Abstract:High temperature and heat waves (HT and HW) directly affect human health and crop growth. Investigating trends in the occurrence of HT and HW is one of the fundamental issues of research on climate change and can provide valuable information for life and production. Most of the previous studies on trends in the occurrence of HT and HW used ordinary least squares (OLS) method to calculate the magnitude of the linear trend and then used the Student’s t-test to determine the statistical significance of this trend. This study examined whether traditional methods are suitable for estimating the trend of HT and HW occurrence in China. By showing a case of the annual HT count, with extremely excessive occurrences in 2018 at a station in northeastern China, the authors illustrate that the OLS method is sensitive to outliers and can give a spurious trend. In addition, through normality tests and autocorrelation calculations, we found at least 91.14% of stations and 90.06% of grid boxes for the annual HT count and 92.18% of stations and 87.74% of grid boxes for the annual count of HW in China are non-Gaussian, and majority of them have serial correlation. Applying a nonparametric method that is insensitive to outliers and takes serial correlation into account, we provide a more accurate estimate of linear trends in the annual HT and HW count for each station and grid box, four typical regional averages, and China area-average for the 1960-2018 period. The results show that stations with statistically significant upward trend in HT occurred mainly in South China and northwestern China, and HW stations occurred almost only in South China and in several stations in the Xinjiang Autonomous Region. In terms of the area-averaged time series of the annual HT and HW count, only South China and northwestern China show a statistically significant upward trend, while North China and northeastern China did not exhibit a significant upward trend; those of the average in China are statistically significant. This study provides reference information for choosing the method for estimating trends and their statistical significance and for statistical predicting for the occurrence of HT and HW.

Abstract:To study the impact of urbanization on the local climate in Chengdu, the urbanization effects during summer and winter over Chengdu are simulated by using data about the underlying surface land use type of different periods and the WRF (Weather Research and Forecasting) model, coupled with a single-layer urban canopy model (UCM). The main conclusions are drawn as follows. First, in summer, the urbanization of Chengdu causes a temperature-increasing area over the urban area. The surface temperature increases by about 2.8℃ in the urban area, and the boundary layer height increases by about 150 m. In winter, the surface temperature increases by about 0.6℃ in the urban area, and the boundary layer height by about 25 m. In addition, the diurnal temperature range in both summer and winter decreases. Second, during summer and winter, the 2-m relative humidity decreases, while the sensible heat flux increases and the latent heat flux decreases due to the impact of urbanization, but the degree of change in summer is stronger than in winter. Third, during both seasons, the wind speed decreases by around 0.1-0.6 m s^-1 in the urban area due to an increase in surface roughness, but the area where the wind speed decreases in summer is larger than in winter. Due to urbanization, in the lower atmosphere over the city, divergence decreases, convergence and vertical velocity increases, while water vapor is transported significantly from the lower layer to the upper layer in summer. Finally, during summer, because of urbanization, the average daily precipitation and daytime precipitation both increase in the urban upwind and downwind directions. On the other hand, nighttime precipitation increases in urban downwind areas, but it has no significant effect on urban upwind areas.

