1.Carbon aerosol isotope data of remote station (Hongyuan) in the east of Tibetan Plateau (2020-2021)

    Carbon particles are an important radiative forcing factor in the atmosphere. Their concentration and composition vary greatly in time and space, especially in remote areas. This data set reports the total suspended particulate matter (TSP), total carbon (TC) and water insoluble particulate carbon (IPC) of PM2.5 at two stations in the remote area of the eastern Qinghai Tibet Plateau (Hongyuan) Δ 14C and δ 13C, the area is affected by severe air pollution from southwest China. The contribution rates of TC fossil fuels in TSP and PM2.5 samples are 18.91 ± 7.22% and 23.13 ± 12.52% respectively, which are far lower than those in Southwest China, indicating the importance of non fossil contributions from local sources. TC in TSP samples at study site δ 13C is 27.06 ± 0.96 ‰, which is between long-distance transport sources (such as the southwest region) and local biomass combustion emissions. This data supplements the database of carbon aerosols in the east of the Tibetan Plateau.

    LI Chaoliu

    doi: 10.11888/Atmos.tpdc.272941 14 0 Open Access 2022-11-23

    2.Light absorption data sets of precipitation and water-soluble organic carbon and black carbon in aerosols at Ranwu (2018-2021), Namco (2013-2016), Everest (2013-2016), Lulang Station (2015-2016)

    This data set includes the light absorption data of carbon components in the atmosphere and precipitation at typical stations on the Tibetan Plateau (Ranwu (2018-2021), Namco (2013-2016), Everest (2013-2016), Lulang (2015-2016)). All samples were collected on the spot from various sampling points. The concentrations of black carbon and water-soluble organic carbon, as well as the light absorption data were measured, using the index (MAC value) representing the light absorption capacity, The MAC values of light absorption of water-soluble organic carbon and black carbon are calculated. This data is of great significance for evaluating the radiative forcing of carbon particles in the atmosphere, and is an important basic data input for model simulation.

    LI Chaoliu

    doi: 10.11888/Atmos.tpdc.272940 15 0 Open Access 2022-11-23

    3.Physical snow process model supported global snow depth product retrieved from the passive microwave AMSR2 sensor (2013-2020)

    1. Data content: global 0.25-degree resolution daily snow depth product for seasonal snow from 2013 to 2020, including land areas in the northern and southern hemispheres except Antarctica and Greenland. 2. Data source and processing method: In view of the underestimation problem of current mainstream passive microwave snow products in mountainous areas, the machine learning algorithm is used to generate a new snow depth product, which trained the representative snow depth measurements from meteorological stations globally, year by year, based on both the AMSR2 (Advanced Microwave Scanning Radiometer 2) brightness temperatures and the prior knowledge of snow parameters from snow process model simulations. The snow priors come from the improved multiple-layer SNTHERM (Snow Thermal Model) simulation driven by the GLDAS (Global Land Data Assistance System) dataset, including snow grain size, snow density and the first-guess snow depth parameters. The machine learning algorithm is mainly used to overcome the biases in snow priors caused by the forcing dataset error. The meteorological station measurements participating in the construction of ground truths are from the GHCN (Global Historical Climate Network), GSOD (Global Surface Summary of the Day), USHCN (The United States Historical Climate Network), ECA&D (European Climate Assessment&Dataset) and daily snow depth measurements from reference meteorological stations in China. The snow cover extent is jointly determined based on MODIS 0.05 ° Climate Grid snow cover product and SNTHERM simulations. 3. Data quality description: 1) Cross validation: The root mean square error (RMSE) of retrieved snow depth validated by 1.72 million samples in eight years worldwide is 12.4 cm. The bias of snow depth is not sensitive to elevation and forest cover fraction. 2) Independent validation: the RMSE validated using the 25 km quadrat, 500 m quadrat and snow course measurements from the China Snow Survey in 2018-2019 years is 4.93-8.9 cm; RMSE validated against the EuroAlps dataset and CanSWE dataset are 17.3 cm and 24.2 cm, respectively, in the 0-100 cm snow depth range; if the snow depth is converted to the snow water equivalent (SWE) using a fixed density of 240 kg/m3, the SWE RMSE validated against the GlobSnow validation dataset - the Russian part and the US SNOTEL dataset are 29.2 mm and 40.1 mm in 0-200 mm range. Due to the differences between the training dataset and the independent validation dataset, it is difficult to have a fully-unbiased validation result in the deep snow range; however, the new product still slightly outperforms the JAXA official AMSR2 product and GlobSnow product in the mountains and at the edge of mountains, respectively. Note: The current validation results are calculated based on the representative in-situ measurements. Please contact the data provider and refer to the paper for representative evaluation criteria. 4. Data application achievements and prospects: research on the spatial and temporal distributions of seasonal snow cover; on requirement of a seasonal snow information to support the study of other elements in the cryosphere; researchers looking for a less-biased passive microwave snow depth product in the mountains at current stage. 5. Others: We would like to express our gratitude to the providers of all kinds of raw datasets to generate our new product. Please refer to the paper for the acknowledgement and data description.

