Airborne desert dust in the Northern Adriatic area (Croatia): Different sources

During the implementation of the INTERREG IT-HR project ECOMOBILITY, whose one of the goals was to estimate the impact of ship emissions on air quality in the port city of Rijeka (Croatia) and Venice (Italy), two particular weekly samples were collected in Rijeka, during the first and the thirteen weeks of sampling, i.e. S01 (16.10.-23.10.2018) and S13 (24.04.-30.04.2019.), respectively. Both samples have similarities regarding species characteristic for desert dust contribution, but HYSPLIT analyses excluded Saharan desert to be the source of the S01 sample. Unlike Saharan dust, this sample had a high contribution of fine and ultrafine particles (>50 % and 9.8 %, respectively), as well as secondary inorganic (sulfates, ammonium) and organic (water soluble organic compounds - WSOC) aerosols. Detailed synoptic situation and HYSPLIT backward trajectories pointed out the Syrian Desert as the source of this collected sample. The same source was proved by MERRA-2 reanalysis of the desert dust emission. Although the Saharan dust episodes, mostly in precipitation, are well known in the Northern Adriatic area, this is the first time to indicate Syrian Desert as a source of airborne particulates. This assumption was confirmed with chemical species characteristic for the Syrian Desert, i.e. higher content of potassium from K- feldspar and phosphates.

Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

Environmental management: a country-level evaluation of atmospheric particulate matter removal by the forests of India

Particulate matter (PM) is a critical air pollutant, responsible for an array of ailments leading to premature mortality worldwide. Nature-based solutions for mitigation of PM and especially role of forests in mitigating PM from an ecosystem perspective are less explored. Forests provide a natural pollution abatement strategy by providing a surface area for the deposition of PM. Depending on their structure and composition, forests have varying capacities for PM adsorption, which is again less explored. Hence, in the present study, we evaluate the removal capacity of PM by the forest-type groups of India. Deposition flux and total PM removal across sixteen forest types were estimated based on the 2019 dataset of PM using Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) data. Externality values and PM removal costs by industrial equipment were used for associating an economic value to the air pollution abatement service by forests. The total PM2.5 removal by forests in 2019 was estimated to be 1361.28 tons and PM10 was estimated to be 303,658.27 tons. Deposition of PM was found to be high in littoral and swamp forests, tropical semi-evergreen forests, tropical moist deciduous forests, and sub-tropical pine forests. Tropical dry deciduous forests had the highest net weight % removal of PM with 39% removal for PM2.5 and 39% removal for PM10. The air pollution abatement service by forests for PM removal was 188 M US dollars (USD) with externality-based removal service by forests of 2009 M USD. The net PM removed by all forests of India was estimated to be approximately worth ₹ 470-648 Crore (59-81 million dollars) for PM2.5 and worth ₹56,746-1,22,617 Crore (7093-15,327 million dollars) for PM10 based on valuation using value transfer method. The study concludes that forests can be a significant contributor to PM reduction at a global level. Especially for India's National Clean Air Programme and further research and policy considerations, the findings would be extremely useful.

Spatio-temporal patterns and trends of the air pollution integrating MERRA-2 and in situ air quality data over Egypt (2013-2021)

For best-informed decision-making to improve climate change adaptation and reduce present and future air pollution health hazards, it is essential to identify major trends in spatiotemporal air quality patterns of common air contaminants. This study examined the patterns and trends of SO2, NO2, CO, O3, and particulate matter (PM) air pollutants over 91 monitoring stations in Egypt during 93 months in the August (2013)-April (2021) period. In situ data with their monthly, seasonal, and yearly spatial trends are defined and used to validate the counterpart satellite reanalysis MERRA-2 data. The Mann-Kendall test characterized the seasonal monotonic trends and their Sen's slope, and annual change rate for both data series. Regression analysis of MERRA-2 against in situ concentrations of SO2 and PM10 revealed underestimation with RMSE values of 13.38 g m-3 and 69.46 g m-3, respectively. Local plumes with variable magnitudes characterized distinct industrial places clarified by patterns of in situ pollutants. As a result of the COVID-19 lockdown, the in situ air pollutants showed a considerable regional decline in the yearly average in 2020 compared to the years before. The in situ air pollutants showed annual trends far more significant than those seen in the MERRA-2 data. The shortcomings of the few and spatiotemporal discontinuities of the in situ contaminants are addressed by MERRA-2 air quality products. The in situ data made trends and magnitudes clear that were hidden in their MERRA-2 counterparts. The results clarified air pollution patterns, trends, and spatial variability over Egypt that are essential for climate risk management and for reducing environmental/health concerns.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11869-023-01357-6.

Atmospheric nourishment of global ocean ecosystems

Over the vast open ocean, vital nutrients for phytoplankton growth in the sunlit surface layer are largely provided through physical transport from deep waters, but some nutrients are also provided through atmospheric deposition of desert dust. The extent and magnitude of dust-mediated effects on surface ocean ecosystems have been difficult to estimate globally. In this work, we use global satellite ocean color products to demonstrate widespread responses to atmospheric dust deposition across a diverse continuum of phytoplankton nutritional conditions. The observed responses vary regionally, with some areas exhibiting substantial changes in phytoplankton biomass, whereas in other areas, the response reflects a change in physiological status or health. Climate-driven changes in atmospheric aerosols will alter the relative importance of this nutrient source.

Spatial and temporal heterogeneity of air pollution in East Africa

East Africa's air pollution levels are deteriorating due to anthropogenic and biomass burning emissions and unfavorable weather conditions. This study investigates the changes and influencing factors of air pollution in East Africa from 2001 to 2021. The study found that air pollution in the region is heterogeneous, with increasing trends observed in pollution hot spots (PHS) while it decreased in pollution cold spots (PCS). The analysis identified four major pollution periods: High Pollution period 1, Low Pollution period 1, High Pollution period 2, and Low Pollution period 2, which occur during Feb-Mar, Apr-May, Jun-Aug and Oct-Nov, respectively. The study also revealed that long range transport of pollutants to the study area is primarily influenced by distant sources from the eastern, western, southern, and northern part of the continent. The seasonal meteorological conditions, such as high sea level pressure in the upper latitudes, cold air masses from the northern hemisphere, dry vegetation, and a dry and less humid atmosphere from boreal winter, further impact the transport of pollutants. The concentrations of pollutants were found to be influenced by climate factors, such as temperature, precipitation, and wind patterns. The study identified different pollution patterns in different seasons, with some areas having minimal anthropogenic pollution due to high vegetation vigor and moderate precipitation. Using Ordinary Least Square (OLS) regression and Detrended Fluctuation Analysis (DFA), the study quantified the magnitude of spatial variation in air pollution. The OLS trends indicated that 66 % of pixels exhibited decreasing trends while 34 % showed increasing trends, and DFA results indicating that 36 %, 15 %, and 49 % of pixels exhibited anti-persistence, random, and persistence in air pollution, respectively. Areas in the region experiencing increasing or decreasing trends in air pollution, which can be used to prioritize interventions and resources for improving air quality, were also highlighted. It also identifies the driving forces behind air pollution trends, such as anthropogenic or biomass burning, which can inform policy decisions aimed at reducing air pollution emissions from these sources. The findings on the persistence, reversibility, and variability of air pollution can inform the development of long-term policies for improving air quality and protecting public health.

The Impact of the First and Second Waves of COVID-19 Pandemic in Nigeria

In recent times, the COVID-19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID-19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID-19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID-19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (<25°C), Low temperatures, and low RH which is mostly observed during the cold/dusty periods.

