Aerosol Optical Depth Observations Research Articles

Synoptic Scale Controls and Aerosol Effects on Fog and Low Stratus Life Cycle Processes in the Po Valley, Italy

AbstractFog and low stratus clouds (FLS) form as a result of complex interactions of multiple factors in the atmosphere and at the land surface and impact both the anthropogenic and natural environments. Here, we analyze the role of synoptic conditions and aerosol loading on FLS occurrence and persistence in the Po valley in northern Italy. By applying k‐means clustering to reanalysis data, we find that FLS formation in the Po valley is either based on radiative processes or moisture advection from the Mediterranean sea. Satellite‐based data on FLS persistence shows longer persistence of radiatively formed FLS events, likely due to air mass stagnation and a temperature inversion. Ground‐based aerosol optical depth observations further reveal that FLS event duration is significantly higher under high aerosol loading. The results underline the combined effect of topography, moisture advection and aerosol loading on the FLS life cycle in the Po valley.

Development of an Aerosol Assimilation System Using a Global Non‐Hydrostatic Model, a 2‐Dimensional Variational Method, and Multiple Satellite‐Based Aerosol Products

AbstractThe computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high‐resolution aerosol assimilation system. A 2‐dimensional variational method (2DVar) was implemented in a non‐hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar‐orbital satellites. The model results were evaluated against ground‐based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite‐estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite‐retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground‐based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.

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.

Addressing observational gaps in aerosol parameters using machine learning: Implications to aerosol radiative forcing

The role of atmospheric aerosols has remained hugely uncertain due to lack of long-term systematic observations. This is particularly the case in the global emission hotspot the Indo-Gangetic Plain. In this paper, we apply a machine learning (ML) approach to fill gaps in aerosol observations by combining meteorological reanalysis, and satellite-observations. Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Asymmetry Parameter (AP) are critical parameters for understanding the atmospheric aerosol properties, radiative forcing and climate patterns. We considered AOD, SSA, and AP over an urban site, Kanpur, in the Indo-Gangetic Plain, which is a global hot-spot of high aerosol loading. We used ML technique to fill the observational gaps in AOD, SSA, and AP at 440 nm. Accurate ground-based daily observations of AOD, SSA, and AP from AERONET (Aerosol Robotic Network) over a period of ∼2 decades (2001–2022) are used in the analysis, which have ∼ 37%, ∼62%, and ∼58% data gap, respectively. To reduce observational gaps, an ML model was trained with reanalysis (meteorological parameters) and satellite (angstrom exponent and absorbing aerosol index) datasets by optimally tuning the hyperparameters. The model tested on unseen data showed that it could simulate the AOD (coefficient of determination, R2 = 0.51 and refined index of agreement, RIA = 0.66), SSA (R2 = 0.60 and RIA = 0.70), and AP (R2 = 0.64 and RIA = 0.71), successfully with moderate accuracies: root mean square error of 0.25, 0.018, and 0.018, respectively. This magnitude of error in AOD is comparable to the errors, 0.26 and 0.25, in satellite (MODIS-Moderate Resolution Imaging Spectroradiometer) derived and reanalysis (MERRA-2- Modern-Era Retrospective analysis for Research and Applications, Version 2) AODs, respectively for the study region. When MODIS AOD was included as a predictor in the ML model, it could reproduce AOD with much smaller errors of 0.14, reproducing 84% of total variability. Thus, based on available input data, we reduced observational gaps of daily AOD, SSA, and AP by ∼10% (∼2 years), ∼23% (∼5 years), and ∼21% (∼4 years), respectively, at the study region. Aerosol Radiative Forcing (ARF) at the top-of-atmosphere was estimated using AOD, SSA, and AP both with and without gap-filling. We found that data gaps can significantly impact the estimations of aerosol climate forcing. A considerable change in ARF, ∼ ± 3 Wm−2 indicating that the observational gaps can mislead our understanding of aerosols' impact on climate. This points toward the need for more comprehensive measurements with minimal observational gaps. Our study highlights the possibility of compensating observational gaps by an optimised ML model and that such innovative approach can potentially lead to better understanding of impact of aerosols on climate. Further, the present study could be of immense interest for the community as our validated approach can be applied for other pollutants and for other data-scarce regions in the South and Southeast Asia.

Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems

Atmospheric aerosols play a pivotal role in shaping our environment, impacting climate, human health, and ecosystems. Characterizing the influence of aerosols on ecosystems, especially in mountain environments, is a challenging task due to their complex-orography and scarcity of aerosol ground stations. Satellite-based aerosol data can improve our knowledge over such complex-orography areas. Thus, we have analyzed the Aerosol Optical Depth (AOD) product from the MODerate resolution Imaging Spectrometer (MODIS) sensor produced by the inversion algorithm MultiAngle Implementation of Atmospheric Correction (MAIAC) over the last two decades for the period 2001–2022 with a spatial resolution of 1 × 1 km. Our study focuses on the Sierra Nevada Mountain region and National Park in Southeastern Spain. As a first step, we have validated the AOD from MODIS+MAIAC against three AERONET stations at different altitudes (680 m, 1800 m, and 2500 m above sea level (a.s.l.)). MODIS+MAIAC AOD showed good agreement with the ground-based AOD observations, with R values ranging from 0.75 to 0.82, RMSE values ranging from 0.047 to 0.066 and having 80% of the samples within the expected error (EE) of the product. The MODIS+MAIAC AOD product is able to characterize the fine-scale features of such complex-orography area and hence evaluate the spatio-temporal distribution of the AOD over the mountainous region. We have generated the most extended AOD dataset for a mountainous region, spanning the past two decades. We have deepened into the spatial and seasonal AOD patterns from 2001 to 2022, unveiling elevated AOD values near valleys and urban areas. In general, the AOD values decrease with increasing altitude with the exception of snow-covered areas at high altitudes (>2800 m a.s.l.), which might affect aerosol retrieval and provide bias due to higher-reflecting surfaces and pixel removal. For the first time, the relationship of aerosol loading with ecosystem type has been assessed in the protected environment of Sierra Nevada Natural Park. Monthly AOD trends across different ecosystem types and altitudinal ranges are analyzed in detail over the last two decades. In addition, Generalized Linear Models (GLM) are applied to reveal significant correlations between ecosystems and AOD, irrespective of altitude, latitude or longitude. Based on the interannual variation of AOD over the last two decades, we have analyzed the relationship of AOD with the different ecosystems of Sierra Nevada at 500 m elevation ranges. The patterns of the ecosystem's types are maintained over the elevation ranges 1200–1700 m and 1700–2200 m a.s.l., which demonstrates that land-type has an impact on the AOD product. Furthermore, it is observed that forest-like ecosystems tend to present lower AOD compared with bare-soil or low-growth vegetation ecosystems. In addition, the areas of the mountain closer to Granada city present generalized higher AOD values on the western part of the mountain, regardless of the ecosystem, showing the significant influence of the proximity of urban sites over the ecosystems and the potential impact on the environment.

Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus

Abstract. The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

On the added value of satellite AOD for the investigation of ground-level PM2.5 variability

To assess and mitigate PM2.5 pollution, it is imperative to accurately characterize its spatiotemporal variability and distribution. As station measurements are spatially selective, additional data sources such as satellite observations or chemical transport models (CTMs) are indispensable for providing essential complementary information. With the increasing accessibility of high-quality reanalysis datasets on air pollutants from CTMs, the use of satellite-based observations with their limited spatial and temporal availability is more and more put into question. Model simulations present a clear advantage concerning spatiotemporal continuity. However, how reliable are these model simulations and how does the information they provide differ from satellite-derived data? By applying a random forest (RF) approach we derive PM2.5 concentrations for Germany based on observations of aerosol optical depth (AOD) and a variety of other atmospheric parameters and systematically compare the satellite-derived PM2.5 dataset to results from the CAMS regional model system. We discern the differences in the information they provide to demonstrate the added value of the satellite AOD-derived data for the investigation of long-term PM2.5 variability. The development in PM2.5 variability is analyzed on a yearly and seasonal base over the time period from 2018 to 2022, including the COVID-19 lockdown in spring 2020. We can demonstrate a better accuracy of the RF-based AOD model compared to CAMS reanalysis data with R2 values of 0.71 and 0.57, respectively. Moreover, we find that the AOD-based data provides more detail in spatial gradients and can better depict local and short-term events of enhanced PM2.5 pollution.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Uncertainties from biomass burning aerosols in air quality models obscure public health impacts in Southeast Asia

