Wind speed data is widely used in many sciences, management, and policy fields to assess renewable energy potential, address wind hazards, investigate biological phenomena, and explore climate change/variability, among other applications. The challenge is obtaining complete and accurate wind datasets, as observations are limited in distribution. Global-scale weather stations suffer from spatial and temporal discontinuities that limit their utility. While reanalysis products and climate model simulations achieve data continuity, they often fail to reproduce significant wind speed trends because few of them assimilate in-situ wind observations on land. Data interpolation helps fill gaps, but the high variability of wind speed data, combined with a low distribution of observations worldwide, prevents standard statistical interpolation methods such as kriging or principal component analysis from being accurate for areas with sparse data. As a result, wind speed data has been the bottleneck in related studies. Here, based on the partial convolutional neural network, we reconstructed the global near-surface wind speed data during 1973-2021 by assimilating simulation outputs from 34 climate models and the HadISD dataset, which the Met Office Hadley Center creates. Our dataset has a spatial resolution of 1.25°×2.5° and containers observed wind speed trends.
ZHOU Lihong , ZENG Zhenzhong , JIANG Xin
This dataset is the biome change data of the Tibetan Plateau since the last glacial maximum which was reconstructed by using a new method. Firstly, a random forest algorithm was applied to establish a pollen-biome classification model for reconstructing past vegetation changes of the Tibetan Plateau, and 1802 modern pollen assemblages from 17 vegetation zones in and around the Tibetan Plateau were used as the training set for the model development. The random forest model showed a reliable performance (accuracy > 76%) in predicting modern biomes from modern pollen assemblages based on a comparison with the observed biomes. Moreover, the random forest model had a significantly higher accuracy than the traditional biomization method. Then, the newly established random forest model is applied to the paleovegetation reconstruction of 51 fossil pollen sequences of the Tibetan Plateau. New age-depth models were developed for these fossil pollen records using the Bayesian method, and all fossil pollen records were linearly interpolated to 500-year time slices. Finally, the spatiotemporal changes of biomes on the Tibetan plateau over the past 22,000 years at an interval of 500 years were reconstructed by using the random forest model. This dataset can provide evidence for understanding the past variation of alpine vegetation and its mechanism; provide the basis for studying the impact of past climate change on vegetation on the Tibetan Plateau; and provide boundary conditions for climate simulation.
QIN Feng , ZHAO Yan, CAO Xianyong
By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Xichengyi site, Jinchankou site, Shannashuzha site, Jiangxifen site, Zongri site, Bangga site and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains, plant fossil, sedimentary sample, and obtained a batch of dating data of radiocarbon dating; pollen data; identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. Based on natural geographical factors and sites in different periods, the method of realizing cumulative connection between nodes under the control of the lowest cost uses GIS(R language) tool to carry out spatial numerical calculation, and the result is used as the communication route in prehistoric times (Neolithic-bronze age). The shape of the route developed from the northeast-east-southeast-southwest edge of Neolithic Age in crescent shape to the trend of network development from the edge to the hinterland of Bronze Age, which is a manifestation of the gradual evolution from the exchange of plateau edge to the exchange of edge-hinterland, which is constantly strengthened. A total of 49 dung samples of grazing livestock (30 yak dung samples, 11 horse dung samples and 8 sheep dung samples) were collected in the alpine meadow area of the eastern Qinghai-Tibet Plateau, and the pollen analysis of dung samples was carried out on the basis of regional vegetation investigation. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and the Bronze age.
