The Third pole

Brief Introduction:The Qinghai-Tibet Plateau, known as the 'three poles' on the earth, has been an important area for the study of global environmental change and has played a profound role in regulating the ecology, environment and climate of the entire planet. It has always been a hot spot of concern to the international community. Under the background of global warming, the elements of the cryosphere such as the three-pole glaciers, frozen soils, and frozen-melt lakes have undergone significant changes. The glaciers are rapidly retreating and the layers of frozen soils are thickened. For a long time, China has carried out systematic and multidisciplinary research on the third pole of the world, which is dominated by the Qinghai-Tibet Plateau, and has formed a rich research accumulation. Compared with the Qinghai-Tibet Plateau, China's current level of scientific research in the Arctic and Arctic regions is relatively low, especially the problems of weak research foundation, scattered research direction, and no system and low level. With the impact of climate warming...

Publish Datetime:2020-06-23

Number of Datasets:177

  • Passive microwave SSM/I brightness temperature dataset for China (1987-2007)

    This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, Row number: 308 *166,each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.

    2022-05-05 0 View Details

  • The spatial dataset of climate on the Tibetan Plateau (1961-2020)

    The meteorological elements distribution map of the plateau, which is based on the data from the Tibetan Plateau National Weather Station, was generated by PRISM model interpolation. It includes temperature and precipitation. Monthly average temperature distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): t1960-90_1.e00,t1960-90_2.e00,t1960-90_3.e00,t1960-90_4.e00,t1960-90_5.e00, t1960-90_6.e00,t1960-90_7.e00,t1960-90_8.e00,t1960-90_9.e00,t1960-90_10.e00, t1960-90_11.e00,t1960-90_12.e00 Monthly average temperature distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): t1991-20_1.e00,t1991-20_2.e00,t1991-20_3.e00,t1991-20_4.e00,t1991-20_5.e00, t1991-20_6.e00,t1991-20_7.e00,t1991-20_8.e00,t1991-20_9.e00,t1991-20_10.e00, t1991-20_11.e00,t1991-20_12.e00, Precipitation distribution map of the Tibetan Plateau from 1961 to 1990 (30-year average values): p1960-90_1.e00,p1960-90_2.e00,p1960-90_3.e00,p1960-90_4.e00,p1960-90_5.e00, p1960-90_6.e00,p1960-90_7.e00,p1960-90_8.e00,p1960-90_9.e00,p1960-90_10.e00, p1960-90_11.e00,p1960-90_12.e00 Precipitation distribution map of the Tibetan Plateau from 1991 to 2020 (30-year average values): p1991-20_1.e00,p1991-20_2.e00,p1991-20_3.e00,p1991-20_4.e00,p1991-20_5.e00, p1991-20_6.e00,p1991-20_7.e00,p1991-20_8.e00,p1991-20_9.e00,p1991-20_10.e00, p1991-20_11.e00,p1991-20_12.e00, The temporal coverage of the data is from 1961 to 1990 and from 1991 to 2020. The spatial coverage of the data is 73°~104.95° east longitude, 26.5°~44.95° north latitude, and the spatial resolution is 0.05 degrees×0.05 degrees (longitude×latitude), and it uses the geodetic coordinate projection. Name interpretation: Monthly average temperature: The average value of daily average temperature in a month. Monthly precipitation: The total precipitation in a month. Dimensions: The file format of the data is E00, and the DN value is the average value of monthly average temperature (×0.01°C) and the average monthly precipitation (×0.01 mm) from January to December. Data type: integer Data accuracy: 0.05 degrees × 0.05 degrees (longitude × latitude). The original sources of these data are two data sets of 1) monthly mean temperature and monthly precipitation observation data from 128 stations on the Tibetan Plateau and the surrounding areas from the establishing times of the stations to 2000 and 2) HadRM3 regional climate scenario simulation data of 50×50 km grids on the Tibetan Plateau, that is, the monthly average temperature and monthly precipitation simulation values from 1991 to 2020. From 1961 to 1990, the PRISM (Parameter elevation Regressions on Independent Slopes Model) interpolation method was used to generate grid data, and the interpolation model was adjusted and verified based on the site data. From 1991 to 2020, the regional climate scenario simulation data were downscaled to generate grid data by the terrain trend surface interpolation method. Part of the source data came from the results of the GCM model simulation; the GCM model used the Hadley Centre climate model HadCM2-SUL. a) Mitchell JFB, Johns TC, Gregory JM, Tett SFB (1995) Climate response to increasing levels of greenhouse gases and sulphate aerosols. Nature, 376, 501-504. b) Johns TC, Carnell RE, Crossley JF et al. (1997) The second Hadley Centre coupled ocean-atmosphere GCM: model description, spinup and validation. Climate Dynamics, 13, 103-134. The spatial interpolation of meteorological data adopted the PRISM (Parameter-elevation Regressions on Independent Slopes Model) method: Daly, C., R.P. Neilson, and D.L. Phillips, 1994: A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140~158. Due to the difficult observational conditions in the plateau area and the lack of basic research data, there were deletions of meteorological data in some areas. After adjustment and verification, the accuracy of the data was only good enough to be used as a reference for macroscale climate research. The average relative error rate of the monthly average temperature distribution of the Tibetan Plateau from 1961 to 1990 was 8.9%, and that from 1991 to 2020 was 9.7%. The average relative error rate of precipitation data on the Tibetan Plateau from 1961 to 1990 was 20.9%, and that from 1991 to 2020 was 22.7%. The area of missing data was interpolated, and the values of obvious errors were corrected.

