Soil temperature is a crucial parameter in surface emissions of carbon, water, and energy exchanges. This study utilized the soil temperature of 836 national basic meteorological observing stations over China to evaluate three soil temperature products. Soil temperature data from the China Meteorology Administration Land Data Assimilation System (CLDAS), European Centre for Medium-Range Weather Forecasts (ERA-Interim), and Global Land Data Assimilation System (GLDAS) during 2017 are evaluated. The results showed that soil temperature reanalysis datasets display a significant north-to-south difference over eastern China with generally underestimated magnitudes. CLDAS data perform soil temperature assessment best at different depths and can be reproduced well in most areas of China. CLDAS slightly overestimates soil temperature in summer. The most significant deviation of ERA-Interim (GLDAS) appears in summer (summer and autumn). As soil depth increases, the soil temperature errors of all three datasets increase. The CLDAS represents the soil temperature over China but owns a more considerable bias in barren or sparsely vegetated croplands. ERA-Interim performs poorest in urban and built-up and barren or sparsely vegetated areas. GLDAS overall owns an enormous bias at the mixed forest, grassland, and croplands areas, which should be improved, especially in summer. However, it performs better in open shrublands and barren or sparsely vegetated areas. The ST of mixed forests shows better results in the south region than the north region. For grasslands, smaller MEs are located in the north and northwest regions. The ST of croplands shows the poorest performance over the northwest region.

In this study, we evaluate the impacts of different datasets (e.g., NCEP global forecast system (GFS) and ERA5) that are used to derive the initial and boundary conditions, various planetary parameterization boundary layer (PBL) schemes and radiation parameterization schemes on wind speed simulations over wind farms near Dunhuang in Northwest of China. The mesoscale community Weather Research and Forecasting (WRF) model is employed to simulate the wind speeds in March of 2014. The sensitivity of numerical simulations to different PBL schemes, including the Yonsei University (YSU), the Asymmetric Convective Model (ACM2) and the Mellor–Yamada–Janjic (MYJ) scheme are examined. Besides, simulations with different radiation parameterization schemes, including the Rapid Radiative Transfer Model for general circulation model (GCM) applications (RRTMG) and the Fu–Liou–Gu radiative transfer scheme (FLG), are compared. Based on hourly observation data from three national basic meteorological observing stations and an anemometer tower in Dunhuang, the simulation results are evaluated. Results show that, using the GFS data as the initial data, the simulation error of 10-m wind speed is rather smaller under the combination of the YSU and FLG. When using the ERA5 data as the initial data, the error of the 2-m temperature simulation is smaller, and it is also less than that of the 10-m wind speed simulation. The simulation results show significant differences at different altitudes. The relative error of wind speed is larger at higher altitude. In the vertical direction, the wind speed is smaller at a lower height and so is the simulation error. In terms of wind speed from the anemometer tower, the error of the wind speed is related to the magnitude of the observed wind speed. Therefore, according to specific conditions of the simulated area, selecting an appropriate combination of initial data and parameterization schemes can effectively reduce the errors of simulated wind speed.

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones.

As the most frequent dust event in the Tarim Basin (TB), persistent floating dust significantly impacts the regional weather and climate. Long-term analysis (2015–2024) showed that the occurrence of persistent floating dust is significantly associated with the presence of the nocturnal low-level jet (NLLJ). To investigate this potential linkage, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the persistent floating dust event accompanied by the NLLJ in the TB from 29 to 31 July 2006. Results indicated that a typical NLLJ occurred during the event, with an easterly jet core (>12 m/s) near 850-hPa facilitating the westward dust transport and accumulation within the TB, as well as strong convergence and vertical uplift on its front side elevating the dust layer height (DLH). Quantification showed that the NLLJ enhanced dust column concentrations (mean maximum > 100 mg/m²) and DLH (mean maximum > 300 m) over the central and western TB, and the cumulative maximum increase in dust emissions exceeded 200 mg/m², in the NLLJ region. Furthermore, nocturnal dust radiative forcing intensified the NLLJ by up to 1 m/s, thereby establishing a positive feedback mechanism. These results reveal the crucial role of the NLLJ in persistent floating dust events and enrich our understanding of such events in the TB.

Near-surface ozone is a secondary pollutant, and its high concentrations pose significant risks to human and plant health. Based on an Extra Tree (ET) model, this study estimated near-surface ozone concentrations with the high spatiotemporal resolution based on Himawari-8 aerosol optical depth (AOD) data and meteorological variables from 1 January 2016 to 31 December 2020. The SHapley Additive exPlanation (SHAP) method was employed to evaluate the contribution of AOD and meteorological factors on ozone concentration. The results indicate that (1) the ET model achieves a sample-based cross-validation R² of 0.75–0.87 and an RMSE (μg/m³) of 17.96–20.30. The coefficient of determination (R²) values of the model in spring, summer, autumn, and winter are 0.81, 0.80, 0.87, and 0.75, respectively. (2) Higher temperature and boundary layer heights were found to positively contribute to ozone concentration, whereas higher relative humidity exerted a negative influence. (3) From 11:00 to 15:00 (Beijing time, UTC+08:00), ozone concentration increases gradually, with the highest occurring in the summer, followed by spring. This study has obtained high spatial and temporal resolution ozone concentration data, offering valuable insights for the development of fine-scale ozone pollution prevention and control strategies.