measurements of AOD (aerosol optical depth), AE (Ångström exponent) and SSA (single scattering albedo) over the period 2012–2020 are used for this purpose. Ten AERONET stations with available measurements during the lockdown and post-lockdown periods with a long previous data record are selected. The stations are well distributed throughout Spain, covering different areas and population densities. A comprehensive set of three statistical tests are applied to assess general changes in the dataset, the central tendency and low and high values for each parameter. The analyses are conducted for the 2020 lockdown and post-lockdown periods by comparing daily aerosol data with the measurements recorded for the same calendar days during the period 2012–2019. The results indicate a general increase in AOD during the lockdown and a decrease during the post-lockdown. While AE shows no overall behaviour, SSA is the parameter most sensitive to changes in anthropogenic contribution, with an overall significant increase in almost all the stations during both lockdown and post-lockdown periods. The study contributes to addressing the impact of the COVID-19 lockdown and provides methodologies to detect its footprint.

Evaluation of MERRA-2 data for aerosols patterns over the Kingdom of Saudi Arabia

Aerosol is one of the major climate-forcing parameters which affect the Kingdom of Saudi Arabia in particular. The most relevant consideration that characterizes the aerosol properties and distribution is the Aerosol Optical Depth (AOD). In this study Modern Era Retrospective Analysis for Research and Applications (MERRA-2) AOD product from the year 1980–2021 is used to investigate aerosols pattern over the Kingdom of Saudi Arabia. The validation of the MERRA-2 AOD product is made by using AOD data retrieved from Aerosol Robotic Network (AERONET) stations located at Solar Village (SV) and at King Abdullah University of Science and Technology (KAUST). Various statistical analyses are performed to test the reliability of MERRA-2 data in the study region. The results of the statistical analysis indicate that MERRA-2 is highly correlated with both AERONET stations data. Thus, annual and seasonal aerosol climatology maps based on 41 years of MERRA-2 data are prepared and analyzed over the study region. The annual and seasonal aerosol climatology analysis of MERRA-2 data shows high density of AOD at southern and eastern regions while the low density emerges over the western and northern regions of the country during the study period. The results of the study are very encouraging, which increases our confidence level to use historical MERRA-2 AOD product to improve the knowledge on aerosols distribution over the region in future.

Feasibility analysis of AERONET lunar AOD for nighttime particulate matter estimation

Several studies have attempted to estimate particulate matter (PM) concentrations using aerosol optical depth (AOD), based on AOD and PM relationships. Owing to the limited availability of nighttime AOD data, PM estimation studies using AOD have focused on daytime. Recently, the Aerosol Robotic Network (AERONET) produced nighttime AOD, called lunar AOD, providing an opportunity to estimate nighttime PM. Nighttime AOD measurements are particularly important as they help fill gaps in our understanding of aerosol variability and its impact on the atmosphere, as there are significant variations in AOD between day and night. In this study, the relationship between lunar AOD and PM was investigated using data from AERONET station, meteorological station, and air pollution station in Seoul Metropolitan area from May 2016 to December 2019, and then PM estimation model was developed covering both daytime and nighttime using random forest machine learning techniques. We have found the differences in the importance of variables affecting the AOD-PM relationship between day and night from the random forest model. The AOD-PM relationship in the daytime was more affected by time-related variables, such as the day of the year among the variables. The new model was developed using additional lunar AOD data to estimate continuous PM concentrations. The results have shown that the model based on lunar AOD data estimated well PM10 and PM2.5 with similar performance of model using solar AOD. The results imply the possibility of seamless near-surface PM concentration data on a large scale once satellites produce nighttime AOD data.