    2022-05-05 0 View Details

  • The NPP spatio-temporal dataset of the Tibetan Plateau (1982-2006)

    This data set contains the results of the calculation of Net Primary Productivity (NPP) on the Tibetan Plateau based on ecological models and remote sensing data from 1982 to 2006. Ecosystem NPP of the Tibetan Plateau was generated based on the remote sensing Advanced Very High Resolution Radiometer (AVHRR) data and the Carnegie-Ames-Stanford Approach (CASA) model(1982-2006), the soil carbon content was generated based on the second soil census data, and the biomass carbon data were generated based on the High Resolution Biosphere Model (HRBM) model. Forest ecosystem NPP of the Tibetan Plateau (1982-2006): npp_forest82.e00,npp_forest83.e00,npp_forest84.e00,npp_forest85.e00,npp_forest86.e00, npp_forest87.e00,npp_forest88.e00,npp_forest89.e00,npp_forest90.e00,npp_forest91.e00, npp_forest92.e00,npp_forest93.e00,npp_forest94.e00,npp_forest95.e00,npp_forest96.e00, npp_forest97.e00,npp_forest98.e00,npp_forest99.e00,npp_forest00.e00,npp_forest01.e00, npp_forest02.e00,npp_forest03.e00,npp_forest04.e00,npp_forest05.e00,npp_forest06.e00 Grassland ecosystem NPP of the Tibetan Plateau(1982-2006): npp_grass82.e00,npp_grass83.e00,npp_grass84.e00,npp_grass85.e00,npp_grass86.e00, npp_grass87.e00,npp_grass88.e00,npp_grass89.e00,npp_grass90.e00,npp_grass91.e00, npp_grass92.e00,npp_grass93.e00,npp_grass94.e00,npp_grass95.e00,npp_grass96.e00, npp_grass97.e00,npp_grass98.e00,npp_grass99.e00,npp_grass00.e00,npp_grass01.e00,npp_grass02.e00,npp_grass03.e00,npp_grass04.e00,npp_grass05.e00,npp_grass06.e00. Biomass carbon and soil carbon of the Tibetan Plateau: Biomass.e00,Socd.e00. The soil carbon content data (Socd) are generated based on data of the second soil census of China and Soil Map of China (1:1,000,000) by soil subclass interpolation. The NPP data are generated from the CASA model and AVHRR data simulation: Potter CS, Randerson JT, Field CB et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811–841. The biomass carbon data are generated via HRBM model simulation: McGuire AD, Sitch S, et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochem. Cycles, 2001, 15 (1), 183-206. The raw data are mainly remote sensing data and field observation data with high accuracy; the verification and adjustment of the measured data in the field during the production were undertaken to maintain the error of the simulation results and the field measured data within the acceptable range as much as possible; the verification results of the NPP data and the field measured data show that the error remains within 15%. The spatial resolution is 0.05°×0.05° (longitude×latitude).

