The Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System (FGOALS-f3-L) model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Global Monsoons Model Intercomparison Project (GMMIP) Tier-1 and Tier-3 experiments are introduced in this paper, and the model descriptions, experimental design and model outputs are demonstrated. There are three simulations in Tier-1, with different initial states, and five simulations in Tier-3, with different topographies or surface thermal status. Specifically, Tier-3 contains four orographic perturbation experiments that remove the Tibetan–Iranian Plateau, East African and Arabian Peninsula highlands, Sierra Madre, and Andes, and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan–Iranian Plateau and surrounding regions at altitudes above 500 m. These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation, and to a better understanding of the orographic impact on the global monsoon system over highlands.
HE Bian
Meteorological elements of the dataset include the near-surface land-air exchange parameters, such as downward/upward longwave/shortwave radiation flux, momentum flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the tropopause are also included. Independent evaluations were conducted for the dataset by comparison between the observational data and the most recent ERA5 reanalysis data. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.
LI Fei, Ma Shupo, ZHU Jinhuan, ZOU Han , LI Peng , ZHOU Libo
The Tibetan Plateau Subregional Dynamical Downscaling Dataset-Standard Year (TPSDD-Standard) is a high spatial-temporal resolution gridded dataset for the study of land-air exchange processes and lower atmospheric structure over the entire Tibetan Plateau, taking into account the climatic characteristics of each subregion of the Tibetan Plateau. Based on the 500 hPa multi-year average of the geopotential height field over the Tibetan Plateau, the year (2014) with the largest pattern correlation coefficient with this geopotential height field is selected as the standard year, which means that it can roughly reflect the multi-year average status of the atmosphere over the Tibetan Plateau. The temporal resolution of this data is 1 hour and the spatial resolution is 5 km. Meteorological elements of the dataset include near-surface land-air exchange parameters such as downward/upward long-wave/short-wave radiation fluxes, sensible heat fluxes, latent heat fluxes, etc. In addition, the 3-dimensional vertical distribution of wind, temperature, humidity, and pressure from the surface to the top of the troposphere is also included. The dataset was independently evaluated by comparing the observed data with the latest ERA5 reanalysis data. The results demonstrate the accuracy and superiority of the dataset, which offers great potential for future climate change studies.
LI Fei, Ma Shupo, ZHU Jinhuan, ZHOU Libo , LI Peng , ZOU Han
This data set is the conventional meteorological observation data of Maqu grassland observation site in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
The high-resolution atmosphere-hydrologic simulation dataset over Tibetan Plateau is prepared by WRFv4.1.1 model with grids of 191 * 355 and spatial resolution of 9 km, and a spatial range covering the entire plateau. The main physics schemes are configured with Thompson microphysics scheme, the rapid radiative transfer model (RRTM), and the Dudhia scheme for longwave and shortwave radiative flux calculations, respectively, the Mellor-Yamada-Janjic (MYJ) TKE scheme for the planetary boundary layer and the Unified Noah Land Surface Model. The time resolution is 3h and the time span is 2000-2010. Variables include: precipitation (Rain), temperature (T2) and water vapor (Q2) at 2m height on the ground, surface skin temperature (TSK), ground pressure (PSFC), zonal component (U10) and meridional component (V10) at 10m heigh on the ground, downward long-wave flux (GLW) and downward short-wave flux (SWDOWN) at surface, ground heat flux (GRDFLX), sensible heat flux (HFX), latent heat flux (LH), surface runoff (SFROFF) and underground runoff (UDROFF). The data can effectively support the study of regional climate characteristics, climate change and its impact over the Tibet Plateau, which will provide scientific basis for the sustainable development of the TP under the background of climate change.
MENG Xianhong, MA Yuanyuan
The distribution data of available wind energy resources with 1km resolution in the Qinghai Tibet Plateau is based on the multi-year average wind speed in the Qinghai Tibet Plateau obtained by numerical simulation, and considering the constraints and restrictions of terrain, water body, urban and other land use on wind energy development, the comprehensive wind energy resource levels are very rich, rich, relatively rich and general. Set the land availability according to the terrain slope and land use type, deduct the 3km range around the town, divide the land availability into 5 intervals from 0 to 1 according to the interval of 0.2, and then divide the annual average wind speed into 4 intervals. The classification of wind energy resources is obtained through the combination of land availability and wind speed. The data are mainly used for detailed survey of wind energy resources and macro site selection of wind farms.
