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There have been fruitful publications on thermal comfort of urban residential buildings in China. However, there is a lack of reviews on this topic to perform a comprehensive analysis and find opportunities to meet occupants' thermal comfort needs while improving building energy efficiencies. This paper addresses this issue by presenting a systematic review on the advancements in research on thermal comfort in urban residential buildings in China. Firstly, two common thermal comfort research approaches, i.e., field studies and laboratory studies, are discussed. Secondly, eleven main thermal comfort evaluation indicators are summarized. Finally, this paper analyzes the thermal comfort survey data from different researchers, discusses the impacts of adaptive behaviors on human thermal comfort, and provides recommendations for future research on urban residential thermal comfort. It was found that people have higher and higher requirements for their indoor thermal environment as time goes by, especially in the winter; the thermoneutral temperature is higher in warmer climate regions in the summer but lower in the winter than in colder climate regions; the thermoneutral temperature tends to increase with the indoor air temperature due to an adaptation to the indoor thermal environment. The outcomes of this paper provide valuable information on thermal comfort behaviors of urban residents in different climate zones in China, which can serve as a resource for the academic community conducting future research on thermal comfort and assist policymakers in enhancing building energy efficiencies without compromising the occupants' comfort. [ABSTRACT FROM AUTHOR]

The intense ultraviolet radiation and large amount of saline-alkali soil in the Golmud region of Province, China, have deteriorated the performance of asphalt pavement. Given the effect of adverse conditions in this area, this paper investigated the collective and interaction effects of salt chemicals, air temperature, and ultraviolet radiation on asphalt and asphalt mixture through laboratory experiments under controlled conditions. In particular, using different concentrations of Na2SO4 and NaCl solutions, simulated salt chemicals for asphalt and asphalt mixture performance impact. The mechanism of performance deterioration of the asphalt mixture suffered from salt attack was explored. The results show that the original asphalt's storage and loss modulus are higher than the modified asphalt at different temperatures with the multi-factors effect. In addition, the modified asphalt's phase angle is 5%∼10% higher than the original asphalt below −2°C. At the same time, strength loss rates of asphalt mixture increase with the cycles and solution concentration, varying from 20%−40%. Wet-dry cycles and the concentration of salt solution are positively correlated with the deterioration of asphalt mixture strength. This research provides the durability prediction and the more reasonable composition design of the asphalt mixture applied in salt enrichment regions. [ABSTRACT FROM AUTHOR]

The impact of climate change on asphalt pavement infrastructure has become increasingly apparent. This paper investigated how climate change impacts the selection of asphalt binder for freeway pavement in China. The climate data predicted by the Global Climate Model were employed to select the appropriate performance grade for asphalt binder. The predicted air temperature converted pavement temperature, and performance grade distribution of the country was analysed. It is suggested that the performance grade of asphalt binder for freeways' pavement should be determined based on ten years' predicted climate data. This study concluded the following four major findings. Firstly, the selection of the appropriate asphalt binder performance grade needs to take into account the effects of future climate change. Secondly, the change in the standard deviation of temperature has a greater impact on the change in asphalt performance grade, as compared to the change in average air temperature. Thirdly, climate change affects the low-temperature performance grade more than the high-temperature performance grade in China. Finally, by 2050s, 15.2% of freeway asphalt pavement needs to upgrade high-temperature performance grade limit, and 17.3% of freeway asphalt pavement needs to upgrade to low-temperature performance grade limit. [ABSTRACT FROM AUTHOR]

The impact of climate change on asphalt pavement infrastructure has become increasingly apparent. This paper investigated how climate change impacts the selection of asphalt binder for freeway pavement in China. The climate data predicted by the Global Climate Model were employed to select the appropriate performance grade for asphalt binder. The predicted air temperature converted pavement temperature, and performance grade distribution of the country was analysed. It is suggested that the performance grade of asphalt binder for freeways' pavement should be determined based on ten years' predicted climate data. This study concluded the following four major findings. Firstly, the selection of the appropriate asphalt binder performance grade needs to take into account the effects of future climate change. Secondly, the change in the standard deviation of temperature has a greater impact on the change in asphalt performance grade, as compared to the change in average air temperature. Thirdly, climate change affects the low-temperature performance grade more than the high-temperature performance grade in China. Finally, by 2050s, 15.2% of freeway asphalt pavement needs to upgrade high-temperature performance grade limit, and 17.3% of freeway asphalt pavement needs to upgrade to low-temperature performance grade limit. [ABSTRACT FROM AUTHOR]

Plum Rains Season (PRS) has the typical characteristics of outdoor air temperature dramatic changes and high air humidity in the hot summer and cold winter region in China. When the outdoor temperature rises rapidly during PRS, the building envelope surface temperature is probably lower than the indoor air dew point temperature, resulting in moisture condensation. This paper evaluates the influence of geographical location and outdoor meteorological parameters on the indoor humidity environment. The effects of critical parameters such as altitude, average temperature, relative humidity, total precipitation, total precipitation days, atmospheric pressure, and wind speed on the building envelope moisture condensation in nine typical cities in the hot summer and cold winter region were simulated and analyzed. The results show that the Condensation Frequency (CFn) in the western, central, and eastern regions reached the highest in April, May, and June, respectively. Among the nine typical cities, Changsha has the highest Condensation Risk (CR). In addition, the altitude, total precipitation, and atmospheric pressure have little effect on the indoor humidity environment, and it is not directly related to CR; The average temperature and total precipitation days were not related to CR in the western and eastern regions and positively correlated with CR in the central region; The wind speed was positively correlated with CR in the western and central regions and negatively correlated in the eastern region; The relative humidity can affect the indoor humidity environment and moisture condensation on the inner surface of walls, when the relative humidity increases, the CR increases. [ABSTRACT FROM AUTHOR]