    2022-04-21 0 View Details

  • Long-term sequence dataset of lake area on the Tibetan Plateau (1970-2013)

    The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.

    2022-04-19 0 View Details

  • The sequence data of livestock number at county level on the Tibetan Plateau (1970-2006)

    This data set contains sequence data of the number variation of livestock in the major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study the social and economic changes of the Tibetan Plateau. The table has ten fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Large livestock (10,000) Interpretation: The number of large livestock such as cattle, horses, mules, donkeys, and camels. Field 6: Cattle herd (10,000) Interpretation: Number of cattle Field 7: Equine animals(10,000) Interpretation: The number of equine animals such as horses, mules and donkeys. Field 8: Horses (10,000) Interpretation: The number of horses Field 9: Sheep (10,000) Interpretation: The number of sheep Field 10: Data Sources Interpretation: Source of Data The data come from the statistical yearbook and county annals. Some are listed as follows. [1] Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009 [2] Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009 [3] Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009 [4] Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009 [5] Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009 [6] Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008. [7] County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993 [8] Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994 [9] Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993 [10] Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996 [11] Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995 [12] Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003 [13] Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993 [14] Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005 [15] Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004 [16] Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [17] Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006 [18] Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996 [19] County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993 [20] Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995 [21] Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [22] Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006 [23] Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997 [24] Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995 [25] County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998 [26] Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006 [27] Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996 [28] Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. Gansu: Gansu Cultural Publishing House, 1999 [29] Zhuoni County Annals Editorial Committee. Zhuoni County Annals [J]. Gansu: Gansu Nationality Publishing House, 1994 [30] Diebu County Annals Editorial Committee. Diebu County Annals [J]. Gansu: Lanzhou University Press, 1998 [31] Pengxian County Annals Editorial Committee. Pengxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1989 [32] Guanxian County Annals Editorial Committee. Guanxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1991 [33] Wenjiang County Annals Editorial Committee. Wenjiang County Annals [J]. Sichuan: Sichuan People's Publishing House, 1990 [34] Shifang County Annals Editorial Committee. Shifang County Annals [J]. Sichuan: Sichuan University Press, 1988 [35] Tianquan County Annals Editorial Committee. Tianquan County Annals [J]. Sichuan: Sichuan Science and Technology Press, 1997 [36] Shimian County Annals Editorial Committee. Shimian County Annals [J]. Sichuan: Sichuan Cishu Publishing House, 1999 [37] Lushan County Annals Editorial Committee. Lushan County Annals [J]. Sichuan: Fangzhi Publishing House, 2000 [38] Hongyuan County Annals Editorial Committee. Hongyuan County Annals [J]. Sichuan: Sichuan People's Publishing House, 1996 [39] Wenchuan County Annals Editorial Committee. Wenchuan County Annals [J]. Sichuan: Bayu Shushe, 2007 [40] Derong County Annals Editorial Committee. Derong County Annals [J]. Sichuan: Sichuan University, 2000 [41] Baiyu County Annals Editorial Committee. Baiyu County Annals [J]. Sichuan: Sichuan University Press, 1996 [42] Batang County Annals Editorial Committee. Batang County Annals [J]. Sichuan: Sichuan Nationality Publishing House, 1993 [43] Jiulong County Annals Editorial Committee. Jiulong County Annals(Sequel) (1986-2000) [J]. Sichuan: Sichuan Science and Technology Press, 2007 [44] County Annals Editorial Committee of Derung-Nu Autonomous County Gongshan. County Annals of Derung-Nu Autonomous County Gongshan [J]. Beijing: Nationality Publishing House, 2006 [45] Lushui County Annals Editorial Committee. Lushui County Annals [J]. Yunnan: Yunnan People's Publishing House, 1995 [46] Deqin County Annals Editorial Committee. Deqin County Annals [J]. Yunnan: Yunnan Nationality Publishing House, 1997 [47] Yutian County Annals Editorial Committee. Yutian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [48] Cele County Annals Editorial Committee. Cele County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2005 [49] Hetian County Annals Editorial Committee. Hetian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [50] Qiemo County Local Chronicles Editorial Committee. Qiemo County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [51] Shache County Annals Editorial Committee. Shache County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [52] Yecheng County Annals Editorial Committee. Yecheng County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1999 [53] Akto County Local Chronicles Editorial Committee. Akto County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [54] Wuqia County Local Chronicles Editorial Committee. Wuqia County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1995

