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Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) is an important link between carbon and water cycles, which is critical for the forests under future global climatic changes. WUENDVI was calculated by accumulated normalized difference vegetation index (NDVI) and actual evapotranspiration (ET) and could reflect the impacts of climatic changes and human activities on WUE. The three forestry regions of China are the northeast, southwest, and southeast areas. Among them, the northeast forest region is a natural forest region with forest stock accounting for more than 1/4 of China, and the southwest mountainous forest region is another natural forest region in China, with forest stock accounting for more than 1/3 of China, while the southeast forest area is mainly plantations. In 2018, the forest areas over three forestry regions of China were around 1,725,988 km2. This paper evaluated the changes in forest WUE and their relationships with climatic change and human activities over three forestry regions of China during 1961–2019. The main findings of this study were summarized as follows: (1) the spatial changes of WUE were gentle in the artificial forest region but fluctuant sharply in the natural forest regions. In the southwest forest region, the WUE increased with elevation, while it showed the opposite trends in the artificial forest region. Overall, the annual mean forest WUE increased in almost all regions of the study areas during 1961-2019; (2) in the northeast and southeast forest regions, the WUE presented a negative relationship with the temperature. In the southwest forest region, the WUE was positively correlated with the temperature and its increase rate slowed down significantly when the temperature increases by more than 1 °C. The WUE was negatively correlated with precipitation in the three regions and was more sensitive to the decrease of precipitation. The sensitivity of WUE to precipitation reduction was highest in the artificial forest region and lowest in the northeast forest region; (3) the forest WUE and WUENDVI were lowest in the artificial forest region but highest in the northeast forest region, while the net increase in forests area during 1980–2018 was largest in the artificial forest region (155,975 km2) but lowest in the northeast forest region (78,766 km2). In general, human activities had the greatest impact on the forest WUE in the northeast forest region. Human activities and climatic change had quite complex and interactive effects on forest WUE. Therefore, more attention should be paid to the joint influences of climate change and human activities on WUE. [ABSTRACT FROM AUTHOR]

In this paper, 16 cities representing four major architectural climate zones of China were selected, and the heating and cooling loads of office building during the period of 1961–2017 were simulated by using TRNSYS software. Daily extreme heating and cooling loads were determined by percentile methods, and the climate change impact on extreme loads was analyzed. The results showed that the days of extreme heating load of office building significantly decreased in almost all the selected cities, whereas there were no significant changes in the days of extreme cooling load. The daily extreme heating load was dominantly affected by the temperature (R2 = 0.43–0.79), but the daily extreme cooling load was related to multiple climate factors, including temperature, relative humidity, and solar radiation. Single temperature can only explain 3–37% of the changes of daily extreme cooling load, and the combination of multiple climate factors can explain 58–85%. Importantly, the effect of temperature on extreme cooling load showed decrease from the cold to hot climate zones, but the impacts of relative humidity and solar radiation gradually increased. These results suggest that the maximum design capacity of heating systems can properly decrease, and the design capacity of cooling systems may not largely increase with the climate warming. In addition, the combined effect of multiple climate factors rather than the single temperature, as well as the different responses of extreme cooling energy consumption to climate change in different climate zones, should be considered when making measures for improving operating efficiency of air-conditioning system. [ABSTRACT FROM AUTHOR]

Climate elements are important indicators of climate change in China's main grain-producing areas during the April–September growth season and affect the growth and yield of crops. This paper combines grain concentration and geographical detector to divide the North and South regions of China's main grain production. The linear trend and Morlet wavelet transform methods are used to analyse the characteristics of climate change based on observational climate data from April to September 1981–2015. The results show that the climate in the North region is warm and dry during the growth season, whereas the climate is warm and humid in the South region. The main periods of the change in temperature in the North and South regions are 3 years, and that in precipitation is 5 years, and that in sunshine hours is 3–4 years. Changes in the climate elements in various provinces show complex, varying and regional characteristics of cold-warm and dry-wet cycles. The changes in climate elements are significant and different climatic conditions and regions have various possible impacts on grain production in China during the growth season. For China's agricultural-economy sustainable development and grain security, the study suggests that governments should place more emphasis on climatic element changes during the growth season and invest more money in disaster prevention and mitigation, especially in the main grain-producing areas. [ABSTRACT FROM AUTHOR]