ZHU Rong, SUN Chaoyang
1) Data content (including elements and significance): 19 stations of Alpine network (Southeast Tibet station, Namuco station, Everest station, mustage station, Ali station, Golmud station, Tianshan station, Qilian mountain station, Ruoergai station (2 points in total, Northwest Institute and Chengdu Institute of Biology), Yulong Snow Mountain station and Naqu station (including stations, Qinghai Tibet Institute, Northwest Institute and Geography Institute), Haibei Station, Sanjiangyuan station, Shenza station,, Lhasa station and Qinghai Lake Station) meteorological observation data set of Qinghai Tibet Plateau in 2020 (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and flux) 2) Data source and processing method: Excel format for field observation of 19 stations of Alpine network 3) Data quality description: Daily resolution of the station 4) Data application achievements and prospects: Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data. In addition, the data set is an update of the meteorological data of the surface environment and observation network in China's high and cold regions (2019).
ZHU Liping
The Holocene single orbit parameter change simulation results (2019-2020) data set uses the earth system model cesm model (horizontal resolution: about 2 ° for the atmosphere and land surface module and about 1 ° for the ocean and sea ice module) to carry out the Holocene transient simulation test considering the change of earth orbit parameters. The spatial resolution is 2 °; Spatial range: North: 50 ° n, South: 20 ° n, West: 60 ° e, East: 130 ° E; Regional scope: Eurasia; The time range is Holocene. The simulation results can be used to analyze the changes of westerly monsoon in Eurasia under the influence of individual orbital parameters in Holocene.
ZHANG Ran ZHANG Ran
The monthly mean wind speed grid data of 3 km resolution over the Qinghai Tibet Plateau is based on the meteorological element database developed by the National Climate Center for Mesoscale Numerical Simulation of long-term time series, with a horizontal resolution of 3 km × 3 km, time resolution 1 hour, time length 1995 ⁓ 2016. The establishment of the database adopts the double nested numerical simulation method of WRF mesoscale model, with the outer grid distance of 9 km, covering most of Eurasia; There are four internal weight grids with a grid distance of 3 km, covering the land and sea areas of China, and the fourth calculation area covers the Qinghai Tibet Plateau (Fig. 1). The top height of WRF model is 10 HPA, with 36 layers in the vertical direction, and 9 layers from the ground to the height of 200 m. The physical process parameterization schemes include Thompson (outer heavy grid) and wsm6 (inner heavy grid) microphysical parameterization schemes; The k-f cumulus parameterization scheme is set in the outer grid, and the cumulus convection parameterization scheme is not set in the second grid; Rrtm (rapid radiative transfer model) long wave radiation parameterization scheme; Dudhia shortwave radiation parameterization scheme; Acm2 boundary layer parameterization scheme; Noah land surface parameterization scheme. The four-dimensional data assimilation technology is used in the numerical simulation, which integrates the grid reanalysis data of global atmospheric circulation model (cfsv2), oisst sea surface temperature data, and the time observation data of more than 2400 surface weather stations and 160 radiosonde weather stations in China. In 2009, China Meteorological Administration established a national wind energy resources professional observation network including 400 wind towers, including 329 70 m wind towers, 68 100 m wind towers and 3 120 m wind towers, which were gradually completed from 2008 to 2009, and mainly distributed in regions rich in wind energy resources in China. Based on the hourly wind direction and wind speed observation data of a complete year from January 2009 to December 2010 at the height of 70 m of the wind tower, the wind speed simulation results of the mesoscale WRF model (horizontal resolution 3 km) output in the same period were analyzed × 3 km), excluding the observation data integrity rate of less than 90% and the annual average wind speed of less than 3.8 m / s, there are 354 wind measuring towers actually used for error test, and the sample number of each tower is about 8700 hours. The results show that the relative error between the measured wind speed and the numerical simulation wind speed is less than 5% in 49% of the tower tests; The relative error is 5-10% for 28% of the wind towers; The relative error of 14.4% wind tower is 10-15%; The relative error of 5.6% wind tower is 15-20%; The relative error of 3% wind tower is more than 20%. The anemometer towers with large relative errors are mainly distributed in mountainous areas with complex inland terrain and coastal mountainous areas. In addition, the correlation coefficient of hourly wind speed comparison across the country is 0.6, and the correlation coefficient of average wind speed in 16 directions is 0.8, which is more than 99.9% of the statistical significance test. It shows that the temporal and spatial variation characteristics of numerical simulation wind speed are consistent with the variation of measured wind speed. There are no anemometer towers in Tibet. There are 13 anemometer towers in Qinghai Province. The relative errors of 6 towers are less than 5%, 3 towers are 5-10%, 3 towers are 10-15%, and 1 tower is 15-20%.
