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CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people's understanding of CO2 spatiotemporal variations, supporting carbon-related research. [ABSTRACT FROM AUTHOR]

The Tarim Basin is a large inland arid basin in the arid region of northwest China and has been experiencing significant "warming and wetting" since 1987. As a result, the purpose of this paper is to determine whether the climate transition phenomenon occurred in the Tarim Basin as well as the role of atmospheric circulation in this process. We use meteorological data and atmospheric circulation indexes to study the seasonal trends of climate change in this region from 1987 to 2020 to understand how they are affected by atmospheric circulation. The findings show that, from 1987 to 2020, the Tarim Basin experienced significant warming and wetting; with the exception of the winter scale, all other seasonal scales exhibited a clear warming and wetting trend. From the perspective of spatial distribution, most of the areas showed a significant warming trend, and the warming amplitude around the basin is greater than that in the central area of the basin. However, there are significant regional differences in precipitation change rates. Meanwhile, wavelet analysis shows that there is a significant oscillation period of 17–20 years between climate change and the atmospheric circulation index during 1987–2020. The correlation analysis shows that the Pacific decadal oscillation (PDO) and El Niño-Southern Oscillation (ENSO) are the main influencing factors of climate change in the Tarim Basin at different seasonal scales, while the teleconnection of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) is low and the PDO dominates the summer and autumn temperature changes in the Tarim Basin. The research results of this paper show that, despite the warming and wetting trends since 1987 in the Tarim Basin, the climate type did not change. From 1987 to 2020, the main teleconnection factors of climate change in the Tarim Basin were PDO and ENSO. [ABSTRACT FROM AUTHOR]

The rainfall distribution in southwest China is uneven, and the rainstorm threshold cannot use in a unified standard. This paper synthesizes a calculation method for the extremely heavy precipitation threshold and the provision of the rainstorm threshold in meteorological operation. It calculates the daily precipitation rainstorm threshold at 400 national ground stations in southwest China. The rainstorm events from 1961 to 2021 were statistically analyzed using the rainstorm threshold and analyzing the spatial-temporal variation characteristics. The results show that the number of single-station rainstorm events and the average precipitation of single-station rainstorm events in southwest China decreased from east to west. The number and frequency of single-station rainstorm events in Guizhou, Sichuan, Tibet, and Chongqing are increasing, while the number of single-station rainstorm events in Yunnan is decreasing. There is no apparent spatial distribution pattern for the continuous rainstorm events in the southwest region. From 1961 to 2021, the number and frequency of rainstorm events at a single station in southwest China followed an upward trend. The number of rainstorm events at a single station increased by 16.7 times · (10a)−1, and the frequency of rainstorms increased by 9.9% · (10a)−1. The continuous rainstorm events show an increasing trend, with an increase of 0.1 times · (10a)−1. Using the rainstorm threshold in southwest China, the early warning threshold for rainstorm disasters can be adjusted. The temporal and spatial characteristics of rainstorm events since 1961 can analyze the changes occurring in rainstorm events under global warming and provide data to support the response of southwest China to climate change. [ABSTRACT FROM AUTHOR]

In the context of global warming, snow cover changes have an extremely important impact on the hydrological cycle and the redistribution of water resources in arid and semi-arid regions. In this paper, based on the daily cloud-free snow area remote sensing product data in the source region of Yangtze River (SRYR) from 2000 to 2019, the snow phenology variables such as the snow cover day (SCD), snow onset date (SOD), snow end date (SED), and snow duration day (SDD) were extracted separately for each hydrological year, and the vertical distribution of snow cover area (SCA) in the SRYR was analyzed by combining with the digital elevation model (DEM). In addition, we also combined climate factors and land cover types to further explore the spatiotemporal variation characteristics of snow phenology in response to different influencing factors, in order to reveal the spatiotemporal variation patterns of snow cover in the SRYR. The results showed that: (1) The SCA in the SRYR has a more obvious vertical distribution, with the maximum SCA reaching 61.58% at high elevation, while at low elevation, the SCA is mostly below 20%. The distribution of SCD in the study area showed a significant exponential correlation with DEM (R2 = 0.87). (2) The area of SOD in the SRYR showed an advanced trend of about 63.37%, while the area of SED showed a delayed trend of about 69.59%, and the area which showed significant trends is 4.29% and 4.36%, respectively. Therefore, the trends of both SOD and SED showed insignificant changes. (3) Temperature change is the main factor affecting the change of snow cover in the SRYR. Among them, 90.9% of the regions showed a significant positive correlation between temperature and SCD, while precipitation showed a significant negative correlation with SCD in about 75.3% of the total area of SRYR. Under the stable snow area (SCD > 60), the land cover type is glacial or permanent snow about 1.5 × 103 km2, which covers almost the entire glacial or permanent snow of the SRYR. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Clues of climate change on the Alashan Plateau since the last glacial period (40 ka) are important for revealing the mechanism of desertification of middle-latitude deserts in the Northern Hemisphere (NH). Studies are still rare for the understanding of the specific relationship of climate changes between the Alashan Plateau and the global. Based on a systematic and comparative analysis of the existing research in China and the international academic community, this paper reviews the environmental evolution history of the Alashan Plateau since the last glacial period from the records of paleo-environment and geomorphological characteristics in different deserts of the plateau (e.g., Badanjilin, Tenggeli, and Wulanbuhe). From about 40 ka to the end of the last glacial maximum, the climate on the plateau was wetter than it is today, and to the end of the Pleistocene, the climate was generally dry and the aeolian activities were enhanced. However, the climate was arid during the whole last glacial period in the Wulanbuhe Desert, evidently different from the overall pattern of the plateau. The Tenggeli Desert was characterized by an arid climate in the early Holocene. The most controversial events for the Alashan Plateau are the drought events in the middle Holocene in the Badanjilin Desert. The role and impact of the westerlies and the East Asian Summer Monsoon (EASM) systems on the climate change of the desert and even the whole plateau is a vexed question that brings different views in different periods. There is still a lack of definite evidence representing the events of global environmental change that occurred on the plateau during the discussed period. The distinctive morphology of dune mountains and the distribution of sand dunes are mutually indicative of the direction and energy of wind systems on the plateau. It is suggested that appropriate wind energy is the significant key to the desertification in these middle-latitude deserts on the plateau. From a global-scale review of climate change, the desertification of the modern-scale sandy desert landscapes on the Alashan Plateau is generally related to the global glacial period and the cold and dry climate during the past 40 ka. [ABSTRACT FROM AUTHOR]

