East Asia is a major dust source in the world. Mineral dusts in the atmosphere and their interactions with clouds and precipitation have great impacts on regional climate in Asia, where there are ...large arid and semiarid regions. In this review paper, we summarize the typical transport paths of East Asian dust, which affect regional and global climates, and discuss numerous effects of dust aerosols on clouds and precipitation primarily over East Asian arid and semiarid regions. We hope to provide a benchmark of our present understanding of these issues. Compared with the aerosols of Saharan dust, those of East Asian dust are more absorptive of solar radiation, and its direct radiative forcing at the top of atmosphere is nearly positive or nil. It means that aerosols of East Asian dust can influence the cloud properties not only by acting as cloud condensation nuclei and ice nuclei (via first indirect effect, second indirect effect, and invigoration effect) but also through changing the relative humidity and stability of the atmosphere (via semidirect effect). Converting visible light to thermal energy, dust aerosols can burn clouds to produce a warming effect on climate, which is opposite to the first and second indirect effects of aerosols. The net dust aerosol radiative effects are still highly unclear. In addition, dust can inhibit or enhance precipitation under certain conditions, thus impacting the hydrological cycle. Over Asian arid and semiarid regions, the positive feedback loop in the aerosol‐cloud‐precipitation interaction may aggravate drought in its inner land.
Key Points
East Asian dust affects regional and global climate by typical transport paths
East Asian dust aerosols are more absorptive than those from Saharan Desert
Dust‐cloud‐precipitation interaction over arid regions may aggravate drought
The frequent occurrence of severe air pollution episodes in China has been a great concern and thus the focus of intensive studies. Planetary boundary layer height (PBLH) is a key factor in the ...vertical mixing and dilution of near-surface pollutants. However, the relationship between PBLH and surface pollutants, especially particulate matter (PM) concentration across China, is not yet well understood. We investigate this issue at 1600 surface stations using PBLH derived from space-borne and ground-based lidar, and discuss the influence of topography and meteorological variables on the PBLH–PM relationship. Albeit the PBLH–PM correlations are roughly negative for most cases, their magnitude, significance, and even sign vary considerably with location, season, and meteorological conditions. Weak or even uncorrelated PBLH–PM relationships are found over clean regions (e.g., Pearl River Delta), whereas nonlinearly negative responses of PM to PBLH evolution are found over polluted regions (e.g., North China Plain). Relatively strong PBLH–PM interactions are found when the PBLH is shallow and PM concentration is high, which typically corresponds to wintertime cases. Correlations are much weaker over the highlands than the plains regions, which may be associated with lighter pollution loading at higher elevations and contributions from mountain breezes. The influence of horizontal transport on surface PM is considered as well, manifested as a negative correlation between surface PM and wind speed over the whole nation. Strong wind with clean upwind air plays a dominant role in removing pollutants, and leads to obscure PBLH–PM relationships. A ventilation rate is used to jointly consider horizontal and vertical dispersion, which has the largest impact on surface pollutant accumulation over the North China Plain. As such, this study contributes to improved understanding of aerosol–planetary boundary layer (PBL) interactions and thus our ability to forecast surface air pollution.
Exposure to fine particulate matter (PM2.5) can significantly harm human health and increase the risk of death. Satellite remote sensing allows for generating spatially continuous PM2.5 data, but ...current datasets have overall low accuracies with coarse spatial resolutions limited by data sources and models. Air pollution levels in China have experienced dramatic changes over the past couple of decades. However, country-wide ground-based PM2.5 records only date back to 2013. To reveal the spatiotemporal variations of PM2.5, long-term and high-spatial-resolution aerosol optical depths, generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle implementation of Atmospheric Correction (MAIAC) algorithm, were employed to estimate PM2.5 concentrations at a 1 km resolution using our proposed Space-Time Extra-Trees (STET) model. Our model can capture well variations in PM2.5 concentrations at different spatiotemporal scales, with higher accuracies (i.e., cross-validation coefficient of determination, CV-R2 = 0.86–0.90) and stronger predictive powers (i.e., R2 = 0.80–0.82) than previously reported. The resulting PM2.5 dataset for China (i.e., ChinaHighPM2.5) provides the longest record (i.e., 2000 to 2018) at a high spatial resolution of 1 km, enabling the study of PM2.5 variation patterns at different scales. In most places, PM2.5 concentrations showed increasing trends around 2007 and remained high until 2013, after which they declined substantially, thanks to a series of government actions combating air pollution in China. While nationwide PM2.5 concentrations have decreased by 0.89 μg/m3/yr (p < 0.001) during the last two decades, the reduction has accelerated to 4.08 μg/m3/yr (p < 0.001) over the last six years, indicating a significant improvement in air quality. Large improvements occurred in the Pearl and Yangtze River Deltas, while the most polluted region remained the North China Plain, especially in winter. The ChinaHighPM2.5 dataset will enable more insightful analyses regarding the causes and attribution of pollution over medium- or small-scale areas.
