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
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
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
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,
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 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 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
The data sets include four sets of data obtained from the Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors using passive microwave remote sensing inversion. SMMR was aboard the Nimbus-7 satellite, and its working period was from October 26, 1978 to July 8, 1987. Since July 1987, the data provided by the SSM/I and the SSMIS aboard the US Defense Meteorological Satellite Program (DMSP) satellite group have been used. The first three data sets contain sea ice concentration data, covering the Antarctic region with a spatial resolution of 25 km: (1) The data were obtained from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Version 1 by applying the NASA Team algorithm inversion. The temporal coverage is from November 1978 to February 2017, with a temporal resolution of one month. A bin file is stored every month. (2) The data source is the same as the first set. The temporal coverage is from 1978-10-26 to 2017-2-28. The temporal resolution is two days, and the spatial resolution is 25 km. A folder was stored every year, and a bin file was stored every other day. (3) The data were obtained from near-real-time DMSP SSMIS by applying the NASA Team algorithm inversion. The temporal coverage is from 2015-1-1 to 2018-2-3, and the temporal resolution is one day. A bin file is stored every day. Each file consists of a 300-byte file title (data time information, projection pattern, file name) and a 316*332 matrix. The fourth set of data is the sea ice coverage and sea ice area time series. The temporal coverage is from November 1978 to December 2017. This data set is a time series sequence of sea ice coverage and sea ice area in the Antarctic. The temporal resolution is one month, and an ASCII file is stored every month. Each file consists of a file title (time, data type), a 39*1 sea ice cover matrix and a 39*1 sea ice area matrix. For further details on the data, please visit the US Ice and Snow Data Center NSIDC website - Data Description http://nsidc.org/data/NSIDC-0051; http://nsidc.org/data/NSIDC-0081; http://nsidc.org/data/G02135
LI Shuanglin, LIU Na
Based on the 33rd Antarctic Scientific Expedition in China, the data set of temporal and spatial distribution of metal element concentrations in snow and ice obtained on the section from Zhongshan Station to Dome A in East Antarctica mainly includes: 1. A shallow ice core obtained 202 km away from Zhongshan Station. The ice core covers the period from 1990 to 2017 with a resolution of years, including metal element iron, hydrogen and oxygen isotopes and other data. 2. Collect a sample every 10km along the Zhongshan Station Dome A section in East Antarctica. The metal elements include rare earth elements, barium and other elements. The data can be used to study the pollution and contribution of natural sources and human activities to Antarctic snow and ice.
Du Zhiheng
Glaciers are sensitive to climate change. With global warming, the melting of glaciers continues to accelerate all over the world. Surging glaciers are glaciers with intermittent and periodic acceleration, which is a sensitive indicator of climate change. Based on Landsat and Sentinel satellite images from 1980s to 2020, the study area images were obtained by filtering, stitching, and cropping. Among them, the L1GS level images collected by Landsat TM sensor were geo-registered using a second-order polynomial, and the error of the geo- registered images was less than one pixel. After image template matching with an orientation correlation algorithm, this data set provides the surface ice flow velocity of a typical surging glacier in the Greenland ice sheet, Sortebræ Glacier in different period from 1980s to 2020. It is expected to contribute to the research on the surging process of Sortebræ Glacier and the discussion on the mechanism of glacier surging in the context of global warming.
QIAO Gang , SUN Zixiang , YUAN Xiaohan
Pine Island Glacier, Swett Glacier, etc. are distributed in the basins of the Antarctic Ice Sheet 21 and 22, which is one of the areas with the most severe melting in the Southwest Antarctica. This dataset first uses Cryosat-2 data (August 2010 to October 2018) to establish a plane equation in each regular grid, taking into account terrain items, seasonal fluctuations, backscattering coefficients, wave front width, lifting rails and other factors, and calculates the elevation change of ice cover surface in the grid through least square regression. In addition, we used ICESat-2 data (October 2018 to December 2020) to calculate the surface elevation change during the two periods by obtaining the elevation difference at the intersection of satellite lifting orbits in each regular grid. The spatial resolution of surface elevation change data in two periods is 5km × 5km, the file format is GeoTIFF, the projection coordinate is polar stereo projection (EPSG 3031), and it is named by the name of the satellite altimetry data used. The data can be opened using ArcMap, QGIS and other software. The results show that the average elevation change rate of the region from 2010 to 2018 is -0.34 ± 0.08m/yr, which belongs to the area with severe melting. The annual average elevation change rate from October 2018 to November 2020 is -0.38 ± 0.06m/yr, which is in an intensified state compared with CryoSat-2 calculation results.