The North Yellow Sea, located at the intersection of the Eurasian continent and North Pacific Ocean at mid-latitudes, is a sensitive area subjected to the joint actions of the ocean, land, and monsoons. On its southern shore, loess and paleosol sedimentary sequences were widely developed during the last interglacial period, which is of great significance for revealing patterns of climate change and dynamic conditions. In this paper, we focus on the Zhifu section (ZFS) on Zhifu Island within the Shandong Province of China. The optically stimulated luminescence (OSL) dating method was used to construct our chronological framework. Grain size and its endmember (EM) components were then analyzed; EM1 is a clay component EM, which represents a weak dynamic environment and strong weathering pedogenesis, while EM2 and EM3 are silt and very fine sand component EMs, respectively, representing a strong dynamic environment and weak weathering pedogenesis. Maximum EM1, mean grain size, clay content, and pH values occur in the paleosol layers (ZF4, ZF6, and ZF8), with minimum values in the loess layers (ZF5 and ZF7); EM3 values show the opposite pattern. This indicates that the ZF4, ZF6, and ZF8 layers represent warm and humid environments with abundant precipitation, where the East Asian summer monsoon (EASM) was enhanced, corresponding to Marine Isotope Stages (MIS) 5a, 5c, and 5e. In contrast, ZF5 and ZF7 represent sub-warm and humid environments with less precipitation, where the EASM was weakened, corresponding to MIS 5b and 5d. Among these stages, MIS5e is the warmest and wettest. These climatic events reveal the pattern of climate fluctuation over a ten-thousand-year timescale; they are synchronous with climate changes recorded in other geological repositories, such as cave stalagmites in southern China and sea-level fluctuations in the Yellow-Bohai Sea, which result from changes in global solar radiation. [ABSTRACT FROM AUTHOR]

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

China's economy has made significant strides in the past three decades. As a direct result of China's "one belt, one road" (OBOR) initiative, the country's rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world's high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China's CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model's results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India's high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions. [ABSTRACT FROM AUTHOR]

In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Research on precipitation regularity in the past 120 years is an important link in analyzing the precipitation characteristics of watersheds. This paper systematically analyzes the characteristic changes of centennial precipitation data in the Haihe River basin with the help of CRU data, PCI, SPI, and the Pearson type III curve. The results show that the spatial and temporal distribution of precipitation in the Haihe River basin has a more obvious inconsistency. The temporal distribution shows the characteristics of relatively stable in the early period and increasing fluctuation in the later period, the concentration of precipitation gradually decreases, and the overall drought level decreases. The spatial distribution shows a general pattern of gradually decreasing from southwest to northeast, the overall trend of summer precipitation changes from stable to north–south extremes, and the distribution probability of extreme precipitation events in the basin decreases from southeast to northwest, while the drought-prone area transitions from the northeast to the west and southwest of the basin. Under the influence of both climate change and human activities, the seasonal distribution of precipitation tends to be average, the area affected by extreme precipitation rises, and the arid area shifts to the inland area. [ABSTRACT FROM AUTHOR]

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions. [ABSTRACT FROM AUTHOR]

Tourism climate research is of great significance to the tourism industry because tourism is closely linked to climate. Based on an analysis of related core papers, this paper reviews the research progress on tourism climate in China in terms of research method, research process, and research topic. Research on tourism climate in China started later than similar research in some Western countries and the topics mainly focused on tourism climate resources, climate comfort for tourism, the impact of climate on tourists' behavior and emotion, climate and tourism seasonality, climate change and tourism development, etc. To provide scientific support for the sustainable development of China's tourism industry, we propose the following for future research, based on our review of the literature: (1) strengthening the theoretical study of tourism climatology, (2) constructing and improving the research content system, and (3) enriching relevant research in climate-sensitive areas. [ABSTRACT FROM AUTHOR]

Previously, the types of impacts of land–atmosphere interactions have scarcely been clarified systematically. In this article, we present a classification of these impacts based on modeling boundary layer variables/parameters, which is grouped into local, regional, and remote impacts. In the narrow sense, land surface processes (LSPs) influence the atmospheric state via vertical land–atmosphere coupling at local scales, which is referred to as local LSP impacts. However, local LSP impacts can lead to the advection effect due to the horizontal heterogeneity in the parameters over a region, which can be defined as regional LSP impacts. Furthermore, remote LSP impacts on the regional atmospheric state are induced by some land/sea surface variables/parameters over remote key areas of the Earth's surface, which are conventionally taken as strong signals of climate variation. Of the three impacts, local impacts are the most important essential, as the other two types of impacts are derived from these impacts. We describe the quantification of local impacts based on our previous studies from the perspective of modeling approaches, and we discuss some issues related to these impacts. Previous investigations showed that local LSP impacts are mostly stronger than regional LSP impacts, e.g., the diabatic process is dominant in the physical processes responsible for daily maximum temperatures, and two first-order physical processes including vertical diffusion largely induce changes in surface wind speed in China. Finally, some aspects for future research are noted. This study provides insights into the research on land–atmosphre interactions at different scales. [ABSTRACT FROM AUTHOR]