The long-term height-resolved wind trend in China under global warming still needs to be discovered. To fill this gap, in this paper we examined the climatology and long-term (1979–2021) trends of the wintertime wind speed at the near-surface and upper atmosphere in China based on long-term radiosonde measurements. At 700, 500, and 400 hPa, much higher wind speed was found over eastern China, compared with western China. At 300, 200, and 100 hPa, maximum wind speed was observed in the latitude zone of around 25–35°N. Furthermore, westerly winds dominated most parts of China between 20°N and 50°N at altitudes from 700 hPa to 100 hPa. A stilling was revealed for the near-surface wind from 1979–2003. From 2004 onward, the near-surface wind speed reversed from decreasing to increasing. This could be largely due to the joint impact of reduced surface roughness length, aerosol optical depth (AOD), and increased sensible heat flux in the ground surface. The decrease of AOD tended to reduce aerosol radiative forcing, thereby destabilizing the planetary boundary layer (PBL). By comparison, the wintertime wind in the upper atmosphere exhibited a significant monotonic upward trend, albeit with varying magnitude for different altitudes. In the upper troposphere, the wintertime maximum wind was observed along a westerly jet stream, with a pronounced upward trend within the zone approximately bounded by latitudes of 25–50°N, particularly above 500 hPa. This accelerating wind observed in the upper troposphere and lower stratosphere could be closely associated with the large planetary-scale meridional temperature trend gradient. Besides, the direction for the wind at the near-surface and lower troposphere (925 and 850 hPa) exhibited a larger variance over the period 1979–2021, which could be associated with the strong turbulence of PBL caused by the heterogeneous land surface. For those pressure levels higher than 850 hPa, large wind directional variance was merely found to the south of 25 °N. The findings from long-term radiosonde measurements in winter over China shed light on the changes in wind speed on the ground and upper atmosphere under global warming from an observational perspective. [ABSTRACT FROM AUTHOR]

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range ensemble forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature. [ABSTRACT FROM AUTHOR]

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research. [ABSTRACT FROM AUTHOR]

The paper took Guang'an City in Sichuan Province of China as a research region, and it built an evaluation index system based on 14 evaluation indices according to four criteria, namely, ecological background, ecological structure, ecological benefits, and ecological stress. The Delphi algorithm and entropy weight (a combination of subjective and objective methods) were used to calculate the weight value of each evaluation index, and an ideal point model was used to calculate the ideal point value. The ideal point ecological grade was classified, and the principal component analysis method was used to obtain the main control factors of each year, which revealed the relationship between the ecological distribution of ideal points and the environmental impact factor, with the changing characteristics of the ecology in the research region according to the hotspot model of the spatial differentiation of land ecological quality as the main control factor. Lastly, the paper also analyzed the land ecological quality of Guangan City in 2000, 2005, 2010, and 2015. The study results show that the overall land ecological quality in Guang'an City generally increased from 2005 to 2015. The proportion of forest land area and the temperature were the most important main control factors. The air temperature and the land ecological quality were positively correlated. [ABSTRACT FROM AUTHOR]

It is well known that El Niño–Southern Oscillation (ENSO) can influence the East Asian winter climate by modifying the atmospheric circulation over the western North Pacific (WNP). While the impact on precipitation in southeastern China has been extensively studied, the ENSO signal in surface air temperature (SAT) remains overlooked. In this paper, we identify robust ENSO footprints in the winter daily minimum SAT in southeastern China, with El Niño winters generally accompanied by warmer-than-normal minimum SAT anomalies. In contrast, the responses of maximum SAT are weak and negligible, suggesting a diurnal cycle–dependent ENSO influence. Further analysis indicates that this diurnal cycle dependence stems primarily from the disparate surface radiative heating between day and night induced by ENSO-related local total cloud cover (TCC) change. The warmer minimum SAT occurring in the early morning of El Niño winters is mainly caused by the enhanced surface downward longwave radiative heating as a result of the TCC increase. However, in the afternoon of El Niño winters, although the anomalous horizontal advection of warm air plays a role, there is surface radiative cooling as the weakening of solar radiation due to TCC reflection overwhelms the increase in downward longwave radiation, which leads to a weakened sensible heat flux and thus has a cooling effect on the SAT. Ultimately, these two processes effectively cancel each other out and together produce insignificant maximum SAT responses. Our conclusions carry important implications for the seasonal to interannual winter climate prediction in southeastern China. Significance Statement The purpose of this study is to better understand how ENSO influences winter surface air temperature in southeastern China. Despite insignificant responses of winter daily maximum temperature to ENSO, we find here a robust ENSO influence on the daily minimum temperature, with warm anomalies tending to occur during El Niño events. Further evidence suggests that this diurnal cycle–dependent ENSO influence is mainly caused by ENSO-induced change in local cloudiness, which has a different radiative heating effect between day and night. Our findings are of great importance for improving regional climate prediction, which will benefit social and economic activities. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

Near-land surface air temperature (NLSAT) is an important meteorological and climatic parameter widely used in climate change, urban heat island and environmental science, in addition to being an important input parameter for various earth system simulation models. However, the spatial distribution and the limited number of ground-based meteorological stations make it difficult to obtain a large range of high-precision NLSAT values. This paper constructs neural network, long short-term memory, bi-directional long short-term memory, support vector machine, random forest, and Gaussian process regression models by combining MODIS data, DEM data, and meteorological station data to estimate the NLSAT in China's mainland and compare them with actual NLSAT observations. The results show that there is a significant correlation between the model estimates and the actual temperature observations. Among the tested models, the random forest performed the best, followed by the support vector machine and the Gaussian process regression, then the neural network, the long short-term memory, and the bi-directional long short-term memory models. Overall, for estimates in different seasons, the best results were obtained in winter, followed by spring, autumn, and summer successively. According to different geographic areas, random forest was the best model for Northeast, Northwest, North, Southwest, and Central China, and the support vector machine was the best model for South and East China. [ABSTRACT FROM AUTHOR]

The development of an effective drying method to extend the shelf life of raw Chinese hickory (Carya cathayensis) is a major postharvest processing in the nut industry. In this paper, the effect of seed size and drying temperature on hot air drying kinetics and quality of Chinese hickory were detected. Our results reveal that the seed size greatly affects the final equilibrium moisture content of Chinese hickory. So, we predict that a larger Chinese hickory must be dried at a higher temperature. An Arrhenius‐type relationship is found between effective diffusivity (Deff) of Chinese hickory and drying temperature where the activation energy is 40.85 kJ/mol. Accelerated storage results show that Chinese hickory drying at 35 and 45°C have lower acid values, peroxide values, higher content of unsaturated fatty acids than those of 55 and 65°C. We suggested that 45°C is a suitable temperature for the industrial‐scale drying of Chinese hickory. Practical applications: In China, the annual output of Chinese hickory is about 21,500 metric tons. After harvest, fresh Chinese hickory is susceptible to decay and germinate during storage. Recently, the proportion of cracked Chinese hickory and kernel products is constantly rose in the market. So the requirements for the quality of raw materials are increased. Drying is the most critical step after harvest. Traditionally, the drying of Chinese hickory is performed in the presence of the natural sun. In rainy days, Chinese hickory often cannot be dried in time. Hot air drying is a common drying method in industry. However, limited reports are available on hot air drying and its effect on the product quality of Chinese hickory seeds. The results of this paper will help to establish an effective industrial‐scale drying method of Chinese hickory. In addition, the effect of seed size on hot air drying kinetics was also first investigated. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

As an important part of the Earth's environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way. [ABSTRACT FROM AUTHOR]