    2022-04-19 0 View Details

  • The statistics of natural disasters in Qinghai (1950-2000)

    This data set contains information on natural disasters in Qinghai over nearly 50 years, including the times, places and the consequences of natural disasters such as droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms, pest plagues, rats, and geological disasters. Qinghai Province is located in the northeastern part of the Tibetan Plateau and has a total area of 720,000 square kilometers. Numerous rivers, glaciers and lakes lie in the province. Because two mother rivers of the Chinese nation, the Yangtze River and the Yellow River, and the famous international river—the Lancang River—originated here, it is known as the "Chinese Water Tower"; there are 335,000 square meters of available grasslands in the province, and the natural pasture area ranks fourth in the country after those of Inner Mongolia, Tibet and Xinjiang. There are various types of grasslands, abundant grassland resources, and 113 families, 564 genera and 2100 species of vascular plants, which grow and develop under the unique climatic condition of the Tibetan Plateau and strongly represent the characteristics of the plateau ecological environment. As the main part of the Tibetan Plateau, Qinghai Province is one of the centers of the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the study of climate and ecological environment in the international field of sciences and technology. The terrain and land-forms in Qinghai are complex, with interlaced mountains, valleys and basins, widely distributed snow and glaciers, the Gobi and other deserts and grassland. Complex terrain conditions, high altitudes and harsh climatic conditions make Qinghai a province with frequent meteorological disasters. The main meteorological disasters include droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms. The data are extracted from the Qinghai Volume of Chinese Meteorological Disaster Dictionary, with manual entry, summarizing and proofreading.

    2022-04-19 0 View Details

  • The representative sequence dataset of surface temperature in the Tibetan Plateau (1951-2006)

    This data set contains the temperature anomaly series for each quarter and month of the years from January, 1951 to December, 2006 on the Tibetan Plateau. Based on the “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006, the monthly average temperature of 123 sites on the Tibetan Plateau and its neighboring areas were gridded using the Climate Anomaly Method (CAM). Further, the average monthly temperature anomaly sequences from 1951 to 2006 were established using the area weighting factor method. To maximize the use of the observation data, the method using the data at a nearby reference station to correct the short series of the climatic standard values of the air temperature data is emphatically discussed. Reference: Yu Ren, Xueqin Zhang, Lili Peng. Construction and Analysis of Mean Air Temperature Anomaly Series for the Qinghai-Xizang Plateau during 1951-2006. Plateau Meteorology, 2010. The “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006 meet the relevant national standards. There are five fields in the monthly temperature anomaly data table. Field 1: Year Field 2: Month Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Number of sites included in the calculation Field 5: Monthly Temperature Anomaly Unit °C There are five fields in the year and quarter temperature anomaly data table. Field 1: Year Field 2: Quarter Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Explanation: Number of sites included in the calculation Field 5: Temperature anomaly °C In the quarter field: 1. If it is null, it is the annual temperature anomaly 2. DJF: Winter (Last December to this February) temperature anomaly °C 3. MAM: Spring (March-May) temperature anomaly °C 4. JJA: Summer (June-August) temperature anomaly °C 5. SON: Fall (September-November) temperature anomaly °C Data accuracy: the monthly average temperature anomaly to the third decimal places, the annual and quarterly average temperature anomaly to the second decimal places.

    2022-04-19 0 View Details

  • Data products of mixed soil moisture of the Tibetan Plateau (2008-2016)

    The data products of mixed soil moisture of the Tibetan Plateau utilize remote sensing observation, in situ measurement and model simulation techniques. In situ soil moisture (SM) observation combines the classification of the Tibetan Plateau climate zone and is used to generate in situ measurements of SM climatology at plateau scales. The resulting in situ SM climatology of the Tibetan Plateau scale is used to scale the SM data simulated by the model, which are then used to scale the SM satellite observations. The climatological-scale satellites and model-simulated SMs are then objectively mixed by applying triple configuration and least square matching. The final mixed SM can replicate SM dynamics in different climate zones, from subhumid areas to semiarid and arid regions of the Tibetan Plateau. - Time resolution: day, starting from 01/05/2008 - Spatial resolution: 0.25° × 0.25° - Data set size: 61 × 121 × 975 - Unit: cm^3 cm^-3 The data quality is open to assessment.

