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Detecting changes in surface air temperature in mid- and low-altitude mountainous regions is essential for a comprehensive understanding of warming trend with altitude. We use daily surface air temperature data from 64 meteorological stations in Wuyi Mountains and its adjacent regions to analyze the spatio-temporal patterns of temperature change. The results show that Wuyi Mountains have experienced significant warming from 1961 to 2018. The warming trend of the mean temperature is 0.20°C/decade, the maximum temperature is 0.17°C/decade, and the minimum temperature is 0.26°C/decade. In 1961–1990, more than 63% of the stations showed a decreasing trend in annual mean temperature, mainly because the maximum temperature decreased during this period. However, in 1971–2000, 1981–2010 and 1991–2018, the maximum, minimum and mean temperatures increased. The fastest increasing trend of mean temperature occurred in the southeastern coastal plains, the quickest increasing trend of maximum temperature occurred in the northwestern mountainous region, and the increase of minimum temperature occurred faster in the southeastern coastal and northwestern mountainous regions than that in the central area. Meanwhile, this study suggests that elevation does not affect warming in the Wuyi Mountains. These results are beneficial for understanding climate change in humid subtropical middle and low mountains. [ABSTRACT FROM AUTHOR]

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid‐latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG‐induced warming was weaker and AA‐induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal‐to‐noise ratio. This weaker GHG‐induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal‐to‐noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable. [ABSTRACT FROM AUTHOR]

Frequent occurrences of extreme heats in recent years pose a threat to the local society, economy, and ecosystem in Southern China. Understanding the process and predictability of these heat events is crucial for scientific research and policy-making. The long-term variation characteristics and the corresponding driving factors of high-temperature days (HTDs) and surface air temperature (SAT) in midsummer in Southern China were analyzed by observed data of meteorological stations and atmospheric reanalysis data. The results reveal an abrupt rise in HTDs around the early 2000s during the period of 1979–2021, rather than a sustained increase. Prior to and after this mutation, no clear trend is observed. The changes in SAT align closely with the variations in HTDs. The abrupt increase in solar radiation, resulting from a decrease in total cloud cover, is identified as the direct cause of the SAT change. In the decade following the abrupt change point, significant anomalous descent motion and low-level divergent winds over Southern China are observed, creating favorable climatic conditions for the rise in SAT. The primary driving factor behind these circulation changes is identified as the Pacific interdecadal oscillation, which shifted from a positive to a negative phase during the same period. This shift led to the strengthening of the Walker circulation and the formation of an anomalous anticyclone over the northwest Pacific. It is worth noting that current mainstream climate dynamical models failed to capture the abrupt rise in SAT, instead exhibiting a sustained increasing trend that compromised the accuracy of the forecast. This discrepancy may be attributed to the models' slow response to rapid changes in sea surface temperature. [ABSTRACT FROM AUTHOR]

The slowly varying soil temperature can exert local and nonlocal influences on regional climate system, and may thus provide a critical source of subseasonal-to-seasonal climate prediction. In this study, we identify that soil temperature in May over the key region of Central Asia (42 °N–50 °N, 62 °E–80°E, KRCA) from Noah, Mosaic, CLM and ERA-interim datasets is closely linked to variations of the surface air temperature, daily maximum temperature and hot days over Northeastern China in summer (June–July–August), with correlation coefficients of regional average detrended time series ranging from 0.42 to 0.54, and all significant at the 99% confidence level for the period of 1979–2018. The possible physical mechanism behind the substantial downstream impacts of soil temperature over Central Asia are explored via diagnostical analysis combined with regional climate model experiments. Warmer soil temperature in May over the KRCA can keep to the summer and cause positive anomalies of geopotential height in summer over Northeastern China through the Rossby wave propagation, and associated stronger subsidence warming, less cloud cover, more solar radiation reaching the surface, higher planetary boundary layer, and stronger thermal advection at 850 hPa, which provide favorable conditions for warmer surface air temperature particularly in the daytime as well as more hot days. This study further reveals that soil temperature over Central Asia in May makes important contribution to prediction of summer surface air temperature, daily maximum temperature and hot days over Northeastern China in terms of regional average time series and spatial patterns. Our findings highlight the previously-unknown substantial role of antecedent soil temperature condition over Central Asia for summer surface air temperature variation and prediction over Northeastern China. [ABSTRACT FROM AUTHOR]

This study focuses on the interannual variability of winter mean surface air temperature (SAT) and subseasonal SAT reversal in North China, which have profound impacts on national life and economic activities. The analysis explores the temporal and spatial distribution characteristics of these variations and their relationship with the Eurasian Zonal Circulation (EZ). The findings reveal distinct interdecadal changes in North China's winter mean SAT, along with a general subseasonal reversal of cold and warm conditions. In comparison to the North Atlantic oscillation/Arctic Oscillation, the EZ has a more significant influence on the interannual variability of winter and subseasonal reversals. Strong EZ years are associated with the weakening of the Siberian High and the Ural Blocking High, causing the East Asian Trough to move eastward, which leads to the retention of cold air from the north and increased SAT in North China. Conversely, weak EZ years exhibit opposite circulation patterns. The winter mean EZ is influenced by preceding autumn Arctic sea ice concentrations (SIC). Reduced autumn SIC results in decreased heat flux and reduced eddy energy, leading to substantial attenuation of the winter storm track across Scandinavia and Western Asia. This attenuation, mediated through the interaction between eddies and mean flow, triggers a slowdown of Eurasian zonal circulation and intensification of the Siberian High. These conditions favor a positive phase of the winter Scandinavia pattern, facilitating the transport of cold Arctic air into North China. Likewise, subseasonal reversals in SIC influence corresponding alterations in the EZ. Plain Language Summary: The winter surface air temperatures (SAT) in North China exhibit notable interdecadal variations and distinct intra‐seasonal changes. These intra‐seasonal variations lead to subseasonal reversals, where early winter may be unusually warm, followed by later winter turning unusually cold, or vice versa. These subseasonal SAT fluctuations are influenced by atmospheric circulation anomalies. In this study, we investigate the spatial and temporal patterns of the mean winter SAT and subseasonal SAT reversals in North China, focusing on their interannual variability. We emphasize the significant impact of anomalous zonal circulation over the middle and high latitudes in Eurasia and explore the relative influence of the Arctic as a boundary forcing factor in circulation anomalies. Key Points: The winter surface air temperature (SAT) and subseasonal SAT reversal in North China have noteworthy interannual variationsThe Eurasian Zonal Circulation (EZ) strongly influences winter SAT and subseasonal reversal variabilityThe physical mechanisms linking autumn Arctic sea ice concentrations to EZ is examined [ABSTRACT FROM AUTHOR]

