Quantifying the level of CO2, the main greenhouse gas (GHG), is essential for research on regional and global climate change, especially in the densely populated North China Plain with its severe CO2 emissions. In this study, 12 airborne flights were managed and conducted during the autumn–winter period of 2018–2019 in downtown Shijiazhuang and its surrounding areas, which are representative of the typical urban conditions in the North China Plain, to explore the spatial and temporal distributions of CO2. The results showed that the measured columnar averages of CO2 ranged between 399.9 ± 1.5 and 443.8 ± 31.8 ppm; the average of the 12 flights was 412.1 ppm, slightly higher than the globally averaged 410.5 ± 0.20 ppm and the 2 background concentrations of 411.6 ± 2.1 ppm and 411.4 ± 0.2 ppm in low-latitude Mauna Loa and middle-latitude Waliguan in 2019, indicating the potential influences of anthropogenic activities. The typical stratification of the planetary boundary layer (PBLH), residual layer (RL), and elevated inversion layer (IL) was crucial in constraining the high CO2 concentrations. This illustrated that the warming effect of CO2 within the PBLH may also have some influences on regulating the thermal structure of the low troposphere. Based on a backward trajectory analysis, it was evidenced that there were three different categories of air masses for autumn and one category for winter. Both trajectories in the PBL, i.e., below 1000 m, from the local and southern areas with tremendous anthropogenic emissions (autumn) and from the western regions (winter) led to comparatively high levels of CO2, but the mid-tropospheric CO2 concentrations above 1000 m were commonly homogeneously distributed, with higher levels appearing in winter because the concentration in the free troposphere followed the global seasonal pattern, with a summer minimum and winter maximum as a result of the seasonality of the net CO2 exchange and the balance between photosynthesis and respiration. These results provide an in-depth understanding of the vertical concentrations of tropospheric CO2 in the North China Plain, which will offer scientific references for the evaluation of carbon accounting and carbon emissions.

The icing on power transmission lines, as a major hazard affecting the safety of electricity usage in China during winter, poses a significant challenge in systematically evaluating the weather conditions and their distribution characteristics during the icing period. Understanding the interaction between the microterrain and micrometeorology and achieving a refined analysis of the physical mechanisms during the icing process remain difficult tasks in this field. These are crucial aspects for the development of more accurate icing prediction models across southern China. Therefore, this study provides a comprehensive review and summary of the current research state and progress in the study of power transmission line icing in southern China from three perspectives: (1) large-scale circulation characteristics; (2) microphysical process, terrain–atmosphere interaction, microtopography and local micrometeorological conditions for the occurrence of icing events; and (3) numerical icing event modeling and forecasting. This study also looks ahead to the scientific issues and technological bottlenecks that need to be overcome for the prediction of ice coating on power transmission lines in southern China. The goal is to provide guidance for the causal analysis and forecasting warnings of power transmission line icing in the complex microterrain of the southern region.

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events.

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%–40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

The bark and leaves of the Eucommia ulmoides Oliv. (E. ulmoides) have good medicinal value. Studies show endophytes play important roles in host medicinal plant secondary metabolite synthesis, with season being a key influencing factor. Therefore, we used 16 S rRNA to detect endophytic bacteria (EB) in E. ulmoides bark and leaves collected in winter, spring, summer, and autumn, and analyzed the contents of major active components respectively. The results showed that the species diversity and richness of EB of the E. ulmoides bark were higher than those of leaves in all seasons except fall. Among them, the higher species diversity and richness were found in the E. ulmoides bark in winter and spring. EB community structure differed significantly between medicinal tissues and seasons. Concurrently, the bark and leaves of E. ulmoides showed abundant characteristic EB across seasons. For active components, geniposidic acid showed a significant positive correlation with EB diversity and richness, while the opposite was true for aucubin. Additionally, some dominant EB exhibited close correlations with the accumulation of active components. Delftia, enriched in autumn, correlated significantly positively with aucubin. Notably, the impact of the same EB genera on active components differed across medicinal tissues. For example, Sphingomonas, enriched in summer, correlated significantly positively with pinoresinol diglucoside (PDG) in the bark, but with aucubin in the leaves. In summary, EB of E. ulmoides was demonstrated high seasonal dynamics and tissue specificity, with seasonal characteristic EB like Delftia and Sphingomonas correlating with the accumulation of active components in medicinal tissues.

