This data includes in-situ major and trace element data of lithium aluminosilicate minerals and beryl, and in-situ major element data of niobium tantalum oxide. The samples were collected from Kuqu leucogranite and granite pegmatite in the eastern Himalaya. The data of major mineral elements are obtained by electron microprobe, and the data of trace mineral elements are obtained by laser ablation inductively coupled plasma mass spectrometry. The obtained data can reveal the complex crystallization environment during mineral formation, show the supersaturation caused by crystallization differentiation and supercooling and fluid action, reflect the evolution degree of crystallization environment and magmatic differentiation, and explore the evolving relationship between leucogranite and granite pegmatite and the prospect of rare metal mineralization.

The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.

Central Asian meteorological station observation data set includes field observation data of temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation, soil heat flux, sunshine time and soil temperature at 10 field weather stations in central Asia. The 10 field stations cover different ecosystem types such as farmland, forest, grassland, desert, desert, wetland, plateau and mountain. The original meteorological data collected by the ground meteorological observation stations in this data set are obtained after format conversion after screening and auditing. The data quality is good. Various types of climate in the Middle East, fragile ecological environment, the frequent meteorological disasters, the establishment of the data set for long-term ecological environment monitoring, disaster prevention and mitigation in central Asia, central Asia, climate change and ecological environment in the areas of study provides data support, ecological environment monitoring in central Asia has been obtained in the study of the application.

This is a comprehensive dataset on microbial abundance, dissolved organic carbon (DOC), and total nitrogen (TN) for glaciers on the TP based on extensive field sampling from 2010. The dataset comprises 5,409 microbial abundance records of ice cores and snow pits from 12 glaciers and 2,532 DOC and TN records of five habitats, including ice core, snow pit, surface ice, surface snow, and proglacial runoff, from 38 glaciers. These glaciers covered broad areas and diverse climate conditions with a multiyear average temperature ranging from -13.4 ℃ (the Guliya glacier) to 2.9 ℃ (the Zhuxigou glacier) and multiyear average precipitation ranging from 76.9 mm (the No.15 glacier) to 927.8 mm (the 24K glacier), which makes this dataset suitable for studies across the entire TP. To the best of our knowledge, this is the first dataset of microbial abundance and TN in glaciers on the TP, and also the first dataset of DOC in ice cores on the TP. These new data could provide valuable information for researches on the glacier carbon and nitrogen cycle and assessing the potential impacts of glacier retreat due to global warming on downstream ecosystems.

Due to its high surface albedo and low thermal conductivity, snowpack on sea ice can effectively adjust the change of sea ice (growth and melting) and control energy budgets. It is an important parameter for sea ice thickness estimation. This product provides the daily snow depth on Arctic sea ice from 2012 to 2020 (September to April). Based on the original reanalysis reconstruction model (NASA Eulerian Snow on Sea Ice Model), we add a melting process, and then combine with the particle filter method to construct the snow depth estimation model. The ERA5 data (snowfall, 2-m air temperature and wind speed data) provided by the ECMWF, sea ice drift data provided by the OSI SAF and sea ice concentration data provided by the NSIDC are used to force the reanalysis reconstruction model to obtain the simulated snow depth, and then the satellite-derived snow depths are assimilated into the model to obtain the cold-season snow depth on Arctic sea ice (October to April). Since there is no remote sensing data used for assimilation in September, the linear regression analysis is used to construct the relationship between the simulated snow depth and the assimilated snow depth to obtain the final snow depth data in September. Finally, the final snow depth on Arctic sea ice from 2012 to 2020 (September-April) is generated at a 50-km spatial resolution. This product can effectively integrate the advantages of satellite data and simulation data, and is in good agreement with three OIB data (i.e., the NSIDC OIB quick look product, NSIDC OIB L4 product and OIB product provided by the NOAA), with root mean square errors (RMSE) of 5.80 cm, 4.61 cm and 6.50 cm, respectively. This data set can provide accurate input parameters for the estimation of sea ice thickness and volume, help to analyze the Arctic mass balance and energy balance, and promote the future development of sea ice models.

We provided the raindrop size distribution (DSD) observations with OTT PARSIVEL2 disdrometers at Dari (4096 m a.s.l., 33.55°N, 99.95°E) and Delingha (3137 m a.s.l., 37.47°N, 96.81°E) in the northeast of the Tibetan Plateau (TP) during summer 2019. The disdrometers adopted in the data are manufactured by OTT HydroMet. The process of the Data Quality Control (QC) are described in the paper Microphysical Characteristics of Raindrop Size Distribution and Implications for Radar Rainfall Estimation over the Northeastern Tibetan Plateau. Several rainfall integral parameters calculated from the disdrometer observation were provided. The parameters concluded NT (total number concentration of raindrops, m−3), W (liquid water content, g m−3), R (rain rate, mm h−1), Z (radar reflectivity, mm6 m−3), D0 (median volume diameter, mm), and σm (mass spectrum standard deviation). The gamma parameters (N0, μ, and λ) estimated using the moment method (MM), the maximum likelihood method (ML) are provided. The standardized gamma distribution parameters Nw and Dm are also provided. The measurement of microphysical characteristics of precipitation is very essential to the study of the physical and dynamic processes of rainfall over the TP.

To understand the potential impact of projected climate changes on the vulnerable agriculture in Central Asia (CA) in the future, six agroclimatic indicators are calculated based on the 9km-resolution dynamical downscaled results of three different global climate models and a high-resolution projection dataset of agroclimatic indicators over CA is produced. These indicators are growing season length (GSL, days), biologically effective degree days (BEDD, ℃), frost days (FD, days), summer days (SU, days), warm spell duration index (WSDI, days), and tropical nights (TR, days). The periods are 1986-2005 and 2031-2050. The spatial resolution is 0.1°. As all the indicators except WSDI are defined with absolute temperature thresholds and particularly sensitive to the systematics biases in the model data, the quantile mapping (QM) method is applied to correct the simulated temperature. Results show the QM method largely reduces the biases in all the indicators. GSL, SU, WSDI, and TR will significantly increase over CA and FD will decrease. However, changes in BEDD are spatially heterogeneous, with the increases in northern CA and the mountainous areas and decreases in the southern and middle part of the plain areas. This dataset can be applied for assessing the future risks in the local agriculture for climate changes and will be beneficial to adaption and mitigation actions for food security in this region.

The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.

This is the supporting data for the paper entitled 'understanding key processes associated with alpine lake ice phenology using a coupled atmosphere-lake model', which is planned to submit to Geophysical Research Letters. This data includes the model code and simulation data at lake Nam-Co based on WRF-Flake.

1) Data content: spatial and temporal dataset of near-surface monthly air temperature of Antarctic ice sheet from 2001 to 2018。 2) Data source and processing method: MODIS (MODerate resolution Imaging Spectroradiometer) Land Surface Temperature measurements in combination with in-situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05°×0.05°, spanning from 2001 to 2018. 3) Data quality description: the accuracy is better than that of ERA5 reanalysis data. 4) Data application achievements and prospects: the database can be used to study the temporal and spatial distribution characteristics of near-surface air temperature of Antarctic ice sheet, and the impact of SAM and ENSO on the interannual variation of Antarctic temperature. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.

1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.

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。