    2022-04-18 0 View Details

  • 0.25 degree climate dataset in the northeastern part of the Tibetan Plateau (1957-2009)

    The 0.25 Degree climate data set in the northeastern part of the Tibetan Plateau from 1957 to 2009 contains four meteorological elements, which are precipitation, maximum and minimum temperatures, and wind speed. The time resolution is daily. The data set contains 2400 text files, each with precipitation (the 1st column), highest (the 2nd column) and lowest (the 3rd column) temperatures and wind speed (the 4th column). Each file name contains latitude and longitude. Each file represents the four meteorological element values for the corresponding grid point (0.25*0.25 degrees). These data are formed by gridding the observation data of 81 meteorological stations in the northeast of the plateau, considering the change of meteorological conditions with the elevation. The gridding methods and steps are as follows. Download the original daily maximum and minimum temperatures, precipitation, and wind speed from the China Meteorological Data Network (http://data.cma.cn). Then, perform quality control on the data. The principle used is 1) to remove daily precipitations below 0 and greater than 150 mm, daily temperatures below -50 °C and greater than 50 °C and wind speeds below 0 m / s, 2) draw annual sequence precipitation, temperature and wind speed, check for abnormal year-to-year changes, and conduct quality control through station migration records. For data with abnormal changes but with station migration records, the data are segmented by modifying the station name. For example, at Xining Station (52866), abnormal temperature changes occurred in 1996, which was found through records that Xining Station migrated after 1996. Therefore, the records before 1996 are recorded as virtual station 52867 data, and after 1996, the data are still recorded as 52866 stations. If the data change abnormally but there is no station migration record, the abnormally changed data are eliminated, for example, the data from Delingha Station before 1975. Some stations have migration records, but the data do not change abnormally; then, it is assumed that the stations before and after the migration are still in the same climate environment, so there is no change in station name and data record. Interpolation begins after quality control. The method begins with (1) calculating the changes in daily average temperature, precipitation and wind speed as the altitude changes. It is concluded that the temperature decreases with altitude by 4.3 °C/km, and the coefficient of determination R2 is 0.65. In the warm and humid season (from May to September), the average daily precipitation has an insignificant increase with altitude (0.5 mm/km, R2 is 0.1). The average daily precipitation in the cold dry season (from October to April) does not change with altitude. The wind speed also has an insignificant increase with altitude, with an increase rate of 0.4 m/s/km and R2 of 0.1. (2) The spatial interpolation is performed using the Synographic Mapping System (SYMAP, Shepard, 1984) method. In this method, the distance between stations and the angle between surrounding stations are taken into account in interpolation to indicate the density of stations. The distance and angle are integrated into a weight. In addition, the stations that are close and have a large angle between each other are given a large weight. (3) The latitude and longitude of the station, the meteorological element value, the altitude, the rate of change with the altitude, and the weight are considered simultaneously, and the value of the target grid is interpolated. The maximum search range for interpolation is 55 stations around, and the smallest search range is 4 stations around. (4) Integrate the precipitation in the warm and dry seasons to form a precipitation sequence throughout the period. (5) During the method test period, some stations are set aside to check the gridded data. (6) After the verification is passed, all 81 stations are used in the final gridding process and form this set of data sets. Shepard, D. S., 1984: Computer Mapping: The SYMAP interpolation algorithm. Spatial Statistics and Models, G.Gaile and C. Willmot, Eds., Reidel 133-145.

    2022-04-18 0 View Details

  • The water level observation of lakes on the Tibetan Plateau (2010-2017)

    The water level observation data set of lakes on the Tibetan Plateau contains the daily variations of water levels for three lakes: Zhari Namco, Bamco and Dawaco. The lake water level was obtained by a HOBO water level gauge (U20-001-01) installed on the lakeshore, then corrected using the barometer installed on the shore or pressure data of nearby weather stations, and then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this data set are as follows: Daily variation data of water level in Zhari Namco from 2009 to 2014; Daily variation data of water level in Bamco from 2013 to 2014; Daily variation data of water level in Dawaco from 2013 to 2014. Water level, unit: m.

