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Net ecosystem productivity (NEP), the difference between gross primary productivity (GPP) and ecosystem respiration (ER), is the basis of forest carbon sinks. Revealing NEP differences between naturally regenerating forests (NF) and planted forests (PF) can benefit for making carbon neutrality strategies. Based on 35 eddy covariance measurements in China, we analyzed NEP differences in values and spatial patterns between NF and PF. The results showed that NF had slightly lower NEP than PF, resulting from the high stand age (SA) and soil fertilizer, while their differences were not significant (p > 0.05). The increasing latitude decreased mean annual air temperature thus decreased GPP both in NF and PF. However, the higher SA and soil fertilizer in NF made most GPP release as ER thus induced no significant NEP spatial variation, while lower SA and soil fertilizer in PF made NEP spatially couple with GPP thus showed a decreasing latitudinal pattern. Therefore, stand characteristics determined the differences in NEP values but indirectly affected the differences in NEP spatial variations through altering GPP allocation. The decreasing latitudinal pattern of NEP in PF indicates a higher sequestration capacity in the PF of South China. Our results provide a basis for improving the forest carbon sequestration. [ABSTRACT FROM AUTHOR]

Investigating the spatial and temporal changes in snow cover over mountain areas is significant for understanding the impact of regional climate variability. In this study, using cloud‐removed snow cover data, which are generated based on Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products, the spatiotemporal changes of snow cover fraction (SCF) and its relationship with temperature and precipitation changes from 2002 to 2020 were examined over Altai Mountains, China. The results demonstrate that the distribution and changes of SCF are highly spatiotemporally heterogeneous. Within a year, the maximum SCF occurs in January at 98.6%, and the minimum appears in July at 8.7%. The annual‐mean SCF shows an increasing trend at 0.09%·annum−1, owing to the significantly increasing SCF in the snow accumulation period at 0.5%·annum−1 and the decreasing SCF in the snow melting period at −0.2%·annum−1. The SCF distribution, as well as its interannual change, is greatly influenced by elevation. During the snow cover period, a positive linear correlation between SCF and elevation is found at 0.02%·m−1 (p <.01). The annual‐mean SCF decreases in the area below 1,200 m, whereas it increases in the area above 1,200 m. Accordingly, the elevation‐dependent SCF results in various SCF distributions on different slopes and watersheds. The SCF shows an apparent pattern in different aspects, with similar SCFs between the north and east aspects and between the west and south aspects but a difference between the northeast aspects and the westsouth aspects. The SCF is negatively correlated with air temperature (r = −0.74, p < 0.01) and positively correlated with precipitation (r = 0.74, p < 0.01). In addition, temperature shows a significant and larger correlation with SCF in both the snow accumulation and melting periods, indicating the major factor of temperature for the changes in SCF. [ABSTRACT FROM AUTHOR]

The surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China. [ABSTRACT FROM AUTHOR]

The built-in refrigerator has been popular in China in recent years due to users' high requirements for the integration of home appliances and furnishings. However, the built-in configuration will cause a significant performance deterioration, which has been less quantitively studied. The condenser and its refrigerator performance are compared experimentally and numerically between built-in and free-standing configurations. By contrast with the free-standing condition, the built-in condenser has poor performance attributed to two reasons: 28.6% lower condenser air flowrate and 10.72 °C higher condenser inlet air temperature caused by the hot short-circuited airflow. This heat dissipation deterioration increases the condensing temperature and discharge temperature, resulting in a refrigerator cooling capacity loss. Correspondingly, the compressor increases the rotating speed and power to compensate for the loss. The compressor ON-time ratio reduces by 8% but the average power during the compressor-on period increases by 56.9%, finally increasing the energy consumption by 43.4%. This study also provides some guidance for further heat dissipation and airflow field optimization of built-in refrigerators. [ABSTRACT FROM AUTHOR]

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

A long-series mass balance (MB) of glaciers can be used to study glacier–climate relationships. Using a distributed simplified energy balance model (SEBM) and an enhanced temperature-index model (ETIM), the MB of Laohugou Glacier No. 12 (LHG12) was reconstructed from 1980 to 2020, driven by a calibrated ERA5 reanalysis dataset. The simulation of SEBM performs better than that of ETIM. The results showed that the annual MB of LGH12 is a fluctuating trend of declining from 1980 to 2020, with annual means of −0.39 ± 0.28 m w.e. a−1 and cumulative value of −16 ± 4 m w.e. During 1980–1990, the annual MB fluctuated in a small range, while after 1990, LHG12 accelerated melting owing to rising air temperature, with annual means of −0.48 m w.e. a−1, three times as large as that of 1980–1990. The largest mass loss occurred during 2001–2010 at an average rate of −0.57 m w.e. a−1. The average equilibrium line altitude (ELA) was 4976 m a.s.l., and since 1980, the ELA has been increasing at a rate of 37.5 m/10 a. LHG12 is most sensitive to air temperature, and the MB sensitivity reaches −0.51 m w.e. a−1 with air temperature increase of 1 °C. The sensitivity of MB to incoming shortwave radiation (+10%) simulated by SEBM is −0.30 m w.e. a−1, three times larger than that simulated by ETIM. This is mainly because the two models have different conditions for controlling melting. Melting is controlled only by air temperature for ETIM, while for SEBM, it is controlled by air temperature and incoming shortwave radiation. [ABSTRACT FROM AUTHOR]

Vegetation phenology is a sensitive indicator of climate change, and can help understand the response of vegetation cycles to climate, which is important for understanding the impact of global climate change on terrestrial ecosystems. In this study, based on the enhanced vegetation index (EVI) time-series data, derived from the moderate-resolution imaging spectroradiometer (MODIS) data, the climate parameters were extracted using the Savitzky–Golay (S–G) filtering method to explore the spatial and temporal variation characteristics of the vegetation phenology in an agro-pasture ecotone in China, from 2000 to 2020. In addition, the response characteristics of the vegetation phenology to the climate elements (temperature and precipitation) were also analyzed. The results showed the following: (1) The start of the growing season (SOS) was widely advanced, and that was caused by climate change. The end of the growing season (EOS) was delayed, and the length of the growing season (LOS) was gradually extended with a large interannual fluctuation in the SOS and the LOS in the region; (2) the SOS showed significant negative correlations with the air temperature and precipitation. Precipitation was mainly positively correlated with the EOS, but there was no significant difference in the correlation between temperature and the EOS. In general, pre-season precipitation is the main driver of the vegetation phenology, while the influence of temperature on the phenology is less obvious; (3) in the semi-arid area and arid area, the phenology was mainly influenced by precipitation. The response of the vegetation phenology to the temperatures in different temperature zones was found to be regular, showing high spatial differences. In general, the higher the cumulus temperature, the lower the negative effect of the temperature on both the SOS and EOS. These results may provide new reference to study the non-systematic changes of the vegetation phenology in response to climate change. [ABSTRACT FROM AUTHOR]