An update on the climate norms each decade is recommended by the World Meteorological Organization (WMO) partly to keep pace with conditions as climate changes over time. In accordance with such update, this study documents the features of the new climate normal defined for 1991–2020 and its impacts on climate monitoring and prediction in China. With on-site observation and model prediction datasets, our analysis reveals that the new normal of national average precipitation of China during winter and summer is respectively 3.0 and 10.8 mm higher than that of the period 1981–2010. As a result, precipitation observations during 1961–2020 consistently fall below the new normal. The adjustment of thresholds for precipitation extremes with new climate normals results in a decrease of extreme precipitation occurrence by 0.2–0.8 d on average over the winter and summer seasons during 1961–2020. Meanwhile, the application of new climate normals induces more pronounced negative temperature anomalies across most areas of China. The adjustments of extreme temperature thresholds have led to an increased occurrence of extremely cold days by 1–2 d on average over 1961–2020, while the frequency of extremely hot days decreases by more than 1.4 d. Furthermore, it is implied that with the development of global warming, the baselines for temperature and precipitation are rising. The application of the new climate normal may result in the omission of relative threshold based extreme events, promoting increased focus on climate risk reduction studies. Additionally, the average anomaly sign consistency rates (Pcs) of precipitation and temperature anomaly predictions, relative to the new normal and produced by the Beijing Climate Center, are consistently lower than those relative to the old normal. This decrease in Pcs implies new challenges for climate prediction, especially for temperature prediction.

The atmospheric environment before the onset of rainfall, frequently obtained from reanalysis, oftentimes varies dramatically at time scales ranging from minute to hour. Nevertheless, the extent to which hourly reanalysis data can capture the pre-rainfall and pre-storm signals remains largely unknown. To this end, three warm-season (April to September of 2015–2017) records of high-resolution radiosonde measurements and the Fifth Generation Global Climate Atmosphere Reanalysis (ERA5) reanalysis data throughout the whole of China are used to derive the pre-storm environmental parameters, including convective available potential energy (CAPE), convective inhibition (CIN), precipitable water (PW), K index, LI index, storm-relative helicity (SRH) and vertical wind shear (WSR). For rainfall and storm events, we separately conducted a comprehensive comparative analysis of ERA5 against soundings at 120 radiosonde stations in China obtained 2 h before the rainfall onset. The CAPE before the onset of storm events is much greater than that for rainfall events, especially over the central and southern China, and the association between CAPE and rain rate for the storm events is much stronger than that for the rainfall events. Pre-rainfall environmental analysis suggests that ERA5 reanalysis tends to overestimate the thermodynamic parameters including CAPE, CIN, PW, and LI index, compared with soundings, and this overestimation exhibits a “west-high east-low” spatial pattern. In contrast, ERA5 reanalysis tends to underestimate the dynamic parameters like WSR and SRH. By comparison, the correlation coefficients between soundings and ERA5 for most pre-storm environmental parameters are lower than those of all rainfall events, suggesting ERA5 has a relatively better performance in characterizing pre-rainfall environment compared with in characterizing pre-storm environment. Besides, the pre-storm parameter discrepancy pattern may be more spatially heterogenous, compared with pre-rainfall counterpart. We also try to explore the evolution of the pre-rainfall atmospheric parameters with lead time that refers to the period between 20 min after the sounding balloon releases and the rainfall onset. Most pre-rainfall environmental parameters from ERA5 undergo more sudden changes within 10 min of the rainfall onset than those from soundings. Overall, ERA5 reanalysis cannot accurately capture the atmospheric convective environment before rainfall in most cases, especially in western China where the weather stations are scarce. Therefore, the findings highlight the need of integrating reanalysis with sounding measurements to better characterize pre-rainfall and pre-storm environment.