ZHU Rong, SUN Chaoyang
The 1km resolution wind energy resource data of Qinghai Tibet Plateau is developed by using the wind energy resource numerical simulation assessment system of China Meteorological Administration (weras / CMA), which includes typical terrain classification module, mesoscale model WRF and Calmet dynamic diagnosis model. Firstly, the typical days are randomly selected from the historical weather types for hourly wind speed simulation, and then the climate average distribution of wind energy resources is obtained according to the statistical analysis of the frequency of weather types. The data set includes wind speed and wind power density over the Qinghai Tibet Plateau. The data accuracy of wind speed is 0.01m/s, the data accuracy of wind power density is 0.01w/m2, and the vertical height of data is 100m. The data have been checked and corrected by the observation data of meteorological stations, and are mainly used for detailed investigation of wind energy resources and macro site selection of wind farms. This data is the output data of the national wind energy resources detailed survey and evaluation project from 2008 to 2012 (the project cost is 290 million yuan), and then becomes the basic data of wind energy resources related research. The Ministry of finance has no plan to invest in extending this data set in the near future.
ZHU Rong, SUN Chaoyang
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
YANG Kun
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. The data includes the daily meteorological observation data sets (air temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) of the Qinghai Tibet Plateau in 2014-2017 from 17 stations of China Alpine network. The data of the three river sources are missing.
ZHU Liping,
This dataset is derived from the Nagqu Station of Plateau Climate and Environment (31.37N, 91.90E, 4509 a.s.l), Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. The ground is flat, with open surrounding terrain. An uneven growth of alpine steppe, with a height of 3–20 cm. The observation time of this dataset is from January 1, 2014 to December 31, 2017. The observation elements primarily included the wind speed, air temperature, air relative humidity, air pressure, downward shortwave radiation, precipitation, evaporation, latent heat flux and CO2 flux. The precipitation , evaporation and CO2 flux data are daily cumulative values, and the other variables are daily average values. The observed data are generally continuous, but some data are missing due to power supply failure, and the missing data in this dataset are marked as NAN.
HU Zeyong, GU Lianglei, SUN Fanglei, WANG Shujin
1.The data content: air temperature, relative humidity, precipitation, air pressure, wind speed and vapor pressure. 2. Data sources and processing methods: campel mountain type automatic meteorological station observation by the United States, including air temperature and humidity sensor model HMP155A;Wind speed and direction finder models: 05103-45;The atmospheric pressure sensor: CS106;The measuring cylinder: TE525MM.Automatic meteorological station every ten minutes automatic acquisition data, after complete automatic acquisition daily meteorological data then daily mean value were calculated statistics. 3.Data quality description: automatic continuous access to data. 4.Data application results and prospects: the weather stations set in the upper of the glacier terminal, meteorological data can be used to simulate for predict the future climate change under the background of type Marine glacial changes in response to global climate change research provides data.
LIU Jing
The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.
MA Yaoming
This data set includes the biomass and photosynthesis observational data of the highland spring barley experimental plot at the Lhasa Farm Experimental Station and the meteorological data observationally obtained at the Damxung Grass Experimental Station. The time range is 2006-2009. Biomass observation method: The sampling area of each sample is 25 cm*25 cm. Photosynthetic data observation: The instrument is a LiCor-6400. The biomass data are manually entered according to the record book. The photosynthetic data are automatically recorded by the instrument. The average wind speed, prevailing wind direction, temperature, atmospheric pressure and relative humidity in the daily values of meteorological data are averaged over half-hour data. The precipitation and total radiation data are automatically recorded by the observation system. The observation process of biomass data is in strict accordance with the agronomic method, and it can be applied to the estimation of agricultural productivity. In the process of photosynthetic data observation, the operation of the instrument and the selection of the observation object are strictly in accordance with professional requirements and can be used in photosynthetic parameter simulations estimating plant leaf and productivity. The Tibetan Plateau farmland ecosystem observation data includes: 1) aboveground biomass; 2) CO2 response photosynthetic data; 3) light-response photosynthetic data; and 4) daily meteorological data in Damxung Monitoring Point. Data collection locations: Lhasa Agricultural Ecology Experimental Station, Chinese Academy of Sciences, Longitude: 91°20’, Latitude: 29°41’, Altitude: 3688 m and Damxung Alpine Meadow Carbon Flux Observation Station, Longitude: 91°05′, Latitude: 30°25′, Altitude: 4333 m.
ZHANG Xianzhou
The data set includes the average wind speed data of main areas in Qinghai Province from 1988 to 2016 such as Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu and Haixi. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The data table records the monthly and annual average wind speed in eight regions of Qinghai. Unit: m / s The data set is mainly applied in geography and socioeconomic research.