Located in the central part of the arid regions of Northwest China, the Heihe River Basin (HRB) plays an important role in wind prevention, sand fixation, and soil and water conservation as the second largest inland river basin. In the context of the warming and wetting climate observed in Northwest China, the situation of the ecological environment in the HRB is of significant concern. Using the data from meteorological observation stations, grid fusion and hydrological monitoring, this study analyzes the multi-scale climate changes in the HRB and their impacts on runoff. In addition, predictive models for runoff in the upper and middle reaches were developed using machine learning methods. The results indicate that the climate in the HRB has experienced an overall warming and wetting trend over the past 60 years. At the same time, there are clear regional variabilities in the climate changes. Precipitation shows decreasing trends in the northwestern part of the HRB, while it shows increases at rates higher than the regional average in the southeastern part. Moreover, the temperature increases are generally smaller in the upper reaches than those in the middle and lower reaches. Over the past 60 years, there has been a remarkable increase in runoff at the Yingluo Gorge (YL) hydrological station, which exhibits a distinct "single-peak" pattern in the variation of monthly runoff. The annual runoff volume at the YL (ZY) hydrological station is significantly correlated with the precipitation in the upper (middle) reaches, indicating the precipitation is the primary influencing factor determining the annual runoff. Temperature has a significant impact only on the runoff in the upper reaches, while its impact is not significant in the middle reaches. The models trained by the support vector machines and random forest models perform best in predicting the annual runoff and monthly runoff, respectively. This study can provide a scientific basis for environmental protection and sustainable development in the HRB. [ABSTRACT FROM AUTHOR]

Amidst global climate change and unsustainable human exploitation of water resources, water has emerged as a critical factor constraining global agricultural food production and ecological environments. Particularly in agricultural powerhouses like China, irrigation water accounts for a significant portion of freshwater resource utilization. However, the inefficiency of irrigation water usage has become a weak link in water resource management. To better assess irrigation water efficiency, an accurate estimation of regional irrigated areas is urgently needed. This study proposes a method for identifying actual irrigated areas based on remote sensing-derived evapotranspiration (ET) to address the challenge of accurately interpreting irrigated areas in tropical regions. Using Yunnan Province's Yuanmou irrigation district as a case study, this research combined ground monitoring data and remote sensing data to identify actual irrigated areas through ET inversion and downscaling methods using the Penman–Monteith–Leuning (PML) model. In 2023, the total irrigated area interpreted from remote sensing in the study area was approximately 15,000 hm2, with a comparison against validation points revealing an extraction error of 16%. The small error indicates that this method can effectively enhance the reliability of monitoring actual irrigated areas, thus providing valuable data support for agricultural irrigation water management. [ABSTRACT FROM AUTHOR]

Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China's regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development. [ABSTRACT FROM AUTHOR]

The driving force of climate change in the monsoon margin is complex, making it a key area for regional and global climate change research. Palaeohydrological studies in the monsoon margin have increased the resolution of research in the long term, transitioning from qualitative to quantitative studies to comprehend climate change processes, patterns, and mechanisms. Testate amoebae (TA) in peat sediments are used as a proxy indicator organism for quantitative reconstruction of palaeohydrology. Thus, their community changes are directly related to precipitation, and widely used to reconstruct the patterns of summer precipitation globally. We investigated TA species and reconstructed palaeohydrological changes in the Greater Khingan Mountains' Hongtu (HT) peatland, located in the East Asian Summer Monsoon (EASM) margin. The result showed that the most abundant TA species were Assulina muscorum (12.4 ± 5.0%) and Nebela tincta (8.9 ± 4.9%) in the HT peat core. The increase in dry indicator species (e.g., A. muscorum and Alabasta militaris) indicated a drying pattern in the HT peatland since 150 cal yr BP. Principal component analysis (PCA) explained 47.6% of the variation in the selected TA assemblages. During 400 to 250 cal yr BP, PCA axis 1 scores ranged from 0.2 to −1.3 (reflecting a drier climate), associating with the Little Ice Age. The paleohydrology of the northern part of the Greater Khingan Mountains was mainly controlled by the EASM, which was associated with changes in North Atlantic Sea surface temperature and solar radiative forcing. The apparent drying pattern may be the result of the gradual intensification of anthropogenic activities and the increase in EASM intensity. [ABSTRACT FROM AUTHOR]

This study aimed to utilize hybrid physics-based models that allow for a comprehensive evaluation of how solar–meteorological variables influence the fraction of absorbed photosynthetically active radiation (FAPAR). By integrating both physical and statistical approaches, these models provided a novel perspective on understanding the complex relationship between solar radiation and FAPAR dynamics. The integration of URE and CLC in studying ecosystem efficiency in the nations of China and India is crucial, owing to their interdependent nature. To achieve this, the coupled model intercomparison project phase 6 (CMIP6) was utilized, which involved interpolating data from the most recent five GCMs from the six-phase CMIP6. By integrating advanced modeling techniques with observational data, this innovative approach enabled a holistic assessment of the intricate connections between urban development, climate dynamics, and the productivity of surrounding ecosystems in these regions. The reported decline in FAPAR productivity in China and India highlights the growing concern over the impact of climate change on the health and resilience of vegetation. China experienced negative changes of −1.425, −0.414, and −0.018, while India reported negative changes of −2.463, 2.199, and 2.915 under different scenarios. Interestingly, despite these variations, both countries observed similar tidal variations in their seasonal bases, suggesting a consistent pattern in the fluctuations of vegetation productivity throughout the year. The authors of this study collected and analyzed extensive data on CLC and URE in order to investigate their impact on FAPAR fluctuations in China and India. The higher impact of URE compared to CLC in terms of effective contributions suggests that URE plays a crucial role in shaping the atmospheric physics of both China, accounting for 45.26%, and India, registering 33.95%. Conversely, the effects of CLC and PAR residual factor (PRF) were more pronounced in India compared to China, with impacts of 8.21% and 53.03% compared to 4.21% and 46.70%, respectively. [ABSTRACT FROM AUTHOR]

Recent studies on China's arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent. [ABSTRACT FROM AUTHOR]

The Dongting Lake basin, located in the middle Yangtze River region, has long been under the threat of climate change. However, there has been a lack of comprehensive analysis and research on the long-term trends and interactions among hydrometeorological factors within the region. To address this gap, this study collected data from 31 meteorological stations in the region and employed statistical analysis methods, including the non-parametric Mann–Kendall test, Sen's slope test, and cross-wavelet analysis. The results revealed significant increases in temperatures, especially in the spring season, while summer, winter, and annual rainfall also exhibited a significant increase. However, spring and autumn rainfall showed a non-significant decrease, and there was a clear decreasing trend in annual streamflow. Interestingly, evaporation demonstrated a significant increasing trend. The annual average temperature and annual runoff exhibited approximately negative correlations in the 6–10-year resonance period and positive correlations in the 4–6-year resonance period. There are significant positive resonance periods in the relationship between annual precipitation and annual runoff within the range of 0–12 years, indicating that precipitation has a substantial impact and serves as the primary source of runoff. Furthermore, there was a transition between "abundance" and "dry" periods in the annual runoff around 4 a, occurring before and after 1973 and 2005. The change points in annual precipitation and runoff were identified as 1993 and 1983. [ABSTRACT FROM AUTHOR]