In winter 2020/2021, several episodes of cold air outbreaks have wreaked havoc across much of China, resulting in exceptionally low temperatures over northern‐to‐southeastern China. Our analysis shows that the anomalous circulation leading to the cold wave in China was characterized as: (a) a strengthening of the Siberian high, (b) a deepening of the East Asian trough, and (c) a weakening of the stratospheric polar vortex. Prior to the winter, in summer and autumn, the largest sea ice loss along the Siberian coast was observed. We therefore address the question whether the substantial sea ice loss has contributed to the cold wave. First, applying singular value decomposition (SVD) analysis, we find strong coherence between the autumn Arctic sea ice coverage and temperatures over the central‐to‐east Asian region with the sea ice component (SVD1_SIC) reaching a record high in 2020 and displaying a decadal shift around 2005. Associated with both the interannual and decadal variations of the SVD1_SIC, there are similar circulation anomalies as described in (a), (b) and (c) above. Second, applying self‐organizing maps, wintertime daily air temperatures are clustered into nine nodes with Node‐1 mostly resembling "warm Arctic, cold Eurasia" pattern. The frequency of Node‐1 shows a similar decadal shift as observed in SVD_SIC1 above, with a correlation of about 0.56. Associated with Node‐1, the circulation anomalies are consistent with features highlighted in (a), (b) and (c) above. Thus, taking all the evidence into account, we conclude that the substantial sea ice loss along the Siberian coast contributed significantly to the 2020/2021 winter cold wave in China. The mechanism behind the contribution in this study highlights that the Arctic sea ice loss is favourable for certain circulation patterns on climate timescale, reinforcing disruptive chain of reactions on weather timescale, and leading to severe cold events. [ABSTRACT FROM AUTHOR]

Quantifying future changes in solar photovoltaic (PV) potential is of great significance for energy planning and policy making to achieve carbon neutrality. To constrain the uncertainties of applying directly Coupled Model Intercomparison Project Phase 6 (CMIP6) projections to such quantification, we construct the performance‐weighted CMIP6 solar irradiance and air temperature data, and then assess the future changes in China's PV potential and accompanying economic benefits under three typical climate change scenarios. We find that the high emission scenario will substantially damage China's PV potential, resulting in a reduction of 314 TWh/year in electricity generation by the planned installed capacity in 2100, with a corresponding loss of US $25 billion/year. However, the low emission scenario will enhance the PV potential, yielding an additional electricity generation of 226 TWh/year and a corresponding bonus of US $18 billion/year in 2100. China's commitment to carbon neutrality will lead to low‐carbon emissions, which brings great economic rewards to its PV industry. Plain Language Summary: Quantifying the impact of climate change on future solar photovoltaic (PV) potential is important for energy planning and policy development in the context of the carbon neutrality. However, directly applying Coupled Model Intercomparison Project Phase 6 (CMIP6) projections to this quantification leads to large uncertainties. To this end, we reconstruct the future solar irradiance and air temperature data by weighting the CMIP6 projections according to their performance in reproducing the historical changes. Afterward, we assess the future changes in China's PV potential and the associated economic impacts under three typical climate change scenarios. We find that China's PV potential would significantly decrease under high emissions scenario, equal to an electricity generation of 314 TWh/year at planned installed capacity in 2100 and economic loss of about $25 billion/year. In contrast, PV potential would increase under low emission scenario, resulting in an additional electricity of 226 TWh/year and economic benefits of $18 billion/year. China is striving to achieve low emission development with the carbon neutrality commitment, from which the Chinese PV industry will reap huge economic rewards. Key Points: Synthesis of Coupled Model Intercomparison Project Phase 6 projections by Bayesian linear regression reduces the uncertainties of solar irradiance and air temperatureChina's future capacity factor will continuously decrease (increase) to about 0.141 (0.152) by 2100 under high (low) emission scenarioHigh emissions will result in a reduction of 314 TWh/year in electricity generation, with a corresponding loss of US $25 billion/year [ABSTRACT FROM AUTHOR]

The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change. [ABSTRACT FROM AUTHOR]

Air temperature is an important parameter in the research of meteorology, environment, and ecology. Obtaining accurate temperature values with high spatial–temporal resolution is the premise for regional climate monitoring and analysis and is also the basis for the calculation of various ecological and environmental factors. In this study, we proposed a temperature refinement method using the ERA5 reanalysis data, which constructed the correlation between the measured temperature derived from weather stations and the interpolated temperature based on the artificial neutral network (ANN) model. Experiments in a high-intensity coal mining area in China were conducted, and the root mean square error (RMSE) and compound relative error (CRE) were adopted as the statistical values in the internal and external accuracy tests. Numerical results showed that the proposed temperature refinement method outperformed the traditional interpolated method with an approximately 42% and 33% RMSE improvement in the internal and external accuracy test, respectively. Moreover, the proposed method effectively improved the geographic differences of the traditional method and obtained temperature estimates with high accuracy at arbitrary sites. [ABSTRACT FROM AUTHOR]

Based on the daily minimum air temperature (Tmin) data and the daily precipitation data from the NCC/CMA combined with the NCEP/DOE reanalysis data, the intraseasonal features and circulations of the winter cold precipitation events (CPEs) in southern China under the influence of strong Madden–Julian oscillation (MJO) were explored. The results show that: (1) Winter temperatures in southern China are characterized by intraseasonal oscillations (ISOs) of 10–30-d and 30–60-d, with six CPEs under strong MJO all occurring during these two intraseasonal scales in cooling phases. The invasion of cold air coupled with the availability of appropriate moisture conditions in southern China is more conducive to the CPEs. (2) A cyclone and anticyclone lying to the east of the Ural Mountains and the northwest of Lake Baikal at 925-hPa gradually move southeastward. The merging of the low-frequency (LF) blocking highs over the Ural Mountains and the North Pacific Ocean at 500-hPa leads to the contraction and southward movement of the LF cold vortex. The following anomalous northerly winds steer the cold air towards southern China. The cold advection is the dominant term in the cooling process, while the adiabatic cooling accompanied with ascending motion is also beneficial to the cooling process. (3) MJO has some effect on the LF blocking highs and the cold vortex in the mid-high latitudes and induces the CPEs over southern China. The joint effect of mid-high and low latitudes on the 30–60-d intraseasonal oscillation scale can have a significant impact on the cooling and precipitation processes of CPEs. [ABSTRACT FROM AUTHOR]

Previous research has documented a tight positive relationship between vegetation activity and growing season air temperature in China's temperate zone (TC). However, this relationship may change over time following alternations in other environmental factors. Using the linear regression analysis and the moving windows based on partial correlation analysis method, the temporal variations of responses of vegetation NDVI to rising air temperature during 1982–2015 in the TC were examined. The results showed that the interannual partial correlation between NDVI and air temperature (RNDVI−T, include RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin, represents the partial correlation between NDVI and Tmean, Tmax, and Tmin, respectively) for the growing season (GS) in a 17−year moving window showed a significant decreasing trend during the last 34 years, mainly due to decreasing RNDVI−T in summer and autumn. The area with a significant decrease of RNDVI−Tmean, RNDVI−Tmax, and RNDVI−Tmin for the GS approximately accounted for 52.36%, 45.63%, and 49.98% of the TC, respectively. For the seasonal patterns of RNDVI−T, the regions with a significant downward trend in all seasons were higher than those with a significant upward trend. We also found a more significant and accelerating decrease of RNDVI−T for warm years compared to cold years, implying a decoupling or even a reverse correlation between NDVI and air temperature with continuous climate warming over the TC. Overall, our study provided evidence that the impact of Tmean, Tmax, and Tmin on vegetation activities exhibited a weakening trend and cautioned using results from interannual time scales to constrain the decadal response of vegetation growth to future global warming. [ABSTRACT FROM AUTHOR]

