Sources, variability, long-term trends, and radiative forcing of aerosols in the Arctic: implications for Arctic amplification

Atmospheric pollution in the Arctic has been an important driver for the ongoing climate change there. Increase in the Arctic aerosols causes the phenomena of Arctic haze and Arctic amplification. Our analysis of aerosol optical depth (AOD), black carbon (BC), and dust using ground-based, satellite, and reanalysis data in the Arctic for the period 2003-2019 shows that the lowest amount of all these is found in Greenland and Central Arctic. There is high AOD, BC, and dust in the northern Eurasia and parts of North America. All aerosols show their highest values in spring. Significant positive trends in AOD (> 0.003 year-1) and BC (0.0002-0.0003 year-1) are found in the northwestern America and northern Asia. Significant negative trends are observed for dust (- 0.0001 year-1) around Central Arctic. Seasonal analysis of AOD, BC, and dust reveals an increasing trend in summer and decreasing trend in spring in the Arctic. The major sources of aerosols are the nearby Europe, Russia, and North America regions, as assessed using the potential source contribution function (PSCF). Anthropogenic emissions from the transport, energy, and household sectors along with natural sources such as wildfires contribute to the positive trends of aerosols in the Arctic. These increasing aerosols in the Arctic influence Arctic amplification through radiative effects. Here, we find that the net aerosol radiative forcing is high in Central Arctic, Greenland, Siberia, and Canadian Arctic, about 2-4 W/m2, which can influence the regional temperature. Therefore, our study can assist policy decisions for the mitigation of Arctic haze and Arctic amplification in this environmental fragile region of the Earth.

Remote sensing and model analysis of biomass burning smoke transported across the Atlantic during the 2020 Western US wildfire season

Biomass burning is the main source of air pollution in several regions worldwide nowadays. This predominance is expected to increase in the upcoming years as a result of the rising number of devastating wildfires due to climate change. Harmful pollutants contained in the smoke emitted by fires can alter downwind air quality both locally and remotely as a consequence of the recurrent transport of biomass burning plumes across thousands of kilometers. Here, we demonstrate how observations of carbon monoxide and aerosol optical depth retrieved from polar orbiting and geostationary meteorological satellites can be used to study the long-range transport and evolution of smoke plumes. This is illustrated through the megafire events that occurred during summer 2020 in the Western United States and the transport of the emitted smoke across the Atlantic Ocean to Europe. Analyses from the Copernicus Atmosphere Monitoring Service, which combine satellite observations with an atmospheric model, are used for comparison across the region of study and along simulated air parcel trajectories. Lidar observation from spaceborne and ground-based instruments are used to verify consistency of passive observations. Results show the potential of joint satellite-model analysis to understand the emission, transport, and processing of smoke across the world.

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

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

The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis

BACKGROUND AND AIM

The associations between COVID-19 transmission and meteorological factors are scientifically debated. Several studies have been conducted worldwide, with inconsistent findings. However, often these studies had methodological issues, e.g., did not exclude important confounding factors, or had limited geographic or temporal resolution. Our aim was to quantify associations between temporal variations in COVID-19 incidence and meteorological variables globally.

METHODS

We analysed data from 455 cities across 20 countries from 3 February to 31 October 2020. We used a time-series analysis that assumes a quasi-Poisson distribution of the cases and incorporates distributed lag non-linear modelling for the exposure associations at the city-level while considering effects of autocorrelation, long-term trends, and day of the week. The confounding by governmental measures was accounted for by incorporating the Oxford Governmental Stringency Index. The effects of daily mean air temperature, relative and absolute humidity, and UV radiation were estimated by applying a meta-regression of local estimates with multi-level random effects for location, country, and climatic zone.

RESULTS

We found that air temperature and absolute humidity influenced the spread of COVID-19 over a lag period of 15 days. Pooling the estimates globally showed that overall low temperatures (7.5 °C compared to 17.0 °C) and low absolute humidity (6.0 g/m3 compared to 11.0 g/m3) were associated with higher COVID-19 incidence (RR temp =1.33 with 95%CI: 1.08; 1.64 and RR AH =1.33 with 95%CI: 1.12; 1.57). RH revealed no significant trend and for UV some evidence of a positive association was found. These results were robust to sensitivity analysis. However, the study results also emphasise the heterogeneity of these associations in different countries.

CONCLUSION

Globally, our results suggest that comparatively low temperatures and low absolute humidity were associated with increased risks of COVID-19 incidence. However, this study underlines regional heterogeneity of weather-related effects on COVID-19 transmission.

Vertical aerosol data assimilation technology and application based on satellite and ground lidar: A review and outlook

Observations and numerical models are mainly used to investigate the spatiotemporal distribution and vertical structure characteristics of aerosols to understand aerosol pollution and its effects. However, the limitations of observations and the uncertainties of numerical models bias aerosol calculations and predictions. Data assimilation combines observations and numerical models to improve the accuracy of the initial, analytical fields of models and promote the development of atmospheric aerosol pollution research. Numerous studies have been conducted to integrate multi-source data, such as aerosol optical depth and aerosol extinction coefficient profile, into various chemical transport models using various data assimilation algorithms and have achieved good assimilation results. The definition of data assimilation and the main algorithms will be briefly presented, and the progress of aerosol assimilation according to two types of aerosol data, namely, aerosol optical depth and extinction coefficient, will be presented. The application of vertical aerosol data assimilation, as well as the future trends and challenges of aerosol data assimilation, will be further analysed.

Improved sub-seasonal forecasts to support preparedness action for meningitis outbreak in Africa

West African countries are hit annually by meningitis outbreaks which occur during the dry season and are linked to atmospheric variability. This paper describes an innovative co-production process between the African Centre of Meteorological Applications for Development (ACMAD; forecast producer) and the World Health Organisation Regional Office for Africa (WHO AFRO; forecast user) to support awareness, preparedness and response actions for meningitis outbreaks. Using sub-seasonal to seasonal (S2S) forecasts, this co-production enables ACMAD and WHO AFRO to build initiative that increases the production of useful climate services in the health sector. Temperature and relative humidity forecasts are combined with dust forecasts to operationalize a meningitis early warning system (MEWS) across the African meningitis belt with a two-week lead time. To prevent and control meningitis, the MEWS is produced from week 1 to 26 of the year. This study demonstrates that S2S forecasts have good skill at predicting dry and warm atmospheric conditions precede meningitis outbreaks. Vigilance levels objectively defined within the MEWS are consistent with reported cases of meningitis. Alongside developing a MEWS, the co-production process provided a framework for analysis of climate and environmental risks based on reanalysis data, meningitis burden, and health service assessment, to support the development of a qualitative roadmap of country prioritization for defeating meningitis by 2030 across the WHO African region. The roadmap has enabled the identification of countries most vulnerable to meningitis epidemics, and in the context of climate change, supports plans for preventing, preparing, and responding to meningitis outbreaks.

