Objective Light-absorbing aerosols have a huge impact on visibility. The atmospheric pollution they cause can pose serious risks to human health. Quantitatively assessing the optical properties and spatiotemporal distribution of light-absorbing aerosols is of vital importance for decision-making in the management and control of complex air pollution. The dynamic changes in the physicochemical properties of light-absorbing aerosols, along with their temporal and spatial heterogeneity, introduce significant uncertainties in simulating their radiative forcing. The challenges arise from difficulties in accurately estimating particle size distribution, chemical composition, and mixed state, impeding precise retrievals through satellite remote sensing, with common model simulations and radiative transfer equations assuming the presence of external mixing for light-absorbing aerosols. However, research indicates that, especially in regions prone to pollution events like East Asia, South Asia, and Southeast Asia, a core-shell mixed state, with black carbon as the core and scattering aerosols like sulfates and nitrates as the shell, best represents the prevailing state of light-absorbing aerosols. Rough assumptions about aerosol states not only introduce significant errors in simulating aerosol number and mass concentrations in the atmosphere but also lead to substantial uncertainties in estimating overall radiative forcing. Methods Data from both satellite and in situ measurements are employed in the present study. First, we employ the AERONET aerosol optical depth (AOD) dataset to identify polluted days at three selected sites, and we match it in space and time with the single scattering albedo (SSA) dataset combined with the TROPOMI ultraviolet (UV) SSA dataset. Second, we utilize the Mie optical model across various combinations of core and shell sizes to establish a preliminary SSA map. Subsequently, we use SSA data from six different wavebands to constrain the SSA output from the Mie model. All calculations are conducted at a daily and grid-level resolution. Upon obtaining probability distributions for core size, shell size, and their corresponding SSA and absorption coefficient (ABS) values, we then apply spatial relationships between the column total absorbing aerosol optical depth (AAOD) from TROPOMI, single-particle absorption, and size distribution. This allows us to assess the column value of black carbon mass concentration and particle number concentration. Results and Discussions Spatial distribution of the mean absorption coefficient obtained from the Mie model simulations during periods of severe pollution shows that the absorption coefficient of the Beijing station is generally higher, with values mainly concentrated between 0. 05 and 0. 07. This indicates a higher presence of light-absorbing aerosols during this period. For the Hong Kong station, most of the absorption coefficients are below 0. 1, with the majority falling below 0. 2 and a low standard deviation of less than 0. 02. Factors related to topography and wind patterns are the primary reasons for the lower values observed in the Hong Kong station (Fig. 3). After applying spatial relationships between the column total AAOD from TROPOMI, the results show that the particle concentrations in the column at the Beijing station generally fall within the range of 3 x 10(19)-5 x 10(19) grid(-1). The number concentrations in Hong Kong are relatively lower than those in Beijing. Except for a few grid points where concentrations reach 2. 5 x 10(19) grid(-1), the overall value range in Hong Kong between 1 x 10(19) and 2 x 10(19) grid(-1). For the Seoul station, particle concentration range is from 1. 5 x 10(19) to 3. 0x 10(19) grid(-1) (Fig. 4). By considering the particle size distribution of black carbon aerosols under the core-shell mixed state simulated by the Mie model, the results of the spatial distribution of black carbon aerosol column mass concentration at each grid point (Fig. 6) shows that over 60% of the area of Beijing have concentrations exceeding 500 kg/grid. In the Hong Kong area, apart from certain regions within the Pearl River Delta urban cluster where black carbon column mass exceeds 500 kg/grid, the values in other areas are below 300 kg/grid. In addition, Seoul has an overall column mass concentration of less than 300 kg/grid.

Study region: The Northwest inland basins of China (NWC).Study focus: Terrestrial water resources, especially groundwater resources, are the main source of water for human activities and for maintaining the stability of the ecological environment in NWC. Excessive consumption of water resources will seriously affect the sustainable utilization of water resources and ecological security in this region. Therefore, it is urgent to clarify the long-term changes in water storage in this area in order to handle the pressure of future water re-sources and the natural environment. Using GRACE satellite datasets and global hydrological models (GHMs) products, this study analyzed spatiotemporal variations in terrestrial water storage anomalies (TWSA), groundwater storage anomalies (GWSA), soil moisture, snow water equivalent, and canopy interception combined anomalies (SSCA) in NWC through the application of the water balance, trend decomposition, and empirical orthogonal decomposition methods. Furthermore, the driving factors of water storage change and feasible water resource manage-ment strategies were discussed. New hydrological insights for the region: TWSA in the NWC has experienced a continuous decline over the past nearly 40 years, while SSCA has shown a weak increasing trend (0.03 cm yr-1). Since the availability of glacial retreat data (2003-2016), glacial water storage in the NWC has decreased by 0.09 cm per year, while TWSA, SSCA, and GWSA have changed at rates of -0.25, 0.02, and -0.18 cm yr-1, respectively. The North Tianshan Rivers Basin has become one of the areas with the most severe groundwater depletion in China. 2005-2010 was a turning period in the changes of TWSA, followed by widespread water loss across the NWC. Glacier and snow melt are the most important factors for the decline of TWSA in the Tianshan mountains area, and over -exploitation of groundwater by human activities is a secondary factor. For other regions, Groundwater losses remain the most significant contributor to TWSA losses. The massive loss of water storage in the Tianshan Mountains area, especially the accelerated retreat of glaciers, will affect the stable water supply to the middle and lower reaches of the oasis region, perhaps leading to increased groundwater extraction, which will threaten regional water security and sustainable development. Developing a water-saving society and implementing inter-basin water transfer arefeasible ways to alleviate the water resource crisis. Conducting a comprehensive analysis of all inland rivers in China helps to facilitate horizontal comparisons between various basins, thereby providing more comprehensive insights of water storage fluctuations. The data on water storage changes, extending back to 1980, provide a longer-term perspective on water resource changes in the region, which can contribute to enhancing water resource security and ecological environ-mental protection.

