This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
High resolution pollen records from ice cores can indicate the relationship between seasonal vegetation changes and climate indicators. High resolution sporopollen analysis was carried out on the 32 m ice core sediments of Zuopu ice core in Qinghai Tibet Plateau. 117 SPOROPOLLEN ASSEMBLAGES were obtained. All the data are sporopollen percentage data, which are arranged in order of depth.
LV Houyuan
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
The melting observation of Hengduan Moutain glacier is mainly carried out on Hailuogou Glacier on the east slope of Gongga and the large and small Gongba glacier on the west slope of Gongga. In addition, some ablation observations have been made on Baishui 1 glacier on the east slope of Yulong. According to the melting observation of the four glaciers in the above two mountains, there are some regional representativeness, which makes them reflect the basic situation of melting glaciers in Hengduan Mountain. This data set records the glacier ablation data of different time and different places: from June to August 1982, the Glacier No. 1 in Baishui on the east slope of Yulong mountain was observed at the altitude of 4200m, 4600m and 4800m. From August 27, 1982 to the end of August 1983, the annual measured data of different heights of Hailuogou Glacier tongue on the east slope of Gongga Mountain were collected. From July 12, 1982 to August 6, 1983, the observation data of Gongba glacier melting on the west slope of Gongga Mountain were recorded.
LI Jijun
This data set includes the average concentrations of chemical species (Na+, K+, Mg2+, Ca2+ and TDS) in meltwater runoff draining 77 glaciers worldwide, annual glacial runoff from eight mountain ranges in Asia, and the mineral compositions of glacial deposits in some typical glacial catchments within Asia. This data set comes from the field monitoring of 19 glaciers in Asia by the data set provider, the previous published data worldwide, and the data shared by the authors of published papers. This data set can be used to evaluate the impact of climate warming on glacier erosion process and chemical weathering process, and the impact of glacier melt caused by climate warming on downstream ecosystems and element cycles.
LI Xiangying
The data in the form of .xlsx store the meteorological varialbes observed on the East Rongbuk glacier from May to July. Two sheets, named "Surface_energy_budget" and "Cycle", respectivley, are included. In the sheet of "surface_energy_budget", the meteorological variables are as follows: Four-component radiations (incident solar radiation, reflected shortwave radiation, incoming longwave radiation, outgoing longwave radiation)、wind speed and direction, air temperature and relative humidity, cloud index, south Asian summer monsoon and albedo. In addition, net shortwave radiation, net longwave radiation, net radiation, sensible heat, latent heat and subsurface heat are also included. Energy fluxes are in unit of W m-2. The sheet of "Cycle" stores the diurnal cycle of the meteorological variables mentioned above. In the first line, the prefixes of "1"、"2" and “3” indicate three observational periods, i.e., "1" represents days from 1 - 28 May, "2" represents the period between 29 May 16 June and "3" indicates time episode from 17 June to 22 July.
LIU Weigang
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
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
Qiangyong glacier: 90.23 °E, 28.88° N, 4898 m asl. The surface is bedrock. The record contains data of absolute pressure and water temperature. Data from the automatic water gauge was collected using USB equipment at 12:00 on June 15, 2021, with a recording interval of one hour, and data was downloaded at 12:00 on Nov. 2, 2021. There is no missing data. Jiagang glacier: 88.69°E, 30.82°N, 5362 m asl. The surface is rubble and weeds. The record contains data of absolute pressure and water temperature. Data from the automatic water gauge was collected using USB equipment at 20:00 on June 19, 2021, with a recording interval of one hour, and data was downloaded at 11:00 onSept 18 , 2021. There is no missing data.
ZHANG Dongqi
The data include K, Na, CA, Mg, F, Cl, so 4 and no 3 in the glacier runoff of zhuxigou, covering most of the inorganic dissolved components. The detection limit is less than 0.01 mg / L and the error is less than 10%; The data can be used to reflect the contribution of chemical weathering processes such as sulfide oxidation, carbonate dissolution and silicate weathering to river solutes in zhuxigou watershed, and then accurately calculate the weathering rates of carbonate and silicate rocks, so as to provide scientific basis for evaluating the impact of glaciation on chemical weathering of rocks and its carbon sink effect.
