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.
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
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.
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.
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
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
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
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.
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.
Gwadar deepwater port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from the Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and the Red Sea in the West. It is a port with a strategic position far away from Muscat, the capital of Oman. This data is the measured meteorological data of Gwadar Port meteorological station (62.329494e, 25.233308n). The data time range is 2014-2015, and the data time resolution is one day.
Coupled Model Intercomparison Project Phase 5 (CMIP5) provides a multiple climate model environment, which can be used to predict the future climate change in the key nodes in the Belts and Road to deal with the environmental and climate problems. Key nodes in the Belt and Road are taken as the study regions of this dataset. The ability of 43 climate models in CMIP5 to predict the future climate change in the study regions was assessed and the optimal models under different scenarios were selected according to the RMSE between the prediction results and real observations. This dataset is composed of the prediciton results of precipitation and near-surface air temperature between 2006 and 2065 using the optimal models in monthly temporal frequncy. The spatial resolution of the dataset has been downscaled to 10 km using statistical downscaling method. Data of each period has three bands, namely maximum near-surface air temperature, minimum near-surface air temperature and precipitation. In this data set, the unit of precipitation is kg / (m ^ 2 * s), and the unit of near-surface air temperature is K. This dataset provides data basis for solving environmental and climate problems of the key nodes in the Belts and Road.
LI Xinyan, LING Feng
1) Data content (including elements and significance): the data includes daily values of temperature (℃), precipitation (mm), relative humidity (%) and wind speed (M / s) 2) Data source and processing method; air temperature, relative humidity and wind speed are daily mean values, precipitation is daily cumulative value; data collection location is 29 ° 39 ′ 25.2 ″ n; 94 ° 42 ′ 25.62 ″ E; 4390m; underlying surface is natural grassland; collector model Campbell Co CR1000, collection 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; 3) Data quality description; the original data of 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 sensors, there may be some errors in winter precipitation. 4) In addition, it is convenient for scientists to update the atmospheric data in the future. This data is updated from time to time every year.
The China-Mongolia-Russia Economic Corridor is confronted with security problems related with global warming, mostly including the increasingly serious of degradation of permafrost and land desertification. On one hand, frozen soil degradation has caused frequent disasters such as debris flow, flood, ice and snow damage along the China-Mongolia-Russia transportation and pipeline, which will cause water and soil erosion followed by exposed pipes in frozen soil, in particular in summer. On the other hand, desertification will drive the ecological environment more vulnerable with the compound hazards of soil erosion and sandstorms occurring frequently. Therefore, this dataset will hopefully provide basic climate data for the research on the climate change and its impacts on permafrost and desertification for the China-Mongolia-Russia Economic Corridor. The original data is extracted from ERA5- Land surface climate reanalysis data (ERA5 – Land) (source: https://cds.climate.copernicus.eu). We adopted the inverse distance weight (IDW) method to interpolate the original data with the spatial resolution of 10 km. Based on this dataset, the spatial and temporal distribution pattern of climatic factors are outlined over the past 40 years for the corridor.
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM2M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours (or 6 hours), daily and yearly scales.
PAN Xiaoduo, ZHANG Lei
Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.
The data are collected from the automatic weather station (AWS, Campbell company) in the moraine area of the 24K glacier in the Southeast Tibet Plateau, Chinese Academy of Sciences. The geographic coordinates are 29.765 ° n, 95.712 ° E and 3950 m above sea level. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), net radiation (w / m2), water vapor pressure (kPa) and air pressure (mbar). In the original data, an average value was recorded every 30 minutes before October 2018, and then an average value was recorded every 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The net radiation probe is nr01, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the data has undergone strict quality control. The original abnormal data of 10 minutes and 30 minutes are removed first, and then the arithmetic mean of each hour is calculated. Finally, the daily value is calculated. If the number of hourly data is less than 24, the data is removed, and the corresponding date data in the data table is empty. In addition to the lack of some parameter data due to the thick snow and low temperature in winter and spring, the data can be used by scientific researchers who study climate, glacier and hydrology through strict quality control.
