Surface Net Solar Radiation Research Articles

Ice Sheet Mass Changes over Antarctica Based on GRACE Data

Assessing changes of the mass balance in the Antarctic ice sheet in the context of global warming is a key focus in polar study. This study analyzed the spatiotemporal variation in the Antarctic ice sheet’s mass balance, both as a whole and by individual basins, from 2003 to 2016 and from 2018 to 2022 using GRACE RL06 data published by the Center for Space Research (CSR) and ERA-5 meteorological data. It explored the lagged relationships between mass balance and precipitation, net surface solar radiation, and temperature, and applied the random forest method to examine the relative contributions of these factors to the ice sheet’s mass balance within a nonlinear framework. The results showed that the mass loss rates of the Antarctic ice sheet during the study periods were −123.3 ± 6.2 Gt/a and −24.8 ± 52.1 Gt/a. The region with the greatest mass loss was the Amundsen Sea in West Antarctica (−488.8 ± 5.3 Gt/a and −447.9 ± 14.7 Gt/a), while Queen Maud Land experienced the most significant mass accumulation (44.9 ± 1.0 Gt/a and 30.0 ± 3.2 Gt/a). The main factors contributing to surface ablation of the Antarctic ice sheet are rising temperatures and increased surface net solar radiation, each showing a lag effect of 1 month and 2 months, respectively. Precipitation also affects the loss of the ice sheet to some extent. Over time, the contribution of precipitation to the changes in the ice sheet’s mass balance increases.

Global patterns of lake microplastic pollution: Insights from regional human development levels

Microplastics have emerged as a pervasive pollutant across various environmental media. Nevertheless, our understanding of their occurrence, sources, and drivers in global lakes still needs to be completed due to limited data. This study compiled data from 117 studies (2016–May 2024) on microplastic contamination in lake surface water and sediment, encompassing surface water samples in 351 lakes and lake sediment samples in 200 lakes across 43 countries. Using meta-analysis and statistical methods, the study reveals significant regional variability in microplastic pollution, with concentrations ranging from 0.09 to 130,000 items/m3 in surface water and from 5.41 to 18,100 items/kg in sediment. Most microplastics were under 1 mm in particle size, accounting for approximately 79 % of lake surface water and 76 % of sediment. Transparent and blue microplastics were the most common, constituting 34 % and 21 % of lake surface water and 28 % and 18 % of sediment, respectively. Fibers were the dominant shape, representing 47 % of lake surface water and 48 % of sediment. The primary identified polymer types were polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET). Countries like India, Pakistan, and China had higher contamination levels. Positive correlations were found between microplastic abundance in surface water and factors like human footprint index (r = 0.29, p < 0.01), precipitation (r = 0.21, p < 0.05), and net surface solar radiation (r = 0.43, p < 0.001). In contrast, negative correlations were observed with the human development index (r = −0.61, p < 0.01) and wind speed (r = −0.42, p < 0.001). In sediment, microplastics abundance correlated positively with the human footprint index (r = 0.45, p < 0.001). This study underscores the variability in microplastic pollution in global lakes and the role of human activities and environmental factors, offering a valuable reference for future research.

Process-Oriented Compound Long-Duration Dry and Hot Events in China: Atmospheric Conditions, Moisture, and Heat Budget Analyses

Abstract Summertime compound dry and hot events pose severe threats to agricultural production and human health, especially those with a long duration. Considering the joint evolution of such hazards in space and time, spatiotemporal compound long-duration dry and hot (SLDDH) events in China during 1961–2022 are identified with a process-oriented method. Here, we investigate the associated large-scale atmospheric circulation patterns and the physical processes causing their precipitation and temperature anomalies. In all regions of China, this persistent dry–hot compound extreme is accompanied by anomalous high pressure systems along with enhanced descending motion, increased net surface solar radiation, and decreased water vapor flux convergence. Moisture budget diagnosis shows that precipitation deficits during the SLDDH events are produced primarily by the suppressed vertical moisture advection associated with the dynamical contribution of anomalous subsidence, while the thermodynamic process due to the anomaly in atmospheric moisture content makes a small contribution. Horizontal temperature advection generally plays a negative role in sustaining SLDDH events, while it helps trigger the events in North China. In most regions, adiabatic warming due to abnormal subsidence plays a dominant role in determining the near-surface high temperatures during the long-lasting warm and dry periods, whereas diabatic heating has a cooling or small effect therein. However, in some northern areas such as North China and northern Xinjiang, hot extremes during SLDDH events arise from a combination of diabatic heating and adiabatic warming. This study thus quantifies and reveals the crucial factors leading to the severity of compound dry and hot events.