Global climate change will modify precipitation and temperatures' temporal and spatial distribution, trigger more extreme weather events, and impact hydrological processes. The Yangtze River basin is one of the world's largest basins, and understanding future climate changes is vital for water resource management and supply. Research on predicting future climate change in the upper Yangtze River basin (UYRB) and introducing machine learning algorithms to analyze the impact of climate factors, including extreme weather indicators, on surface runoff is urgently needed. In this study, a statistical downscaling model (SDSM) was used to forecast the future climate in the UYRB, and the Mann–Kendall (MK) or modified Mann–Kendall (MMK) trend test at a 5% level of significance was applied to analyze temporal trends. The Spearman rank correlation (SRC) test at a 5% level of significance and random forest regression (RFR) model were employed to identify the key climatic factors affecting surface runoff from annual precipitation, annual temperature, maximum 5-day precipitation (R×5Day), number of tropical nights (TR), and consecutive dry days (CDD), and the RFR model was also used to predict future runoff. Based on the results, we found that, compared to the selected historical period (1985–2014), the mean annual precipitation (temperature) during the mid-term (2036–2065) increased by 18.93% (12.77%), 17.78% (14.68%), 20.03% (17.03%), and 19.67% (19.29%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively, and during the long term (2071–2100), increased by 19.44% (12.95%), 22.01% (21.37%), 30.31% (30.32%), and 34.48% (37.97%), respectively. The warming and humidification characteristics of the northwestern UYRB were more pronounced. The key climatic factors influencing surface runoff were annual precipitation, maximum 5-day precipitation (R×5day), and annual temperature. Because of warming and humidification, surface runoff in the UYRB is expected to increase relative to the historical period. The surface runoff during the mid-term (long term) increased by 12.09% (12.58%), 8.15% (6.84%), 8.86% (8.87%), and 5.77% (6.21%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. The implementation of sustainable development pathways under the low radiative forcing scenario can be effective in mitigating climate change, but at the same time, it may increase the risk of floods in the UYRB. [ABSTRACT FROM AUTHOR]

Several countries are currently evaluating the potential health impacts of climate change (CC), particularly in relation to the complex connections between CC-induced weather fluctuations. China, heavily affected by CC, provides clear evidence of its effects. Previous research in animal sciences indicates that factors like temperature, humidity, precipitation, and wind speed can affect animal epidemics. In China, a major global hub for animal husbandry, these factors pose significant challenges, warranting further investigation into their quantitative relationship with disease outbreaks. This study investigates the influence of these climatic conditions on epizootic diseases in China. In the current study, using data from 278 village-level surveys and daily meteorological data spanning 2012 to 2018, we used a fixed-effect model for analysis. The findings reveal that increasing temperatures and wind speeds exacerbate disease development, while the precipitation anomaly index negatively impacts animal epidemics, with humidity showing minimal influence. Addressing CC's potential impact on animal disease, governments, organizations, and farmers need to pay more attention to the impacts of climate change on animal diseases and work together to better cope with the impacts through policies, measures, and research. [ABSTRACT FROM AUTHOR]

This study examines the projected impact of climate change on the density altitude (DA) at Chinese airports during the summer by the end of the 21st century. Findings indicate that climate change is expected to significantly increase the DA at all Chinese airports, with an estimated rise between 300 and 800 feet. The analysis suggests that temperature increases will universally contribute to a rise in DA. Pressure changes, however, are more variable. Most airports are predicted to see an increase in pressure, which could offset some temperature effects on the DA. Airports in Eastern China are expected to see a decrease in pressure, amplifying the effects on DA and creating operational challenges. [ABSTRACT FROM AUTHOR]

As global warming produces dramatic climate changes, water management is facing increasingly serious challenges. Given to the process of climate change and its complex effects on watershed hydrology, this paper investigates the spatial and temporal variation characteristics of major climatic factors (i.e., precipitation and temperature) over the upper Yangtze River basin (UYRB), China. The statistical analyses are based on annual and seasonal scales during 1951–2020 with a recorded period of seven decades. The Mann–Kendall nonparametric test and R/S analysis are used to record the temporal trends (past and future) of climate variables; the Pettitt test, standard normal homogeneity test and Buishand test are used to detect the homogeneity in climate series. The sensitivities of the streamflow to climatic parameters are assessed at the watershed scale, especially considering the Three Gorges Dam's (TGD) effect on changing runoff. The results of the study indicate that the annual precipitation of 29 out of 34 series indicate homogeneity, while 31 out of 34 annual mean temperature series show heterogeneity, with jump points around 1997 in the mean temperature of 20 sites. Detectable changes in precipitation were not observed during 1951–2020; however, the temperature increased significantly in the whole basin on annual and seasonal scales, except for several stations in the eastern part. The magnitude of increase in air temperature in high altitudes (Tibet Plateau) is higher than that in low altitudes (Sichuan Plain) over the last seven decades, and future temperatures continue to sharply increase in high altitudes. The TGD plays an important role in explaining the seasonal variations in streamflow at Yichang station, with streamflow experiencing a sharp increase in winter and spring (dry season) and a decrease in summer and autumn (rainy season) compared to the pre-TGD period. The streamflow variation at an annual scale is mainly regulated by climate fluctuation (variation in precipitation). During the last seven decades, increasing air temperature and decreases in rainfall and runoff signify reduced water resources availability, and the climate tends to be warmer and drier over the basin. The sensitivity of the streamflow to watershed precipitation is higher than that to temperature, with variation in annual rainfall explaining 71% of annual runoff variability. [ABSTRACT FROM AUTHOR]

