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In situ measurements are the most important basis of obtaining precise meteorological datasets. However, it is difficult to accurately extrapolate the plausible meteorological field in certain regions based on in situ measurements alone, especially in areas with complex topography like the Tibetan Plateau (TP). Gridded products, remote sensing, and data assimilation technique overcome this problem but they have their own weaknesses, such as low resolution, huge computation, and time‐consuming. Here we applied the state‐of‐the‐art generative adversarial networks (GAN) in image inpainting to construct the surface temperature over the TP combined with high‐resolution China Meteorological Forcing Dataset (CMFD) air temperature product and in situ observations. In this study, the surface air temperature at 2 m dataset with grids of 0.1° over the TP from 1979 to 2020 based on GAN‐based model have been generated, which can capture the spatial patterns and trends of surface air temperature over the TP. Moreover, the GAN‐based daily temperature has higher correlation coefficients (0.99) and lower root‐mean‐square errors (1.06°C) than CMFD (0.98, 1.43°C) and reanalysis products when compared with in situ observations. Finally, the temperature fields over the TP from 2019 to 2020 is constructed, whereas CMFD is only available from 1979 to 2018. The results reflect the advantages of GAN on big‐data computation and fully utilizing effective information over the TP, and GAN‐based temperature dataset can be used to detect climate change over the region. This study demonstrated the potential and applicability of artificial intelligence for developing and extending practicable high‐resolution meteorological dataset over regions with sparse stations and rugged topography. [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]

This study presents a comprehensive evaluation of the mean and extreme surface air temperature and precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel ensemble simulations over the Tibetan Plateau. Simulations from 29 climate models are compared against gridded observations for the period 1961–2012. Results show that models reasonably reproduce the overall spatial patterns of the 1961–2012 averages of seasonal mean temperature and precipitation. Nevertheless, models tend to underestimate mean temperature and overestimate precipitation accumulations. To be specific, the multimodel mean estimates of seasonal average temperatures over the plateau are 0.5°C–2.6°C colder than observed, while the corresponding estimates of seasonal precipitation accumulations are 218% (spring: March–May), 76% (summer: June–August), 129% (autumn: September–November), and 533% (winter: December–February) of those observed. As in observations, models also well capture increasing trend of mean temperature and precipitation in all seasons but underestimate the rates of increasing trends for both variables in all seasons. Models' ability to simulate temperature and precipitation extremes is also evaluated in terms of a set of chosen extreme indices defined by the Expert Team on Climate Change Detection and Indices. On average, models tend to underestimate the averages of annual maximum daily maximum temperature, annual minimum daily minimum temperature, frost days, warm nights, and consecutive dry days but overestimate the corresponding averages of annual maximum 5‐day precipitation accumulations and simply daily intensity. Generally, models are able to simulate the signs of the trends in extreme indices but underestimate their magnitudes and misrepresent spatial patterns. Key Points: CMIP6 models reasonably reproduce the spatial patterns and temporal variations of mean and extreme temperature and precipitationCMIP6 models continue to suffer from cold bias in temperature and wet bias in precipitation similar as its predecessor CMIP5Most models underestimate the observed trends in mean and extreme temperature and precipitation [ABSTRACT FROM AUTHOR]

Surface air temperatures affect a diverse set of physical and biological systems in many parts of the world. For regional‐scale studies, gridded surface air temperature data sets are frequently used as input variables. Here we evaluate 10 commonly used gridded air temperature products with spatial resolutions ranging from 0.1° × 0.1° to 5.0° × 5.0°, relative to observations from in situ weather stations on the Tibetan Plateau. Gridded temperatures are consistently lower, with mean annual air temperature biases ranging from −4.68 °C to −1.72 °C, and root mean square error (RMSE) from 3.24 °C to 6.11 °C. The mean biases of mean seasonal air temperatures for spring, summer, autumn, and winter are −3.21 °C, −2.98 °C, −2.89 °C, and −2.98 °C, respectively, and RMSEs of 4.81 °C, 4.39 °C, 4.3 °C, and 4.7 °C. Of all 10 products, the China meteorological forcing data set compares best with observations. Annual and season temperatures all increase significantly during 1980–2018 on the Tibetan Plateau, with winter dominating the warming rate. Key Points: Out of 10 assessed gridded products, the China meteorological forcing data set compares best with temperature observationsTibetan Plateau temperatures increase significantly during 1980–2018, with winter dominating the warming rateThe limitations related to the irregular spatial distribution of in‐situ sites in this complex terrain, especially elevation biases result in uncertainties. [ABSTRACT FROM AUTHOR]