Environmental impacts of aerosol radiative effect and urbanization and their interactions over the Beijing-Tianjin-Hebei City cluster

With fast economic development, urban expansion and air pollution are two main eco-environmental challenges confronting China. Though both are associated with intensified human activities, their impacts on urban environment are investigated separately, and the synthetic effect and intricate interaction of the two have not been fully understood. Here, based on in-situ measurements and online-coupled meteorology-chemistry modelling, the impacts of anthropogenic aerosol and urbanization are investigated simultaneously at Beijing-Tianjin-Hebei city cluster during a typical pollution season. The two processes exhibit opposite influences on meteorology and air quality. Aerosol via its radiative effect tends to cause net loss of solar energy with 3 °C cooling at the ground surface and 1 °C warming in the upper boundary layer. In contrast, urbanization yields large increment of net surface solar radiation as well as sensible heat flux, leading to over 1 °C warming in the lower atmosphere. Such modifications of boundary layer structure alter the accumulation and formation of air pollution. Urbanization effect dominated in the modification of near-surface air temperature and air pollution, making the synthetic effect a linear combination of two processes. The study highlights a comprehensive understanding of air pollution and urbanization and their synergy effect on urban environment over megacities.

Spatial and Temporal Changes in Soil Freeze-Thaw State and Freezing Depth of Northeast China and Their Driving Factors

It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China.

Corn, soybeans and winter wheat water requirements over the contiguous United States between 2013 and 2021: The application of the SEBALIGEE v2 global model

The open-source fully-automated Surface Energy Balance Algorithm for Land-Improved (SEBALI) Google Earth Engine (SEBALIGEE) estimates 30-m actual evapotranspiration (ET) at a monthly rate, a much needed parameter in many hydrological and agricultural applications. An improved version of the basin-based SEBALIGEE v1 is proposed in this paper. The improvement of SEBALIGEE v1, designated as v2, focuses primarily on incorporating advanced machine learning approaches which have enabled for the first time to implement SEBALIGEE over any scale application and enhance its overall performance. More particularly, an evaluation of the monthly ET estimated from the new algorithm across several fluxnet sites in the US, China, Italy, Belgium, Germany, and France, yielded a Mean Absolute Error (MAE) of 12.22 mm/month versus 14.54 mm/month in the original SEBALIGEE v1. Furthermore, we applied the new any-scale capability of SEBALIGEE v2 over the contiguous United States (CONUS) while emphasizing on the three main crops, including corn, soybeans and winter wheat. Our analysis indicated that all three crops presented two similar ET seasonal cycles with peaks occurring in late spring to the summer (May-Aug) and between October and January, corresponding well to the key stages of crop life cycle. Moreover, corn and soybeans exhibited similar magnitudes of ET (36 ∼ 168 mm/month) and higher than winter wheat (33 ∼ 122 mm/month), with large standard deviations observed in the ET estimates amogst all the crops. On interannual comparisons, the corn and soybeans ET and aKc showed higher values than winter wheat, with the highest and lowest years identified and discussed. Following an exploratory analysis against some of the most common interfering variables such as air temperature, dewpoint temperature, surface net solar radiation, wind speed, SPEI drought index calculated on 14 days and 30-days, it was noted that the surface net solar radiation had the most influencing factor on ET in corn and soybeans plantations with R2 values of ∼0.72. The SPEI-30 stands out for winter wheat, showing a water scarcity tolerance up to a month in most of its developing stages. Different management practices are then recommended in each of these two crops’ categories (corn and soybeans vs. winter wheat).