Evaluation of the CAMS reanalysis for atmospheric black carbon and carbon monoxide over the north China plain

Black carbon (BC) and carbon monoxide (CO) at different model levels from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis were comprehensively evaluated against observations performed simultaneously on both surface and mountain sites in winter and summer in the North China Plain for the first time. CAMS could capture the seasonal difference in BC and CO emission on both sites but showed significant and persistent biases. Biases were high on the surface site and low on the mountain site for both seasons, implying the uncertainties in emission inventories used in the CAMS reanalysis which may have more influence near source. Biases were reduced and the correlation coefficient of CAMS BC with observed BC increased when two datasets were compared on a daily basis, which suggests daily or longer time averaged CAMS BC could be more suitable for trend analysis. Although CAMS could generally reproduce the distinct diurnal variation of BC and CO on both sites, the inaccurate representation of the daily evolution of planetary boundary layer (PBL) in model may bring more uncertainties to the concentration biases on surface from midnight to early morning. BC hydrophilic ratio from CAMS displayed large biases compared to observations with no seasonal difference on both sites, which was probably resulted from the initial emission state of BC hygroscopicity for all source types in model. Uncertainties in the removal processes and the simplified aging processes in model could further induce uncertainty in modelling BC hydrophilic ratio in the CAMS. These results could not only be referenced for the improvement on CAMS reanalysis but also facilitate model or trend analysis of BC and CO pollution by utilizing the CAMS reanalysis product from both short- and long-term perspectives, which will be beneficial to both the mitigation and policy-making on primary emissions in China.

Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application

With an increasing number of air quality monitoring stations installed around the Chinese mainland, high-resolution aerosol observations become available, allowing improvements in air pollution monitoring and aerosol forecasting. However, the multi scales (especially small-scale) information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method (3DVAR). This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps, two-scale-3DVAR (TS-3DVAR), to improve the effectiveness of assimilating high-resolution observations. In TS-3DVAR, the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples. The data assimilation (DA) analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field’s multi-scale characteristics, which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting. Particulate matter with an aerodynamic diameter of less than 2.5 μm (PM_2.5) and 10.0 μm (PM₁₀) from the surface air quality monitoring stations from November 01 to November 30, 2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy. The results showed that TS-3DVAR better constrained both large-scale and small-scale, especially the spatial wavelengths in a range of 54–216 km and those above 351 km. The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70% and 35.33% higher than those of 3DVAR. As a result, the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field, and thereby the forecasting capability for PM_2.5. In the initial chemical field, the 30-day average correlation coefficient (Corr) of PM_2.5 of TS-3DVAR was 0.052 (6.12%) higher than that of 3DVAR, and the root mean square error (RMSE) of TS-3DVAR was 3.446 μg m⁻³ (16.4%) lower than that of 3DVAR. For the forecasting capability for PM_2.5 mass concentration, the 30-day average Corr of TS-3DVAR during the 0–24 hour forecast period was 0.025 (5.08%) higher than that of 3DVAR, and the average RMSE was 2.027 μg m⁻³ (4.85%) lower. The positive effect of TS-3DVAR on the improvement of forecasting capability can last for more than 24 h.

Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls

In China, the rapid development of the economy and implementation of multiple emission control policies in recent decades have been accompanied by dramatic changes in air quality. In this study, PM_2.5 concentrations estimated by using MERRA-2 reanalysis data were integrated into the Global Exposure Mortality Model (GEMM) to explore the spatiotemporal variation of nationwide PM_2.5-related premature mortality from 1990 to 2019, and the driving factors behind decadal changes were evaluated. Since 2000, as a result of PM_2.5 pollution, air quality in China has deteriorated substantially, especially in the fast-developing eastern and southern parts. In 2009, the nationwide population-weighted (PW) PM_2.5 concentration peaked at 41.4 μg/m³ (95% confidence interval [CI], 36.7-46.2). Simultaneously, the GEMM results revealed that nationwide PM_2.5-related deaths increased remarkably from 1089 (95% CI, 965-1210) thousand in 1990 to 1795 (1597-1986) thousand in 2009. The implementation of the toughest-ever Air Pollution Prevention and Control Action Plan (APPCAP) in 2013 effectively controlled PM_2.5 pollution in China. By 2018, the nationwide PW PM_2.5 concentration had decreased to 34.0 (29.2-38.9) μg/m³. Dynamic trend prediction revealed that, although the APPCAP achieved substantial health benefits, the policy did not result in further remarkable reductions in PM_2.5-related deaths; in 2019, deaths peaked at 1932 (1716-2140) thousand. PM_2.5-related deaths in 2030 were projected for each of four emission control scenarios. The results of the driving factor analysis and the future projections indicated that the health benefits from improving air quality are likely to be counterbalanced by changes in the population age structure. Because population ageing is becoming more and more rapid in China and the challenge of climate change is increasing, the results of this study imply that policymakers need to implement more stringent measures and set more ambitious emission control targets to reduce nationwide PM_2.5-related premature mortality in the future.

A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD550. Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s−1, the C4C and MODIS AOD550s also overestimate CAOD550 and FAOD550, respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.

Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe

We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from −34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations.

Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand

Air pollutants, especially particulate matter (PM) ≤ 2.5 µm (PM2.5) and PM ≤ 10 µm (PM10), are a major concern in upper northern Thailand. Data from a retrospective cohort comprising 9820 lung cancer patients diagnosed from 2003 to 2018 were obtained from the Chiang Mai Cancer Registry, and used to evaluate mortality and survival rates. Cox proportional hazard models were used to identify the association between the risk of death and risk factors including gender, age, cancer stage, smoking history, alcohol-use history, calendar year of enrollment, and time-updated PM2.5, PM10, NO₂ and O₃ concentrations. The mortality rate was 68.2 per 100 persons per year of follow-up. In a multivariate analysis, gender, age, cancer stage, calendar year of enrollment, and time-varying residential concentration of PM2.5 were independently associated with the risk of death. The lower the annually averaged PM2.5 and PM10 concentrations, the higher the survival probability of the patient. As PM2.5 and PM10 were factors associated with a higher risk of death, lung cancer patients who are inhabitant in the area should reduce their exposure to high concentrations of PM2.5 and PM10 to increase survival rates.