Abstract:Meteorological forecasting is an important factor affecting the accuracy of atmospheric heavy pollution prediction. In response to a heavy pollution event in Beijing during 16-21 December 2016, this paper carried out a sensitivity test for the parameterization scheme of a mesoscale meteorological model Weather Research and Forecasting (WRF). Combining microphysical, long-wave radiation, short-wave radiation, land surface, boundary layer, near-surface, and cumulus convective parameterization processes, a total of 51 sets of parameterization schemes were designed to analyze the simulation accuracy and sensitivity of the temperature, relative humidity, and 10-m height wind speed of eight meteorological stations in Beijing under different simulation schemes. The temperature simulation is the most sensitive to a long-wave process parameterization scheme, the set dispersion is 2.4-7.4℃, followed by the short-wave process parameterization scheme. Additionally, the relative humidity simulation is the most sensitive to the long-wave process parameterization scheme, followed by the land surface process and the wind speed simulation had little difference in sensitivity to different process parameterization schemes. In the comparison of the statistical results of the simulation results with observations, we prefer the combination of the smallest simulation error: Lin microphysical, RRTMG long-wave, RRTMG short-wave, Tiedtke cumulus convection, Noah land surface, MYNN 3rd boundary layer and MYNN near-surface scheme, and compared the best scheme to the ensemble mean and baseline scheme. For the ensemble mean, the correlation coefficient between the temperature simulation and observation was 0.69, which is greater than the baseline scheme. The simulated deviation and root-mean-square error were 25% and 11% less than the baseline scheme and the ensemble mean relative humidity and wind speed simulation were less variable than the baseline scheme. Compared with the ensemble mean, the best scheme can simultaneously improve the temperature, relative humidity, and wind speed simulation, such that the temperature simulation deviation and root-mean-square error decreases by 35% and 17% compared with the baseline scheme, the relative humidity simulation deviation and root-mean-square error decreases by 43% and 13%, and the wind speed simulation deviation and root-mean-square error decreases by 33% and 24%. The above results show that the sensitivity test and optimization of the parameterization scheme can significantly reduce the simulation error of meteorological elements during heavy pollution. The improvement of heavy pollution prediction needs to focus on the uncertainty of the parametric scheme simulation. Additionally, the MYNN 3rd boundary layer scheme has good performance in the simulation of meteorological elements in this heavy pollution process, which can provide reference for future improvements of heavy pollution forecasting.

Abstract:The separate and joint effects of the Great Khingan Strip (GKS) and Changbai Mountain (CM) on summer rainfall in Northeast China are investigated based on high resolution simulations using a regional numerical model WRF-ARW (V3.9). The results indicate that the GKS and CM can significantly influence rainfall and atmospheric circulation in Northeast China. The two mountains block the southerly winds at the windward slopes, leading to moisture convergence and upward movement over the regions; therefore, summer rainfall increase on the windward slopes of the two mountains. On the leeward slopes, there are moisture divergence and downward motion of winds, which results in decreased precipitation over the regions. The existence of the GKS can increase summer rainfall by about 1.09 mm d^-1 (30% of climatological mean summer precipitation in control run) in the region from the eastern slope of the GKS to the Songnen Plain, but decrease it by about 0.69 mm d^-1 (24% of climatological mean summer precipitation in control run) over Eastern Mongolia. The existence of the CM can increase summer rainfall by about 1.76 mm d^-1 (26% of climatological mean summer precipitation in control run) in the region from the southern slope of the CM to the Korean Peninsula, but decrease it by about 0.81 mm d^-1 (22% of climatological mean summer precipitation in control run) over the Sanjiang Plain. The combined effect of the GKS and CM has an offset effect on summer rainfall in Eastern Mongolia, the Songnen Plain, and the Korean Peninsula, but has an enhancement effect on the Sanjiang Plain. The results of this study are important to better understand the formation of the current summer climate in Northeast China.

Abstract:Based on observations of monthly mean temperature, precipitation, and relative humidity at Lhasa, Shigatse, Zedang, and Jiangzi stations over the Yarlung Zangbo River valley in the hinterland of the Tibetan Plateau in the past 57 years (1961-2017), the evolution characteristics of interannual and interdecadal variations of the climate series in the region are analyzed. The relationships between the climate series and their connections to the total cloud cover and surface water vapor pressure during the same period on interannual and interdecadal scales are also discussed. The results show the following: (1) In the past 57 years, the summer climate in the region has exhibited a trend of warming and drying. The temperature (relative humidity) increased (decreased) significantly, and the precipitation trend is not obvious. (2) A close relationship exists between the summer climate factors in the region and the interannual and interdecadal variations: A significant negative correlation exists between temperature and relative humidity (precipitation), while a significant positive correlation exists between precipitation and relative humidity; (3) The interannual and interdecadal variations of summer climatic factors in the region are related to the total cloud cover and surface water vapor changes over the same period. The continuous reduction of the total cloud cover in the past 57 years is one of the main causes of the significant increase in temperature; however, the significant increase in temperature and the change in precipitation do not cause a significant decrease in relative humidity.

