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
We utilized 12 datasets covering the period 900–1999 CE, including two summer temperature gridded datasets from the Qinghai–Tibetan Plateau, two summer temperature series from the Arctic, a summer temperature gridded dataset from the Arctic, six global gridded annual temperature reconstruction datasets, and a last millennium reanalysis dataset with seasonal resolution. We used the optimal information extraction method to reconstruct the summer temperature anomalies in the Qinghai-Tibet Plateau and the Arctic over the past millennium (900–1999 CE) with annual resolution. The range of the Qinghai-Tibetan Plateau is 27°N–36°N, 77°E–106°E, and the range of the Arctic is 60°N–90°N. The reconstruction target is the summer (June–August) temperature anomalies (with respect to 1961–1990 CE period) in the instrumental CRUTEM4v dataset. The data can be used to study the mechanism of temperature variability in the Qinghai-Tibetan Plateau and Arctic over the past millennium.
SHI Feng
The atmospheric and oceanic thermal conditions over the Indian Ocean-Third Pole (Qinghai-Tibet Plateau) are important for affecting the Asian monsoon activity and pan-Third Pole climate. At seasonal and interannual timescales, the meridional atmospheric and oceanic heat sources are closely related to Indian monsoon, Bay of Bengal monsoon, and the sea surface temperature (SST) mode in the tropical Indian Ocean. Therefore, we calculate and establish the meridional atmospheric and oceanic heat sources dataset for the Indian Ocean-Third pole section. In order to obtain the horizontal distribution of atmospheric heating rate on each pressure level, we use the inverse algorithm from Yanai et al. (1973): Q_1=c_p [∂T/∂t+V ⃑∙∇T+(p/p_0 )^κ ω ∂θ/∂p] Q_1 is the atmospheric apparent heat source, which can be affected by temperature local variation, temperature advection and potential temperature vertical variation. T, θ, V ⃑, and ω respectively represent the temperature, potential temperature, horizontal wind vector, and vertical velocity. p_0=1013.25hPa. κ=R/c_p, R and c_p are the gas constant and specific heat of dry air at constant pressure respectively, κ≈0.286。 Based on the ERA5 Atmospheric Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) atmospheric heating rate (unit: K/s) for the Indian Ocean-Third pole section (30°S-60°N) with horizontal resolution of 1°×1° and vertical range of 1000-100hPa at 27 levels. With reference to Hall and Bryden (1982), the vertical Ocean Heat Transport (OHT) at given longitudes can be calculated by the following formula: OHT=∮_(Θ=Θ_i)▒∫_(z_b)^(z_0)▒〖ρ_0 c_p (θ-θ_r ) 〗∙udz Where ρ_0, c_p, θ, θ_r, and u represent the density, specific heat, capacity potential temperature, reference temperature (0℃), and zonal velocity of sea water respectively. z_0 and z_b are the depths of sea surface and sea floor. Based on the CMEMS (Copernicus Marine Service) Oceanic Reanalysis data from 2000 to 2019, we calculate the monthly meridional (along 60°E, 70°E, 80°E, 90°E) OHT (eastward positive, unit: PW(1015W)) over the Indian Ocean-Third pole region (30°S-30°N) with horizontal resolution of 1°×1° and vertical range from sea surface to sea floor at a depth of about 5900m on 75 levels. This dataset can reflect the close relationship between meridional atmospheric and oceanic thermal conditions of Indo-Tibetan Plateau region and Indian monsoon, Bay of Bengal monsoon, and SST mode over tropical Indian Ocean. For example, from the monthly evolution of meridional atmospheric heating rate along 70°E for the Indian Ocean-Third pole section (Figure 1), the atmospheric heat source area above the tropical southern Indian Ocean gradually advances northward from Marth to May. In particular, from May to June, this tropical atmospheric heat source area moves to the tropical northern Indian Ocean with its intensity strengthened and scope expanded, at the same time, the Indian summer monsoon onsets. For instance, from the monthly evolution of meridional atmospheric heating rate along 90°E for the Indian Ocean-Third pole region (Figure 2), we can see that the atmospheric heat source area above the tropical Indian Ocean expands to the south of Qinghai-Tibet Plateau and increases significantly from April to June, coinciding with the onset and northward advance of the Bay of Bengal monsoon. Another example, from the monthly evolution of meridional OHT along 60°E and 90°E for the Indian Ocean-Third pole section (Figures 3 and 4), it can be found the ocean heat at the equatorial Indian Ocean subsurface transports from west to east, and its position is very close to the Equatorial undercurrent. And this subsurface OHT intensity in the west is obviously higher than that in the east, which is related to the wind-thermocline-SST feedback mechanism. It is also worth noting that this subsurface OHT is strong in spring (March-May), weakens in summer, and significantly strengthens in late autumn and early winter (October-December), interacting with the development and formation of Indian Ocean Dipole.
LI Delin , XIAO Ziniu, ZHAO Liang
The high-resolution atmosphere-hydrologic simulation dataset over Tibetan Plateau is prepared by WRFv4.1.1 model with grids of 191 * 355 and spatial resolution of 9 km, and a spatial range covering the entire plateau. The main physics schemes are configured with Thompson microphysics scheme, the rapid radiative transfer model (RRTM), and the Dudhia scheme for longwave and shortwave radiative flux calculations, respectively, the Mellor-Yamada-Janjic (MYJ) TKE scheme for the planetary boundary layer and the Unified Noah Land Surface Model. The time resolution is 3h and the time span is 2000-2010. Variables include: precipitation (Rain), temperature (T2) and water vapor (Q2) at 2m height on the ground, surface skin temperature (TSK), ground pressure (PSFC), zonal component (U10) and meridional component (V10) at 10m heigh on the ground, downward long-wave flux (GLW) and downward short-wave flux (SWDOWN) at surface, ground heat flux (GRDFLX), sensible heat flux (HFX), latent heat flux (LH), surface runoff (SFROFF) and underground runoff (UDROFF). The data can effectively support the study of regional climate characteristics, climate change and its impact over the Tibet Plateau, which will provide scientific basis for the sustainable development of the TP under the background of climate change.