A key element for reducing energy consumption and improving thermal comfort on high-speed rail is controlling air-conditioning temperature. Accurate prediction of air supply temperature is aimed at improving control effects. Existing studies of supply air temperature prediction models are interdisciplinary, involving heat transfer science and computer science, where the problem is defined as time-series prediction. However, the model is widely accepted as a complex model that is nonlinear and dynamic. That makes it difficult for existing statistical and deep learning methods, e.g., autoregressive integrated moving average model (ARIMA), convolutional neural network (CNN), and long short-term memory network (LSTM), to fully capture the interaction between these variables and provide accurate prediction results. Recent studies have shown the potential of the Transformer to increase the prediction capacity. This paper offers an improved temporal fusion transformers (TFT) prediction model for supply air temperature in high-speed train carriages to tackle these challenges, with two improvements: (i) Double-convolutional residual encoder structure based on dilated causal convolution; (ii) Spatio-temporal double-gated structure based on Gated Linear Units. Moreover, this study designs a loss function suitable for general long sequence time-series forecast tasks for temperature forecasting. Empirical simulations using a high-speed rail air-conditioning operation dataset at a specific location in China show that the temperature prediction of the two units using the improved TFT model improves the MAPE by 21.70% and 11.73%, respectively the original model. Furthermore, experiments demonstrate that the model effectively outperforms seven popular methods on time series computing tasks, and the attention of the prediction problem in the time dimension is analyzed. [ABSTRACT FROM AUTHOR]

Atmospheric weighted mean temperature (Tm) is a key parameter used by the Global Navigation Satellite System (GNSS) for calculating precipitable water vapor (PWV). Some empirical Tm models using meteorological or non-meteorological parameters have been proposed to calculate PWV, but their accuracy and reliability cannot be guaranteed in some regions. To validate and determine the optimal Tm model for PWV retrieval in China, this paper analyzes and evaluates some typical Tm models, namely, the Linear, Global Pressure and Temperature 3 (GPT3), the Tm model for China (CTm), the Global Weighted Mean Temperature-H (GTm-H) and the Global Tropospheric (GTrop) models. The Tm values of these models are first obtained at corresponding radiosonde (RS) stations in China over the period of 2011 to 2020. The corresponding Tm values of 87 RS stations in China are also calculated using the layered meteorological data and regarded as the reference. Comparison results show that the accuracy of these five Tm models in China has an obvious geographical distribution and decreases along with increasing altitude and latitude, respectively. The average root mean square (RMS) and Bias for the Linear, GPT3, CTm, GTm-H and GTrop models are 4.2/3.7/3.4/3.6/3.3 K and 0.7/−1.0/0.7/−0.1/0.3 K, respectively. Among these models, Linear and GPT3 models have lower accuracy in high-altitude regions, whereas CTm, GTm-H and GTrop models show better accuracy and stability throughout the whole China. These models generally have higher accuracy in regions with low latitude and lower accuracy in regions with middle and high latitudes. In addition, Linear and GPT3 models have poor accuracy in general, whereas GTm-H and CTm models are obviously less accurate and stable than GTrop model in regions with high latitude. These models show different accuracies across the four geographical regions of China, with GTrop model demonstrating the relatively better accuracy and stability. Therefore, the GTrop model is recommended to obtain Tm for calculating PWV in China. [ABSTRACT FROM AUTHOR]

In this paper, the spatial and temporal characteristics of convective precipitation (CP) and large-scale precipitation (LSP) in southern China during 1980–2020 are analyzed using monthly mean precipitation data from MERRA-2. In addition, the possible effects of relative humidity on CP and LSP are explored. The results indicate the following. (1) The LSP dominates the proportion of total precipitation (TP). Both LSP and CP are more prevalent in the south and less prevalent in the north, but there is a difference in the regions of their maximum centers. (2) Significant interannual and seasonal variations are observed in precipitation. TP and LSP tended to be higher than average after the 1990s, while for the CP, a significant negative trend has dominated the past ten years. There are significant increasing trends for TP and LSP, with area-averaged linear trends of 78.1 mm/decade and 85.9 mm/decade, respectively, while that of CP is − 17.1 mm/decade. The increasing trends of LSP are mainly contributed by the precipitation of summer and autumn. (3) The variations of LSP are affected by relative humidity in the troposphere, while CP is only influenced by the changes in relative humidity due to air temperature or specific humidity. The trend of relative humidity is − 0.33%/decade, mainly due to rising temperature in the troposphere. (4) Changes in relative humidity caused by temperature or specific humidity alone act on large-scale precipitation through both interannual and interdecadal processes, causing large-scale precipitation to increase, and the convective precipitation is mainly affected by the interdecadal processes. [ABSTRACT FROM AUTHOR]

During a total eclipse of the Sun, the solar disk blocked by the lunar disk produces an instant and strong decline of energy at the surface. This loss of energy leads to a decreasing air temperatures near the surface. Anyone under a completely clear sky, with a total solar eclipse (TSE) in progress, feels a cooling, whose minimum is reached a few minutes after totality. This drop in temperature is known as an anomaly and this delay is called thermal lag. During a TSE air temperature changes appreciably not only in magnitude but also in timing, depending on weather and geographical conditions. If the eclipse is partially or totally obscured by clouds, some effects are produced on the thermal lag. Under clear skies, the temperature response lags behind the change in solar flux as one expects in TSE; however, under cloudy skies, the lag can reverse in early and/or late stages of partial phases. The normal heating of the surface by the Sun, which drives turbulent motion in the air layer near the surface, is disrupted during the eclipse. The 2009 TSE in China provided an opportunity to have a look at these kinds of perturbations caused by this eclipse. In this paper, the second of a series of three, we analyze the near-surface air temperature response, at three different heights over the ground, recorded by the Williams College expedition under meteorological conditions characterized by cloudy skies during the longest total solar eclipse of the 21st century on 2009 July 22, at Tianhuangping (Zhejiang), China. An analysis of the relationship between solar radiation and air temperature was made by applying a study previously published in the first paper of this series in which we evaluated the cloudiness contribution in estimating the impact on global solar radiation throughout this phenomenon at that site. The analysis of this response includes linear and absolute negative anomalies as well as fluctuations, which was undertaken through a statistical study to get information on the convection activity produced by the latter. The fluctuations generated by turbulence were studied by analyzing variance and residuals. The results, indicating a steady decrease and recovery of both perturbations, were consistent with those published by other studies for this total solar eclipse. [ABSTRACT FROM AUTHOR]

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [ABSTRACT FROM AUTHOR]