The frequency and duration of observed concurrent hot and dry events (HDEs) over China during the growing season (April–September) exhibit significant decadal changes across the mid-1990s. These changes are characterized by increases in HDE frequency and duration over most of China, with relatively large increases over southeastern China (SEC), northern China (NC), and northeastern China (NEC). The frequency of HDEs averaged over China in the present day (PD, 1994–2011) is double that in the early period (EP, 1964–81); the duration of HDEs increases by 60%. Climate experiments with the Met Office Unified Model (MetUM-GOML2) are used to estimate the contributions of anthropogenic forcing to HDE decadal changes over China. Anthropogenic forcing changes can explain 60%–70% of the observed decadal changes, suggesting an important anthropogenic influence on HDE changes over China across the mid-1990s. Single-forcing experiments indicate that the increase in greenhouse gas (GHG) concentrations dominates the simulated decadal changes, increasing the frequency and duration of HDEs throughout China. The change in anthropogenic aerosol (AA) emissions significantly decreases the frequency and duration of HDEs over SEC and NC, but the magnitude of the decrease is much smaller than the increase induced by GHGs. The changes in HDEs in response to anthropogenic forcing are mainly due to the response of climatological mean surface air temperatures. The contributions from changes in variability and changes in climatological mean soil moisture and evapotranspiration are relatively small. The physical processes associated with the response of HDEs to GHG and AA changes are also revealed. [ABSTRACT FROM AUTHOR]

The Daxing'anling forest region represents a crucial forestry hub in China and confronts some of the nation's most severe freezing–thawing hazards. This study delved into the temporal trends and spatial distributions of various parameters related to freezing and thawing, including air temperature, ground surface temperature, freezing index, thawing index, and freezing–thawing frequency. Furthermore, this study assessed and delineated freezing–thawing hazards within the research area. The findings revealed a rapid increase in air temperature and ground surface temperature within the Daxing'anling forest region yet a lower rate of increase in ground surface temperature compared to Northeast China. Latitude had the strongest influence on mean annual air temperature, mean annual ground surface temperature, air freezing index, air thawing index, ground surface freezing index, ground surface thawing index, air freezing–thawing frequency, and ground surface freezing–thawing frequency, followed by longitude and elevation. Overall, freezing index, and air freezing–thawing frequency increased from south to north, whereas mean annual air temperature, mean annual ground surface temperature, air thawing index, ground surface thawing index, and ground surface freezing–thawing frequency decreased from south to north. The assessment outcomes underscore the importance of closely monitoring freezing–thawing hazards in regions north of the 50th parallel. [ABSTRACT FROM AUTHOR]

The Paris Agreement proposed two global warming levels relative to the preindustrial period, with the ideal objective of 1.5°C warming and an upper boundary of 2.0 °C. However, the years when the temperature will first reach 1.5 or 2.0°C vary in different regions. Therefore, climate extremes and their population exposure are still not clear at regional or global warming levels. This study investigated climate extremes in China at different Chinese and global warming with 16 CMIP6 models under the middle SSP245 scenario. In China, the year when 1.5°C warming is projected to occur is 2020 and 2035 for 2.0°C warming. These values are more than 10 years earlier than the corresponding global warming levels of 2030 and 2049. Population exposure to percentile‐based heat extremes at global 1.5°C warming is projected to greatly increase relative to those when 1.5°C warming occurs in China due to increases in climate extremes, and exposure to absolute heat extreme indices is projected to decrease from Chinese to global 2.0°C warming under the joint impacts of increases in extremes and population decreases. Furthermore, from Chinese to global 1.5°C warming, about 344 million people will experience increased exposure to heat, wet and dry extremes, and around 468 and 371 million people will be affected by increased exposure to heat–wet and heat–dry extremes, respectively. Thus, a more adaptive strategy should be proposed to cope with the future possible natural hazards caused by heat–wet and heat–dry extremes. Plain Language Summary: In general, heat extremes are projected to increase in a warmer climate. However, changes in surface air temperature will vary among different regions at regional and global warming levels which might impact future climate extremes and their population exposure. The heat extremes, heavy precipitation amount and consecutive days with no precipitation, and population exposure to these extremes were investigated at global and Chinese 1.5 or 2.0°C warming levels. The years when 1.5 and 2.0°C warming will occur in China are more than 10 years earlier than those of global warming levels, respectively. About 344 million people will be involved in increased exposure to above three types of extremes moving from Chinese to global 1.5°C warming. Thus, more adaptive strategies should be established to cope with the possible increased risks in the future. Key Points: Warming by 1.5 and 2.0°C will occur more than 10 years earlier in China compared with global warming levelsPopulation exposure to percentile‐based heat extremes in China is projected to greatly increase from Chinese to global 1.5°C warmingAbout 400 million people will experience increased exposure to heat–wet and heat–dry extremes from Chinese to global 1.5°C warming [ABSTRACT FROM AUTHOR]

Prolonged low temperature significantly threatens transportation and planting in populous areas and is closely linked to slowly varying forcing such as sea surface temperature (SST). In this study, we reveal the impact of the north–south dipole pattern in extra-tropical North Atlantic SST (DNA) anomaly on surface air temperature (SAT) over southern China in February. The positive phase of the DNA is favorable for a positive North Atlantic Oscillation-like pattern at the troposphere via synoptic eddy vorticity forcing and triggers a southeastward-propagating Rossby wave train associated with the deepened East Asian trough and the strengthened Siberian high, which are conducive to enhancement of the East Asian winter monsoon. A stronger monsoon leads to lower-than-normal SAT over southern China by intensifying southward cold air outbreaks. Considerably, the extremely low temperature over southern China in February 2022 is explainable by this physical mechanism. Furthermore, experiments with a linear baroclinic model verify that the proposed downstream propagating Rossby wave train is mainly associated with the midlatitude North Atlantic SST anomaly. These results emphasize that the extra-tropical North Atlantic SST anomaly should be focused when interpreting and predicting the late winter SAT over southern China. [ABSTRACT FROM AUTHOR]