This data set is the data set of climate elements in Hoh Xil area of Qinghai Province, covering the data of 14 observation stations, recording the climate observation data in 1990 in detail. Hoh Xil area in Qinghai Province has a high terrain with an average altitude of over 5000m. The climate is cold, the air is thin and the natural environment is bad. The vast area is still no man's land, known as "forbidden zone for human beings". Due to less interference from human activities, most of the area still maintains its original natural state. Its special geographical location, crustal structure and natural environment, as well as the unique composition of the biological flora, have been the focus of domestic surgical circles. The original data of the data set is digitized from the book "natural environment of Hoh Xil, Qinghai Province". The climate observation data include solar radiation, temperature, precipitation, air pressure, wind speed, etc. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
When using the 3DVAR for data assimilation, it is necessary to use error covariance to determine the contribution of background field and observation. Among them, the background field error covariance depends not only on the atmospheric prediction model (such as resolution, parameterization scheme, etc.), but also on the simulation area. Based on the Weather Forecast and Research (WRF) model, this data is estimated by NMC method through the simulation of the Central Asian Great Lakes region (27 km horizontal resolution) in 2017. The variables include stream function, velocity potential function, temperature, relative humidity and surface pressure. This data can be applied to the study and application of data assimilation in the Central Asia Great Lakes region based on WRF model.
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Airborne pollen is mainly produced and disseminated during the process of plant flowering, controlled by plant phenology and climatic conditions. As an important bioindicator of plant behavior, airborne pollen can supply information about reproductive phenology, climate and atmospheric circulations. From 2011 to 2013, airborne pollen samples were collected using a volumetric Burkard pollen trap at the Qomolangma Station for Atmospheric and Environmental Observation and Research, Chinese Academy of Sciences (QOMS, 28.21°N, 86.56°E; 4276 m a.s.l.), on the northern slope of the Himalayas. The sampler is a volumetric air-suction device capable of continuously gathering pollen and spore particles. Air is drawn in at a speed of 10 l/min, and airborne particles are deposited on a sticky tape mounted on a drum that makes one complete rotation per week. The tape is changed weekly after a complete rotation. Then, the tape is removed and cut into seven pieces, with each piece representing one day of sampling. The pieces are mounted on slides using glycerin and safranin. Identification and counting of pollen grains were performed under an Olympus BX41 microscope at 400× magniﬁcation; all pollen grains on each slide were counted . Pollen concentration was expressed as the daily pollen grains per cubic meter of air using a constant air intake speed of 10 l/min. The pollen concentration and percentage of each pollen taxon in each year were calculated. The pollen sampling and lab process were followed the standard methods to ensure the authenticity and reliability of the data. The pollen data can provides insights into vegetation response to climate change and has significance for interpreting fossil pollen records.
The daily values of air temperature, air pressure, relative humidity, wind speed, wind direction, precipitation, radiation, water vapor pressure, etc. observed by the comprehensive observation and research station of the west wind belt of mostag.
This data set includes the daily average values of air temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, p2.5 concentration, short wave radiation, etc. observed by the comprehensive observation and research station of atmosphere and environment of Everest from 2017 to 2018.
This data set includes the daily average data of air temperature, relative humidity, precipitation, wind speed, wind direction, net radiation, air pressure, etc. of Southeast Tibet station from January 1, 2017 to December 31, 2018.
Lun LUO, Liping ZHU
1) The data set driven by the surface meteorological elements of the surface meteorological observation data product (2017-2018) of the Qinghai Tibet Plateau includes four elements: near surface temperature, surface precipitation rate, short wave radiation and long wave radiation. 2) The data set is based on the existing Princeton reanalysis data, GLDAS data, gewex-srb radiation data and TRMM Precipitation Data in the world as the background field, and integrates the conventional meteorological observation data of China Meteorological Administration, and is formed by spatial interpolation. 3) The data is TIFF format, the temporal resolution is daily value, and the spatial resolution is 0.1 °. 4) It is convenient for researchers and students who do not use such assimilation data in NC format. Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing.
