Surface Solar Radiation Research Articles

Evaluation of the hourly ERA5 radiation product and its relationship with aerosols over China

Surface solar radiation (SSR) and its components are important parameters in studies of land, hydrology and the atmosphere. However, the SSR of ECMWF Reanalysis 5 (ERA5) rather than observations is often used in related research fields due to the sparse and uneven spatial distribution of observations. Therefore, understanding the deviation from observations and the attribution is essential for the appropriate use of reanalysis SSR. Based on station observations in China for 2014–2020, this study evaluates the ERA5 SSR products including global radiation (Rs), direct radiation (Rdir) and diffuse radiation (Rdif) under various PM2.5 concentrations to explore the influence of aerosols on ERA5 radiation. Overall, Rs from ERA5 has higher accuracy than Rdir and Rdif. The ERA5 product generally overestimates (underestimates) Rdir (Rdif), and this overestimate (underestimate) increases with the intensification of particle pollution. Under the influence of biomass burning (BB) events, the Rdir (Rdif) of ERA5 exhibits overestimation (underestimation) similar to that under the heaviest particle pollution. In terms of the time series, the deviations of Rdir and Rdif from ERA5 in 1999–2020 still vary with the local aerosol optical depth (AOD). The diurnal variation in Rdir (Rdif) from ERA5 also shows larger deviations under air pollution conditions than under clean conditions, especially at the hours around noon. Furthermore, the misrepresentation of aerosols also causes large deviations in the Rdif/Rdir of ERA5 from observations, especially at stations in eastern China. Similar changes in the deviation of ERA5 radiation from observations with increasing aerosol loading are found under both all sky and clear sky conditions. The high association with aerosol pollution found here indicates that ERA5 SSR products should be used with caution in the presence of atmospheric particle pollution.

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.

Benefit of aerosol reduction to winter wheat during China's clean air action: A case study of Henan Province

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action, yet its impact on wheat (Triticum aestivum L.) production remains unclear. We use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to 2018. After calibrating parameters with the extended Fourier Amplitude Sensitivity Test (EFAST) and the generalized likelihood uncertainty estimation (GLUE) method, the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more accurately. We found that the aerosol negatively impacted wheat biomass by 21.87% and yield by 22.48% from 2006 to 2018, and the biomass effects from planting to anthesis were more significant compared to anthesis to maturity. Due to the strict clean air action, under all-sky conditions, the surface solar shortwave radiation (SSR) in 2018 increased by about 7.08% over 2006-2013 during the wheat growing seasons. As a result of the improvement of crop photosynthesis, winter wheat biomass and yield increased by an average of 5.46% and 2.9%, respectively. Our findings show that crop carbon uptake and yield will benefit from the clean air action in China, helping to ensure national food and health security.

Sub-regional variation in atmospheric and land variables regulates tea yield in the Dooars region of West Bengal, India.

Climatic variables can have localized variations within a region and these localized climate patterns can have significant effect on production of climate-sensitive crops such as tea. Even though tea cultivation and industries significantly contribute to employment generation and foreign earnings of several South Asian nations including India, sub-regional differences in the effects of climatic and soil variables on tea yield have remained unexplored since past studies focused on a tea-producing region as a whole and did not account for local agro-climatic conditions. Here, using a garden-level panel dataset based on tea gardens of Dooars region, a prominent tea-producing region in India, we explored how sub-regional variations in climatic and land variables might differently affect tea yield within a tea-producing region. Our analysis showed that the Dooars region harboured significant spatial variability for different climatic (temperature, precipitation, surface solar radiation) and soil temperature variables. Using graph-based Louvain clustering of tea gardens, we identified four spatial sub-regions which varied in terms of topography, annual and seasonal distribution of climatic and land variables and tea yield. Our sub-region-specific panel regression analyses revealed differential effects of climatic and land variables on tea yield of different sub-regions. Finally, for different emission scenario, we also projected future (2025-2100) tea yield in each sub-region based on predictions of climatic variables from three GCMs (MIROC5, CCSM4 and CESM1(CAM5)). A large variation in future seasonal production changes was projected across sub-regions (-23.4-35.7% changes in premonsoon, -4.2-3.1% changes in monsoon and -10.9-10.7% changes in postmonsoon tea production, respectively).