MENG Xianhong, MA Yuanyuan
This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity（specific humidity in 2020）, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
Near-surface air temperature variability and the reliability of temperature extrapolation within glacierized regions are important issues for hydrological and glaciological studies that remain elusive because of the scarcity of high-elevation observations. Based on air temperature data in 2019 collected from 12 automatic weather stations, 43 temperature loggers and 6 national meteorological stations in six different catchments, this study presents air temperature variability in different glacierized/nonglacierized regions and assesses the robustness of different temperature extrapolations to reduce errors in melt estimation. The results show high spatial variability in temperature lapse rates (LRs) in different climatic contexts, with the steepest LRs located on the cold-dry northwestern Tibetan Plateau and the lowest LRs located on the warm-humid monsoonal-influenced southeastern Tibetan Plateau. Near-surface air temperatures in high-elevation glacierized regions of the western and central Tibetan Plateau are less influenced by katabatic winds and thus can be linearly extrapolated from off-glacier records. In contrast, the local katabatic winds prevailing on the temperate glaciers of the southeastern Tibetan Plateau exert pronounced cooling effects on the ambient air temperature, and thus, on-glacier air temperatures are significantly lower than that in elevation-equivalent nonglacierized regions. Consequently, linear temperature extrapolation from low-elevation nonglacierized stations may lead to as much as 40% overestimation of positive degree days, particularly with respect to large glaciers with a long flowline distances and significant cooling effects. These findings provide noteworthy evidence that the different LRs and relevant cooling effects on high-elevation glaciers under distinct climatic regimes should be carefully accounted for when estimating glacier melting on the Tibetan Plateau.
YANG Wei
1) The Qinghai Tibet plateau surface meteorological driving data set (2019-2020) includes four meteorological elements: land surface temperature, mean total precipitation rate, mean surface downward long wave radiation flux and mean surface downward short wave radiation flux. 2) The data set is based on era5 reanalysis data, supplemented by MODIS NDVI, MODIS DEM and fy3d mwri DEM data products. The era5 reanalysis data were downscaled by multiple linear regression method, and finally generated by resampling. 3) All data elements of the Qinghai Tibet plateau surface meteorological driving data set (2019-2020) are stored in TIFF format. The time resolution includes (daily, monthly and annual), and the spatial resolution is unified as 0.1 ° × 0.1°。 4) This data is convenient for researchers and students who will not use such assimilated data in. NC format. 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.
ZHU Liping, DU Baolong
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
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
This data is based on the modified radiosonde observation data of 2008 used by Chen et al. 2016, Chen et al. 2011 and Chen et al. 2013. The vertical resolution of the processed atmospheric wind speed, wind direction, temperature, relative humidity and pressure is 20m. The data of three observation stages in 2008 are processed, namely iop1, IOP2 and iop3. Iop1 started from February 25, 2008 to March 19, 2008, IOP2 from May 13, 2008 to June 12, 2008, and iop3 from July 7, 2008 to July 16, 2008.
CHEN Xuelong, MA Yaoming
1) 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.
ZHU Liping,
(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.
Da Wei
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,
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 , MA Jin
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.
MA Yaoming
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
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
Based on the WRF model, using ERA5 reanalysis data as the initial and boundary fields, the high-resolution low-level atmospheric structure and the earth atmosphere exchange data set of the Qinghai Tibet Plateau are preliminarily obtained by the method of dynamic downscaling. The time range of this data set is from August 1 to August 31, 2014, with a time resolution of 1 hour, a horizontal range of 25 °N-40 °N, 70oE-105oE, and a horizontal resolution of 0.05 °. The data format is NetCDF, and one file is output every hour. The file is named after the date. The lower atmospheric structure data includes temperature, relative humidity, water vapor mixing ratio, potential height, meridional wind and latitudinal wind meteorological elements, with 34 isobaric surfaces in the vertical direction; the surface air exchange data set includes the upward / downward short wave radiation, upward / downward long wave radiation, surface sensible heat and flux, 2m air temperature and water vapor mixing ratio, 10m wind, etc. The data set can provide data support for the study of weather process and climate environment in the Tibetan Plateau.
Ma Shupo
1) Data content (including elements and meanings): Gridded multiyear-average monthly air temperature lapse rate data over the Tibetan Plateau at three kinds of resolutions (i.e. 0.25°, 0.75° and 2°) 2) Data source and processing method: Locally reliable temperature lapse rates are created from filtered MODIS LST-elevation samples by using the thresholds of standard error of elevation and correlation coefficient 3) Data quality description: For ERA-Interim, the validation accuracy (based on 1980-2014 daily mean aire temperature records from 113 stations across the Tibetan Plateau) decreases from ~4℃ to ~2℃ after using the 0.75° temperaturel lapse rate. 4) Data application results and prospects: This dataset can be used for downscaling air temperature from multiple reanalysis datasets.
ZHANG Fan, ZHANG Hongbo
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
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