Asymmetric modulation of solar activity on tropical cyclone frequency over the western North Pacific and the possible mechanism

The impacts of solar activity on the tropical Pacific climate have been widely reported. However, few studies focus on the effects of solar activity on the tropical cyclone (TC). Based on the observational and reanalysis data for 1979–2020, this study investigated the solar modulation of TC frequency over the western North Pacific in different solar cycle phases. Results suggest that the regressions of TC frequency to solar activity are asymmetric in the high- and low-solar activity years (HS and LS). Specifically, the intensified solar activity could markedly induce more TCs in HS; however, no significant modulation can be found in LS. Further exploration reveals a possible air–sea coupled mechanism for this interesting phenomenon. The relatively cloud-free area in the western North Pacific could receive more incoming solar radiation at the surface in HS than in LS. This increased regional surface net solar radiation in HS could produce a stronger surface upward latent heat flux and, thus, greater evaporation. Along with that, the local upward motion is dramatically enhanced over the TC source. Then, for compensation, the regional sea level pressure is reduced, and the low-level winds become cyclonic over the TC origin. All of these solar-caused regional atmospheric anomalies in HS contribute to more TC generations. The key to this possible mechanism is the increased regional solar forcing at the ocean surface that is amplified by regionally enhanced upward latent heat flux and evaporation.

Meteorological influences on PM2.5 variation in China using a hybrid model of machine learning and the Kolmogorov-Zurbenko filter

Quantitatively evaluating the contribution of meteorology and emissions to PM2.5 reduction during the Three-year Action Plan to Fight Air Pollution (2018–2020) is important to guide future emission control measures in China. In this study, a Kolmogorov-Zurbenko (KZ) filter and an artificial neural network (ANN) combined model was used to separate the meteorology-influenced PM2.5 concentration by considering seven meteorological factors including boundary layer height, surface pressure, surface net solar radiation, total precipitation, atmospheric temperature, wind speed and wind direction in China. To compensate for the black-box nature of the ANN, the Shapley additive explanation (SHAP) value was introduced to explain the effect of each meteorological factor on long-term PM2.5 concentration. Results showed that PM2.5 concentration decreased by 14% from 2018 to 2020 in China with annual average concentrations of 37.19, 35.28, and 31.94 μg/m3, respectively. Although the decline in PM2.5 was mainly due to emission reductions (58% for all of China), the contribution of prevailing meteorological conditions should not be ignored, especially in some regions such as Northeast China, which had the highest contribution of meteorology (58%) on PM2.5 reduction. In general, the contributions of meteorological conditions to the long-term trend of PM2.5 varied greatly by region and season. SHAP analysis indicated that temperature, surface pressure and boundary layer height were the dominant meteorological factors affecting long-term PM2.5 with contributions of 65%, 11% and 6% on average. We also found that regions with larger average PM2.5 concentration were more affected by emission changes than by meteorology. Therefore, we recommend the continuation of emission control measures in heavily polluted regions such as North and Central China.

PM2.5 and O3 concentration estimation based on interpretable machine learning

High concentrations of PM2.5 and ozone (O3) seriously threaten human health. In this study, we constructed a machine learning-based model to predict PM2.5 and O3 concentrations, with the Fenwei Plain in China, as our study target. We evaluated the performance of RF, XGB, and CatBoost models for predicting PM2.5 and O3 concentrations and found that the CatBoost model performed the best, capturing most of the PM2.5 and O3 concentration changes (with R2 values above 0.7). Using the SHAP-based machine learning interpretation method, we analyzed the factors contributing to the variation of PM2.5 and O3 concentrations. We found that PM10 had the largest effect on PM2.5 concentration, while net surface solar radiation significantly affected O3 concentration. Additionally, meteorological variables such as temperature and humidity play a role in the variation of PM2.5 and O3 concentrations. Finally, based on the results of the SHAP analysis, we propose some feasible management methods for PM2.5 and O3, providing suggestions for managing PM2.5 and O3 concentrations.

