Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
HE Bian
Meteorological elements of the dataset include the near-surface land-air exchange parameters, such as downward/upward longwave/shortwave radiation flux, momentum flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the tropopause are also included. Independent evaluations were conducted for the dataset by comparison between the observational data and the most recent ERA5 reanalysis data. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.
LI Fei, Ma Shupo, ZHU Jinhuan, ZOU Han , LI Peng , ZHOU Libo
This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
CMIP6 is the sixth climate model comparison plan organized by the World Climate Research Program (WCRP). Original data from https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 。 This dataset contains four SSP scenarios of Scenario MIP in CMIP6. (1) SSP126: Upgrade of RCP2.6 scenario based on SSP1 (low forcing scenario) (radiation forcing will reach 2.6W/m2 in 2100). (2) SSP245: Upgrade of RCP4.5 scenario based on SSP2 (moderate forcing scenario) (radiation forcing will reach 4.5 W/m2 in 2100). (3) SSP370: New RCP7.0 emission path based on SSP3 (medium forcing scenario) (radiation forcing will reach 7.0 W/m2 in 2100). (4) SSP585: Upgrade the RCP8.5 scenario based on SSP5 (high forcing scenario) (SSP585 is the only SSP scenario that can make the radiation forcing reach 8.5 W/m2 in 2100). Using GRU data to correct the post-processing deviation of the original CMIP data, the post-processing data set of monthly precipitation (pr) and temperature (tas) estimates from 2046-2065 was obtained, with a reference period of 1985-2014.
YE Aizhong
Freeze-thaw index is an important sensitive indicator of climate change, and is also widely used in the study of frozen soil changes. The research on the spatial distribution characteristics and time variation trend of freezing and thawing index in the global scope can provide a basis for the global frozen soil environment assessment, engineering construction and coping with climate change. This data set is based on the daily temperature observation data of more than 14000 stations covering the global land from 1973 to 2021 to calculate the air freezing index (FI) and air melting index (TI). The freezing/thawing index is the cumulative value of the daily average temperature below/above 0 ℃ during the freezing/thawing period. Considering that the index calculation should cover the whole freezing/thawing period and ensure the continuity of the calculation period, the northern hemisphere takes July 1 of that year to June 30 of the next year as a freezing period, and takes January 1 to December 30 of that year as a melting period, while the southern hemisphere has the opposite freezing/thawing period. The stations with missing survey years were not filled, which, on the one hand, avoided the uncertainty error caused by interpolation on the results, and on the other hand, retained the authenticity and accuracy of the data as much as possible. The study of global freeze-thaw index can effectively and comprehensively understand the near surface heat state, and can provide important support for exploring the changes of freeze-thaw state.
PENG Xiaoqing, CHEN Cong , MU Cuicui
We utilized 12 datasets covering the period 900–1999 CE, including two summer temperature gridded datasets from the Qinghai–Tibetan Plateau, two summer temperature series from the Arctic, a summer temperature gridded dataset from the Arctic, six global gridded annual temperature reconstruction datasets, and a last millennium reanalysis dataset with seasonal resolution. We used the optimal information extraction method to reconstruct the summer temperature anomalies in the Qinghai-Tibet Plateau and the Arctic over the past millennium (900–1999 CE) with annual resolution. The range of the Qinghai-Tibetan Plateau is 27°N–36°N, 77°E–106°E, and the range of the Arctic is 60°N–90°N. The reconstruction target is the summer (June–August) temperature anomalies (with respect to 1961–1990 CE period) in the instrumental CRUTEM4v dataset. The data can be used to study the mechanism of temperature variability in the Qinghai-Tibetan Plateau and Arctic over the past millennium.
SHI Feng
As a huge elevated surface and atmospheric heat source in spring and summer, the Qinghai Tibet Plateau (TP) has an important impact on regional and global climate and climate. In order to explore the thermal forcing effect of TP, the sensitivity test data set of sensible heat anomaly on the Qinghai Tibet Plateau was prepared. This data includes three groups of sensitivity tests: (1) in the fully coupled model cesm1.2.0, the plateau sensible heat is stronger CGCM from March to may in spring_ lar_ mon_ 3-12-2.nc and plateau thermal sensitivity are weak (CGCM)_ sma_ mon_ 3-12-2. Sensitivity test of NC; (2) In the single general circulation model cam4.0, the sensible heat of the plateau is stronger in spring (March may)_ lar_ Mon 3-8.nc and low sensible heat cam_ sma_ Mon3-8.nc sensitivity test. Including: 3D wind, potential height, air temperature, surface temperature, specific humidity, sensible heat flux, latent heat flux, precipitation and other conventional variables Space scope: global simulation results
DUAN Anmin
The Qinghai-Tibet Engineering Corridor runs from Golmud to Lhasa. It passes through the core region of the Qinghai-Tibet Plateau and is an important passage connecting the interior and Tibet. As the primary parameter in the surface energy balance, the land surface temperature represents the degree of energy and water exchange between the earth and the atmosphere, and is widely used in the research of climatology, hydrology and ecology. The annual average surface land temperature is obtained by using the four day and night observations of Aqua and Terra. Therefore, the 8-day land surface temperature synthesis products MOD11A2 and MYD11A2 with a resolution of 1km were downloaded first, and then the data were batch projected by MRT (MODIS Reprojection Tool). Finally, the annual average MODIS land surface temperature data after 2010 was calculated by IDL.
