The diurnal evolution of the atmospheric boundary layer—the lowermost part of the atmosphere where the majority of human activity and meteorological phenomena take place—is described by its depth. Additionally, the boundary layer height (BLH) and the turbulence intensity strongly impact the pollutant diffusion, especially during transition periods. Based on integrated observations from a 325-m meteorological tower and a Doppler Wind lidar in the center of Beijing, the entire diurnal cycle of urban BLH in December 2016 was characterized. Results highlight that the Doppler lidar exhibited it is well suited for monitoring convective BLH while it trudges in monitoring stable BLH, while a 325-m meteorological tower provided an important supplement for Doppler lidar under nocturnal boundary layer and heavily polluted conditions. For the diurnal cycle, under light wind condition, the pattern of urban BLH was largely modulated by thermal forcing of solar radiation and may partly be affected by wind speed. While under strong wind condition, the pattern of urban BLH was largely modulated both by thermal forcing and dynamical forcing. The present work also presented evidence for several new features in the morning and afternoon transitions of the urban boundary layer, showing the duration of the morning transition varied between 1 and 5 h, with the largest value occurring under weak wind with high PM_2.5 concentration; while the afternoon transition ranged from 3 to 6 h, which was positively (negatively) correlated to wind speed (PM_2.5 concentration). Our work highlights that weak wind speed (weak dynamic motion) and heavy aerosol pollution (weak thermal forcing due to the effect of cooling) can dramatically affect the evolution of the boundary layer.

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models.

The correlations between the planetary boundary layer height (PBLH) and surface meteorological factors, including the surface wind speed (R_WS), temperature (R_TS), specific humidity (R_SH), pressure (R_PS), and lower tropospheric stability (R_LTS), are investigated using hourly profiles from the Aircraft Meteorological Data Reports at 54 major airports over the contiguous United States (CONUS) and surface observations from weather stations. At the diurnal cycle, the R_WS and R_TS are positive and the R_PS and R_LTS are negative across the CONUS, while the R_SH is negative over most areas but tends to be positive in the inland humid region. At the annual cycle, the correlations show significant diurnal variations that cannot be well captured by routine twice‐daily radiosonde data. The R_WS is significantly positive at night but less significant during the daytime over most regions. The R_TS and R_SH are positive and the R_PS and R_LTS are negative at the daytime, which are of the opposite sign at the nighttime, with exceptions in some coastal areas. The diurnal variations of the correlations at the annual cycle also exhibit significant geographic variability, with the diurnal variation generally stronger in central eastern humid areas, weaker in some coastal areas, and moderate in relatively arid or mountain areas. This study highlights that the correlations between the PBLH and meteorological variables depend on the thermal stratification and are affected by the local hydrological and synoptic factors.

This study evaluated the reliability of satellite-derived atmospheric temperature and humidity profiles derived from occultations of Fengyun-3D (FY-3D), the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2), the Meteorological Operational Satellite program (METOP), and the microwave observations of NOAA Polar Orbital Environmental Satellites (POES) using various conventional sounding datasets from 2020 to 2021. Satellite-derived profiles were also used to explore the precursors of severe convective precipitations in terms of the atmospheric boundary layer (ABL) characteristics and convective parameters. It was found that the satellite-derived temperature profiles exhibited high accuracy, with RMSEs from 0.75 K to 2.68 K, generally increasing with the latitude and decreasing with the altitude. Among these satellite-derived profile sources, the COSMIC-2-derived temperature profiles showed the highest accuracy in the middle- and low-latitude regions, while the METOP series had the best performance in high-latitude regions. Comparatively, the satellite-derived relative humidity profiles had lower accuracy, with RMSEs from 13.72% to 24.73%, basically increasing with latitude. The METOP-derived humidity profiles were overall the most reliable among the different data sources. The ABL temperature and humidity structures from these satellite-derived profiles showed different characteristics between severe precipitation and non-precipitation regions and could reflect the evolution of ABL characteristics during a severe convective precipitation event. Furthermore, some convective parameters calculated from the satellite-derived profiles showed significant and rapid changes before the severe precipitation, indicating the feasibility of using satellite-derived temperature and humidity profiles as precursors to severe convective precipitation.

The lack of observational data in Taklamakan Desert makes it very difficult to study its unique boundary layer structure. As a common means of supplementing observational data, the mesoscale boundary layer parameterization scheme in the numerical model method is difficult to capture small-scale turbulent processes, which may lead to large deviations in simulation. In order to obtain more accurate simulation data of desert atmospheric boundary layer, nested LES into WRF (WRF-LES) was configured to simulate the seasonal variations in Taklamakan Desert. By comparing LES with the conventional boundary layer parameterization scheme, the error characteristics between the two schemes are analyzed. The results show that LES exhibits superior performance in solving key atmospheric features such as small-scale processes and low-level jet streams. The simulation results in winter and summer have great uncertainty due to the boundary condition errors, respectively. LES also shows the maximum and minimum optimization degree in summer and winter, respectively, while the simulation results in spring and autumn are relatively stable. In the analysis of turbulence parameters, there are clear seasonal differences in turbulence characteristics, and the intensity of turbulence in summer is significantly higher than that in other seasons. When turbulent activity is strong, the difference in potential temperature and horizontal wind speed simulated between the two schemes is closely related to intense turbulent kinetic energy in LES. More accurate turbulence reproduced in LES leads to the better potential temperature and horizontal wind speed simulations in summer. In addition, large-scale cloud systems can lead to considerable simulation bias. Neither scheme can accurately simulate the cloud emergence process, and large differences between the two schemes occur at this point.