An intercomparison of ozone taken from the Copernicus atmosphere monitoring service and the second Modern-Era retrospective analysis for research and applications over China during 2018 and 2019

Spatiotemporal variations of ozone (O₃) taken from the Copernicus Atmosphere Monitoring Service (CAMS) and the second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) were intercompared and evaluated with ground and ozone-sonde observations over China in 2018 and 2019. Intercomparison of the surface ozone from CAMS and MERRA-2 reanalysis showed significant negative bias (CAMS minus MERRA-2, same below) at Tibetan Plateau of up to 80 µg/m³, and the average R² was about 0.6 across China. Evaluated with the ground observations from China National Environmental Monitoring Center (CNEMC), we found that CAMS and MERRA-2 reanalysis were capable of capturing the key patterns of monthly and diurnal variations of surface ozone over China except for the western region, and MERRA-2 overestimated the observations compared to CAMS. Vertically, the CAMS profiles overestimated the ozone-sonde from the World Ozone and Ultraviolet Radiation Data Center (WOUDC) above 200 hPa with the magnitude reaching up to 150 µg/m³, while little bias was found between the reanalysis and observations below 200 hPa. Intercomparison drawn from the vertical distribution between CAMS and MERRA-2 reanalysis showed that the negative bias appeared throughout the troposphere over China, while the positive bias emerged in the upper troposphere and lower stratosphere (UTLS) with high order of magnitude exceeding 100 µg/m³, indicating large uncertainties at higher altitudes. In summary, we concluded that CAMS reanalysis showed better agreement with the observations in contrast to MERRA-2, and the large discrepancy especially at higher altitudes between these two reanalysis datasets could not be ignored.

15-Year Analysis of Direct Effects of Total and Dust Aerosols in Solar Radiation/Energy over the Mediterranean Basin

The direct radiative effects of atmospheric aerosols are essential for climate, as well as for other societal areas, such as the energy sector. The goal of the present study is to exploit the newly developed ModIs Dust AeroSol (MIDAS) dataset for quantifying the direct effects on the downwelling surface solar irradiance (DSSI), induced by the total and dust aerosol amounts, under clear-sky conditions and the associated impacts on solar energy for the broader Mediterranean Basin, over the period 2003–2017. Aerosol optical depth (AOD) and dust optical depth (DOD) derived by the MIDAS dataset, along with additional aerosol and dust optical properties and atmospheric variables, were used as inputs to radiative transfer modeling to simulate DSSI components. A 15-year climatology of AOD, DOD and clear-sky global horizontal irradiation (GHI) and direct normal irradiation (DNI) was derived. The spatial and temporal variability of the aerosol and dust effects on the different DSSI components was assessed. Aerosol attenuation of annual GHI and DNI were 1–13% and 5–47%, respectively. Over North Africa and the Middle East, attenuation by dust was found to contribute 45–90% to the overall attenuation by aerosols. The GHI and DNI attenuation during extreme dust episodes reached 12% and 44%, respectively, over particular areas. After 2008, attenuation of DSSI by aerosols became weaker mainly because of changes in the amount of dust. Sensitivity analysis using different AOD/DOD inputs from Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset revealed that using CAMS products leads to underestimation of the aerosol and dust radiative effects compared to MIDAS, mainly because the former underestimates DOD.

Differential impact of government lockdown policies on reducing air pollution levels and related mortality in Europe

Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO₂, O₃, PM_2.5, and PM₁₀ levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO₂ compared to PM_2.5 and PM₁₀ concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.

Spatio-temporal variation and sensitivity analysis of aerosol particulate matter during the COVID-19 phase-wise lockdowns in Indian cities

At the pandemic of COVID-19, the movement of business and other non-essential activities were majorly restricted at the end of March 2020 in India and continued in different lockdown phases until June 2020. By categorically, studying sensitivity towards anthropogenic factors with other environmental implications in urban Indian cities during phase-wise lockdown scenarios will pave the way for a refined Clean Air Programme (CAP). In this study, the aerosol particulate matter variations between the lockdown phases in both spatial and temporal scales have been explored along with cities exceeding national ambient air quality (NAAQ) standards covering different geographical regions of India for their air quality level. The results of the spatial pattern of Copernicus Atmosphere Monitoring System (CAMS) near-real-time data showed a negative change both in Aerosol Optical Depth (AOD) (-0.2 to 0.1) and black carbon AOD (bcAOD) (-0.9 to -0.75). The changes were evident in successive phases of lockdown with an overall AOD reduction of about 70-90%. Southern urban cities showed a significant impact of mobile sources from temporal analysis than other cities. Principal Component Analysis (PCA) for effects of pollutants by anthropogenic factors (mobile and point source) and meteorological factors (wind speed, wind direction, solar radiation, relative humidity) revealed the two significant driving factors. PM reduction was about 50-70%, predominantly due to anthropogenic factors. The factor analysis revealed the influence of meteorological factors between the major urban cities (Delhi, Kolkata, Mumbai, Chennai, Bengaluru, and Hyderabad). Cities that exceed NAAQ standard performed well during phase-wise lockdowns, exceptional to cities in Gangetic plain. This study helps to frame region-specific strategic action plans for the CAP.

Improving the Representation of Whitecap Fraction and Sea Salt Aerosol Emissions in the ECMWF IFS-AER

The European Centre for Medium-Range Weather Forecasts (ECMWF) operates the Integrated Forecasting System aerosol module (IFS-AER) to provide daily global analysis and forecast of aerosols for the Copernicus Atmosphere Monitoring Service (CAMS). New estimates of sea salt aerosol emissions have been implemented in the IFS-AER using a new parameterization of whitecap fraction as a function of wind speed and sea surface temperature. The effect of whitecap fraction simulated by old and new parameterizations has been evaluated by comparing the IFS-AER new sea salt aerosol characteristics to those of aerosol retrievals. The new parameterization brought a significant improvement as compared to the two parameterizations of sea salt aerosol emissions previously implemented in the IFS-AER. Likewise, the simulated sea salt aerosol optical depth and surface concentration are significantly improved, as compared against ground and remote sensing products.

Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis

Mobility restrictions during the COVID-19 pandemic ostensibly prevented the public from transmitting the disease in public places, but they also hampered outdoor recreation, despite the importance of blue-green spaces (e.g., parks and natural areas) for physical and mental health. We assess whether restrictions on human movement, particularly in blue-green spaces, affected the transmission of COVID-19. Our assessment uses a spatially resolved dataset of COVID-19 case numbers for 848 administrative units across 153 countries during the first year of the pandemic (February 2020 to February 2021). We measure mobility in blue-green spaces with planetary-scale aggregate and anonymized mobility flows derived from mobile phone tracking data. We then use machine learning forecast models and linear mixed-effects models to explore predictors of COVID-19 growth rates. After controlling for a number of environmental factors, we find no evidence that increased visits to blue-green space increase COVID-19 transmission. By contrast, increases in the total mobility and relaxation of other non-pharmaceutical interventions such as containment and closure policies predict greater transmission. Ultraviolet radiation stands out as the strongest environmental mitigant of COVID-19 spread, while temperature, humidity, wind speed, and ambient air pollution have little to no effect. Taken together, our analyses produce little evidence to support public health policies that restrict citizens from outdoor mobility in blue-green spaces, which corroborates experimental studies showing low risk of outdoor COVID-19 transmission. However, we acknowledge and discuss some of the challenges of big data approaches to ecological regression analyses such as this, and outline promising directions and opportunities for future research.

