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The Tibetan Plateau (TP) region, also known as the "Asian water tower", provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d−1 (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP. [ABSTRACT FROM AUTHOR]

Surface runoff is a major component of the hydrological cycle, and it is essential for supporting the ecosystem services provided by grassland and forest ecosystems. It is of practical importance to understand the mechanisms and the dynamic processes of runoff in a river's basin, and in this study, we focused on the restored montane Pailugou Basin in the Qilian Mountains, Gansu Province, China, since its water status is extremely important for the large arid area and local economies therein. Our purpose was to determine the annual variation in the surface runoff in the Pailugou Basin because it is important to understand the influence of climate fluctuations on surface water resources and the economy of the basin. In addition, little is known about the annual variations in precipitation and runoff in this region of the world. Daily atmospheric precipitation, air temperature and runoff data from 2000 to 2019 were analyzed by the calculation of the uneven annual distribution of surface runoff, the calculation of the complete adjustment coefficient, and the vector accumulation expressed by the concentration degree. We also used the cumulative anomaly approach to determine the interannual variation trend of runoff, while the change trend was quantified by the sliding average method. Finally, we used the Mann–Kendall mutation test method and regression analysis to establish the time-series trend for precipitation and runoff and to determine the period of abrupt runoff changes. The results indicated concentrated and positive distributions of surface runoff on an annual basis, with a small degree of dispersion, and an explicit concentration of extreme flows. The relative variation ranges exhibited a decreasing trend, and the distribution of the surface runoff gradually was uniform over the year. The runoff was highest from July to September (85% of the annual total). We also determined that annual surface runoff in the basin fluctuated over the 20-year period but showed an overall increasing trend, increasing by 3.94 × 105 m3, with an average increase rate of 0.42 × 105 m3 every ten years. From 2005 to 2014, the annual runoff and the proportion of runoff in the flood season (July to September) to the annual runoff fluctuated greatly. The correlation between the runoff and precipitation was significant (r = 0.839, p < 0.05), whereas the correlation between air temperature and surface runoff was low (r = 0.421, p < 0.05). [ABSTRACT FROM AUTHOR]

Solar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts. Plain Language Summary: The Tibetan Plateau (TP) is characterized by high elevation and complex topography. Interaction between solar radiation and the undulating topography has important impacts on the regional surface energy balance and hydrologic cycle. Here we use Earth System Model simulations to show the local and remote impacts of the TP's solar radiation‐topography interaction on the surface climate of the Asian regions. Such interaction overall increases the air temperature especially in winter over the TP and reduces the summer precipitation in southern TP. Teleconnectedly, the interaction further alters the precipitation patterns in South and East Asia, by altering the atmospheric circulation that influences moisture transport and clouds. Accounting for such interaction generally improves the model performance when benchmarked against observations. These findings underscore the important roles of the TP's solar radiation‐topography interaction in modulating the climate of the local and remote Asian regions. Key Points: Solar radiation‐topography interaction over the Tibetan Plateau (TP) increases annual average regional near‐surface air temperature by 0.26 KSolar radiation‐topography interaction over the TP also affects the precipitation patterns in South and East AsiaIncluding solar radiation‐topography interaction improves the simulation of surface climate over the TP and Asian regions [ABSTRACT FROM AUTHOR]

