1) Data content (including elements and significance): the data includes the daily values of air temperature (℃), precipitation (mm), relative humidity (%), wind speed (M / s) and radiation (w / m2) 2) Data source and processing method; Air temperature, relative humidity, radiation and wind speed are daily mean values, and precipitation is daily cumulative value; Data collection location: 29 ° 39 ′ 25.2 ″ n near the forest line on the east slope of Sejila Mountain; 94°42′25.62″E; 4390m； The underlying surface is natural grassland; Collector model Campbell Co CR1000, acquisition time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; The wind speed sensor is 05103; The precipitation is te525mm; The radiation is li200x; 3) Data quality description; The original data of air temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; The daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of the sensor, the precipitation in winter may have a certain error. 4) Data application achievements and prospects: this data is the update of the existing data "Sejila Mountain meteorological data (2007-2017)" and "basic meteorological data of Sejila east slope forest line of South Tibet station of Chinese Academy of Sciences (2018)". The data time scale span is large, which is convenient for scientists or graduate students in Atmospheric Physics, ecology and atmospheric environment. This data will be updated from time to time every year.
LUO Lun
This data is obtained from the automatic weather station of Nam Co Station for Multisphere Observation and Research, Chinese Academy of Sciences. Geographical coordinates are 30.77° N, 90.96° E, and the altitude is 4730m, and the precipitation data have been corrected. Data set elements include temperature, precipitation, relative humidity, wind speed, total radiation and air pressure. The time range is from January 1st, 2019 to December 29th, 2020. During the monitoring period, the data is stable and continuous. Through the analysis of meteorological data, it is important to understand the local climate change in the region. At the same time, this data has a wide application prospect and can serve graduate students and scientists with backgrounds such as atmospheric science, hydrology, climatology, physical geography and ecology.
WANG Junbo
The meteorological data are the basic meteorological data such as air temperature, relative humidity, wind speed, precipitation and air pressure observed in the observation field of Southeast Tibet station of Chinese Academy of Sciences (94.738286 ° e, 29.76562 ° n, 3326m), and the underlying surface is forest grassland. The time resolution of the original data is 10min, the air temperature, relative humidity, wind speed and air pressure are calculated by arithmetic mean, and the precipitation is the daily cumulative value. The meteorological station was set up at the end of 2006 and the probes were replaced in August 2020. Please note that the models of instrument probes before and after the update are as follows: the model of temperature and humidity probe was changed from HMP45C to hmp155; The model of air pressure probe is changed from PTB220 to ptb110; The model of wind speed sensor is changed from 034b to 0513, and the model of rain gauge sensor is rg13h The data can be used by students and researchers engaged in meteorology, atmospheric environment or ecology (Note: when using, it must be indicated in the article that the data comes from South East Tibetan Plateau station for integrated observation and research of alpine environment, CAS)
LUO Lun
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments in ecological and hydrological systems. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains ten meteorological elements that are widely used to drive ecological and hydrological models. They are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
QIU Yuan
This data set is the result of dynamic downscaling simulation of CORDEX region 8 (Central Asia) using WRF model driven by MPI-ESM-HR1.2 model data in CMIP6 plan. The data include 2m temperature (variable T2) and precipitation and precipitation was divided into convective (variable RAINC) and non-convective (variable RAINNC) precipitation. The time period includes historical test (1995-2014), near future (2021-2040) and medium future (2041-2060). The future time period includes SSP1-2.6 and SSP5-8.5. The time resolution of the simulation is once every 6 hours, the spatial resolution is 25km, the number of vertical layers is 51, a whole year in 1994 is used as spin up, the SST update is used, and the parameterized scheme combination with good performance in this area is selected. The data set can better reflect the future climate change characteristics of Central Asia and the Qinghai Tibet Plateau, and provide guidance for relevant countries to adapt to climate change.
LUO Yong, ZHOU Jiewei, SHI Wen
The dataset is from the transient experiment TRN40ka in Zhang et al (2021, Nature Geoscience), spanning 40ka-32ka BP with changing orbital parameters. For detailed description of experimental design, please refer to the original paper. Model details: COSMOS (ECHAM5-JSBACH-MPI-OM), a comprehensive fully coupled atmosphere–ocean general circulation model (AOGCM), is used to generate the dataset. The atmospheric model ECHAM5, complemented by the land surface component JSBACH, is used at T31 resolution (∼3.75°), with 19 vertical layers. The ocean model MPI-OM, including sea-ice dynamics that is formulated using viscous-plastic rheology, has a resolution of GR30 (3°×1.8°) in the horizontal, with 40 uneven vertical layers.