Potential impacts of climate and anthropogenic-induced changes on DOM dynamics among the major Chinese rivers

Dissolved organic matter (DOM) is closely linked to human activities in drainage basins and plays a crucial role in maintaining ecosystem functioning and reflecting environmental quality. However, the impacts of climate and anthropogenic-induced changes on DOM in riverine systems under increasingly warming conditions still need to be better understood, particularly at large regional scales. To address this knowledge gap, we analyzed a dataset containing 386 published measurements for nine major Chinese river systems, examining dissolved organic carbon (DOC) concentrations and optical properties of chromophoric DOM (CDOM) under diverse environmental conditions, including mean air temperature, precipitation, surface solar radiation, population density, and land use. Our findings indicate that riverine DOC concentrations are significantly higher in northern China (at ∼46.8%) than in the south. This disparity is primarily due to the high input of soil erosion-induced DOM from drying-affected lands (57.0%), farmland (49.1%), and forests in the north. The high temperate and strong hydrological conditions would lead to DOM degradation easily in the riverine system in the south of China. Our study highlights that various climatic and anthropogenic factors, such as agriculture, vegetation coverage, soil erosion, surface solar radiation, and precipitation, individually or in combination, can affect DOM dynamics in river systems. Therefore, considering alterations in DOM dynamics resulting from climate and environmental changes is crucial for carbon-neutral policies and sustainable river ecosystem assessments.

Correction to: Intraseasonal and synoptic climate variability of surface solar radiation over South‐West Indian Ocean: Regional climate modelling

Correction to: Intraseasonal and synoptic climate variability of surface solar radiation over <scp>South‐West</scp> Indian Ocean: Regional climate modelling

Decomposition of meteorological and anthropogenic contributions to near-surface ozone trends in Northeast China (2013–2021)

Recent years have seen an increase in regional ozone (O3) pollution in China. This study explored the interannual variability in daily maximum 8-h average O3 concentration (MDA8-O3) in Northeast China (NEC) and its three subregions (Liaoning, Jilin, and Heilongjiang) during 2013–2021, and identified the key meteorological drivers underlying the observed variability. The Kolmogorov–Zurbenko filtering technique was applied to a stepwise multiple linear regression model to decompose the meteorological and anthropogenic contributions to annual MDA8-O3 trends. The results showed that the spatiotemporal variation of MDA8-O3 in NEC is characterized by a high–south and low–north pattern with an MDA8-O3 hotspot in the Bohai Rim area. Over the 9-year study period, a significant increasing trend (∼2.5% a−1, P < 0.05) in the regional mean of the annual 90th percentile of MDA8-O3 was detected across NEC. This trend was strongly relevant to changes in relative humidity and surface solar radiation downward (SSRD). Statistical analyses revealed that SSRD dominated MDA8-O3 variability spatially over almost the entire NEC region. Additionally, the contribution decomposition suggested that the trend of increase in annual average MDA8-O3 in NEC was dominated by anthropogenic emission change, which explained 91.9% of the total variation. Regionally, although the dominant role of emissions has not changed, the meteorology-driven anomalies also explained −4.3%–3.3% of the annual average MDA8-O3 variation. This study provides insight into decoupling the complex relationships between long-term variability in regional O3 pollution and both anthropogenic emissions and meteorology, which could provide valuable information for future efforts to address the O3 pollution in NEC.

Sensitivity of Arctic CH4 emissions to landscape wetness diminished by atmospheric feedbacks

Simulations using land surface models suggest future increases in Arctic methane emissions to be limited by the thaw-induced drying of permafrost landscapes. Here we use the Max Planck Institute Earth System Model to show that this constraint may be weaker than previously thought owing to compensatory atmospheric feedbacks. In two sets of extreme scenario simulations, a modification of the permafrost hydrology resulted in diverging hydroclimatic trajectories that, however, led to comparable methane fluxes. While a wet Arctic showed almost twice the wetland area compared with an increasingly dry Arctic, the latter featured greater substrate availability due to higher temperatures resulting from reduced evaporation, diminished cloudiness and more surface solar radiation. Given the limitations of present-day models and the potential model dependence of the atmospheric response, our results provide merely a qualitative estimation of these effects, but they suggest that atmospheric feedbacks play an important role in shaping future Arctic methane emissions.