Reliable precipitation simulation over the Tibetan Plateau (TP) remains a challenge, manifested by a prominent systematic wet bias in the warm season. Previous studies have generally neglected the potential linkage between surface radiation energy budget and precipitation bias. Prevalent scattered cumulus and thunderstorms over the TP in summer strongly influence surface radiation. A cloud fraction scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF model and tested for a month‐long simulation. It is found that the scheme better reproduces the surface solar radiation compared to a default cloud fraction scheme in the WRF model. Using abundant surface observations, we find that overestimation of the downward surface shortwave radiation (DSSR) would lead to wet bias. DSSR overestimation contributes to higher surface temperature and larger evaporation and enhanced atmospheric instability, which favor more simulated convective precipitation. The study suggests that a better simulation of clouds and surface radiation would benefit precipitation simulation over the plateau. Plain Language Summary: Summer precipitation over the Tibetan Plateau (TP) is generally overestimated in general circulation models as well as regional weather forecasts and climate models. The prevailing "popcorn‐like" clouds over the TP in summer would strongly impact surface radiation. Considering the potential linkage between surface radiation energy budget and precipitation bias, a statistical cloud fraction and subgrid condensation scheme (GS‐PDF scheme) is implemented in the WRF model and tested for a month‐long simulation. The GS‐PDF scheme better captures clouds over the TP with more mid‐low cloud water (ice) than the default Xu‐Randall scheme. Increased mid‐low cloud water in the GS‐PDF scheme reduced simulated downward surface shortwave radiation, reduced simulated surface temperatures, atmospheric instabilities and convective precipitation, leading to alleviated wet bias over the TP. This study suggests that better simulations of cloud and surface radiation would be beneficial for simulations of hydrological cycles over the TP. Key Points: A new cloud macrophysics scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF modelThe new macrophysics scheme reduced simulated downward surface shortwave radiation, surface temperatures and atmospheric instabilitiesA better simulation of clouds and surface radiation would benefit precipitation simulation over the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Soil moisture over the Tibetan Plateau (TP) can affect hydrological cycles on local and remote scales through land–atmosphere interactions. However, TP long-term surface soil moisture characteristics and their response to climate change are still unclear. In this study, we firstly evaluate two satellite-based products—SSM/I (the Special Sensor Microwave Imagers) and ECV COMBINED (the Essential Climate Variable combined)—and three reanalysis products—ERA5-Land (the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis), MERRA2 (the second version of Modern-Era Retrospective Analysis for Research and Applications), and GLDAS Noah (the Noah land surface model driven by Global Land Data Assimilation System)—against two in situ observation networks. SSM/I and GLDAS Noah outperform the other soil moisture products, followed by MERRA2 and ECV COMBINED, and ERA5-Land has a certain degree of uncertainty in evaluating TP surface soil moisture. Analysis of long-term soil moisture characteristics during 1988–2008 shows that annual and seasonal mean soil moisture have similar spatial distributions of soil moisture decreasing from southeast to northwest. Additionally, a significant increasing trend of soil moisture is found in most of the TP region. With a non-linear machine learning method, we quantify the contribution of each climatic variable to warm-season soil moisture. It indicates that precipitation dominates soil moisture changes rather than air temperature. Pixel-wise partial correlation coefficients further show that there are significant positive correlations between precipitation and soil moisture over most of the TP region. The results of this study will help to understand the role of TP soil moisture in land–atmosphere coupling and hydrological cycles under climate change. [ABSTRACT FROM AUTHOR]

The northeastern Tibetan Plateau is located in a climatic junction, which is considered an ideal region to explore the interactions between the summer monsoons and the westerly circulation patterns. However, to date, the needed long-term precipitation-based isotopic dataset is too limited to predict the interactions and patterns. This paper presents an evaluation of hydrometeorological processes and climate dynamics in the northeastern Tibetan Plateau based on a 7-yr precipitation isotope dataset covering the summer monsoon periods from 2012 to 2018. Results illustrated remarkable seasonal isotopic variability, characterized by lower δ18O and δ2H values in June with an average of −10‰ and −66.7‰, respectively. Higher δ18O and δ2H values in July averaged −6.7‰ and −39.5‰, respectively. This clear isotopic variability is largely related to seasonal changes of moisture sources and hydrometeorological processes. These precipitation isotopic values were primarily determined by the amount of precipitation, relative humidity, and convective activity, but showed no correlation with air temperature. Backward trajectory model results showed that Xinjiang, northern China, the Arctic, central Asia, and the South China Sea (SCS) were the primary sources of precipitation for the study site with varying seasonal contributions. The maritime moisture source of the SCS primarily resulted in the lowest precipitation δ18O values during the prevailing summer monsoon, which is mainly as a result of the strong convective activity and rainout processes along the air trajectory. The higher average deuterium excess (d-excess) value of precipitation in September indicated continental sources from central Asia (e.g., 75.4%) as land vapor recycling increases d-excess concentration in the atmosphere. These findings provide further insights into the main factors of precipitation isotopic variability related to atmospheric processes along the trajectory and the relevant factors in the monsoon regions. Significance Statement: Recently, scientists and policy makers have become aware that Tibetan hydroclimate variability provides evidence of changes in regional and global circulation patterns that may result in the intensification of climate-driven extremes. However, these studies largely depend on crucial paleoclimate records of past precipitation isotopes in monsoon regions, which contain great uncertainties because of the complex relationship between climatic variability and precipitation isotopes. This study first presented a 7-yr isotopic dataset to understand the hydrological processes and climate dynamics controlling the isotopic variability in the northeastern Tibetan Plateau. The findings reveal important factors on the isotopic variability associated with atmospheric processes and their key climatic variables, which can enhance our interpretation of the paleoclimate records in monsoon regions. [ABSTRACT FROM AUTHOR]