Based on the daily average temperature data of 2374 stations in China from 1993 to 2022 and the monthly average temperature data of European Centre for Medium‐Range Weather Forecasts (ECMWF) System5 in winter, this article analyses the intraseasonal variations of winter surface air temperature (SAT) over China by using seasonal empirical orthogonal function decomposition (S‐EOF) and compare the prediction skill differences of the first three dominant modes in ECMWF System5. The first mode (S‐EOF1) is characterized by out‐of‐phase changes in SAT anomalies (SATA) between December and January. Both the Siberian high (SH) and the central Pacific El Niño‐Southern Oscillation (CP ENSO) can impact S‐EOF1. The second mode (S‐EOF2) is characterized by consistency in warm anomalies over the Northeast and Qinghai‐Tibet Plateau's variation, while the other areas are characterized by the out‐of‐phase change in SATA. S‐EOF2 is closely related to Arctic oscillation (AO). The third mode is characterized by alternating changes over 3 months in winter, which is related to the Pacific meridional mode (PMM) and the North Atlantic SST tripole (NAT). Comparing the ECMWF System5 prediction skill for the first three leading modes for the intraseasonal variations in winter SAT, the prediction skill is not high. The model did not predict the extent of warm anomalies and the intraseasonal fluctuation in S‐EOF1, which may be due to the poor prediction skills for the range and intensity of the CP ENSO and the SH. The second mode has the highest prediction skill among the three modes. For the prediction skill of AO is good. The model can capture the intraseasonal reverse SATA between December and January but did not capture the reverse SATAs between January and February. Because the model does not predict obvious PMM and NAT well. [ABSTRACT FROM AUTHOR]

The systematic simulated cold biases in models for surface air temperature (SAT) over China have been under discussion for a long time, but the related attribution is still unclear. In this study, we investigate the main contributors and relevant physical processes of SAT biases over China based on 31 models participating in the sixth phase of the Coupled Model Intercomparison Project from a surface energy budget perspective. On annual and seasonal scales, less downward clear‐sky longwave radiation in the models, which is due to the underestimated tropospheric temperature and water vapor amount, is the main cause of cold biases over the Tibetan Plateau, Tarim Basin, Sichuan Basin, and most of eastern China. Meanwhile, the simulation biases in surface albedo and sensible heat flux induce cold biases depending on regions and seasons. In winter and spring, the overestimated surface albedo in association with more snow cover leads to prevalent cold biases over the western Tibetan Plateau and Northeast China. Excessive sensible heat release caused by the overestimated ground–air temperature difference facilitates SAT underestimation in the Sichuan Basin during summer and autumn. In addition, the cold biases in winter and spring are larger than those in summer and autumn, due to the stronger snow cover overestimation and tropospheric temperature underestimation. Plain Language Summary: The global climate model is a complex system used for describing the atmosphere, land surface, ocean, sea ice components, and their interactions. It has been widely applied to investigate climate change and its associated mechanisms. However, there are still deficiencies in current models for surface air temperature (SAT) simulations. The underlying cause for insufficient simulation is a hot topic of research. In past decades, many studies have shown prevalent SAT cold biases over China in most models, while the related mechanisms remain unknown. Based on the state‐of‐the‐art models, we investigate the causes of the cold biases over China from the perspective of a surface energy budget. The results indicate that the downward clear‐sky longwave radiation simulation error due to the bias of atmospheric states is the primary reason, and the cold biases induced by surface albedo and sensible heat flux vary among regions and seasons. Key Points: We investigate the reasons for the simulated surface air temperature cold biases in sixth phase of the Coupled Model Intercomparison Project models over ChinaLess downward clear‐sky longwave radiation due to underestimated tropospheric temperature and water vapor amount is the main causeCold biases induced by surface albedo and sensible heat flux vary among regions and seasons [ABSTRACT FROM AUTHOR]

Taking the semiannual cycle of surface air temperature (SAT) into account in fitting the finer structure of the seasonal cycle, this study reveals the specific role of the semiannual cycle in modulating variations of the season onsets and lengths for the first time. The time-varying amplitudes and phases of the annual and semiannual SAT cycle for the period 1964 to 2017 in China are extracted from daily observations based on harmonic analysis. The results show that the phase differences between the two harmonics remain essentially unchanged, while the amplitude ratio of the semiannual to annual harmonics tends to increase after 1988. This enhanced semiannual cycle together with the global warming and annual cycle changes jointly led to a much longer summer with an average speed of 7.45 days/10 years and shorter spring, autumn, and winter with speeds of − 2.43, − 1.63, and − 4.26 days/10 years, respectively. Such obvious asymmetry cannot be found in fitting seasonal cycles by only annual harmonics. The average absolute percentages of the linear trends of season onsets and lengths caused by the intensification of the semiannual cycle are 52.63% and 78.66%. It indicates that season onsets and length not only depend on the phase of annual and semiannual harmonic, but also highly related to the strength of semiannual cycle. The time evolutions of semiannual cycle strength in China are found to be significantly correlated to the meridional temperature advection. The anomalous stronger southward wind in December can result in colder temperature and hence intensify the semiannual component. [ABSTRACT FROM AUTHOR]

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

Air temperature (Ta) datasets with high spatial and temporal resolutions are needed in a wide range of applications, such as hydrology, ecology, agriculture, and climate change studies. Nonetheless, the density of weather station networks is insufficient, especially in sparsely populated regions, greatly limiting the accuracy of estimates of spatially distributed Ta. Due to their continuous spatial coverage, remotely sensed land surface temperature (LST) data provide the possibility of exploring spatial estimates of Ta. However, because of the complex interaction of land and climate, retrieval of Ta from the LST is still far from straightforward. The estimation accuracy varies greatly depending on the model, particularly for maximum Ta. This study estimated monthly average daily minimum temperature (Tmin), average daily maximum temperature (Tmax) and average daily mean temperature (Tmean) over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST data (MYD11A2) and some auxiliary data using an artificial neural network (ANN) model. The data from 2003 to 2010 were used to train the ANN models, while 2011 to 2012 weather station temperatures were used to test the trained model. The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean, with root mean square errors (RMSEs) of 1.04°C and 1.01°C, respectively. Moreover, the best RMSE of Tmax estimation was 1.27°C. Compared with the other two published Ta gridded datasets, the produced 1 km × 1 km dataset accurately captured both the temporal and spatial patterns of Ta. The RMSE of Tmin estimation was more sensitive to elevation, while that of Tmax was more sensitive to month. Except for land cover type as the input variable, which reduced the RMSE by approximately 0.01°C, the other vegetation-related variables did not improve the performance of the model. The results of this study indicated that ANN, a type of machine learning method, is effective for long-term and large-scale Ta estimation. [ABSTRACT FROM AUTHOR]