NIU Fujun
According to the data of three future scenarios of CMIP5 (RCP2.6、RCP4.5、RCP8.5), the spatial variation characteristics and temporal variation trend of the global mean annual air temperature from 2006 to 2100 are analyzed. Under rcp2.6 scenario, the mean annual air temperature shows an increasing trend, with the growth rate ranging from 0.0 ° c/decade to 0.2 ° c/decade (P<0.05), the growth in high latitude regions is faster, ranging from 0.1 ° c/decade to 0.2 ° C / decade. Based on the spatial and temporal characteristics of the mean annual air temperature in the northern hemisphere in the 21st century, under different scenarios, the mean annual air temperature shows a warming trend, and the high latitudes show a more sensitive and rapid growth.
NIU Fujun
The global high-resolution simulated near sea surface temperature precipitation SST data set from 1990 to 2020 is from the latest cmip6 project. Cmip6 is the sixth climate model comparison program organized by the world climate research project (WCRP). Original data source: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 。 The data set includes the global near ocean surface temperature (TMP), precipitation (PR) and sea surface temperature (TOS). The air temperature and precipitation data include the rectangular combination of shared social economic path (SSP) and representative concentration path (RCP) of four different experimental scenarios of scenario MIP in cmip6. (1) Ssp126: upgrade rcp2.6 scenario based on ssp1 (low forcing scenario) (radiation forcing will reach 2.6w/m2 in 2100). (2) Ssp245: upgrade rcp4.5 scenario based on SSP2 (moderate forcing scenario) (radiation forcing will reach 4.5 w / m2 in 2100). (3) Ssp370: a new rcp7.0 emission path based on ssp3 (medium forcing scenario) (radiation forcing will reach 7.0 w / m2 in 2100). (4) Ssp585: upgrade rcp8.5 scenario based on ssp5 (high forcing scenario) (ssp585 is the only SSP scenario that can make radiation forcing reach 8.5 w / m2 in 2100). SST data provides ssp126 scenario data.
YE Aizhong
The atmospheric and oceanic thermal conditions over the Indian Ocean-Third Pole (Qinghai-Tibet Plateau) are important for affecting the Asian monsoon activity and pan-Third Pole climate. At seasonal and interannual timescales, the meridional atmospheric and oceanic heat sources are closely related to Indian monsoon, Bay of Bengal monsoon, and the sea surface temperature (SST) mode in the tropical Indian Ocean. Therefore, we calculate and establish the meridional atmospheric and oceanic heat sources dataset for the Indian Ocean-Third pole section. In order to obtain the horizontal distribution of atmospheric heating rate on each pressure level, we use the inverse algorithm from Yanai et al. (1973): Q_1=c_p [∂T/∂t+V ⃑∙∇T+(p/p_0 )^κ ω ∂θ/∂p] Q_1 is the atmospheric apparent heat source, which can be affected by temperature local variation, temperature advection and potential temperature vertical variation. T, θ, V ⃑, and ω respectively represent the temperature, potential temperature, horizontal wind vector, and vertical velocity. p_0=1013.25hPa. κ=R/c_p, R and c_p are the gas constant and specific heat of dry air at constant pressure respectively, κ≈0.286。 Based on the ERA5 Atmospheric Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) atmospheric heating rate (unit: K/s) for the Indian Ocean-Third pole section (30°S-60°N) with horizontal resolution of 1°×1° and vertical range of 1000-100hPa at 27 levels. With reference to Hall and Bryden (1982), the vertical Ocean Heat Transport (OHT) at given longitudes can be calculated by the following formula: OHT=∮_(Θ=Θ_i)▒∫_(z_b)^(z_0)▒〖ρ_0 c_p (θ-θ_r ) 〗∙udz Where ρ_0, c_p, θ, θ_r, and u represent the density, specific heat, capacity potential temperature, reference temperature (0℃), and zonal velocity of sea water respectively. z_0 and z_b are the depths of sea surface and sea floor. Based on the CMEMS (Copernicus Marine Service) Oceanic Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) OHT (eastward positive, unit: PW(1015W)) over the Indian Ocean-Third pole region (30°S-30°N) with horizontal resolution of 1°×1° and vertical range from sea surface to sea floor at a depth of about 5900m on 75 levels. This dataset can reflect the close relationship between meridional atmospheric and oceanic thermal conditions of Indo-Tibetan Plateau region and Indian monsoon, Bay of Bengal monsoon, and SST mode over tropical Indian Ocean. For example, from the monthly evolution of meridional atmospheric heating rate along 70°E for the Indian Ocean-Third pole section (Figure 1), the atmospheric heat source area above the tropical southern Indian Ocean gradually advances northward from Marth to May. In particular, from May to June, this tropical atmospheric heat source area moves to the tropical northern Indian Ocean with its intensity strengthened and scope expanded, at the same time, the Indian summer monsoon onsets. For instance, from the monthly evolution of meridional atmospheric heating rate along 90°E for the Indian Ocean-Third pole region (Figure 2), we can see that the atmospheric heat source area above the tropical Indian Ocean expands to the south of Qinghai-Tibet Plateau and increases significantly from April to June, coinciding with the onset and northward advance of the Bay of Bengal monsoon. Another example, from the monthly evolution of meridional OHT along 60°E and 90°E for the Indian Ocean-Third pole section (Figures 3 and 4), it can be found the ocean heat at the equatorial Indian Ocean subsurface transports from west to east, and its position is very close to the Equatorial undercurrent. And this subsurface OHT intensity in the west is obviously higher than that in the east, which is related to the wind-thermocline-SST feedback mechanism. It is also worth noting that this subsurface OHT is strong in spring (March-May), weakens in summer, and significantly strengthens in late autumn and early winter (October-December), interacting with the development and formation of Indian Ocean Dipole.
