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Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R2 values for all six moisture indices exceeding 0.96 and root mean square error and mean absolute error values within a reasonable range. The dataset exhibits high consistency with in situ observations over various spatial and temporal regimes, demonstrating broad applicability and strong reliability.
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Heatwaves are consecutive hot days with devastating impacts on human health and the environment. These events may evolve across both space and time, characterizing a spatiotemporally contiguous propagation pattern that has not been fully understood. Here, we track the spatiotemporally contiguous heatwaves in both reanalysis datasets and model simulations and examine their moving patterns (i.e., moving distance, speed, and direction) in different continents and periods. Substantial changes in contiguous heatwaves have been identified from 1979 to 2020, with longer persistence, longer traveling distance, and slower propagation. These changes have been amplified since 1997, probably due to the weakening of eddy kinetic energy, zonal wind, and anthropogenic forcing. The results suggest that longer-lived, longer-traveling, and slower-moving contiguous heatwaves will cause more devastating impacts on human health and the environment in the future if greenhouse gas emissions keep rising and no effective measures are taken immediately. Our findings provide important implications for the adaption and mitigation of globally connected extreme heatwaves.
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Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.
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Compared with interdecadal, interannual, or seasonal scales, the variations of diurnal temperature range (DTR) at the intraseasonal scale and their driving forces are less understood. Using surface meteorological observations and multi-source satellite retrievals during 2013-2017, together with Random Forest modeling, this study examines the intraseasonal variation of summer DTR in the Yangtze River Delta (YRD) region, China, and determines its potential driving factors [i.e., daily maximum/minimum surface air temperature (SATmax/SATmin), sunshine duration (SSD), rainfall, altitude, land vegetation cover, and land surface thermal environment including daytime/nighttime land surface temperature (LSTD/LSTN) and anthropogenic heat flux (AHF)]. It is found that the intraseasonal variation of DTR at both 8-day and monthly scales in the YRD exhibits regional differences and is modulated by different primary factors across the region. The evident intraseasonal variation of DTR, with a peak in June, in the northern YRD, is largely attributable to nighttime temperatures (SATmin and LSTN), which in turn are mainly attributable to different LSTN responses to the underlying surface cover changes associated with crop rotation. In contrast, as the YRD metropolitan area (MYRD) is covered by a large proportion of built-up surfaces, and the weather stations there are surrounded by a higher surface thermal environment and AHF, the MYRD has stably higher LST and SATmin in the whole summer season. Thus, the summer DTR in the MYRD exhibits marginal intraseasonal variations. In the southern YRD, there is also a distinct DTR characteristic, with a maximum in July and minimum in June, since this region is largely covered by forests with constantly high-density vegetation cover, and its DTR variation is mainly forced by SSD, which directly affects SATmax. The findings reported here have important implications for understanding the influences of human activities on regional climate and environmental change for other regions of the world that experience various external forcings.
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We investigated the factors influencing the daily maximum boundary layer height (hmax) and their relationship with air quality in the Sichuan Basin, China. We analyzed the factors influencing hmax on cloudy and sunny days in winter using five years of observational data and a reanalysis dataset and investigated the relationship between hmax and air quality. The inversion layer in the lower troposphere has a critical impact on hmax on cloudy days. By contrast, the sensible heat flux and wind shear are the main influencing factors on sunny days, although the contribution of the sensible heat flux to hmax is less than that of the wind shear. This is because the turbulence is mainly affected by mechanical mixing induced by the topographic effect of the Tibetan Plateau to the west of the Sichuan Basin. The secondary circulation over the Sichuan Basin is weaker on cloudy days than on sunny days. These results are important for understanding the dispersion of air pollutants over the Sichuan Basin.