Approximately 40% of the Tibetan Plateau (TP) is underlain by continuous permafrost, yet its impact on fluvial water and sediment dynamics remains poorly investigated. Here we show that water and sediment dynamics in the permafrost‐dominated Tuotuohe basin on the TP are driven by air temperature and permafrost thaw, based on 33‐year daily in situ observations (1985–2017). Air temperature regulates the seasonal patterns of discharge and suspended sediment concentration (SSC) by controlling the changes in active contributing drainage area (ACDA, the unfrozen erodible landscape that contributes hydrogeomorphic processes within a catchment) and governing multiple thermal processes such as glacier‐snow melt and permafrost thaw. Rainstorms determine the short‐lived fluvial extreme events by intensifying slope processes and channel erosion and likely also by enhancing thaw slumps. Furthermore, the SSCs at equal levels of discharges are lower in autumn (September–October) than in spring (May–June) and summer (July–August). This reduced sediment availability in autumn can possibly be attributed to the increased supra‐permafrost groundwater runoff and the reduced surface runoff and erosion. Due to rapid climate warming, the ACDA has increased significantly from 1985 to 2017, implying expanding erodible landscapes for hydrogeomorphic processes. As a result, the fluvial water and sediment fluxes have substantially increased. In a warmer and wetter future for the TP, the fluvial sediment fluxes of similar permafrost‐underlain basins will continue to increase with expanding erodible landscapes and intensifying thermal and pluvial‐driven geomorphic processes. Thus, permafrost thaw should be considered as an important driver of past and future water and sediment changes for the TP. Key Points: Climate warming expands the erodible landscape and increases fluvial water and sediment fluxes on the Tibetan PlateauAir temperature regulates seasonal discharge and sediment dynamics by controlling glacier‐snow melt and permafrost processesIncreasing rainstorms results in more frequent fluvial extreme events [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) has important influences on regional and global climate change. Here, we perform an in‐depth study of the relationship between cloud parameters and meteorological factors over the TP. The spatiotemporal variations in cloud cover and cloud optical thickness over the TP during the daytime from 2003 to 2015 are analysed using the Aqua‐MODIS level 2 atmospheric product data MYD06. Results show that the annual average cloud cover over the TP decreases from the southeast to the northwest. The cloud cover of the western TP is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. The cloud covers in most areas of the TP exceed 30% in spring and summer, and the cloud optical thickness in the southeastern TP exceeds 10 in summer, with substantial cloud cover and cloud optical thickness changes. Compared to other seasons, the cooling effect of the near surface cloud net radiative forcing is greater during daytime in summer, which is likely associated with thick cloud optical thickness or large cloud cover. The results of the analysis of relationships among cloud cover, water vapour and air temperature show that positive correlations exist between cloud cover and water vapour, and that significantly negative correlations exist between cloud cover and air temperature over the TP. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. The cloud cover of the western Tibetan Plateau (TP) is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer. [ABSTRACT FROM AUTHOR]

Circulation‐type classifications (CTCs) are useful tools for systematically describing atmospheric circulation and studying its variation and impact on surface climate. However, few circulation classification methods (CCMs) have been applied in the Tibetan Plateau (TP). In this study, six frequently used CCMs using 500 hPa geopotential height data were assessed with respect to their abilities in explaining observed daily mean temperature and precipitation variations in the central and eastern TP for the period 1980–2014. Two statistical measures, the explained variance and the Kolmogorov–Smirnov test, were used to quantify the performance of the CTCs in describing climate variable variations. SAN (simulated annealing and diversified randomization clustering) and KRZ (Kruizinga's eigenvector‐based scheme) were identified as optimum CCMs for synoptic typing over the study region. The daily mean temperatures are well conditioned on SAN‐CTC with 43.9–62.4% explained variance at annual scale and 11.1–25.2% at seasonal scale. For daily precipitation variations, the explained variance at the annual scale is less than 40%. The spatial characters of climate variation affected by large‐scale circulation are identified. Daily mean temperature variations in the Qaidam Basin and the Qilian Mountains (90°–103°E and 35°–40°N) all year round and in the region between the Tanggula Mountains and the Himalayan Mountains (88°–97°E and 30°–34°N) in wet season (May–September) are well resolved by the CTCs. Daily precipitation variations in the eastern TP in the dry season (October–April) and a region from the Brahmaputra River basin to the Tanggula Mountains (85°–92°E and 28°–32°N) and the southern Hengduan Mountains (98°–102°E and 27°–31°N) in the wet season show a close relationship with the identified CTCs by SAN. Schematic diagram of the circulation systems related to TP climate variations. The boundaries of the four spatial domains for circulation classification are drawn with different kinds of dashed lines (domain 1: 40°–135°E, 5°S–70°N, domain 2: 50°–125°E, 5°–60°N, domain 3: 60°–115°E, 15°–50°N, domain 4: 70°–105°E, 25°–40°N). [ABSTRACT FROM AUTHOR]