Three-Dimensional Distributions of the Direct Effect of anExtended and Intense Dust Aerosol Episode (16–18 June 2016) over the Mediterranean Basin on Regional Shortwave Radiation, Atmospheric Thermal Structure, and Dynamics

In the present study, we used the FORTH deterministic spectral Radiation Transfer Model (RTM) to estimate detailed three-dimensional distributions of the Direct Radiative Effects (DREs) and their consequent modification of the thermal structure of the regional atmosphere during an intense dust episode that took place from 16 to 18 June 2016 over the Mediterranean Basin (MB). The RTM operated on a 3-hourly temporal and 0.5 × 0.625° spatial resolution, using 3-D aerosol optical properties (i.e., aerosol optical depth, single scattering albedo, and asymmetry parameter) and other surface and atmospheric properties from the MERRA-2 reanalysis and cloud properties (i.e., cloud amount, cloud optical depth, and cloud top height) from the ISCCP-H dataset. The model ran with and without dust aerosols, yielding the upwelling and downwelling solar fluxes at the top of the atmosphere, in the atmosphere, and at the Earth’s surface as well as at 50 levels in the atmosphere. The dust direct radiative effect (DDRE) was estimated as the difference between the two (one taking into account all aerosol types and one taking into account all except for dust aerosols) flux outputs. The atmospheric heating rates and subsequent convection induced by dust radiative absorption were calculated at 50 levels to determine how the DDRE affects the thermal structure and dynamics of the atmosphere. The results showed that such a great and intense dust transport event significantly reduces the net surface solar radiation over the MB (by up to 62 W/m2 on a daily mean basis, and up to 200 W/m2 on an hourly basis, at 12:00 UTC) while increasing the atmospheric solar absorption (by up to 72 W/m2 daily and 187 W/m2 hourly, at 12:00 UTC). At the top of the atmosphere, both heating (over desert areas) and cooling (over oceanic and other continental areas) are observed due to the significantly different surface albedos. Transported dust causes considerable heating of the region’s atmosphere, which becomes maximum at altitudes where the dust loadings are highest (0.14 K/3 h on 17 June 2016, 12:00 UTC, at 3–5 km above sea level). The dust solar absorption and heating induce a buoyancy as strong as 0.014 m/s2, resulting in considerable changes in vertical air motions and possibly contributing to the formation of middle- and high-level clouds over the Mediterranean Basin.

144P Exposure-lag response of surface net solar radiation on lung cancer incidence: A worldwide interdisciplinary and time-series study

144P Exposure-lag response of surface net solar radiation on lung cancer incidence: A worldwide interdisciplinary and time-series study

Conversion of surface CH4 concentrations from GOSAT satellite observations using XGBoost algorithm

Methane (CH4), the second most significant greenhouse gas after carbon dioxide, contributes significantly to global warming. Owing to its wide monitoring range and long observation time, satellite remote sensing has emerged as a popular method for monitoring CH4. Although the existing algorithm for satellite retrievals of CH4 column amount is mature, it has some drawbacks: the retrieval calculation process is complicated, and there is still a discrepancy between the satellite-retried column amount of CH4 and surface CH4 concentrations. To obtain more accurate near-surface CH4 concentrations from satellite observations, this paper proposed a conversion method based on the Extreme Gradient Boosting (XGBoost) algorithm, taking column amount retrieved by the Greenhouse Gas Observation SATellite (GOSAT) satellite, meteorological factors and near-surface methane concentrations as predictor variables. Using this method, we analyzed the importance of the characteristic factors and predicted methane concentrations at four ground monitoring sites in World Center for Greenhouse Gases (WDCGG), and reached the following conclusions: (1) The model performed well on the test set, with a sample-based cross-validation coefficient of determination (R2) of 0.79, a root mean square error (RMSE) of 0.0251 ppm and a mean absolute percentage error (MAPE) of 0.88%. (2) Of the five meteorological input features, surface net solar radiation had a greater impact on the construction of the model than the other four. (3) The CH4 monthly average concentrations predicted by this model are generally consistent with the trend of the CH4 concentrations of ground monitoring stations.

Combining discrete wavelet decomposition with soft computing techniques to predict monthly evapotranspiration in semi-arid Hakkâri province, Türkiye.