Evaluation of the simulated aerosol optical properties over India: COALESCE model inter-comparison of three GCMs with ground and satellite observations

Within the framework of COALESCE project (Carbonaceous aerosol emissions, source apportionment, and climate impacts) initiative, spatio-temporal distribution of aerosol optical properties from three general circulation models are evaluated against aerosol data from satellite observations (MODIS and CALIPSO) and ground-based measurements (AERONET) for the period 2005-2014. The GCMs, NICAM-SPRINTARS (N-S), ECHAM6.3-HAM2.3 (E-H), CAM5.3 (CAM), input with identical emissions from the SMoG-India-v1 emission inventory over India nested in the CEDS global inventory, including all emission sectors except sea salt and soil dust. The annual mean total aerosol optical depth (AOD) averaged over the Indian land region is 0.38, 0.27, and 0.17 from the N-S, CAM, and E-H models respectively, while the annual mean value from the MODIS observational dataset is 0.43. Single scattering albedo predicted by E-H is lower compared to CAM and N-S while model predictions of Angstrom exponent are closer to MERRA2 dataset. However, the average total aerosol column burden over Indian landmass simulated by the models is very close and comparable to the reanalysis results. Statistical analysis of AOD between model and AERONET measurements at nine sites shows that the root mean square error varies from 0.1 to 0.4 and the index of agreement (average value) is ∼0.4. The aerosol emission and transport models, methodology for calculation of aerosol optical properties and their mixing states contributes to the diversity in the results from various models. The present study provides an analysis of limitations and uncertainties contributing to the differences between the simulations and observations, and the inter-model diversity.

Generating a long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)

Generating a long-term high-spatiotemporal resolution global PM_2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM_2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM_2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m³ and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m³. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.

Anthropogenic pollutants could enhance aridity in the vicinity of the Taklimakan Desert: A case study

With the intensification of human activities, the mixture of anthropogenic pollutants and natural dust aerosols in the vicinity of the Taklimakan Desert (TD) has become a new uncertainty in the weather and climate system. In this study, using a Weather Research and Forecasting model version 4.0 with the Thompson aerosol-aware microphysics scheme, we investigated the impact of anthropogenic aerosols on clouds and precipitation in an atmospheric environment with abundant dust aerosols in the vicinity of the TD. Our findings indicate that anthropogenic aerosols can increase cloud droplet number concentrations in the vicinity of the TD, and the maximum percentage increase can reach 50 %. In addition, the effective radius of water clouds decreases significantly due to anthropogenic aerosols, which means that more numerous but smaller cloud droplets are formed with enhanced anthropogenic aerosol loading under a dusty background. Meanwhile, anthropogenic aerosols can decrease raindrops below 650 hPa, graupel and snow particles, causing less precipitation in the dusty atmosphere surrounding the TD. Furthermore, the anthropogenic aerosol-induced changes in daily precipitation accumulation are also large, with a regionally averaged maximum reduction of up to 4.2 %. Therefore, anthropogenic aerosols are an important factor that exacerbates aridity in the vicinity of the TD, and there is an urgent need to control anthropogenic pollutants around the TD.

Aerosol-Radiation Interactions in China in Winter: Competing Effects of Reduced Shortwave Radiation and Cloud-Snowfall-Albedo Feedbacks Under Rapidly Changing Emissions

Since 2013, Chinese policies have dramatically reduced emissions of particulates and their gas-phase precursors, but the implications of these reductions for aerosol-radiation interactions are unknown. Using a global, coupled chemistry-climate model, we examine how the radiative impacts of Chinese air pollution in the winter months of 2012 and 2013 affect local meteorology and how these changes may, in turn, influence surface concentrations of PM_2.5, particulate matter with diameter <2.5 μm. We then investigate how decreasing emissions through 2016 and 2017 alter this impact. We find that absorbing aerosols aloft in winter 2012 and 2013 heat the middle- and lower troposphere by ∼0.5-1 K, reducing cloud liquid water, snowfall, and snow cover. The subsequent decline in surface albedo appears to counteract the ∼15-20 W m^-2 decrease in shortwave radiation reaching the surface due to attenuation by aerosols overhead. The net result of this novel cloud-snowfall-albedo feedback in winters 2012-2013 is a slight increase in surface temperature of ∼0.5-1 K in some regions and little change elsewhere. The aerosol heating aloft, however, stabilizes the atmosphere and decreases the seasonal mean planetary boundary layer (PBL) height by ∼50 m. In winter 2016 and 2017, the ∼20% decrease in mean PM_2.5 weakens the cloud-snowfall-albedo feedback, though it is still evident in western China, where the feedback again warms the surface by ∼0.5-1 K. Regardless of emissions, we find that aerosol-radiation interactions enhance mean surface PM_2.5 pollution by 10%-20% across much of China during all four winters examined, mainly though suppression of PBL heights.

Extensive characterization of aerosol optical properties and chemical component concentrations: Application of the GRASP/Component approach to long-term AERONET measurements

A recently developed GRASP/Component approach (GRASP: Generalized Retrieval of Atmosphere and Surface Properties) was applied to AERONET (Aeronet Robotic Network) sun photometer measurements in this study. Unlike traditional aerosol component retrieval, this approach allows the inference of some information about aerosol composition directly from measured radiance, rather than indirectly through the inversion of optical parameters, and has been integrated into the GRASP algorithm. The newly developed GRASP/Component approach was applied to 13 AERONET sites for different aerosol types under the assumption of aerosol internal mixing rules to analyze the characteristics of aerosol components and their distribution patterns. The results indicate that the retrievals can characterize well the spatial and temporal variability of the component concentration for different aerosol types. A reasonable agreement between GRASP BC retrievals and MERRA-2 BC products is found for all different aerosol types. In addition, the relationships between aerosol component content and aerosol optical parameters such as aerosol optical depth (AOD), fine-mode fraction (FMF), absorption Ångström exponent (AAE), scattering Ångström exponent (SAE), and single scattering albedo (SSA) are also analyzed for indirect verifying the reliability of the component retrieval. It was demonstrated the GRASP/Component optical retrievals are in good agreement with AERONET standard products [e.g., correlation coefficient (R) of 0.93-1.0 for AOD, fine-mode AOD (AODF), coarse-mode AOD (AODC) and Ångström exponent (AE); R = ~ 0.8 for absorption AOD (AAOD) and SSA; RMSE (root mean square error) < 0.03 for AOD, AODF, AODC, AAOD and SSA]. Thus, it is demonstrated the GRASP/Component approach can provide aerosol optical products with comparable accuracy as the AERONET standard products from the ground-based sun photometer measurements as well as some additional important inside on aerosol composition.

COVID-19 persuaded lockdown impact on local environmental restoration in Pakistan

Coronavirus disease 2019 (COVID-19) pandemic adversely affected human beings. The novel coronavirus has claimed millions of lives all over the globe. Most countries around the world, including Pakistan, restricted people's social activities and ordered strict lockdowns throughout the country, to control the fatality of the novel coronavirus. The persuaded lockdown impact on the local environment was estimated. In the present study, we assessed air quality changes in four cities of Pakistan, namely Islamabad, Karachi, Lahore, and Peshawar, based on particulate matter (PM_2.5), using "Temtop Airing 1000," which is capable of detecting and quantifying PM_2.5. The Air Quality Index (AQI) was evaluated in three specific time spans: the COVID-19 pandemic pre- and post-lockdown period (January 1, 2020 to March 20, 2020, and May 16, 2020 to June 30, 2020 respectively), and the COVID-19 pandemic period (March 21 2020 to May 15, 2020). We compared land-monitored AQI levels for the above three periods of time. For validation, air quality was navigated by the Moderate Resolution Imaging Spectrometer (MODIS) satellite during the first semester (January 1 to June 30) of 2019 and 2020. It is seen that the concentration of PM_2.5 was considerably reduced in 2020 (more than 50%), ranging from ~ 0.05 to 0.3 kg⋅m³, compared to the same period in 2019. The results revealed that the AQI was considerably reduced during the lockdown period. This finding is a very promising as the inhabitants of the planet Earth can be guaranteed the possibility of a green environment in the future.

