The Tibetan Plateau (TP), acting as a large elevated land surface and atmospheric heat source during spring and summer, has a substantial impact on regional and global weather and climate. To explore the multi-scale temporal variation in the thermal forcing effect of the TP，The data set of atmospheric heat source/sink in Tibetan Plateau was prepared as a quantitative analysis tool for calculating heat budget of gas column. the atmospheric heat source/sink dataset consists of three variables: surface sensible heat flux SH, latent heat release LH and net radiation flux RC. here we calculated the surface sensible heat and latent heat release based on 6-h routine observations at 80 (32) meteorological stations during the period 1979–2016：air temperature at 1.5 m and surface temperature and wind speed at 10 m are used to calculate surface sensible heat flux,the latent heat release is estimated precipitation data.The satellite datasets used to calculate the net radiation flux were the Global Energy and Water Cycle Experiment surface radiation budget satellite radiation(GEWEX/SRB) and Clouds and Earth’s Radiant Energy Systems/Energy Balanced And Filled (CERES/EBAF). The monthly shortwave and longwave radiation fluxes at the surface and at the top of the atmosphere (TOA) in GEWEX/SRB and CERES/EBAF were utilized to obtain the net radiation flux for the period 1984–2015 via statistical methods。

The radiogenic isotope composition of strontium (Sr) and neodymium (Nd) on surface of the Earth are a powerful tool for tracing dust sources and sinks on surface of the Earth. To differentiate the spatial variability of aeolian dust sources in key cryospheric regions at the three poles (including the ‘Third Pole’ covering the high mountain area in Asia, the Arctic and Antarctica). A dataset of the Sr-Nd isotopic compositions from the terrestrial extremely cold or arid environments in this study was compiled as the similar method by Blanchet (2019). The database identified on snow, ice, sand, soil (loess) and sediment from the modern dust samples and paleoclimatic records of the three poles based on 43 different references with 967 data points in total. There are 274 data points from the third pole, 302 data points from the Arctic, and 391 data points from Antarctica. The sampling and measurement methods, and quality of these data are recognized and introduced. In each pole, geographical coordinates and other information are provided. The main scientific purpose of this dataset is to provide our own measurement and collect documentation of the Sr-Nd dataset, which will be useful for determining the sources and transport pathways of dust at the three poles and to investigate whether there are multiple dust sources for each of the poles. These datasets provide detail exhaustive documentation of the isotopic signature from the three poles at specific time intervals, which would be useful for understanding the dust source or sink of the three poles.

Since the original zoning data only divides China into six regions according to regional geographical location, but does not consider the special geographical region of Qinghai Tibet Plateau and the differences in economic development between central and southern China, there are certain limitations in analyzing the economic development and ecological environment of different regions in China. Based on the original distribution of six regions in China, The division of Qinghai Tibet Plateau is added, and the original central south is divided into central China and South China according to the geographical location. Therefore, the data includes a total of eight divisions. The data can be used to compare the differences of climate change, ecological environment, economic development and landform between different regions of China.

Lakes collect runoff, sediment and nutrients from upstream watersheds and are an important "destination" of material migration at the watershed scale. Therefore, the attributes of lake water and sediment are affected by catchment attributes (e.g. climate, terrain and vegetation conditions) to a large degree. This dataset delineates the watershed boundaries of 1525 Lakes (with an area from 0.2 to 4503 square kilometers) on the Tibetan Plateau, and calculates 721 catchment-scale attributes on the aspects of lake body, terrain, climate, vegetation, soil/geology and anthropogenic activities. This is the first dataset of lake-catchment characteristics on the Tibetan Plateau, which can provide foundamental data for the study of lakes in the Tibetan Plateau.

This dataset includes annual mosaics of Antarctic ice velocity derived from Landsat 8 images between December, 2013 and April, 2019, which was updated in 2020 in order to produce multi-year annual ice velocity mosaics and improve the quality of products including non-local means (NLM) filter, and absolute calibration using rock outcrops data. The resulting Version 2 of the mosaics offer reduced local errors, improved spatial resolution as described in the README file.