Ground solar absorption observations of total column CO, CO2, CH4, and aerosol optical depth from California's Sequoia Lightning Complex Fire: emission factors and modified combustion efficiency at regional scales

Abstract. With global wildfires becoming more widespread and severe, tracking their emissions of greenhouse gases and air pollutants is becoming increasingly important. Wildfire emissions have primarily been characterized by in situ laboratory and field observations at fine scales. While this approach captures the mechanisms relating emissions to combustion phase and fuel properties, their evaluation on regional-scale plumes has been limited. In this study, we report remote observations of total column trace gases and aerosols during the 2020 wildfire season from smoke plumes in the Sierra Nevada of California with an EM27/SUN solar Fourier transform infrared (FTIR) spectrometer. We derive total column aerosol optical depth (AOD), emission factors (EFs) and modified combustion efficiency (MCE) for these fires and evaluate relationships between them, based on combustion phase at regional scales. We demonstrate that the EM27/SUN effectively detects changes in CO, CO2, and CH4 in the atmospheric column at ∼10 km horizontal scales that are attributed to wildfire emissions. These observations are used to derive total column EFCO of 120.5±12.2 and EFCH4 of 4.3±0.8 for a regional smoke plume event in mixed combustion phases. These values are consistent with in situ relationships measured in similar temperate coniferous forest wildfires. FTIR-derived AOD was compared to a nearby AERONET (AErosol RObotic NETwork) station and observed ratios of XCO to AOD were consistent with those previously observed from satellites. We also show that co-located XCO observations from the TROPOspheric Monitoring Instrument (TROPOMI) satellite-based instrument are 9.7±1.3 % higher than our EM27/SUN observations during the wildfire period. Finally, we put wildfire CH4 emissions in context of the California state CH4 budget and estimate that 213.7±49.8 Gg CH4 were emitted by large wildfires in California during 2020, about 13.7 % of the total state CH4 emissions in 2020. Our work demonstrates a novel application of the ground-based EM27/SUN solar spectrometers in wildfire monitoring by integrating regional-scale measurements of trace gases and aerosols from smoke plumes.

Temporal Evolution and Regional Properties of Aerosol over the South China Sea

Aerosol robotic network (AERONET) data from Dongsha Island (20.699N, 116.729E) and Taiping Island (35.90N, 3.03W) over the South China Sea (SCS) from January 2018 to December 2020 were used to analyze and discuss the temporal evolution properties of aerosols in the South China Sea. Surrounding AERONET stations (Hong Kong, NSPO, Nha Trang and Singapore) were also used to analyze regional characteristics. High aerosol loads over Dongsha were strongly associated with the anthropogenic fine particle transport from the southeastern coast of China and occasional advection of desert dust from Mongolian areas. The high fine aerosol loading in Taiping originates from the region between Singapore and Indonesia. Compared with other marine islands in the world, SCS was not a pure marine aerosol environment and was affected by terrestrial aerosols. In the Taiping area, aerosol optical depth τ (500 nm) was 0.17 ± 0.13 and the average Ångström exponent α (440–870 nm) was 0.96 ± 0.36. However, that of Dongsha shows the larger values of τ (0.26 ± 0.21) and α (1.1 ± 0.38), indicating that there are large fluctuations in aerosol concentration and size. Aerosol loads in different regions of the SCS due to uneven socioeconomic and complex meteorological systems, such as those of the coastal cities of China, Singapore, and the region between Singapore and Indonesia, contribute to the high optical depth. The special meteorological regime and aerosol source mechanism in the SCS leads to the obvious seasonal cycle of aerosol optical depth and Ångström index. Moreover, the loading variations of aerosols on Dongsha Island and Taiping Island were highly consistent with those of coastal cities around them, suggesting the significant effect of the aerosol in the SCS by the surrounding coastal cities, although the aerosol optical depth in these two places was much lower than that in the surrounding cities.