Impact of Vertical Profiles of Aerosols on the Photolysis Rates in the Lower Troposphere from the Synergy of Photometer and Ceilometer Measurements in Raciborz, Poland, for the Period 2015–2020

The effect of the aerosol vertical distribution on photolysis frequencies of O3 and NO2 is studied. Aerosol measurements in Raciborz (50.08° N, 18.19° E), Poland, made using the CIMEL Sun photometer and collocated CHM-15k “Nimbus” ceilometer are analyzed for the period 2015–2020. Vertical profiles of the aerosol extinction are derived from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm combining the ceilometer measurements of the aerosol backscattering coefficient with the collocated CIMEL measurements of the columnar characteristics of aerosols. The photolysis frequencies are calculated at the three levels in the lower troposphere (the surface and 0.5 and 2 km above the surface) using a radiative transfer model, Tropospheric Ultraviolet and Visible (TUV), for various settings of aerosol optical properties in the model input. The importance of the aerosol vertical distribution on photolysis frequencies is inferred by analyzing statistics of the differences between the output of the model, including the extinction profile from the GRASP algorithm, and the default TUV model (based on columnar aerosol characteristics by the CIMEL Sun photometer and Elterman’s extinction profile). For model levels above the surface, standard deviation, 2.5th percentile, 97.5th percentile, and the extremes, calculated from relative differences between these input settings, are comparable with the pertaining statistical values for the input pair providing changes of photolysis frequencies only due to the variability of the columnar aerosol characteristics. This indicates that the vertical properties of aerosols affect the distribution of the photolysis frequencies in the lower troposphere on a similar scale to that due to variations in columnar aerosol characteristics.

The Influence of Aerosols on Satellite Infrared Radiance Simulations and Jacobians: Numerical Experiments of CRTM and GSI

For a variational data assimilation (DA) system that assimilates radiance observations, the simulated brightness temperature (BT) at the top of the atmosphere and the corresponding Jacobians carried out by the radiance observation operator are needed information. Previous studies reported that the incorporation of aerosol information into the radiance observation operator leads to cooler simulated infrared (IR) BTs and warmer temperature analyses at low levels of the atmosphere. However, the role of the aerosol-affected Jacobians in the DA system, which not only affect the determination of analysis increments but also influence the quality control and the bias correction algorithm, is yet to be investigated. This study examines the aerosol impacts on the sensitivity of IR radiance simulations, Jacobians, and the analysis increments by conducting two experiments: (i) sensitivity tests to assess how the different aspects of the aerosol profiles (i.e., mass loading, peak aerosol level, aerosol thickness layer, and bin partition) affect the simulated BT and the Jacobians from the Community Radiative Transfer Model (CRTM), which is the radiance observation operator in the Gridpoint Statistical Interpolation (GSI) analysis system; (ii) single IR observation experiments using GSI to investigate how the aerosol-affected atmospheric Jacobians influence the analysis increment. The results show that dust aerosols produce the strongest cooling to simulated BTs under similar aerosol optical depths; simulated BTs and Jacobians are most sensitive to the loading and peak altitude of the aerosol layer; simulated BTs become more sensitive to the temperature of the aerosol layer; aerosol-induced differences in atmospheric Jacobians lead to considerable changes to temperature and moisture increments. These results provide a better understanding of the aerosol impacts on each component involved in radiance DA, which can provide guidance for assimilating aerosol-affected IR observations.

Sensitivity of Summertime Convection to Aerosol Loading and Properties in the United Arab Emirates

The Weather Research and Forecasting (WRF) model is used to investigate convection–aerosol interactions in the United Arab Emirates (UAE) for a summertime convective event. Both an idealized and climatological aerosol distributions are considered. The convection on 14 August 2013 was triggered by the low-level convergence of the cyclonic circulation associated with the Arabian Heat Low (AHL) and the daytime sea-breeze circulation. Numerical experiments reveal a high sensitivity to aerosol properties. In particular, replacing 20% of the rural aerosols by carbonaceous particles has a comparable impact on the surface radiative fluxes to increasing the aerosol loading by a factor of 10. In both cases, the UAE-averaged net shortwave flux is reduced by ~90 W m−2 while the net longwave flux increases by ~51 W m−2. However, when the aerosol composition is changed, WRF generates 20% more precipitation than when the aerosol loading is increased, due to a broader and weaker AHL. The surface downward and upward shortwave and upward longwave radiation fluxes are found to scale linearly with the aerosol loading. An increase in the amount of aerosols also leads to drier conditions and a delay in the onset of convection due to changes in the AHL.

Passive versus Active Transport of Saharan Dust Aerosols by African Easterly Waves

Theory and modeling are combined to reveal the physical and dynamical processes that control Saharan dust transport by amplifying African easterly waves (AEWs). Two cases are examined: active transport, in which the dust is radiatively coupled to the circulation; passive transport, in which the dust is radiatively decoupled from the circulation. The theory is built around a dust conservation equation for dust-coupled AEWs in zonal-mean African easterly jets. The theory predicts that, for both the passive and active cases, the dust transports will be largest where the zonal-mean dust gradients are maximized on an AEW critical surface. Whether the dust transports are largest for the radiatively passive or radiatively active case depends on the growth rate of the AEWs, which is modulated by the dust heating. The theoretical predictions are confirmed via experiments carried out with the Weather Research and Forecasting model, which is coupled to a dust conservation equation. The experiments show that the meridional dust transports dominate in the passive case, while the vertical dust transports dominate in the active case.

Evaluation of Nine Operational Models in Forecasting Different Types of Synoptic Dust Events in the Middle East

This study investigates four types of synoptic dust events in the Middle East region, including cyclonic, pre-frontal, post-frontal and Shamal dust storms. For each of these types, three intense and pervasive dust events are analyzed from a synoptic meteorological and numerical simulation perspective. The performance of 9 operational dust models in forecasting these dust events in the Middle East is qualitatively and quantitatively evaluated against Terra-MODIS observations and AERONET measurements during the dust events. The comparison of model AOD outputs with Terra-MODIS retrievals reveals that despite the significant discrepancies, all models have a relatively acceptable performance in forecasting the AOD patterns in the Middle East. The models enable to represent the high AODs along the dust plumes, although they underestimate them, especially for cyclonic dust storms. In general, the outputs of the NASA-GEOS and DREAM8-MACC models present greater similarity with the satellite and AERONET observations in most of the cases, also exhibiting the highest correlation coefficient, although it is difficult to introduce a single model as the best for all cases. Model AOD predictions over the AERONET stations showed that DREAM8-MACC exhibited the highest R2 of 0.78, followed by NASA_GEOS model (R2 = 0.74), which both initially use MODIS data assimilation. Although the outputs of all models correspond to valid time more than 24 h after the initial time, the effect of data assimilation on increasing the accuracy is important. The different dust emission schemes, soil and vegetation mapping, initial and boundary meteorological conditions and spatial resolution between the models, are the main factors influencing the differences in forecasting the dust AODs in the Middle East.

