The data include oxygen isotope data and core age data of Jiang Co Lake in Qinghai Tibet Plateau. The first column is age, and the second column is oxygen isotope value; This data records the changes of oxygen isotopes in the past 84-2015.
0 2022-04-15
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
0 2022-04-15
The maximum freezing depth is an important index of the thermal state of seasonal frozen ground. Due to global warming, the maximum freezing depth of seasonal frozen ground continues to decline. The maximum freezing depth data set of five provinces in Northwest China, Tibet and surrounding areas from 1961 to 2020 was released, with a spatial resolution of 1 km. The data set is a support vector regression (SVR) model based on the measured data of maximum freezing depth from 2001 to 2010 and spatial environmental variables, which simulates the maximum freezing depth in Northwest China, Tibet and surrounding areas from 1961 to 2020. The validation results show that the SVR model has good spatial generalization ability, and there is a high consistency between the predicted value and the observed value of the maximum soil freezing depth. The determination coefficients of the simulation results in the four periods of 1980s, 1990s, 2000s and 2010s are 0.77, 0.83, 0.73 and 0.71 respectively. The percentile range of the prediction results shows that the simulation results have good stability. Based on this data set, it is found that the maximum soil freezing depth in Northwest China continues to decline, among which Qinghai has the fastest decline rate, with an average decline of 0.53 cm every decade. The data set provides data support for the study of seasonal frozen soil in Northwest China, High Mountain Asia and the Third Pole.
0 2022-04-15
CAMELE: Collocation-Analyzed Multi-Source Ensembled Land Evapotranspiration data provide an estimation of global land total evapotranspiration at 0.1°-8daily and 0.25°-daily resolutions. The 0.1°-8daily collection covers the period from 20010101 to 20190829, while the 0.25°-daily provides the estimation from 19810101 to 20200831. TCA-based algorithms are used to evaluate the uncertainties and the error cross-correlation value of five widely used global land evapotranspiration products, including ERA5-land total evaporation, FLUXCOM-RS, PMLV2 (Penman-Monteith-Leuning model version 2 global evaporation), GLEAM v3.3a and GLDASv2.1 Noah. By minimizing the mean square error, the optimal weights of each product for linear combination are given using the evaluation results. Multiple information including the core collection method, synthetic experiments, site-based validation and evaluation of the merging data were described in our paper.
0 2022-04-15
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.
0 2022-04-15
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.
0 2022-04-15
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.
0 2022-04-15
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.
0 2022-04-15
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.
0 2022-04-15
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.
0 2022-04-15
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