A cross-sectional analysis of meteorological factors and SARS-CoV-2 transmission in 409 cities across 26 countries

There is conflicting evidence on the influence of weather on COVID-19 transmission. Our aim is to estimate weather-dependent signatures in the early phase of the pandemic, while controlling for socio-economic factors and non-pharmaceutical interventions. We identify a modest non-linear association between mean temperature and the effective reproduction number (Re) in 409 cities in 26 countries, with a decrease of 0.087 (95% CI: 0.025; 0.148) for a 10 °C increase. Early interventions have a greater effect on Re with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by government interventions is 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and government interventions are more important drivers of transmission.

The effect of national protest in Ecuador on PM pollution

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM_2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM_2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall − 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Enhanced Simulation of an Asian Dust Storm by Assimilating GCOM-C Observations

Dust aerosols have great effects on global and regional climate systems. The Global Change Observation Mission-Climate (GCOM-C), also known as SHIKISAI, which was launched on 23 December 2017 by the Japan Aerospace Exploration Agency (JAXA), is a next-generation Earth observation satellite that is used for climate studies. The Second-Generation Global Imager (SGLI) aboard GCOM-C enables the retrieval of more precious global aerosols. Here, the first assimilation study of the aerosol optical thicknesses (AOTs) at 500 nm observed by this new satellite is performed to investigate a severe dust storm in spring over East Asia during 28–31 March 2018. The aerosol observation assimilation system is an integration of the four-dimensional local ensemble transform Kalman filter (4D-LETKF) and the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-Hydrostatic Icosahedral Atmospheric Model (NICAM). Through verification with the independent observations from the Aerosol Robotic Network (AERONET) and the Asian Dust and Aerosol Lidar Observation Network (AD-Net), the results demonstrate that the assimilation of the GCOM-C aerosol observations can significantly enhance Asian dust storm simulations. The dust characteristics over the regions without GCOM-C observations are better revealed from assimilating the adjacent observations within the localization length, suggesting the importance of the technical advances in observation and assimilation, which are helpful in clarifying the temporal–spatial structure of Asian dust and which could also improve the forecasting of dust storms, climate prediction models, and aerosol reanalysis.

Data Assimilation of AOD and Estimation of Surface Particulate Matters over the Arctic

In this study, more accurate information on the levels of aerosol optical depth (AOD) was calculated from the assimilation of the modeled AOD based on the optimal interpolation method. Additionally, more realistic levels of surface particulate matters over the Arctic were estimated using the assimilated AOD based on the linear relationship between the particulate matters and AODs. In comparison to the MODIS observation, the assimilated AOD was much improved compared with the modeled AOD (e.g., increase in correlation coefficients from −0.15–0.26 to 0.17–0.76 over the Arctic). The newly inferred monthly averages of PM10 and PM2.5 for April–September 2008 were 2.18–3.70 μg m−3 and 0.85–1.68 μg m−3 over the Arctic, respectively. These corresponded to an increase of 140–180%, compared with the modeled PMs. In comparison to in-situ observation, the inferred PMs showed better performances than those from the simulations, particularly at Hyytiala station. Therefore, combining the model simulation and data assimilation provided more accurate concentrations of AOD, PM10, and PM2.5 than those only calculated from the model simulations.

Spatiotemporal Dynamics of Suspended Sediments in the Negro River, Amazon Basin, from In Situ and Sentinel-2 Remote Sensing Data

Monitoring suspended sediments through remote sensing data in black-water rivers is a challenge. Herein, remote sensing reflectance (Rrs) from in situ measurements and Sentinel-2 Multi-Spectral Instrument (MSI) images were used to estimate the suspended sediment concentration (SSC) in the largest black-water river of the Amazon basin. The Negro River exhibits extremely low Rrs values (<0.005 sr−1 at visible and near-infrared bands) due to the elevated absorption of coloured dissolved organic matter (aCDOM at 440 nm > 7 m−1) caused by the high amount of dissolved organic carbon (DOC > 7 mg L−1) and low SSC (<5 mg L−1). Interannual variability of Rrs is primarily controlled by the input of suspended sediments from the Branco River, which is a clear water river that governs the changes in the apparent optical properties of the Negro River, even at distances that are greater than 90 km from its mouth. Better results were obtained using the Sentinel-2 MSI Red band (Band 4 at 665 nm) in order to estimate the SSC, with an R2 value greater than 0.85 and an error less than 20% in the adjusted models. The magnitudes of water reflectance in the Sentinel-2 MSI Red band were consistent with in situ Rrs measurements, indicating the large spatial variability of the lower SSC values (0 to 15 mg L−1) in a complex anabranching reach of the Negro River. The in situ and satellite data analysed in this study indicates sedimentation processes in the lower Negro River near the Amazon River. The results suggest that the radiometric characteristics of sensors, like sentinel-2 MSI, are suitable for monitoring the suspended sediment concentration in large tropical black-water rivers.

Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region

This study analyzes six frontal dust storms in the Middle East during the cold period (October–March), aiming to examine the atmospheric circulation patterns and force dynamics that triggered the fronts and the associated (pre- or post-frontal) dust storms. Cold troughs mostly located over Turkey, Syria and north Iraq played a major role in the front propagation at the surface, while cyclonic conditions and strong winds facilitated the dust storms. The presence of an upper-atmosphere (300 hPa) sub-tropical jet stream traversing from Egypt to Iran constitutes also a dynamic force accompanying the frontal dust storms. Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to monitor the spatial and vertical extent of the dust storms, while model (Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Copernicus Atmospheric Monitoring Service (CAMS), Regional Climate Model-4 (RegCM4)) simulations are also analyzed. The WRF-Chem outputs were in better agreement with the MODIS observations compared to those of CAMS and RegCM4. The fronts were identified by WRF-Chem simulations via gradients in the potential temperature and sudden changes of wind direction in vertical cross-sections. Overall, the uncertainties in the simulations and the remarkable differences between the model outputs indicate that modelling of dust storms in the Middle East is really challenging due to the complex terrain, incorrect representation of the dust sources and soil/surface characteristics, and uncertainties in simulating the wind speed/direction and meteorological dynamics. Given the potential threat by dust storms, more attention should be directed to the dust model development in this region.

