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There are many types of atmospheric heat engines in land-air systems. The accurate definition, calculation and interpretation of the efficiency of atmospheric heat engines are key to understanding energy transfer and transformation of land-air systems. The atmosphere over the Qinghai-Tibet Plateau (QTP) in summer can be regarded as a positive heat engine. The study of the heat engine efficiency is helpful to better understand land-air interaction and thermal-dynamic processes on the QTP. It also provides a new perspective to explain the impact of the QTP on the climate of China, East Asia and even the world. In this paper, we used MOD08 and ERA5 reanalysis data to calculate the atmospheric heat engine efficiency, surface heat source and atmospheric heat source on the QTP in summer (May to September) from 2000 to 2020. The average atmospheric heat engine efficiency on the QTP in summer from 2000 to 2020 varies between 1.2% and 1.5%, which is less than 1.6%; the heat engine efficiency in summer is higher than that in June, July and August; the Qaidam Basin is the region with the highest atmospheric heat engine efficiency, followed by the western QTP. The mean surface heat source on the QTP in summer from 2000 to 2020 is 96.0 W m−2, the atmospheric heat source is 90.7 W m−2, and the release of precipitation condensation latent heat is the most important component of the atmospheric heat source on the QTP in summer. There is a strong and significant positive correlation between the atmospheric heat engine efficiency and the surface heat source on the QTP in summer. The precipitation con densation latent heat is the most important component of the atmospheric heat source in summer and can reflect the precipitation process. There is a strong and significant negative correlation between the atmospheric heat engine efficiency and the atmo spheric heat source on the QTP in summer. [ABSTRACT FROM AUTHOR]

Land surface temperature(LST) is an important indicator to study climate change and test the performance of regional climate model simulation. RegCM4.6 is the representative version of regional climate model RegCM, which is coupled with advanced third-generation land surface model NCAR CLM4.5. Currently, RegCM4.6 has become an important tool to study regional climate change in China. However, its ability to simulate land surface temperature in mainland China and the reasons for its deviation have not been systematically studied, and targeted improvement work is lacking. The present study is the first to employ LST data collected from 809 Chinese meteorological stations from the last 30 years to comprehensively assess the ability of CLM4.5 to simulate LST. Sensitivity tests of soil thermal conductivity (STC) were carried out to improve the model. Although the coupled regional climate model could accurately simulate the temporal and spatial variation of LST, a cold bias of 2~8 °C existed for all of mainland China, which was larger in seasons with more precipitation and greater soil moisture than other seasons. Deviation increased from southeast to northwest. which was caused by the incoming long-wave radiation, sensible heat, and latent heat simulated. There was a significant linear relationship between the observed and simulated LSTs, with correlation coefficients for all the stations ranged from 0.75 to 0.9 (P < 0.001). The observed LST increased at a rate of 0.58 °C/decade, but the simulated LST increased at a lower rate. Assessment of three different STC schemes showed that the Lu-Ren scheme was the most suitable for LST simulation in mainland China. Developing a new STC scheme that considers the role of water vapor can effectively improve the model when used in mainland China. [ABSTRACT FROM AUTHOR]

Rapid progress of solar photovoltaic (PV) technology has caused growing interest in understanding interactions between large scale PV plants and near-surface atmosphere. However few attempts have been made to quantify the impact of PV modules on surface radiative forcing and energy partitioning process. Here, this issue is explored experimentally and analytically in the two adjacent sites located at the PV plant and the natural barren field respectively in Wujiaqu in Xinjiang of China. The results showed that the physical effect of PV panels is not symmetrical in the whole day. During the daytime, compared with the reference site, net shortwave radiative forcing increases 8%, a warming effect on the integrated underlying surface (0.1 K) and a cooling effect (∼-2.6 K) on the ground surface were found in the PV plant. 9.2% of the net radiation (NR) was converted into electric energy (PE), sensible heat flux (H) increased by 30.6% hence resulted in the convection heating effects of 0.64 K and 0.32 K on the near-surface air temperature at the height of 2 m and 10 m respectively, while latent heat flux (LE) and ground surface heat flux (GS) decreased by 49% and 3% respectively related to the reference site. At night, PV panels produce a cooling effect of −0.2 K and −2.3 K on the ground and integrated underlying surface respectively, and less GS is released in the PV plant which contribute to the cooling effects of −0.24 K and −0.08 K on the air temperature at the height of 2 m and 10 m respectively related to the reference site. In the whole day, in the PV plant, H increased by 27.6%, LE and GS decreased by 47.4% and 6.7% respectively, air temperature increased by 0.16 K and 0.1 K at 2 m and 10 m respectively. • The physical effect of PV modules is not symmetrical in the whole day. • Net shortwave radiative forcing induced by PV modules increases 7.2%. • Sensible heat increases 34.4% and latent heat decreased 20.9% in PV plant related to reference site. • Ground surface heat decreases 7.4%. • The reasons of physical effect of PV modules were explained. [ABSTRACT FROM AUTHOR]