Based on daily mean temperature data from 32 meteorological stations in the study area, this paper discusses the relationship between the changes in the climatic growing season and altitude and the differences in the northern (NSQM) and southern (SSQM) slopes of the Qinling Mountains (QMs) from 1985 to 2015. Our analyses generated three primary results. First, the spatial distribution patterns of the growing season start (GSS), growing season end (GSE), and growing season length (GSL) exhibited an altitudinal gradient and differences between the NSQM and SSQM. The occurrence time of the GSS, GSE, and the GSL of the NSQM was approximately 15 days later, 10 days earlier, and 25 days shorter respectively than that of the SSQM. Second, the three factors exhibited regular changes with an increasing elevation gradient: the GSS, GSE, and GSL were gradually delayed, advanced, and shortened, respectively. In the area below 1800 m above sea level, the GSS, GSE, and GSL of the NSQM was later, earlier, and shorter, respectively, than that of the SSQM; while the changing trends in the three were opposite in the areas above 2700 m; and the three in the areas between 1800 and 2700 m varied little in the NSQM and SSQM. The sensitivities of the GSS, GSE, and GSL of the NSQM and SSQM to elevation also reflected in the trends of inter-annual variability. Third, among the different altitudinal vegetation belts from low-altitude to high-altitude areas, the occurrence time of the GSS and GSE and the duration of the GSL in the alpine meadow areas were all exchanged in the NSQM and SSQM. The GSS, GSE, and GSL of the NSQM were earlier, later, and longer respectively than that of the SSQM. [ABSTRACT FROM AUTHOR]

One of the effects of global climate change is increase in the frequency and severity of drought, which strongly affects the Chinese agricultural production. In order to cope these changes more effectively, it is important to document and analyze the agricultural losses caused by drought. We collected and analyzed conventional meteorological data and agricultural statistics data, in order to outline trends in drought occurrence and decline in agricultural yield. Data were assembled for the period 1960-2010. The study pays particular attention to regional differences between northern and southern China. Our results show the drought-caused agricultural loss rates (DCALR) in China have increased by approximately 0.5% per decade in the past 50 years. The study area in this paper is for the whole of the People's Republic of China, minus the Qinghai-Tibetan Plateau; when we analyzed regional differences, we found that losses increased by approximately 0.6% per decade in northern China, close to twice the increase in southern China. Moreover, drought risks and agricultural losses are rising faster in northern China. Our results also indicate that the agriculture in northern China is more sensitive to changes in precipitation, whereas the agriculture in southern China is more sensitive to temperature changes. [ABSTRACT FROM AUTHOR]

Analyses of the variation characteristics for aridity index (AI) can further enhance the understanding of climate change and have effect on hydrology and agriculture. In this paper, based on the data of 283 standard meteorological stations, the temporal-spatial variations and the influences of climate factors on AI were investigated and the relationship between AI and two climate indices (the Arctic Oscillation (AO); El Nino-Southern Oscillation (ENSO)) were also assessed in northern China (NC) during the period from 1961 to 2012. The results revealed that the annual mean AI decreased at the rate of −0.031 per decade in the past 52 years and the trend was statistically significant at the 0.01 level. The Mann-Kendall (M-K) test presented that the percentages of stations with positive trends and negative trends for AI were 10 and 81.9 % (22.6 % statistically significant), respectively. Spatially, in the western part of 100° E, the extremely dry area declined and the climate tended to become wet obviously. In the eastern part of 100° E, dry area moved toward the east and the south, which resulted in the enhancement of semiarid area and the shrinkage of subhumid area. The contributions of sunshine duration and precipitation to the decline of AI are more than those of other meteorological variables in NC. Moreover, the average temperature has risen significantly and AI decreased in NC, which indicated the existence of 'paradox.' Relationship between climate indices (AO and ENSO) and AI demonstrated that the influence of ENSO on AI overweight the AO on AI in NC. [ABSTRACT FROM AUTHOR]

The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

Warming of the climate system is unequivocal, and the change of climate variables will eventually have a great impact on vegetation cover and agricultural practices, especially in the arid area Xinjiang in China, whose agriculture and ecosystems are heavily vulnerable to climate change. In this paper, normalized difference vegetation index (NDVI) was used to study the vegetation growth and its response to climate change in Xinjiang. Firstly, two NDVI datasets (Global Inventory Modeling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS)) were merged through a pixel-wise regression analysis to obtain a long time series of NDVI data, and then, relationships between yearly NDVI and yearly climate variables, and monthly NDVI and monthly climate variables were extensively investigated for grassland and cropland in northern and southern Xinjiang, respectively. Results show the following: (1) there was an increasing trend in NDVI for both grassland and cropland in both northern and southern Xinjiang over the past decades and trends were significant except that for grassland in northern Xinjiang; (2) precipitation and evaporation were more important than temperature for grassland in northern Xinjiang, while precipitation and temperature were more important than evaporation for grassland in southern Xinjiang and cropland in both northern and southern Xinjiang; (3) NDVI was highly correlated with accumulated monthly precipitation instead of monthly precipitation, and there was a lagged effect of precipitation, temperature, and evaporation on NDVI change. However, lagged effects were only significant in specific months. The results could be helpful to agricultural practices; e.g., based on lagged effect of precipitation, irrigation in July is very important for crop growth. [ABSTRACT FROM AUTHOR]