Estimating daily ground-level PM2.5 in China with random-forest-based spatiotemporal kriging

Ambient fine particulate matter (PM_2.5) plays an important role in cardiovascular- and respiratory-related death. Empirical statistical models have been widely applied to estimate ambient PM_2.5 concentrations with correlated variables. However, empirical statistical models ignore the nonlinear relationship between PM_2.5 and covariates and assume that residuals are independent and identically distributed random variables. Here, a hybrid approach, which integrates random forest (RF) model and spatiotemporal kriging, is proposed to estimate the daily PM_2.5 concentration. The proposed RF-based spatiotemporal kriging (RFSTK) model effectively captures nonlinear interactions among different predictors and accounts for the detailed spatiotemporal dependence of the PM_2.5 concentration. The RFSTK model performs well in predicting the daily PM_2.5 concentration. The 10-fold overall cross-validation R² value is 0.881, the mean absolute error (MAE) is 6.89 μg/m³ and the root-mean-square error (RMSE) is 11.48 μg/m³, indicating better performance than the original RF model (R² = 0.848, MAE = 7.88 μg/m³ and RMSE = 13.26 μg/m³). The spatiotemporal prediction of the PM_2.5 concentration shows that approximately 90.04% of China had a daily exposure to PM_2.5 in 2018 that was below the nation's air quality standard of 75 μg/m³. The proposed hybrid method is entirely general and can be applied to map the ambient PM_2.5 concentration over a large spatiotemporal domain.

The Dark Target Algorithm for Observing the Global Aerosol System: Past, Present, and Future

The Dark Target aerosol algorithm was developed to exploit the information content available from the observations of Moderate-Resolution Imaging Spectroradiometers (MODIS), to better characterize the global aerosol system. The algorithm is based on measurements of the light scattered by aerosols toward a space-borne sensor against the backdrop of relatively dark Earth scenes, thus giving rise to the name “Dark Target”. Development required nearly a decade of research that included application of MODIS airborne simulators to provide test beds for proto-algorithms and analysis of existing data to form realistic assumptions to constrain surface reflectance and aerosol optical properties. This research in itself played a significant role in expanding our understanding of aerosol properties, even before Terra MODIS launch. Contributing to that understanding were the observations and retrievals of the growing Aerosol Robotic Network (AERONET) of sun-sky radiometers, which has walked hand-in-hand with MODIS and the development of other aerosol algorithms, providing validation of the satellite-retrieved products after launch. The MODIS Dark Target products prompted advances in Earth science and applications across subdisciplines such as climate, transport of aerosols, air quality, and data assimilation systems. Then, as the Terra and Aqua MODIS sensors aged, the challenge was to monitor the effects of calibration drifts on the aerosol products and to differentiate physical trends in the aerosol system from artefacts introduced by instrument characterization. Our intention is to continue to adapt and apply the well-vetted Dark Target algorithms to new instruments, including both polar-orbiting and geosynchronous sensors. The goal is to produce an uninterrupted time series of an aerosol climate data record that begins at the dawn of the 21st century and continues indefinitely into the future.

Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition

Anthropogenic aerosols are hypothesized to enhance planetary albedo and offset some of the warming due to the buildup of greenhouse gases in Earth's atmosphere. Aerosols can enhance the coverage, reflectance, and lifetime of warm low-level clouds. However, the relationship between cloud lifetime and aerosol concentration has been challenging to measure from polar orbiting satellites. We estimate two timescales relating to the formation and persistence of low-level clouds over [Formula: see text] spatial domains using multiple years of geostationary satellite observations provided by the Clouds and Earth's Radiant Energy System (CERES) Synoptic (SYN) product. Lagrangian trajectories spanning several days along the classic stratus-to-cumulus transition zone are stratified by aerosol optical depth and meteorology. Clouds forming in relatively polluted trajectories tend to have lighter precipitation rates, longer average lifetime, and higher cloud albedo and cloud fraction compared with unpolluted trajectories. While liquid water path differences are found to be negligible, we find direct evidence of increased planetary albedo primarily through increased drop concentration ([Formula: see text]) and cloud fraction, with the caveat that the aerosol influence on cloud fraction is positive only for stable atmospheric conditions. While the increase in cloud fraction can be large typically in the beginning of trajectories, the Twomey effect accounts for the bulk (roughly 3/4) of the total aerosol indirect radiative forcing estimate.

Aerosol optical depth assimilation for a modal aerosol model: Implementation and application in AOD forecasts over East Asia

A new aerosol optical depth (AOD) data assimilation (DA) module was developed in Gridpoint Statistical Interpolation (GSI) 3-dimensional variational (3DVAR) system, named FastJ/CRTM-AOD DA module. And applied to the Modal Aerosol Dynamics Model for Europe with the Secondary Organic Aerosol Model (MADE/SORGAM) in the Weather Research and Forecasting/Chemistry model (WRF/Chem). The Fast-J optical module in WRF/Chem was used as the observation operator of AOD. The corresponding Jacobian code was modified from the one of CRTM-AOD in GSI. This way obviated the need for the Jacobian code's generation, which was complex and difficult for the highly nonlinear observation operator. During the studying period (January and April of 2014), compared to the ground AOD observations, AOD DA reduced about 20% fractional error (FE) with MADE/SORGAM. The original DA framework, which applied to the Goddard Chemistry Aerosol Radiation and Transport (GOCART) mechanism, performed slightly better than the new assimilation scheme for the low-value AOD situations (value < 0.4). However, compared to the original DA framework, the new DA scheme show a notable improvement for the high-value (0.4 < value ≤ 1.2) and extreme-high-value (value > 1.2) AOD situations. FE can be reduced by 48% and 64%, respectively. It indicates that the AOD DA impacts on AOD forecasts vary significant between different aerosol mechanisms. Moreover, FastJ/CRTM-AOD DA module can be easily and efficiently applied to the other aerosol schemes and the other optical modules, which is important to the development on AOD assimilation.

A First Case Study of CCN Concentrations from Spaceborne Lidar Observations

We present here the first cloud condensation nuclei (CCN) concentration profiles derived from measurements with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), for different aerosol types at a supersaturation of 0.15%. CCN concentrations, along with the corresponding uncertainties, were inferred for a nighttime CALIPSO overpass on 9 September 2011, with coincident observations with the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft, within the framework of the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign over Thessaloniki, Greece. The CALIPSO aerosol typing is evaluated, based on data from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis. Backward trajectories and satellite-based fire counts are used to examine the origin of air masses on that day. Our CCN retrievals are evaluated against particle number concentration retrievals at different height levels, based on the ACEMED airborne measurements and compared against CCN-related retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard Terra and Aqua product over Thessaloniki showing that it is feasible to obtain CCN concentrations from CALIPSO, with an uncertainty of a factor of two to three.

