CAS FGOALS-f3-H, with a 0.25° horizontal resolution, and CAS FGOALS-f3-L, with a 1° horizontal resolution, were forced by the standard external conditions, and two coordinated sets of simulations were conducted for 1950–2014 and 2015–50 with the Experiment IDs of ‘highresSST-present’ and ‘highresSST-future’, respectively. The model outputs contain multiple time scales including the required hourly mean, three-hourly mean, six-hourly transient, daily mean, and monthly mean datasets.
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.
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
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.
This product provides the data set of key variables of the water cycle of major Arctic rivers (North America: Mackenzie, Eurasia: Lena from 1971 to 2017, including 7 variables: precipitation, evapotranspiration, surface runoff, underground runoff, glacier runoff, snow water equivalent and three-layer soil humidity, which are numerically simulated by the land surface model vic-cas developed by the project team. The spatial resolution of the data set is 0.1degree and the temporal resolution is month. This data set can be used to analyze the change of water balance in the Arctic River Basin under long-term climate change, and can also be used to compare and verify remote sensing data products and the simulation results of other models.
ZHAO Qiudong, WANG Ninglian, WU Yuwei
Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth’s surface water and energy fluxes. Therefore, discriminating between these forms is of significant importance, especially under a changing climate. We applied a state-of-the-art parameterization scheme with wet-bulb temperature, relative humidity, surface air pressure, and elevation as inputs, as well as observational gridded datasets with a maximum spatial resolution of 0.25◦, to generate a gridded dataset of different forms of daily precipitation (snow, sleet, and rain) and their temperature threshold across mainland China from 1961-2016. The annual snow, sleet, and rain amount were further calculated. The dataset may benefit various research communities, such as cryosphere science, hydrology, ecology, and climate change.
SU Bo , ZHAO Hongyu
Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
The Central Asia Reanalysis (CAR) dataset is generated based on the Weather Research and Forecast (WRF) model version 4.1.2 and WRF Data Assimilation (WRFDA) Version 4.1.2. Variables include temperature,, pressure, wind speed, precipitation and radiation. The reanalysis is established through cyclic assimilation, which performs data assimilation every 6 hours by 3DVAR. The assimilated data include conventional atmospheric observation and satellite radiation data. The main source of conventional data is Global Teleconnection System (GTS), including surface station, automatic station, radiosonde and aircraft report, and the observation elements include temperature, air pressure, wind speed and humidity. Satellite observations include retrievals and radiation data, The retrievals are mainly atmospheric motion vectors from polar orbiting meteorological satellites (NOAA-18, NOAA-19, MetOP-A and MetOP-B) and resampled to a horizontal resolution of 54km; the radiation data includes microwave radiation from MSU, AMSU and MHS and HIRS infrared radiation data. The simulation applies nesting with a horizontal resolution of 27km and 9km respectively, a total of 38 layers in the vertical direction and a top of the model layer of 10hPa. The lateral boundary conditions of the model are provided by ERA-Interim every 6 hours. The physical schemes used in the model are Thompson microphysics scheme, CAM radiation scheme, MYJ boundary layer scheme, Grell convection scheme and Noah land surface model. The data covers five countries in Central Asia, including Kazakhstan, Tajikistan, Kyrgyzstan, Turkmenistan and Uzbekistan, as well as lakes in Central Asia, such as Caspian Sea, Aral Sea, Balkash lake and Isaac lake, which can be used for the study of climate, ecology and hydrology in the region. Compared with gauge-based precipitation in Central Asia, the simulation by CAR shows similar performance with MSWEP ( a merged product) and outperforms ERA5 and ERA-Interim.
Simulation results of four cmip6 models in 2015-2100 under the scenario of shared socio-economic path (SSP) 5-8.5. The selection standard is that the resolution of the four modes is less than 1 °, and there are daily data. Eight variables representing extreme climate are extracted from the original simulation results, which are the extremely high value of daily maximum temperature (TXX), the extremely high value of daily minimum temperature (TNX), the extremely low value of daily maximum temperature (TxN), the extremely low value of daily minimum temperature (TNN), the number of continuous dry days (CDD), the number of continuous wet days (CWD), precipitation intensity (SDII) and the number of heavy precipitation days (r20mm). The time resolution of the data is years, the spatial range is the Qinghai Tibet Plateau, and the time range is 2015-2100.