Global Clear-Sky Aerosol Speciated Direct Radiative Effects over 40 Years (1980–2019)

We assess the 40-year climatological clear-sky global direct radiative effect (DRE) of five main aerosol types using the MERRA-2 reanalysis and a spectral radiative transfer model (FORTH). The study takes advantage of aerosol-speciated, spectrally and vertically resolved optical properties over the period 1980–2019, to accurately determine the aerosol DREs, emphasizing the attribution of the total DREs to each aerosol type. The results show that aerosols radiatively cool the Earth’s surface and heat its atmosphere by 7.56 and 2.35 Wm−2, respectively, overall cooling the planet by 5.21 Wm−2, partly counterbalancing the anthropogenic greenhouse global warming during 1980–2019. These DRE values differ significantly in terms of magnitude, and even sign, among the aerosol types (sulfate and black carbon aerosols cool and heat the planet by 1.88 and 0.19 Wm−2, respectively), the hemispheres (larger NH than SH values), the surface cover type (larger land than ocean values) or the seasons (larger values in local spring and summer), while considerable inter-decadal changes are evident. These DRE differences are even larger by up to an order of magnitude on a regional scale, highlighting the important role of the aerosol direct radiative effect for local and global climate.

Experimental OMPS Radiance Assimilation through One-Dimensional Variational Analysis for Total Column Ozone in the Atmosphere

This experiment is the first ultraviolet radiance assimilation for atmospheric ozone in the troposphere and stratosphere. The experiment has provided better understanding of which observations need to be assimilated, what bias correction scheme may be optimal, and how to obtain surface reflectance. A key element is the extension of the Community Radiative Transfer Model (CRTM) to handle fully polarized radiances, which presents challenges in terms of computational resource requirements. In this study, a scalar (unpolarized) treatment of radiances was used. The surface reflectance plays an important role in assimilating the nadir mapper (NM) radiance of the Ozone Mapping and Profiler Suite (OMPS). Most OMPS NM measurements are affected by the surface reflection of solar radiation. We propose a linear spectral reflectance model that can be determined inline by fitting two OMPS NM channel radiances at 347.6 and 371.8 nm because the two channels have near zero sensitivity on atmospheric ozone. Assimilating a transformed reflectance measurement variable, the N value can overcome the difficulty in handling the large dynamic range of radiance and normalized radiance across the spectrum of the OMPS NM. It was found that the error in bias correction, surface reflectance, and neglecting polarization in radiative transfer calculations can be largely mitigated by using the two estimated surface reflectance. This study serves as a preliminary demonstration of direct ultraviolet radiance assimilation for total column ozone in the atmosphere.

Cloud-to-Ground Lightning Response to Aerosol over Air-Polluted Urban Areas in China

The effect of aerosols on lightning has been noted in many studies, but much less is known about the long-term impacts in air-polluted urban areas of China. In this paper, 9-year data sets of cloud-to-ground (CG) lightning, aerosol optical depth (AOD), convective available potential energy (CAPE), and surface relative humidity (SRH) from ground-based observation and model reanalysis are analyzed over three air-polluted urban areas of China. Decreasing trends are found in the interannual variations of CG lightning density (unit: flashes km−2day−1) and total AOD over the three study regions during the study period. An apparent enhancement in CG lightning density is found under conditions with high AOD on the seasonal cycles over the three study regions. The joint effects of total AOD and thermodynamic factors (CAPE and SRH) on CG lightning density and the percentage of positive CG flashes (+CG flashes/total CG flashes × 100; PPCG; unit: %) are further analyzed. Results show that CG lighting density is higher under conditions with high total AOD, while PPCG is lower under conditions with low total AOD. CG lightning density is more sensitive to CAPE under conditions with high total AOD.

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

Cloud drop number concentrations (N _d) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol optical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak in spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N _d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH), low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N _d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N _d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N _d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N _d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol-N _d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N _d.

Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001-2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.

Improved representation of the global dust cycle using observational constraints on dust properties and abundance

Even though desert dust is the most abundant aerosol by mass in Earth's atmosphere, atmospheric models struggle to accurately represent its spatial and temporal distribution. These model errors are partially caused by fundamental difficulties in simulating dust emission in coarse-resolution models and in accurately representing dust microphysical properties. Here we mitigate these problems by developing a new methodology that yields an improved representation of the global dust cycle. We present an analytical framework that uses inverse modeling to integrate an ensemble of global model simulations with observational constraints on the dust size distribution, extinction efficiency, and regional dust aerosol optical depth. We then compare the inverse model results against independent measurements of dust surface concentration and deposition flux and find that errors are reduced by approximately a factor of two relative to current model simulations of the Northern Hemisphere dust cycle. The inverse model results show smaller improvements in the less dusty Southern Hemisphere, most likely because both the model simulations and the observational constraints used in the inverse model are less accurate. On a global basis, we find that the emission flux of dust with geometric diameter up to 20 μm (PM20) is approximately 5,000 Tg/year, which is greater than most models account for. This larger PM20 dust flux is needed to match observational constraints showing a large atmospheric loading of coarse dust. We obtain gridded data sets of dust emission, vertically integrated loading, dust aerosol optical depth, (surface) concentration, and wet and dry deposition fluxes that are resolved by season and particle size. As our results indicate that this data set is more accurate than current model simulations and the MERRA-2 dust reanalysis product, it can be used to improve quantifications of dust impacts on the Earth system.

Comparison and evaluation of the simulated annual aerosol characteristics over China with two global aerosol models

In this study, simulations of the annual mean aerosol budget, aerosol optical properties, and surface mass concentration in 2006 in China are performed with two aerosol interactive global atmosphere models, namely, the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) and the Beijing Climate Center Atmospheric General Circulation Model (BCC_AGCM) coupled with the Canadian Aerosol Module (CAM) online. The observed and simulated aerosol optical depths (AODs) exhibit similar horizontal distributions with large values over eastern and central China, and sulfate aerosols contribute the main differences between the AODs simulated by NICAM and BCC_AGCM. The simulated sulfate and dust surface concentrations are more consistent with observations compared with the simulated carbonaceous surface concentrations, and both models can reproduce the decreasing tendency of the sulfate surface concentration from urban sites to rural sites. However, the dust emission and deposition levels in China simulated by BCC_AGCM are three times as high as those simulated by NICAM, and the major sink processes of the anthropogenic sulfate, black carbon (BC), and organic carbon (OC) aerosols over China are very different between the two models. The emission and deposition results, which are closely related to the model-assumed aerosol particle size distribution, indicate that the current aerosol size distribution used in the two models should be further improved. The differences in dust emission parameterizations also lead significant discrepancies in aerosol cycles and the dust emission scheme is an important factor determining the magnitudes of global and regional dust emission fluxes.

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

The Impact of Aerosols on Satellite Radiance Data Assimilation Using NCEP Global Data Assimilation System

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13 μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39 μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

A satellite-based algorithm is developed and used to determine the presence of dust aerosols on a global scale. The algorithm uses as input aerosol optical properties from the MOderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Collection 6.1 and Ozone Monitoring Instrument (OMI)-Aura version v003 (OMAER-UV) datasets and identifies the existence of dust aerosols in the atmosphere by applying specific thresholds, which ensure the coarse size and the absorptivity of dust aerosols, on the input optical properties. The utilized aerosol optical properties are the multiwavelength aerosol optical depth (AOD), the Aerosol Absorption Index (AI) and the Ångström Exponent (a). The algorithm operates on a daily basis and at 1° × 1° latitude-longitude spatial resolution for the period 2005–2019 and computes the absolute and relative frequency of the occurrence of dust. The monthly and annual mean frequencies are calculated on a pixel level for each year of the study period, enabling the study of the seasonal as well as the inter-annual variation of dust aerosols’ occurrence all over the globe. Temporal averaging is also applied to the annual values in order to estimate the 15-year climatological mean values. Apart from temporal, a spatial averaging is also applied for the entire globe as well as for specific regions of interest, namely great global deserts and areas of desert dust export. According to the algorithm results, the highest frequencies of dust occurrence (up to 160 days/year) are primarily observed over the western part of North Africa (Sahara), and over the broader area of Bodélé, and secondarily over the Asian Taklamakan desert (140 days/year). For most of the study regions, the maximum frequencies appear in boreal spring and/or summer and the minimum ones in winter or autumn. A clear seasonality of global dust is revealed, with the lowest frequencies in November–December and the highest ones in June. Finally, an increasing trend of global dust frequency of occurrence from 2005 to 2019, equal to 56.2%, is also found. Such an increasing trend is observed over all study regions except for North Middle East, where a slight decreasing trend (−2.4%) is found.