Abstract:To completely understand the influence of temperature and relative humidity in the boundary layer on the vertical distribution of PM2.5 and near-surface pollution, in this study, an unmanned aerial vehicle (UVA) equipped with multiparameter atmospheric environment detector was used for the vertical observation of PM2.5 concentration, temperature, and relative humidity in Nanjing from 3 December to 4 December 2017 and from 23 December to 24 December 2017. Combined with the analysis of meteorological data and the application of HYSPLIT4 (Hybrid Single Particle Lagrangian Integrated Trajectory version 4) trajectory calculation model, the vertical distribution characteristics of PM2.5 and the causes of PM2.5 pollution processes from 3 December to 4 December 2017 and from 23 December to 24 December 2017 were analyzed. The results showed a significant positive correlation between PM2.5 concentration and relative humidity. Moreover, the average correlation coefficient reached 0.96 in the six observations from 23 December to 24 December , 2017. Under the inversion layer, the PM2.5 concentration and relative humidity were high and the vertical difference was small. Over the inversion layer, the PM2.5 concentration and relative humidity rapidly decreased with the increase in height. Because of the poor atmospheric diffusion conditions, PM2.5 continuously accumulates in the south of the North China Plain and moves to the south and southeast under the influence of the high-pressure system. Both PM2.5 pollution processes were significantly affected by external transport. Atmospheric inversion significantly inhibited the upward transport of PM2.5 and water vapor. Moreover, external transport and local inversion were the main causes of these two PM2.5 pollution processes.

Abstract:To improve the accuracy of radar quantitative precipitation estimation (QPE), a high-precision dual-polarization radar QPE method is established, and its performance in operational application is evaluated. In this study, a nonspherical particle scattering model (i.e., T-matrix model) is used to simulate and calculate different polarization quantities on the basis of the data obtained using an LPA10 disdrometer. On the basis of the calculated results, the measured raindrop spectrum data are classified and fitted to optimize the precipitation estimation algorithm of CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization). Two rainfall cases occurring in 2016 and 2017 in South China are selected to assess the performance of the modified algorithm (CSU-HIDRO_I). The R(Z_H) method PPS (WSR-88D Precipitation Processing System) and the CSU-HIDRO_I method are used to estimate the hourly precipitation. On the basis of different rainfall intensities and ranges (i.e., 20-60 km and 60-100 km) obtained by radar, the two precipitation estimation methods are evaluated. Moreover, the hourly precipitation estimated by radar is compared with that estimated by rain gauges. The main results are as follows: (1) The CSU-HIDRO_I method achieves good QPE results, and its estimation accuracy and stability are better than that of the R(Z_H) method. (2) The PPS method overestimates during light rainfall (R<2.5 mm/h) and underestimates during heavy rainfall and rainstorm (R>8 mm/h). By contrast, the CSU-HIDRO_I method can effectively reduce the underestimation of heavy rainfall and improve the estimation accuracy during light rainfall. Compared with the PPS method, the estimation deviation of the CSU-HIDRO_I method for light rainfall, moderate rainfall, heavy rainfall, and rainstorm is reduced by 38%, 24%, 17%, and 15%, respectively. (3) The PPS method is more sensitive to the distance from the radar during precipitation estimation than the other methods. Under the same rainfall intensity, the relative error at different distances fluctuates considerably. By contrast, the CSU-HIDRO_I method is less sensitive to the range from the radar than the other methods. Moreover, the variation of its relative error at different distances is smaller than that of the other methods.