Accurate prediction of evapotranspiration values is important in planning agricultural irrigation, crop growth research, and hydrological modeling. This study is aimed at estimating monthly evapotranspiration (ET) values in Hakkâri province by combining support vector regression, bagged tree, and boosted tree methods with wavelet transform. For this purpose, precipitation, runoff, surface net solar radiation, air temperatures, and previous ET values were divided into sub-signals with various mother wavelets such as Daubechies 4, Meyer, and Symlet 2 and presented as input to machine learning (ML) algorithms. The study's main contribution to the literature is to reveal which wavelet-based machine learning model, mother wavelet type, and combination of meteorological data show the most realistic results in ET estimation. While establishing the models, the data were divided into 80% training and 20% testing. The models' performances were based on the widely used root mean square error, mean absolute error, determination coefficient, and Taylor diagrams. As a result of the study, it was revealed that the hybrid wavelet ML, which is established with input combinations separated into subcomponents by wavelet transform, generally produces more successful predictions than the stand-alone ML model. In addition, it was revealed that the optimum ET forecasting model was obtained with the wavelet bagged tree algorithm with Symlet 2 mother wavelet. Even though the best model established is based on the precipitation and temperature inputs, it was revealed that past ET, solar radiation, and runoff values are also effective inputs in ET prediction. The results can also be used in other regions of the world with semi-arid climates, such as Hakkâri. The study's outputs provide essential resources to decision-makers and planners to manage water resources and plan agricultural irrigation.

Detection of Antarctic Surface Meltwater Using Sentinel-2 Remote Sensing Images via U-Net With Attention Blocks: A Case Study Over the Amery Ice Shelf

Surface meltwater critically impacts the Antarctic mass balance and global sea level rise. Quantifying the extent of surface meltwater in Antarctica on a large scale is a challenging task. Traditional methods, such as thresholding, have many limitations. We used a deep learning method, the U-Net with attention blocks, to automatically extract surface meltwater from Sentinel-2 images. We inserted attention mechanism blocks into U-Net to assign different weights to all pixels and channels to utilize the high resolution and multiple channels of Sentinel-2 images. The model was used to map surface water bodies in Sentinel-2 images, and the average accuracy reached 0.9969 on the test dataset. In East Antarctica, the Amery Ice Shelf (AIS) exhibits the largest surface meltwater area. Studying surface meltwater dynamics on the AIS is useful for understanding the East Antarctic mass balance and demonstrating the model performance. We analyzed the classification results for surface water bodies on the AIS from January 2017-2022. Spatially, 96% of surface water bodies are concentrated inland of the AIS from 70-73°S and account for 93% of the region 20 km from the coastline of the AIS. Temporally, the water body area varies considerably in different years, with a maximum in 2017 (932.54 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and a minimum in 2021 (58.34 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The spatial distribution of surface water body on the AIS is controlled by the firn air content, katabatic winds, bare rocks and blue ice. The interannual variability is associated with complex climate factors, including temperature, surface net solar radiation, snowfall, and snowmelt, among which temperature and snowfall show strong correlation.

Extreme droughts and corresponding Summer Monsoon: A Case Study of 2009 Indian Drought

Drought is a sustained result of continuous surface and atmospheric heating and moisture deficit. In general, drought assessment is made based on various indices. However, the drought dynamics and associated surface energetics about the corresponding Indian Summer Monsoon (ISM, i.e., June, July, August and September-JJAS) still needs to be better understood. Prolonged surface heating causes reduction of soil moisture, surface/subsurface runoff and atmospheric moisture. Excess surface heating results from positive surface energy budget, which is computed using the term, surface net solar radiation (SNSR), surface net thermal radiation (SNTR), surface sensible heat flux (SSHF), and surface latent heat flux (SLHF). It may also be the possible reason for enhanced convection. Convection also depends on the moisture holding capacity of the atmosphere, which increases with increasing air temperature. These processes lead to moisture content deficit and rainfall suppression due to moisture divergence from the convective sources. India Meteorological Department (IMD) (station and 0.25° gridded) and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (0.25° resolution) datasets are used to study the extreme drought events. Drought indices such as Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI), Standardized Precipitation Evapotranspiration Index (SPEI), Soil Moisture Index (SMI) and Sensible Heat Index (SHI) are used. Results indicate that anomalous lowering of available surface soil moisture and increase of surface sensible heat flux is a possible cause for enhancing extreme drought during the 2009 ISM. The associated Hadley circulation shows anomalous weakening, which led to reduced northward moisture transport from the southern oceans, further acerbating moisture deficit. Most parts of India suffered from anomalous decrement in specific humidity in the lower to upper troposphere and related precipitation scarcity during the drought period. The atmosphere’s increased moisture holding capacity sets a weak monsoon due to moisture divergence from ocean/local convection. However, likely, the excessive surface warming (due to SNSR/SNTR trapped into the surface) led to the extreme drought during 2009 ISM.