•A 1-km-resolution PM2.5 dataset from 2000 to 2018 across China is generated.•The ChinaHighPM2.5 dataset yields a higher data quality and outperforms those from most previous studies.•PM2.5 pollution has changed greatly and diversely across China during the last two decades.•The ChinaHighPM2.5 dataset is potentially useful for air pollution studies over small-scale areas.
Surface Solar Irradiance (SSI) is a key parameter dictating surface-atmosphere interactions, driving radiative, hydrological, and land surface processes, and can thus impinge greatly upon weather and ...climate. It is thereby a prerequisite of many studies and applications. Estimating SSI from satellites began in the 1960s, and is currently the principal way to map SSI spatiotemporal distributions from regional to global scales. Starting from an overview of historical studies carried out in the past several decades, this paper reviews the progresses made in methodology, validation, and products over these years. First, the requirements of SSI in various studies or applications are presented along with the theoretical background of SSI satellite estimation. Methods to estimate SSI from satellites are then summarized as well as their advantages and limitations. Validations of satellite-based SSI on two typical spatial scales are discussed followed by a brief description of existing products and their accuracies. Finally, the challenges faced by current SSI satellite estimation are analyzed, and possible improvements to implement in the future are suggested. This review not only updates the review paper by Pinker et al. (1995) on satellite methods to derive SSI but also offers a more comprehensive summary of the related studies and applications.
•A comprehensive review of estimating SSI from satellites is presented.•Methods, validations, and products from the past few decades are reviewed.•Current challenges and future directions are suggested.
The planetary boundary layer height (PBLH) is an important parameter for understanding the accumulation of pollutants and the dynamics of the lower atmosphere. Lidar has been used for tracking the ...evolution of PBLH by using aerosol backscatter as a tracer, assuming aerosol is generally well-mixed in the PBL; however, the validity of this assumption actually varies with atmospheric stability. This is demonstrated here for stable boundary layers (SBL), neutral boundary layers (NBL), and convective boundary layers (CBL) using an 8-year dataset of micropulse lidar (MPL) and radiosonde (RS) measurements at the ARM Southern Great Plains, and MPL at the GSFC site. Due to weak thermal convection and complex aerosol stratification, traditional gradient and wavelet methods can have difficulty capturing the diurnal PBLH variations in the morning and forenoon, as well as under stable conditions generally. A new method is developed that combines lidar-measured aerosol backscatter with a stability dependent model of PBLH temporal variation (DTDS). The latter helps “recalibrate” the PBLH in the presence of a residual aerosol layer that does not change in harmony with PBL diurnal variation. The hybrid method offers significantly improved PBLH detection, with better correlation and smaller biases, under most thermodynamic conditions, especially for SBL and CBL. Relying on the physical process of PBL diurnal development, different schemes are developed for growing, maintenance, and decaying periods. Comprehensive evaluation of this new method shows much better tracking of diurnal PBLH variation and significantly smaller biases under various pollution levels.
•A lidar-based algorithm retrieving planetary boundary layer height was developed.•This method was evaluated in detail under various thermodynamic stabilities.•This method showed a great improvement for the stable boundary layer.•The biases were largely reduced under various stabilities and pollution levels.
•We developed a new deep learning model for satellite-based real-time PM2.5 estimation.•We validated EntityDenseNet with ground-based measurements over mainland China in 2019.•EntityDenseNet ...displayed the best performance compared to four other machine learning models.