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p < 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349. [ABSTRACT FROM AUTHOR]

Natural ecosystems are changing more quickly because of human activities, the type and intensity of which are directly correlated with vegetation greenness. To effectively determine how human activities affect trends in vegetation under climate change, we must differentiate between various types of human activities. The GTWR model can study the spatiotemporal non-stationary relationship between the NDVI trend and climate change. The GTWR model was incorporated into multiple climate variables and improved residual analysis to quantify the contributions of climate change and human activities on vegetation change trends in the Hexi region during different periods. This study divides human activities into four groups based on land use change: urbanization, agricultural expansion, desertification, and ecological restoration to further investigate their contribution to vegetation greenness change. The results showed that in 56.9% of the significant vegetation greening trends between 1982 and 2015, climate factors contributed only 7.4%, while human factors contributed a significant 22.7%. Since the ecological restoration project implemented in 2000, the expansion intensity of ecological restoration and urbanization increased significantly, followed by agricultural expansion and desertification. For the considerable greening trends in the Hexi region, the ecological restoration project contributed 26.7%, while agricultural expansion and urbanization contributed 17.5% and 4.6%, respectively. This study aims to provide new insights for more accurate simulation and evaluation of the interaction effects of climate change and human socio-economic development on vegetation growth. [ABSTRACT FROM AUTHOR]

For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

The existence of global warming is common knowledge. However, it can be predicted that there may be cooling zones worldwide based on the mechanism of terrestrial biophysical processes. Here, the Theil–Sen median trend, the Mann–Kendall trend test method, continuous wavelet transformation, and the Hurst exponent were used to study the cooling trends, abrupt change times, periodicity, and future sustainability of temperature changes in different cooling zones since the 1900s based on the CRU dataset. We found an amazing result; 8,305,500 km2 of land surface had been cooling since the 1900s, covering five continents and 32 countries, accounting for 86% of land area in China, and distributed over 16 zones. The average cooling rate of the cooling zones was −0.24 °C/century. The maximum cooling rate was −1.40 °C/century, and it was 1.43 times the average rate of global land warming (0.98 °C/century). The cooling zones near the sea were greatly influenced by ocean currents and were mainly affected by a small time scale periodicity of less than 30 years, whereas the cold zones located relatively far from the sea and less affected by ocean currents were mainly affected by medium time scales of more than 30 years. Moreover, 32.33% of the cooling zones, involving 2,684,900 km2, will be continuously cooling in the future, and the rest will probably warm up in 2114, 2041, 2096, 2099, 2119, 2073, 2048, and 2101, respectively. The study will help us to further understand the essential characteristics of global climate change, and to find more theoretical bases for mitigating global warming and exploring surface cooling mechanisms. [ABSTRACT FROM AUTHOR]

Variations in runoff and sediment discharge are important characteristic variables for revealing the coupled effects of climate change (including both the natural variability of climate and anthropogenic climate change) and human activities (including soil and water conservation measures, land use changes, and hydraulic engineering construction). Based on the meteorological data from 19 meteorological stations and the hydrological data from the watershed control station of Hongqi Station, the temporal and spatial evolution of runoff and sediment discharge and the water–sand relationship were analyzed, and the response mechanisms of runoff and sediment discharge changes were clarified using Mikhail Budyko's theory and other qualitative and quantitative methods. The results determined that: (1) The runoff and sediment discharge showed significant downward trends, with linear change rates of −0.28 × 108 m3/a and −46.10 × 104 t/a, respectively. The change points of the runoff and sediment discharge occurred in 1987 and 1996, respectively. (2) The spatial distribution of water and sediment was different, and the upper and middle reaches produced water, while the downstream produced sediment. (3) Comparing potential evapotranspiration and rainfall based on Budyko theory and the regression relationship, runoff is more closely related to rainfall, and runoff changes are more affected by it. The change in sediment discharge is most closely related to sediment concentration, followed by rainfall and potential evaporation. (4) The contribution rates of runoff and sediment discharge changes influenced by climate change were 24% and 3%, respectively, and the contribution rates by human activities were 76% and 97%, respectively. Human activities, including soil and water conservation measures, land use changes, and hydraulic engineering construction were the main influencing factors, and the impacts of human activities increased from 1960 to 2019. The research results are of great significance for erosion control and ecological restoration in the Tao River Basin under the conditions of the changing environment. [ABSTRACT FROM AUTHOR]

Linking sinuosity, a fairly recently developed metric, with high temperature extremes (HTEs) can be both useful and insightful to better understand the physical mechanisms behind HTEs. However, it is not clear whether there exists a relationship between the sinuosity changes and HTE changes in present and future climate conditions over southeastern China. In this paper, the anomalous characteristics of the atmospheric circulation are quantified by sinuosity. Three sinuosity metrics are used in this study: individual sinuosity (SIN), aggregate sinuosity (ASIN), and comprehensive sinuosity (CSIN). Furthermore, we examine the relationship between sinuosity changes and HTE changes in present and future climate conditions. ASIN is strongly correlated with surface air temperature (SAT). We find that the influence of individual sinuosity (SIN) at different latitudes on the SAT of southeastern China is different. The SIN of low (middle) latitude isohypses has significant positive (negative) correlations with the SAT of southeastern China. The SIN of high-latitude isohypses is rather limited and can therefore be ignored. The projected relationship between the sinuosity changes and HTE changes in the late 21st century suggests similar results. The change in SAT is related to the changes in climate variables over southeastern China in the future, and these changes increase with the increase in Z500 or V850 and the decrease in U500. Moreover, the frequencies of large (small) comprehensive sinuosity (CSIN) values at low (mid) latitudes will increase. At the end of the 21st century, Z500 isohypses at different latitudes will have an obvious poleward shift. Our results indicate that measuring the aggregate waviness of the midtropospheric flow (via sinuosity) can provide insight regarding HTEs, and the climate model output can be used to examine the future likelihood of increased HTE. [ABSTRACT FROM AUTHOR]

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover. [ABSTRACT FROM AUTHOR]