Near-Real-Time Application of SEVIRI Aerosol Optical Depth Algorithm

Within the framework of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project, the near-real-time (NRT) operation has been documented for an in-house developed algorithm used for the retrieval of aerosol optical depth (AOD) maps from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the Meteosat Second Generation (MSG). With the frequency of 15 min at a spatial resolution of roughly 5.5 × 5.5 km the AOD maps are provided for the country domains of Poland, the Czech Republic, Romania, and Southern Norway. A significant improvement has been reported in terms of modification of the existing prototype algorithm that it suits the operational NRT AOD retrieval for an extended area. This is mainly due to the application of the optimal interpolation method for the AOD estimation on reference days with the use of ground-based measurements of the Aerosol Robotic Network (AERONET) and the Aerosol Research Network (PolandAOD-NET) as well as simulations of the Copernicus Atmosphere Monitoring Service (CAMS). The main issues that have been addressed regarding surface reflectance estimation, cloud screening and uncertainty calculation. Exemplary maps of the NRT retrieval have been presented.

Five Years of Dust Episodes at the Southern Italy GAW Regional Coastal Mediterranean Observatory: Multisensors and Modeling Analysis

The Mediterranean area is a climate-change hotspot because of the natural and anthropogenic pollution pressure. The presence of natural aerosols, such as dust, influences solar radiation and contributes to the detection, in storm episodes, of significant concentrations of PM10 in Southern Italy, where generally fresh and clean air is due to local circulation, and particulate matter concentrations are very low. We present the results of medium-term observations (2015–2019) at Lamezia Terme GAW (Global Atmospheric Watch) Regional Observatory, with the purpose of identifying the dust incursion events by studying the aerosol properties in the site. To achieve this goal, the experimental data, collected by several instruments, have been also correlated with the large-scale atmospheric patterns derived by the ERA5 reanalysis dataset, in order to study the meteorological conditions that strongly influence dust outbreaks and their spatio-temporal behavior. An intense dust-outbreak episode, which occurred on 23–27 April 2019, was chosen as a case study; a detailed analysis was carried out considering surface and column optical properties, chemical properties, large-scale pattern circulation, air-quality modeling/satellite products, and back-trajectory analysis, to confirm the capability of the modeled large-scale atmospheric fields to correctly simulate the conditions mainly related to the desert dust-outbreak events.

Evaluating the Impact of Assimilating Aerosol Optical Depth Observations on Dust Forecasts Over North Africa and the East Atlantic Using Different Data Assimilation Methods

This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropical North Atlantic. To do so, seven experiments are conducted using the Weather Research and Forecasting dust model and the Gridpoint Statistical Interpolation analysis system. Six of these experiments differ in whether or not AOD observations are assimilated and the DA method used, the latter of which includes the three-dimensional variational (3D-Var), ensemble square root filter (EnSRF), and hybrid methods. The seventh experiment, which allows us to assess the impact of assimilating deep blue AOD data, assimilates only dark target AOD data using the hybrid method. The assimilation of MODIS AOD data clearly improves AOD analyses and forecasts up to 48 hr in length. Results also show that assimilating deep blue data has a primarily positive effect on AOD analyses and forecasts over and downstream of the major North African source regions. Without assimilating deep blue data (assimilating dark target only), AOD assimilation only improves AOD forecasts for up to 30 hr. Of the three DA methods examined, the hybrid and EnSRF methods produce better AOD analyses and forecasts than the 3D-Var method does. Despite the clear benefit of AOD assimilation for AOD analyses and forecasts, the lack of information regarding the vertical distribution of aerosols in AOD data means that AOD assimilation has very little positive effect on analyzed or forecasted vertical profiles of backscatter.

Bounding Global Aerosol Radiative Forcing of Climate Change

Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth's radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This review provides a new range of aerosol radiative forcing over the industrial era based on multiple, traceable, and arguable lines of evidence, including modeling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes constrain the forcing from aerosol-radiation interactions. A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol-driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction is less clear, and the influence on mixed-phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to a 68% confidence interval for the total aerosol effective radiative forcing of -1.6 to -0.6 W m-2, or -2.0 to -0.4 W m-2 with a 90% likelihood. Those intervals are of similar width to the last Intergovernmental Panel on Climate Change assessment but shifted toward more negative values. The uncertainty will narrow in the future by continuing to critically combine multiple lines of evidence, especially those addressing industrial-era changes in aerosol sources and aerosol effects on liquid cloud amount and on ice clouds.

A Decade of Aerosol Optical Properties Measurements over Athens, Greece

Long-term ground-based measurements of aerosol optical properties in Athens, Greece, for the period 2008–2018 performed by the National Observatory of Athens are used in order to investigate the aerosol climatology of the area. In this study, we utilize quality-assured measurements of the aerosol optical depth (AOD), Single Scattering Albedo (SSA) and Ångström exponent obtained by CIMEL photometers in the framework of the Aerosol Robotic Network (AERONET) to extract the seasonality and the trends of aerosols in the region. Higher aerosol loads are found during spring and summer months. A 1.1% per year decrease for AOD at 440 nm and 0.4% decrease per year for SSA during the studied period are recorded. Collocated and synchronous PM10 values, for a five-year period, are used in order to study ground-level conditions. Also, the Planetary Boundary Layer Height from ERA-5 is used to investigate the stratification of the particles. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental dominant aerosol mixtures. Also, the characterization of AOD provided by Copernicus Atmosphere Monitoring Service (CAMS) is investigated. Finally, seasonal AOD trends recorded from AERONET from satellite sensors (MODIS-Aqua/MODIS-Terra) and estimated by CAMS are examined, and significant differences have been found.

Orange Snow—A Saharan Dust Intrusion over Romania During Winter Conditions

On the morning of 23 March 2018, an unusual phenomenon was observed over Romania where the southeastern part of the country was covered in a fresh-layer of orange snow. The event was extensively reported in mass-media and social-media and raised questions about the origin and the possible impact of the orange snow. Even if this type of events, intrusions of Saharan dust, have been reported before in Romania, and in Europe in general, their occurrence during negative temperature conditions is very rare. Saharan dust intrusion occurs over Europe mainly during spring and, in general, is not accompanied by snow at low altitudes. In this article, for the first time, the synoptic-scale conditions leading to the Saharan dust intrusion over Romania and the chemical and physical properties of the deposited dust particles in a snow layer were analyzed. The Saharan dust event affected a permanent atmospheric measurement research infrastructure located southwest of Bucharest, the capital city of Romania. In-situ and remote sensing measurements conducted at this research infrastructure allowed the identification of the dust source as the north Sahara. The source was confirmed by the elemental ratios of the main components (e.g., Al, Ca, Mg, Fe, K). For example, the (Ca+Mg)/Fe ratio of 1.39 was characteristic for the north Sahara. The dust morphology and the minerals were analyzed by scanning electron microscopy with energy disperse X-ray spectrometry (SEM/EDX). The size distribution of the particle geometric diameter showed that they are centred on 1 μ m, but larger particles up to 40 μ m are also present. To visualize the minerals, an approach was developed which emphasized the presence of the calcite, quartz or clay minerals. The optical parameters of dust were measured by re-suspending the particles. Values of the optical parameters (i.e., asymmetry parameter at 550 nm was 0.604, single scattering albedo was 0.84–0.89) were similar to those measured for Saharan dust intrusions over the Iberian Peninsula. Also, the non-refractory particles found in the dust-contaminated snow layer were analyzed, indicating the presence of HULIS-like compounds, most probably advected from the Mediterranean sea.