Trends in Forest Greening and Its Spatial Correlation with Bioclimatic and Environmental Factors in the Greater Mekong Subregion from 2001 to 2020

Understanding trends of vegetation evolution and its spatial characteristics is critical for sustainable social development in the Greater Mekong Subregion (GMS), which is densely populated and still has uneven economic development. Through Theil–Sen/Mann–Kendall tests, polynomial regression and bivariate local autocorrelation analyses, we investigated vegetation greening trends and their spatial correlation with bioclimatic and environmental variables. The study yielded the following results: (1) Land cover in the GMS has changed significantly over the last 20 years. Conversion between forest and grassland was the main type of change. (2) The upward trend in the forest enhanced vegetation index (EVI) significantly exceeded the downward trend in countries over 20 years. In GMS, the spatial variation in forest trend slope values ranged from −0.0297 a−1 to 0.0152 a−1. (3) Anthropogenic activities have played an important role in forest greening; planted, plantation and oil palm forests exhibit the largest contributions to greening. (4) Changes in forest EVI were most spatially correlated with radiation (12.19% for surface net solar radiation and 12.14% for surface solar radiation downwards) and least spatially correlated with seasonality precipitation (8.33%) and mean annual temperature (8.19%). The results of the analysis of EVI trends in vegetation and their spatial correlation with bioclimatic and environmental variables can provide a reference for strategies aimed for protecting the vegetation ecology.

Study on the spatiotemporal dynamic of ground-level ozone concentrations on multiple scales across China during the blue sky protection campaign

Surface ozone (O3), one of the harmful air pollutants, generated significantly negative effects on human health and plants. Existing O3 datasets with coarse spatiotemporal resolution and limited coverage, and the uncertainties of O3 influential factors seriously restrain related epidemiology and air pollution studies. To tackle above issues, we proposed a novel scheme to estimate daily O3 concentrations on a fine grid scale (1 km × 1 km) from 2018 to 2020 across China based on machine learning methods using hourly observed ground-level pollutant concentrations data, meteorological data, satellite data, and auxiliary data including digital elevation model (DEM), land use data (LUD), normalized difference vegetation index (NDVI), population (POP), and nighttime light images (NTL), and to identify the difference of influential factors of O3 on diverse urbanization and topography conditions. Some findings were achieved. The correlation coefficients (R2) between O3 concentrations and surface net solar radiation (SNSR), boundary layer height (BLH), 2 m temperature (T2M), 10 m v-component (MVW), and NDVI were 0.80, 0.40, 0.35, 0.30, and 0.20, respectively. The random forest (RF) demonstrated the highest validation R2 (0.86) and lowest validation RMSE (13.74 μg/m3) in estimating O3 concentrations, followed by support vector machine (SVM) (R2 = 0.75, RMSE = 18.39 μg/m3), backpropagation neural network (BP) (R2 = 0.74, RMSE = 19.26 μg/m3), and multiple linear regression (MLR) (R2 = 0.52, RMSE = 25.99 μg/m3). Our China High-Resolution O3 Dataset (CHROD) exhibited an acceptable accuracy at different spatial–temporal scales. Additionally, O3 concentrations showed decreasing trend and represented obviously spatiotemporal heterogeneity across China from 2018 to 2020. Overall, O3 was mainly affected by human activities in higher urbanization regions, while O3 was mainly controlled by meteorological factors, vegetation coverage, and elevation in lower urbanization regions. The scheme of this study is useful and valuable in understanding the mechanism of O3 formation and improving the quality of the O3 dataset.