LI Delin , XIAO Ziniu, ZHAO Liang
The high-resolution atmosphere-hydrologic simulation dataset over Tibetan Plateau is prepared by WRFv4.1.1 model with grids of 191 * 355 and spatial resolution of 9 km, and a spatial range covering the entire plateau. The main physics schemes are configured with Thompson microphysics scheme, the rapid radiative transfer model (RRTM), and the Dudhia scheme for longwave and shortwave radiative flux calculations, respectively, the Mellor-Yamada-Janjic (MYJ) TKE scheme for the planetary boundary layer and the Unified Noah Land Surface Model. The time resolution is 3h and the time span is 2000-2010. Variables include: precipitation (Rain), temperature (T2) and water vapor (Q2) at 2m height on the ground, surface skin temperature (TSK), ground pressure (PSFC), zonal component (U10) and meridional component (V10) at 10m heigh on the ground, downward long-wave flux (GLW) and downward short-wave flux (SWDOWN) at surface, ground heat flux (GRDFLX), sensible heat flux (HFX), latent heat flux (LH), surface runoff (SFROFF) and underground runoff (UDROFF). The data can effectively support the study of regional climate characteristics, climate change and its impact over the Tibet Plateau, which will provide scientific basis for the sustainable development of the TP under the background of climate change.
MENG Xianhong, MA Yuanyuan
This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity（specific humidity in 2020）, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from January 1 to October 9 in 2021. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 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_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from January 1 to October 13, 2021. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to October 13 in 2021. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30.
Li Xiaoyan
Data content: temperature data of Nukus irrigation area from January 2021 to December 2021, unit: 0.1 ℃. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Data application achievements and prospects: in the context of climate change, it can be used to analyze the correlation between meteorological elements and groundwater characteristics, and can also be combined with other hydrometeorological data to analyze the temporal and spatial distribution and change characteristics of groundwater. At the same time, it can also be used as basic data for research in related fields such as extreme climate, food production reduction and human health.
LIU Tie
This dataset provides the monitoring data of runoff, precipitation and temperature of the Duodigou Runoff Experimental Station located in the northern suburbs of Lhasa city. Among the dataset, there are two runoff monitoring stations, which provide discharge data from June to December 2019, with a data step of 10 minutes. There are five precipitation monitoring stations, which provide precipitation data from 2018 to 2021, with a data step of 1 day. There are eight air temperature monitoring stations, which provide air temperature data from 2018 to 2021 in 30 minute steps. The discharge, the precipitation and the temperature data are the measured values. The dataset can provide data support for the study of hydrological and meteorological processes in the Tibet Plateau.
LIU Jintao
As the “water tower of Asia”, the Tibetan Plateau has a profound impact on the global natural environment and climate change. Therefore, analyzing the distribution characteristics of troposphere-stratospheric water vapor over the Qinghai-Tibet Plateau is an important part of understanding the water vapor source and change characterize. In situ observations are limit in this region, and the water vapor sounding data set is needed. Therefore, we carried out balloon-borne measurements at Lhasa and Kunming over the Qinghai-Tibet Plateau, and then obtained the vertical distribution of water vapor in the troposphere and lower stratosphere over the Qinghai-Tibet Plateau. The dataset is named “Pan-Third Pole Water Vapor Sounding”, which is mainly the water vapor profile data obtained by balloon sounding conducted at Lhasa and Kunming in August from July 2009 to 2019. Altitude (Altitude), Water vapor (H2O), temperature (Temp), potential temperature (K), and air pressure (Press) from near the surface to 20 km are obtained by conventional balloons soundings payloaded with the Cryogenic Frost Point Hygrometer (CFH) and radiosonde (iMet). Data is transmitted in real time to the ground receiving station via a radiosonde.
BIAN Jianchun
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