Soil temperature is an important variable of earth's surface system, and studying it is of great benefit to understanding the energy and hydrological cycles and climate change. Based on soil temperature data at a depth of 3.2 m from 113 stations in China from 1980 to 2017, the spatial distribution and temporal variation of the soil temperatures in China were analyzed by using a fuzzy C-means algorithm (FCM), linear fitting and the Mann–Kendall method. The results show that the average annual soil temperature at 3.2 m in China gradually increased from north to south, with the lowest temperatures in the northeast and Qinghai–Tibetan Plateau ( T 3.2 m ≤ 5 ∘ C ) and the highest in South China ( T 3.2 m ≥ 25 ∘ C ). The country is divided into 10 regions using the FCM method. The soil temperature anomalies show a continuous increase over time, but there are significant regional differences in the warming speeds. The northeast and northwest regions exhibit the largest temperature increases, approximately 0.57 ° C / 10 a , North China has the second highest increases, with approximately 0.36 ° C / 10 a , and the values in other areas are less than 0.3 ° C / 10 a . Over the 38-year period, the soil temperature increased by approximately 1.4 ° C , and the temperature increase mainly occurred during the last 20 years. The positive center of the annual soil temperature anomaly at 3.2 m in the Qinghai–Tibetan Plateau during the last 2 decades of the twentieth century became cooling to a negative soil temperature anomaly at 3.2 m during the first 2 decades of the twenty-first century. The negative centers for the annual soil temperature anomaly at 3.2 m in the south of Shaanxi and the middle reaches of the Yangtze River during the last 2 decades of the twentieth century became warming to positive soil temperature anomalies at 3.2 m in the first 2 decades of the twenty-first century. In the other regions, different turning points of the positive–negative temperature anomaly are exhibited at 3.2 m. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively. [ABSTRACT FROM AUTHOR]

• PV plants in China's northwestern Gobi Deserts would favor lower evaporation and wind. • Local climate effects of PV plants are equivalent to or even greater than projected climate variability. • PV-induced climate effects could contribute to improving ecological conditions in Gobi Deserts. In this study, a promising photovoltaic (PV) deployment scenario is firstly designed to represent China's solar energy development in the context of its dual carbon target. Then, the potential climate effects of the designed PV plants under different emission scenarios are simulated using a newly developed weather research and forecasting (WRF)-PV regional climate model, which is initiated by the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. The modeling results indicate that the projected PV plants in China's Gobi Deserts could impact the local climate, causing positive change of 3.71 ± 0.03 % in the surface relative humidity, and negative changes of 1.60 ± 1.36 W/m2 in the latent heat flux and 0.39 ± 0.00 m/s in the 10-m wind speed in July by about 2060 under the intermediate emission scenario, and these PV-induced climate effects are approximately equivalent to or even greater than the projected climate variability, but almost confined to the local area. These findings suggest that the projected PV deployment in China will lead to low carbon emissions while causing favorable climate effects, such as lower evaporation and wind speed in Gobi Deserts, which will bring environmental and ecological benefits to the PV industry. [ABSTRACT FROM AUTHOR]

Using data observed at a photovoltaic (PV) power plant at the edge of the Gurbantünggüt Desert and at an undeveloped site in the Gobi desert in the summers of 2019 and 2020, we compared and analyzed the variations of radiation and surface albedo in various wavelength bands. Components of the solar radiation received by the surface of the arid regions of China were ranked infrared (IR) > visible (VIS) > ultraviolet (UV). The observed surface albedo of the PV site was significantly lower than that at the undeveloped desert site. The mean surface albedo values for shortwave broadband (SW), IR, VIS, and UV at the PV site were 0.14, 0.15, 0.13, and 0.08, respectively, and the corresponding values for the undeveloped site were 0.22, 0.23, 0.23, and 0.10, respectively. We also developed a parametric formula for the surface albedo at the PV plant given data observed on typical sunny days. We then validated the parametric formula using observations taken in the summers of 2019 and 2020. The results showed that the parametric formula could precisely simulate variations of the surface albedo and reflected radiation of the PV plant. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time. [ABSTRACT FROM AUTHOR]

• The impact of wind farms on the local climate is not simply warming and cooling. • Air temperature becomes colder and the land surface temperature becomes warmer at night. • The significant increase in evapotranspiration may be due to the impact of wind farms. • Wind farm has no or negligible effect on vegetation growth both spatially and temporally. To explore the possible local climatic and environmental impacts of an onshore wind farm in North China, meteorological observation and satellite remote sensing data around the wind farm were collected, and the soil chemical composition within and outside of the wind farm was determined. Our analysis revealed changes in the spatial patterns of wind, air temperature (T a), ground surface temperature (T s), land surface temperature (LST), evapotranspiration (ET), and the normalized difference vegetation index (NDVI) from before to after the construction of the wind farm. The two datasets of meteorological observations showed that wind speed decreased after the construction of the wind farm, with one dataset indicating a significant decrease. Since the construction of the farm, T a and T s have shown increasing trends, and ET has shown a significant increasing trend. The nighttime T a and LST showed opposite trends, with T a decreasing and LST increasing. The change in LST was the greatest at the wind farm and in its downwind zone, indicating that the operation of the wind farm affects the LST within and beyond the 10 km buffer zone. After the wind farm construction, the NDVI showed a significant positive trend. However, increases in the concentrations of Ti and Co were observed, suggesting that the construction and operation of wind farm may promote vegetation growth and cause soil pollution. The monitoring of wind farms needs to be strengthened to reveal the long-term impacts of wind farms on local climates and ecosystems. [Display omitted] [ABSTRACT FROM AUTHOR]