The accuracy improvement of clear-sky surface shortwave radiation derived from CERES SSF dataset with a simulation analysis

Towards the Xiaotang region along the northern margin of the China's largest desert, a quantitative assessment of the precision of clear-sky satellite observations (the Single Scanner Footprint TOA/Surface Fluxes and Clouds downward surface shortwave radiation product of Clouds and the Earth's Radiant Energy System (CERES), DSSR_CER) is conducted, the localized inversion mode of "absolutely clear-sky" downward surface shortwave radiation (DSSR) is established, and the "absolutely clear-sky" DSSR in Xiaotang during 2005-2018 is simulated by the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model. In general, under the "absolutely clear-sky" condition of Xiaotang region, there is a significant error in DSSR_CER, and the simulated results of SBDART (DSSR_SBD) with same input parameters as DSSR_CER is better and more comparable. Single scattering albedo (SSA), asymmetry parameter (ASY) and aerosol optical depth (AOD) play crucial roles in deciding the accuracy of DSSR, and after parameter adjustment, the DSSR_SBD is better than the initial, which is improved remarkably with all indexes of the fitting results greatly improved. The temporal variation of the DSSR during 2005-2018 indicates that the highest annual average value is found in 2008 (770.00 W·m^-2), while the lowest appears in 2010 (600.97 W·m^-2). Besides, the highest seasonal mean DSSR appears in summer, which between 860.6 and 935.07 W·m^-2, while reaches the lowest in winter (403.79-587.53 W·m^-2). Moreover, the monthly average DSSR changes as a curve with a single peak and is close to normal distribution, the highest appears in June (934.61 W·m^-2), while the minimum with the value of 390.34 W·m^-2 is found in December. All of the solar elevation angle, the characteristics of climate and aerosol particles in different seasons may contribute to the temporal variation.

Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

Aerosol-induced atmospheric heating rate decreases over South and East Asia as a result of changing content and composition

Aerosol emissions from human activities are extensive and changing rapidly over Asia. Model simulations and satellite observations indicate a dipole pattern in aerosol emissions and loading between South Asia and East Asia, two of the most heavily polluted regions of the world. We examine the previously unexplored diverging trends in the existing dipole pattern of aerosols between East and South Asia using the high quality, two-decade long ground-based time series of observations of aerosol properties from the Aerosol Robotic Network (AERONET), from satellites (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)), and from model simulations (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The data cover the period since 2001 for Kanpur (South Asia) and Beijing (East Asia), two locations taken as being broadly representative of the respective regions. Since 2010 a dipole in aerosol optical depth (AOD) is maintained, but the trend is reversed—the decrease in AOD over Beijing (East Asia) is rapid since 2010, being 17% less in current decade compared to first decade of twenty-first century, while the AOD over South Asia increased by 12% during the same period. Furthermore, we find that the aerosol composition is also changing over time. The single scattering albedo (SSA), a measure of aerosol’s absorption capacity and related to aerosol composition, is slightly higher over Beijing than Kanpur, and has increased from 0.91 in 2002 to 0.93 in 2017 over Beijing and from 0.89 to 0.92 during the same period over Kanpur, confirming that aerosols in this region have on an average become more scattering in nature. These changes have led to a notable decrease in aerosol-induced atmospheric heating rate (HR) over both regions between the two decades, decreasing considerably more over East Asia (− 31%) than over South Asia (− 9%). The annual mean HR is lower now, it is still large (≥ 0.6 K per day), which has significant climate implications. The seasonal trends in AOD, SSA and HR are more pronounced than their respective annual trends over both regions. The seasonal trends are caused mainly by the increase/decrease in anthropogenic aerosol emissions (sulfate, black carbon and organic carbon) while the natural aerosols (dust and sea salt) did not change significantly over South and East Asia during the last two decades. The MERRA-2 model is able to simulate the observed trends in AODs well but not the magnitude, while it also did not simulate the SSA values or trends well. These robust findings based on observations of key aerosol parameters and previously unrecognized diverging trends over South and East Asia need to be accounted for in current state-of-the-art climate models to ensure accurate quantification of the complex and evolving impact of aerosols on the regional climate over Asia.

Differences in the Evolution of Pyrocumulonimbus and Volcanic Stratospheric Plumes as Observed by CATS and CALIOP Space-Based Lidars

Recent fire seasons have featured volcanic-sized injections of smoke aerosols into the stratosphere where they persist for many months. Unfortunately, the aging and transport of these aerosols are not well understood. Using space-based lidar, the vertical and spatial propagation of these aerosols can be tracked and inferences can be made as to their size and shape. In this study, space-based CATS and CALIOP lidar were used to track the evolution of the stratospheric aerosol plumes resulting from the 2019–2020 Australian bushfire and 2017 Pacific Northwest pyrocumulonimbus events and were compared to two volcanic events: Calbuco (2015) and Puyehue (2011). The pyrocumulonimbus and volcanic aerosol plumes evolved distinctly, with pyrocumulonimbus plumes rising upwards of 10 km after injection to altitudes of 30 km or more, compared to small to modest altitude increases in the volcanic plumes. We also show that layer-integrated depolarization ratios in these large pyrocumulonimbus plumes have a strong altitude dependence with more irregularly shaped particles in the higher altitude plumes, unlike the volcanic events studied.

The Regional Impact of Ecological Restoration in the Arid Steppe on Dust Reduction over the Metropolitan Area in Northeastern China

A massive ecological restoration program has been implemented in northern China with the aim of protecting the Beijing-Tianjin-Hebei metropolitan area of eastern China from dust events. However, some current studies have cast doubt on the efficacy of such ecological restoration projects, partly due to the constraint of available water in northern China, leading to poor survival rates of planted trees in semiarid regions (15%). In this study, using a logical framework combining statistical analysis, partial least-squares path model analysis, and a regional climate model (RegCM) simulation with multisource dust indicators, we found that there was a reduction of dust in northern China that was synchronous with the increase in vegetation growth after ecological restoration. In contrast to previous reports of a decrease in wind speed due to ecological restoration, this study found that the increase in vegetation had an insignificant impact on local wind speed (p = 0.30). Instead, ecological restoration mainly reduced the sand emission in steppe area by improving the soil conditions of the underlying surface, and hence contributed 15% of the reduction of dust events in the Beijing-Tianjin-Hebei metropolitan area through dust transmission (p = 0.002). The effect of ecological restoration in the northern steppe on dust reduction over the northeastern metropolitan area of China should not be overstated.

Abrupt decline in tropospheric nitrogen dioxide over China after the outbreak of COVID-19

China's policy interventions to reduce the spread of the coronavirus disease 2019 have environmental and economic impacts. Tropospheric nitrogen dioxide indicates economic activities, as nitrogen dioxide is primarily emitted from fossil fuel consumption. Satellite measurements show a 48% drop in tropospheric nitrogen dioxide vertical column densities from the 20 days averaged before the 2020 Lunar New Year to the 20 days averaged after. This decline is 21 ± 5% larger than that from 2015 to 2019. We relate this reduction to two of the government's actions: the announcement of the first report in each province and the date of a province's lockdown. Both actions are associated with nearly the same magnitude of reductions. Our analysis offers insights into the unintended environmental and economic consequences through reduced economic activities.