The Yangtze River Delta (YRD) region, located over East China, is an international urban agglomeration radiating Asia Pacific with convenient transportation, advanced manufacturing industry, and modern service industry. The intensive human activities have exerted substantial effects on the weather and climate over the YRD region during the past decades, thus bringing more challenges to accurate weather and climate forecasting. However, the weekly cycles of surface air temperature and precipitation linked closely to human activities and their corresponding physical processes remain insufficiently understood. Here, we investigate the weekly cycles of summertime surface air temperature and precipitation and the weekend effect over the YRD region for the period of 2008–2019 using high-resolution observational data and reanalysis data. The results demonstrate that lower surface air temperature and higher precipitation generally manifest during weekends compared to weekdays over the YRD region particularly over the areas with intensive human activities. Further analysis of the underlying physical processes points to the aerosol-cloud-radiation interaction which largely explains the weekend effect in summertime temperature and precipitation over the YRD region. [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]

Public spaces in residential areas are the main places for the daily activities of residents, and thermally comfortable activity space is particularly important for promoting the activities and public health of residents. Previous studies have mostly focused on tropical regions, with less research on outdoor thermal comfort in subtropical regions. In this study, we aimed to investigate outdoor thermal benchmarks and outdoor activities in summer subtropical residential areas. Through physical measurements, questionnaires, and field observations, four residential areas in Changsha city were studied. The outdoor thermal comfort and activity of residents were studied with the physiological equivalent temperature (PET) as a model. The results showed that: (1) the neutral PET and neutral PET ranges for residents were 26.2 °C and 24.7–27.8 °C, respectively, and the range of 90% acceptable PET was lower than 28.5 °C; (2) globe temperature and air temperature were the primary factors influencing the thermal sensation of the residents; and (3) residents adapted to the thermal environment by adjusting their activity time and space in summer. These results help us to better understand the outdoor thermal comfort and activities of people living in subtropical regions and can provide guidance for the design of future residential open spaces. [ABSTRACT FROM AUTHOR]

Reanalysis datasets have been widely used in meteorological research, including studies of Northeast China cold vortices (NCCVs), where these datasets act as effective substitutes for observations. However, to date, no studies have focused on their performance in reproducing NCCVs. To address this knowledge gap, we adopted an automatic three-step identification algorithm (TIA) and used it to detect NCCVs from ERA5 and MERRA-2 reanalysis datasets spanning 39 warm seasons (May–September) during the period from 1980 to 2018. A comparative method was employed for a rough verification of the characteristics of the reproduced NCCVs. Moreover, a dataset derived from 1370 Chinese ground-based observational stations was used to verify the performance of the reanalysis models in reproducing the precipitation and air temperature associated with NCCVs. The results show that the TIA identified the majority of NCCVs, with an accuracy of approximately 90% from ERA5 or MERRA-2. Both reanalysis models can reproduce the characteristics of NCCVs (including location, strength, and duration), and both replicate air temperature better than precipitation. ERA5 and MERRA-2 showed strong consistency in reproducing the central longitude, central latitude, central height, and range of NCCVs, with correlation coefficients of 0.974, 0.972, 0.996, and 0.919, respectively, at the 99.9% significance level. The daily average 2-m temperatures in both reanalysis datasets were in good agreement with observations; however, overestimations of approximately 7°–8°C arose in steep high-altitude regions. In addition, both models tended to overestimate light rain (≤5 mm day−1) by approximately 1.2 mm and underestimate heavy rain (≥20 mm day−1) by over 6.7 mm. [ABSTRACT FROM AUTHOR]

Foxtail millet (Setaria italica (L.) P. Beauv.) is an important traditional cereal crop in dryland ecological agriculture in China and is widely grown in India, the United States, and Nigeria. It is of significance to understand the variety–location–fertilizer (V–L–F) interaction for highly efficient production. Therefore, a two-year field experiment was conducted with six varieties in five locations, and data were analyzed by combined ANOVA analysis, redundancy analysis (RDA), and additive main multiplicative interaction (AMMI). The results showed that the mean sum of squares was significantly different among years, locations, varieties, fertilizations, and their interactions, except for Y–V and V–F interactions. The contributions of various factors to yield variation varied, location was the largest contributor (38.7%), followed by year (33.6%), and variety and fertilizer contributed 7.1% and 3.2%, respectively. JI25 was widely adapted, and its yield was stable and higher than that of others over diverse environments in two years. The RDA results showed that two principal components explained more than 66.1% of the yield variance, while more than 63.0% of the variances were clustered in the first factor. Excessive single rainfall or total rainfall and air temperature (especially minimum temperature) were significantly associated with the millet yield. The results offered an important reference for variety layout, natural resource potential mining, and formulation of efficient green cultural practices. [ABSTRACT FROM AUTHOR]

The spatiotemporal characteristics of dry-wet trends were identified and assessed, and the dominant meteorological factors were identified for the climate of Jiangsu province in humid southeastern China for the period 1960–2019. We conducted the research using data for the entire Jiangsu province as well as three major regions in Jiangsu (Huaibei, Jianghuai, and Sunan) with different regional climates. The results showed that decreased precipitation and relative humidity in spring and autumn over the study period were mainly responsible for the dry trends of the climates of all three regions and the entire province. Precipitation had a greater influence in spring and relative humidity in autumn. Decreases in sunshine hours and wind speed were responsible for the summer wet trends of the climates of Huaibei and Jianghuai and the entire province. However, precipitation increased significantly in the summer and was responsible for the increasing wet trend in Sunan. Significantly increased precipitation in winter was primarily responsible for the increasing wetness in Jianghuai and Sunan and the entire province in that season. However, the wet trend in northern Huaibei in winter was mainly caused by the decrease in wind speed over the study period. For the growing season and annually, the positive effects of changes in wind speed, sunshine hours, and precipitation led to increased humidity index in Jianghuai, Sunan, and the entire province. Precipitation showed a decreasing trend that countered the positive effects of decreases in wind speed and sunshine hours, which resulted in a slight decrease in the humidity index in Huaibei for both the growing season and annually. Sensitivity analysis indicated that the humidity index was positively sensitive to precipitation and relative humidity and negatively sensitive to air temperature, wind speed, and sunshine hours in Jiangsu province during 1960–2019. Overall, the humidity index in this region of southeastern China was most sensitive to changes in precipitation followed, in order of sensitivity, by sunshine hours, air temperature, wind speed, and relative humidity. Our findings provide a theoretical basis for adjusting irrigation programs and efficient utilization of water resources at the regional scale in humid southeastern China. [ABSTRACT FROM AUTHOR]

A heatwave (HW) is a spatiotemporally contiguous event that is spatially widespread and lasts many days. HWs impose severe impacts on many aspects of society and terrestrial ecosystems. Here, we systematically investigate the influence of the selected threshold method (the absolute threshold method ( A B S ), quantile-based method ( Q T L ), and moving quantile-based method ( Q T L m o v )) and selected variables (heat index ( H I ), air temperature ( T a i r )) on the change patterns of spatiotemporally contiguous heatwave (STHW) characteristics over China from 1961–2017. Moreover, we discuss the different STHW change patterns among different HW severities (mild, moderate, and severe) and types (daytime and nighttime). The results show that (1) all threshold methods show a consistent phenomenon in most regions of China: STHWs have become longer-lasting (6.42%, 66.25%, and 148.58% HW days ( HWD ) increases were found from 1991–2017 compared to 1961–1990 corresponding to A B S , Q T L , and Q T L m o v , respectively, as below), more severe (14.83%, 89.17%, and 158.92% increases in HW severity ( HWS ) increases), and more spatially widespread (14.92%, 134%, and 245.83% increases in the summed HW area ( HWA sum )). However, the HW frequency ( HWF ) of moderate STHWs in some regions decreased as mild and moderate STHWs became extreme; (2) for threshold methods that do not consider seasonal variations (i.e., A B S and Q T L ), the spatial H I exceedance continuity was relatively weak, thus resulting in underestimated STHW characteristics increase rates; (3) for different variables defining STHWs, the relative changing ratio of the H I -based STHW was approximately 20% higher than that of the T a i r -based STHW for all STHW characteristics, under the Q T L m o v threshold; (4) for different STHW types, the nighttime STHW was approximately 60% faster than the daytime STHW increase considering the Q T L threshold and approximately 120% faster for the Q T L m o v method. This study provides a systematic investigation of different STHW definition methods and will benefit future STHW research. [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]