Background concentration of atmospheric PM2.5 in the Beijing–Tianjin–Hebei urban agglomeration: Levels, variation trends, and influences of meteorology and emission

The determination of background PM2.5 and the separation of the meteorology-related and emission-related influences on background concentration in urban areas are important to evaluate the effectiveness of local anthropogenic emission control measures. In this study, the baseline separation technique was used to estimate the urban background concentration of PM2.5 in the Beijing–Tianjin–Hebei Urban agglomeration (JJJUA) by comparing its results with the background level monitored at the Shangdianzi regional background site. The Kolmogorov–Zurbenko (KZ) filter and stepwise regression model were further used to isolate the impacts of meteorology and emission on background levels. The results showed that the annual average background levels of PM2.5 in JJJUA were 27.18, 24.77, and 22.20 μg/m3 in 2018, 2019, and 2020, respectively. The background concentration showed significant differences in the seasonal and spatial distributions, with the highest levels obtained during winter in the southern inland plains. Boundary layer height, surface net solar radiation, total precipitation, temperature, and wind direction were negatively correlated with the background level, whereas surface pressure was positively correlated with the background level. A significant contribution of anthropogenic emissions (89%, −5.71 μg/m3•yr) on the decrease in trend of long-term background concentration was observed in JJJUA, indicating the strong influence of long-range transport of PM2.5 from surrounding areas. The results emphasize the strong influence of regional air pollution levels on background PM2.5, and coordinated control of regional air pollution is suggested to potentially reduce the regional background level of PM2.5.

Influence of Dust Aerosols on Snow Cover Over the Tibetan Plateau

Dust in the atmosphere and snow on the Tibetan Plateau (TP) remarkably influence the Asian climate, which can influence snow cover by changing radiative forcing. In this study, we investigated the spatial and temporal distributions of dust and snow cover over the TP from 2009 to 2018 and estimated the relative contributions of atmospheric dust and dust-on-snow to the change in snow cover over the northern TP through the use of reanalysis datasets and satellite retrievals. The results show that the high and low centers of aerosol and dust aerosol optical depth (AOD) are roughly similar. Dust concentrations over the TP generally decrease from north to south and from west to east, showing decreasing trends in the winter half-year (December to May). The correlation coefficients between the dust concentration and snow cover over the northern TP are −0.6 in spring. Dust in the atmosphere and on snow over the TP could significantly influence snow cover by changing the radiative forcing, and the influence of dust deposited on snow is greater than that in the atmosphere. Atmospheric dust reduces the surface net solar radiation by −3.84 W m−2 by absorbing shortwave radiation, decreasing the surface temperature by −2.27°C, and finally increasing the snow cover by 1.04%. However, dust deposited on snow can decrease the surface albedo by −0.004 by reducing the surface optical properties, induce surface warming at 0.42°C, and reduce snow cover by −2.00% by rapid snowmelt in the northern TP.

Recent regional warming across the Siberian lowlands: a comparison between permafrost and non-permafrost areas

The northern mid-high latitudes experience climate warming much faster than the global average. However, the difference in the temperature change rates between permafrost and non-permafrost zones remains unclear. In this study, we investigated the temporal changes in temperature means and extremes across the Siberian lowlands (<500 m) over the past six decades (1960–2019) using in situ observations and reanalysis data. The results show that permafrost zones (0.39 °C/decade) have warmed faster than non-permafrost zones (0.31 °C/decade). The minimum values of the daily maximum (TXn) and minimum (TNn) temperatures changed faster than their maximum values (TXx, TNx), suggesting that low minimum temperatures increase faster, as evidenced by the considerably higher warming rate in the cool season (October–April, 0.43 ± 0.10 °C/decade, n= 126) than that in the warm season (May–September, 0.25 ± 0.08 °C/decade, n= 119). The change rates of TXx and TNx in permafrost areas were 2–3 times greater than those in non-permafrost areas; however, over the last ten years, TXx and TNx in non-permafrost areas showed decreasing trends. Moreover, faster-warming permafrost regions do not exhibit a faster increase in surface net solar radiation than slower-warming non-permafrost regions. While our findings suggest that carbon emissions from thawing soils are likely a potential driver of rapid warming in permafrost-dominated regions, the potential feedback between ground thawing and climate warming in permafrost regions remains uncertain.