    PAN Jinmei, YANG Jianwei , JIANG Lingmei, Xiong Chuan, PAN Fangbo , SHI Jiancheng, GAO Xiaowen

    doi: 10.11888/Cryos.tpdc.272937 79 2 Open Access 2022-11-24

    4.In-situ observations of lake level on the western Tibetan Plateau (2016-2021)

    This dataset contains in-situ lake level observations at Lumajiangdong Co, Memar Co,Camelot Lake and Jieze Caka on the western Tibetan Plateau. The lake water level was monitored by HOBO water level logger (U20-001-01) or Solist water level logger, which was installed on the lake shore. Lake level data was then calibrated by using the barometer installed near the lake. Then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this dataset are as follows: Daily lake level changes at Lumajiangdong Co from 2016 to 2021; Daily lake level changes at Memar Co from 2017 to 2019 and from 2020 to 2021; Daily lake level changes at Luotuo Lake from 2019 to 2020. Daily lake level changes at Jieze Caka Lake from 2019 to 2020. Water level, unit: m.

    LEI Yanbin

    doi: 10.11888/Terre.tpdc.272314 267 45 Open Access 2022-04-20

    5.Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-automatic weather station-10m tower, 2021)

    This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2021. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    LIU Shaomin, XU Ziwei

    doi: 10.11888/Atmos.tpdc.272925 371 6 Requestable 2022-11-17

    6.Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin(Huailai station-automatic weather station-40m tower, 2021)

    This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2021. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua

    doi: 10.11888/Atmos.tpdc.272924 368 3 Requestable 2022-11-17

    7.Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2021)

    This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2021. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    LIU Shaomin, XU Ziwei

    doi: 10.11888/Terre.tpdc.272923 379 2 Requestable 2022-11-17

    8.Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2021)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from April 13 to December 31 in 2021. The site (115.7923° E, 40.3574° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class1-9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua

    doi: 10.11888/Atmos.tpdc.272926 419 3 Requestable 2022-11-17

    9.Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2021)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2021. The site (115.7880° E, 40.3491°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class 1 to 9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    LIU Shaomin, XU Ziwei

    doi: 10.11888/Atmos.tpdc.272922 386 4 Requestable 2022-11-17

    10.Data set of agricultural pattern of five Central Asian countries (V1.0, 2020)

    Based on ESA's CCI-LC Maps data, we mapped the agricultural landscape of Central Asia, including Kazakhstan, Turkmenistan, Tajikistan, Kyrgyzstan, and Uzbekistan, for sustainable agricultural development in the five Central Asian countries, and classified the existing agricultural land into six categories: rainfed cropland, rainfed cropland (herbaceous cover), rainfed cropland (forest cover), irrigated cropland, cropland (>50%)/natural vegetation (<50%), and cropland (<50%)/natural vegetation (>50%). 50%)/natural vegetation (<50%) and arable land (<50%)/natural vegetation (>50%). The data year is 2020 and the spatial resolution of the data is 300m × 300m, i.e., about 0.003° × 0.003°. The dataset can provide basic data support for future land resource development and utilization and agricultural development of the five Central Asian countries.

    ZHANG Junjun , JIANG Xiaohui

    doi: 10.11888/HumanNat.tpdc.272897 385 11 Open Access 2022-11-01

    11.Agricultural patterns dataset in five Central Asian countries (V1.0)

    Facing the sustainable agricultural development of the five Central Asian countries, with the goal of land resources, in order to explore the land resources evaluation in Central Asia under the climate change in the past 20 years and the land resources situation in Central Asia under the climate change in the next 30 years, we collected the land resources evaluation elements in Central Asia, including: soil elements (soil salinization degree, soil texture, soil organic matter content, soil pH value, soil total nitrogen), terrain elements (elevation, slope) Climatic elements (rainfall, temperature, solar radiation). Topographic elements and soil elements are based on 2020. Climate elements include 2000, 2010, 2020, and the average precipitation and temperature in 2030 and 2050 under the future SSP5-8.5 scenarios estimated by the ESM1 climate model in CMIP6, with a spatial resolution of 0.01 ° × 0.01°。 The data set can provide basic data support for the future development and utilization of land resources and agricultural development of the five Central Asian countries.