Abstract. Models suggest that biomass burning causes thousands of premature deaths annually in Southeast Asia due to excessive exposure to particulate matter (PM) in smoke. However, measurements of surface air quality are sparse across the region, and consequently estimates for the public health impacts of seasonal biomass burning, are not well constrained. We use the nested GEOS-Chem model of chemistry and transport (horizontal resolution of 0.25°×0.3125°) to simulate atmospheric composition over Southeast Asia during the peak burning months of March and September in the moderate burning year of 2014. Model simulations with GEOS-Chem indicate that regional surface levels of PM2.5 (fine particulate matter with a diameter ≤2.5 µm) greatly exceed World Health Organization guidelines during the burning seasons, resulting in up to 10 000 premature deaths in a single month. However, the model substantially underestimates the regional aerosol burden compared to satellite observations of aerosol optical depth (AOD) (20 %–52 %) and ground-based observations of PM (up to 54 %), especially during the early burning season in March. We investigate potential uncertainties limiting the model representation of biomass burning aerosols and develop sensitivity simulations that improve model–measurement agreement in March (to within 31 %) and increase the estimated number of PM2.5-related premature deaths that month by almost half. Our modifications have a much smaller impact on the same metrics for September, but we find that this is due to canceling errors in the model. Compared to PM2.5 simulated directly with GEOS-Chem, PM2.5 derived from satellite AOD is less sensitive to model uncertainties and may provide a more accurate foundation for public health calculations in the short term, but continued investigation of uncertainties is still needed so that model analysis can be applied to support mitigation efforts. Further reduction of uncertainties can be achieved with the deployment of more aerosol measurements across Southeast Asia.

Absolute radiance calibration in the UV and visible spectral range using atmospheric observations during twilight

Abstract. We present an improved radiance calibration method for UV–Vis spectroscopic instruments with a narrow field of view (up to a few degrees) based on the calibration method by Wagner et al. (2015). The updated method uses only measurements during the twilight period instead of several hours as for the original method. The calibration is based on the comparison of measurements and simulations of the radiance of zenith-scattered sunlight. The main advantage of our method compared to radiance calibration methods in the laboratory is that the calibration can be directly applied in the field. This allows routine radiance calibrations whenever the sky is clear during twilight. The calibration can also be performed retrospectively and will thus be applicable for the large number of existing data sets. Also, potential changes in the instrument properties during transport from the laboratory to the field are avoided. The new version of the calibration method presented here has two main advantages. First, the required measurement period can be rather short (only a few minutes during twilight for cloud-free conditions). Second, even without knowledge of the aerosol optical depth (AOD), the errors in the calibration method are rather small, especially in the UV spectral range where they range from about 4 % at 340 nm to 8 % at 420 nm. If the AOD is known, the uncertainties are even smaller (about 3 % at 340 nm to 4 % at 420 nm). For visible wavelengths, good accuracy is only obtained if the AOD is approximately known with uncertainties from about 4 % at 420 nm to 10 % between about 550 and 700 nm (generally the AOD is nevertheless smaller in the visible than in the UV spectral range). One shortcoming of the method is that it is not possible to determine the AOD exactly at the time of the (twilight) measurements because AOD observations from sun photometer measurements or the MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) measurements are usually not meaningful for such high solar zenith angle (SZA). But the related uncertainty can be minimised by repeating the radiance calibrations during the twilight periods of several days.

Aerosol optical depth disaggregation: toward global aerosol vertical profiles

Abstract Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of anthropogenic climate change. This uncertainty arises in part from the difficulty in measuring the vertical distributions of aerosols, and only sporadic vertically resolved observations are available. We often have to settle for less informative vertically aggregated proxies such as aerosol optical depth (AOD). In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, that is, the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure, or relative humidity. Using Bayesian nonparametric modeling, we devise a simple Gaussian process prior over aerosol vertical profiles and update it with AOD observations to infer a distribution over vertical extinction profiles. To validate our approach, we use ECHAM-HAM aerosol-climate model data which offers self-consistent simulations of meteorological covariates, AOD, and extinction profiles. Our results show that, while very simple, our model is able to reconstruct realistic extinction profiles with well-calibrated uncertainty, outperforming by an order of magnitude the idealized baseline which is typically used in satellite AOD retrieval algorithms. In particular, the model demonstrates a faithful reconstruction of extinction patterns arising from aerosol water uptake in the boundary layer. Observations however suggest that other extinction patterns, due to aerosol mass concentration, particle size, and radiative properties, might be more challenging to capture and require additional vertically resolved predictors.