Land surface temperature (LST) is an essential variable for high-temperature prediction, drought monitoring, climate, and ecological environment research. Several recent studies reported that LST observations in China warmed much faster than surface air temperature (SAT), especially after 2002. Here we found that the abrupt change in daily LST was mainly due to the overestimation of LST values from the automatic recording thermometer under snow cover conditions. These inhomogeneity issues in LST data could result in wrong conclusions without appropriate correction. To address these issues, we proposed three machine learning models—multivariate adaptive regression spline (MARS), random forest (RF), and a novel simple tree-based method named extreme gradient boosting (XGBoost)—for accurate prediction of daily LST using conventional meteorological data. Daily air temperature (maximum, minimum, mean), sunshine duration, precipitation, wind speed, relative humidity, daily solar radiation, and diurnal temperature range of 2185 stations over 1971–2002 from four regions of China were used to train and test the models. The results showed that the machine learning models, particularly XGBoost, outperformed other models in estimating daily LST. Based on LST data corrected by the XGBoost model, the dramatic increase in LST disappeared. The long-term trend for the new LST was estimated to be 0.32° ± 0.03°C decade−1 over 1971–2019, which is close to the trend in SAT (0.30° ± 0.03°C decade−1). This study corrected the inhomogeneities of daily LST in China, indicating the strong potential of machine learning models for improving estimation of LST and other surface climatic factors. [ABSTRACT FROM AUTHOR]

Termination II (T‐II) is a natural reference for evaluating the potential effect of substantial ice sheets melting on monsoon and temperature changes caused by global warming. However, the phase relationship of East Asian summer monsoon (EASM) and temperature during T‐II remains enigmatic due to the lack of paleotemperature proxies. Here, we provide high‐resolution mean annual atmospheric temperature (MAAT) estimates during T‐II in northern China using speleothem clumped isotopes. Compared with present, the maximum MAAT at the last interglacial was 1.8 ± 1.5°C higher, and the minimum MAAT at the penultimate glaciation was 6.8 ± 1.3°C lower. EASM and temperature decoupled during the first 2100 years of the record and coupled onwards. This variable phase relationship is plausibly modified by the strength of the cold anomaly in North Atlantic triggered by collapse of ice sheets. When the cold anomaly exceeds a boundary condition, it could not only weaken EASM but also lower temperature. Plain Language Summary: Ice Age Termination II (T‐II) represents the transition from the penultimate glaciation (PG) to the last interglacial (LIG). It is a natural reference for evaluating the potential effect of substantial ice sheets melting caused by global warming on monsoon and temperature changes. Speleothem oxygen and clumped isotopes are monsoon and temperature proxies, respectively. We reconstructed the evolution of monsoon and temperature during T‐II using theses proxies from a northern Chinese stalagmite. We found that, it was ∼2°C warmer at the LIG and ∼7°C colder at the PG than present in East Asia. Interestingly, East Asian monsoon and temperature decoupled initially and coupled onwards during T‐II, which is plausibly ascribed to the modulation of extreme cooling triggered by collapse of great ice sheets and resultant large freshwater flux during deglacial. Key Points: Mean annual atmospheric temperature estimates during Termination II (T‐II) in northern China using speleothem clumped isotopesCompared with present, it was ∼2°C warmer at the last interglacial and ∼7°C colder at the penultimate glaciationVariable phase relation of East Asian monsoon and temperature at T‐II is due to the modulation of the cold anomaly in North Atlantic [ABSTRACT FROM AUTHOR]

Vegetation activity and phenology are significantly affected by climate change, and changes in vegetation activity and phenology can in turn affect regional or global climate patterns. As one of the world’s great biomes, temperate grasslands have undergone remarkable changes in recent decades, but the connections between vegetation activity and phenology changes and regional climate there have remained unclear. Using the observation minus reanalysis (OMR) method, this study investigated the possible effects of vegetation activity and vegetation growing season changes on air temperatures in temperate grasslands of China. The results showed that average NDVI of the temperate grassland significantly increased by 0.011 decade21 for the growing season during 1982–2015. The growing season started earlier and ended later, resulting in an extension. Increased vegetation activity during spring and autumn significantly warmed spring and autumn air temperatures by reducing albedo. By contrast, summer greening had no significant effect on summer temperature, due to the opposing effects of decreased albedo and enhanced evapotranspiration on temperature. The earlier start and later end of the growing season contributed to warmer spring and autumn air temperatures. As phenological changes had no significant effect on summer temperature, the extended growing season warmed air temperature. Our results suggest that the climate change–induced increasing vegetation activity and extended growing seasons can further aggravate regional warming in temperate grasslands of China, implying that the effects of vegetation activity and phenology changes on regional climate should be considered in climate models for accurately simulating climate change in temperate grasslands. [ABSTRACT FROM AUTHOR]

This study investigated the impacts of horizontal resolution of lateral boundary condition (LBC) and downscaling method on the performance of RegCM4.6 in simulating the 2 m air temperature and precipitation over central eastern China. Two groups of experiments (direct downscaling experiments using LBC derived from the same reanalysis data but with different horizontal resolutions [0.75°, 1.5°, and 2.5°] and double‐nested downscaling experiment) with the model horizontal resolution of 20 km over central eastern China have been carried out. Results show that the direct downscaling experiments consistently outperform the double‐nested experiment to simulate the 2 m air temperature and precipitation over central eastern China, suggesting that adopting the direct downscaling method is appropriate to simulate the surface climate over central eastern China. Meanwhile, the direct downscaling experiments driven by the LBC derived from the reanalysis data with the horizontal resolution ranging from 0.75° to 2.5° display very comparable performance with a little better performance by adopting the LBC derived from the reanalysis data of much finer resolution. Further mechanism analysis indicates that much larger errors of LBC produced by the double‐nested downscaling method lead to the positive pressure biases and negative biases of water vapor convergence flux over Jianghuai Valley, which contribute to the much larger biases of precipitation and 2 m air temperature in the double‐nested downscaling experiment compared to the direct downscaling experiments. This study emphasizes that the direct downscaling method is superior to the double‐nested downscaling method for the simulations of 2 m air temperature and precipitation over central eastern China. Key Points: Direct downscaling method is more suitable to simulate the surface climate over central eastern China than double‐nested approachLimited impact of the horizontal resolution of reanalysis data for lateral boundary condition generation on the model performance by adopting direct downscaling [ABSTRACT FROM AUTHOR]

With the intensification of global warming, extreme precipitation events occur frequently all over the world. Extreme precipitation has brought huge challenges to the development of human society and economy, and it is urgent to strengthen the analysis of the causes of extreme precipitation. In this study, we first extracted precipitation events from the precipitation time series from 1960 to 2018 in the Huaihe River Basin (HRB) and then extracted extreme precipitation events based on precipitation amount and precipitation intensity. The results show that extreme precipitation in the HRB has an increasing trend after 2000, although the increasing trend is not obvious, and the uncertainty of the occurrence of extreme precipitation events had increased. Extreme precipitation mainly had a strong correlation with the surface air temperature in the North Pacific, Indian Ocean, and Qinghai–Tibet Plateau. The El Niño year and the year of abnormal circulation contribute to the production of extreme precipitation in the HRB. In the years when extremely heavy precipitation occurred in the HRB, the subtropical high was northerly. Although most of the water vapor is brought from the Pacific by the East Asian monsoon, the water vapor that causes extreme precipitation in the HRB mainly comes from the Indian Ocean. Through multivariate wavelet coherence (MWC) analysis, annual precipitation is greatly affected by multiple climate variables, while extreme precipitation is greatly affected by a single climate variable. This study provides an important reference for the analysis and prediction of extreme precipitation. [ABSTRACT FROM AUTHOR]