A physical retrieval approach based on the one-dimensional variational (1D-Var) algorithm is applied in this paper to simultaneously retrieve atmospheric temperature and humidity profiles under both clear-sky and partly cloudy conditions from FY-4A GIIRS (geostationary interferometric infrared sounder) observations. Radiosonde observations from upper-air stations in China and level-2 operational products from the Chinese National Satellite Meteorological Center (NSMC) during the periods from December 2019 to January 2020 (winter) and from July 2020 to August 2020 (summer) are used to validate the accuracies of the retrieved temperature and humidity profiles. Comparing the 1D-Var-retrieved profiles to radiosonde data, the accuracy of the temperature retrievals at each vertical level of the troposphere is characterized by a root mean square error (RMSE) within 2 K, except for at the bottom level of the atmosphere under clear conditions. The RMSE increases slightly for the higher atmospheric layers, owing to the lack of temperature sounding channels there. Under partly cloudy conditions, the temperature at each vertical level can be obtained, while the level-2 operational products obtain values only at altitudes above the cloud top. In addition, the accuracy of the retrieved temperature profiles is greatly improved compared with the accuracies of the operational products. For the humidity retrievals, the mean RMSEs in the troposphere in winter and summer are both within 2 g kg−1. Moreover, the retrievals performed better compared with the ERA5 reanalysis data between 800 hPa and 300 hPa both in summer and winter in terms of RMSE. [ABSTRACT FROM AUTHOR]

The moisture and the temperature variation of shallow loess during the freeze-thaw process have tremendous influences on the freeze-thaw disease and agricultural development. The present paper, based on the field test of the temperature and water content of the shallow loess in the seasonal frozen region, probes into the variation of the temperature and moisture fields of shallow loess in the freeze-thaw period. Field test shows that the daily variation of surface temperature reaches the maximum with the peak temperature appearing around 14:00, and the time when the peak temperature appears gradually postpones as the depth increases. Moreover, the daily variation of soil temperature decreases with the increase of temperature, and the variation can be ignored given the depth of more than 30 cm. In addition, there is an obvious variation of moisture content in shallow soil under the action of freeze-thaw cycles. In this regard, the coupled calculation model is proposed in this paper to simulate the dynamic variation of temperature and moisture in shallow soil in Bin County located in Western China under natural conditions, compare the calculation value with the measurement value, and further verify the rationality of the numerical calculation method and parameter selection. Furthermore, the variation law of the maximum frozen depth with the air temperature is obtained through the simulation of extreme cold climate conditions. [ABSTRACT FROM AUTHOR]

• Optimal Window-to-Wall Ratios were recommended for various building orientation. • Integrated models with different glazing area and orientation combinations were proposed. • Daylight factor, air temperature and air velocity were computed for each test scenario. • Three optimal intervals of Window-to-Wall Ratio were determined respectively. • Intersection interval of the three subsets is the optimal interval of Window-to-Wall Ratio. Since the ratio of glazing and opaque areas on building facade has significant impact on the indoor visual and thermal comforts as well as energy consumption, the optimal Window-to-Wall Ratio is necessary to be explored for the rural residences with a large quantity in China. Due to the traditional dwellings has been representative of housing samples among rural residences, Sizhai traditional dwellings situated in Zhejiang Province were chosen as the study buildings in this paper. The building model is rotated to 20° increments clockwise to create 18 building orientation intervals. Furthermore, the Window-to-Wall Ratio of front facade for the building model was divided into 8 intervals at increments of 0.1, from [0.1–0.2] to [0.8–0.9]. Through indoor environment simulation, the daylight factor, air temperature and air velocity were computed for the test scenarios with different building orientation and Window-to-Wall Ratio combinations. Three optimal intervals of Window-to-Wall Ratios at a series of building orientations corresponding to the daylight factor, mean maximum indoor temperature and mean indoor air velocity are determined respectively, based on the criteria of national codes and thermal comfort ranges. The intersection interval of the three subsets is the optimal interval of Window-to-Wall Ratio for the study building. In addition, for verifying the accuracy of research results, two validation techniques (i.e., comparison of experimental measurement data, and comparison of computational results from other simulation tools) were adopted in this paper. [ABSTRACT FROM AUTHOR]

Influent quality and quantity were important factors that caused the abnormal operation of WWTP. In this study, the prediction models of influent quality and quantity were established based on four machine learning methods of Linear Regression, Ridge Regression, ElasticNet Regression and Lasso Regression. The meteorological conditions (precipitation and air temperature) and influent indicators (influent quantity, COD, and NH3-N) were used as training data. The influent quantity prediction of the models were evaluated using the historical data obtained from a WWTP located in western China, and the results showed that the normal rates of influent quantity were ranged from 98.9%-100%. The highest accuracy was obtained with Ridge method which was 86.19%. For influent quality (COD) prediction, Ridge method is relatively ideal, with 82% accuracy. For influent quality (NH3-N) prediction, because of higher data normality rates, Lasso and ElasticNet method were more ideal, both with 74% accuracy. Further, in view of the reason of low prediction accuracy, this paper puts forward the idea of model improvement from the three directions of data fluctuation, correlation and amount. It is expected that this study will provide reference for similar research and provide a reference and thought for similar research. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children's health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons. [ABSTRACT FROM AUTHOR]

[ Objective) This paper aimed to the quantification of the detailed ranges and dynamics of the environmental factors of the light, temperature, air and moisture in morel cultivation. ( Methods ] Environmental factors in greenhouses for Morchella importuna cultivation were collected in Chengdu plain area using Internet of Things (IoT) technologies. Illumination intensity, air temperature, air relative humidity, oxygen percentage in the air, carbon dioxide concentration in the air, soil temperature and soil water content were recorded every ten mi nutes, in greenhouses with different layers of sunshade-net. Fluctuation models in 24-hour cycle were established for the environmental fac tors at different stages. [ Results] The sunlight in the greenhouses showed a dynamic that indirectly regulated the fluctuating features of the ox Oxygen percentage, relative humidity and temperature in the air, while the average illumination intensity in the greenhouse with the highest mo rel yield were about 2500 - 3500 ks at the noon of sunny days. Sunshade-net effectively prevented over-heating during daytime and over-chill ing during night., diminishing the occurrence of extreme values of air temperature and soil temperature, whereas the cumulative temperatures of the air and the soil, as well as the cumulative values of the relative humidity. oxygen percentage and carbon dioxide concentration in the air, and the soil water content, showed no significant difference between the regimes of different sunshade net layers. The effects of the sunshade-net layers were attenus ting the fluctuations of the environmental factors, The air tem perature was either higher than or similar to the soil temperature during the fruiting stage. (Conclusion) Field conditions ideal for morel cultivation were summarized from the analytical results of the environmental factors, for the reference of field management of morel cul tivation in plain area of southwestern China, which further provided some inspirations for the engineers hoping to develop factory-mode tech nologies of morel production. [ABSTRACT FROM AUTHOR]

