1) This data is the reconstructed autumn sea ice from 1289 to 1993 in Barents Kara Sea, Arctic ; 2) Based on multiple statistical methods modeling, this sea ice time series is reconstructed by the ice core and tree ring proxy record; 3) This long term sea ice series is annual resolution and have a high reliability; 4) This data can help us know the historical changes of Arctic sea ice and its response and impact on climate change. The Barents Sea Kara Sea area is the key sea area where the extreme cold air flows southward in winter and spring in China. However, the lack of observation data limits our understanding of its mechanism. It is very important to reconstruct the characteristics of long-term Arctic sea ice change to study the Arctic sea ice change in the global context and its impact on China's historical climate.

Sea ice is the ice formed by the freezing of sea water on the sea surface, and the re freezing of precipitation on the sea ice surface also becomes a part of sea ice. Sea ice changes not only affect the stratification, stability and convection of the ocean, but also affect the large-scale temperature and salt environment. In addition, due to the high albedo and insulation of sea ice, it can change the radiation state of the polar surface and affect the energy and material exchange between air and sea. The change of sea ice not only affects the local marine ecological environment and the local atmospheric environment, but also affects the weather and climate of other regions in the way of remote correlation through complex feedback process. Through the evaluation, this data set presents four parameters related to polar sea ice: sea ice density, range, thickness and albedo. To provide a basis for the study of polar and global climate change.

Svalbard, Spitsbergen. The archipelago in the Arctic region is the territory of the northernmost border of Norway. It is located in the north of the European continent, between the Norwegian continent and the Arctic point. Vegetation is mainly lichens and bryophytes, the only trees are small polar willow and birch. The vegetation spectrum data set collected in this area is mainly based on the pioneer plant survey of 283 sample points in the new Olson area of Svalbard Islands in the Arctic. The survey time is July 6-22, 2018. The collection place includes London Island, the Yellow River Station area and the front of glaciers, which provides background information for the study of plant distribution and change in the Arctic tundra area.

Antarctic administrative boundary datasets consist of the properties of the state boundaries of the Antarctic states (properties properties), and the corresponding names and types of those properties :(CITY_POP), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (YEAR). The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.

The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2016 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format.

The "poles AOD Collection 1.0" aerosol optical thickness (AOD) data set adopts the self-developed visible band remote sensing inversion method, combined with the merra-2 model data and the official NASA product mod04. The data covers from 2000 to 2019, with the time resolution of day by day, covering the "three poles" (Antarctic, Arctic and Qinghai Tibet Plateau) area, and the spatial resolution of 0.1. Degree. The inversion method mainly uses the self-developed APRs algorithm to invert the aerosol optical thickness over ice and snow. The algorithm considers the BRDF characteristics of ice and snow surface, and is suitable for the inversion of aerosol optical thickness over ice and snow. The experimental results show that the relative deviation of the data is less than 35%, which can effectively improve the coverage and accuracy of the aerosol optical thickness in the polar region.

The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is from 1998 to 2016, and the time resolution is hour by hour. The coverage site is the Arctic Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The optical characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format.

This data was reconstructed based on the history of perchlorate from 1956 to 2004 in Miaoergou ice core (94°19 'E,43°03 'N, 4518 m) in east Tianshan mountain. Data content: perchlorate from 1956 to 2004 (including: Cl-, NO3- and SO42-). Data was measured by ESI-MS/MS; Data quality: the blank sample was significantly lower than the sample values, and the quality was good. Data application result and prospect: The data has been published, the detailed information can be found in the published paper. Zhiheng Du, Cunde Xiao, Vasile I. Furdui C,Wangbin Zhang. (2019). The perchlorate record during 1956–2004 from Tienshan ice core, East Asia. Science of the Total Environment. Time range and resolution: 1956-2004 AD, and annual resolution.

These data are a digitization of the frozen soil distribution map of the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000). Considering the unification with the global frozen soil classification system, the permafrost is divided into the following five types: (1) Discontinuous permafrost: continuous coefficient 50%-90% (2) Island permafrost: continuous coefficient <50% (3) Plateau discontinuous permafrost: continuous coefficient 50%-90% (4) Plateau island permafrost: continuous coefficient 50%-90% (5) Mountain permafrost The compilation basis of the frozen soil map includes (1) the measured field survey data and exploration of frozen soil; (2) aerial image and satellite image interpretation; (3) TOPO30 1-km resolution ground elevation data; and (4) and temperature and ground temperature data. The distribution of frozen soil on the Tibetan Plateau adopted the research results of Zhuotong Nan et al. (2002). Using the average annual temperature data of 76 boreholes along the Qinghai-Tibet Highway, a statistical regression analysis was performed to obtain the relation between annual mean ground temperature, latitude and elevation. Based on the relation combined with GTOPO30 elevation data (global 1-km digital elevation model data developed by the Earth Resources Observation and Technology Center of the U.S Geological Survey), the annual average ground temperature distribution over the entire Tibetan Plateau was simulated. Taking the annual average ground temperature of 0.5 °C as the boundary between permafrost and seasonal frozen soil and the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988) as a reference, the boundary between the plateau discontinuous permafrost and plateau island permafrost was determined. In addition, taking the Distributions Map of Permafrost in Daxiao Hinganling Northeast China (Dongxin Guo, et al. 1981), the Distribution Map of Permafrost and Ground Ice in Circum-Arctic (Brown et al. 1997) and the latest field data as references, the permafrost boundary of northeast China has been revised; the mountain permafrost boundary in the northwest mostly adopted the boundary delineated in the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988). According to this data set, permafrost area in China is approximately 1.75×106 km2, accounting for 18.25% of the territory of China, among which the mountain permafrost area is 0.29×106 km2, which accounts for 3.03% of the territory of China. For more information, please refer to the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000) specification (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006).

This data set includes daily average data of atmospheric temperature, relative humidity, precipitation, wind speed, wind direction, net radiance, and atmospheric pressure from 1 January 2007 to 31 December 2016 derived from the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data set has been used by students and researchers in the fields of meteorology, atmospheric environment and ecological research. The units of the various meteorological elements are as follows: temperature °C; precipitation mm; relative humidity %; wind speed m/s; wind direction °; net radiance W/m2; pressure hPa; and particulate matter with aerodynamic diameter less than 2.5 μm μg/m3. All the data are the daily averages calculated from the raw observations. Observations and data collection were carried out in strict accordance with the instrument operating specifications and the guidelines published in relevant academic journals; data with obvious errors were eliminated during processing, and null values were used to represent the missing data. In 2015, due to issues related to the age of the observation probe at the station, only the wind speed data for the last 8 months were retained.

In the late June and early July of 2014, the dominant species of desert plants in the lower reaches of Heihe River, Lycium barbarum and Sophora alopecuroides, were selected. Using the LI-6400 portable photosynthesis system (LI-COR, USA), the photosynthetic and water physiological characteristics of desert plants were measured and analyzed.

This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}. The production of this data is supported by the following Natural Science Foundation Projects: Environmental and Ecological Science Data Center of West China (90502010), Land Data Assimilation System of West China (90202014) and Active and Passive Microwave Radiation Transmission Simulation and Radiation Scattering Characteristics of the Frozen Soil (41071226).