Fine and Coarse Dust Effects on Radiative Forcing, Mass Deposition, and Solar Devices Over the Middle East

AbstractIn desert regions like the Middle East, dust has a profound impact on the environment, climate, air quality, and solar devices. The size of dust particles determines the extent of these effects. Dust deposition (DD) measurements show that coarse dust particles with geometric radius r > 10 μm comprise most of the deposited mass. Still, these particles are not represented in the current models that are tuned to fit the observed aerosol optical depth (AOD). As a result, the existing models and reanalysis products underestimate DD and dust emission (DE) almost 3 times. This is the first study to constrain the dust simulations by both AOD and DD measurements to quantify the effect of coarse and fine dust using the WRF‐Chem model. We found that, on average, coarse dust contributes less than 10% to dust shortwave radiative forcing (RF) at the surface but comprises more than 70% of DE. Annual mean net RF over the Arabian Peninsula and regional seas locally reaches −25 W m−2. Airborne fine dust particles with radii r < 3 μm are mainly responsible for the significant dimming (5%–10%) of solar radiation, cooling the surface, and hampering solar energy production. However, dust mass deposition is primarily linked to coarse particles, decreasing the efficiency of photovoltaic panels by 2%–5% per day. Therefore, incorporating coarse dust in model simulations and data assimilation would improve the overall description of the dust mass balance and its impact on environmental systems and solar devices.

Impact of assimilating NOAA VIIRS aerosol optical depth (AOD) observations on global AOD analysis from the Copernicus Atmosphere Monitoring Service (CAMS)

Abstract. Global monitoring of aerosols is required to analyse the impacts of aerosols on air quality and to understand their role in modulating the climate variability. The Copernicus Atmosphere Monitoring Service (CAMS) provides near-real-time forecasts and reanalyses of aerosols using the ECMWF Integrated Forecasting System (IFS), constrained by the assimilation of MODIS and Polar Multi-Sensor Aerosol Optical Properties (PMAp) aerosol optical depth (AOD). Given the potential end of lifetime of MODIS AOD, implementing new AOD observations in the CAMS operational suite is a priority to ensure the continuity of the CAMS forecast performances. The objective of this work is to test the assimilation of the NOAA VIIRS AOD product from S-NPP and NOAA20 satellites in the IFS model. Simulation experiments assimilating VIIRS on top or in place of MODIS were carried out from June 2021 to November 2021 to evaluate the impacts on the AOD analysis. For maritime aerosol background, the assimilation of VIIRS and the use of VIIRS from NOAA20 as an anchor reduce the analysis AOD values compared to MODIS-based experiments, in which the analysis values were too high due to the positive bias of MODIS/Terra over ocean. Over land, the assimilation of VIIRS induces a large increase in the analysis over biomass burning regions where VIIRS shows larger AOD than MODIS due to differences in the aerosol models and cloud filtering between MODIS and VIIRS retrieval algorithms. For dust source regions, the analysis is reduced when VIIRS is assimilated on top of or in place of MODIS, particularly over the Sahara, the Arabian Peninsula and a few places in Asia in the July–August period. The assimilation of VIIRS leads to an overall reduction of the bias in AOD analysis evaluated against AERONET measurements, with the largest bias reduction over Europe and desert and maritime sites.

Empirical Correlation Weighting (ECW) Spatial Interpolation Method for Satellite Aerosol Optical Depth Products by MODIS AOD over Northern China in 2016

Satellite aerosol products are pivotal in studies of regional air quality and global climate change. Compared with accurate in situ observations, satellite measurements provide valuable large-scale atmospheric information. However, limitations such as clouds and retrieval assumptions result in a significant number of missing values in satellite aerosol optical depth (AOD) products, which severely hampers the representativeness. To address this issue, spatial interpolation of the AOD data is necessary to improve data coverage. In this study, one year of AOD observation data from the MODIS C6.1 version was applied to analyze the spatiotemporal correlated characteristics. The statistical parameters were used as dynamic interpolation weights to develop a novel interpolation method called empirical correlation weighting (ECW) based on MODIS AOD over Northern China in 2016. The ECW interpolation results were obtained at a 0.05° resolution (~5 km). The results showed that the spatial coverage of the Deep Blue (DB) and Dark Target (DT) products increased from 43.88% to 70.65% and from 15.04% to 32.62%, respectively. The reconstruction of the ECW method illustrated good agreement with original values in three cases and in two experimental areas. The mean absolute error (MAE) and root mean square error (RMSE) in the two experiments were 0.1171 and 0.0809, and 0.1212 and 0.0838, respectively, indicating that the ECW exhibited the better accuracy than ordinary Kriging (OK) and Thin Plate Spline (TPS). The AERONET validation results indicated that the values of RMSE and MAE were slightly higher after interpolation compared with those before interpolation, maintaining relatively low values, 0.241 and 0.257, 0.140 and 0.150, respectively.