The spring of 2018 was the hottest on record since 1951 over eastern China based on station observations, being 2.5°C higher than the 1961–90 mean and with more than 900 stations reaching the record spring mean temperature. This event exerted serious impacts in the region on agriculture, plant phenology, electricity transmission systems, and human health. In this paper, the contributions of human-induced climate change and anomalous anticyclonic circulation to this event are investigated using the newly homogenized observations and updated Met Office Hadley Centre system for attribution of extreme events, as well as CanESM2 (Second Generation Canadian Earth System Model) simulations. Results indicate that both anthropogenic influences and anomalous anticyclonic circulation played significant roles in increasing the probability of the 2018 hottest spring. Quantitative estimates of the probability ratio show that anthropogenic forcing may have increased the chance of this event by ten-fold, while the anomalous circulation increased it by approximately two-fold. The persistent anomalous anticyclonic circulation located on the north side of China blocked the air with lower temperature from high latitudes into eastern China. Without anthropogenic forcing or without the anomalous circulation in northern China, the occurrence probability of the extreme warm spring is significantly reduced. [ABSTRACT FROM AUTHOR]

Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters. [ABSTRACT FROM AUTHOR]

• A dual-evaporator system couples with a new fresh air conditioner. • Five independent affecting parameters are included for parametric analysis. • Four typical cities are selected for energy-saving potential evaluation. • Average seasonal energy-saving rate is 48.87%. • Average seasonal power saving per hour is 24.85 W m−2. Energy recovery, evaporative cooling and multiple evaporator are regarded as the effective and energy-efficient techniques to improve the energy efficiency of air conditioning system and reduce building energy consumption. In this paper, a parametric analysis and energy-saving potential evaluation for a new hybrid air conditioning system (HAC) under hot-humid climates are carried out theoretically based on detailed models. The proposed HAC incorporates into a dual-evaporator air conditioning system with an independent fresh air conditioner for both total and sensible energy recovery. One evaporator is installed between the total and sensible energy recovery loop of the fresh air conditioner to deeply dehumidify the fresh air for indoor humidity control. The other evaporator is installed inside the conditioned room to handle the indoor return air for temperature and humidity independent control. The parametric analysis mainly for the effect of air-to-water heat capacity ratio, ambient temperature, enthalpy-humidity ratio of the air-conditioned room and the number of heat/mass transfer units of the internal heat exchangers. The energy-saving potential of the HAC is also evaluated for applications in four typical cities (Changsha, Guangzhou, Fuzhou and Hangzhou) located in southern China over the summer period (June–August). The results show that, for the same air supply requirement, the HAC has an average energy-saving rate of 48.87% compared to conventional system with electric heater, and its power saving per hour is 24.85 W m−2. [ABSTRACT FROM AUTHOR]

This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. In response to soil moisture increase, the local air temperature significantly decreases, and precipitation increases in summer. The wetter ANWC results in a warmer and rainless climate over the Central part of North China Plain (CNCP). While the influence of increasing soil moisture on the Tibetan Plateau (TP) precipitation is negligible due to the high altitude. A cooler climate over ANWC leads to an abnormal descending motion occurring. Meanwhile, an anomalous high pressure and an abnormal anticyclonic circulation occurs in 500 hPa over the west of ANWC. Correspondingly, a huge anomalous cyclone circulation occurs over the northeastern part of China and Mongolia, which can hinder the northward movement of the East Asian summer Monsoon (EASM) and reduce the water vapor transport into Central China Plain. As a result, the weakened EASM thus leads to rainless over CNCP. As soil moisture increases over ANWC is stronger, the magnitudes of the influence on the atmospheric field become larger. This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. • This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. • It finds that the wetter ANWC can result in a warmer and rainless climate over the Central part of North China Plain (CNCP) through a complex atmospheric circulation response. • A cooler climate leads to an abnormal descending motion occurring over ANWC. • A huge anomalous cyclone circulation over the northeastern part of China and Mongolia can block the East Asian summer Monsoon northward and decrease the water vapor transport into Central China Plain. • This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. [ABSTRACT FROM AUTHOR]

In this paper, by using forecasts of the highest and lowest daily surface air temperatures from the Coupled Forecast System version 2 (CFSv2) over a period of 9 months, NCEP/DOE reanalysis data from the National Centers for Environmental Prediction (NCEP), and data from the U.S. NOAA Climate Prediction Center (CPC), three cold waves are identified during 2015–2016 in China, and the CFSv2 forecasts of these cold waves are evaluated. The results show that the high-resolution CPC data and the low-resolution NCEP/DOE data both very clearly show the temperature drops and temperature anomalies of these cold waves. The earlier the forecast lead time is, the greater the errors in the beginning and ending dates of the cold waves between the CFSv2 forecasts and the observations; there are errors of 1–2 days in the durations of the cold waves between the CFSv2 forecasts and the observations. CFSv2 exhibits certain abilities to forecast the overall temperature drops and temperature anomalies of cold waves at forecast lead times of 0, 5, 10, and 15 days, but the CFSv2 prediction ability is poor at forecast lead times exceeding 20 days. CFSv2 can predict the spatial distribution of temperature drops better than it can predict that of temperature anomalies, but the absolute errors in the observed temperature drops are greater than those in the observed temperature anomalies. The temperature drops from the CFSv2 forecasts are obviously underestimated in North China, South China, and their nearby areas but overestimated in Mongolia and Northeast China. The temperature anomalies from the CFSv2 forecasts are obviously underestimated in the inland areas of China but overestimated in the surrounding oceans and adjacent coastal areas. The difference fields at forecast lead times of 0 and 30 days are basically the same, but the absolute values of the differences increase with increases in the forecast lead time (with a few exceptions). [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

• Capture appropriate models in the discussion of TNn, TN5d and CR. • TNn and TN5d extreme events influence on pavement temperature with depth. • Identify the low-temperature performance based on winter temperature extremes. Winter temperature extremes are of major interest due to their potential damaging impacts related to pavement temperature and thermal cracking. In the present paper, three climate indices are adopted to describe the feature of winter temperature extremes. Statistical models for indices are analyzed based on the observed data in Harbin. The threshold values with their 99.9% cumulative frequency are proposed. Experiments were carried out in order to investigate the impacts of winter temperature extremes on pavement temperature and low-temperature performance of asphalt mixture. The results show that Weibull distribution is an appropriate model in the discussion of air temperature and its duration, and Frechet is the best-fitted model for the rate of air temperature change. Comparisons of experimental results indicate the importance of winter temperature extremes to asphalt pavement temperature. The influence of low-temperature properties by different temperature conditions is also analyzed. The low-temperature performance in Harbin can be accurately quantified by linear interpolation. [ABSTRACT FROM AUTHOR]