Bias correction and variability attribution analysis of surface solar radiation from MERRA-2 reanalysis

Bias correction and variability attribution analysis of surface solar radiation from MERRA-2 reanalysis

Soil Moisture Influences on Summer Arctic Cyclones and Their Associated Poleward Moisture Transport

Abstract Moisture transport into the Arctic is an important modulator for clouds, radiative forcing, and sea ice change. Transport events—namely, moist-air intrusions—are often associated with Arctic cyclones, and during the summer season we find that the high-latitude land surface is a significant moisture source for intrusions. Summer Arctic cyclones typically originate from the surrounding continental interior and shorelines where during the early stages of intensification the warm sector experiences strong latent heat fluxes from the land surface. In this study, we use multiyear reanalysis data and back-trajectory calculations to quantify the linkages between key continental moisture source regions and water vapor within cyclone-induced intrusions. We also conduct regional soil moisture sensitivity experiments using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) to diagnose the land surface moisture contribution for an August 2016 Arctic cyclone case. Results from reanalysis show that land regions on average account for more than 30% of the total moist-air intrusion flux at 70°N during summer. COAMPS case-study experiments reaffirm this result, showing that land surface moisture flux on average accounts for 30% of the intrusion water vapor content. COAMPS experiments further reveal that land surface moisture impacts cyclone intensification and moist-air intrusion cloud water vapor. When the regional soil moisture is reduced, intrusion cloud cover is also reduced, resulting in an increase in the surface solar radiation &gt; 90 W m−2. These results demonstrate that the high-latitude land surface plays an important role in the Arctic summer hydrological cycle and may be increasingly impactful as traditionally cold or frozen soils warm. Significance Statement The purpose of this study is to better understand to what extent high-latitude land surface moisture flux is entrained into summer Arctic cyclones and their poleward moisture transport. Moisture transport into the Arctic is important for regional clouds, radiative forcing, and sea ice change. Our results show that land surface evaporation augments cyclone-induced moisture transport into the Arctic on average by 30%. Results are important because traditionally cold or partially frozen high-latitude soils continue to warm and exhibit more positive evaporative trends, including areas undergoing permafrost thaw.

A novel data gaps filling method for solar PV output forecasting

This study proposes a modified gaps filling method, expanding the column mean imputation method and evaluated using randomly generated missing values comprising 5%, 10%, 15%, and 20% of the original data on power output. The XGBoost algorithm was implemented as a forecasting model using the original and processed datasets and two sources of solar radiation data, namely, Shortwave Radiation (SWR) from Advanced Himawari Imager 8 (AHI-8) and Surface Solar Radiation Downward (SSRD) from ERA5 global reanalysis data. The accuracy of the two sets of forecasted power output was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Results show that by applying the proposed gap filling method and using SWR in forecasting solar photovoltaic (PV) output, the improvement in the RMSE and MAE values range from 12.52% to 24.30% and from 21.10% to 31.31%, respectively. Meanwhile, using SSRD, the improvement in the RMSE values range from 14.01% to 28.54% and MAE values from 22.39% to 35.53%. To further evaluate the accuracy of the proposed gap-filling method, the proposed method could be validated using different datasets and other forecasting methods. Future studies could also consider applying the said method to datasets with data gaps higher than 20%.