WU Guangjian
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
In recent years, the melting of the Antarctic ice sheet has accelerated, and a large amount of surface melt water has appeared on the surface of the Antarctic ice sheet. Understandings of the spatial distribution and dynamics of surface melt water on the Antarctic ice sheet is of great significance for the study of the mass balance of the Antarctic ice sheet. This dataset is 2000-2020 surface melt water dataset of Antarctica Ice Sheet typical melting area (Prydz bay) based on 10-30m Landsat-7, 8 and Sentinel-2 images. The projections are polar azimuthal projections in vector format (ESRI Shapefile) and raster format (GeoTIFF) and the time is Southern Hemisphere summer (December-to-February).
YANG Kang
Based on GRACE Level-1b satellite gravity data, a time series of mass change over Greenland for the period 2002 to 2016, with a spatial resolution of 1 degree × 1 degree and a time resolution of one month was developed by the satellite gravity team led by Professor Shen Yunzhong from Tongji University. The reference time of this time series is the mean time span between January 2004 and December 2009. During data processing, ICE5G model was used to reduce the effect of GIA, and the contribution of GAD was added back by using AOD1B RL06 from GFZ
SHEN Yunzhong
This is a comprehensive dataset on microbial abundance, dissolved organic carbon (DOC), and total nitrogen (TN) for glaciers on the TP based on extensive field sampling from 2010. The dataset comprises 5,409 microbial abundance records of ice cores and snow pits from 12 glaciers and 2,532 DOC and TN records of five habitats, including ice core, snow pit, surface ice, surface snow, and proglacial runoff, from 38 glaciers. These glaciers covered broad areas and diverse climate conditions with a multiyear average temperature ranging from -13.4 ℃ (the Guliya glacier) to 2.9 ℃ (the Zhuxigou glacier) and multiyear average precipitation ranging from 76.9 mm (the No.15 glacier) to 927.8 mm (the 24K glacier), which makes this dataset suitable for studies across the entire TP. To the best of our knowledge, this is the first dataset of microbial abundance and TN in glaciers on the TP, and also the first dataset of DOC in ice cores on the TP. These new data could provide valuable information for researches on the glacier carbon and nitrogen cycle and assessing the potential impacts of glacier retreat due to global warming on downstream ecosystems.
LIU Yongqin
In recent years, with the acceleration of the melting of the Antarctic ice sheet, a large amount of ice melt has formed on the surface of the ice sheet from 2000 to 2019. It is of great significance to study the material balance of the Antarctic ice sheet to deeply understand the spatial-temporal distribution and dynamic changes of the melt water on the Antarctic ice sheet. This data set is based on Landsat7 and landsat8 images with 30 m spatial resolution from 2000 to 2019. By using normalized water body index, Gabor filtering and morphological path opening operations, the ice melt grid data set is generated, and the grid water body mask is converted into vector data in ArcGIS. This data set is based on the 250m ice surface melt water data set of the Antarctic ice sheet melting area (Alexander Island, Antarctic Peninsula) from 2000 to 2019 extracted from Landsat images. The time is concentrated from December to February (Southern Hemisphere summer)
YANG Kang
The dataset of of potential glacial lakes (PGLs) distribution in the Tibetan Plateau and its surrounding (TPS) are vector data (. SHP). The data set contains the ID, area, perimeter, volume and elevation of each PGL. The TPS region was divided into 17 subregions based on the river basins’ borders, including 8 outflow river basins, i.e., the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, Indus, and Ob river basins, and 9 exorheic river basins, i.e., the Qiangtang, Hexi, Tarim, Qiadam, Junggar, Yili, Syr Darya, Amu Darya, and Mongolia river basins. This data is processed from theGlacier ice thickness distribution dataset (provided by Farinotti et al. (2019)). The grid difference between the initial DEM and the glacier ice thickness distribution was used to produce the DEM without glaciers. The overdeepenings were detected via two steps. First, we filled the depressions of the DEM without glaciers using a hydrology tool in the ArcGIS software. Second, using the filled DEM to subtract the DEM without glaciers, we ascertained the PGLs’ locations, areas, depths, and volumes. The quality of this data set depends on the quality of the original glacier thickness data, and the quality of the ice thickness dataset is the best of all similar data at present. The dataset of of potential glacial lakes distribution in the Tibetan Plateau and its surroundings can provide a new perspective from which to understand the future formation and evolution of glacial lakes in the TPS. It is anticipated that approximately 16,000 PGLs areas of greater than 0.02 km2 will be formed in the TPS, covering an area of 2253.95 ± 1291.29 km2 and holding a water volume of 60.49 ± 28.94 km3, which would contribute to a 0.16 ± 0.08 mm equivalent sea-level rise.