    2022-04-18 0 View Details

  • The 30-m land cover data of Tibetan Plateau (2010)

    These data contain two data files: GLOBELAND30 TILES (raw data) and TIBET_ GLOBELAND30_MOSAIC (mosaic data). The raw data were downloaded from the Global Land Cover Data website (GlobalLand3) (http://www.globallandcover.com) and cover the Tibetan Plateau and surrounding areas. The raw data were stored in frames, and for the convenience of using the data, we use Erdas software to splice and mosaic the raw data. The Global Land Cover Data (GlobalLand30) is the result of the “Global Land Cover Remote Sensing Mapping and Key Technology Research”, which is a key project of the National 863 Program. Using the American Landsat images (TM5, ETM+) and Chinese Environmental Disaster Reduction Satellite images (HJ-1), the data were extracted by a comprehensive method based on pixel classification-object extraction-knowledge checks. The data include 10 primary land cover types—cultivated land, forest, grassland, shrub, wetland, water body, tundra, man-made cover, bare land, glacier and permanent snow—without extracting secondary types. In terms of accuracy assessment, nine types and more than 150,000 test samples were evaluated. The overall accuracy of the GlobeLand30-2010 data is 80.33%. The Kappa indicator is 0.75. The GlobeLand30 data use the WGS84 coordinate system, UTM projection, and 6-degree banding, and the reference ellipsoid is the WGS 84 ellipsoid. According to different latitudes, the data are organized into two types of framing. In the regions of 60° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 6° (longitude); in the regions of 60° to 80° north and south latitudes, the framing is carried out according to a size of 5° (latitude) × 12° (longitude). The framing is projected according to the central meridian of the odd 6° band. GLOBELAND30 TILES: The original, unprocessed raw data are retained. TIBET_ GLOBELAND30_MOSAIC: The Erdas software is used to mosaic the raw data. The parameter settings use the default value of the raw data to retain the original, and the accuracy is consistent with that of the downloading site.

    2022-04-18 0 View Details

  • The fundamental database of atmospheric boundary layer of the north Tibetan Plateau (1997-2008)

    The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.

    2022-04-18 0 View Details

  • The meteorological observation dataset of Guoluo meadow on the Tibetan Plateau (2005-2009)

    This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²

    2022-04-18 0 View Details

  • Vector dataset of glaciers and glacial lakes in the Boqu Basin in Central Himalaya (1976-2010)

    This is the 1976, 1991, 2000, and 2010 vector data set of glaciers and glacial lakes in the Boqu Basin in Central Himalaya based on Landsat satellite images. The data source is from Landsat remote images. 1976: LM21510411975306AAA05, LM21510401976355AAA04 1991: LT41410401991334XXX02, LT41410411991334XXX02 2000: LE71410402000279SGS00, LE71400412000304SGS00, LE71410402000327EDC00, LE71410412000327EDC00 2010: LT51400412009288KHC00, LT51410402009295KHC00, LT51410412009311KHC00, LT51410402011237KHC00. The boundaries of glaciers and glacial lakes are extracted manually from the various remote sensing images. The extraction error of the boundaries of glaciers and glacial lakes is estimated to be 0.5 pixels. Data file: Glacial_1976: Glacier vector data in 1976 Glacial_1991: Glacier vector data in 1991 Glacial_2000: Glacier vector data in 2000 Glacial_2010: Glacier vector data in 2010 Glacial_Lake_1976: Glacial lake vector data in 1976年 Glacial_Lake_1991: Glacial lake vector data in 1991 Glacial_Lake_2000: Glacial lake vector data in 2000 Glacial_Lake_2010: Glacial lake vector data in 2010 The glacial lake vector data fields include Number, name, latitude and longitude, altitude, area, orientation, type of glacial lake, length, width, and distance from the glacier.