Abstract:Based on conventional observation data, in this study, we analyzed Jinan Doppler weather radar data, FY-2G data, and automatic weather station data with respect to the formation and splitting processes of a supercell storm that occurred in the background of a cold vortex in North China on June 14, 2016. The radar echo characteristics and environmental conditions of the splitting supercell were analyzed in particular detail. The results showed that the supercell storm occurred near the mesoscale convergence line on the ground, in front of the centrally located short-wave slot trough. In an environmental condition characterized by hollow jets at high altitudes, which triggered an easterly moving convective cloud cluster, unstable layers, and strong vertical wind shear, the convective storms split, with those that were right-shifting strengthening. After splitting, the storm monomer on the left side of the environmental wind was not significantly restrained. The dew-point front near the mesoscale convergence line offset the resistance of the anticyclonic storm, which strengthened and extended the life of the anticyclonic storm. The storm monomer on the right side of the environmental wind was strengthened and lasted 2 h. The storm splitting process began at the initial stage of monomer formation, with the split initiating at the middle and upper levels and then extending downward. After the division, relative to the direction of the environmental wind, the left monomer was an anticyclonic leftward-moving storm, and the right monomer was a cyclonic rightward-moving storm. The cyclonic rightward-moving storm featured an inflow notch at the low level, a bounded weak echo region at the middle-upper level, and strong storm-top divergence at the upper level. These echo features are similar to those of a classic supercell storm. After the division, the rightward-moving storm was accompanied by a deep and long-lasting mid-cyclone that had originated at the middle level (four to five kilometers), and then developed both upward and downward. Its strongest rotation occurred at a high level with a rotation speed of 29 m/s. This differs from the behavior of the classic supercell monomer, which has its strongest rotation in the middle level.

Abstract:On the basis of the annual averaged surface air temperature data from eight meteorological stations in the source region of the Yellow River obtained using the ensemble empirical mode decomposition (EEMD) approach, the multi-timescale temperature features of meteorological stations, with Madoi as a representative, during 1953-2017 and their contributions to the temperature variations are revealed. The correlations between different timescale temperature oscillations and sea surface temperature (SST) indices, particularly the Atlantic Multidecadal Oscillation (AMO), are analyzed. The results indicated that: (1) The long-term temperature trend during 1953-2017 in the source region of the Yellow River was 0.31 ℃/10 years; the warming period started in the late 1980s and accelerated in the late 1990s. (2) There were 3-, 6-, 11-, 25-, 64-, and 65-year quasi-cycle oscillations for temperature during 1953-2017. Among them, the 3- and 65-year quasi-cycle oscillations were significant. The amplitude of the 3-year timescale oscillation increased before the 21st century and decreased after the 21st century. By contrast, the amplitude of the 65-year timescale oscillation increased after the 21st century. (3) The three-year quasi-cycle oscillation was dominant during 1953-1997. Moreover, the contribution of the 65-year oscillation increased nearly five times, which was equivalent to the contribution of the 3-year oscillation during the rapid warming period since 1998. (4) The correlations of temperature with the Nino3.4 and Pacific Decadal Oscillation (PDO) indices were not significant. However, the maximum significant correlation was observed during the 22-year temperature-led PDO. In contrast to PDO, the maximum significant correlation was observed when the AMO led the original temperature and its three interdecadal components 0 and 3-7 years that supported the finding that the AMO had a significant effect on the temperature variation in the source region of the Yellow River. (5) The positive warm phase of the AMO corresponded to the warming period of East Asia, including China, and the source region of the Yellow River was only a part of that area. The negative cold phase of the AMO from the early 1960s to the early 1990s and the positive warm phase of the AMO from the middle and late 1990s to the present corresponded to the negative and positive phases of the temperature in the source region of the Yellow River. The AMO highly correlated with the 65-year oscillation. These results supported the finding that the AMO was an important climatic oscillation that affects the temperature variation, particularly the interdecadal timescales, in the source region of the Yellow River.