ZHANG Taigang, WANG Weicai, YAO Tandong, GAO Tanguang, AN Baosheng
Global solar radiation and diffuse horizontal solar radiation at Dome C (Antarctica) are measured by radiation sensors (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground are obtained from the IPEV/PNRA Project “Routine Meteorological Observation at Station Concordia”, http://www.climantartide.it. 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.; Lanconelli, C.; Lupi, A.; Driemel, A.; Vitale, V.; Li, K.; Song, T. 2022. Long-Term Variations of Global Solar Radiation and Its Potential Effects at Dome C (Antarctica). Int. J. Environ. Res. Public Health, 19, 3084. https://doi.org/10.3390/ijerph19053084). The observed global solar radiation and meteorological parameters are available at https://doi.org/10.1594/PANGAEA.935421. The data set can be used to study solar radiation and its attenuation at Dome C, Antarctica.
BAI Jianhui
The alpine region of Asia is the third pole in the world, and it is called the "Asian water tower". Affected by climate warming, glaciers continue to lose money, which has profoundly changed the supply-demand relationship of glacial water resources. In order to systematically understand the response of glaciers to climate change, the project reveals the relationship between the change of glacier material balance and climate factors through the sensitivity of glacier material balance. The data includes two maps: the sensitivity distribution map of material balance to temperature and precipitation and the climate sensitivity zoning. In the past 70 years, there have been significant differences in the evolution sequence of glacier material balance among mountain systems in the high mountain region of Asia. The glaciers in the Karakoram and West Kunlun regions have shown a stable state, and the material balance is a weak positive balance, while the Himalayas, Tianshan and Qilian Mountains have shown an accelerated trend after 1990. This is mainly due to the sensitivity of material balance to temperature and precipitation. The monthly scale material balance model is driven by 0.5 ° resolution era5 temperature and precipitation data, and the material balance calibration parameters of 43 monitored glaciers are 1 ° from 2000 to 2016 × The parameters are spatially constrained by the 1 ° aster material balance data, and the material balance sequences of 95085 glaciers in the high mountain region of Asia from 1951 to 2020 are reconstructed by using the method of extrapolation of spatial parameters. The sensitivity of glacier material balance to temperature (± 0.5K, ± 1K, ± 1.5k) and precipitation (± 10%, ± 20%, ± 30%) is analyzed, In combination with the influencing factors of glacier material balance (distribution of summer temperature, ratio of summer precipitation, distribution of glacier types, distribution of clear sky solar radiation in summer, etc.), the glacial climate sensitivity in the high mountain region of Asia is classified and divided into four categories, as shown in Fig. 4: the main control area of air temperature: the temperature is the main control factor of glacier material balance change, and precipitation occupies a secondary position; Precipitation control area: the glacier is mainly controlled by precipitation, and the temperature in the glacier area is lower than 0 ° C throughout the year; Temperature and precipitation control area of accumulated glacier in winter: refers to that the glacier is mainly supplied by precipitation in winter, and the change of material balance of the glacier is the result of the joint action of temperature and precipitation; Summer cumulative glacier temperature and precipitation control area: refers to the supply mode of glacier is summer precipitation, and the material balance of glacier is the result of the joint action of temperature and precipitation.
SHANGGUAN Donghui
The data set includes the observed and simulated runoff into the sea and the composition of each runoff component (total runoff, glacier runoff, snowmelt runoff, rainfall runoff) of two large rivers in the Arctic (North America: Mackenzie, Eurasia: Lena), with a time resolution of months. The data is a vic-cas model driven by the meteorological driving field data produced by the project team. The observed runoff and remote sensing snow data are used for correction. The Nash efficiency coefficient of runoff simulation is more than 0.85, and the model can also better simulate the spatial distribution and intra/inter annual changes of snow cover. The data can be used to analyze the runoff compositions and causes of long-term runoff change, and deepen the understanding of the runoff changes of Arctic rivers.
ZHAO Qiudong, WU Yuwei
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