Global evaluation of simulated surface shortwave radiation in CMIP6 models

Surface solar radiation (SSR) is the main energy source of Earth system, and has direct influence on climate change, the hydrological cycle, and the development of renewable energy technologies. In this study, we compared simulated monthly SSR profiles generated from 62 Coupled Model Intercomparison Project Phase 6 (CMIP6) models against natural SSR observational data collected at 226 GEBA sites during 1990–2014. The accuracy with which CMIP6 models simulated spatial features of SSR was further assessed using ISCCP-ITP satellite data. Our results show that most CMIP6 models overestimate the monthly SSR averaged over all GEBA sites, with an averaged mean bias error (MBE) of approximately 7.8 W/m2. The root mean square error (RMSE) for individual models ranged from 26 to 38 W/m2, and the RMSE for their ensemble mean decreased by an average of 8.1 W/m2. Averaging of the multi-model ensemble led to a significant improvement in the correlation coefficient, which increased from 0.90 to 0.96. Ground-based data showed that the accuracy and annual trend for the CMIP6 multi-model mean (MMM) simulations were more accurate than any individual model. The spatial distribution of CMIP6 MMM multi-year average SSR values agrees well with ISCCP-ITP satellite data retrievals; however, the overall model performance is overestimated in most locations on Earth (e.g., the southern Sahara Desert, the land along the Mediterranean Sea, and the Yangtze River Delta plain), but underestimated in some others (e.g., the Rocky Mountains, the Andes Mountains, Indonesia). The results of this study will allow more accurate predictions to be made of future SSR data produced by CMIP6 models.

METHODS AND CHALLENGES IN TIMESERIES ANALYSIS OF VEGETATION IN THE GEOSPATIAL DOMAIN

Abstract. The increasing availability of remotely sensed data have offered unprecedented possibilities for monitoring and analysis of environmental variables, including boosting recent studies in the field of ecosystem resilience relying on indicators derived from timeseries analysis, such as the temporal autocorrelation of vegetation indices. A forest ecosystem with decreased resilience will be more susceptible to external drivers and their change and could shift into an alternative system configuration by crossing a tipping point. Nevertheless, remote sensing data quantifying vegetation and forests properties inherently carry information related to the climate as well, which has to be accounted for before performing any modelling exercise. In this paper, we aim to present the general workflow and the challenges encountered in processing and analysing the historical, high-frequency and high-resolution timeseries of vegetation and climatic data. The final aim is training a machine learning model (Random Forest) in order to model and explore the performance and importance of a set of climatic and environmental metrics in predicting an indicator of the resilience of forests. In this case, the resilience of forests is quantified through the temporal autocorrelation (TAC) of the kernel NDVI (kNDVI). Climatic and environmental predictors include 2-meter air temperature, total precipitation, vapour pressure deficit, surface solar radiation, forest cover and soil organic carbon content. Results show a good performance of the Random Forest model and the ranking in the importance of the predicting variables captured in terms of background climate and climate variability. This application allows to separate and identify the main drivers of the temporal autocorrelation of kNDVI.

Internal Variability of the Climate System Mirrored in Decadal‐Scale Trends of Surface Solar Radiation

AbstractDecadal‐scale unforced climate variability associated with low‐frequency ocean (or coupled) modes can be identified within the time‐evolving sea surface temperature (SST) pattern. We seek to find a relationship between 12 well‐known climate modes of variability (El Niño–Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, etc.), which are reflected in the SST pattern, and decadal trends in downwelling shortwave radiation at Earth's surface (surface solar radiation, surface shortwave radiation [SSR]). The analysis is performed using the pre‐industrial control runs (piControl) of 57 models from the Coupled Model Intercomparison Project—Phase 6. We find that regional SSR trends occur during periods when a climate mode is transitioning and show a mirroring pattern (opposite sign SSR trends) when the mode transitions in the opposite direction. Unforced trends in clear‐sky SSR are mostly driven by SST‐induced water vapor changes, while all‐sky SSR trends show a complex spatial structure with trends of different signs in different regions. Unforced SSR trends are generally weaker in simulations with climatological SSTs. The role of natural aerosols as another potentially relevant factor for SSR variability is briefly addressed and we find that constantly higher aerosol loads result in increased variability for clear‐sky conditions, but not for all‐sky conditions. The results from this study suggest that all‐sky dimming and brightening in different parts of the world are enhanced rather than suppressed by internal variability related to an SST‐pattern with the exception of India and China, where both effects are present. A practical application can be the planning of photovoltaic energy facilities given areas where internal variability is generally smaller or affects SSR in opposite directions.