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The meteorological causes of winter droughts over Southwest China (SWC) have been widely investigated in recent years; however, little information is available on the impact of these droughts on air pollution. This study (1) characterized and quantified the impact of winter droughts on air pollution over SWC and (2) investigated the atmospheric teleconnections associated with the winter droughts in this region using air pollution monitoring data, routinely observed meteorological data, and National Centers for Environmental Prediction/National Center for Atmospheric Research and ERA-Interim reanalysis data. The main results are as follows: (1) A surface high pressure system together with a weak descent in the middle troposphere was the main cause of the SWC drought in December 2017. (2) It has been found that precipitation, the number of precipitation days and the atmospheric boundary layer height all decreased during the droughts, resulting in unfavorable conditions for the dispersion of air pollutants. (3) The concentrations of PM2.5 and PM10 were much higher during dry periods than those during non-dry periods over SWC, especially in the Sichuan Basin of the SWC. (4) WRF-Chem simulations reproduced the observed changes in air pollutant concentrations between dry and non-dry conditions. (5) Atmospheric teleconnections associated with the winter droughts in SWC were negative phases of the conventional Eurasian teleconnection, Eastern Atlantic/Western Russia pattern, Arctic Oscillation and North Atlantic Oscillation, and a La Niña event. Overall, this study provides scientific support for the long-term potential and accurate short-term predictions of air pollution in SWC.
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Sichuan Basin, located in southwest China, has been ranked as the fourth of heavily air polluted regions in China partly due to its deep mountain-basin topography. However, spatial-temporal distribution of air pollution over the basin is still unclear due to the lack of monitoring data and poor knowledge. Since January 2015, six criteria air pollutants began to be monitored in 20 cities across the basin. The measured data enable us to analyze the basin-wide spatial-temporal distribution characteristics of these air pollutants. Results revealed heavy air pollution in the bottom zone, medium in the slope zone, and light pollution in the edge zone of the Basin in terms of the altitudes of air quality monitoring stations across the Basin. The average concentrations of PM2.5 and PM10 were 55.87µg/m3 and 86.49µg/m3 in the bottom, 33.76µg/m3 and 63.33µg/m3 in the slope, and 19.71µg/m3 and 35.06µg/m3 in the edge, respectively. In the bottom and slope of the basin, high PM2.5 concentration events occurred most frequently in winter. While in summer, ozone became primary pollutant. Among the six air pollutants, concentrations of PM2.5 and PM10 decrease dramatically with increasing altitude which was fitted by a nonlinear relationship between particulate matter (PM) concentrations and altitude. This relationship was validated by extinction coefficient profiles from CALIPSO observations and EV-lidar data, and hence used to reflect vertical distribution of air PM concentrations. It has been found that the thickness of higher PM concentrations is less than 500m in the basin. In the bottom of the basin, PM concentrations exhibited stronger horizontal homogeneities as compared with those in the North China Plain and Yangtze River Delta. However, gaseous pollutants seemed not to show clear relationships between their concentrations and altitudes in the basin. Their horizontal homogeneities were less significant compared to PM.
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OBJECTIVE: To evaluate the distribution characteristics of atmospheric pollutants in Lanzhou City and their health-effects on daily respiratory disease hospital admissions. METHODS: Using the same period atmospheric pollutants (PM10, SO2 and NO2) data, meteorological data and daily respiratory disease hospital admissions data, from 2001 to 2009 in Lanzhou, to analysis of the distribution of three air pollutants(PM10, SO2 and NO2), and their correlation with meteorological factors. On this basis, a time series semi-parametric generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollution and daily respiratory disease hospital admissions. RESULTS: The concentrations of PM10 and SO2 showed a decreased trends during the study period of 9 years, and NO2 showed weakly fluctuations and remained substantially unchanged. The highest concentrations of the three pollutants were presented in the winter and the lowest value were presented in the summer. PM10 concentration monthly distribution was bimodal distribution, SO2 and NO2 were unimodal distribution. There were significantly correlation between meteorological factors and pollutants. The results showed that there was certain lag effect of three kinds of air pollutants on daily respiratory disease hospital admissions. The lag time of three air pollutants all were 0 - 6 day moving average (avg06). An increase of 10 µg/m in PM10, SO2 and NO2 were significantly associated with the excess risk (ER) of 0. 45%, 1. 35% and 3. 02% for all respiratory hospital admissions. CONCLUSION: The ambient NO2, SO2 and PM10 concentrations have an adverse effect on daily hospital admissions for respiratory diseases of residents in Lanzhou City.