Under the summer sunlight, the snow covering the ice melts, forming different shapes and sizes of ice pools on the ice. The melting pool caused by the melting of the sea ice surface will reduce the sea ice albedo, which will have a significant impact on the energy balance in the polar region, increasing absorption and thus accelerating the sea ice melting process. Among the factors that affect the sea ice albedo, melting pool is one of the most important and most violent factors. With climate change, the rate of ice melting in summer is also getting faster and faster. The energy balance on the Earth's surface has a significant impact, and the acceleration of ice melting speed may also make the melting pool, an important natural phenomenon, one of the most significant ice surface features during the Arctic sea ice melting season. The albedo of melting pool is between sea water and sea ice. The study of melting pool on ice is also an important part of the study of the rapid change mechanism of Arctic sea ice. Due to the similar microwave signal characteristics between sea ice melting pools and the sea surface, and the significant uncertainty of using microwave data to map melting pool coverage due to factors such as wind speed and sea ice melting, the most reliable remote sensing method for melting pool coverage is to use medium resolution optical remote sensing data (such as MODIS) to map sub pixel melting pool coverage. This dataset includes the use of MODIS data for sub pixel decomposition inversion of Arctic sea ice melting pool coverage and sea ice concentration based on dynamic end element reflectance.
Xiong Chuan, REN Yan, QIU Yubao
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
Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project (PAMIP) were carried out by the model group of the Chinese Academy of Sciences (CAS) Flexible Global Ocean-Atmosphere-Land System (FGOALS-f3-L). Eight groups of experiments forced by different combinations of the sea surface temperature (SST) and sea ice concentration (SIC) for pre-industrial, present-day, and future conditions were performed and published. The time-lag method was used to generate the 100 ensemble members, with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period. All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
HE Bian
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.
BAO Qing
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
The water level observation data set of lakes on the Tibetan Plateau contains the daily variations of water levels for three lakes: Zhari Namco, Bamco and Dawaco. The lake water level was obtained by a HOBO water level gauge (U20-001-01) installed on the lakeshore, then corrected using the barometer installed on the shore or pressure data of nearby weather stations, and then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this data set are as follows: Daily variation data of water level in Zhari Namco from 2009 to 2014; Daily variation data of water level in Bamco from 2013 to 2014; Daily variation data of water level in Dawaco from 2013 to 2014. Water level, unit: m.
LEI Yanbin
The Antarctic McMurdo Dry Valleys ice velocity product is based on the Antarctic Ice Sheet Velocity and Mapping Project (AIV) data product, which is post-processed with advanced algorithms and numerical tools. The product is mapped using Sentinel-1/2/Landsat data and provides uniform, high-resolution (60m) ice velocity results for McMurdo Dry Valleys, covering the period from 2015 to 2020.
JIANG Liming JIANG Liming JIANG Liming
Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
The Qinghai Tibet Engineering Corridor starts from Golmud in the north and ends at Lhasa in the south. It passes through the core area of the Qinghai Tibet Plateau and is an important channel connecting the mainland and Tibet. Permafrost temperature is not only an important index to study ground thermal state in permafrost regions, but also a key factor to be considered in permafrost engineering construction. The core of GIPL1.0 is the Kudryavtesv method, which considers the thermophysical properties of snow cover, vegetation and different soil layers. However, Yin found that compared with the Kudryavtesv method, the accuracy of TTOP model was higher. Therefore, the model was improved in combination with the freezing/thawing index. Through the verification of field monitoring data, it was found that the simulation error of permafrost temperature was less than 1 ℃. Therefore, the improved GIPL1.0 model is used to simulate the permafrost temperature of the Qinghai Tibet project corridor, and predict the future permafrost temperature under the SSP2-4.5 climate change scenario.
NIU Fujun
Retrogressive thaw slumps (RTSs) are slope failures caused by the thawing of ice-rich permafrost. Once developed, they usually retreat at high speeds (meters to tens of meters) towards the upslope direction, and the mudflow may destroy infrastructure and release carbon stored in frozen ground. RTSs are frequently distributed in permafrost areas and increase dramatically but lack investigation. Qinghai Tibet Engineering Corridor crosses the permafrost, links the inland and the Tibet. However, in this critical area, we lack knowledge of the distribution and impact of RTSs. To compile the first comprehensive inventory of RTSs, this study uses an iterative semi-automatic method based on deep learning and manual inspection to delineate RTSs in 2019 images. The images from PlanetScope CubeSat have a resolution of 3 meters, have four bands, cover a corridor area of approximately 54,000 square kilometers. The method combines the high efficiency and automation of deep learning and the reliability of the manual inspection to map the entire region ninth, which minimize the missings and misidentification. The manual inspection is based on geomorphic features and temporal changes (2016 to 2020) of RTSs. The inventory which includes 875 RTSs with their attributes, including identification, Longitude and Latitude, possibilities and time, provides a benchmark dataset for quantifying permafrost degradation and its impact.