ZHU Liping, PENG Ping
Near surface atmospheric forcing data were produced by using Wether Research and Forecasting (WRF) model over the Heihe River Basin at hourly 0.05 * 0.05 DEG resolution, including the following variables: 2m temperature, surface pressure, water vapor mixing ratio, downward shortwave & upward longwave radiation, 10m wind field and the accumulated precipitation. The forcing data were validated by observational data collected by 15 daily Chinese Meteorological Bureau conventional automatic weather station (CMA), a few of Heihe River eco-hydrological process comprehensive remote sensing observation (WATER and HiWATER) site hourly observations were verified in different time scales, draws the following conclusion: 2m surface temperature, surface pressure and 2m relative humidity are more reliable, especially 2m surface temperature and surface pressure, the average errors are very small and the correlation coefficients are above 0.96; correlation between downward shortwave radiation and WATER site observation data is more than 0.9; The precipitation agreed well with observational data by being verified based on rain and snow precipitation two phases at yearly, monthly, daily time scales . the correlation coefficient between rainfall and the observation data at monthly and yearly time scales were up to 0.94 and 0.84; the correlation between snowfall and observation data at monthly scale reached 0.78, the spatial distribution of snowfall agreed well with the snow fractional coverage rate of MODIS remote sensing product. Verification of liquid and solid precipitation shows that WRF model can be used for downscaling analysis in complex and arid terrain of Heihe River Basin, and the simulated data can meet the requirements of watershed scale hydrological modeling and water resources balance. The data for 2000-2012 was provided in 2013. The data for 2013-2015 was updated in 2016. The data for 2016-2018 was updated in 2019.
This dataset includes the monthly minimum temperature data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2017. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 ℃. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea.
The surface air temperature dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean surface air temperature and 3-hourly instantaneous surface air temperature. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 1979 to 2018. Spatial dimension of data: 73°E-106°E, 23°N-40°N. The surface air temperature with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.
DING Lirong, ZHOU Ji, WANG Wei
This dataset includes the monthly maximum temperature data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2017. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 ℃. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea.
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels)，’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.
1) Data content: including the central Asian region, the regional scope: 30°N ~ 60°N, 40°E ~ 90°E; 2) Data source: process the CMIP data set and use bilinear interpolation to interpolate the data of different resolution modes to 0.5°× 0.5°，CRU observation data from 1901 to 2014;; 3) Data quality: the time length is long, the data quality is good, and the missing values are marked by 999; 3) Prospect of data application achievement set: the data has been used to evaluate the simulation capability of temperature in central Asia, and the capability of climate system model to simulate historical climate change in central Asia has been evaluated through calculation and analysis of regional mean, relative error, root-mean-square error, Taylor diagram, EOF. 4) data reliability: by comparing and analyzing the annual changes of the observed and simulated data, the data results show a significant warming trend. By carrying out correlation test on the data results, they all pass the 99% reliability test.At the same time, CMIP plan data and CRU data are also common data sets, which are often used in many studies on climate change.
This data is conventional and satellite data of six hour resolution for the Great Lakes region of Central Asia. The conventional data include the observation of ground stations and sounding stations in the Great Lakes region of Central Asia and its surrounding areas (China, Kazakhstan, Kyrgyzstan, Turkmenistan, Tajikistan, Uzbekistan, Afghanistan, Russia, Iran, Pakistan, India, etc.), and the observation elements include temperature, pressure, wind speed and humidity, with the average number of stations in each time It is about 600, and the interval between stations is between 10-100km; the satellite data comes from the cloud guide wind retrieved by polar orbiting satellites (NOAA series and MetOp Series). All the data are from the global telecommunication system (GTS), and the observation data with poor quality are eliminated through quality control. The data can be applied to the data assimilation of the Great Lakes region in Central Asia, and also to the numerical simulation of the Great Lakes region in Central Asia.