As an important part of remote sensing data, weather radar plays an important role in convective weather forecasts to reduce extreme precipitation disasters. The existing radar echo extrapolation methods do not utilize the local natural characteristics of the radar echo effectively but only roughly extract the whole characteristics of the radar echo. To address these challenges, we design a spatiotemporal difference and generative adversarial fusion model (STDGAN). Specifically, a spatiotemporal difference module (STD) is designed to extract local weather patterns and model them in detail. In our model, spatiotemporal difference information and spatiotemporal features captured by the model itself are fused together. In addition, our model is trained in a generative adversarial network (GAN) framework; it helps to generate a clearer map of future radar echoes at the image level. The discriminator consists of multi-scale feature extractors, which can simulate weather models of various scales more completely. Finally, extrapolation experiments were conducted using actual radar echo data from Shijiazhuang and Nanjing. The experiments have shown that our model has a more accurate prediction performance for predicting local weather patterns and overall echo change trajectories compared with previous research models. Our model achieved MSE, PSNE, and SSIM values of 132.22, 37.87, and 0.796, respectively, on the Shijiazhuang radar echo dataset. In addition, our model also showed better performance results on the Nanjing radar echo dataset. The results show that the MSE was 49.570, the PSNR was 0.714, and the SSIM was 30.633. The CC value was 0.855.

Abstract The temporal and spatial resolutions of rainfall data directly affect the accuracy of hydrological simulation. Weather radar has been used in business in China, but the uncertainty of data is large. At present, research on radar data and fusion in small and medium-sized basins in China is very weak. In this paper, taking the Duanzhuang watershed as an example, based on station data, Shijiazhuang's radar data are preprocessed, optimized and fused. Eleven rainfall events are selected for fusion by three methods and quality evaluation, and three flood simulations are used to test their effect. The results show that preprocessing and initial optimization have poor effects on radar data improvement. The rainfall proportional coefficient fusion method performs best in rainfall spatial estimation, where the R2 values of the three inspection stations are increased to 0.51, 0.78 and 0.82. Three fusion datasets in the peak flow and flood volume of flood simulation perform better than station data. For example, in the No.20210721 flood, the NSE of the three fusion data increased by 39, 30 and 48%. This shows that the fusion method can effectively improve the data accuracy of radar and can obtain high temporal and spatial resolution rainfall data.

As a key indicator of agricultural production capacity, crop production potential is critical to evaluate the impacts of climate variability on agriculture. However, less attention has been paid to the pattern of biomass accumulation in response to crop climatic production potential and its relation to grain yield formation at an intra-seasonal time scale, especially under evolving soil water limitation. In this study, we combined a mechanism-based empirical model with field experiments conducted at different growth stages of maize on the Northern China Plain (NCP) to assess the dynamic response of biomass accumulation to climatic production potential and its relation to grain yield. The results showed that the ability of climatic production potential to estimate biomass was significantly improved when a vapor pressure deficit (VPD) was incorporated, with the root mean square error (RMSE) reduced by 33.3~41.7% and 45.6~47.2% under rainfed and evolving soil water limitation conditions, respectively. Drought significantly decreased biomass accumulation mainly by decreasing the intrinsic growth rate and delaying the occurrence timing of maximum growth. Moreover, grain yield shared a nonlinear and saturating relationship with biomass across rainfed and water deficit conditions. The results imply that evolving soil water limitation changes the process of biomass accumulation but not its relationship with grain yield. These findings provide useful information to estimate crop production potential under abiotic stresses and improve the accuracy of crop yield prediction.