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

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

Quantitative assessment of urbanization effect on surface air temperature (SAT) change provides crucial basis for formal detection and attribution analyses of climate change. However, debates about urbanization‐related warming bias in documented regional SAT trend still persist, mainly due to different determination of rural stations. Here the urbanization effect on SAT change over the Beijing–Tianjin–Hebei region in China during 1980–2019 is estimated through three kinds of ways (i.e., comparisons between urban and rural stations [arithmetically station‐averaged], urban‐dominated and rural‐dominated patches [patch‐weighted mean], and realistic urban and rural areas [area‐weighted mean]). The last method explicitly takes urban and rural land cover fractions into account when calculating urban/rural and regional mean SAT trends. Urbanization‐induced warming in the annual mean SAT change of urban stations (areas) through the three ways are estimated as 0.159°C, 0.195°C, and 0.138°C per decade, respectively. And urbanization effect on regional averaged annual mean SAT calculated by patch‐weighted and area‐weighted methods are 0.113°C and 0.050°C per decade, respectively, which account for 33.8% and 14.8% of the total regional warming. The urbanization effect on observed SAT change estimated by considering realistic urban/rural land cover proportions is much lower than traditional station‐unweighted way. [ABSTRACT FROM AUTHOR]

Cloud-radiation process has a strong impact on surface air temperature (SAT). Using the Weather Research and Forecasting (WRF) model, this study investigates the effects of cumulus and radiation parameterization on SAT simulation over Eastern China (EC) during the summer season from 2001 to 2020. Four experiments are performed at a 30 km resolution using the combination of two cumulus schemes (KF and KF-CUP) and two radiation schemes (CAM and RRTMG). The results indicate that the KF and RRTMG scheme can produce warmer SAT than KF-CUP and CAM, respectively. By decomposing the differences in SAT simulation, it is found that KF and RRTMG have greater surface downward shortwave radiation (DSR), and the DSR shows a significant positive correlation with SAT in most parts of EC. Further analysis reveals that low-level cloud (LC) can strongly reflect the DSR, and the LC fraction (LCF) of KF and RRTMG is less than that of KF-CUP and CAM, respectively. The reason for this phenomenon is that the sub-grid cumulus heating rate is higher in KF and RRTMG, resulting in their higher air temperature (T) and greater differences between T and dew point (Td), which is not conducive to the formation of large-scale stratiform cloud and the increase of LCF. As a result, KF and RRTMG have more DSR and higher SAT than KF-CUP and CAM, respectively. The same mechanism can also explain the differences in the sub-seasonal cycle simulations between the four experiments. By comparing the SAT error in each subregion, this study can also provide a reference for future dynamic downscaling over the EC region. [ABSTRACT FROM AUTHOR]

This study analyses the spring climate anomalies under different configurations of western North Pacific anticyclone (WNPA) and Siberian high (SH). Compared to the WNPA alone with increased precipitation over southern China, the out‐of‐phase configuration (strong‐WNPA–weak‐SH) can enhance the WNPA‐related southwesterlies over eastern China, favouring increased precipitation with larger amplitude over southern China and southern Northeast China. Differently, the in‐phase configuration (strong‐WNPA–strong‐SH) decreases and confines WNPA‐related southwesterlies over southern China, causing decreased precipitation over eastern Southwest China; additionally, the WNPA‐related southwesterlies converge with SH‐related northwesterlies over the lower reaches of the Yangtze River, causing increased precipitation there. For surface air temperature, the WNPA alone induces warming over western Southwest China through changing heat flux, while the SH alone induces cooling over northern China through changing temperature advection, and over Northwest and southern China through changing heat flux. In the out‐of‐phase configuration, warming expands into Northern China due to the cooperation of weakening SH. In the in‐phase configuration, SH‐related cooling mainly occurs to the north of the Yangtze River because of the obstruction of WNPA. Further analysis indicates the eastern Pacific (EP) and central Pacific (CP) El Niño‐Southern Oscillation (ENSO), European low and Ural high contribute to the different configurations of WNPA and SH. The EP ENSO‐related zonal overturning circulation intensifies the WNPA and European low‐induced warm advection weakens the SH, resulting in the out‐of‐phase configuration. The CP ENSO‐related zonal overturning circulation intensifies the WNPA with a northeast–southwest tilt and Ural high‐induced cold advection intensifies SH, resulting in the in‐phase configuration. [ABSTRACT FROM AUTHOR]

The maximum signal-to-noise ratio empirical orthogonal function (MSN-EOF) is used to evaluate the midsummer 2 m temperature over Eastern China using subseasonal forecast data in the ECMWF model. The predictable sources of the most predictable components in the ECMWF model are analyzed, and the improvement of reconstructed predictions with leading predictable components is also investigated. ECMWF has the highest forecast ability among all S2S models but decays significantly after 10 d. The leading predictable mode mainly presents a dipole mode over Eastern China. Both the El Niño-Southern Oscillation and tropical Indian Ocean sea surface temperature anomalies are the main predictable sources of MSN-EOF1. A positive MSN-EOF1 is accompanied by El Niño decay, which can cause the strong and westward West Pacific Subtropical High, accompanied by anomalous southwesterlies over the north of Eastern China. The second predictable mode is the warmer characteristic of the Yangtze-Yellow River Basin, and the sea ice anomalies over the Barents Sea in the previous winter are the main predictable sources. This is accompanied by the wave train propagating from northwestern Russia to northeast Asia. The third predictable mode is mainly the temperature trend item extracted from the ECMWF model. The reconstructed predictions with the leading four MSN-EOF components show higher ability than the model raw predictions, therefore, this method can use the predictable components to filter the model noise and reduce the model error. [ABSTRACT FROM AUTHOR]

Examining large-scale characteristics of the difference between ground surface temperature (GST) and surface air temperature (SAT) and its long-term trend will help understand land surface energy exchange and the effect of land-atmosphere interaction on climate change and variability. Based on a homogenized monthly dataset of GST and SAT from 1961 to 2018, this study analyzes the spatial distribution and long-term trend of the difference between ground surface temperature and surface air temperature (GST–SAT) in the warm season (April to October) over China mainland. The results show that the warm-season mean GST–SAT in the Qinghai-Tibet Plateau and the northwestern deserts have the largest GST–SAT. On average, the GST–SAT in China is the greatest in summer, with the maximum monthly value occurring in July. During 1961–2018, the warm-season mean GST–SAT undergoes a significant increasing trend (0.04 °C/10yr, p < 0.01), with the largest increase seen in mid-late spring (April and May), and the smallest increase in August. Spatially, the GST–SAT increases significantly in the northern region, decreases slightly in the southern region, and remains unchanged in the Qinghai-Tibet Plateau. The warm-season mean GST–SAT is significantly positively correlated with altitude and sunshine duration (R = 0.50, 0.40; p < 0.05), and significantly negatively correlated with relative humidity and precipitation (R = 0.48, −0.42; p < 0.05), in the country on a whole in the analysis period. [ABSTRACT FROM AUTHOR]