The basic data source of this dataset is from the website of the National Oceanic and Atmospheric Administration (NOAA). NOAA satellites are meteorological observation satellites. Provide meteorological environment information including temperature, precipitation, dew point, wind speed, etc. This dataset mainly covers key nodes in the Southeast Asia and Middle East regions. The main steps of data processing are as follows: firstly, the daily maximum temperature data is obtained by screening from a large number of basic meteorological data; the daily maximum temperature relative humidity relationship is integrated, and the daily relative humidity calculation is completed based on the dew point temperature data of the weather station. This data set provides basic information and a strong reference for evaluating the high temperature weather process in key node areas.
GE Yong, LIU Qingsheng
Meteorological data is a group of data reflecting the weather, which can be divided into climate data and weather data. This data set mainly includes the rainfall data and temperature data in the meteorological data (pre represents rainfall and T2 represents temperature in the data set). This data set is from the CRU (climate research unit) global grid data provided by the University of East Anglia in the UK (http://www.cgiar-csi.org/). Cru data set is obtained by interpolating the data of 365 observation stations in Central Asia, which has high accuracy in Central Asia. This data set uses CRU to obtain rainfall and temperature data of five Central Asian countries through ArcGIS batch cutting. Data format: GeoTIFF; spatial resolution: 0.5 °; time scale: Monthly Scale. The meteorological data is widely used and can be integrated with resources in different fields. It plays an important role in the development and construction of transportation, new energy, agriculture, mobile Internet software development and service, public management, smart city based on big data technology, smart transportation, smart food and other fields.
Climatic Research Unit CRU
The temporal resolution of temperature and radiation data in Central Asia is monthly scale, and the spatial resolution is 0.5 degree and 0.05 degree, respectively. The GCS_WGS_1984 projection coordinate system was used. Among them, the downward short wave radiation, air temperature and vapor pressure data of GLDAS, surface temperature / emissivity data of MOD11C3, surface albedo data of MCD43C3 and ASTER_GEDv4.1 are used for radiation data calculation; the temperature data was calculated by MOD06_ L2 cloud products and MOD07_ L2 atmospheric profile data was calculated. This data is based on the advanced remote sensing algorithm and makes full use of the current high-precision remote sensing data and products, which is different from the traditional climate model for the estimation of climate elements. The data can be used to analyze the spatial and temporal variation characteristics of water resources in Central Asia, analyze the supply-demand relationship of agricultural water resources and evaluate the development potential of water resources.
Jinxi SONG, Xiaohui JIANG
The site's daily and monthly statistical data sets are the key parameters reflecting the weather conditions of the site, and are the GSOM data. Meteorology plays an important role in the lithosphere, biosphere, soil circle and the atmosphere, providing a basis for assessing the regional contribution and response of climate factors to the world. This data set takes 34 key node regions of the pan third pole as the study area (Abbas, Astana, Bangkok, etc.),based on the site climate data from 2000 to 2016, the meteorological factors in different regions were counted, and the data series of meteorological observations in key nodes were obtained. The main parameters are: annual average maximum value, average minimum value and average temperature; monthly total precipitation and snowfall.