The data set of ice core-snow black carbon content on the Tibetan plateau (1950-2006) contains five (5) tables: 1 Xu et al. 2006 AG, 2 Xu et al. 2009 PNAS_Conc., 3 Xu et al. 2009 PNAS_flux, 4 Xu et al. 2012 ERL, 5 Wang et al. 2015 ACP. The data collection sites include the Meikuang glacier, Dongkemadi, Qiangyong, Kangwure, Naimona’nyi, Muztagata, Rongbuk, Tanggula Mountain, Ningjin Gangsang, Zuoqipu, and Glacier No. 1 at the headwaters of the Ürüqi River. The latitudes and longitudes of the collection locations, elevations and other information are marked in the data. The main indicators of the data are location, time, organic carbon (OC), elemental carbon (EC), black carbon (BC) content and flux. Location: latitude and longitude Time: year or date OC: organic carbon EC: elemental carbon BC: Black carbon Conc.: content, unit: ng g-1 Flux: flux, unit: mg m-2a-1 The data come from the following subjects. 1. National Program on Key Basic Research Project (973 Program)：Temporal and Spatial Characteristics and Remote Sensing Modeling of Global Change Sensitive Factors; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 2. National Key Basic Research Program: The Response of Formation and Evolution on the Tibetan Plateau to Global Changes and Adaptation Strategy; Person in charge: Tandong Yao; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 3. The General Program of National Natural Science Foundation of China: High-resolution Carbon Black Recording in Snow Ice of the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 4. The General Program of the National Natural Science Foundation of China: Extraction of Climate and Environment Information from Ice Core Encapsulated Gas on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 5. National Natural Science Foundation of China for Distinguished Young Scholars: Snow and Ice-Atmospheric Chemistry and Environmental Changes on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 6. National Natural Science Foundation of China for Distinguished Young Scholars: Study on the Changes of Aerosol Emissions and Combustion in Human Activities in South Asia in the Past 100 Years; Person in charge: Mo Wang; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). Observation methods: two-step heating method, thermal/optical carbon analysis method, and single-particle black carbon aerosol photometer.

The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.

Snow water equivalent (SWE) is an important parameter of the surface hydrological model and climate model. The data is based on the ridge regression algorithm of machine learning, which integrates a variety of existing snow water equivalent data products to form a set of snow water equivalent data products with continuous time series and high accuracy. The spatial range of the data is Pan-Arctic (45 N° to 90 N °), The data time series is 1979-2019. The dataset is expected to provide more accurate snow water equivalent data for the hydrological and climate model, and provide data support for cryosphere change and global change.

The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.

Surface melting is the primary reason that affects the mass balance of Greenland ice sheet. At the same time, ice and snow have high albedo, and ice sheet surface melting will cause the difference of radiation energy budget, and then affects the energy exchange between sea-land-air. The high-resolution ice sheet surface melting product provides important information support for the study of Greenland ice sheet surface melting and its response to global climate change. This dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Greenland ice sheet surface melt daily product for 1985, 2000, 2015 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Greenland ice sheet, and the data set is stored as *.nc.

Previous studies suggest an accelerated water cycle over the Tibetan Plateau (TP) in recent decades, mainly based on observed precipitation. However, the exact changes to evapotranspiration (ETa) over this period remain largely unknown. Although multiple ETa products for the TP region report that ETa experienced a significant increasing trend of around 8.4 ± 2.2 mm/10 a during 1982–2018, there exist large uncertainties in the annual ETa estimates over different climate zones. Here, we quantified and explained the ETa trend using a comprehensive process-based ETa model refined on ground-based observations from nine stations over the TP. Attribution analysis revealed that a large part of the increasing ETa trend was caused by higher temperature (53.8%) and more soil moisture (23.1%) caused by the melting cryosphere and increased precipitation. The increasing rate of ETa on the TP was approximately twice that of the global ETa, providing strong and independent evidence for an accelerated hydrological cycle. The dominant role of increased temperature in ETa implies a continued acceleration of the water cycle in the future.

The data are the physicochemical indexes of cultivated soils in the Nianchu River Basin in the "One River, Two Rivers" region of Tibet. The data include soil bulk weight, soil mass water content, soil volume water content, soil total porosity, soil texture (clay, powder and gravel), soil pH, soil organic matter, soil total nitrogen, soil total phosphorus, soil total potassium, soil alkaline nitrogen, soil effective phosphorus and soil fast-acting potassium, etc.; the soil samples are mixed samples consisting of 3-5 sample points, and the experimental analysis participates in the relevant national standards. Soil moisture content, volumetric water content and total soil porosity were determined by ring knife drying method, soil texture was determined by laser particle size meter, pH was determined by glass electrode method; organic matter was determined by potassium dichromate volumetric method; total nitrogen was determined by Kjeldahl method; total phosphorus was determined by acid melting method - molybdenum di-resistance colorimetric method; total potassium was determined by acid melting method - flame photometer method; alkaline nitrogen was determined by sodium hydroxide - alkaline diffusion method; effective phosphorus was determined by Olsen method; fast-acting potassium by NH4Ac leaching, flame photometric method. Soil duplicate samples deviated within 3%. The data can be used for regional soil environmental quality analysis and provide scientific guidance for sustainable use of arable land.