Spatial-Temporal Pattern of Black Carbon (BC) Emission from Biomass Burning and Anthropogenic Sources in New South Wales and the Greater Metropolitan Region of Sydney, Australia

Biomass burnings either due to Hazards Reduction Burnings (HRBs) in late autumn and early winter or bushfires during summer periods in various part of the world (e.g., CA, USA or New South Wales, Australia) emit large amount of gaseous pollutants and aerosols. The emissions, under favourable meteorological conditions, can cause elevated atmospheric particulate concentrations in metropolitan areas and beyond. One of the pollutants of concern is black carbon (BC), which is a component of aerosol particles. BC is harmful to health and acts as a radiative forcing agent in increasing the global warming due to its light absorption properties. Remote sensing data from satellites have becoming increasingly available for research, and these provide rich datasets available on global and local scale as well as in situ aethalometer measurements allow researchers to study the emission and dispersion pattern of BC from anthropogenic and natural sources. The Department of Planning, Industry and Environment (DPIE) in New South Wales (NSW) has installed recently from 2014 to 2019 a total of nine aethalometers to measure BC in its state-wide air quality network to determine the source contribution of BC and PM2.5 (particulate Matter less than 2.5 μm in diameter) in ambient air from biomass burning and anthropogenic combustion sources. This study analysed the characteristics of spatial and temporal patterns of black carbon (BC) in New South Wales and in the Greater Metropolitan Region (GMR) of Sydney, Australia, by using these data sources as well as the trajectory HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) modelling tool to determine the source of high BC concentration detected at these sites. The emission characteristics of BC in relation to PM2.5 is dependent on the emission source and is analysed using regression analysis of BC with PM2.5 time series at the receptor site for winter and summer periods. The results show that, during the winter, correlation between BC and PM2.5 is found at nearly all sites while little or no correlation is detected during the summer period. Traffic vehicle emission is the main BC emission source identified in the urban areas but was less so in the regional sites where biomass burnings/wood heating is the dominant source in winter. The BC diurnal patterns at all sites were strongly influenced by meteorology.

Impacts of meteorology and vegetation on surface dust concentrations in Middle Eastern countries

Severe dust events have occurred frequently in arid regions, which greatly impacted air quality, climate, and public health. The Middle East is one of the areas in the world impacted by intense dust storms. We investigated the characteristics of airborne dust levels in five Middle Eastern countries (Kuwait, Iraq, Iran, Saudi Arabia, and Syria) from 2001 to 2017. Surface level dust concentrations were determined using the Modern-Era Retrospective analysis for Research and Applications version 2. Kuwait was selected as an example to assess sources and other factors influencing dust levels in arid regions. We performed backward trajectory analysis to identify the dust transport pathways. We quantitatively assessed the impacts of meteorological parameters along with the Normalized Difference Vegetation Index (NDVI). Dust levels in Kuwait were higher than the other four countries, and had a distinct seasonal pattern, with the highest in summer and the lowest in winter. Our results showed that dust levels in Kuwait in January were influenced largely by local emissions, whereas in June they were affected more by emissions attributable to long-distance transport. There were significant positive associations between wind speed in the five countries, particularly Iraq, with dust levels in Kuwait, indicating the impact of nearby desert areas. Significant negative associations were observed between NDVI in Kuwait, Iraq, and Saudi Arabia with dust levels in Kuwait. Our result highlights that climatic variations and vegetation conditions are associated with changes in dust levels in arid regions.

GEOS-S2S Version 2: The GMAO High Resolution Coupled Model and Assimilation System for Seasonal Prediction

The Global Modeling and Assimilation Office (GMAO) has recently released a new version of the Goddard Earth Observing System (GEOS) Sub-seasonal to Seasonal prediction (S2S) system, GEOS-S2S-2, that represents a substantial improvement in performance and infrastructure over the previous system. The system is described here in detail, and results are presented from forecasts, climate equillibrium simulations and data assimilation experiments. The climate or equillibrium state of the atmosphere and ocean showed a substantial reduction in bias relative to GEOS-S2S-1. The GEOS-S2S-2 coupled reanalysis also showed substantial improvements, attributed to the assimilation of along-track Absolute Dynamic Topography. The forecast skill on subseasonal scales showed a much-improved prediction of the Madden-Julian Oscillation in GEOS-S2S-2, and on a seasonal scale the tropical Pacific forecasts show substantial improvement in the east and comparable skill to GEOS-S2S-1 in the central Pacific. GEOS-S2S-2 anomaly correlations of both land surface temperature and precipitation were comparable to GEOS-S2S-1, and showed substantially reduced root mean square error of surface temperature. The remaining issues described here are being addressed in the development of GEOS-S2S Version 3, and with that system GMAO will continue its tradition of maintaining a state of the art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as to evaluate additional sources of predictability in the earth system through the expanded coupling of the earth system model and assimilation components.

Aerosol Optical Depth of the Main Aerosol Species over Italian Cities Based on the NASA/MERRA-2 Model Reanalysis

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) provides data at 0.5° × 0.625° resolution covering a period from 1 January 1980 to the present. Natural and anthropogenic aerosols are simulated in MERRA-2, considering the Goddard chemistry, aerosol, radiation, and transport model. This model simulates the sources, sinks, and chemistry of mixed aerosol tracers: dust, sea salt, hydrophobic and hydrophilic black carbon and organic carbon, and sulfate. MERRA-2 aerosol reanalysis is a pioneering tool for investigating air quality issues, noteworthy for its global coverage and its distinction of aerosol speciation expressed in the form of aerosol optical depth (AOD). The aim of this work was to use the MERRA-2 reanalysis to study urban air pollution at a national scale by analyzing the AOD. AOD trends were evaluated for a 30-year period (1987–2017) over five Italian cities (Milan, Rome, Cagliari, Taranto, and Palermo) in order to investigate the impacts of urbanization, industrialization, air quality regulations, and regional transport on urban aerosol load. AOD evolution predicted by the MERRA-2 model in the period 2002–2017 showed a generalized decreasing trend over the selected cities. The anthropogenic signature on total AOD was between 50% and 80%, with the largest contribution deriving from sulfate.

Multi-Model Evaluation of Meteorological Drivers, Air Pollutants and Quantification of Emission Sources over the Upper Brahmaputra Basin

The temporal distributions of meteorological drivers and air pollutants over Dibrugarh, a location in the upper Brahmaputra basin, are studied using observations, models and reanalysis data. The study aims to assess the performance of the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), the WRF coupled with Sulfur Transport dEposition Model (WRF-STEM), and Copernicus Atmosphere Monitoring Service (CAMS) model over Dibrugarh for the first time. The meteorological variables and air pollutants viz., black carbon(BC), carbon monoxide(CO), sulphur dioxide(SO2), Ozone(O3), and oxides of Nitrogen(NOx) obtained from WRF-Chem, WRF-STEM and CAMS are evaluated with observations. The source region tagged CO simulated by WRF-STEM delineate the regional contribution of CO. The principal source region of anthropogenic CO over Dibrugarh is North-Eastern India with a 59% contribution followed by that from China (17%), Indo-Gangetic Plains (14%), Bangladesh (6%), other parts of India (3%) and other regions (1%). Further, the BC-CO regression analysis is used to delineate the local emission sources. The BC-CO correlations estimated from models (0.99 for WRF-Chem, 0.96 for WRF-STEM, 0.89 for CAMS), and reanalysis (0.8 for Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA2) are maximum in pre-monsoon whereas surface observations show highest correlations (0.81) in winter. In pre-monsoon season, 90% of the modeled CO is due to biomass burning over Dibrugarh.