Hydrological processes depend directly on climate conditions [e.g., precipitation, potential evapotranspiration ( PE)] based on the water balance. This paper examines streamflow datasets at four hydrological stations and meteorological observations at 79 weather stations to reveal the streamflow changes and underlying drivers in four typical watersheds (Meigang, Saitang, Gaosha, and Xiashan) within Poyang Lake Basin from 1961 to 2000. Most of the less than 90th percentile of daily streamflow in each watershed increases significantly at different rates. As an important indicator of the seasonal changes in the streamflow, CT (the timing of the mass center of the streamflow) in each watershed shows a negligible change. The annual streamflow in each watershed increases at different rates, with a statistically significant trend (at the 5 % level) of 9.87 and 7.72 mm year, respectively, in Meigang and Gaosha watersheds. Given the existence of interactions between precipitation and PE, the original climate elasticity of streamflow can not reflect the relationship of streamflow with precipitation and PE effectively. We modify this method and find the modified climate elasticity to be more accurate and reasonable using the correlation analysis. The analyses from the modified climate elasticity in the four watersheds show that a 10 % increase (decrease) in precipitation will increase (decrease) the annual streamflow by 14.1-16.3 %, while a 10 % increase (decrease) in PE will decrease (increase) the annual streamflow by −10.2 to −2.1 %. In addition, the modified climate elasticity is applied to estimate the contribution of annual precipitation and PE to the increasing annual streamflow in each watershed over the past 40 years. Our result suggests that the percentage attribution of the increasing precipitation is more than 59 % and the decreasing in PE is less than 41 %, indicating that the increasing precipitation is the major driving factor for the annual streamflow increase for each watershed. [ABSTRACT FROM AUTHOR]

Extreme weather exerts a huge impact on human beings and it is of vital importance to study the regular pattern of meteorological and hydrological factors. In this paper, a selection of seven extreme indices is used to analyze the trend of precipitation extremes of 18 meteorological stations located in Zhejiang Province, east China using the Mann-Kendall test. Then the precipitation trends in the plum season (from May to July) and typhoon season (from August to October) are studied separately. The results show that the precipitation trend varies from east to west. There is a positive trend in the east and a negative one in the west. The largest part of Zhejiang Province shows a positive trend in heavy precipitation and the most significant upward trend is detected in Dinghai with 3.4 mm/year for precipitation on very wet days. Although the upward trend of extreme precipitation is not prevailing, the range of increase in specific areas is apparent, like Dinghai with 1.3 mm/year. Precipitation intensity exhibits an upward trend in most areas and a typical upward trend can be found in Dachendao, Tianmushan, and Yuhuan with 0.04, 0.02, and 0.05 mm/year respectively. Precipitation intensity in both plum and typhoon seasons has increased too, especially for the coastal stations. [ABSTRACT FROM AUTHOR]

This paper focuses on the primary causes of changes in potential evapotranspiration (ETo) in order to comprehensively understand climate change and its impact on hydrological cycle. Based on modified Penman-Monteith model, ETo is simulated, and its changes are attributed by analyzing the sensitivity of ETo to influence meteorological variables together with their changes for 595 meteorological stations across China during the period 1961–2008. Results show the decreasing trends of ETo in the whole country and in most climate regions except the cold temperate humid region in Northeast China. For China as a whole, the decreasing trend of ETo is primarily attributed to wind speed due to its significant decreasing trend and high sensitivity. Relative humidity is the highest sensitive variable; however, it has negligible effect on ETo for its insignificant trend. The positive contribution of temperature rising to ETo is offset by the effect of wind speed and sunshine duration. In addition, primary causes to ETo changes are varied for differing climate regions. ETo changes are attributed to decreased wind speed in most climate regions mainly distributed in West China and North China, to declined sunshine duration in subtropical and tropical humid regions in South China, and to increased maximum temperature in the cold temperate humid region. [ABSTRACT FROM AUTHOR]

