1) Data content: spatial and temporal dataset of near-surface monthly air temperature of Antarctic ice sheet from 2001 to 2018。 2) Data source and processing method: MODIS (MODerate resolution Imaging Spectroradiometer) Land Surface Temperature measurements in combination with in-situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05°×0.05°, spanning from 2001 to 2018. 3) Data quality description: the accuracy is better than that of ERA5 reanalysis data. 4) Data application achievements and prospects: the database can be used to study the temporal and spatial distribution characteristics of near-surface air temperature of Antarctic ice sheet, and the impact of SAM and ENSO on the interannual variation of Antarctic temperature. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.
0 2022-04-15
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
0 2022-08-03
This is a dataset of shrubline shifts and recruitment including 24 willow shrubline plots on the eastern Tibetan Plateau. It includes the following information: 1) Shrub recruitment series; 2) Climatic sensitivity of shrub recruitment; 3) Shrubline shifts and their potential drivers.
194 2021-08-02
Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.
0 2022-07-22
China's high-quality natural gauge-based streamflow dataset (CNRD_gauge) was developed from a well-trained and tested land surface model (VIC) that coupled to a routing model with flow direction correction. The dataset currently covers multiple hydrological stations for the period 1961–2018 , and will continue to update. The land surface model was trained by a comprehensive parameter uncertainty framework, including parameter sensitivity, optimization, and regionalization. The rooting model was corrected based on high-resolution river flowlines, as well the ascertained gauge locations and catchment areas. Supported by a well-trained model system, about 83% of the catchments across China exhibited NSE > 0.7, and about 56% of the catchments exhibited KGE > 0.7. The systematic bias of estimated natural streamflow from a calibrated land surface model was reduced by the statistical post-processing technique with the Pbias metric decreased from 17.13% to 2.27%. The reconstructed gauge-based streamflow dataset provides a reliable representation of natural hydrological processes in regions affected by intensive human activity.
0 2022-07-18
The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited. Compared with the previous version released in 2019, Western China Daily 1km spatial resolution all-weather land surface temperature data set (2003-2018) V1, this data set (V2) adopts a new preparation method, namely satellite thermal infrared remote sensing reanalysis data integration method based on new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST was used as the reference value, the mean deviation (MBE) of the data set in daytime and nighttime was -0.28 K and -0.29 K respectively, and the standard deviation (STD) of the deviation was 1.25 K and 1.36 K respectively. The test results based on the measured data of six stations in the Qinghai Tibet Plateau and Heihe River Basin show that under clear sky conditions, the data set is highly consistent with the measured LST in daytime / night, and its MBE is -0.42-0.25 K / - 0.35-0.19 K; The root mean square error (RMSE) was 1.03 ~ 2.28 K / 1.05 ~ 2.05 K; Under the condition of non clear sky, the MBE of this data set in daytime / night is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K; The RMSE was 2.24-3.87 K / 2.03-3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version. The time span of this data set is from 2000 to 2021 and will be updated continuously; The time resolution is twice a day (corresponding to the two transit times of aqua MODIS in the daytime and at night); The spatial resolution is 1 km. In order to facilitate the majority of colleagues to carry out targeted research around the Qinghai Tibet Plateau and its adjacent areas, and reduce the workload of data download and processing, the coverage of this data set is limited to Western China and its surrounding areas (72 ° E-104 ° E，20 ° N-45 ° N）。 Therefore, this dataset is abbreviated as trims lst-tp (thermal and reality integrating modem resolution spatial seamless LST – Tibetan Plateau) for user's convenience.