Aerosol Pollution over St. Petersburg in Different Seasons Based on the Results of Lidar Measurements from 2014 to 2022

The results of long-term (during the 8-year period) observations of aerosol optical depth in the center of St. Petersburg are presented, which are performed using the stationary lidar complex of the Resource Center "Observatory of environmental safety", Research Park, St. Petersburg University. It has been established that the content of aerosol particles over the city prevails in the summer and spring seasons, while the minimum content of aerosols is observed in winter.

The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget.

Global lockdown measures to prevent the spread of the coronavirus disease 2019 (COVID-19) led to air pollutant emission reductions. While the COVID-19 lockdown impacts on both trace gas and total particulate pollutants have been widely investigated, secondary aerosol formation from trace gases remains unclear. To that end, we quantify the COVID-19 lockdown impacts on NOx and SO2 emissions and sulfate-nitrate-ammonium aerosols using multiconstituent satellite data assimilation and model simulations. We find that anthropogenic emissions over major polluted regions were reduced by 19 to 25% for NOx and 14 to 20% for SO2 during April 2020. These emission reductions led to 8 to 21% decreases in sulfate and nitrate aerosols over highly polluted areas, corresponding to >34% of the observed aerosol optical depth declines and a global aerosol radiative forcing of +0.14 watts per square meter relative to business-as-usual scenario. These results point to the critical importance of secondary aerosol pollutants in quantifying climate impacts of future mitigation measures.

Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models

Abstract. As operational support to define the Clouds–Atmospheric Dynamics–Dust Interactions in West Africa (CADDIWA) field campaign which took place in the Cape Verde area, the coupled regional model WRF–CHIMERE is deployed in forecast mode during the summer 2021. The simulation domain covers West Africa and the eastern Atlantic and allows the modeling of dust emissions and their transport to the Atlantic. On this route, we find Cape Verde, which was used as a base for measurements during the CADDIWA campaign. Meteorological variables and mineral dust concentrations are forecasted on a horizontal grid with a 30 km resolution and from the surface to 200 hPa. For a given day D, simulations are initialized from D−1 analyses and run for 4 d until D+4, yielding up to six available simulations on a given day. For each day, we thus have six different calculations, with better precision expected the closer we get to the analysis (lead D−1). In this study, a quantification of the forecast variability of wind, temperature, precipitation and mineral dust concentrations according to the modeled lead is presented. It is shown that the forecast quality does not decrease with time, and the high variability observed on some days for some variables (wind, temperature) does not explain the behavior of other dependent and downwind variables (mineral dust concentrations). A new method is also tested to create an ensemble without perturbing input data, but considering six forecast leads available for each date as members of an ensemble forecast. It has been shown that this new forecast based on this ensemble is able to give better results for two AErosol RObotic NETwork (AERONET) stations than the four available for aerosol optical depth observations. This could open the door to further testing with more complex operational systems.

On the importance of the model representation of organic aerosol in simulations of the direct radiative effect of Siberian biomass burning aerosol in the eastern Arctic

Shortcomings and uncertainties in the model representation of atmospheric transformations (the aging) of organic aerosol (OA) have long been identified as one of the potential sources of considerable uncertainty in OA simulations with both global and regional models. However, the impact of this uncertainty on predictions of radiative and climate effects of both anthropogenic and biomass burning (BB) aerosol yet needs to be understood. This study examines the importance of the model representation of OA for simulating the direct radiative effect (DRE) of Siberian BB aerosol in the eastern Arctic. We employ a regional coupled chemistry-meteorology model and a global fire emission database to simulate the optical properties and DRE of BB aerosol emitted from intense Siberian fires in July 2016 and compare the DRE estimates that were obtained using two alternative representations of Siberian BB OA. One of them is a “default” OA representation that predicts very little secondary OA (SOA), and another involves a simple original OA parameterization that has been developed previously within the volatility basis set (VBS) framework and features a strong production of SOA. The simulations of the aerosol optical properties are evaluated against satellite observations of the aerosol optical depth (AOD) in Siberia and the Arctic as well as against values of the single scattering albedo derived from in situ observations of the aerosol absorption and scattering coefficients at four Arctic sites. While the simulations with the default OA representation are found to strongly underestimate AOD both in Siberia and the eastern Arctic, the use of the VBS parameterization considerably improves the agreement between the AOD simulations and observations in both regions. Simulations of the single scattering albedo are found to be overall rather adequate with both representations. Differences in the OA representations are found to result in major differences in the estimates of the DRE of Siberian BB aerosol in the eastern Arctic. Specifically, although the simulations with both representations predict that the DRE is predominantly negative at the top of the atmosphere (TOA), the magnitude of the mean DRE is found to be more than twice as large (6.0 W m−2) with the VBS parameterization than with the default OA representation (2.8 W m−2). An even larger difference (by a factor of 3.5) is found between the estimates of the DRE over the snow- or ice-covered areas. The different treatments of the BB OA evolution are associated also with considerably different contributions of black and brown carbon to the DRE estimates. Overall, our results indicate that model estimates of the DRE of Siberian BB aerosol in the eastern Arctic are strongly sensitive to the assumptions regarding the evolution of OA in Siberian BB plumes and that the SOA formation in these plumes is one of the major factors determining the magnitude of the radiative effects of Siberian BB aerosol in the real atmosphere.