YANG Bojin , HUANG Huabing , LIANG Shuang , LI Xinwu
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
BAI Jianhui
Based on the CMIP6 model data (see Table 1 for the model list), the distribution and thickness of frozen soil in the Qinghai Tibet Plateau and the circum Arctic region, as well as the terrestrial ecosystem carbon flux (total primary productivity GPP and ecosystem carbon source sink NEP) data in the frozen soil area under different climate change scenarios (including SSP126, SSP245 and SSP585) in the historical period (1990-2014) and the future (2046-2065) are estimated, with a spatial resolution of 1 ° × 1°。 Among them, the distribution of frozen soil is estimated under the future climate warming scenario by using the spatial constraint method (Chadburn et al., 2017), based on the probability of frozen soil occurrence under different temperature gradients at the current stage, and combined with the future temperature change simulated by the Earth system model. For the change of active layer thickness, the sensitivity of active layer thickness to temperature change estimated by remote sensing at this stage is used to constrain the change of active layer thickness simulated by the Earth System Model, so as to correct the error of the model in simulating the thickness of frozen soil active layer. The future permafrost carbon flux is the multi model ensemble average of the Earth system model simulation results. The simulation results show that the permafrost in the Qinghai Tibet Plateau will be significantly degraded under the future climate change scenario. With the future temperature rise, the continuous permafrost regions will be shown as carbon sources, but the temperature rise will promote the growth of vegetation, and the carbon sink capacity in the discontinuous permafrost regions will be enhanced. Similar to the Qinghai Tibet Plateau, the permafrost around the Arctic will also be generally degraded in the future, and the future climate warming will promote the growth of vegetation in the Arctic, thus enhancing regional carbon sinks.
WANG Tao, LIU Dan , WEI Jianjun
The ground-based observation dataset of aerosol optical properties over the Tibetan Plateau was obtained by continuous observation with a Cimel 318 sunphotometer, involving two stations: Qomolangma Station and Nam Co Station. These products have taken the process of cloud detection. The data cover the period from January 1, 2021 to December 31, 2021, and the time resolution is daily. The sunphotometer has eight observation channels from visible light to near infrared, and the central wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm, respectively. The field of view angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. Six bands of aerosol optical thickness can be obtained from direct solar radiation, and the accuracy is estimated to be 0.01-0.02. Finally, AERONET unified inversion algorithm was used to obtain the aerosol optical thickness, Ångström index, aerosol particle size distribution, single scattering albedo, phase function, complex refraction index and asymmetry factor.
CONG Zhiyuan
The active layer thickness in the Wudaoliang permafrost region of the Qinghai Tibet Plateau is retrieved based on the seasonal deformation obtained by SBAS-InSAR technology and ERA5-Land spatio-temporal multi-layer soil moisture data corrected by variational mode decomposition method. The time range of the is 2017-2020, and the spatial resolution is 1km. This data can be used to study the change of the active layer thickness in the permafrost region of the Qinghai Tibet Plateau and analyze its interaction with climate change, water cycle and energy cycle. It is significance to understand the permafrost degradation, environment evolution and the impact of permafrost degradation on ecology and climate.
LU Ping , HAO Tong , LI Rongxing
Both a decrease of sea ice and an increase of surface meltwater, which may induce ice-flow speedup and frontal collapse, have a significant impact on the stability of the floating ice shelf in Greenland. However, detailed dynamic precursors and drivers prior to a fast-calving process remain unclear due to sparse remote sensing observations. Here, we present a comprehensive investigation on hydrological and kinematic precursors before the calving event on 26 July 2017 of Petermann Glacier in northern Greenland, by jointly using remote sensing observations at high-temporal resolution and an ice-flow model. Time series of ice-flow velocity fields during July 2017 were retrieved with Sentinel-2 observations with a sub-weekly sampling interval. The ice-flow speed quickly reached 30 m/d on 26 July (the day before the calving), which is roughly 10 times quicker than the mean glacier velocity.
JIANG Liming
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