Spatially continuous surface air temperature (SAT) is of great significance for various research areas in geospatial communities, and it can be reconstructed by the SAT estimation models that integrate accurate point measurements of SAT at ground sites with wall-to-wall datasets derived from remotely sensed observations of spaceborne instruments. As land surface temperature (LST) strongly correlates with SAT, estimation models are typically developed with LST as a primary input. Geostationary satellites are capable of observing the Earth's surface across large-scale areas at very high frequencies. Compared to the substantial efforts to estimate SAT at daily or monthly scales using LST derived from MODIS, very limited studies have been performed to estimate SAT at high-temporal scales based on LST from geostationary satellites. Estimation models for hourly SAT based on the LST derived from FY-4A, the first geostationary satellite in China's new-generation meteorological observation mission, were developed for the first time in this study. The models were fully cross-validated for a very large-scale region with diverse geographic settings using random forest, and specified differently to explore the influence of time and location variables on model performance. Overall predictive performance of the models is about 1.65–2.08 K for sample-based cross-validation, and 2.22–2.70 K for site-based cross-validation. Incorporating time or location variables into the hourly models significantly improves predictive performance, which is also confirmed by the analysis of predictive errors at temporal scales and across sites. The best-performing model with an average RMSE of 2.22 K was utilized for reconstructing maps of SAT for each hour. The hourly models developed in this study have general implications for future studies on large-scale estimating of hourly SAT based on geostationary LST datasets. [ABSTRACT FROM AUTHOR]

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

Using the daily 2 m maximum temperature (Tmax), 2 m minimum temperature (Tmin) and cloud cover data measured at ground sites of the China Meteorological Administration in North China from 2000 to 2017, this study investigates the influence of clouds on the daily temperature range (DTR) defined as the difference between Tmax and Tmin. As expected, the cloud cover shows the similar averaged spatial distribution and monthly variation with Tmin. Surprisingly, it also shows the similar average spatial distribution and monthly variation with Tmax, suggesting the more important roles of regions (latitude) and seasons associated with the variations of land surface temperature, which is further related to solar radiation absorbed and surface heat capacity. By comparing monthly variations of temperature between cloudy and clear skies, we find that clouds can weaken Tmax and increase Tmin, and thus decrease DTR. As a result, the spatial distribution of DTR is opposite to the cloud cover. The clouds have relatively stronger impact on Tmin and DTR over mountain region, which is most likely caused by the stronger longwave cloud radiative forcing associated with higher cloud tops over the mountain region. [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]

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]

The intensity and direction of elevation-dependent warming (EDW) could vary in different parts of the mountain, while less attention is paid to the local EDW effect. The Tibetan Plateau (TP) and the T'ai-hang & Lvliang Mountains in Shanxi Province of China (TLMsx) were taken as cases to examine the relationship between EDW and the long-term trend of surface air temperature (SAT) lapse rate (SATLR). For the observed data, the intensity of regional EDW (I EDW) was estimated with linear regression model, while the SATLR was estimated with geographically weighted regression (GWR) model. The long-term trend of SATLR was estimated applying the modified Theil-Sen method, and the regional average SATLR trend (k SATLR ¯) was calculated and analyzed. Both observed data and simulation experiment revealed that there was not significant difference between I EDW and − k SATLR ¯ , with a little difference caused by the noise in the SAT trends. As the SATLR trend measured the difference in the surface air warming rate within 1-km vertical distance, i.e. the intensity of EDW in a small spatial scale, in the context of climate warming, it could be used to estimate the intensity of local EDW. The SATLR trend thus provided a new perspective to study the EDW effect in the future, and facilitated the quantitative analysis on the mechanisms of EDW. • Two kinds of elevation-dependent warming (EDW) effect, positive EDW and negative EDW, are found. • The regional average surface air temperature lapse rate (SATLR) trend (k SATLR ¯) was broadly opposite to that of the intensity of EDW (I EDW). • There was not significant difference between I EDW and − k SATLR ¯. [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]

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]

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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]

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]

Stratospheric aerosol intervention (SAI) geoengineering is a proposed scheme to counteract anthropogenic global warming, but the climate response to SAI, with great regional disparities, remains uncertain. In this study, we use Geoengineering Model Intercomparison Project G4 experiment simulations from six models that counteract anthropogenic forcing under medium–low emissions (Representative Concentration Pathway 4.5 – RCP4.5) by injecting a certain amount of SO 2 into the stratosphere every year to investigate the surface air temperature response to SAI geoengineering over China. We have found that SAI has led to surface cooling over China during the last 40 years of injection simulation (2030–2069), which varies among models, regions, and seasons. Decreased tropospheric temperature and water vapor and increased stratospheric aerosols induce robust decreases in downward clear-sky longwave and shortwave radiation fluxes at the surface, respectively, dominating the temperature change over China. Changes in cloud effective forcing and surface albedo feedback also relate to the temperature response but with large spatial and seasonal variations. We find that the increased summer cloud cover and winter surface albedo lead to strong cooling, while the decreased summer cloud cover and winter surface albedo lead to weak cooling or even insignificant warming for the certain subregions and models. Our results suggest that cloud and land surface processes in models dominate the spatial pattern of SAI-induced surface air temperature change over China. [ABSTRACT FROM AUTHOR]

Leaf nitrogen (N) status and stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) were used to study environmental factors that control mountain individuals of Picea neoveitchii trees, a coniferous species endemic and endangered in China. From May to September 2016, we carried out observations at four different altitude locations extending southeast of Daba Mountain in western Hubei Province. Needle-shaped leaf δ13C was positively correlated with needle N and C content calculated from the needle area (Narea and Carea content), needle δ15N, needle mass, and leaf mass per area (LMA), respectively. Needle δ15N was also positively correlated with monthly temperature and precipitation for the current month and last month. The seasonal normalised difference vegetation index (NDVI) was highest in June at the lowest altitude and August at the highest altitude. We found that N availability as an important driving factor for tree growth is controlled by surface soil temperature, while in summer, air temperatures above 23 °C exceed the physiological threshold of trees and limit the growth of trees. We concluded that the negative effect of higher temperature on tree growth is greater than the positive effect of higher nitrogen. [ABSTRACT FROM AUTHOR]