Lidar data assimilation method based on CRTM and WRF-Chem models and its application in PM2.5 forecasts in Beijing

A three-dimensional variational (3DVAR) lidar data assimilation method is developed based on the Community Radiative Transfer Model (CRTM) and Weather Research and Forecasting model coupled to Chemistry (WRF-Chem) model. A 3DVAR data assimilation (DA) system using lidar extinction coefficient observation data is established, and variables from the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) mechanism of the WRF-Chem model are employed. Hourly lidar extinction coefficient data from 12:00 to 18:00 UTC on March 13, 2018 at four stations in Beijing are assimilated into the initial field of the WRF-Chem model; subsequently, a 24 h PM_2.5 concentration forecast is made. Results indicate that assimilating lidar data can effectively improve the subsequent forecast. PM_2.5 forecasts without using lidar DA are remarkably underestimated, particularly during heavy haze periods; in contrast, forecasts of PM_2.5 concentrations with lidar DA are closer to observations, the model low bias is evidently reduced, and the vertical distribution of the PM_2.5 concentration in Beijing is distinctly improved from the surface to 1200 m. Of the five aerosol species, improvements of NO₃^- are the most significant. The correlation coefficient between PM_2.5 concentration forecasts with lidar DA and observations at 12 stations in Beijing is increased by 0.45, and the corresponding average RMSE is decreased by 25 μg·m^-3, which respectively compared to those without DA.

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.

Advanced Ultraviolet Radiation and Ozone Retrieval for Applications (AURORA): A Project Overview

With the launch of the Sentinel-5 Precursor (S-5P, lifted-off on 13 October 2017), Sentinel-4 (S-4) and Sentinel-5 (S-5)(from 2021 and 2023 onwards, respectively) operational missions of the ESA/EU Copernicus program, a massive amount of atmospheric composition data with unprecedented quality will become available from geostationary (GEO) and low Earth orbit (LEO) observations. Enhanced observational capabilities are expected to foster deeper insight than ever before on key issues relevant for air quality, stratospheric ozone, solar radiation, and climate. A major potential strength of the Sentinel observations lies in the exploitation of complementary information that originates from simultaneous and independent satellite measurements of the same air mass. The core purpose of the AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project is to investigate this exploitation from a novel approach for merging data acquired in different spectral regions from on board the GEO and LEO platforms. A data processing chain is implemented and tested on synthetic observations. A new data algorithm combines the ultraviolet, visible and thermal infrared ozone products into S-4 and S-5(P) fused profiles. These fused products are then ingested into state-of-the-art data assimilation systems to obtain a unique ozone profile in analyses and forecasts mode. A comparative evaluation and validation of fused products assimilation versus the assimilation of the operational products will seek to demonstrate the improvements achieved by the proposed approach. This contribution provides a first general overview of the project, and discusses both the challenges of developing a technological infrastructure for implementing the AURORA concept, and the potential for applications of AURORA derived products, such as tropospheric ozone and UV surface radiation, in sectors such as air quality monitoring and health.

The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is NASA's latest reanalysis for the satellite era (1980 onward) using the Goddard Earth Observing System, version 5 (GEOS-5), Earth system model. MERRA-2 provides several improvements over its predecessor (MERRA-1), including aerosol assimilation for the entire period. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer and the Advanced Very High Resolution Radiometer instruments. Additionally, MERRA-2 assimilates (non bias corrected) AOD from the Multiangle Imaging SpectroRadiometer over bright surfaces and AOD from Aerosol Robotic Network sunphotometer stations. This paper, the second of a pair, summarizes the efforts to assess the quality of the MERRA-2 aerosol products. First, MERRA-2 aerosols are evaluated using independent observations. It is shown that the MERRA-2 absorption aerosol optical depth (AAOD) and ultraviolet aerosol index (AI) compare well with Ozone Monitoring Instrument observations. Next, aerosol vertical structure and surface fine particulate matter (PM_2.5) are evaluated using available satellite, aircraft, and ground-based observations. While MERRA-2 generally compares well to these observations, the assimilation cannot correct for all deficiencies in the model (e.g., missing emissions). Such deficiencies can explain many of the biases with observations. Finally, a focus is placed on several major aerosol events to illustrate successes and weaknesses of the AOD assimilation: the Mount Pinatubo eruption, a Saharan dust transport episode, the California Rim Fire, and an extreme pollution event over China. The article concludes with a summary that points to best practices for using the MERRA-2 aerosol reanalysis in future studies.

The MERRA-2 Aerosol Reanalysis, 1980 - onward, Part I: System Description and Data Assimilation Evaluation

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) updates NASA's previous satellite era (1980 - onward) reanalysis system to include additional observations and improvements to the Goddard Earth Observing System, Version 5 (GEOS-5) Earth system model. As a major step towards a full Integrated Earth Systems Analysis (IESA), in addition to meteorological observations, MERRA-2 now includes assimilation of aerosol optical depth (AOD) from various ground- and space-based remote sensing platforms. Here, in the first of a pair of studies, we document the MERRA-2 aerosol assimilation, including a description of the prognostic model (GEOS-5 coupled to the GOCART aerosol module), aerosol emissions, and the quality control of ingested observations. We provide initial validation and evaluation of the analyzed AOD fields using independent observations from ground, aircraft, and shipborne instruments. We demonstrate the positive impact of the AOD assimilation on simulated aerosols by comparing MERRA-2 aerosol fields to an identical control simulation that does not include AOD assimilation. Having shown the AOD evaluation, we take a first look at aerosol-climate interactions by examining the shortwave, clear-sky aerosol direct radiative effect. In our companion paper, we evaluate and validate available MERRA-2 aerosol properties not directly impacted by the AOD assimilation (e.g. aerosol vertical distribution and absorption). Importantly, while highlighting the skill of the MERRA-2 aerosol assimilation products, both studies point out caveats that must be considered when using this new reanalysis product for future studies of aerosols and their interactions with weather and climate.

Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM2.5 Predictions

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM_2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM_2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM_2.5, PM₁₀, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM_2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be -29.9W/m² at the surface, 27.0W/m² inside the atmosphere, and -2.9W/m² at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China.

Spatiotemporal variability and contribution of different aerosol types to the Aerosol Optical Depth over the Eastern Mediterranean

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the Aerosol Optical Depth (AOD) over the Eastern Mediterranean as derived from MODIS Terra (3/2000-12/2012) and Aqua (7/2002-12/2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sunphotometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium sized cities, industrial zones, and power plant complexes, seasonal variabilities, and decadal averages. The average AOD at 550 nm (AOD₅₅₀) for the entire region is ~ 0.22 ± 0.19 with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in Central and Eastern Europe, and transport of dust from the Sahara Desert and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry-aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD₅₅₀. The spatial and temporal variability of anthropogenic, dust and fine mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD₅₅₀ exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine mode natural aerosols account for ~ 51 %, ~ 34 % and ~ 15 % of the total AOD₅₅₀ over land, while, anthropogenic aerosols, dust and marine aerosols account ~ 40 %, ~ 34 % and ~ 26 % of the total AOD₅₅₀ over the sea, based on MODIS Terra and Aqua observations.

Spatiotemporal variability and contribution of different aerosol types to the Aerosol Optical Depth over the Eastern Mediterranean

This study characterizes the spatiotemporal variability and relative contribution of different types of aerosols to the Aerosol Optical Depth (AOD) over the Eastern Mediterranean as derived from MODIS Terra (3/2000-12/2012) and Aqua (7/2002-12/2012) satellite instruments. For this purpose, a 0.1° × 0.1° gridded MODIS dataset was compiled and validated against sunphotometric observations from the AErosol RObotic NETwork (AERONET). The high spatial resolution and long temporal coverage of the dataset allows for the determination of local hot spots like megacities, medium sized cities, industrial zones, and power plant complexes, seasonal variabilities, and decadal averages. The average AOD at 550 nm (AOD₅₅₀) for the entire region is ~ 0.22 ± 0.19 with maximum values in summer and seasonal variabilities that can be attributed to precipitation, photochemical production of secondary organic aerosols, transport of pollution and smoke from biomass burning in Central and Eastern Europe, and transport of dust from the Sahara Desert and the Middle East. The MODIS data were analyzed together with data from other satellite sensors, reanalysis projects and a chemistry-aerosol-transport model using an optimized algorithm tailored for the region and capable of estimating the contribution of different aerosol types to the total AOD₅₅₀. The spatial and temporal variability of anthropogenic, dust and fine mode natural aerosols over land and anthropogenic, dust and marine aerosols over the sea is examined. The relative contribution of the different aerosol types to the total AOD₅₅₀ exhibits a low/high seasonal variability over land/sea areas, respectively. Overall, anthropogenic aerosols, dust and fine mode natural aerosols account for ~ 51 %, ~ 34 % and ~ 15 % of the total AOD₅₅₀ over land, while, anthropogenic aerosols, dust and marine aerosols account ~ 40 %, ~ 34 % and ~ 26 % of the total AOD₅₅₀ over the sea, based on MODIS Terra and Aqua observations.

Effect of aerosol vertical distribution on aerosol-radiation interaction: A theoretical prospect

This study presents a theoretical investigation of the effect of the aerosol vertical distribution on the aerosol radiative effect (ARE). Four aerosol composition models (dust, polluted dust, pollution and pure scattering aerosols) with varying aerosol vertical profiles are incorporated into a radiative transfer model. The simulations show interesting spectral dependence of the ARE on the aerosol layer height. ARE increases with the aerosol layer height in the ultraviolet (UV: 0.25–0.42 μm) and thermal-infrared (TH-IR: 4.0–20.0 μm) regions, whereas it decreases in the visible-near infrared (VIS-NIR: 0.42–4.0 μm) region. Changes in the ARE with aerosol layer height are associated with different dominant processes for each spectral region. The combination of molecular (Rayleigh) scattering and aerosol absorption is the key process in the UV region, whereas aerosol (Mie) scattering and atmospheric gaseous absorption are key players in the VIS-NIR region. The longwave emission fluxes are controlled by the environmental temperature at the aerosol layer level. ARE shows maximum sensitivity to the aerosol layer height in the TH-IR region, followed by the UV and VIS-NIR regions. These changes are significant even in relatively low aerosol loading cases (aerosol optical depth ∼0.2–0.3). Dust aerosols are the most sensitive to altitude followed by polluted dust and pollution in all three different wavelength regions. Differences in the sensitivity of the aerosol type are explained by the relative strength of their spectral absorption/scattering properties. The role of surface reflectivity on the overall altitude dependency is shown to be important in the VIS-NIR and UV regions, whereas it is insensitive in the TH-IR region. Our results indicate that the vertical distribution of water vapor with respect to the aerosol layer is an important factor in the ARE estimations. Therefore, improved estimations of the water vapor profiles are needed for the further reduction in uncertainties associated with the ARE estimation.

Validation of the McClear clear-sky model in desert conditions with three stations in Israel

Abstract. The new McClear clear-sky model, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded Copernicus Atmosphere Monitoring Service (CAMS) to estimate the solar direct and global irradiances received at ground level in cloud-free conditions at any place any time. The work presented here focuses on desert conditions and compares the McClear irradiances to coincident 1 min measurements made in clear-sky conditions at three stations in Israel which are distant from less than 100 km. The bias for global irradiance is comprised between 2 and 32 W m−2, i.e. between 0 and 4 % of the mean observed irradiance (approximately 830 W m−2). The RMSE ranges from 30 to 41 W m−2 (4 %) and the squared correlation coefficient is greater than 0.976. The bias for the direct irradiance at normal incidence (DNI) is comprised between −68 and +13 W m−2, i.e. between −8 and 2 % of the mean observed DNI (approximately 840 W m−2). The RMSE ranges from 53 (7 %) to 83 W m−2 (10 %). The squared correlation coefficient is close to 0.6. The performances are similar for the three sites for the global irradiance and for the DNI to a lesser extent, demonstrating the robustness of the McClear model combined with CAMS products. These results are discussed in the light of those obtained by McClear for other desert areas in Egypt and United Arab Emirates.

New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean

A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process, that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach AOT fields have lognormal PDFs and structure functions having the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale-invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a year-long global MODIS AOT dataset (over ocean) with 10 km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5° spacing. The observed shapes of the structure functions indicated that in a large number of cases the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

The implementation of NEMS GFS Aerosol Component (NGAC) Version 1.0 for global dust forecasting at NOAA/NCEP

The NOAA National Centers for Environmental Prediction (NCEP) implemented NEMS GFS Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5 day dust forecasts at 1°×1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.