XIA Zhuoxuan, HUANG Lingcao, LIU Lin
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
On the basis of RGI6.0, we use remote sensing and geographic information system technology to update the glacier inventory data in Alaska. The updated glacier inventory uses a data source for 2018 Landsat OLI spatial resolution 15m remote sensing image, and the method used is manual interpretation. The results show that the Alaska Glacier inventory includes 27043 glaciers with a total area of 81285km2. The uncertiany of this data is 4.3%. The data will provide important data support for the study of glacier change in Alaska and the regional and global impact of glacier change in the context of global change.
SHANGGUAN Donghui,
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
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
This data is the simulation of Antarctic sea ice density data from 2020 to 2100 under the medium emission scenario (ssp245) of the 6th International Coupled Model Comparison Program (CMIP6). The 25 mode data of CMIP6 were uniformly interpolated and then aggregated averaged. The size of sea ice density data is 0-1, the data time range is from January 2020 to December 2100, the time resolution is month, the spatial range is south of 45 ° S, and the spatial resolution is 1 ° × 1°。 This data provides the status and evolution of Antarctic sea ice under the medium emission scenario, and can provide reference for future changes in Antarctica.
LI Shuanglin, WANG Hui
The extraction of glacier surface movement is of great significance in the study of glacier dynamics and material balance changes. In view of the shortcomings of the current application of autonomous remote sensing satellite data in glacier movement monitoring in China, the SAR data covering typical glaciers in alpine areas of the Qinghai Tibet Plateau from 2019 to 2020 obtained under the GF-3 satellite FSI mode was used to obtain the glacier surface velocity distribution in the study area with the help of a parallel offset tracking algorithm. With its good spatial resolution, GF-3 image has significant advantages in extracting glacier movement with small scale and slow movement, and can better reflect the details and differences of glacier movement. This study is helpful to analyze the movement law and spatio-temporal evolution characteristics of glaciers in the Qinghai Tibet Plateau under the background of climate change.
YAN Shiyong
The basic data of hydrometeorology, land use and DEM were collected through the National Meteorological Information Center, the Hydrological Yearbook, the China Statistical Yearbook and the Institute of Geographic Sciences and Resources of the Chinese Academy of Sciences. The distributed time-varying gain hydrological model with independent intellectual property rights is used for modeling, and the Qinghai Tibet Plateau is divided into 10937 sub basins with a threshold of 100 square kilometers. In Heihe River, Yarlung Zangbo River, the source of Yangtze River, the source of Yellow River, Yalong River, Minjiang River and Lancang River basins, 14 flow stations were selected to observe the daily flow data to develop and verify the model. The daily scale Naxi efficiency coefficient is above 0.7, and the correlation coefficient is above 0.8. The precipitation and temperature data output from 13 models and 4 scenarios provided by CMIP6 are used to post process the future precipitation and temperature data. The post processed precipitation and temperature driven hydrological model simulates the water cycle process from 2046 to 2065, and gives the possible future spatial and temporal distribution of 0.1 degree daily scale runoff across the Qinghai Tibet Plateau.
YE Aizhong
The data is an excel file, which includes four tables named as follows: Altay Snow DOC Time Series, Altay Snow Pit Data, Altay Snow MAC (absorption section) and Central Asia Mos Island Glacier BC, OC, DUST Data. Altay snow DOC table includes seven columns including sample number, sampling date, sampling time, sampling depth, DOC-PPM, BC-PPb and TN-PPM, and 47 sample data. Altay snow pit table includes 8 columns including snow pit number, sample number, sampling date, sampling time, sampling depth, DOC-PPM, BC-PPb and TN-PPM, and 238 sample data. Altay snow MAC table includes: sampling time, MAC and AAE, a total of three columns, and 46 sample data. The BC, OC and DUST data tables of glaciers in Central Asia's Muse Island include 8 columns: code no (sample number), Latitude (latitude), Longitude (longitude),/m a.s.l (altitude), snow type (snow type), BC, OC and DUST, which are analyzed by sampling time. There are 105 rows of data in total. Abbreviation explanation: DOC: Dissolved Organic Carbon MAC: mass absorption cross section BC: black carbon DUST: Dust OC: Organic carbon TN: Total Nitrogen PPM: ug g-1 (microgram per gram) PPb: ng g-1 (nanogram per gram)
ZHANG Yulan
This data set is a code file set of TCA (triple collision analysis) algorithm, which is used to generate the global daily-scale soil moisture fusion dataset from 2011 to 2018.
XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, JIA Li , HU Guangcheng
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
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