The increasing application of floor heating technology promotes the development of floor radiant cooling technology (abbreviated as FRC technology). Many office buildings in northern China try to use FRC technology to cool in summer, but thermal comfort is the key problem restricting the promotion of this technology. The thermal comfort problems of an office room with floor radiant cooling were studied in this paper by the methods of numerical simulation, control variable, and data fitting, and the experimental results were verified in multiple ways. It was found that, for an office room using floor radiant cooling, the effect of the floor surface temperature on thermal comfort was about 16%, while the effect of indoor air temperature was about 84%, and relative humidity had little effect on thermal comfort. A simplified thermal comfort calculation model was proposed, which could be used as an indicator to adjust the floor surface and indoor air temperature, or could be used to calculate the PMV-PPD value. The findings have guiding significance for the design and control of FRC technology. [ABSTRACT FROM AUTHOR]

The two leading modes of winter surface air temperature (SAT) over China during 1961–2017 are a spatially consistent pattern and a north-south dipole pattern. Based on the two leading modes, the characteristics of the extreme cold and warm days in the two patterns, defined by the standard deviation larger than 1.28 or smaller than −1.28 in the time series of the two leading modes, are analyzed. With the increase of winter SAT during 1961–2017, the number of spatially consistent extreme cold days decreased and their occurrence was restricted to late December to early January, whereas the number of spatially consistent extreme warm days increased significantly in January and February. Global warming is associated with an increase in the spatially consistent extreme warm days and a decrease in spatially consistent extreme cold days, but has little relation to the sum of extreme cold and warm days of either the spatially consistent or north-south dipole pattern. The Siberian High (SH) is the main factor controlling the sum of spatially consistent extreme warm and cold days. The strong (weak) SH before (after) the 1990s corresponds to an increase (decrease) in the sum of the spatially consistent extreme warm and cold days. The occurrences of extreme south-cold-north-warm and extreme south-warm-north-cold days are related to the north-south difference of the SH. When the center of the SH is in mid-high latitudes, the extreme south-warm-north-cold (south-cold-north-warm) days occur more (less) often. During the winters of 1961–2017, the total number of extreme cold and warm days of the north-south dipole pattern changes negligibly. The North Atlantic meridional overturning circulation (AMOC) may be the main factor affecting the sum of the extreme cold and warm days of the two types of SAT pattern in China. [ABSTRACT FROM AUTHOR]

As an intrinsic feature of daily surface air temperature (SAT) variability found in station measurements, temporal asymmetry (TA) can be taken as an evaluation metric to access the quality of SAT re-analysis product. In this study, TA calculated from four SAT variables, i.e., daily mean SAT (Tmean), daily maximum SAT (Tmax), daily minimum SAT (Tmin) and diurnal temperature range (TDTR = Tmax − Tmin), is applied to evaluate synoptic-scale performance of four reanalysis products (NCEP-2, JRA-55, ERA-I, and ERA-5) over China. The results show that four re-analyses overall overestimate the TA of daily Tmax and Tmin variability over China, but with a comparatively consistent estimated TA for Tmean. Moreover, the TA of Tmean variability for these four re-analyses shares high spatial consistency with those from the observation. However, four re-analyses own the similar region-dependent spatial patterns of overestimated TA for Tmax and Tmin variability, especially for Tmax. Since high TA is an indicator for strong nonlinear feature, only Tmean reanalysis is the most suitable to explore synoptic-scale extreme events, such as heat waves and cold waves, which are highly related to the strong nonlinear processes. [ABSTRACT FROM AUTHOR]

A thorough understanding of regional differences in change patterns of surface air temperature (SAT) at various spatial scales can help people cope well with global warming. In this study, a SAT change pattern recognition method was firstly established based on k-mean++ algorithm. By creating a clustering effect evaluation index (DBWk) to select the optimal cluster number k, the pattern of SAT change in 1960–2016 of China was recognized at national and regional scales. Results showed that China's SAT change patterns were grouped into 3 clusters, namely, Clusters I, II and III, at the national scale. These clusters were further divided into 3, 7, and 4 subclusters at the regional scale, respectively. The SAT change in Cluster I was intense, with a relatively cold period (1960–1987) and a relatively warm period (1988–2016). The SAT of Cluster II decreased slightly in the first phase (1960–1983), minimally increased in the third phase (1999–2016), but rose strongly in the second phase (1984–1998). The linear trend (LT) of SAT increase of Cluster III was high and statistically significant, especially in 1983–2016. The analysis of the SAT change pattern of subclusters showed that the SAT fluctuations of the Altai Mountains and Junggar Basin were the strongest. The Northern Qinghai–Tibet Plateau had the highest warming rate, and the LT of warming was statistically the most significant in China. [ABSTRACT FROM AUTHOR]

The climate of Yangtze River Basin in China is cold and humid in winter. Conventional air-conditioning systems may cause high energy consumption and uncomfortable microclimatic conditions especially for lower body of indoor occupants. This study investigated four personalized seat heating systems, in a typical office room in Shanghai during winter, based on heated floor panels including heated floor panels + ordinary chair (HF-OC), heated floor panels + insulated chair (HF-IC), heated floor panels +insulated chair and leg box (HF-IC-LB) and overall personalized heating (OPH). The surface temperature of walls and heated floor panels, and the indoor air temperature at different positions were recorded with thermocouples. The hourly energy consumptions of the proposed personalized seat heating systems were measured and compared with a conventional split type air conditioner. Questionnaires of thermal sensation and comfort were carried out among 10 university students. Compared with HF-OC, HF-IC could improve the thermal comfort to a certain extent, while HF-IC-LB provided the optimal thermal micro-environment for the lower body other than other body parts. The OPH systems were proven effective to provide satisfactory thermal environment for all body parts at lower indoor temperature (12–16°C) with much less energy consumption than room air conditioners. [ABSTRACT FROM AUTHOR]