The land-sea thermal contrast is an important driver for monsoon interannual and interdecadal variability and the monsoon onset. The importance of the thermal contrast between the Tibetan Plateau (TP) and the Indian Ocean (IO) in driving the establishment of Indian Summer Monsoon (ISM) has been recognized. The South Asian Summer Monsoon (SASM) is primarily a tropical summer monsoon. As a direct dynamic response to the diabatic heating, the difference between upper and lower-layer winds can be closely linked to the strength of the heat source. The upper-layer thermal contrast is more important for the SASM (Sun et al., 2010; Sun and Ding，2011; Dai et al., 2013). Thermal contrast between the TP and the IO at the mid-upper troposphere is closely related to the onset and the variability of ISM. Considering that the temperature above the TP and IO are the two centers which are most sensitive to the change of ISM, a thermal contrast index (TCI) is proposed based on 500-200hPa air temperature: TCI = Nor[T(25°N-38°N, 65°E-95°E) - T(5°S-8°N, 65°E-95°E)] Where Nor represents standardization and T is 500-200hPa air temperature. The TCI is larger, and the ISM is stronger. The TCI can capture the interannual and interdecadal variability of ISM well. The cooperative thermal effect between TP and IO may contributes more to the ISM than the separately temperature of TP or IO. In addition, from the view of climate mean state, the pentad-by-pentad increment of TCI has a 15-pentad lead when the correlation coefficient between it and the ISM index reaches the maximum. And the correlation coefficient between the pentad-by-pentad increment of TCI and the ISM index is significant when the pentad-by-pentad increment of TCI has a 3-pentad lead. The result indicates the advantage of the TCI for prediction of the ISM. Meanwhile, the averaged pentad-by-pentad increment of TCI for the first 25 (TCI25) pentads may be a predictor of the early or late onset of the ISM. The ISM onset will be earlier when the TCI25 is larger.
LI Zhangqun, XIAO Ziniu, ZHAO Liang
In order to understand the temporal and spatial variation characteristics of temperature changes in the Northern Hemisphere, the study used CRU (Climatic Research Unit) grid data to calculate the spatial distribution of the average annual temperature of 30 years (1971-2000). The annual average temperature decreases with the latitude increasing, and varies from greater than 30 °C to less than -25 °C. In the regions of the same latitudes, the annual average temperature in high altitude areas (such as the Tibetan Plateau, the Mongolian Plateau, and the Western Siberian Mountains) presented the trend of low temperature. At the same time, the annual average temperature trend distribution map of the Northern Hemisphere with a resolution of 0.5 ° × 0.5 ° from 1901 to 2016 was completed.
Guoan Yin, Shi Yaya
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;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 (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -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_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) (°), 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_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), 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), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. 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: 2018-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The observation data set of Central Asia field meteorological station includes the field observation data of temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation, soil heat flux, sunshine time and soil temperature of 10 Central Asia field meteorological stations. 10 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other ecosystem types. The original meteorological data collected by the ground meteorological observation station is obtained after screening and review, and format conversion. Data quality is good. Central Asia has a variety of climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of Central Asia ecological environment monitoring, disaster prevention and mitigation, climate change and ecological environment in Central Asia, and has been applied in the research of Central Asia ecological environment monitoring.
This data is the aridity index (AI) under the rcp4.5 scenario. AI data is the ratio of precipitation to potential evapotranspiration. This data is calculated by the average of 14 models. These 14 modes are canesm2; ccsm4; cnrm-cm5; csiro-mk3-6-0; giss-e2-r; hadgem2-cc; hadgem2-es; inmcm4; ipsl-cm5a-lr; miroc5; miroc-esm-chem; miroc-esm; mpi-esm-lr; mri-cgcm3. The spatial resolution is 2 * 2 degrees, and the temporal resolution is from January 2020 to December 2099. This data set can be used to analyze the future dry and wet change scenarios in the Great Lakes region of Central Asia, as well as the dry and wet past and pattern in other regions of the world under the future scenarios.
This data set includes the daily values of temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, etc. observed at Namuco station from January 1, 2017 to December 31, 2018.
WANG Junbo, WU Guangjian
The basic data set of water resources research of Southeast Asian countries and Lancang Mekong basin (1901-2010) collected and sorted out the main hydrometeorological data of Southeast Asian countries and Lancang Mekong basin, including precipitation, average temperature, maximum temperature, minimum temperature, water vapor pressure, etc. the data came from CRU TS v. 4.03 (clinical research unit time series version 4.03), which is widely used in the whole world The format is NC, the time resolution is month by month, and the time length is from January 1901 to December 2018. Hydrological data includes surface runoff and underground runoff simulated by the hydrological model. The data comes from GLDAS (Global Land Data Assimilation System). The data format is NC, the time resolution is month by month, and the time length is from January 1979 to February 2019.