DONG Guanghui , HOU Guangliang, YANG Xiaoyan
Since the first Industrial Revolution, human activity has profoundly affected all spheres of the earth, and this influence will continue to expand and intensify. As an ecosystem unit with global significance, the Qinghai-Tibet Plateau (QTP) is also an important ecological security barrier in China, playing a crucial role in soil and water conservation, biodiversity conservation, water conservation and carbon balance. However, in the past 30 years, with the expansion of the scope and rapid growth of the intensity of human activities on the QTP, a series of ecological and environmental issues caused by human activities have become increasingly prominent and seriously affected the ecological functions of the QTP. The comprehensive spatial dataset that records human activity intensity will contribute to a deeper understanding of the intensity and scope of human activities in the region, reveal the law of change of human activities in the context of climate warming, and have important significance for further quantitative identification of the impact of human activities and climate change on the ecosystem, as well as promoting the sustainable development of the region. In this study, the human footprint index method was adopted to evaluate the intensity of human activity on the QTP, which used six types of spatial data as indicators of human activities, including population density, land use, grazing density, night lighting, railway and road. The dataset records indicators of human activity intensity in the seven phases, namely, 1990, 1995, 2000, 2005, 2010, 2015 and 2017. The optimization and adjustment of the human footprint method in this dataset mainly include: (1) Six kinds of data including population density, land use, night lighting, grazing density, road and railway were selected to calculate the intensity of human activities; (2) Adjust the assignment of different land use types; (3) The maximum intensity threshold of population density was set at 50 people/km2, and the logarithmic method was used to assign the value. (4) The cattle and sheep density data were used to characterize the grazing density, and the maximum intensity threshold was set as 1000 sheep units/km2, and the logarithmic method was used to assign the value. (5) The corrected DMSP/OLS night lighting data were used for assigning values; (6) Divide the road into five grades, namely expressway, national road, provincial road, county road and other roads, and assign values respectively; (7) The maximum influence range of railway is set as 3.5km; (8) Using glacier and lake spatial data for quality control . The dataset contains the data from "Duan, Q., & Luo, L. (2020). A dataset of human footprint over the Qinghai-Tibet Plateau during 1990–2015. China Scientific Data, 5(3). https://doi.org/10.11922/csdata.2019.0082.zh", and the newly produced data of 2017. This dataset can provide spatial data for exploring the characteristics and rules of spatial changes of human activities in the Qinghai-Tibet Plateau, and can also provide support for exploring the interaction between human activities and ecological environment in the region. it can play a guiding role in promoting the ecological environment protection and sustainable development of the entire Qinghai-Tibet Plateau.
DUAN Quntao, LUO Lihui
The data sources of this dataset mainly include domestic satellite images such as HJ-1A/B, GF-1/2, ZY-3, and Landsat TM/ETM+/OLI series satellite image data. Using the domestic satellite images supplemented by Google Earth images to generate the component training sample and validation sample data of different geographical divisions. Using Google Earth Engine (GEE) to test and correct the model algorithm parameters. The normalized settlement density index (NSDI) is obtained based on random forest algorithm, Landsat TM/ETM+/OLI series satellite images and auxiliary data. The vector boundary of urban built-up area is obtained by density segmentation method after manual interactive interpretation and correction. The NSDI, vegetation coverage index and vector boundary of the Tibetan Plateau are used to produce the original data of urban impervious surface and urban green space fractions in the Tibetan Plateau. After correction and accuracy evaluation, the datasets of urban impervious surface area and green space fractions in the Tibetan Plateau from 2000 to 2020 are generated. The resolution of the data product is 30 m, and the coordinate system and storage format of the data files are unified. The geographic coordinate system is WGS84, the projected coordinate system is Albers, and the data storage format is GeoTIFF, the data unit is percentage (the value range is 0~10000), and the scale factor is 0.01. In order to quantify the change of urban land cover more accurately, samples from several typical cities are selected to verify the dataset. The specific verification methods and accuracy are shown in the published results. The data can be used to analyze and reveal the impact of land cover change and future scenario simulation on the Tibetan Plateau, to provide a scientific basis for building environmentally livable cities and improving the quality of human settlements on the Tibetan Plateau.