Particulate matter with a mass concentration of particles with a diameter less than 2.5 μm (PM2.5) is a key air quality parameter. A real-time knowledge of PM2.5 is highly valuable for lowering the risk of detrimental impacts on human health. To achieve this goal, we developed a new deep learning model-EntityDenseNet to retrieve ground-level PM2.5 concentrations from Himawari-8, a geostationary satellite providing high temporal resolution data. In contrast to the traditional machine learning model, the new model has the capability to automatically extract PM2.5 spatio-temporal characteristics. Validation across mainland China demonstrates that hourly, daily and monthly PM2.5 retrievals contain the root-mean-square errors of 26.85, 25.3, and 15.34 μg/m3, respectively. In addition to a higher accuracy achievement when compared with various machine learning inversion methods (backpropagation neural network, extreme gradient boosting, light gradient boosting machine, and random forest), EntityDenseNet can “peek inside the black box” to extract the spatio-temporal features of PM2.5. This model can show, for example, that PM2.5 levels in the coastal city of Tianjin were more influenced by air from Hebei than Beijing. Further, EntityDenseNet can still extract the seasonal characteristics that demonstrate that PM2.5 is more closely related within three month groups over mainland China: (1) December, January and February, (2) March, April and May, (3) July, August and September, even without meteorological information. EntityDenseNet has the ability to obtain high temporal resolution satellite-based PM2.5 data over China in real-time. This could act as an important tool to improve our understanding of PM2.5 spatio-temporal features.
•A high-resolution (1 km) and high-quality PM10 dataset in China (i.e., ChinaHighPM10) is generated.•The ChinaHighPM10 dataset yields a high accuracy (R2 = 0.86, RMSE = 24.28 μg/m3) and outperforms ...most previous studies.•PM10 concentrations significantly decreased by 5.81 μg/m3/yr (p < 0.001) from 2015 to 2019 across China.
Respirable particles with aerodynamic diameters ≤ 10 µm (PM10) have important impacts on the atmospheric environment and human health. Available PM10 datasets have coarse spatial resolutions, limiting their applications, especially at the city level. A tree-based ensemble learning model, which accounts for spatiotemporal information (i.e., space-time extremely randomized trees, denoted as the STET model), is designed to estimate near-surface PM10 concentrations. The 1-km resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product and auxiliary factors, including meteorology, land-use cover, surface elevation, population distribution, and pollutant emissions, are used in the STET model to generate the high-resolution (1 km) and high-quality PM10 dataset for China (i.e., ChinaHighPM10) from 2015 to 2019. The product has an out-of-sample (out-of-station) cross-validation coefficient of determination (CV-R2) of 0.86 (0.82) and a root-mean-square error (RMSE) of 24.28 (27.07) μg/m3, outperforming most widely used models from previous related studies. High levels of PM10 concentration occurred in northwest China (e.g., the Tarim Basin) and the Northern China Plain. Overall, PM10 concentrations had a significant declining trend of 5.81 μg/m3 per year (p < 0.001) over the past five years in China, especially in three key urban agglomerations. The ChinaHighPM10 dataset is potentially useful for future small- and medium-scale air pollution studies by virtue of its higher spatial resolution and overall accuracy.
Ozone (O3) is an important trace and greenhouse gas in the atmosphere, posing a threat to the ecological environment and human health at the ground level. Large-scale and long-term studies of O3 ...pollution in China are few due to highly limited direct ground and satellite measurements. This study offers a new perspective to estimate ground-level O3 from solar radiation intensity and surface temperature by employing an extended ensemble learning of the space-time extremely randomized trees (STET) model, together with ground-based observations, remote sensing products, atmospheric reanalysis, and an emission inventory. A full-coverage (100%), high-resolution (10 km) and high-quality daily maximum 8-h average (MDA8) ground-level O3 dataset covering China (called ChinaHighO3) from 2013 to 2020 was generated. Our MDA8 O3 estimates (predictions) are reliable, with an average out-of-sample (out-of-station) coefficient of determination of 0.87 (0.80) and root-mean-square error of 17.10 (21.10) μg/m3 in China. The unique advantage of the full coverage of our dataset allowed us to accurately capture a short-term severe O3 pollution exposure event that took place from 23 April to 8 May in 2020. Also, a rapid increase and recovery of O3 concentrations associated with variations in anthropogenic emissions were seen during and after the COVID-19 lockdown, respectively. Trends in O3 concentration showed an average growth rate of 2.49 μg/m3/yr (p < 0.001) from 2013 to 2020, along with the continuous expansion of polluted areas exceeding the daily O3 standard (i.e., MDA8 O3 = 160 μg/m3). Summertime O3 concentrations and the probability of occurrence of daily O3 pollution have significantly increased since 2015, especially in the North China Plain and the main air pollution transmission belt (i.e., the “2 + 26” cities). However, a decline in both was seen in 2020, mainly due to the coordinated control of air pollution and ongoing COVID-19 effects. This carefully vetted and smoothed dataset is valuable for studies on air pollution and environmental health in China.