Current state of the global operational aerosol multi-model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)

Since the first International Cooperative for Aerosol Prediction (ICAP) multi-model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP-MME over 2012-2017, with a focus on June 2016-May 2017. Evaluated with ground-based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate-resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP-MME AOD consensus remains the overall top-scoring and most consistent performer among all models in terms of root-mean-square error (RMSE), bias and correlation for total, fine- and coarse-mode AODs as well as dust AOD; this is similar to the first ICAP-MME study. Further, over the years, the performance of ICAP-MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP-MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP-MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012-2017 suggests a general tendency for model improvements in fine-mode AOD, especially over Asia. No significant improvement in coarse-mode AOD is found overall for this time period.

Three-Dimensional Modelling of Precipitation Enhancement by Cloud Seeding in Three Different Climate Zones

This study numerically investigates precipitation enhancement from cumuliform clouds in three different climate regions: (1) Arid climate of the United Arab Emirates (UAE); (2) maritime climate of Thailand; and (3) continental climate of Serbia. Recently developed core/shell sodium chloride (NaCl)/titanium dioxide (TiO2) nanostructure (CSNT) aerosol was tested as a precipitation enhancer in all three climate regions. Previous experimental studies in cloud chambers and idealized numerical simulations demonstrated that CSNT is a significantly more effective precipitation enhancer than the traditional NaCl. Here, CSNT and NaCl seeding agents are incorporated into the WRF (Weather Research and Forecasting) model microphysics with explicate treatment of aerosol. Our results show that CSNT is a profoundly more effective precipitation enhancer in the case of arid climate characterized with low humidity. The accumulated surface precipitation in the arid test was 1.4 times larger if CSNT seeding agent was used instead of NaCl. The smallest difference in the effectiveness between CSNT and NaCl was observed in the maritime case due to their similar activation properties at high values of relative humidity.

The Weather Research and Forecasting Model with Aerosol-Cloud Interactions (WRF-ACI): Development, Evaluation, and Initial Application

The Weather Research and Forecasting Model with Aerosol-Cloud Interactions (WRF-ACI) is developed for studying aerosol effects on gridscale and subgrid-scale clouds using common aerosol activation and ice nucleation formulations and double-moment cloud microphysics in a scale-aware subgrid-scale parameterization scheme. Comparisons of both the standard WRF and WRF-ACI models' results for a summer season against satellite and reanalysis estimates show that the WRF-ACI system improves the simulation of cloud liquid and ice water paths. Correlation coefficients for nearly all evaluated parameters are improved, while other variables show slight degradation. Results indicate a strong cloud lifetime effect from current climatological aerosols increasing domain average cloud liquid water path and reducing domain average precipitation as compared to a simulation with aerosols reduced by 90%. Increased cloud-top heights indicate a thermodynamic invigoration effect, but the impact of thermodynamic invigoration on precipitation is overwhelmed by the cloud lifetime effect. A combination of cloud lifetime and cloud albedo effects increases domain average shortwave cloud forcing by ~3.0 W m^-2. Subgrid-scale clouds experience a stronger response to aerosol levels, while gridscale clouds are subject to thermodynamic feedbacks because of the design of the WRF modeling framework. The magnitude of aerosol indirect effects is shown to be sensitive to the choice of autoconversion parameterization used in both the gridscale and subgrid-scale cloud microphysics, but spatial patterns remain qualitatively similar. These results indicate that the WRF-ACI model provides the community with a computationally efficient tool for exploring aerosol-cloud interactions.

A Laboratory Experiment for the Statistical Evaluation of Aerosol Retrieval (STEAR) Algorithms

We have developed a method for evaluating the fidelity of the Aerosol Robotic Network (AERONET) retrieval algorithms by mimicking atmospheric extinction and radiance measurements in a laboratory experiment. This enables radiometric retrievals that use the same sampling volumes, relative humidities, and particle size ranges as observed by other in situ instrumentation in the experiment. We use three Cavity Attenuated Phase Shift (CAPS) monitors for extinction and University of Maryland Baltimore County’s (UMBC) three-wavelength Polarized Imaging Nephelometer (PI-Neph) for angular scattering measurements. We subsample the PI-Neph radiance measurements to angles that correspond to AERONET almucantar scans, with simulated solar zenith angles ranging from 50 ∘ to 77 ∘ . These measurements are then used as input to the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm, which retrieves size distributions, complex refractive indices, single-scatter albedos, and bistatic LiDAR ratios for the in situ samples. We obtained retrievals with residuals less than 8% for about 90 samples. Samples were alternately dried or humidified, and size distributions were limited to diameters of less than 1.0 or 2.5 μ m by using a cyclone. The single-scatter albedo at 532 nm for these samples ranged from 0.59 to 1.00 when computed with CAPS extinction and Particle Soot Absorption Photometer (PSAP) absorption measurements. The GRASP retrieval provided single-scatter albedos that are highly correlated with the in situ single-scatter albedos, and the correlation coefficients ranged from 0.916 to 0.976, depending upon the simulated solar zenith angle. The GRASP single-scatter albedos exhibited an average absolute bias of +0.023–0.026 with respect to the extinction and absorption measurements for the entire dataset. We also compared the GRASP size distributions to aerodynamic particle size measurements, using densities and aerodynamic shape factors that produce extinctions consistent with our CAPS measurements. The GRASP effective radii are highly correlated (R = 0.80) and biased under the corrected aerodynamic effective radii by 1.3% (for a simulated solar zenith angle of θ ∘ = 50 ∘ ); the effective variance indicated a correlation of R = 0.51 and a relative bias of 280%. Finally, our apparatus was not capable of measuring backscatter LiDAR ratios, so we measured bistatic LiDAR ratios at a scattering angle of 173 degrees. The GRASP bistatic LiDAR ratios had correlations of 0.71 to 0.86 (depending upon simulated θ ∘ ) with respect to in situ measurements, positive relative biases of 2–10%, and average absolute biases of 1.8–7.9 sr.

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Spatial variation of modelled total, dry and wet nitrogen deposition to forests at global scale

Forests are an important biome that covers about one third of the global land surface and provides important ecosystem services. Since atmospheric deposition of nitrogen (N) can have both beneficial and deleterious effects, it is important to quantify the amount of N deposition to forest ecosystems. Measurements of N deposition to the numerous forest biomes across the globe are scarce, so chemical transport models are often used to provide estimates of atmospheric N inputs to these ecosystems. We provide an overview of approaches used to calculate N deposition in commonly used chemical transport models. The Task Force on Hemispheric Transport of Air Pollution (HTAP2) study intercompared N deposition values from a number of global chemical transport models. Using a multi-model mean calculated from the HTAP2 deposition values, we map N deposition to global forests to examine spatial variations in total, dry and wet deposition. Highest total N deposition occurs in eastern and southern China, Japan, Eastern U.S. and Europe while the highest dry deposition occurs in tropical forests. The European Monitoring and Evaluation Program (EMEP) model predicts grid-average deposition, but also produces deposition by land use type allowing us to compare deposition specifically to forests with the grid-average value. We found that, for this study, differences between the grid-average and forest specific could be as much as a factor of two and up to more than a factor of five in extreme cases. This suggests that consideration should be given to using forest-specific deposition for input to ecosystem assessments such as critical loads determinations.