KUANG Wenhui, GUO Changqing, DOU Yinyin
The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
The basic principle of ancient recipe analysis based on carbon and nitrogen stable isotope analysis method is you are what you eat, that is, the chemical composition of animal tissues and organs is closely related to their diet. Through the detection of isotope ratio of relevant elements, the food structure of ancient people and animals can be directly revealed Then it discusses the research means of people's livelihood and livestock domestication. The collagen of human and animal bones from shilinggang site in Nujiang, Yunnan Province in the southwest of Qinghai Tibet Plateau was analyzed by carbon and nitrogen stable isotopes.
DONG Guanghui , REN Lele
Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of hourly and daily of the new SWDR are 104.9 and 31.5 Wm-2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm-2, respectively), ERA5 (at 176.6 and 39.5 Wm-2, respectively) and GLASS (daily of 36.5 Wm-2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm-2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.
HUSI Letu, WANG Tianxing, DU Yihan
The dataset is from the transient experiment TRN40ka in Zhang et al (2021, Nature Geoscience), spanning 40ka-32ka BP with changing orbital parameters. For detailed description of experimental design, please refer to the original paper. Model details: COSMOS (ECHAM5-JSBACH-MPI-OM), a comprehensive fully coupled atmosphere–ocean general circulation model (AOGCM), is used to generate the dataset. The atmospheric model ECHAM5, complemented by the land surface component JSBACH, is used at T31 resolution (∼3.75°), with 19 vertical layers. The ocean model MPI-OM, including sea-ice dynamics that is formulated using viscous-plastic rheology, has a resolution of GR30 (3°×1.8°) in the horizontal, with 40 uneven vertical layers.
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.
CHEN Yongzhe, FENG Xiaoming, TIAN Hanqin, WU Xutong, GAO Zhen, FENG Yu, PIAO Shilong, LV Nan, PAN Naiqing, FU Bojie
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
Based on the analysis of brgdgts and hydrogen isotopes of leaf wax in lake sediments from Tengchong Qinghai (tcqh) in Yunnan Province, this study shows for the first time the high-resolution annual average temperature change history of low latitude land since the last glacial period (since the last 88000 years). According to the annual average temperature of South Asia established by tcqh core, there are two warm periods of 88000-71000 years and 45000-22000 years in this region, and the temperature range is about 2-3 ° C. Since the Holocene, the temperature has been increasing for about 1-2 years ° C。
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, Wu Tonghua
Relationship between modern pollen and climate, and its representative to vegetation are the important references in explaining and reconstructing past climate and vegetation qualitatively or quantitatively. To extrct past climate and vegetation signals from fossil pollen spectrum of a lacustrine sediment, a corresponding modern pollen dataset collected from lake-sediment surface is necessary. At present, there are a few modern pollen datasets extracted from lake sediment-surface established on the Tibetan Plateau, however, the geographic gaps (e.g. the central and east Tibetan Plateau) of available sampled lakes influence the correct understanding. To ensure the even distribution of the representative lakes, we collected lake sediment-surface samples (n=117) covering the alpine meadow evenly on the east and central Tibetan Plateau, in July and August 2018. For pollen extraction, approximately 10 g (wet original sediment) per sample were sub-sampled. Pollen sample was processed by the standard acid-alkali-acid procedures followed by 7-μm-mesh sieving. More than 500 terrestrial pollen grains were counted for each sample. Pollen assemblages of the dataset from alpine meadow are dominated by Cyperaceae (mean is 68.4%, maximum is 95.9%), with other herbaceous pollen taxa as commen taxa including Poaceae (mean is 10.3%, maximum is 87.7%), Ranunculaceae (mean is 4.8%, maximum is 33.6%), Artemisia (mean is 3.7%, maximum is 24.5%), Asteraceae (mean is 2.1%, maximum is 33.6%), etc. Salix (mean is 0.4%, maximum is 5.3%) is the major shrub taxon in these pollen assemblages, while arboreal taxa occur with low percentages generally (mean of total arboreal percentages is 0.9% (maximum is 5.8%), including mainly Pinus (mean is 0.3%, maximum is 1.8%), Betula (mean is 0.1%, maximum is 0.9%) and Alnus (mean is 0.1%, maximum is 0.7%). These pollen assemblages represent the plant components well in the alpine meadow communities, although they are influenced slightly by long-distance pollen grain transported by wind or river (such as these arboreal pollen taxa). Together with pollen counts and percentages, we also provided the modern climatic data for the sampled lakes. The China Meteorological Forcing Dataset (CMFD; gridded near-surface meteorological dataset) with a temporal resolution of three hours and a spatial resolution of 0.1° was employed, and the climatic data of the nearest pixel of one sampled lake was defined to represent climatic conditions of the lake. Finally, the mean annual precipitation (Pann), mean annual temperature (Tann) and mean temperature of the coldest month (Mtco) and warmest month (Mtwa) are calculated for each sampled lake.