Based on the latest series of homogenized ground surface temperature (GST) and surface air temperature (SAT) data for China, this study performed a detailed analysis of the trend of the differences between the two homogenized series in 1961–2016. The differences, referred to as surface–air temperature differences (SATDs) in this study, were separately averaged by month, season, and year. The long-term and spatial changes in the trends of SATDs were investigated. Moreover, interdecadal trend breakpoints were identified to understand the characteristics of trends in fluctuation. The possible influences of precipitation, Pacific Decadal Oscillation (PDO), and global warming on SATDs were also analyzed. The results showed that during the 12 months of a year, only three months, March, April, and May, exhibited increasing trends of station-averaged, monthly mean SATDs while the other nine months exhibited decreasing trends. In addition, the station-averaged annual and seasonal mean SATDs of summer, autumn, and winter all showed significant decreasing trends, while the spring mean SATDs showed a significant increasing trend. The spatial distribution pattern of the linear trends of monthly, seasonal, and annual SATDs in meteorological stations indicated that SATDs had more obviously increasing trends in the northern regions than the southern regions of China. The trends of station-averaged monthly mean SATDs (except for April) and station-averaged seasonal and annual mean SATDs experienced interdecadal breakpoints, fully indicative of obvious interdecadal fluctuations with temporal complexity among China's SATD trends. By the regression analysis of monthly STADs against simultaneous precipitation, as well as the comparative analysis of their linear trends, we found that both amount of precipitation and the change of precipitation type have important influences on SATDs. The results of convergent cross mapping analysis also revealed the causal effect of precipitation on SATDs. [ABSTRACT FROM AUTHOR]

Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, while reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions, or with limited temporal and spatial coverage. In this study, an all-sky daily mean Ta product at 1 km spatial resolution over mainland China for 2003-2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The validation results showed that R2 and root mean square error (RMSE) values of the three models ranged from 0.984 to 0.986 and 1.342 K to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. The relative contributions of different features to models were also quantitatively analysed. Finally, values of our Ta product in 2010 were validated and compared with the China Land Data Assimilation System (CLDAS) dataset at 0.0625° spatial resolution, China Meteorological Forcing Data (CMFD) dataset at 0.1° spatial resolution and GLDAS dataset at 0.25° spatial resolution. The R2 and RMSE values of our product were 0.992 and 1.010 K, respectively, indicating this high-resolution satellite product has significantly higher accuracy. In summary, the all-sky daily mean Ta dataset developed in this study has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and hydrological cycle. This dataset is freely available at http://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta%5FChina/) currently. A sub-dataset that covers Beijing generated from this dataset is publicly available at http://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a). [ABSTRACT FROM AUTHOR]

The soil temperature (ST) is closely related to the surface air temperature (AT), but their coupling may be affected by other factors. In this study, significant effects of the AT on the underlying ST were found, and the time taken to propagate downward to 320 cm can be up to 10 months. Besides the AT, the ST is also affected by memory effects—namely, its prior thermal conditions. At deeper depth (i.e., 320 cm), the effects of the AT from a particular season may be exceeded by the soil memory effects from the last season. At shallower layers (i.e., < 80 cm), the effects of the AT may be blocked by the snow cover, resulting in a poorly synchronous correlation between the AT and the ST. In northeastern China, this snow cover blockage mainly occurs in winter and then vanishes in the subsequent spring. Due to the thermal insulation effect of the snow cover, the winter ST at layers above 80 cm in northeastern China were found to continue to increase even during the recent global warming hiatus period. These findings may be instructive for better understanding ST variations, as well as land-atmosphere interactions. [ABSTRACT FROM AUTHOR]

The atmospheric water holding capacity will increase with temperature according to Clausius-Clapeyron scaling and affects precipitation. The rates of change in future precipitation extremes are quantified with changes in surface air temperature. Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model, RegCM4, and 17 global climate models that participated in CMIP5. First, we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period (RF: 1982–2001). The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes, as well as those based on observations: OBS and XPP. Precipitation extremes over four subregions in China are predicted to increase in the mid-future (MF: 2039–58) and far-future (FF: 2079–98) relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean. The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058, and the RCM results show higher interannual variability relative to that of the CMIP5 models. Then, we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature. Finally, based on the water vapor equation, changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity (significant at the p < 0.1 level). [ABSTRACT FROM AUTHOR]

The low‐latitude highlands (LLH) of China are situated in the subtropical southwest China. The spring climatic characteristics of the LLH were previously regarded as being governed by remote sea surface temperature forcing and large‐scale atmospheric circulations. Recent studies qualitatively demonstrated that the LLH is a relatively strong soil moisture (SM)‐surface air temperature (SAT) coupling region in China during spring. In this study, the quantitative spring SM‐SAT coupling (mean of March and April before wet season) is further statistically analysed using two different SM data sources (one reanalysis data set and one land surface assimilation product). The results show that significant negative SM‐SAT feedbacks mainly occur in the western and middle part of the LLH. Over these areas, 10–50% of the total SAT variance could be explained by the SM‐induced negative feedbacks with a feedback parameter of −0.2°C·(standardized SM)−1 to −0.8°C·(standardized SM)−1. Further analyses show that SM negatively impacts Bowen ratio in the western and middle part of the LLH where Bowen ratio positively affects SAT, therefore significant negative SM‐SAT feedbacks occur therein. The potential impact of India–Burma Trough (IBT) on the SM‐SAT coupling is also investigated in this study, the results of which suggest that SM‐SAT coupling and the effects of SM on land surface energy balance may be immune to the influence of the IBT. Furthermore, SM and IBT may comparably affect SAT in the western and middle part of the LLH. This highlights that SM might have prominent effects on the subseasonal predictability of spring SAT over these areas. [ABSTRACT FROM AUTHOR]

Spring soil moisture over the vast region from the lower and middle reaches of the Yangtze River valley to North China (YRNC) can significantly influence East Asian summer monsoon (EASM) via May surface temperature. This study further validates the role of May surface temperature in linking spring soil moisture with summer monsoon circulation and rainfall over eastern China. The role of May surface temperature over YRNC in the ensuing EASM and precipitation over eastern China are investigated by observational analyses and numerical simulations. Given the long "memory" of soil temperature (ST) and its highly consistent with surface temperature, experiments are performed by altering ST as a substitution. The results show that YRNC spring surface temperature anomalies have a significant influence on EASM and precipitation over eastern China. The negative May ST anomalies correspond to an abnormal cooling in the surface and tropospheric atmosphere around the YRNC, which narrows the land‐ocean thermal contrast, resulting in a weakened EASM. An anomalous anticyclone extends from southeastern China to the Philippine Islands, and an anomalous cyclone occupies northeastern China. Consequently, rainfall anomalously decreases over southeastern China and increases over northeastern China. The convergence of warm and humid southerly airflows and cold northerly winds conduces to an enhanced rainfall around the Yangtze River valley. The opposite occurs for the positive ST anomalies. Further simulations show that abnormal wet (dry) spring soil can lead to negative (positive) ST and surface temperature anomalies in May over the YRNC. Hence, the surface air temperature in May is the bridge for the effect of the YRNC spring soil moisture on the EASM and precipitation over eastern China. [ABSTRACT FROM AUTHOR]