    2022-04-18 0 View Details

  • Annual variation characteristic value of runoff at the major hydrological stations of the Yarlung Zangbo River (1956-2000)

    This dataset contains the annual variation of runoff from the major hydrological stations in the Yarlung Zangbo River (annual average runoff volume, annual extremum ratio, coefficient of variation, etc.). It can be used to study the hydrological characteristics of the Yarlung Zangbo River. The original data are the national hydrological station data, and the quality requirements are the same as the national standards. Spatial Coverage: 4 hydrological stations in the main streams of the Yarlung Zangbo River basin, which are Lazi, Nugesha, Yangcun and Nuxia. This data sheet has five fields. Field 1: Station Name Field 2: Annual average runoff volume Field 3: Annual Extreme Ratio Field 4: Coefficient of variation Field 5: Data Series Length

    2022-04-18 0 View Details

  • The precipitation dataset of the Third Pole region (1951-2010)

    The precipitation dataset of the Third Pole region mainly contains two EXCEL files: (1) Daily precipitation data in China in the Third Pole region, named as China_daily.xlsx. The precipitation data in China were obtained from the China Meteorological Administration-National Meteorological Information Center (http://data.cma.gov.cn/site/index.html). (2) Daily precipitation data in other countries in the Third Pole region, named as Foreign_daily.xlsx. The precipitation data in other countries were obtained from NCDC International Climatic Data Center - NOAA Satellite Information Service Center (http://www7.ncdc.noaa.gov/CDO/country), Pakistan Meteorological Administration, Nepal Meteorological Administration, etc. There are seven variables in these two EXCEL data files: precipitation, corrected precipitation, correction factor, wind-induced loss, evaporation loss, wet loss, and trace precipitation. The detail characteristics of TPE stations were described in an EXCEL file either, named as "TPE station and gauge type.xls". The raw data has been strictly quality controlled by the relevant meteorological departments and has been applied in relevant academic papers.

    2022-04-15 0 View Details

  • Dataset of ice core-snow black carbon content on the Tibetan Plateau (1950-2006)

    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.

    2022-04-15 0 View Details

  • Dataset of gridded daily precipitation in China (Version 2.0) (1961-2013)

    The National Meteorological Information Center Meteorological Data Room has detected, controlled and corrected the quality of 2474 national-level ground stations' basic meteorological data and formed a set of high-quality, national and provincial ground-based basic data files. On the basis of the basic ground data of the precipitation data files, the thin-plate spline method is used, introducing the digital elevation data to eliminate the influence of the elevation on the precipitation precision under the unique terrain conditions in China. A dataset of 0.5°×0.5° grid values for the surface precipitation in China since 1961 is established. It provides a data basis for accurately describing the trends and magnitudes of precipitation changes in China. One of two data sources for the development of “Dataset of Gridded Daily Precipitation in China (Version 2.0)” was 1) the monthly and daily precipitation data of 2474 national-level stations in the country archived by the Meteorological Data Room for nearly 50 years. The information comes from the monthly information of the “Monthly Report of the Surface Meteorological Record” reported by the climate data processing departments of all the provinces, municipalities and autonomous regions. That information is collected, organized and strictly checked and reviewed by the National Meteorological Information Center. Since the establishment of the station, many stations in the country have undergone historical changes such as business reform and station migration. In 1961, the total number of stations had stabilized above 2,000, and the number of backstage stations in the late 1970s reached 2,400. 2) The second data source was a Chinese range of 0.5°×0.5° digital elevation model data DEMs generated by GTOP030 data (resolution 30′′×30′′) resampling. For the quantitative analysis and evaluation of the data, please see the Dataset of Gridded Daily Precipitation in China - Data Specification.

    2022-04-03 0 View Details

  • 2002-2019 Global AMSR-E/2 Near-surface Freeze/Thaw state (0.25°)

    The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E and AMSR2 passive microwave brightness temperature data, and the freeze-thaw discriminant function algorithm is used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.25° from 2002 to 2019. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.

    2022-03-30 0 View Details

  • 2002-2017 Global AMSR-E/2 Near-surface Freeze/Thaw state (0.05°)

    The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E, AMSR2 passive microwave brightness temperature data and MODIS optical remote sensing data. The freeze-thaw discriminant function algorithm and downscaling algorithm are used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.05° from 2002 to 2017. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.

    2022-03-28 0 View Details