ZHANG Xu
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 Janurary 1to December 31, 2020. 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
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
The dataset contains observed climate data (1/1/2019-12/31/2019) from two automatic meteorological station located in the Qinghai Lake Basin. The niaodao station (36°58′N，99°52′E) is located in Gonghe County, Hainan Prefecture, Qinghai Province, and the wayanshan station (37°44′ N，100°05′ E) is located in Gangcha County, Haibei Prefecture, Qinghai Province. The observed elements include air temperature (℃) and relative humidity (%) at three layers (1m, 5m, and 10m), atmospheric pressure (hPa), and photosynthetically active radiation (W/m2). Both stations use CR1000 to collect climate data and record it every half an hour, the air temperature and humidity were measured by hmp155a, the atmospheric pressure was measured by CS106 and the photosynthetically active radiation was measured by LI200R. Our dataset will support the study of optimizing the ecological security barrier system in the key urbanized areas of the Tibetan Plateau.
CHEN Kelong, CHEN Zhirong
This dataset includes daily minimum temperature (Tmin), maximum temperature (Tmax) and precipitation (PPT) data of NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) (v1.0) over the periods of 2000–2009 and 2090–2099. The unit of Tmax and Tmin is K, and the unit of PPT is kgm-2s-1; the background filling value is -999. This dataset is a subset extraction fromthe original data. The original data was downloaded from https://portal.nccs.nasa.gov/datashare/NEXGDDP/BCSD/ in August 2020; The NEX-GDDP data set is obtained from CMIP5 (Coupled Model Intercomparison Project Phase 5) historical climate and General Circulation Models (General Circulation Models) operating in RCP (Representative Concentration Pathways) 4.5 scenario mode, including 21 atmospheric circulation models; among them, 2000 –2005 is a historical climate scenario, and 2006–2009 and 2090-2099 are RCP 4.5 scenarios. For the description of the original data, please refer to https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp.
Shen Miaogen, JIANG Nan
Qiangyong glacier: 90.23 °E, 28.88° N, 4898 m asl. The surface is bedrock. The record contains data of 1.5 m temperature, 1.5 m humidity, 2 m wind speed, 2 m wind orientation, surface temperature, etc. Data from the automated weather station was collected using USB equipment at 19:10 on August 6, 2019, with a recording interval of 10 minutes, and data was downloaded on December 20, 2020. There is no missing data but a problem with the wind speed data after 9:30 on July 14, 2020 (most likely due to damage to the wind vane). Jiagang glacier: 88.69°E, 30.82°N, 5362 m asl. The surface is rubble and weeds. The records include 1.5 meters of temperature, 1.5 meters of humidity, 2 meters of wind speed, 2 meters of wind direction, surface temperature, etc. The initial recording time is 15:00 on August 9, 2019, and the recording interval is 1 minute. The power supply is mainly maintained by batteries and solar panels. The automatic weather station has no internal storage. The data is uploaded to the Hobo website via GPRS every hour and downloaded regularly. At 23:34 on January 5, 2020, the 1.5 meter temperature and humidity sensor was abnormal, and the temperature and humidity data were lost. The data acquisition instrument will be retrieved on December 19, 2020 and downloaded to 19:43 on June 23, 2020 and 3:36 on September 25, 2020. Then the temperature and humidity sensors were replaced, and the observations resumed at 12:27 on December 21. The current data consists of three segments (2019.8.9-2020.6.30; 2020.6.23-2020.9.25; 2020.12.19-2020.12.29), Some data are missing after inspection. Some data are duplicated in time due to recording battery voltage, which needs to be checked. The meteorological observation data at the front end of Jiagang mountain glacier are collected by the automatic weather station Hobo rx3004-00-01 of onset company. The model of temperature and humidity probe is s-thb-m002, the model of wind speed and direction sensor is s-wset-b, and the model of ground temperature sensor is s-tmb-m006. The meteorological observation data at the front end of Jianyong glacier are collected by the US onset Hobo u21-usb automatic weather station. The temperature and humidity probe model is s-thb-m002, the wind speed and direction sensor model is s-wset-b, and the ground temperature sensor model is s-tmb-m006.