The Forecast Skill of the Summer Precipitation Over Tibetan Plateau Improved by the Adoption of a 3D Sub‐Grid Terrain Solar Radiative Effect Scheme in a Convection‐Permitting Model

AbstractWe have successfully incorporated a 3‐dimensional sub‐grid terrain solar radiative effect (3D STSRE) parameterization scheme into a convection‐permitting Weather Research and Forecasting model (WRF_CPM) in this study. Impacts of 3D STSRE scheme on the ability of WRF_CPM in forecasting the precipitation in summer over the Tibetan Plateau (TP) and nearby regions with complex terrain have been systematically addressed by conducting experiments without and with the 3D STSRE scheme. Results show that the application of 3D STSRE scheme can obviously mitigate the overestimation of surface solar radiation (SSR) and rainfall over TP and nearby regions, especially over the areas with much more rugged terrain (i.e., southern TP) in the WRF_CPM without 3D STSRE scheme. Further mechanism analyses indicate that the decreased surface heating induced by the reduction of SSR reduces the intensity of the thermal‐low pressure over the TP, which leads to the diminished strength of southwesterly winds and thereafter the weaker convergence of moisture flux over the southern TP. Moreover, the weakened surface thermal forcing makes the local atmosphere more stable, suppressing the vertical water vapor transport and local convection. These effects greatly alleviate the overestimation of precipitation over the southern TP produced by the WRF_CPM without the 3D STSRE scheme.

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.

GIScience can facilitate the development of solar cities for energy transition

The energy transition is increasingly being discussed and implemented to cope with the growing environmental crisis. However, great challenges remain for effectively harvesting and utilizing solar energy in cities related to time and location-dependant supply and demand, which needs more accurate forecasting- and an in-depth understanding of the electricity production and dynamic balancing of the flexible energy supplies concerning the electricity market. To tackle this problem, this article discusses the development of solar cities over the past few decades and proposes a refined and enriched concept of a sustainable solar city with six integrated modules, namely, land surface solar irradiation, three-dimensional (3D) urban surfaces, spatiotemporal solar distribution on 3D urban surfaces, solar photovoltaic (PV) planning, solar PV penetration into different urban systems, and the consequent socio-economic and environmental impacts. In this context, Geographical Information Science (GIScience) demonstrates its potent capability in building the conceptualized solar city with a dynamic balance between power supply and demand over time and space, which includes the production of multi-sourced spatiotemporal big data, the development of spatiotemporal data modelling, analysing and optimization, and geo-visualization. To facilitate the development of such a solar city, this article from the GIScience perspective discusses the achievements and challenges of (i) the development of spatiotemporal big data used for solar farming, (ii) the estimation of solar PV potential on 3D urban surfaces, (iii) the penetration of distributed PV systems in digital cities that contains the effects of urban morphology on solar accessibility, optimization of PV systems for dynamic balancing between supply and demand, and PV penetration represented by the solar charging of urban mobility, and (iv) the interaction between PV systems and urban thermal environment. We suggest that GIScience is the foundation, while further development of GIS models is required to achieve the proposed sustainable solar city.

Surface Solar Extremes in the Most Irradiated Region on Earth, Altiplano

Abstract Satellites have consistently pointed to the Altiplano of the Atacama Desert as the place on Earth where the world’s highest surface irradiance occurs. This region, near the Tropic of Capricorn, is characterized by its high elevation, prevalent cloudless conditions, and relatively low concentrations of ozone, aerosols, and precipitable water. Aimed at studying the variability of the surface solar irradiance and detecting atmospheric composition changes in the Altiplano, an atmospheric observatory was set up in 2016 at the northwestern border of the Chajnantor Plateau (5,148 m MSL, 22.95°S, 67.78°W, Chile). Here, we report on the first 5 years of measurements at this observatory that establish the Altiplano as the region that receives the highest-known irradiation on Earth and illuminate the unique features of surface solar extremes at high-altitude locations. We found that the global horizontal shortwave (SW) irradiance on the plateau is on average 308 W m−2 (equivalent to an annual irradiation of 2.7 MWh m−2 yr−1, the highest worldwide). We also found that forward scattering by broken clouds often leads to intense bursts of SW irradiance; a record of 2,177 W m−2 was measured, equivalent to the extraterrestrial SW irradiance expected at approximately 0.79 astronomical units (AU) from the Sun. These cloud-driven surface solar extremes occur on the Chajnantor Plateau at a frequency, intensity, and duration not previously seen anywhere in the world, making the site an ideal location for studying the response of photovoltaic (PV) power plants to periods of enhanced SW variability.