Classification of MODIS fire emission data based on aerosol absorption Angstrom exponent retrieved from AERONET data

Biomass burning emits a large quantity of gaseous pollutants and aerosols into the atmosphere, which perturbs the regional and global climate and has significant impacts on air quality and human health. In order to understand the temporal and spatial distributions of biomass burning and its contribution to aerosol optical and radiative impacts, we examined fire emission data and its contribution to aerosol optical and radiative impacts over six major hot-spot continents/sub-continents across the globe, namely North-Central (NC) Africa, South America, US-Hawaii, South Asia, South East Asia, and Australia-New Zealand, using long-term satellites, ground-based and re-analysis data during 2000–2021. The selected six sites contributed ∼70% of total global fire data. The classification of biomass burning, such as pre, active, and post burning phases, was performed based on the Absorption Angstrom Exponent (AAE) estimated from 55 AERONET (AErosol RObotic NETwork) stations. The study found the highest contribution of fire count (55 %) during the active burning phase followed by post (36 %) and pre (8 %) burning phases. Such high fire counts were associated with high absorption aerosol optical depth (AAOD) during the active fire event. Strong dominance of fine and coarse mode mixed aerosols were also observed during active and post fire regimes. High AAOD and low Extinction Angstrom Exponent (EAE) over NC Africa during the fire events suggested presence of mineral dust mixed with biomass burning aerosols. Brightness temperature, fire radiative power and fire count were also dominated by the active burning followed by post and pre burning phases. The maximum heating rate of 3.15 K day−1 was observed during the active fire events. The heating rate profile shows clear variations for three different fire regimes with the highest value of 1.80 K day−1 at ∼750 hPa altitude during the active fire event.

Modeling radiative and climatic effects of brown carbon aerosols with the ARPEGE-Climat global climate model

Abstract. Organic aerosols are predominantly emitted from biomass burning and biofuel use. The fraction of these aerosols that strongly absorbs ultraviolet and short visible light is referred to as brown carbon (BrC). The life cycle and the optical properties of BrC are still highly uncertain, thus contributing to the uncertainty of the total aerosol radiative effect. This study presents the implementation of BrC aerosols in the Tropospheric Aerosols for ClimaTe In CNRM (TACTIC) aerosol scheme of the atmospheric component of the Centre National de Recherches Météorologiques (CNRM) climate model. This implementation has been achieved using a BrC parameterization based on the optical properties of Saleh et al. (2014). Several simulations have been carried out with the CNRM global climate model, over the period of 2000–2014, to analyze the BrC radiative and climatic effects. Model evaluation has been carried out by comparing numerical results of single-scattering albedo (SSA), aerosol optical depth (AOD), and absorption aerosol optical depth (AAOD) to data provided by Aerosol Robotic Network (AERONET) stations, at the local scale, and by different satellite products, at the global scale. The implementation of BrC and its bleaching parameterization has resulted in an improvement of the estimation of the total SSA and AAOD at 350 and 440 nm. This improvement is observed at both the local scale, for several locations of AERONET stations, and the regional scale, over regions of Africa (AFR) and South America (AME), where large quantities of biomass burning aerosols are emitted. The annual global BrC effective radiative forcing (all-sky conditions) has been calculated in terms of both aerosol–radiation interactions (ERFari, 0.029 ± 0.006 W m−2) and aerosol–cloud interactions (ERFaci, −0.024 ± 0.066 W m−2). This study shows, on an annual average, positive values of ERFari of 0.292 ± 0.034 and 0.085 ± 0.032 W m−2 over the AFR and AME regions, respectively, which is in accordance with the BrC radiative effect calculated in previous studies. This work also reveals that the inclusion of BrC in the TACTIC aerosol scheme causes a statistically significant low-level cloud fraction increase over the southeastern Atlantic Ocean during the burning season partially caused by a vertical velocity decrease at 700 hPa (semi-direct aerosol effect). Lastly, this study also highlights that the low-level cloud fraction changes, associated with more absorbing biomass burning aerosols, contribute to an increase in both solar heating rate and air temperature at 700 hPa over this region.