0 2022-05-16
Land surface temperature (LST) is one of the important parameters of the interface between the earth's surface and atmosphere. It is not only the direct reflection of the interaction between the surface and the atmosphere, but also has a complex feedback effect on the earth atmosphere process. Therefore, land surface temperature is not only a sensitive indicator of climate change and an important prerequisite for mastering the law of climate change, but also a direct input parameter of many models, which has been widely used in many fields, such as meteorology, climate, environmental ecology, hydrology and so on. With the deepening and refinement of Geosciences and related fields, there is an urgent need for all weather LST based on satellite remote sensing. The generation principle of this dataset is a satellite thermal infrared remote sensing reanalysis data integration method based on a new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. The method makes full use of the high-frequency and low-frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data, and finally reconstructs a high-quality all-weather land surface temperature data set. The evaluation results show that this data set has good image quality and accuracy, which is not only seamless in space, but also highly consistent with the amplitude and spatial distribution of 1 km daily Aqua MODIS LST products widely used in current academic circles. When MODIS LST is used as reference, the mean deviation (MBE) of the data set is 0.08k to 0.16k, and the standard deviation of deviation (STD) is 1.12k to 1.46k. Compared with the daily 1km AATSR LST product released by ESA, the MBE and STD of the product are -0.21k to 0.25k and 1.27k to 1.36k during the day and night. Based on the measured data of 15 stations in Heihe River Basin, Northeast China, North China and South China, the test results show that the MBE is -0.06k to -1.17k, and the RMSE is 1.52k to 3.71k, and there is no significant difference between clear sky and non clear sky. The time resolution of this data set is twice a day, the spatial resolution is 1km, and the time span is from 2000 to 2021; The spatial scope includes the main areas of China's land (including Hong Kong, Macao and Taiwan, excluding the islands in the South China Sea) and the surrounding areas (72 ° E-135 ° E，19 ° N-55 ° N）。 This dataset is abbreviated as trims LST (thermal and reality integrating modem resolution spatial sealing LST) for users to use. It should be noted that the spatial subset of trims LST, trims lst-tp (1 km daily land surface temperature data set in Western China, trims lst-tp; 2000-2021) V2) has also been released in the national Qinghai Tibet Plateau scientific data center to reduce the workload of data download and processing for relevant users.
0 2022-05-16
As an important part of the global carbon pool, Arctic permafrost is one of the most sensitive regions to global climate change. The rate of warming in the Arctic is twice the global average, causing rapid changes in Arctic permafrost. The NDVI change data set of different types of permafrost regions in the Northern Hemisphere from 1982 to 2015 has a temporal resolution of every five years, covers the entire Arctic Rim countries, and a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, GIS method and ecological method are used to quantify the regulation and service function of permafrost in the northern hemisphere to the ecosystem, and all the data are subject to quality control.
0 2022-07-15
Antarctic digital elevation model (DEM) is essential for human fieldwork, ice topography monitoring and ice mass change estimation. A new-generation satellite laser altimeter ICESat-2 is used to generate a new and specific time-stamped Antarctic DEM for both ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2019 to November 2020 are used to estimate surface elevations at resolutions of 500 m and 1 km based on a spatiotemporal fitting method, which posts this DEM at a modal resolution of 500 m. About 74% of Antarctica are observed and the remaining observation gaps are interpolated using the ordinary kriging method. National Aeronautics and Space Administration Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter call it ICESat-2 DEM). Overall, a median bias of -0.19 m and root-mean-square deviation of 10.83 m are found from appropriately 5.2 × 106 spatiotemporal matched measurement points. The accuracy and uncertainty of ICESat-2 DEM vary in relation to the surface slope and roughness, more reliable estimates can be found in the flat ice sheet interior. ICESat-2 DEM is comparable to previous DEMs derived from satellite altimeters, stereo-photogrammetry and interferometry. The high accuracy and a specific time stamp make ICESat-2 DEM an essential addition to the existing Antarctic DEM groups and can be further used for other scientific applications.
0 2022-07-14
Automatic weather stations have been proved to be a powerful tool for monitoring the near surface meteorological elements of glaciers/caps to determine the surface energy budget, so as to quantify glaciers/caps ablation and its response to climate change. This data set introduces the PANDA transect automatic weather station network, which includes 11 automatic weather stations (AWS), Zhongshan, Panda 100, Panda 200, Panda 300, Panda 400, Taishan, Eagle, Panda 1100, Dome A, Kunlun and Panda s. The transect network spans the Prydz Bay Amery ice shelf Dome A area, from the coast to the top of the southeast Antarctica ice sheet. The transect network is roughly along longitude ~77 ° e, and the latitude range is 69.37°S-82.33°S, covering all geographical and climatic units in the southeast polar region. All automatic weather stations in the network measure air temperature, relative humidity, air pressure, wind speed and direction every hour, and some automatic weather stations can also measure surface temperature and short/longwave radiation. All automatic station data is transmitted in real time through Argos system. The data quality is very reliable, and the data of Dome A and Eagle station have been widely used. At present, the data set has been updated by us to 2021. Except Zhongshan and Panda S, all other stations are multi-layer observations, mainly with four heights of 1/2/4/6m. The data has been subject to strict quality control. We plan to update it once a year. This data set is of great value to climate change estimation, extreme weather event diagnosis, data assimilation, weather forecasting, etc. in the Antarctic region.
0 2022-07-14
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