Abstract Air temperature is an important parameter affecting biosphere processes, air quality, regional climate, and human health, but is difficult to estimate, especially on a nationwide scale. Remote sensing provides high spatial resolution data that influence air temperature and can help overcome this problem in spatially heterogeneous environments. Generally, the simple linear relationships between air temperature, land surface temperature (LST), and auxiliary data, seldom consider the complex spatial variations in air temperature. The random forest (RF) regression model can characterize the complex temperature distribution by establishing the non-linear relationships among them. The main purpose of this paper is to present an RF regression method to estimate monthly average temperatures in China, using MODIS LST, normalized differential vegetation index (NDVI), night-time light, and a digital elevation model (DEM). Results based on cross-validation show that mean absolute error (MAE) and root mean square error (RMSE) at national level in different months ranged from 1.15 to 1.44 °C (mean ± standard deviation: 1.30 ± 0.10 °C) and from 1.57 to 1.99 °C (1.79 ± 0.13 °C), respectively. Potential sources of error and uncertainty were analyzed and investigated. Results show that regression model performs better in eastern (MAE: 0.68 ± 0.79 °C; RMSE: 0.75 ± 0.81 °C) and central China (MAE: 0.68 ± 0.41 °C; RMSE: 0.74 ± 0.41 °C). The model performance was strongly dependent on elevation, slope, land cover, and satellite data quality. In addition, the percentage increase in mean squared error from RF regression model was used to quantify the importance of variables. Positive importance scores indicated an improvement in accuracy with the assistance of NDVI, night-time lights, and the DEM. LST, and more auxiliary data sources, when integrated into an RF regression model, may be ways in which to improve the modeling of air temperature variations in the future. [ABSTRACT FROM AUTHOR]

Abstract Vegetation-atmosphere interaction is thought to have significant impacts on modulating regional climate. The main purpose of this paper is to reveal the impact of vegetation change on regional climate simulation over Eastern China. Therefore, we have performed three 30-year simulations from 1982 to 2011 with the WRF model coupled with the Noah land surface model as follows: a control experiment with climatology green vegetation fraction (GVF) (CTL), a sensitive experiment with WRF default climatology GVF (CLMV), and a sensitive experiment with dynamic vegetation (DYNV). The climatology difference between the CTL and CLMV experiment points out that during 1982–2011 over Eastern China, the increase of vegetation causes the decrease of temperature; on the other hand, the increase of temperature often corresponds to the reduction of vegetation. In the meantime, the increase/decrease in GVF is quite consistent with the simulated increase/decrease in precipitation, which is caused by both circulations and water vapor distributions at lower levels. The difference in the temperature trend between the CTL and DYNV experiment is almost opposite to that between the DYNV and GVF trend, which means that the decreased/increased vegetation will increase/decrease the surface air temperature. However, the trend of precipitation variation shows no clear relation with that of GVF. In response to GVF change, the results show that in the vegetation-climate feedback the moisture feedback is dominant over the radiation and momentum feedback. Significant decrease in GVF and LAI is detected across the East China, which leads to the decrease in the latent heat flux and increase in the sensible heat flux. For extremes and heat waves, it is shown that DYNV leads to better stimulation on the maximum of daily maximum temperature (TXx), warm spell days (WSDI), heat wave days (HWNY) and the averaged heat wave duration (HWDU) than CTL does over most of the Southeast China, although it may introduce some new biases in the Eastern coastal regions as well as the Northeastern China. Highlights • Dynamic green vegetation fraction (GVF) data was used for simulation. • Studied the effect of vegetation variation on climate extremes. • The results are also compared with actual observations. [ABSTRACT FROM AUTHOR]

Using the Weather Research and Forecasting (WRF) model, this paper analyzes the spatiotemporal features of heat waves in 20-year regional climate simulations over East Asia, and investigates the capability of WRF to reproduce observational heat waves in China. Within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX), the WRF model is driven by the ERA-Interim (ERAIN) reanalysis, and five continuous simulations are conducted from 1989 to 2008. Of these, four runs apply the interior spectral nudging (SN) technique with different wavenumbers, nudging variables and nudging coefficients. Model validations show that WRF can reasonably reproduce the spatiotemporal features of heat waves in China. Compared with the experiment without SN, the application of SN is effectie on improving the skill of the model in simulating both the spatial distributions and temporal variations of heat waves of different intensities. The WRF model shows advantages in reproducing the synoptic circulations with SN and therefore yields better representations for heat wave events. Besides, the SN method is able to preserve the variability of large-scale circulations quite well, which in turn adjusts the extreme temperature variability towards the observation. Among the four SN experiments, those with stronger nudging coefficients perform better in modulating both the spatial and temporal features of heat waves. In contrast, smaller nudging coefficients weaken the effects of SN on improving WRF's performances. [ABSTRACT FROM AUTHOR]

The apparent temperature (APT), or human-perceived temperature, is commonly defined as a function of the surface air temperature (SAT), vapour pressure (or humidity) and wind speed. This paper demonstrates that the APT over China, as revealed by daily station-observed data, has generally increased faster than the SAT during summertime in the past 50 years (1968-2017). The rate of increase in APT was significantly faster than that of SAT in 60.1% of stations, and the difference between the average Chinawide APT and SAT was 0.11°C decade-1. This phenomenon is occurring nationwide, but it is more intense over western, north-eastern and eastern coastal China. The more rapid increasing trend in APT indicates that human beings actually experience surplus heat stress under a certain change in SAT, and the increased SAT explains 67.0% of the average APT warming for the country, contributing to the change in the base APT. Apart from the increasing SAT, a decrease in surface wind speed and an increase in surface vapour pressure have also been observed, contributing to 21.6% of the increase in APT and explaining the remaining 11.4%, respectively. [ABSTRACT FROM AUTHOR]