Canada has extensive forests and peatlands that play key roles in global carbon cycle. Canadian soils and peatlands are assumed to store approximately 20% of the world's soil carbon stock. However, the spatial and vertical distributions of the soil organic carbon (SOC) concentration in Canada have not been systematically investigated. SOC concentration, expressed in g C KG(-1) soil, affects the chemical and physical properties of the soil, such as water infiltration ability, moisture holding capacity, nutrient availability, and the biological activity of microorganisms. In this study, we tested a three dimensional (3D) machine learning approach and 40 spatial predictors derived from 20 years of optical and microwave satellite observations to estimate the spatial and vertical distributions of SOC concentration in Canada in six depth intervals up to 1 m. A quantile regression forest method was used to build an uncertainty map showing 80% of prediction intervals. Results showed that a random forest model associated with 25 covariates was successful in capturing 83% of spatial and vertical SOC variation over the country. Soil depth was the most important covariate to predict SOC concentration, followed by surface temperature and elevation. The SOC concentration in forested areas was highest in the top layers (0-5 cm), but soils located in peatlands showed higher C concentration in all soil depths. Among the terrestrial ecozones of Canada, Pacific Maritime and the Hudson Plain mostly covered by forest trees and peatlands, respectively, show highest SOC concentration, while the lowest concentration are observed in the Prairies and Mixed Wood Plain ecosystems that are the agricultural areas of the country. This study provides a deeper understanding of the major factors controlling SOC concentration in Canada and shows potential areas with high carbon reserves, which are crucial in view of the ongoing climate change. In addition, the presented methodological framework has great potential to be used in future soil carbon storage inventories using satellite observations. Mapping SOC concentration and associated uncertainties in Canada are fundamental to detect trends of gains or losses in SOC linked to recent and future national or global policy decisions related to soil quality and carbon sequestration.

Arid regions of Central Asia have sensitive ecosystems that rely heavily on terrestrial water storage which is composed of surface water storage, soil moisture storage and groundwater storage. Therefore, we employed three Gravity Recovery and Climate Experiment (GRACE) satellite datasets and five global hydrological models (GHMs) to explore the terrestrial water storage (TWS) changes over arid regions of Central Asia from 2003 to 2014. We observed significantly decreasing water storage trends in the GRACE data, which were underestimated by the GHMs. After averaging the three GRACE satellite datasets, we found that the water storage was decreasing at a rate of -4.74 mm/year. Contrary to the prevailing declining water storage trends, northeastern Kazakhstan (KAZ), and southern Xinjiang increased their water storage over the same period. The GRACE data showed that Turkmenistan (TKM), Uzbekistan (UZB) and KAZ experienced the most severe water depletions, while Tajikistan (TJK) and northwest China (NW) experienced the least significant depletions. With respect to the major river and lake basins, the Aral Sea Basin exhibited the most serious water loss (-0.60 mm/month to -0.38 mm/month). The water storage positively correlates with the precipitation; and negatively correlates, with a three-month lag, with temperature and potential evapotranspiration (PET). Partial least square regression (PLSR) had the high capability in simulating and predicting the TWS. These results provide scientific evidence and guidance for local policy makers working toward sustainable water resource management, and the resolution of international water resource disputes among Central Asian countries.

As an important factor of surface processes, soil moisture has great influence on atmospheric circulation and weather climate of local and adjacent areas. Because the observation sites of soil moisture in the Tibetan Plateau (TP) are sparse and the observation time is short, we use a set of satellite retrieval data which has validated by field observations, to study the relationship between earlier soil moisture of TP and later precipitation of eastern China and its mechanism. The results indicate that with the global warming, the general soil moisture of TP has an obvious trend to increase. After removing the linear trend, we define the Tibetan Plateau soil moisture index (TPSMI) to characterize the interannual variation of TP soil moisture. Such variations of soil moisture have great conformance in 0 similar to 10 cm, 10 similar to 40 cm and 40 similar to 100 cm, which makes soil moisture interannual signal from spring continue into summer. The correlation coefficient between spring and summer TPSMI is 0. 56. When the TPSMI is bigger, which means that the soil moisture of eastern TP is bigger, and when the soil moisture of western TP is smaller, there is a latent heat source (sensible heat source) in eastern (western) TP. The two heat sources together induce a cyclone-anticyclone-cyclone wave train from the west of TP through China mainland to northeast China, which presents a prominent quasi-barotropic structure through the middle and upper troposphere. This has great contribution to the enhancement of Northeast Cold Vortex, which leads to the outburst of cold air. At the same time, the South Asian anticyclone gets enhanced and eastward, while the Sub-tropical anticyclone gets enhanced and westward with the converge of warm moist airflow from south and cold dry airflow from north in the Yangtze River basin. In addition to the stronger rising movement, the summer precipitation of the Yangtze River basin is much more. On the contrary, when the TPSMI is smaller, the precipitation of the Yangtze River basin is much less.