Identifying the response of runoff changes to extreme climate evolution was of great scientific significance for the rational regulation of watershed water resources and the prevention of hydrological disasters. However, the time–frequency response relationships were not clear. The Yinjiang River watershed, a typical watershed with karst trough valley areas, was chosen to identify the impact of different climatic driving factors on runoff changes from 1984 to 2015. Continuous wavelet transform (CWT), cross-wavelet transform (XWT), and wavelet coherence transform (WTC) were performed to study the response relationship and time–frequency effect between runoff changes and extreme climate change at different time scales. The main results showed that: (1) Twelve extreme climate indices (ECIs) were detected to have a significant impact on runoff changes, mainly on a 6-year time scale; (2) The R10 and Rx1day in extreme precipitation index and SU34.4 and TNx in the extreme temperature index were the main driving factors of runoff changes, which had relatively large impacts on runoff changes in high and low energy vibration regions. However, the remaining eight ECIs that passed the 0.05 confidence level showed relatively large impacts on runoff changes only in low energy vibration regions; (3) The transition of the interaction between ECIs and runoff changes in high and low time–frequency scales was related to the abrupt change characteristics of the ECIs. The correlation of abrupt change was an important reason for the emergence of highly correlated regions that trigger high and low energy vibrations; (4) As a whole, the extreme precipitation events were ahead of runoff changes at the high time–frequency scale and exhibited small lag effects at the low time–frequency scale, while extreme temperature events were mainly ahead of runoff changes. This study has effectively revealed the impact of climate factors at different scales on runoff changes, and provides a theoretical understanding for regulating and managing water resources in karst basins. [ABSTRACT FROM AUTHOR]

Given the interdependence of climate change (CLC) and urban expansion (URE) on ecosystem productivity in China and India, hybrid physics-based models were fitted in this study to evaluate the effects of these variables on photosynthetically active radiation (PAR). This was accomplished by interpolating the most recent five general circulation models (GCMs) from coupled model intercomparison project phase 6 (CMIP6) into the CMIP6 multi-ensemble model. The potential of PAR is projected to increase by 0.001 to 2.077% in China and by 0.002 to 6.737% in India, on a seasonal and annual basis, if the warming is kept at 1.5 °C from now until the end of this century. The effects of CLC and URE on the changes in PAR in China and India were investigated, and URE had a greater impact than CLC when compared to effective contributions, with 49.47% for China and 28.41% for India in the entire case scenario. In contrast, CLC and PAR residual factor (PRF) have a greater impact in India than in China, with effects of 13.79% and 57.79% compared to 0.89% and 49.64%, respectively. Preferences for exotic, high-productivity plant species, irrigation, CO2 fertilization, and nitrogen deposition are suggested as measures for replenishing PAR in both countries. [ABSTRACT FROM AUTHOR]

Along with tree ring width, carbon isotopes are also good proxies for climate change. Water use efficiency (WUE) can be calculated more quickly and accurately based on carbon isotopes. In this study, according to the principle of dendroclimatology, the sequence of δ13C and WUE of tree rings of Picea meyeri are built. Pearson correlation analysis and multiple regression analysis are used to explore the response of carbon stable isotopes of Picea meyeri to climate change, which revealed the relationship between δ13C of Picea meyeri and climatic factors. Based on δ13C, we calculated the WUE of Picea meyeri and analyzed its response to climate change. The main conclusions are as follows: (1) The δ13C of Picea meyeri decreases year-by-year from 1957 to 2020, in the range from −23.89‰~−21.67‰, and the average value is −22.67‰. The water use efficiency of Picea meyeri increases in the range from 17.26~61.31, with an average of 39.45. (2) The δ13C of Picea meyeri is negatively correlated with temperature, which has the highest correlation with the temperature of the growing season (c5–c9), and its coefficient is higher than that of the mean temperature of each month. (3) There is a significant positive correlation between WUE sequence and temperature. Meanwhile, due to the effect of precipitation and temperature, the Picea meyeri is subject to drought stress to some extent. Above all, temperature is the main climatic factor affecting the δ13C and WUE of Picea meyeri on Luya mountain. [ABSTRACT FROM AUTHOR]

Analyzing trends in flood magnitude changes, and their underlying causes, under climate change, is a key challenge for the effective management of water resources in arid and semi-arid regions, particularly for inland rivers originating in the Qilian Mountains (QMs). Sen's slope estimator and the Mann–Kendall test were used to investigate the spatial and temporal trends in flood magnitude, based on the annual maximum peak discharge (AMPD) and Peaks Over Threshold magnitude (POT3M) flood series, of twelve typical rivers, from 1970 to 2021. The results showed that, in the AMPD series, 42% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends; in the POT3M series, 25% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends. The regional differences in the QMs from east to west were that, rivers in the eastern region (e.g., Gulang, Zamu, and Xiying rivers) showed significantly decreasing trends in the AMPD and POT3M series; most rivers in the central region had non-significant trends, while the Shule river in the western region showed a significantly increasing trend. Temperatures and precipitation showed a fluctuating increasing trend after 1987, which were the main factors contributing to the change in flood magnitude trends of the AMPD and POT3M flood series in the QMs. Regional differences in precipitation, precipitation intensity, and the ratio of glacial meltwater in the eastern, central and western regions, resulted in the differences in flood magnitude trends between the east and west. [ABSTRACT FROM AUTHOR]