ZHANG Dongqi
The data set records the average temperature in the main areas of Qinghai Province, and the data are divided by region. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 18 data tables with the same structure. For example, the data table in 2001 has nine fields: Field 1: month Field 2: Xining Field 3: Ping An Field 4: source Field 5: chabcha Field 6: colleagues Field 7: Dawu Field 8: Jiegu Field 9: Delingha
Qinghai Provincial Bureau of Statistics
The West Pamir glacier meteorological station in Tajikistan (38 ° 3 ′ 15 ″ n, 72 ° 16 ′ 52 ″ e, 3730m) is jointly constructed by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The observational data include hourly meteorological elements (average wind direction (°), average internal wind speed (M / s), maximum wind speed (°), maximum wind speed (M / s), average temperature (℃), maximum temperature (℃), minimum temperature (℃), average relative humidity (%), minimum relative humidity (%), average atmospheric pressure (HPA), maximum atmospheric pressure (HPA), minimum atmospheric pressure (HPA)). The data period is from November 1, 2019 to November 30, 2020 Meteorological observation data can provide important basic data for the study of the relationship between climate change, glaciers and water resources in the West Pamir mountains, and provide important data for the economic construction of the lower reaches of the Amu Darya River Basin in Tajikistan.
HUO Wen, Ruibo Zhang
This data is based on the modified radiosonde observation data of 2008 used by Chen et al. 2016, Chen et al. 2011 and Chen et al. 2013. The vertical resolution of the processed atmospheric wind speed, wind direction, temperature, relative humidity and pressure is 20m. The data of three observation stages in 2008 are processed, namely iop1, IOP2 and iop3. Iop1 started from February 25, 2008 to March 19, 2008, IOP2 from May 13, 2008 to June 12, 2008, and iop3 from July 7, 2008 to July 16, 2008.
CHEN Xuelong, MA Yaoming
(1) This data is the meteorological data of mustag station from 2015 to 2018. The observation point is located at 75.29 ° E and 38 ° 40 'n, with an altitude of 4924 meters. The parameters include temperature, relative humidity, air pressure, precipitation and wind speed. (2) Data source and processing method: the data comes from the half-hour data of the automatic weather station of the station. Firstly, the abnormal data in the original records are removed, and then the daily values of these parameters are calculated. (3) Data quality description: data is discontinuous in some periods from January to March (4) The meteorological data can be used in the research of atmospheric science, climatology, physical geography and ecology.
XIE Ying
(1) This data is the meteorological data of mustag station in 2019. The observation point is located at 75 ° 03.35'e and 38 ° 24.77'n, with an altitude of 3650m. The parameters include temperature, relative humidity, air pressure, precipitation, radiation and wind speed. (2) Data source and processing method: the data comes from the half-hour data of the automatic weather station of the station. Firstly, the abnormal data in the original records are removed, and then the daily values of these parameters are calculated. (3) The meteorological data can be used in the research of atmospheric science, climatology, physical geography and ecology.
XIE Ying
This data is the data of automatic weather station (AWS, Campbell company) set up at the top of the mountain in the west slope of Sejila by the comprehensive observation and research station of Southeast Tibet alpine environment of Chinese Academy of Sciences in 2016. The geographical coordinates are 29.5919 n, 94.6102 e, with an altitude of 4640 m, and the underlying surface is alpine grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average of 30 minutes before October 2018, and an average of 10 minutes after that. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument model is rg3-m, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. In terms of data quality: the obvious abnormal values are eliminated, the battery is damaged due to snow in the first half of 2019, and the data is missing. The missing temperature data is corrected by using the temperature fitting regression of 43900 m at nearby stations, and the data is yellow. Please pay attention when using it; the monitoring of precipitation starts from August 2019. The data station is a high altitude meteorological station in Southeast Tibet, which will be updated from time to time. It can be used by scientific researchers studying ecology, climate, hydrology, glaciers, etc.
LUO Lun
This data is the data of the automatic weather station (AWS, Campbell company) set up in Yigong Zangbu basin by the Southeast Tibet alpine environment comprehensive observation and research station of Chinese Academy of Sciences in 2018. The geographic coordinates are 30.1741 n, 94.9334 e, and the altitude is 2282m. The underlying surface is grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), water vapor pressure (kPa) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average value recorded in 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument is tb4, the atmospheric pressure sensor is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the quality of the original data is better, less missing. The data station is a meteorological station in the lower altitude of the Qinghai Tibet Plateau, which will be updated from time to time in the future. It can be used by researchers studying climate, hydrology, glaciers, etc.
LUO Lun
This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.
QIU Yuan