ZHANG Ran ZHANG Ran
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, 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. 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 four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements 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 QIU Yuan
This data is precipitation data, which is the monthly precipitation product of tropical rainfall measurement mission TRMM 3b43. It integrates the main area of the Qinghai Tibet Plateau (25 ~ 40 ° n; 25 ~ 40 ° n); The precipitation data of 332 meteorological stations are from the National Meteorological Information Center of China Meteorological Administration. The reanalysis data set is obtained by the station 3 ° interpolation optimization variational correction method. For the monthly sample data from January 1998 to December 2018, the spatial coverage is 25 ~ 40 ° n; 73 ~ 105 ° e, the spatial resolution is 1 ° * 1 °.
XU Xiangde, SUN Chan
Meteorological forcing dataset for Arctic River Basins includes five elements: daily maximum, minimum and average temperature, daily precipitation and daily average wind speed. The data is in NetCDF format with a horizontal spatial resolution of 0.083°, covering Yenisy, Lena, ob, Yukon and Mackenzie catchments. The data can be used to dirve hydrolodical model (VIC model) for hydrological process simulation of the Arctic River Basins. The further quality control were made for daily observation data from Global Historical Climatology Network Daily database(GHCN-D), Global Summary of the Day (GSPD),The U.S. Historical Climatology Network (USHCN),Adjusted and homogenized Canadian climate data (AHCCD) and USSR / Russia climate data set (USSR / Russia). The thin plate spline interpolating method, which similar to the method used in PNWNAmet datasets (Werner et al., 2019), was employed to interpolate daily station data to 5min spatial resolution daily gridded forcing data using WorldClim and ClimateNA monthly climate normal data as a predictor.
ZHAO Qiudong, WU Yuwei
The data set contains the data set (98 ° 29′16″E, 31 ° Based on hobo temperature, moisture and small meteorological station, the monitoring data of shallow ground temperature, moisture and field meteorological elements of 36 ′ 36 ″ n) freeze-thaw landslide and thaw mud flow are obtained through field monitoring. The observation time is between August 31, 2019 and July 14, 2020. Through on-site monitoring of a complete freeze-thaw cycle, the monitoring data of ground temperature, moisture and meteorological elements automatically obtained by on-site sensors are downloaded. Through certain quality control, the data when the sensors are not fully adapted to the soil environment and the system error caused by sensor failure are eliminated. The observation depth of ground temperature is 10cm, 20cm, 40cm, 60cm, 80cm, 100cm, 150cm and 200cm, with a total of 8 layers. The observation depth of water is 20cm, 50cm, 100cm and 200cm, with a total of 4 layers. Meteorological observation elements mainly include temperature, rainfall, wind speed, wind direction and solar radiation. The observation interval is 30 minutes (Note: the maximum range of solar radiation sensor is 1276.8 w / m2, and the actual solar radiation value is 1276.9 w / m2 when it is greater than the maximum range; The minimum starting wind speed of the wind speed sensor is 0.5m/s. When the actual wind speed is less than the starting wind speed, the display value is 0. Therefore, the data can not reflect the phenomenon of super solar constant and wind speed below 0.5m/s). Quality control includes eliminating the data when the sensor is not fully adapted to the soil environment and the system error caused by sensor failure. The corrected final data is stored in Excel file. The integrity and accuracy of the obtained field data are more than 95% after review by many people. The monitoring data can provide the necessary data support for the research of freeze-thaw landslide and thaw mud flow in Southeast Tibet.
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.
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang, CHEN Jing, LI Jiangang
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 QIU Yuan
Contact SupportNorthwest Institute of Eco-Environment and Resources, CAS 0931-4967287 firstname.lastname@example.org
LinksNational Tibetan Plateau Data Center