Thick Quaternary alluvial and floodplain sediments in north Hebei Plain provide important information for understanding local paleoenvironmental and paleoclimatic variations. A 120.8 m drilled core (SHBZK-1) was recovered to determine the late Pleistocene climatic fluctuations, sedimentary environment and their coevolutionary relationship. Laboratory analysis, including grain size distribution, magnetic susceptibility, and optical stimulated luminescence dating, was carried out. Lithofacies and grain size showed that the sediments are of fluvial origin and contain two subfaces: river sand bar and flood plain. The good correlation between magnetic susceptibility and grain size show that climate change is the main factor controlling the variation of sedimentary environment in Hebei Plain, rather than tectonic factors. Furthermore, variations of the magnetic susceptibility and lithofacies reflect the intensity and fluctuations of Asian monsoons and couple well with glacial-interglacial cycles, suggesting that the variation of ice volume in the Northern Hemisphere drives climate change in the Hebei Plain, which, in turn, regulates the variation of the sedimentary environment and facies through controlling precipitation changes, as well as the input amount of magnetic minerals. This research provides a useful continental archive for understanding the late Pleistocene environmental and climatic variation and suggests the prevalence of climate-driven environmental change. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on runoff and its extremes is of great significance for water resource assessment and adaptation strategies, especially in water-scarce regions. This study aims to analyze the impact of future climate change on runoff and its extremes in the upper reaches of the Heihe River basin in northwest China. The projected runoff was derived using the Soil Water Assessment Tool with climate data from the CSIRO-MK-3-6-0 model under the scenario of RCP4.5, and a frequency analysis of runoff was performed by generalized extreme value distribution. The results indicate that, compared with the baseline period of 1961 to 2000, the minimum and maximum temperatures in the period 2031 to 2070 were predicted to increase by 2.5 °C on average. The precipitation in most months was also predicted to increase, with an average rise of 16.5%. The multi-year average runoff was projected to increase by 8%. The annual mean and extreme flows were also expected to rise under future climate change at different return periods, and the low flow was expected to increase the most. [ABSTRACT FROM AUTHOR]

Climate change has become one of the leading problems around the world. The transport sector is one of the major contributors to climate change. At the same time, climate change is also affecting transportation facilities and travel behaviour. This study proposed a bibliometric approach to explore the structure evolution development trends of this knowledge domain with a broader search scope and more objective results compared with a manual review. A total of 4073 peer-reviewed articles were collected from the WoS core collection database to conduct scientometric analysis. The collaboration analysis shows that the US, China, and European countries dominate this field, and international organisations' and government agencies' reports on climate change form the basis of this research field. A total of 14 co-citation clusters were identified, and the research on climate change and transportation primarily focused on the topics of policy options, travel behaviour, the COVID-19 lockdown, environmental cost, and air quality. Keyword co-occurrence evolution analysis was also conducted to track the latest research trends. Based on the results, we propose trends in four areas for future research. This study provides a holistic knowledge map for climate change and transportation research's past, present, and future. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? The HRB most frequently suffers from continued dryness followed by transition from dry-ness to wetness. Dryness–wetness combinations generally occur more frequent under climate change. Frequency of prolonged dryness/wetness escalates more rapidly than abrupt transition events. What is the implication of the main finding? The spatial and temporal preference of inter-seasonal dryness–wetness combinations are elucidated. Bivariate hazard assessment of dryness–wetness combinations enriches water-related hazard atlas and facilitates more integrated mitigation planning. Accumulated evidence reminds one that abrupt transitions between dry and wet spells, though attracting less attention, have harmful influences upon global continents as extensively investigated droughts and floods. This study attempts to incorporate dryness–wetness transitions into the current hazard assessment framework through bivariate frequency analysis and causal attribution from a teleconnection perspective. In the study, regional dry and wet conditions are monitored using the multivariate standardized drought index (MSDI) which facilitates the integrated evaluation of water deficits/surplus from a combined viewpoint of precipitation (largely denoting the received atmospheric water) and runoff (representing an important source of surface water). On such a basis, a copula-based method is subsequently utilized to calculate joint return periods of dryness–wetness combinations in three (i.e., moderate, severe and extreme) severity scenarios. The changing frequency of diverse dryness–wetness combinations is also estimated under a changing climate using a 25-year time window. Furthermore, the cross-wavelet transform is applied to attribute variations in dry and wet conditions to large-scale climate indices, which benefits the early warning of dryness–wetness combinations by providing predictive information. A case study conducted during 1952–2010 in the Huai River basin (HRB)—a typical climatic transition zone in China—shows that the HRB is subject to prolonged dryness with the highest frequency, followed by the abrupt transition from dryness to wetness. Spatially, abrupt dryness–wetness transitions are more likely to occur in the southern and central parts of the HRB than in the rest of the proportion. The past half-century has witnessed the dominantly higher frequency of occurrence of dryness–wetness combinations under three severity scenarios. In particular, the occurrence of the continued dry/wetness escalates more rapidly than transition events under climate change. Moreover, a preliminary attribution analysis discloses the link of the dry and wet conditions in the HRB with climate indices, such as the El Niño southern oscillation, the Pacific decadal oscillation and the Arctic oscillation, as well as sunspot activities. The results of the study enrich the current atlas of water-related hazards, which may benefit more effective hazard mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Better understanding of the spatiotemporal characteristics of precipitation is essential in developing the best management practices for ecological restoration and soil erosion control on China's Loess Plateau, an arid and semiarid region with severe soil erosion. This study aimed to explore the spatiotemporal trends of precipitation using long-term precipitation data from 1957 to 2018 from the 100 national standard meteorological stations across the Loess Plateau. The nonparametric Mann–Kendall statistical test and geospatial interpolation were used to detect the trends and to analyze spatial patterns of annual and seasonal precipitation. Wavelet analysis was applied to examine periodical variation across the plateau. The results reveal that regional annual precipitation over the Loess Plateau decreased during the 62 years of the study at a mean rate of −1.05 mm/10 years (p > 0.1). The changes in annual precipitation showed a periodical fluctuation with an increasing trend from 1957 to 1969, a decreasing trend from 1970 to 1999, and an increasing trend again in the first 18 years of the 21st century. The annual precipitation decreased in all eight sub-annual periods except for winter. Spatially, a decreasing trend occurred in the southern and eastern parts of the Loess Plateau, whereas a slight increase existed in the northwest in all periods. The decrease in six stations was statistically significant (p < 0.05), and a significant increase occurred in four stations (p < 0.1). These changes can be explained by an evident southward shift of the precipitation isohyets especially for 350 mm, 450 mm and 550 mm from the 1960s to the 1990s, and a clearly northward shift after the 1990s. Findings from this study facilitate an understanding of the spatial temporal trends of precipitation so appropriate countermeasures can be developed for effective vegetation restoration and soil erosion control across the Loess Plateau. [ABSTRACT FROM AUTHOR]