    ZHANG Junjun , JIANG Xiaohui

    doi: 10.11888/HumanNat.tpdc.272896 361 11 Open Access 2022-11-01

    12.Atmospheric circulation data set output by climate feedback simulation under different geographical patterns of 60Ma and 25Ma

    Numerical test: The climate model used is the regional climate model RegCM4.1. RegCM4.1 developed by the Italian Research Center for Theoretical Physics (ICTP). In the test of regional model simulation, the horizontal resolution of the atmospheric model is 50 km and the vertical direction is 18 layers; Online coupling sand dust module. Sea surface temperature The sea surface temperature interpolated by OISST is used. The test includes two groups: the Middle Paleocene topographic test (MP,~60Ma BP, test name 60ma_regcm4.1_xxx. nc) and the Late Oligocene (LO,~25Ma BP, test name 25ma_regcM4.1_xxx. nc) The MP regional terrain modification test removed the northern part of the plateau and approximately replaced the terrain distribution of Asian land during the 60Ma period. BP regional terrain modification test only removed the terrain of Pamirs Plateau, approximately replacing the terrain distribution of Asian land during the 25Ma period. The sand and dust source areas of the two tests have not changed, and the sand and dust circulation process has been opened online. Output time: All tests were integrated for 22 years, using the average results of the last 20 years of each test. The data can be used to explain the difference of drought evolution in different regions around the plateau.

    SUN Hui

    doi: 10.11888/Atmos.tpdc.272908 18 0 Open Access 2022-11-03

    13.Fungal spores data and A/C ratio data from Tian’E Lake over the past ~3500 years

    Data description: The data sets contains the content of fungal spores and A/C ratio data from Tian’E Lake (39°14′20″N, 97°55′26″E, altitude 3012 m) in the western part of the Qilian Mountains at the northern margin of the Qinghai-Tibetan Plateau over the past ~3,500 years. We have completed the fungal spores and pollen analyses of 520 sedimentary samples. We use analyses of pollen A/C ratio and Sporormiella-type coprophilous fungal spores to reconstruct changes in regional moisture and grazing intensity over the past ~3,500 years in an arid mountain-basin system. Our results provide a historical reference for understanding how ancient people adapted to the climate change in arid region with high mountains. The data is stored in Excel format.

    ZHANG Jun, HUANG Xiaozhong

    doi: 10.11888/Paleoenv.tpdc.271569 112 104 Open Access 2021-06-30

    14.Root uptake dominates mercury accumulation in permafrost plants of Qinghai-Tibet Plateau (2020)

    Uptake of atmospheric elemental mercury via foliage is thought to be the dominant pathway of mercury accumulation in terrestrial ecosystems, including those in the Arctic permafrost regions. Whether a similar process operates in alpine permafrost regions remains unknown. Here we report mercury concentrations and stable isotopic signatures in a large cluster alpine permafrost regions of mid-latitude Qinghai-Tibet Plateau. We find a transition from foliage to root uptake of mercury as elevation increases. In alpine permafrost regions, we find that root uptake of mercury from the surrounding soil is the dominant accumulation pathway. We estimate that root uptake accounts for 70±19% of plant mercury in permafrost regions of the Qinghai-Tibet Plateau and propose that this may be related to the harsh climate conditions suppressing foliage growth and promoting lateral root growth.

    WANG XUN

    doi: 10.11888/Cryos.tpdc.272903 30 6 Open Access 2022-11-04

    15.Atmospheric circulation data set of sensitivity test simulation output under different geographical patterns of 60Ma and 25Ma

    Numerical experiments: The climate model used is the fast air sea coupling model (FAMOUS) jointly developed by the British Meteorological Office and British universities The horizontal resolution of the atmospheric model in the FAMOUS model is 5 ° × 7.5 °, 11 layers in vertical direction; The horizontal resolution of the ocean model is 2.5 ° × 3.75 °, 20 layers in vertical direction The atmosphere and ocean are coupled once a day without flux adjustment The tests included the Middle Paleocene (MP,~60Ma BP, test name flat_60ma_1xCO2_sea_3d_ * * 100yr_mean. nc) and the Late Oligocene (LO,~25Ma BP, test name orog_25ma_1xCO2_sea_3d_ * * 100yr_mean. nc) The sea land distribution data is mainly taken from the global coastline basic data set (abbreviated as Gplates, website: http://www.gplates.org/ )Considering that the initial uplift of Cenozoic terrains such as the Qinghai Tibet Plateau started at about 50~55 Ma (Searle et al., 1987), the global terrain height was set to 0 in the MP test to omit the role of plateau terrain. At 25 Ma, Greenland (Zachos et al., 2001) and the Qinghai Tibet Plateau (for example, Wang et al., 2014; Ding et al., 2014; Rowley and Currie, 2006; DeCells et al., 2007; Polisar et al., 2009) were revised The change of ancient latitude is also considered when reconstructing the ancient topography of the Qinghai Tibet Plateau (Besse et al., 1984; Chatterjee et al., 2013; Wei et al., 2013) At the same time, referring to the change of Cenozoic atmospheric CO2 (Beerling and Royer, 2011), the atmospheric CO2 concentration in the two periods of experiments was 280 ppmv (1 ppmv=1 mg L – 1) before the industrial revolution For simplicity, all land vegetation and soil properties are set to globally uniform values, that is, various land surface properties on each land grid point except Antarctica are assigned to the global average value of non glacial land surface before the industrial revolution, which is also convenient for highlighting the impact of land sea distribution and topographic changes In addition, since we mainly discuss the average climate state and its change in the characteristic geological period on the scale of millions of years, we can omit the influence of orbital forcing, that is, the Earth's orbital parameters are set to their modern values in all experiments Output time: All tests were integrated for 1000 years, using the average results of the last 100 years of each test. This data is helpful to explore the formation and evolution mechanism of the Cenozoic monsoon and drought.