CAO Xianyong, TIAN Fang, LI Kai, NI Jian
These datasets include mean annual ground temperature (MAGT) at the depth of zero annual amplitude (approximately 3 m to 25 m), active layer thickness (ALT), the probability of the permafrost occurrence, and the new permafrost zonation based on hydrothermal condition for the period of 2000-2016 in the Northern Hemisphere with an 1-km resolution by integrate unprecedentedly large amounts of field data (1,002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modelling with an ensemble strategy, and thus more accurate than previous circumpolar maps.
RAN Youhua, LI Xin, CHENG Guodong, CHE Jinxing, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, JIN Huijun, Jaroslav Obu, Masahiro Hori, YU Qihao, CHANG Xiaoli
We comprehensively estimated water volume changes for 1132 lakes larger than 1 km2. Overall, the water mass stored in the lakes increased by 169.7±15.1 Gt (3.9±0.4 Gt yr-1) between 1976 and 2019, mainly in the Inner-TP (157.6±11.6 or 3.7±0.3 Gt yr-1). A substantial increase in mass occurred between 1995 and 2019 (214.9±12.7 Gt or 9.0±0.5 Gt yr-1), following a period of decrease (-45.2±8.2 Gt or -2.4±0.4 Gt yr-1) prior to 1995. A slowdown in the rate of water mass increase occurred between 2010 and 2015 (23.1±6.5 Gt or 4.6±1.3 Gt yr-1), followed again by a high value between 2015 and 2019 (65.7±6.7 Gt or 16.4±1.7 Gt yr-1). The increased lake-water mass occurred predominately in glacier-fed lakes (127.1±14.3 Gt) in contrast to non-glacier-fed lakes (42.6±4.9 Gt), and in endorheic lakes (161.9±14.0 Gt) against exorheic lakes (7.8±5.8 Gt) over 1976−2019.
The data includes the daily mean value of stable isotope δ18O in precipitation, the air temperature and precipitation amounts in Bomi in 2008; the precipitation samples are collected by Bomi meteorological station, and the stable isotope of precipitation is measured at the Laboratoire des Sciences du Climat et de l’Environnement, France., The δ18O amounts were measured by equilibration on a MAT-252 mass spectrometer, with an analytical precision of 0.05‰. The air temperatures and precipitation amounts were recorded for each precipitation events at Bomi meteorological stations, through the average of the observed temperature before and after the precipitation event, and through the total precipitation amount for each event. The data study has been published in the Journal of Climate, entitled Precipitation Water Stable Isotopes in the South Tibetan Plateau: Observations and Modeling.
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM2M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours (or 6 hours), daily and yearly scales.
PAN Xiaoduo, ZHANG Lei
The data set is the daily precipitation stable isotope data (δ 18O, δ D, d-excess) from Satkhira, Barisal and sylhet3 stations in Bangladesh from 2017 to 2018. The data set was collected by Bangladesh Atomic Energy Commission (BAEC) and measured by picarro l2130i wavelength scanning cavity ring down spectrometer in the Key Laboratory of environment and surface processes, Institute of Qinghai Tibet Plateau, Chinese Academy of Sciences. Sampling location and time of three observation points: Satkhira ：2017.03.11-2018.07.16 Barisal：2017.03.05-2018.07.02 Sylhet : 2017.02.20-2018.09.04
Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.