Extreme heat events have serious effects on human daily life. Accurately capturing the dynamic variance of extreme high-temperature distributions in a timely manner is the basis for analyzing the potential impacts of extreme heat, thereby informing risk prevention strategies. This paper demonstrates the potential application of multiple source remote sensing data in mapping and monitoring the extreme heat events that occurred on Aug. 8, 2013 in Jiangsu Province, China. In combination with MODIS products, the thermal sharpening (TsHARP) method and a binary linear model are compared to downscale the original daytime FengYun 2F (FY-2F) land surface temperature (LST) imagery, with a temporal resolution of 60 min, from 5 km to 1 km. Using the meteorological measurement data from Nanjing station as the reference, the research then estimates the instantaneous air temperature by using an iterative computation based on the Surface Energy Balance Algorithm for Land (SEBAL), which is used to analyze the spatio-temporal air temperature variance. The results show that the root mean square error (RMSE) of the LST downscaled from the binary linear model is 1.30°C compared to the synchronous MODIS LST, and on this basis the estimated air temperature has the RMSE of 1.78°C. The spatial and temporal distribution of air temperature variance at each geographical location from 06:30 to 18:30 can be accurately determined, and indicates that the high temperature gradually increases and expands from the city center. For the spatial distribution, the air temperature and the defined scorching temperature proportion index increase from northern to middle, to southern part of Jiangsu, and are slightly lower in the eastern area near the Yellow Sea. In terms of temporal characteristics, the percentage of area with air temperature above 37°C in each city increase with time after 10:30 and reach the peak value at 14:30 or 15:30. Then, they decrease gradually, and the rising and falling trends become smaller from the southern cities to the northern regions. Moreover, there is a distinct positive relationship between the percentage of area above 37°C and the population density. The above results show that the spatio-temporal distributions of heat waves and their influencing factors can be determined by combining multiple sources of remotely sensed image data. [ABSTRACT FROM AUTHOR]

The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of highlatitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24Wm-2, yielding a climate sensitivity feedback parameter of -0:98Wm-2 K-1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1%yr-1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper. [ABSTRACT FROM AUTHOR]

Atmospheric temperature and directed solar radiation have a significant effect on the temperature field of high-speed railway (HSR) concrete bridge and ballastless track structure. However, temperature actions are random process of which distribution laws are difficult to explore, and existing statistical methods for structure temperature analysis are still not precise. So far, there are few researches for annual temperature spectra and design codes for bridge–track system. Based on the one-year observation data, this paper investigated the temperature actions for Chinese HSR bridge–track structure. By utilizing reliability high moment theory, a statistical method which could built virtual distribution was put forward. Based on the renewed study, the effects of waterproof for deck were taken into consideration, a temperature action model was proposed which is suitable for both bridge and track structure. In addition, for track structure, the previous temperature load models were modified. Apart from that, by proposing the concepts of temperature uniform and fluctuant spectra, the research evaluated service performance of structure. Finally, the distribution regularities of uniform temperature spectra were fitted by Fourier series, and the relationship between structural and atmospheric uniform temperature was established (formula (25) ). As a result, according to 50 years recorded atmospheric temperature data, the prediction model of the structure extreme temperature was suggested, and by taking the recurrence interval of 100, 150 and 300 years, the extreme temperatures of the system are 52.23, 54.34 and 57.77 °C. [ABSTRACT FROM AUTHOR]

Under the background of global warming, occurrence of heat waves has increased in most part of Europe, Asia and Australia along with enhanced ozone level. In this paper, observational air temperature and surface ozone in the Yangtze River Delta (YRD) region of China during summer of 2013, and the regional chemistry-climate model (RegCM-CHEM4) were applied to explore the relationship between heat wave and elevated ground-level ozone. Observations indicated that YRD experienced severe heat waves with maximum temperature up to 41.1 °C, 6.1 °C higher than the definition of heat wave in China, and can last for as long as 27 days. Maximum ozone reached 160.5 ppb, exceeding the national air quality standard (secondary level) as 74.7 ppb. Moreover, ozone was found to increase at a rate of 4–5 ppb K − 1 within the temperature range of 28–38 °C, but decrease by a rate of − 1.3 ~ − 1.7 ppb K − 1 under extremely high temperature. A typical heat wave case (HW: 24/7–31/7) and non-heat wave case (NHW: 5/6–12/6) were selected to investigate the mechanism between heavy ozone and heat waves. It was found that chemical reactions play the most important role in ozone formation during HW days, which result in 12 ppb ozone enhancement compared to NHW days. Chemical formation of ozone can be influenced by several factors. During heat waves, a more stagnant condition, controlled by anti-cyclone with sink airflow, led to less water vapor in YRD from south and contributed to less cloud cover, which favored a strong solar radiation environment and ozone significantly increasing. High temperature also slightly promote the effect of vertical turbulence and horizontal advection, which beneficial to ozone remove, but the magnitude is much smaller than chemical effect. Our study suggests that the chemical reaction will potentially lead to substantial elevated ozone in a warmer climate, which should be taken into account in future ozone related issues. [ABSTRACT FROM AUTHOR]

In this paper, the monitoring data of road surface temperature, air temperature, wind speed, wind direction, relative humidity and precipitation from the automatic weather stations of Hurongxi, Hanyi, Wuhuang and Huanghuang on Huyu expressway from June 2013 to August 2014 were used to Investigate the change characteristics of different sections' road surface temperatures in different seasons and sky conditions. The forecast models of the maximum and minimum road surface temperatures were established on different sections by statistical analysis methods, and the forecast results were verified. The results showed that the road surface temperature and air temperature of Hurongxi, Hanyi, Wuhuang and Huanghuang displayed obvious diurnal variation, but the difference between the road surface temperature and air temperature was larger. Compared with the other three sections, the maximum difference between the road surface temperature and air temperature on Hurongxi increased in winter and decreased in summer overall. The road surface temperature was close to air temperature on Hurongxi after sunset on sunny to cloudy and overcast in winter, while less than air temperature on Hanyi, Wuhuang and Huanghuang. The air temperature was less than road surface temperature on the four sections on rainy day and significant on Hurongxi. In summer, the air temperature was less than road surface temperature on the four sections under three sky conditions and the difference between them in afternoon was the biggest on sunny to cloudy and overcast. The road surface temperature was very close to each other among the four sections in January, while which was rising with the decrease of altitude in April, July and October. The forecast result of the road surface temperature was close to actual result on Hurongxi and Huanghuang, so which can be for reference. But there were some big errors between the forecast result and actual result in several timings on Hanyi and Wuhuang, so the forecast result should be corrected for actual business work. [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]