Based on the daily national meteorological data in spring in 2022, relationships between meteorological factors and agricultural production in China were analyzed using statistical methods. The results showed that the national average air temperature in spring in 2022 was 12.1Ž, which was the highest during the same period since 1961. The national average air temperature in the first ten days and the second ten days in March, and also in the first ten days in April, was abnormally higher than the same period from 1991 to 2020. The national average air temperature in the second ten days in May was obviously lower than the same period in the normal year. The year 2022 was a typical cold year in late spring. The national average precipitation in spring in 2022 was 154.0 mm, which was more than that in the same period in the normal year. In particular, the average precipitation in Sichuan in spring this year was the most since 1961. The national average sunshine was 649.2 hours, close to the same period in the normal year. The suitable sunshine and temperature, and also good soil moisture conditions were favorable for winter wheat growth with delayed sowing date in autumn in 2021 in Northern China, and the delayed-sowing winter wheat growing conditions continued to improve and upgrade in spring in 2022. The meteorological conditions in spring in 2022 were generally conducive to winter wheat growth and yield increase per hectare. The soil moisture and temperature conditions were suitable in most of the spring sowing areas, and the influence of phased low temperature was relatively light, which was conducive to the sowing and emergence of crops in spring. The progress of spring sowing was generally faster than that of the previous year. The seedlings growed well. It was sunny during most of winter wheat mature and harvest period in southern China, and the wheat harvest progressed well. But obvious drought in western Liaoning was unfavorable to spring sowing. In the late spring, heavy rainfall occurred in the east of Jianghan, the south of Jianghuai, the west of South China and the Southwest China. There were many rainy days and little sunshine in the south of Southwest China. At the end of spring, hot and dry weather appeared in some wheat areas in Eastern North China, Huang Huai. The bad weather had a certain negative impact on spring sowing and late grain filling of winter wheat in some of the above regions. [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]

The relationship between land surface temperature (LST) and environmental factors is complex and nonlinear. To determine these relationships for China, this study analyzed the driving effects of air temperature, vegetation index, soil moisture, net surface radiation, precipitation, aerosols, evapotranspiration, and water vapor on LST based on remote-sensing and reanalysis data from 2003–2018, using a convergent cross-mapping method. During the study period, air temperature and net surface radiation were the dominant drivers of LST with a cross-mapping skill above 0.9. Vegetation index and evapotranspiration were the secondary drivers of LST with a cross-mapping skill that was higher than 0.5. Except for air temperature and net surface radiation, the direction and strength of the effects of the driving factors on LST were related to the climate type. The effects of air temperature and net radiation on LST diminished from north to south, indicating that LST was more sensitive to air temperature and net radiation in energy-limited regions. However, the effects of vegetation index and evapotranspiration on LST varied significantly across climate zones; that is, positive effects were mostly in non-monsoonal zones and negative effects were primarily in monsoonal zones. Our results quantified the driving role of environmental factors on LST and provided a comprehensive understanding of LST dynamics. [ABSTRACT FROM AUTHOR]

An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at 10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at 10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature, and at 10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature. [ABSTRACT FROM AUTHOR]

Harbin, located in northeast China (NEC), has obvious monsoon climate characteristics due to the influence of its geographical environment. Under the control of the polar continental air mass, winter in Harbin is exceedingly cold and long, with the frequent invasion of the cold and dry air from the north. Because of its intensely cold climate in winter, Harbin has created a local form of tourism with its own characteristics: the snow and ice landscape attracts a large number of tourists. Therefore, the anomalies of air temperature and precipitation in winter have an important impact on the livelihood of the local people and economy. In the winter of 2018/2019, the ice and snow tourism in Harbin was harshly affected by the extreme weather, and the direct cause is the anomalies of atmospheric circulation. There is a center of strong positive geopotential height anomalies over east China, which favors the movement of warm air northwards to the NEC, resulting in warmer-than-normal air temperature. Anomalous precipitation is largely controlled by the anomalies of local water vapor and air temperature. The aim of this study was to determine whether the warmer-than-normal temperature, which made the atmosphere more resistant to saturation, was the primary cause of the reduced snowfall. The relative importance of water vapor and air temperature anomalies to the anomalous precipitation was compared. The results suggest that the warmer-than-normal temperature affected all levels, but its impact on the near-surface level was greater. At the middle and upper levels (above 850 hPa), in addition to the warmer-than-normal temperature, the amount of water vapor was less than normal. These conditions both reduced the amount of snow; however, by comparison, the dryness of the air contributed more significantly. [ABSTRACT FROM AUTHOR]

Air/soil temperatures play important roles in land–atmosphere interactions. The three-dimensional (temporal, spatial, and vertical) variations of maximum, mean, and minimum ground soil temperature at 0 cm (GSTx, GSTm, and GSTn, respectively), surface air temperature at 2 m (SATx, SATm, and SATn, respectively), and soil–air temperature difference (SATDx, SATDm, and SATDn, respectively) and their potential linkages with large-scale indexes in Southwest China during 1980–2019 were analyzed. Variations of GST and SAT at the majority of stations (pixels) exhibited significant (p ≤ 0.05) warming, albeit at different rates; consequently, SATD exhibited different variation. Moreover, the period of GST, SAT, and SATD was similar in intra-annual and interannual oscillation but was different in interdecadal oscillation. The variation rate of GST, SAT, and SATD exhibited significant (p ≤ 0.05) correlation with elevation, but with different variation gradient. Notably, asymmetric variation of SATDx (downward trend) and of SATDn (upward trend) with elevation was found at elevations >3 km. Wavelet coherence showed that the Atlantic Multidecadal Oscillation is the dominant factor affecting GST and SAT, whereas the Pacific Decadal Oscillation and the North Atlantic Oscillation make the greatest contributions to SATD. It was found that GST, SAT, and SATD exhibit different variations under the effects of global warming, the driving mechanism of which requires further study. [ABSTRACT FROM AUTHOR]

The transfer process and variation characteristics of water and heat fluxes play an important role in both the ecosystem function and the climatic patterns of arid and semi-arid areas. However, both water (latent heat flux, Fe) and heat (sensible heat flux, Fh) fluxes, and the factors impacting each, operate on different time scales. Therefore, the wavelet transformation was used to reconstruct water and heat flux data measured by an eddy covariance system installed in Horqin sandy land, China. The distribution and response regularity of water and heat fluxes in response to control factors were then analysed across different time scales. The results indicated that the responses of Fe and Fh to controlling factors were similar on 30 min and daily scales, to which the energy terms of net radiation (Fn) and available energy (Fa) showed an extremely significant positive contribution. Removing the interference of the energy terms, Fe and Fh are strongly correlated with the vapour pressure deficit (VPD) and air temperature (Ta) at the daily scale. The water conditions of VPD and relative humidity (RH) were the main influencing factors at the monthly scale. At the seasonal scale, Fe was positively correlated with Fa and Fn but negatively correlated with soil heat flux (Fg), RH and wind speed (WS). On the contrary, Fh was positively correlated with RH but negatively correlated with Fn. At the annual scale, Fe and Fh showed a significant positive correlation with all factors. By the increasing time scale, the cross-correlations between various hydroclimatic processes become better. Generally, at the daily and annual scales, there is a high correlation between Fe, Fh and the control factors, and a pronounced periodicity is exhibited. Another factor that is known to cause such effects is the so-called long-term persistence (or else long-range dependence or the Hurst phenomenon). These results suggest that the time scales of flux and impacting factors should be considered when undertaking water and heat flux analyses of sandy land and other ecosystems. [ABSTRACT FROM AUTHOR]