Snow and glacier are important components in the hydrological cycle of the Tibetan Plateau (TP). Air temperature, as the main driver in freezing and thawing processes, becomes vital for hydrological modelling and prediction in this region. Due to a sparse ground gauging network, spatial density of air temperature measurement is insufficient for hydro‐meteorological studies. Therefore, the aim of this study is to identify the best representative temperature data for hydrological applications from four widely used reanalysis products, including ERA‐Interim, ERA‐5, GLDAS‐2.1 and NCEP‐R2, with reference to in situ measurements and gridded snow depth from the year 2008–2017 over the entire TP. To reduce errors, Bayesian Joint Probability (BJP) approach based on K‐Nearest Neighbour (KNN) classification algorithm (KNN‐BJP) is proposed to post‐process gridded reanalyses. The results indicate that all the reanalysis datasets provide highly correlated but cold biased air temperature. The correlation ecoefficiency is greater than 0.85. The cold biases are near −3 ° C and mainly distributed in the southeastern TP. Bias in daily maximum temperature during Spring is greater than −8 ° C for most stations. ERA‐Interim is found to have the closest agreement with in situ measurements, closely followed by GLDAS‐2.1. KNN‐BJP is found to be effective within a distance smaller than 5°. After post‐processing, the prominent underestimation is efficiently corrected with Bias near 0. RMSE is markedly reduced to be smaller than 2.5 ° C. The post‐processed ERA‐5 and GLDAS‐2.1 are as accurate as ERA‐Interim, but able to provide more detailed information for extreme events due to their finer spatial resolution. Thus, ERA‐5 and GLDAS‐2.1 are more recommended to represent air temperature in the TP. Snow depth as complementary reference data is able to present spatial variance of air temperature. Our study can help alleviate the problem of sparse air temperature data over the TP. [ABSTRACT FROM AUTHOR]

Highlights • T a with a medium spatial resolution is in urgent demand for associated studies. • A method was developed to downscale T a based on correlations between T a and terrain. • The downscaled T a is closer to ground measurements and has better image quality. • A 0.01° T a dataset from 2000 to 2015 over the Tibetan Plateau was generated. Abstract Surface air temperature (T a) is critical to the studies of radiation balance, energy budget, and water cycle. It is a necessary input for associated models. Most of the current T a datasets of reanalysis products have limitations at local scales due to their coarse spatial resolutions. For better modeling the radiation balance, energy budget, and water cycle over the Tibetan Plateau, this study proposes a practical method for T a downscaling based on the digital elevation model. This method is applied to downscale T a of the China regional surface meteorological feature dataset (CRSMFD) at 0.1° and the ERA-interim (ERAI) product at 0.125° to 0.01°. The daily mean T a and the 3-hourly instantaneous T a with a 0.01° are obtained. The downscaled T a are evaluated from the perspectives of accuracy and image quality. Results show that the daily mean T a downscaled from the CRSMFD product has a RMSE of 1.13 ± 1.0 K at 105 meteorological stations and RMSEs of 0.96 K to 2.34 K at three experimental stations; the instantaneous T a downscaled from CRSMFD has RMSEs of 1.02 K to 4.0 K at the three experimental stations. T a after downscaling has better agreement with the ground measured T a than before downscaling, especially in mountain areas. By contrast, T a downscaled from the ERAI product has unacceptable accuracy due to the great uncertainty of the ERAI T a over the Tibetan Plateau. With the proposed method, a 0.01° T a dataset from 2000 to 2015 over the Tibetan Plateau was generated to satisfy related studies and applications. [ABSTRACT FROM AUTHOR]