• Indoor thermal environment and AC usage behavior was investigated for low-quality centrally heated dwellings. • 90% of AC trigger temperature is 15 ℃ for the living room and 14 ℃ for the bedroom. • The heating demand drivers of cool housing are identified by heteroskedasticity-robust Ordinary Least Square. • The study presents new knowledge for appropriate way to meet actual heating demand while reducing energy use. • Relationships between indoor temperature, heath and energy use are discussed. In northern China, district heating systems without terminal control typically serve thousands of end-users, thus inevitably some users suffer from insufficient warmth and others are overheated indoors due to hydraulic imbalances. This paper aimed to understand the indoor thermal conditions and the actual heating demand in low-quality centrally heated dwellings. The driving force of heating demand was analyzed based on a smartphone survey of 233 households in cold climates, and field measurements of winter indoor thermal environment and AC supplemental heating behavior were conducted in a sample of 13 cool dwellings. Logistic regression and heteroskedasticity-robust Ordinary Least Squares were adopted to assess AC usage patterns and driving forces of heating demand, respectively. New knowledge has been gathered regarding average temperatures of 17.4 ℃ and 16.8 °C in cool housing living rooms and bedrooms, respectively, outside the human thermal comfort zone. Simultaneously, intermittent and partial AC usage patterns induced by time and air temperature were observed in these cool end-users for the comfort. The average daily running time of AC devices was 2.0 h for the living room and 1.8 h for the bedroom. There were statistically significant base linking occupants' heating demands to education level, household income, tenure type, presence of children, thermal experience and heating system. However, it was not a significant factor for household size. In addition, a rebound effect of building energy efficiency improvements on the interior temperature was also noticed. The results of this study shed light on better and target-oriented residential internal temperature or heating system design for both thermal comfort requirements and energy-savings. [ABSTRACT FROM AUTHOR]

Abstract: Reducing the energy consumption of air-conditioning systems is becoming increasingly important. This paper focuses on the air-conditioning system in Terminal 3 in Xi’an Xianyang International Airport, the first airport terminal to adopt radiant cooling in China. In the large spaces of this airport terminal, a temperature and humidity independent control (THIC) system has been adopted instead of a conventional nozzle air supply method, which significantly reduces the energy consumption of the air supply. The radiant floor is the main mechanism for indoor temperature control, while a liquid desiccant outdoor air processor combined with displacement ventilation is responsible for humidity control. Ice storage is adopted to reduce operating costs, and the supplied chilled water temperature can be as low as 3°C. This paper examines the performance of the THIC system in detail. The indoor environment is determined through measurements to be comfortable, and the on-site performances of key components in both the airport terminal and the cooling plant are investigated. The outdoor air processor supplies air of appropriate temperature and humidity ratio; two kinds of radiant floors are used for temperature control, both with measured cooling capacities of about 30–40W/m2; and the energy efficiency ratio (EER) of the cooling plant is 2.62. [Copyright &y& Elsevier]

The Northeast China is one of typical regions experiencing intensive human activities within short time worldwide. Particularly, as the significant changes of agriculture land and forest, typical characteristics of pattern and process of agroforestry ecotone change formed in recent decades. The intensive land use change of agroforestry ecotone has made significant change for regional land cover, which had significant impact on the regional climate system elements and the interactions among them. This paper took agroforestry ecotone of Nenjiang River Basin in China as study region and simulated temperature change based on land cover change from 1950s to 1978 and from 1978 to 2010. The analysis of temperature difference sensitivity to land cover change based on Weather Research and Forecasting (WRF) model showed that the land cover change from 1950s to 1978 induced warming effect over all the study area, including the change of grassland to agriculture land, grassland to deciduous broad-leaved forest, and deciduous broad-leaved forest to shrub land. The land cover change from 1978 to 2010 induced cooling effect over all the study area, including the change of deciduous broad-leaved forest to agriculture land, grassland to agriculture land, shrub land to agriculture land, and deciduous broad-leaved forest to grassland. In addition, the warming and cooling effect of land cover change was more significant in the region scale than specific land cover change area. [ABSTRACT FROM AUTHOR]

To expand torrential rain, which is a meso- and microscale weather process, to a meso- and long-scale weather process, in this paper, we choose South China as a sample region and propose the conception of the 'Cumulative Effect' of torrential rain (CETR) by using daily precipitation observational data from 740 stations. Through a statistical analysis of the observations, three indexes-continuous time ( L ), control area ( A ), and precipitation contribution rate ( Q )-are used to define the CETR and indicate the torrential rain processes. The relationships between the CETR and simultaneous total precipitation over South China are analyzed in the pre-flooding and latter flooding seasons. This analysis shows that on both interannual and interdecadal scales, the three indexes are highly correlated with simultaneous total precipitation over South China in the pre-flooding season and latter flooding season. Moreover, an empirical orthogonal function (EOF) analysis is performed to classify the spatial distribution of the CETR. In both the pre-flooding season and the latter flooding season, the four major spatial models of torrential rain are similar to those of total precipitation over South China. With regard to the amount of precipitation caused by the CETR, the latter flooding season is affected more significantly than the pre-flooding season. Regarding the geographical distribution of precipitation, the opposite result occurs. In conclusion, in both the pre-flooding season and the latter flooding season, the CETR influences and even determines the amount and distribution of precipitation over South China. [ABSTRACT FROM AUTHOR]

This paper addresses the interannual variation of winter air temperature over Northeast China and its connection to preceding Eurasian snow cover. The results show that there is a significant negative correlation between October Eurasian snow cover and following-winter air temperature over Northeast China. The snow cover located in eastern Siberia and to the northeast of Lake Baikal plays an important role in the winter air temperature anomaly. More (less) eastern Siberia snow in October can cause an atmospheric circulation anomaly pattern in which the atmospheric pressure is higher (lower) than normal in the polar region and lower (higher) in the northern mid-high latitudes. Due to the persistence of the eastern Siberia snow from October to the following winter, the winter atmospheric anomaly is favorable (unfavorable) to the widespread movement of cold air masses from the polar region toward the northern mid-high latitudes and, hence, lower (higher) temperature over Northeast China. Simultaneously, when the October snow cover is more (less), the SST in the northwestern Pacific is continuously lower (higher) as a whole; then, the Aleutian low and the East Asia trough are reinforced (weakened), favoring the lower (higher) temperature over Northeast China. [ABSTRACT FROM AUTHOR]

A large amount of heat and piston wind is generated during the train braking. The piston wind will be heated by the braking energy simultaneously, which has a significant influence on the air temperature and the flow field of subway stations and tunnels. The study conducted a train-induced experiment in Tai yuanjie (TYJ) station, and the air temperature and velocity data were recorded. Meanwhile, a train-induced numerical simulation was carried out for full-scaled model based on dynamic mesh and the theory of braking energy. The experimental data was compared with the simulation results to analyze the unsteady air temperature field and unsteady air flow field of the actual station. The results indicate that the braking energy of the train cannot be ignored in the simulation of the subway station in winter. However, the utilization of the braking energy is inefficient. To improve the utilization of the braking energy, this paper put forward one passive approach and three active approaches. The simulation results indicated that the active approaches could improve the heat utilization of braking energy significantly. Ventilation system could transport part of the piston wind to the station by fans so that it can prevent cold air entering the station. [ABSTRACT FROM AUTHOR]