Two long continuous regional climate simulations over China have been carried out using the Regional Climate Model version 4 (RegCM4) and Weather Research and Forecasting model (WRF). The Simulations are forced by NCEP Reanalysis II data with a horizontal grid spacing of 30 km during 1981–2010. It is demonstrated that the RegCM4 and WRF had pronounced temperature and precipitation downscaling ability, producing more regional details and smaller biases than the driving R2. Overall, WRF tended to better capture the temperature and precipitation pattern and magnitude, daily temperature frequency, the monsoon rain belt movement, and seasonal precipitation variations over most wet regions, while larger deficits than RegCM4 were shown over some dry regions. The extreme precipitation indices of the two RCMs were quite different, with strong regional and seasonal dependence. WRF was better at simulating the annual mean temperature and precipitation trends, with higher spatial pattern correlations. The optimal ensemble approach combining the advantages of RegCM4 and WRF showed improved simulation compared to the individual models. The optimal ensemble reduced the annual temperature biases from the two models by 15–30%, and increased the precipitation spatial pattern correlations by 0.08–0.13. Further works are needed to improve the performances of the ensemble approach by using more RCMs. [ABSTRACT FROM AUTHOR]

Observed surface air temperature (SAT) warming at urban stations often contains both the signal of global warming and that of local urban heat island (UHI) effects; these signals are difficult to separate. In this study, an urban impact indicator (Uii) developed by the authors was modified to represent the extent to which the observed temperature from a given station was influenced by UHI effects. The Uii of a city was calculated by simplifying the city's shape to a circle. In addition, a modified Uii (MUii) was calculated by considering the realistic horizontal distribution of the urban land. We selected 45 urban stations in mainland China, along with an adjacent station for each to give a station pair. Background climate changes across each pair were near-homogeneous. Thus, differences in the trends of annual averaged daily mean SAT (Trendmean), maximum SAT (Trendmax), and minimum SAT (Trendmin) between the urban and adjacent stations (ΔTrend) could be mainly attributed to differences in MUii changes between the urban and adjacent stations (ΔMUii). Several linear regressions between ΔTrend and ΔMUii for the 45 station pairs were calculated to estimate UHI effects on Trendmean (UTmean), Trendmax (UTmax), and Trendmin (UTmin). The results showed that the mean MUii of the 45 urban stations increased from 0.06 to 0.35 during 1992 - 2013. Positive correlations between ΔMUii and ΔTrend for the 45 station pairs were significant at the 0.001 significance level (except for Trendmax). The average UTmean and UTmin of the 45 urban stations during 1954 - 2013 were approximately 0.05 and 0.11°C decade-1, respectively, accounting for 18% and 31% of the overall warming trends, respectively. The UTmin estimated in this study is about twice that of previous results based on regression equations between Uii and SAT trends. [ABSTRACT FROM AUTHOR]

Although many studies have analyzed the effects of urbanization on temperature changes, the urbanization's effect on temperature change remains controversial. Northeast China is the largest old industrial base in China, which experienced a rapid urbanization in the past decades. Under the background of climate change, understanding the changes of surface air temperature and urbanization effects on temperature changes in the Northeast China is important to predict climate change in China. By analyzing the historical climate data, our results suggested that minimum temperature (Tmin) over the Northeast China increased significantly (0.40℃ decade-1) from 1960 to 1989, but showed no significant change (-0.02℃ decade-1) during 1990 - 2016. Due to slight change of maximum temperature (Tmax), the diurnal temperature range (DTR) showed a significant decreasing trend before 1989 (-0.34℃ decade- 1), but reached a stable level after 1990 (-0.06℃ decade-1). In Northeast China, urbanization had a significant warming effect on Tmin during the night-time, but had different effects on Tmax during the daytime under the different changes of solar radiation before and after 1990. There were moderate warming effects of urbanization on Tmax during the solar dimming period of 1960 - 1989, but weak cooling effects on Tmax during the solar stable period after 1990. Due to obvious warming effect of urbanization on Tmin, urbanization in Northeast China tends to result in an increase of mean temperature but a decrease of DTR. [ABSTRACT FROM AUTHOR]

Temperature perturbations under the ground surface are a direct thermal response to temperature changes on the ground surface. Thus, borehole temperature measurements can be used to reconstruct the temperature history of the ground surface using borehole Paleothermometry. In this study, we use seven borehole temperature profiles to reconstruct the ground surface temperature variation for the past 514 years in the Qaidam Basin, northwestern China. Borehole transient temperature measurements from seven sites in the northwestern Qaidam Basin were separated from the geothermal gradients and analyzed using the functional space inversion method to determine the ground surface temperature variation history of the region. All of the temperature profiles show the effects of recent climatic disturbances. The inversion reveals that during the last 514 years, the ground surface temperature has increased by an average of 1.2 °C (−0.11 to 2.21 °C). Clear signs of a cold period between 1500 and 1900 A.D. were found, which corresponds to the Little Ice Age. The coldest period occurred between 1780 and 1790 A.D. with a ground surface temperature of 5.4 °C. The reconstructed ground surface temperature shows an increasing trend during the 19th and the 20th centuries, but the temperature began to decrease in the late twentieth century. However, during the past 514 years, the highest temperature occurred in the 1990s. This reconstructed ground surface temperature variation history is verified by the simulated annual surface air temperature computed using EdGCM, while the cooling trend is confirmed by reconstructions of the winter half-year minimum temperatures from tree rings on the northeastern Tibetan Plateau. [ABSTRACT FROM AUTHOR]

Analyses of in situ and reanalysis output are performed to examine linkages between surface sensible heat fluxes over the central and eastern Tibetan Plateau (CETP) and indices of the East Asian winter monsoon (EAWM) and wintertime near-surface air temperatures over China. The results demonstrate that the sensible heat fluxes over CETP exhibit substantial decadal variability with positive, negative and positive phase during 1980–1987, 1988–2002 and 2003–2014, respectively. This decadal variability exhibits statistically significant associations with sub-components of EAWM and surface temperature anomalies over eastern China. The recovery of decadal change in wintertime sensible heat fluxes from negative to positive phase over CETP over the past decade (since 2003) has been associated with intensification and northward displacement of the East Asian subtropical jet (EASJ), enhanced and westward displacement of East Asian trough and strengthening of the Siberian High. Such processes are associated with the strengthening of EAWM, as well as cold air advection from high latitudes towards the south. Increased wintertime sensible heat fluxes over CETP is associated with markedly changes in the meridional temperature gradient that in term intensifies upper-level zone flow in the EASJ region, which provides a key physical factor linking the anomalies of sensible heat fluxes and EAWM. [ABSTRACT FROM AUTHOR]