The rising of near‐surface air temperature is the most obvious manifestation of global warming. The analysis of the probability distribution function (PDF) of temperature can deep our understanding of the current characteristics of climate and extreme temperature changes. In this study, the daily maximum, minimum and mean temperature observations from meteorological stations are analysed to construct the temperature anomaly PDF during 1961–2016 over China. The skewness and kurtosis of the PDF are compared between the 1961–1990 and 1991–2016 periods. The non‐Gaussian tails of the PDF are also identified to qualify the deviation of the temperature probability density curve. The results show that near‐surface air temperature in China increased during the recent decades. The daily minimum temperature increased more than the daily maximum temperature did, while the temperature increase in winter was larger than that in summer. The largest increase detected was 1.29°C for the daily minimum temperature in winter, contrasting with the smallest increase of 0.51°C recorded for the daily minimum temperature in summer between the two study periods. The temperature anomaly PDF generally presented a positive skewness in winter and a slightly negative skewness in summer. Obvious non‐Gaussian tails in the temperature anomaly PDF were present in the majority of the study sites. Most areas showed a short cold tail in winter (except for southwestern China), and long cold tails were mainly observed in summer. These results can improve our understanding of the variation of extreme temperatures, and of the responses to global warming. [ABSTRACT FROM AUTHOR]

Farmland shelterbelts provide an ecological barrier to oases, but their growth is inhibited or even dies under water scarcity. Therefore, it is necessary to understand the response of farmland shelterbelts to environmental changes for their own survival in order to promote the long-term healthy development of agroforestry systems and improve water management in oases in the northwest arid region. In this study, we measured the sap flow and environmental factors to determine the transpiration dynamics and its influencing factors. The seasonal variations in water use strategy of Populus alba var. pyramidalis Bunge were identified by using stable isotopes of δ18O in xylem, soil water and groundwater coupled with the Iso-Source model. The results showed that the transpiration of the populus was significantly higher in the growing season (more than 80% of the total transpiration) than in the non-growing season, and the transpiration was closely related to air temperature, vapor pressure deficit, soil water content, and soil temperature. The water use strategy had seasonal variations. The populus obtained water predominantly from 0–40 cm soil water (approximately 75%) in the wet spring and then switched to deeper water sources (40–150 cm soil water and groundwater) as the season progressed. The contribution of deeper water sources increased to 84–92% in summer and autumn. This suggested that Populus alba var. pyramidalis Bunge has ecological plasticity, as it could explore deeper water sources as the water stress increased. The ability to switch rapidly among different water sources could put a plant at an advantage if competition for water occurs within the ecosystem. Our findings can provide insights into temporal transpiration dynamics as well as the strategy adopted by farmland shelterbelts to counteract the drought stress associated with the shallow soils in desert oasis ecosystems. [Display omitted] • The growing season's shelterbelt transpiration accounted for about 80% of total annual transpiration. • Transpiration in the desert oasis shelterbelt is more sensitive to deep soil water. • Shelterbelt water sources changed from 0–40 cm soil water to deeper sources as the season progressed. • The shelterbelt coped with variations in soil water availability through root plasticity. [ABSTRACT FROM AUTHOR]

The middle and lower reaches of the Yangtze River (MLRYR) exhibit notable interannual variability in seasonal-mean air temperature amid ongoing global warming. This study elucidates the specific mechanisms governing anomalous air temperatures during early (June) and late summer (July to August) in the MLRYR. Early summer temperatures arise from anomalous sinking driven by negative anomalies in moist enthalpy advection in the lower troposphere, as a response to warm sea surface temperature anomalies (SSTA) in the tropical western Pacific. This induces a low-level cyclone anomaly over Southeast Asia, reducing specific humidity and determining anomalous vertical motion over the MLRYR. In contrast, late summer temperatures are induced by mid-upper tropospheric descending anomalies over the MLRYR. The combined influence of SSTA in the western Pacific and North Atlantic sustains warmer air by reinforcing a barotropic anticyclone over North China through a mid-latitude wave train. Subsequent anomalous negative potential vorticity advection in the upper troposphere strengthens sinking motion over the MLRYR. The seasonal evolution of the East Asian summer monsoon and SSTA is pivotal in the distinct mechanisms governing warmer air temperatures over the MLRYR. While numerical experiments capture general features, the regulation of SSTA is subject to uncertainty due to the atmospheric intrinsic patterns. This research distinguishes predictability sources of air temperature anomalies over the MLRYR between early and late summer, emphasizing the combined influence of SSTA beyond the ENSO domain on East Asian climate. • Hotter summers in the MLRYR primarily come from the persistent higher temperature in situ. • The mechanisms for the MLRYR higher temperature are distinct between early and late summer. • SSTA outside the ENSO domain determines monthly evolution of summer air temperature anomalies over the MLRYR. [ABSTRACT FROM AUTHOR]

• Evaporative fraction increased by 16–49 % on cloudy days compared with clear days. • Clearness index controlled evaporative fraction through direct and indirect paths. • High NDVI reduced evaporative fraction response to cloudiness. Cloudiness influences gross primary productivity and evapotranspiration in terrestrial ecosystems by increasing diffuse radiation and improving the photosynthesis of shaded leaves. However, the effect of cloudiness on energy partitioning in terrestrial ecosystems is not well understood. Using latent heat (LE) and sensible heat (H) datasets observed by the eddy covariance systems and meteorological variables, the regulation of biophysical factors on energy partitioning under different sky conditions was investigated at 6 plantation ecosystems (2 deciduous broadleaf and 4 coniferous plantations) in eastern China from 2019 to 2021. The averages of evaporative fraction (EF) during the growing season were 0.34±0.06–0.50±0.04. The EF values increased by 16−49% on cloudy days, which was attributed to the lower decrease in LE compared to H. On the half-hourly scale, EF was mainly driven by air temperature (T a), relative extractable soil water (REW) and clearness index (CI) (i.e., a metric to quantify cloudiness). For plantation ecosystems, CI had an indirect effect on EF mainly through net radiation (R n). Moreover, the positive effect of cloudy sky conditions on EF was higher under low T a , vapor pressure deficit (VPD) and REW conditions. The increment of EF on cloudy days was weaker at high normalized difference vegetation index, which may be explained by higher T a and VPD limiting canopy transpiration during the mid-growing season. Cloudiness can also influence energy partitioning by regulating stomatal behavior in plantation ecosystems. These results contribute to understanding the drivers of energy partitioning in response to cloudiness in plantation ecosystems. [ABSTRACT FROM AUTHOR]