    LIU Xiaodong

    doi: 10.11888/Paleoenv.tpdc.272904 979 36 Open Access 2022-11-03

    16.Simulations of land use and cover change in the future (2015-2030)

    Simulation results of land use and cover in the upper, middle and lower reaches of the Heihe River basin(HRB) under the Basic, Ecology and Rapid Economic Development scenarios, time frame: 2015-2030, spatial resolution: 1km. Upper HRB land use data simulation, spatial resolution is 1km; Middle HRB land use data simulation, spatial resolution is 1km; Downstream HRB land use data simulation, spatial resolution is 1km. The spatial resolution of land use data in the downstream area of the HRB is 1 km. The land use data in the Economic scenario focuses on rapid economic development and rapid expansion of land for construction, while forest and grassland protection is neglected. The data were obtained based on historical land use data using DLS model simulations.

    WU Feng

    doi: 10.11888/Terre.tpdc.272849 864 16 Open Access 2022-10-14

    17.Simulation data of socio-economic resource circulation network in the Heihe Riverbasin (2012)

    The data set includes the implied water resources and land resource flows among 11 cities and counties in the Heihe River basin, including Ganzhou, Sunan, Minle, Linze, Gaotai, Shandan, Suzhou, Jinta, Jiayuguan and Ejina. Table 1 includes the transfer volume of virtual water resources and virtual land resources among multiple regions. Table 2 includes the virtual water resources export volume of each regional sub sector and the virtual water resources import volume of each regional sub sector. Table 3 includes the export volume of virtual land resources of each regional sub sector and the import volume of virtual land resources of each regional sub sector. Based on the input-output tables of 11 cities and counties in the Heihe River Basin, we investigate the consumption, loss and flow of water and land resources in each economic sector, construct a coupled water-land resource accounting statement, and calculate the virtual water resources and virtual land resources flow by sector in each region based on the input-output analysis method. The water consumption and land use data of each region and sector are obtained from official statistical yearbook data.

    CHEN Bin

    doi: 10.11888/HumanNat.tpdc.272886 89 12 Open Access 2022-10-17

    18.Archaeological site plant and animal resource utilization in the Tibet Plateau and neighbouring areas during the Neolithic Age and the Bronze age (2021)

    By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Xichengyi site, Jinchankou site, Shannashuzha site, Jiangxifen site, Zongri site, Bangga site and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains, plant fossil, sedimentary sample, and obtained a batch of dating data of radiocarbon dating; pollen data; identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. Based on natural geographical factors and sites in different periods, the method of realizing cumulative connection between nodes under the control of the lowest cost uses GIS(R language) tool to carry out spatial numerical calculation, and the result is used as the communication route in prehistoric times (Neolithic-bronze age). The shape of the route developed from the northeast-east-southeast-southwest edge of Neolithic Age in crescent shape to the trend of network development from the edge to the hinterland of Bronze Age, which is a manifestation of the gradual evolution from the exchange of plateau edge to the exchange of edge-hinterland, which is constantly strengthened. A total of 49 dung samples of grazing livestock (30 yak dung samples, 11 horse dung samples and 8 sheep dung samples) were collected in the alpine meadow area of the eastern Qinghai-Tibet Plateau, and the pollen analysis of dung samples was carried out on the basis of regional vegetation investigation. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and the Bronze age.

    DONG Guanghui , HOU Guangliang, YANG Xiaoyan

    doi: 10.11888/HumanNat.tpdc.271856 1023 171 Open Access 2021-11-27

    19.Simulation data of active layer thickness and ground temperature of permafrost in Qinghai Tibet Plateau (2000-2015, 2061-2080)

    A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.

    Ni Jie, Wu Tonghua

    doi: 10.17632/hbptbpyw75.1 7243 830 Open Access 2021-03-09

    20.Comprehensive integrated datasets over the Pan-Third Pole (1980-2020)

    LI Hu, PAN Xiaoduo, LI Xin, GE Chunmei, RAN Youhua

    doi: 10.11888/Geogra.tpdc.271328 2888 190 Open Access 2021-05-16