Analysis and Validation of the Aerosol Optical Depth of MODIS Products in Gansu Province, Northwest China

The accurate determination of aerosol optical depth (AOD) is of great importance for climate change research and environmental monitoring. To understand the applicability of the MODIS aerosol product inversion algorithm in Gansu Province, this work uses ground-based solar photometer AOD observation data to validate the MODIS C6 version of the AOD product. Additionally, the retrieval accuracy of MODIS C6 Deep Blue (DB) algorithm AOD products and Deep Blue and Dark Target Fusion (DB–DT combined) algorithm AOD products for Gansu Province when setting different spatial sampling windows is compared and analyzed. Meanwhile, the monitoring effects of these two AOD algorithms in typical polluted atmospheric conditions in Gansu Province are compared. The results show that (1) the correlation between the MODIS AOD products of the two algorithms and the ground-based observation data decreases with an increasing spatial sampling window size. When the spatial sampling window of the two algorithms is set at 30 km × 30 km, it is more representative of the AOD value in Gansu Province, thus reflecting local characteristics. (2) When the spatial sampling window is set at 30 km × 30 km, the inversion effect of the DB algorithm AOD is better than that of the DB–DT combined algorithm AOD on different underlying surfaces. (3) The seasonal variability in the inversion accuracy of the DB algorithm AOD is less than that of the DB–DT combined algorithm, and it has inversion advantages in spring, autumn and winter, while the DB–DT combined algorithm outperforms the DB algorithm only in winter. The inversion effect of the two algorithms on AOD is influenced by the spatial sampling window setting. (4) Both the DB algorithm AOD and the DB–DT combined algorithm AOD can monitor the distribution of AOD in the central and western regions of Gansu, especially for high values of AOD under polluted atmospheric conditions, which represents a good monitoring effect. However, the two algorithms perform poorly in monitoring the southeast region of Gansu, while there is a discontinuous AOD distribution in the northwest region of Gansu. Overall, the MODIS DB algorithm AOD product has higher applicability in Gansu Province. This work provides a good reference for local air pollution and climate prediction.

Assessing the Accuracy of PRISMA Standard Reflectance Products in Globally Distributed Aquatic Sites

PRISMA is the Italian Space Agency’s first proof-of-concept hyperspectral mission launched in March 2019. The present work aims to evaluate the accuracy of PRISMA’s standard Level 2d (L2d) products in visible and near-infrared (NIR) spectral regions over water bodies. For this assessment, an analytical comparison was performed with in situ water reflectance available through the ocean color component of the Aerosol Robotic Network (AERONET-OC). In total, 109 cloud-free images over 20 inland and coastal water sites worldwide were available for the match-up analysis, covering a period of three years. The quality of L2d products was further evaluated as a function of ancillary parameters, such as the trophic state of the water, aerosol optical depth (AOD), observation and illumination geometry, and the distance from the coastline (DC). The results showed significant levels of uncertainty in the L2d reflectance products, with median symmetric accuracies (MdSA) varying from 33% in the green to more than 100% in the blue and NIR bands, with higher median uncertainties in oligotrophic waters (MdSA of 85% for the entire spectral range) than in meso-eutrophic (MdSA of 46%) where spectral shapes were retained adequately. Slight variations in the statistical agreement were then noted depending on AOD values, observation and illumination geometry, and DC. Overall, the results indicate that water-specific atmospheric correction algorithms should be developed and tested to fully exploit PRISMA data as a precursor for future operational hyperspectral missions as the standard L2d products are mostly intended for terrestrial applications.