Qinghai Provincial Bureau of Statistics
This data set mainly includes meteorological data and soil moisture data collected from 2005 to 2008 at the Sherjila Mountain Alpine Timberline Observation Site of the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data set of alpine timberline observations in southeast Tibet includes 1) the meteorological data set and 2) the soil moisture data set. The meteorological data set includes wind speed, temperature (1, 3 m), relative humidity (1, 3 m), soil heat flux (-5, -20, -60 cm), soil temperature (-5, -20, -60 cm), air pressure, total radiation, net radiation, photosynthetically active radiation, infrared radiation (660, 730 nm), atmospheric longwave radiation, ground longwave radiation, surface temperature, precipitation, and snow thickness. The soil moisture data set includes vegetation type and soil water content (-5, -20, -60 cm). Instruments used for each variable: Temperature: Air temperature probe, produced in Taiwan, model TRH-S. Relative humidity: Model TRH-S, produced in Taiwan. Wind speed: Anemoscope, produced in Taiwan, model 03102. Barometric Pressure: Barometric pressure sensor, produced in Taiwan, model BP0611A. Atmospheric longwave radiation: Pyrgeometer, produced by the Kipp & Zonen Company of the Netherlands, model CG3. Ground longwave radiation: Pyrgeometer, produced by the Kipp & Zonen Company of the Netherlands, model CG3. Total radiation: Pyranometer, produced by the Kipp & Zonen Company of the Netherlands, model CM3. Net radiation: Net radiometer, produced by the Kipp & Zonen Company of the Netherlands, model NR-Lite. Photosynthetically active radiation: PAR-Sensor, produced by the Kipp & Zonen Company of the Netherlands, model MS-PAR. Infrared radiation: Infrared radiation sensor, produced by the Skye Company of the UK, model SKY110. Rainfall: Rain gauge, produced in Taiwan, model 7852 M. Snow thickness: Ultrasonic snow depth sensor, produced in the United States, model 260-700. Soil temperature: Soil temperature probe, produced by the Onset Company of the United States, model 12-Bit. Soil heat flux: Soil heat flux plate, produced by the Hukseflux Company of the Netherlands, model HFP01. Soil moisture content: Soil moisture sensor, produced by the Onset Company of the United States, model S-SMA-M003. The observations and data acquisition were carried out in strict accordance with the instrument operating specifications. Each instrument was rigorously validated and calibrated by the supplier before installation to ensure the accuracy of the observation data. Data with significant errors were removed when processing the data table.
LIU Xinsheng, LUO Tianxiang
This data set contains meteorological observation data from three meteorological stations in the Shandong section of the Qilian Mountains (Xiying Reservoir [XYSCZ], Forest Protection Station [XYHLZ] and Shangchigou [XYSCG]), including temperature, precipitation, relative humidity, wind speed, main wind direction, total radiation and air pressure, and the temporal resolution is one day. The raw data were observed and collected in strict accordance with the instrument operating specifications. The accuracy of the data meets the requirements of the National Meteorological Administration and the World Meteorological Organization (WMO) for meteorological observation data. The observation system is maintained by professionals 2-3 times a year, during which the sensor is calibrated or replaced and the collected data are downloaded and reorganized. The data are the continuous sequence generated by quality controlling the raw data, and some obvious systematic error data caused by missing points and sensor failure are eliminated.
GAO Hongshan
This data set contains the daily values of temperature, air pressure, relative humidity, wind speed, precipitation, and total radiation observed at the Namco station from 1 October 2005 to 31 December 2016. The data set was processed as a continuous time series after the original data were quality controlled. After the systematic error caused by missing data points and sensor failure was eliminated, the data set reaches the accuracy of raw meteorological observation data required by the National Weather Service and the World Meteorological Organization (WMO). The data can provide information for professionals engaged in scientific research and training related to atmospheric physics, atmospheric environment, climate, glaciers, frozen soils and other disciplines. This data set has mainly been applied in the fields of glaciology, climatology, environmental change, cold zone hydrological processes, frozen soil science, etc. The measured parameters had the following units and accuracies: Air temperature, unit: °C, accuracy: 0.1 °C; air relative humidity, unit: %, accuracy: 0.1%; wind speed, unit: m/s, accuracy: 0.1 m/s; wind direction, unit: °, accuracy: 0.1 °; air pressure, unit: hPa, accuracy: 0.1 hPa; precipitation, unit: mm, accuracy: 0.1 mm; total radiation, unit: W/m2, accuracy: 0.1 W/m2.
WANG Yuanwei, WU Guangjian
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