Improved Clear Sky Model from In Situ Observations and Spatial Distribution of Aerosol Optical Depth for Satellite-Derived Solar Irradiance over the Korean Peninsula

In solar resource assessment, the climatological environment of the target area is objectively quantified by the cloudiness or clear sky index, which is defined as the ratio of global horizontal irradiance to clear sky solar insolation. The clear sky model calculates incoming solar irradiance on the ground surface considering several atmospheric parameters such as water vapor and aerosol optical depth. This study investigated the importance of aerosol optical depth for deriving clear sky irradiance in radiative transfer models and examined its viability in a universal or community model for public use. The evaluation was conducted based on ground observations at the Korea Institute of Energy Research (KIER) station from January to December 2021. The original simulation was performed using the monthly mean of aerosol optical depth obtained from the Aerosol Robotic Network station; the mean absolute error was 29.9 W m−2. When the daily mean of in situ observations at KIER was incorporated into the clear sky model, the mean absolute error was reduced to 9.7 W m−2. Our results confirm that the clear sky model using gridded datasets of aerosol optical depth is suitable for use as a universal or community model.

A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data

The 30 m resolution Landsat data have been used for high resolution aerosol optical depth (AOD) retrieval based on radiative transfer models. In this paper, a Landsat-8 AOD retrieval algorithm is proposed based on the deep neural network (DNN). A total of 6390 samples were obtained for model training and validation by collocating 8 years of Landsat-8 top of atmosphere (TOA) data and aerosol robotic network (AERONET) AOD data acquired from 329 AERONET stations over 30°W–160°E and 60°N–60°S. The Google Earth Engine (GEE) cloud-computing platform is used for the collocation to avoid a large download volume of Landsat data. Seventeen predictor variables were used to estimate AOD at 500 nm, including the seven bands TOA reflectance, two bands TOA brightness (BT), solar and viewing zenith and azimuth angles, scattering angle, digital elevation model (DEM), and the meteorological reanalysis total columnar water vapor and ozone concentration. The leave-one-station-out cross-validation showed that the estimated AOD agreed well with AERONET AOD with a correlation coefficient of 0.83, root-mean-square error of 0.15, and approximately 61% AOD retrievals within 0.05 + 20% of the AERONET AOD. Theoretical comparisons with the physical-based methods and the adaptation of the developed DNN method to Sentinel-2 TOA data with a different spectral band configuration are discussed.

Inferring Polluted Asian Absorbing Aerosol Properties Using Decadal Scale AERONET Measurements and a MIE Model

AbstractAbsorbing aerosols uniquely impact radiation, aerosol transport, and meteorology. This paper quantifies black carbon core and sulphate shell size and mass using decadal measurements of multi‐spectral aerosol optical depth, single scattering albedo, and angstrom exponent from Aerosol Robotic Network stations located throughout East, Southeast, and South Asia, in connection with a MIE model. All sites are uniquely characterized into four types: urban, biomass burning, long‐range transport, and clean. Unique size and mass probability distributions of both the core and shell are calculated within each classification. Well known urban, biomass burning, and clean sites are all properly identified. Furthermore, two unique sites previously thought to not have multiple characteristics are identified, with urban and biomass burning significant in Beijing and long‐range transport significant in the otherwise clean South China Sea at Taiping Island. It is hoped that these results will allow for advances in attribution and radiative forcing studies.

Casual Rerouting of AERONET Sun/Sky Photometers: Toward a New Network of Ground Measurements Dedicated to the Monitoring of Surface Properties?