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation. [ABSTRACT FROM AUTHOR]

Sanjiang Plain is the most extensive marsh distribution region in China. Marshes in this region can protect biodiversity, regulate climate, and provide habitats for wild animals and plants. The normalized difference vegetation index (NDVI) is a crucial indicator of vegetation coverage, which may reflect ecosystem structure and functional features. Clarifying the spatiotemporal change of marsh coverage and its climatic drivers is vital for observing and predicting vegetation change in Sanjiang Plain. Using meteorological dataand MODIS NDVI data from 2000 to 2020, we analyzed the spatiotemporal variation in marsh vegetation coverage and climatic change effects in Sanjiang Plain. We found that the growing season vegetation NDVI of marsh increased significantly at a rate of 0.011/decade, indicating that the marsh vegetation growth has obviously improved during the past 21 years. Furthermore, we found that the increase of minimum (Tmin) and maximum (Tmax) temperature in July can significantly promote the marsh plant growth, and increasing nighttime Tmin has a stronger impact on promoting the growth than increasing daytime Tmax in this month. In addition, the increase of daytime Tmax in August can promote the marsh vegetation growth, whereas the increasing precipitation in August was unfavorable for the growth in Sanjiang Plain. [ABSTRACT FROM AUTHOR]

The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction. [ABSTRACT FROM AUTHOR]

Examining the effects of climate change on spring maize, and its suitability under dynamic cultivation patterns, will aid strategic decision-making for future agricultural adaptation. This paper investigates the climate suitability of spring maize, based on daily data from 50 meteorological stations, and statistics on maize yield and area at the county level in Jilin Province, China, between 1986 and 2015. Based on a significant correlation between the cultivation patterns indicator ≥10 °C accumulated temperature (AAT10) and the average yield (R2 = 0.503), the yield data are used to determine suitable thresholds for meteorological factors under the dynamic cultivation pattern, and a fuzzy fitness approach is used to evaluate the climate suitability. The results showed a good agreement between suitability estimates and scaled observed yields (average d = 0.705). Moreover, good consistency between cultivation patterns, climate suitability and yield show that the late-maturing varieties of maize have gradually moved northward and eastward, and the areas of high suitability and high yield have gradually expanded eastward. In addition, drought and chilling hazard factors limit the suitability of climate resources, especially in the eastern and western regions. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems in China are threatened by land use and future climate change. Understanding the effects of these changes on vegetation and the climate-vegetation interactions is critical for vegetation preservation and mitigation. However, land-use impacts on vegetation are neglected in terrestrial ecosystems exploration, and a deep understanding of land-use impacts on vegetation dynamics is lacking. Additionally, few studies have examined the contribution of vegetation succession to changes in vegetation dynamics. To fill the above gaps in the field, the spatiotemporal distribution of terrestrial ecosystems under the current land use and climate baseline (1970–2000) was examined in this study using the Comprehensive Sequential Classification System (CSCS) model. Moreover, the spatiotemporal variations of ecosystems and their succession under future climate scenarios (the 2030s–2080s) were quantitatively projected and compared. The results demonstrated that under the current situation, vegetation without human disturbance was mainly distributed in high elevation regions and less than 10% of the national area. For future vegetation dynamics, more than 58% of tundra and alpine steppe would shrink. Semidesert would respond to climate change with an expansion of 39.49 × 104 km2, including the succession of the steppe to semidesert. Although some advancement of the temperate forest at the expense of substantial dieback of tundra and alpine steppe is expected to occur, this century would witness a considerable shrinkage of them, especially in RCP8.5, at approximately 55.06 × 104 km2. Overall, a warmer and wetter climate would be conducive to the occurrence and development of the CSCS ecosystems. These results offer new insights on the potential ecosystem response to land use and climate change over the Chinese domain, and on creating targeted policies for effective adaptation to these changes and implementation of ecosystem protection measures. [ABSTRACT FROM AUTHOR]

The South-to-North Water Diversion Project (SNWDP) is a national strategic project for water shortages in northern China. Climate change will affect the availability of water resources in both source and receiving areas. A grid-based RCCC-WBM model based on climate projections from nine Global Climate Models under SSP2-4.5 was used for analyzing the changes in temperature, precipitation, and streamflow in the near future (2025–2045, NF) and far future (2040–2060, FF) relative to the baseline (1956–2000). The results showed that: (1) the temperature of the western route will increase significantly in the NF and FF with an extent of 1.6 °C and 2.0 °C, respectively, (2) precipitation will very likely increase even though Global Climate Model (GCM) projections are quite dispersed and uncertain, and (3) over half of the GCMs projected that streamflow of receiving area will slightly increase with a rate of 1.68% [−8.67%, 12.3%] and 2.78% [−3.30%, 11.0%] in the NF and FF, respectively. Climate change will support the planning of the western route to a certain extent. However, water supply risk induced by the extreme situation of climate change should be paid adequate consideration when the project operates in practice due to the large dispersion and uncertainty of GCM projections. [ABSTRACT FROM AUTHOR]

A significant greening trend has been reported globally in recent decades. The greening indicates the improvement in net primary production (NPP) in vegetation. Adopting statistics-based regression models, we investigated the dynamics of NPP and its climatic drivers in main ecosystems (forest land, grass land, and unused land) over China during the period 2000–2021. The results confirmed an increasing NPP covering approximately 86% area in the main ecosystems. NPP exhibited an increase rate of 6.11 g C m−2 yr−1 in forest land, 4.77 g C m−2 yr−1 in grass land, and 1.25 g C m−2 yr−1 in unused land, respectively. Over the same period, warm–wet climate trend was observed covering approximately 90% of the main ecosystems. The warm–wet climate has had a positive effect rather than negative effect on NPP in the main ecosystems, judging by their significant positive correlation. Our results suggested that the increase in annual precipitation exerted much more important effect on the increasing NPP. The warm–wet climate trend contributes to the upward trend in NPP, even if variability in NPP might involve the influence of solar radiation, atmospheric aerosols, CO2 fertilization, nitrogen deposition, human intervention, etc. [ABSTRACT FROM AUTHOR]