Climatic Research Unit CRU, Global Land Data Assimilation System GLDAS
The dataset records the Ali Desert Environment Integrated Observation and Research Station, the meteorological dataset for 2017-2018, and the time resolution of the data is days. It includes the following basic meteorological parameters: temperature (1.5 meters from the ground, once every half hour, unit: Celsius), relative humidity (1.5 meters from the ground, half an hour, unit: %), wind speed (1.5 meters from the ground, half an hour) , unit: m / s), wind direction (1.5 meters from the ground, once every half hour, unit: degrees), air pressure (1.5 meters from the ground, once every half hour, unit: hPa), precipitation (24 hours, unit: mm ), water vapor pressure (unit: Kpa), evaporation (unit: mm), downward short-wave radiation (unit: W/m2), upward short-wave radiation (unit: W/m2), downward long-wave radiation (unit: W/m2) ), upward long-wave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). Data collection location: Observation Field of Ali Desert Environment Comprehensive Observation and Research Station, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Longitude: 79°42'5"; Latitude: 33°23'30"; Altitude: 4264 meters.
The data include daily precipitation (Precip) amount and daily mean near-surface air temperature (T2M) over the Pan Third Pole region. The data is downscaled by using the Weather Research and Forecasting (WRF) model (3.7.1). The boundary and initial condition come from the fifth-generation global reanalysis product by the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The seasonal cycle and summer mean of precipitation over Tibet is well reproduced in comparison to the in situ observations.
Data description: This dataset includes the grid data of annual temperature and annual precipitation on the Tibetan Plateau from 1998 to 2017. It is the basic data for study of climate change and its impact on the ecological environment. Data source and processing: The meta data was aquired from the temperature and precipitation daily data of China's ground high-density stations (above 2,400 national meteorological stations) based on the latest compilation of the National Meteorological Information Center's basic data. After removing the missing stations, the software's thin plate spline method in ANUSPLIN was used to perform spatial interpolation, in order to generate grid data with spactial resolution of 1 km on the Tibetan Plateau . Data application: This data can be used to indentify the impact of climate change on the ecological environment.
The RCM employed is the International Center for Theoretical Physics (ICTP) Regional Climate Model version 4 (RegCM4, Giorgi et al., 2012). The domain used is the Coordinated Regional Climate Downscaling Experiment (CORDEX) Phase II East Asia domain, covering whole of China and its surrounding East Asia areas. The model is run at 25 km gird spacing, with its standard configuration of 18 vertical sigma layers with a model top at 10 hPa. Configuration of the model follows Gao et al. (2016, 2017), with land cover data over China was updated as reported by Han et al. (2015) to better represent the realistic vegetation. The initial and lateral boundary conditions needed to drive RegCM4 are derived from the CMIP5 models of HadGEM2-ES (RCP4.5 pathways), and the data set include temperature and precipitation.
This data set is the data set of climate factors in the Qinghai Tibet Plateau from 1990 to 2015. It records the spatial distribution change of annual average temperature in the past 25 years. The data is in TIF grid format, with a spatial resolution of 1km and an annual average temperature unit of 0.1C. The data comes from the daily observation data of meteorological stations on the Qinghai Tibet Plateau, which is generated by time aggregation calculation and spatial interpolation processing. As an important climate factor, the data set can be used to study the annual average temperature change and climate change of the Qinghai Tibet Plateau. As the climate background of the ecological environment change of the Qinghai Tibet Plateau, it provides data support for the study of urbanization and ecological environment interaction stress Bracing.
DU Yunyan, YI Jiawei