This paper presents an innovative method for observing vegetation health at a very high spatial resolution (~5 × 5 cm) and low cost by upgrading an existing Aerosol RObotic NETwork (AERONET) ground station dedicated to the observation of aerosols in the atmosphere. This study evaluates the capability of a sun/sky photometer to perform additional surface reflectance observations. The ground station of Toulouse, France, which belongs to the AERONET sun/sky photometer network, is used for this feasibility study. The experiment was conducted for a 5-year period (between 2016 and 2020). The sun/sky photometer was mounted on a metallic structure at a height of 2.5 m, and the acquisition software was adapted to add a periodical (every hour) ground-observation scenario with the sun/sky photometer observing the surface instead of being inactive. Evaluation is performed by using a classical metric characterizing the vegetation health: the normalized difference vegetation index (NDVI), using as reference the satellite NDVI derived from a Sentinel-2 (S2) sensor at 10 × 10 m resolution. Comparison for the 5-year period showed good agreement between the S2 and sun/sky photometer NDVIs (i.e., bias = 0.004, RMSD = 0.082, and R = 0.882 for a mean value of S2A NDVI around 0.6). Discrepancies could have been due to spatial-representativeness issues (of the ground measurement compared to S2), the differences between spectral bands, and the quality of the atmospheric correction applied on S2 data (accuracy of the sun/sky photometer instrument was better than 0.1%). However, the accuracy of the atmospheric correction applied on S2 data in this station appeared to be of good quality, and no dependence on the presence of aerosols was observed. This first analysis of the potential of the CIMEL CE318 sun/sky photometer to monitor the surface is encouraging. Further analyses need to be carried out to estimate the potential in different AERONET stations. The occasional rerouting of AERONET stations could lead to a complementary network of surface reflectance observations. This would require an update of the software, and eventual adaptations of the measurement platforms to the station environments. The additional cost, based on the existing AERONET network, would be quite limited. These new surface measurements would be interesting for measurements of vegetation health (monitoring of NDVI, and also of other vegetation indices such as the leaf area and chlorophyll indices), for validation and calibration exercise purposes, and possibly to refine various scientific algorithms (i.e., algorithms dedicated to cloud detection or the AERONET aerosol retrieval algorithm itself). CIMEL is ready to include the ground scenario used in this study in all new sun/sky photometers.

Evaluation of natural aerosols in CRESCENDO Earth system models (ESMs): mineral dust

Abstract. This paper presents an analysis of the mineral dust aerosol modelled by five Earth system models (ESMs) within the project entitled Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO). We quantify the global dust cycle described by each model in terms of global emissions, together with dry and wet deposition, reporting large differences in the ratio of dry over wet deposition across the models not directly correlated with the range of particle sizes emitted. The multi-model mean dust emissions with five ESMs is 2836 Tg yr−1 but with a large uncertainty due mainly to the difference in the maximum dust particle size emitted. The multi-model mean of the subset of four ESMs without particle diameters larger than 10 µ m is 1664 (σ=651) Tg yr−1. Total dust emissions in the simulations with identical nudged winds from reanalysis give us better consistency between models; i.e. the multi-model mean global emissions with three ESMs are 1613 (σ=278) Tg yr−1, but 1834 (σ=666) Tg yr−1 without nudged winds and the same models. Significant discrepancies in the globally averaged dust mass extinction efficiency explain why even models with relatively similar global dust load budgets can display strong differences in dust optical depth. The comparison against observations has been done in terms of dust optical depths based on MODIS (Moderate Resolution Imaging Spectroradiometer) satellite products, showing global consistency in terms of preferential dust sources and transport across the Atlantic. The global localisation of source regions is consistent with MODIS, but we found regional and seasonal differences between models and observations when we quantified the cross-correlation of time series over dust-emitting regions. To faithfully compare local emissions between models we introduce a re-gridded normalisation method that can also be compared with satellite products derived from dust event frequencies. Dust total deposition is compared with an instrumental network to assess global and regional differences. We find that models agree with observations within a factor of 10 for data stations distant from dust sources, but the approximations of dust particle size distribution at emission contributed to a misrepresentation of the actual range of deposition values when instruments are close to dust-emitting regions. The observed dust surface concentrations also are reproduced to within a factor of 10. The comparison of total aerosol optical depth with AERONET (AErosol RObotic NETwork) stations where dust is dominant shows large differences between models, although with an increase in the inter-model consistency when the simulations are conducted with nudged winds. The increase in the model ensemble consistency also means better agreement with observations, which we have ascertained for dust total deposition, surface concentrations and optical depths (against both AERONET and MODIS retrievals). We introduce a method to ascertain the contributions per mode consistent with the multi-modal direct radiative effects, which we apply to study the direct radiative effects of a multi-modal representation of the dust particle size distribution that includes the largest particles.