In the scientific literature on diatom ecology whether temperature drives changes in diatom assemblage directly or indirectly remains a subject of debate. In order to clarify the relationship between temperature and diatom assemblage composition, we focused on a region with significant seasonal changes but without a freezing period. From March 2016 to March 2018, sediment trap samples were collected monthly to monitor the seasonal succession of diatoms in Douhu Lake, in subtropical southeast China. The results show that in 2016 the variations in abundance of Aulacoseira granulata and Aulacoseira ambigua followed an obvious seasonal pattern, corresponding to the seasonal variation in water and air temperature. In 2016, the thermophilic taxon A. granulata was dominant in the warm season, and in the cold season its abundance declined, while that of A. ambigua, a taxon better adapted to cooler conditions, increased relatively. In 2017, however, there was no such typical seasonal variation. In the cold season of 2017, A. granulata did not decline and there was no significant increase in the cold-preferring A. ambigua. This was related to the large drop in the lake level associated with human activity (substantial water extraction) that occurred during the winter of 2017, which disturbed the natural seasonal fluctuation in water temperature. Thus, in 2017, while air temperature followed the regular seasonal cycle, lake water temperature did not decrease significantly in winter, which allowed A. granulata to maintain a relatively high abundance in the cold season. From these results and an extensive survey of the literature, we conclude that water temperature, more than the seasonal succession, directly drives the fluctuation in abundance of these two Aulacoseira species. [ABSTRACT FROM AUTHOR]

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed 'bell-shaped' curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region. [ABSTRACT FROM AUTHOR]

Monitoring drought precisely and evaluating drought effects quantitatively can establish a scientific foundation for understanding drought. Although solar-induced chlorophyll fluorescence (SIF) can detect the drought stress in advance, the performance of SIF in monitoring drought and assessing drought-induced gross primary productivity (GPP) losses from lush to senescence remains to be further studied. Taking the 2019 drought in the middle and lower reaches of the Yangtze River (MLRYR) as an example, this study aims to monitor and assess this drought by employing a new global, OCO-2-based SIF (GOSIF) and vegetation indexes (VIs). Results showed that the GPP, GOSIF, and VIs all exhibited significant increasing trends during 2000–2020. GOSIF was most consistent with GPP in spatial distribution and was most correlated with GPP in both annual (linear correlation, R2 = 0.87) and monthly (polynomial correlation, R2 = 0.976) time scales by comparing with VIs. During July–December 2019, the precipitation (PPT), soil moisture, and standardized precipitation evapotranspiration index (SPEI) were generally below the averages during 2011–2020 and reached their lowest point in November, while those of air temperature (Tem), land surface temperature (LST), and photosynthetically active radiation (PAR) were the contrary. For drought monitoring, the spatial distributions of standardized anomalies of GOSIF and VIs were consistent during August–October 2019. In November and December, however, considering vegetation has entered the senescence stage, SIF had an obvious early response in vegetation physiological state monitoring compared with VIs, while VIs can better indicate meteorological drought conditions than SIF. For drought assessment, the spatial distribution characteristics of GOSIF and its standardized anomaly were both most consistent with that of GPP, especially the standardized anomaly in November and December. All the above phenomena verified the good spatial consistency between SIF and GPP and the superior ability of SIF in capturing and quantifying drought-induced GPP losses. Results of this study will improve the understanding of the prevention and reduction in agrometeorological disasters and can provide an accurate and timely method for drought monitoring. [ABSTRACT FROM AUTHOR]

The Northeast China cold vortex (NCCV) often occurs in spring and summer, causing extreme weather such as rainstorm and hail in Northeast China. The brightness temperature (TB) observations of Microwave Temperature Sounder-2 (MWTS-2) on board Fengyun-3D (FY-3D), which can provide atmospheric temperature in various vertical layers, are firstly limb-corrected and then applied to track the origin and movement of four NCCV cases in June and July 2019. Results show that a cold core is observed at the location of NCCVs in TB observations of channels 4 and 5, whose peak weighting function (WF) altitudes are 700 and 400 hPa, respectively, indicating the cold structure of NCCVs in the middle and lower troposphere. The TB observations of channels 6 and 7, which measure the atmospheric temperature around 250 and 200 hPa, respectively, capture a warm core structure of NCCVs in the upper troposphere and lower stratosphere. Being less affected by the low-level cloud and rain, TB observations of channels 6 and 7 are applied to identify and track the warm cores of NCCVs. The NCCV tracks of movement identified by MWTS-2 observations compare well with those determined by the 500 hPa geopotential height and the 300 hPa potential vorticity (PV) anomaly from the ERA5 reanalysis. Both clearly show that the NCCVs were originated from high latitudes, then moved southeastward, and finally entered Northeast China. The entire process took several days. Therefore, TB observations of MWTS-2 can be used to identify the precursors of NCCVs and monitor their appearances, developments, and movements in time. With the flourishing development of Fengyun satellite series in China as well as the already existing 40 years of microwave sounder observations worldwide, this research provides a new way to investigate the synoptic and climatological features of NCCVs. [ABSTRACT FROM AUTHOR]

Climacium dendroides is a rare bryophyte resource with important medicinal value, which is mainly concentrated in the southwest and northeast China. In recent years, we found in the wild investigation that the population of C. dendroides had shrunk. In our study, based on high-resolution environmental variables and geographical occurrence records, the Maximum Entropy Model (MaxEnt) combined with ArcGIS was used to predict the spatial-temporal distributional pattern of C. dendroides in China under the current and future scenarios. Simulation results revealed that the total suitable area decreases by more than a third (–36.02%) in future scenarios. Further, the distributional centers moved to the northwest. Annual precipitation, max temperature of warmest month, and annual mean air temperature were the key environmental factors affecting the distribution of C. dendroides. Global warming coupled with the adverse effects of anthropogenic activities might accelerate the disappearance of suitable habitats for C. dendroides. Overall, our research provides useful indications for protecting the diversity of medical bryophytes and for their conservation. Supplemental data for this article is available online at http://dx.doi.org/10.1080/11263504.2021.1906348 [ABSTRACT FROM AUTHOR]

In this study, we used hourly observations to investigate the cooling effect of summer rainfall on surface air temperature (Ta) in a subtropical area, Guangdong province, South China. Data were categorized step-by-step by rainfall system (convection, monsoon, and typhoon), daily rainfall amount, and relative humidity (RH) level. Moreover, the average hourly Ta variation due to solar radiation was removed from all observations before statistical analysis. The results showed that the linear relationship between hourly Ta variation and rainfall intensity did not exist. However, the cooling effect of rainfall on Ta variation was dominant. In addition, convective rainfall does cause a greater temperature drop than the other two rainfall systems. After further partitioning all samples by RH level preceding the rainfall, the relationship between hourly Ta variation and rainfall intensity became distinctive. When RH was below 70%, rainfall-induced cooling became more substantial and scaled linearly with event intensity, but when RH exceeded 70%, the rainfall cooling effect was generally restrained by the RH increase. A strong correlation between hourly Ta variation and RH level preceding the rainfall suggests the importance of RH on the rainfall cooling effect. [ABSTRACT FROM AUTHOR]

To evaluate the effects of temperature and lactic acid bacteria (LAB) inoculants on oat silage in Loess Plateau of China, oat was harvested at dough stage, inoculated without (Control) or with LAB inoculants Synlac I (SLI, Lactobacillus plantarum and Pedioccocus acidilactici) and a selected strain HT1 (L. rhamnosus) and ensiled at 25°C (T25), 35°C (T35) and 45°C (T45). The fermentation quality was measured after 60 d of ensiling and the aerobic exposure was conducted at 30°C for 9 d. The results showed that control silage (stored at 25°C) had better fermentation quality than that ensiled at 35°C or 45°C. High temperature of 45°C resulted in sharp decreases in LAB counts and lactic acid concentration and increases in pH and NH3-N concentration in the control group. Inoculation improved the fermentation quality, and HT1 was more effective than SLI at 35°C and 45°C, while SLI showed better performance at 25°C. All silages displayed mild fluctuation for all treatments at the first 3 d of aerobic exposure, and significant differences were observed among treatments after that. Both control and inoculated silages stored at 25°C showed a sharp pH increase, while HT1 treated silages stored at 35°C and 45°C maintained stable pH and better fermentation quality during the aerobic exposure. In conclusion, SLI was suitable for oat silage fermentation at normal atmospheric temperature (25°C), while HT1 was more effective in improving DM recovery, fermentation quality and aerobic stability of oat silage at high temperature during summer in the Loess Plateau of China. [ABSTRACT FROM AUTHOR]