Based on homogenized land surface air temperature (SAT) data (derived from China Homogenized Historical Temperature (CHHT) 1.0), the warming trends over Northeast China are detected in this paper, and the impacts of urban heat islands (UHIs) evaluated. Results show that this region is undergoing rapid warming: the trends of annual mean minimum temperature (MMIT), mean temperature (MT), and mean maximum temperature (MMAT) are 0.40 C decade−1, 0.32 C decade−1, and 0.23 C decade−1, respectively. Regional average temperature series built with these networks including and excluding “typical urban stations” are compared for the periods of 1954–2005. Although impacts of UHIs on the absolute annual and seasonal temperature are identified, UHI contributions to the long-term trends are less than 10% of the regional total warming during the period. The large warming trend during the period is due to a regime shift in around 1988, which accounted for about 51% of the regional warming. [ABSTRACT FROM AUTHOR]

Snow is an important component of a climate system, and very sensitive to climate change, and it can be an indicator of checking and monitoring global change. This paper is concerned with identifying changes in the time series of snowfall in Qinghai, located in western China. We used the Mann-Kendall test to evaluate the annual and monthly trends in snowfall in Qinghai during the periods 1957/58–2006/07 based on data obtained from 25 stations. Significant positive and negative trends at the 90, 95, and 99 percent confidence levels were detected for numerous stations. In order to understand the regional pattern of snowfall in Qinghai, the detected trends are spatially interpolated using the inverse distance weighted interpolation method in a GIS environment. The results indicate that in January snowfall in the most area of Qinghai shows a significant upward trend, especially in the Delingha and Nomhon area in northern Qaidam Basin, as well as southeastern Qinghai, however, there is no significant downward trend area. The trend analysis indicates that annual snowfall has a significant upward trend in Delingha and Nomhon area in northern Qaidam Basin, and the downward trends occurred in the Qilian mountain area and Gonghe Basin. This is especially the case for highlighting the spatial structure of snowfall trends. The research results help in further understanding the regional pattern of snowfall in Qinghai and its response to global climatic warming. [ABSTRACT FROM AUTHOR]

Monthly precipitation data of 42 rain stations over the Pearl River basin for 1960–2005 were analyzed to classify anomalously wet and dry conditions by using the standardized precipitation index (SPI) and aridity index ( I) for the rainy season (April–September) and winter (December–February). Trends of the number of wet and dry months decided by SPI were detected with Mann-Kendall technique. Furthermore, we also investigated possible causes behind wet and dry variations by analyzing NCAR/NCEP reanalysis dataset. The results indicate that: (1) the Pearl River basin tends to be dryer in the rainy season and comes to be wetter in winter. However, different wetting and drying properties can be identified across the basin: west parts of the basin tend to be dryer; and southeast parts tend to be wetter; (2) the Pearl River basin is dominated by dry tendency in the rainy season and is further substantiated by aridity index ( I) variations; and (3) water vapor flux, moisture content changes in the rainy season and winter indicate different influences of moisture changes on wet and dry conditions across the Pearl River basin. Increasing moisture content gives rise to an increasing number of wet months in winter. However, no fixed relationships can be observed between moisture content changes and number of wet months in the rainy season, indicating that more than one factor can influence the dry or wet conditions of the study region. The results of this paper will be helpful for basin-scale water resource management under the changing climate. [ABSTRACT FROM AUTHOR]

The Inner Mongolia grassland of China is representative of semi-arid grasslands in temperate zones. Studying land surface processes in this region will improve the understanding of regional climate formation and the feedback with global climate change. Satellite remote sensing provides an excellent opportunity to study land-atmosphere interactions at the regional scale. It is necessary to develop feasible and reasonable remote sensing-based methods to map surface energy fluxes for a specific study area. In this paper, previously published algorithms and empirical formulae were tested with Landsat 7 ETM+ data to derive the regional distributions of land surface reflectance, surface temperature, NDVI and land surface energy fluxes (net radiation, soil heat flux, sensible heat flux and latent heat flux) over the upper Xilin River watershed in Inner Mongolia, China. A new land use/land cover classification was developed and applied for regionalization analysis. Validation of remote sensing derived surface reflectance, surface temperature, net radiation and sensible heat flux with field measurements shows differences of about 13, 4, 1, and 28%, respectively. This study provides valuable guidance for further investigation of the whole watershed. [ABSTRACT FROM AUTHOR]