Impact of snow-darkening by deposition of light-absorbing aerosols on snow cover in the Himalaya-Tibetan-Plateau and influence on the Asian Summer monsoon: A possible mechanism for the Blanford Hypothesis

The impact of snow darkening by deposition of light absorbing aerosols (LAAs) on snow cover over the Himalaya-Tibetan-Plateau (HTP) and influence on the Asian summer monsoon are investigated using the NASA Goddard Earth Observing System Model Version 5 (GEOS-5). We find that during April-May-June, deposition of LAAs on snow leads to a reduction in surface albedo, initiating a sequence of feedback processes, starting with increased net surface solar radiation, rapid snowmelt in HTP and warming of the surface and upper troposphere, followed by enhanced low-level southwesterlies and increased dust loading over the Himalayas-Indo-Gangetic Plain. The warming is amplified by increased dust aerosol heating, and subsequently amplified by latent heating from enhanced precipitation over the Himalaya foothills and northern India, via the Elevated Heat Pump (EHP) effect during June-July-August. The reduced snow cover in the HTP anchors the enhanced heating over the Tibetan Plateau and its southern slopes, in conjunction with an enhancement of the Tibetan Anticyclone, and the development of an anomalous Rossby wavetrain over East Asia, leading to weakening of the subtropical westerly jet, and northward displacement and intensification of the Mei-Yu rainbelt. Our results suggest that atmosphere-land heating induced by LAAs, particularly desert dust play a fundamental role in physical processes underpinning the snow-monsoon relationship proposed by Blanford more than a century ago.

Dust Impacts on the 2012 Hurricane Nadine Track during the NASA HS3 Field Campaign

During the 2012 deployment of the NASA Hurricane and Severe Storm Sentinel (HS3) field campaign, several flights were dedicated to investigating Hurricane Nadine. Hurricane Nadine developed in close proximity to the dust-laden Saharan Air Layer, and is the fourth longest-lived Atlantic hurricane on record, experiencing two strengthening and weakening periods during its 22-day total lifecycle as a tropical cyclone. In this study, the NASA GEOS-5 atmospheric general circulation model and data assimilation system was used to simulate the impacts of dust during the first intensification and weakening phases of Hurricane Nadine using a series of GEOS-5 forecasts initialized during Nadine's intensification phase (12 September 2012). The forecasts explore a hierarchy of aerosol interactions within the model: no aerosol interaction, aerosol-radiation interactions, and aerosol-radiation and aerosol-cloud interactions simultaneously, as well as variations in assumed dust optical properties. When only aerosolradiation interactions are included, Nadine's track exhibits sensitivity to dust shortwave absorption, as a more absorbing dust introduces a shortwave temperature perturbation that impacts Nadine's structure and steering flow, leading to a northward track divergence after 5 days of simulation time. When aerosol-cloud interactions are added, the track exhibits little sensitivity to dust optical properties. This result is attributed to enhanced longwave atmospheric cooling from clouds that counters shortwave atmospheric warming by dust surrounding Nadine, suggesting that aerosol-cloud interactions are a more significant influence on Nadine's track than aerosol-radiation interactions. These findings demonstrate that tropical systems, specifically their track, can be impacted by dust interaction with the atmosphere.

Remote sensing of aerosols with small satellites in formation flight

Determination of aerosol optical properties with orbital passive remote sensing is a difficult task, as observations often have limited information. Multi-angle instruments, such as the Multi-angle Imaging SpectroRadiometer (MISR) and the POlarization and Directionality of the Earth's Reflectances (POLDER), seek to address this by making information rich multi-angle observations, which can be used to better retrieve aerosol optical properties. The paradigm for such instruments is that each angle view is made from one platform, with, for example, a gimbaled sensor or multiple fixed view angle sensors. This restricts the observing geometry to a plane within the scene Bidirectional Reflectance Distribution Function (BRDF ) observed at the top of the atmosphere (TOA). New technological developments, however, support sensors on small satellites flying in formation, which could be a beneficial alternative. Such sensors may have only one viewing direction each, but the agility of small satellites allows one to control this direction and change it over time. When such agile satellites are flown in formation and their sensors pointed to the same location at approximately the same time, they could sample a distributed set of geometries within the scene BRDF . In other words, observations from multiple satellites can take a variety of view zenith and azimuth angles, and are not restricted to one azimuth plane as is the case with a single multi-angle instrument. It is not known, however, if this is as potentially capable as a multi-angle platform for the purposes of aerosol remote sensing. Using a systems engineering tool coupled with an information content analysis technique, we investigate the feasibility of such an approach for the remote sensing of aerosols. These tools test the mean results of all geometries encountered in an orbit. We find that small satellites in formation are equally capable as multi-angle platforms for aerosol remote sensing, as long as their calibration accuracies and measurement uncertainties are equivalent. As long as the viewing geometries are dispersed throughout the BRDF , it appears the quantity of view angles determines the information content of the observations, not the specific observation geometry. Given the smoothly varying nature of BRDF 's observed at the TOA, this is reasonable, and supports the viability of aerosol remote sensing with small satellites flying in formation. The incremental improvement in information content that we found with number of view angles also supports the concept of a resilient mission comprised of multiple satellites that are continuously replaced as they age or fail.

Differentiating between local and remote pollution over Taiwan

In this study, an approach has been developed for differentiating between local and remote pollution over Taiwan, based on homogeneity perspective (variations of the standard deviation) of both AERONET measurements and NASA MERRA aerosol reanalysis (version 2, MERRA-2) over a 15-year period (2002 - 2017). The analysis of seasonal variations of the standard deviation of aerosol optical depth (AOD) measurements at six AERONET sites and MERRA AOD data in Taiwan showed that, in spring when remote aerosols dominate, the standard deviation is almost three times lower than that in autumn, when aerosols from local sources dominate. This finding was supported by MERRA AOD over the open ocean area: total AOD data were used to differentiate between local and remote pollution over both Taiwan and the open ocean area in the vicinity of Taiwan. Over Taiwan, MERRA total AOD showed a primary maximum in spring and a secondary one in autumn. Over the open ocean area, where there are no local sources of anthropogenic aerosols, MERRA total AOD showed only one maximum in spring and no maximum in autumn. This suggests that, in Taiwan, the maximum in autumn is attributed to local air pollution, while the pronounced maximum in spring is mainly caused by air pollution from continental Asia. The analyses of spatial distribution of 15-year monthly mean MERRA winds confirmed the above-mentioned results. Furthermore, similar to total AOD, MERRA sulfate AOD peaked in autumn over Taiwan, but not over the oceanic area: this indicates the contribution of local emissions of anthropogenic aerosols from the industrial sector. The standard deviation of MERRA sulfate AOD in spring is two-three times lower than the standard deviation in autumn: this is additional evidence that, in spring, sulfate aerosols from remote sources are predominant; while in autumn sulfate aerosols from local sources dominate.

Exploring systematic offsets between aerosol products from the two MODIS sensors

Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm is applied to Moderate-resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55 μm) over both land and ocean, and Angstrom Exponent (AE derived from 0.55 and 0.86 μm) over ocean. Here, we analyse the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by ~13% (~0.02 over land and ~0.015 over ocean), and this offset (MOD04 - MYD04), has seasonal as well as long-term variability. Focusing on 2008, and deriving yearly gridded mean AOD and AE, we find that over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model, and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensors' polarization sensitivity, response-versus-scan angle, and to cross-calibration from MODIS-T to MODIS-A. A second convolves the de-trending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval, using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A Collection (C6.1) of all MODIS-atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that: a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. b) Because the MODIS-A product appears to have overall smaller bias compared to ground-truth, it may be more suitable for some applications, however c) since the AOD offset is only ~0.02 and within noise level for single retrievals, both MODIS products may be adequate for most applications.