•A full-coverage of daily MDA8 O3 dataset (10 km) from 2013 to 2020 in China is generated.•The ChinaHighO3 dataset yields a high-quality information (CV-R2 = 0.87, RMSE = 17.10 μg/m3).•Ground-level O3 showed a significant increasing trend of 2.49 μg/m3/yr (p < 0.001) during 2013–2020.•Rapid O3 increase and recovery were observed during and after the COVID-19 lockdown.
The depth of the planetary boundary layer (PBL) and its temporal evolution have important effects on weather, air quality and climate. While there are methods to detect the PBL depth from atmospheric ...profiles, few can be applied to different types of measurements and cope with changing atmospheric conditions. Many require supporting information from other instruments. In this study, two common methods for PBL depth detection (wavelet covariance and iterative curve-fitting) are combined, modified and applied to long-term time series of radiosonde profiles, micropulse lidar (MPL) measured backscatter and atmospheric emitted radiance interferometer (AERI) data collected at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. Intercomparison among the three PBL retrieval products shows the robustness of the algorithm. The comparisons were made for different times of day, four seasons, and variable sky conditions. While considerable uncertainties exist in PBL detection using all three types of measurements, the agreement among the PBL products is promising under certain conditions, and the different measurements have complementary advantages. The best agreement in the seasonal cycle occurs in winter, and the best agreement in the diurnal cycle when the boundary-layer regime is mature and changes slowly. PBL depths from instruments with higher temporal resolution (MPL and AERI) are of comparable accuracy to radiosonde-derived PBL depths; AERI excels for shallow PBLs, MPL for cloudy conditions. The new continuous PBL data set can be used to improve model parameterizations of PBL and our understanding of atmospheric transport of pollutants which affect clouds, air quality and human health.
•A new method for detecting planetary boundary layers is introduced.•The method is valid for use with micropulse lidar, radiosonde and infrared spectrometer.•PBLs spanning eight years are obtained and compared among different input data.•Diurnal and seasonal variations of PBL are presented over the SGP of US.•Strengths and limitations are revealed.
The increasing severity of droughts/floods and worsening air quality from increasing aerosols in Asia monsoon regions are the two gravest threats facing over 60% of the world population living in ...Asian monsoon regions. These dual threats have fueled a large body of research in the last decade on the roles of aerosols in impacting Asian monsoon weather and climate. This paper provides a comprehensive review of studies on Asian aerosols, monsoons, and their interactions. The Asian monsoon region is a primary source of emissions of diverse species of aerosols from both anthropogenic and natural origins. The distributions of aerosol loading are strongly influenced by distinct weather and climatic regimes, which are, in turn, modulated by aerosol effects. On a continental scale, aerosols reduce surface insolation and weaken the land‐ocean thermal contrast, thus inhibiting the development of monsoons. Locally, aerosol radiative effects alter the thermodynamic stability and convective potential of the lower atmosphere leading to reduced temperatures, increased atmospheric stability, and weakened wind and atmospheric circulations. The atmospheric thermodynamic state, which determines the formation of clouds, convection, and precipitation, may also be altered by aerosols serving as cloud condensation nuclei or ice nuclei. Absorbing aerosols such as black carbon and desert dust in Asian monsoon regions may also induce dynamical feedback processes, leading to a strengthening of the early monsoon and affecting the subsequent evolution of the monsoon. Many mechanisms have been put forth regarding how aerosols modulate the amplitude, frequency, intensity, and phase of different monsoon climate variables. A wide range of theoretical, observational, and modeling findings on the Asian monsoon, aerosols, and their interactions are synthesized. A new paradigm is proposed on investigating aerosol‐monsoon interactions, in which natural aerosols such as desert dust, black carbon from biomass burning, and biogenic aerosols from vegetation are considered integral components of an intrinsic aerosol‐monsoon climate system, subject to external forcing of global warming, anthropogenic aerosols, and land use and change. Future research on aerosol‐monsoon interactions calls for an integrated approach and international collaborations based on long‐term sustained observations, process measurements, and improved models, as well as using observations to constrain model simulations and projections.
Key Points
The fast‐developing Asia has suffered severe air pollution problem
Aerosol affects the Asian monsoon
Aerosol‐monsoon interactions dictate the climate change in the region
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