Exploring the spatial distribution of the multi-fractal scaling behaviours in atmospheric CO2 concentration time series is useful for understanding the dynamic mechanisms of carbon emission and absorption. In this work, we utilise a well-established multi-fractal detrended fluctuation analysis to examine the multi-fractal scaling behaviour of a column-averaged dry-air mole fraction of carbon dioxide (XCO2) concentration time series over China, and portray the spatial distribution of the multi-fractal scaling behaviour. As XCO2 data values from the Greenhouse Gases Observing Satellite (GOSAT) are insufficient, a spatio-temporal thin plate spline interpolation method is applied. The results show that XCO2 concentration records over almost all of China exhibit a multi-fractal nature. Two types of multi-fractal sources are detected. One is long-range correlations, and the other is both long-range correlations and a broad probability density function; these are mainly distributed in southern and northern China, respectively. The atmospheric temperature and carbon emission/absorption are two possible external factors influencing the multi-fractality of the atmospheric XCO2 concentration. Highlight: (1) An XCO2 concentration interpolation is conducted using a spatio-temporal thin plate spline method. (2) The spatial distribution of the multi-fractality of XCO2 concentration over China is shown. (3) Multi-fractal sources and two external factors affecting multi-fractality are analysed. [ABSTRACT FROM AUTHOR]

We retrospectively investigate the skill of climate models within the IPCC TAR, AR4, and AR5 for the near-surface (2 m) air temperature changes in China. Those models are found to be skillful in projecting the subsequent climatology and trend of the temperature changes in China during 2002–2018 from several to ten years ahead, with higher scores for the climatology than for the trend. Among various emission scenarios for all the three assessment reports, although the long-term projections at the end of the 21st century are remarkably diverse, the near-term temperature projections differ slightly. [Display omitted] Climate change has attracted significant attention due to its increasing impacts on various aspects of the world, and future climate projections are of vital importance for associated adaptation and mitigation, particularly at the regional scale. However, the skill level of the model projections over China in the past more than ten years remains unknown. In this study, we retrospectively investigate the skill of climate models within the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) for the near-term projections of near-surface (2 m) air temperature changes in China. Those models are revealed to be skillful in projecting the subsequent climatology and trend of the temperature changes in China during 2002–2018 from several to ten years ahead, with higher scores for the climatology than for the trend. The model projections display cold biases against observations in most of China, while the nationally averaged trend is overestimated by TAR models during 2002–2018 but underestimated by AR4 models during 2008–2018. For all emission scenarios, there is no obvious difference between the equal- and unequal-weighted averages based on the arithmetic averaging and reliability ensemble averaging method respectively, however the uncertainty range of projection is narrowed after weighting. The near-term temperature projections differ slightly among various emission scenarios for the climatology but are largely different for the trend. [ABSTRACT FROM AUTHOR]

High-resolution gridded 2 m air temperature datasets are important input data for global and regional climate change studies, agrohydrologic model simulations and numerical weather predictions, etc. In this study, the digital elevation model (DEM) is used to correct temperature forecasts produced by ECMWF. The multi-grid variation formulation method is then used to fuse the data from corrected temperature forecasts and ground automatic station observations. The fused dataset covers the area over (0–60°N, 70–140°S), where different underlying surfaces exist, such as plains, basins, plateaus, and mountains. The spatial and temporal resolutions are 1 km and 1 h, respectively. The comparison of the fusion data with the verification observations, including 2400 weather stations, indicates that the accuracy of the gridded temperature is superior to European Centre for Medium-Range Weather Forecasts (ECMWF) data. This is because a more significant number of stations and high-resolution terrain data are used to generate the fusion data than are utilized in the ECMWF. The obtained dataset can describe the temperature feature of peaks and valleys more precisely. Due to its continuous temporal coverage and consistent quality, the fusion dataset is one of China's most widely used temperature datasets. However, data uncertainty will increase for areas with sparse observations and high mountains, and we must be cautious when using data from these areas. [ABSTRACT FROM AUTHOR]

Air temperature (Ta) is an essential parameter for science research and engineering practice. While the traditional site-based approach is only able to obtain observations in limited and discrete locations, satellite remote sensing is promising to retrieve some environmental variables with spatially continuous coverage. Nowadays, land surface temperature (Ts) measurements can be obtained from some satellite sensors (e.g., MODIS), further enabling us to estimate Ta in view of the relationship between Ta and Ts. In this article, we proposed a two-phase integrated framework to estimate daily mean Ta nationwide. In the first phase, multivariate linear regression models were fitted between site-based observations of daily mean air temperature (Ta-mean) and MODIS land surface temperature products (including Terra day: TMOD-day, Terra night: TMOD-night, Aqua day: TMYD-day, and Aqua night: TMYD-night) conditional on some covariates of environmental factors. The fitted models were then used to predict Ta-mean from those covariates at unobserved locations. The predicted Ta-mean were looked on as stochastic variables, and their distributions were also obtained. In the second phase, Bayesian maximum entropy (BME) methods were used to produce spatially continuous maps of Ta-mean taking the meteorological station observations as hard data and the predicted Ta-mean in the first phase as soft data. It is shown that the proposed approach is promising to improve the interpolation accuracy significantly, comprehensively considering the prior knowledge and the context of space variability and correlation, which will enable it to compile spatially continuous air temperature products with higher accuracy. [ABSTRACT FROM AUTHOR]

Background: Bulinus globosus, the main intermediate snail host of Schistosoma haematobium. The increased contacts between Africa and China could even lead to large-scale dissemination of B. globosus in China. Temperature is the key factor affecting fresh-water snail transmission. This study predicted potential risk of colonization of B. globosus in the mainland of China under climate change. Methods: We investigated minimum and maximum temperatures for B. globosus eggs, juveniles and adult snails kept under laboratory conditions to find the most suitable range by pinpointing the median effective temperatures (ET50). We also assessed the influence of temperature on spawning and estimated the accumulated temperature (AT). The average air temperatures between 1955 and 2019 in January and July, the coldest and hottest months in China, respectively, were collected from national meteorological monitoring stations and investigated in a geographic information system (GIS) using empirical Bayesian Kriging to evaluate the theoretical possibility for distribution of B. globosus in southern China based on temperature. Results: The effective minimum temperature (ET50min) for eggs, juveniles, adult snails and spawning were 8.5, 7.0, 7.0, 14.9 °C, respectively, with the corresponding maximum values (ET50max) of 36.6, 40.5, 40.2 and 38.1 °C. The AT was calculated at 712.1 ± 64.9 °C·d. In 1955, the potential B. globosus distribution would have had a northern boundary stretching from the coastal areas of Guangdong Province and Guangxi Autonomous Region to southern Yunnan Province. Since then, this line has gradually moved northward. Conclusions: Annual regeneration of B. globosus can be supported by the current climate conditions in the mainland of China, and a gradual expansion trend from south to north is shown in the study from 2015 to 2019. Thus, there is a potential risk of colonization of B. globosus in the mainland of China under climate change. [ABSTRACT FROM AUTHOR]