Evaluation and uncertainty analysis of Himawari-8 hourly aerosol product version 3.1 and its influence on surface solar radiation before and during the COVID-19 outbreak

The hourly Himawari-8 version 3.1 (V31) aerosol product has been released and incorporates an updated Level 2 algorithm that uses forecast data as an a priori estimate. However, there has not been a thorough evaluation of V31 data across a full-disk scan, and V31 has yet to be applied in the analysis of its influence on surface solar radiation (SSR). This study firstly investigates the accuracy of V31 aerosol products, which includes three categories of aerosol optical depth (AOD) (AODMean, AODPure, and AODMerged) as well as the corresponding Ångström exponent (AE), using ground-based measurements from the AERONET and SKYNET. Results indicate that V31 AOD products are more consistent with ground-based measurements compared to previous products (V30). The highest correlation and lowest error were seen in the AODMerged, with a correlation coefficient of 0.8335 and minimal root mean square error of 0.1919. In contrast, the AEMerged shows a larger discrepancy with measurements unlike the AEMean and AEPure. Error analysis reveals that V31 AODMerged has generally stable accuracy across various ground types and geometrical observation angles, however, there are higher uncertainties in areas with high aerosol loading, particularly for fine aerosols. The temporal analysis shows that V31 AODMerged performs better compared to V30, particularly in the afternoon. Finally, the impacts of aerosols on SSR based on the V31 AODMerged are investigated through the development of a sophisticated SSR estimation algorithm in the clear sky. Results demonstrate that the estimated SSR is significant consistency with those of well-known CERES products, with preservation of 20 times higher spatial resolution. The spatial analysis reveals a significant reduction of AOD in the North China Plain before and during the COVID-19 outbreak, resulting in an average 24.57 W m−2 variation of the surface shortwave radiative forcing in clear sky daytime.

Geomorphic influences on land use/cover diversity and pattern

Landforms directly or indirectly affect land use/cover diversity and its spatial distribution. However, the interactions between complex topography and land use/cover patterns remain unclear. To explore this concern, a study was performed in the Altay region, Xinjiang, which is a typical region with multiple geomorphic and land use/cover types. Topographic variables, including elevation, slope, aspect, relief amplitude, topographic wetness index (TWI), surface solar radiation value (SRV) and land use/cover diversity indexes (LDIs) were extracted and calculated. Firstly, the dominant distribution area of each land use/cover type was calculated by a topographic distribution index, and the results showed that forest and steppe land covers were mainly distributed on mountains with slopes > 5°, small relief platforms and hills and large relief mountains; agricultural, urban, water and Gobi desert land uses/covers were concentrated on plains, gently rolling hills and low mountains with slopes < 5° with elevation of approximately 800 m. Further research indicated that the moving-window method providing the characteristic scale of land use/cover diversity was 210 m, and the spatial distributions of the LDIs were obtained. Combined with landscape structure information entropy (LSIE), the results showed that the LDIs and LSIE were highest on plains, hills and low-mountains. Regularity occurred between landform and land use/cover: a higher elevation, greater relief amplitude, higher TWI, and gentle slope resulted in a simpler land use/cover type and smaller LDIs. The interrelationships between LDIs and topographic variables were evaluated via redundancy analysis (RDA) and Spearman correlation coefficient analysis, and results indicated that elevation, slope and TWI, strongly influenced the land use/cover diversity and pattern. Fully considering the relationship between geomorphic conditions and land use/cover is essential for rationally developing land resources and sustainable development.