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This data set covers the CO2 flux monitoring station of Haibei Station from 2002 to 2020 in early May, Early or late June to September and mid-October each year. 12 periods of biomass monitoring data of alpine Kobresia humilis meadow were collected as EXCEL files. The biomass monitoring data of Kobresia humilis were aboveground (green grass, dead grass, debris) and underground (0-10cm, 10-20cm, 20-40cm). The data is retained to two decimal places in g/m2. For lack of test data in the "empty", or due to causes such as the sample lost data measurement, data set corresponding cell expressed as a blank. This data set covers the CO2 flux monitoring station of Haibei Station from 2002 to 2020 in early May, Early or late June to September and mid-October each year. 12 periods of biomass monitoring data of alpine Kobresia humilis meadow were collected as EXCEL files. The biomass monitoring data of Kobresia humilis were aboveground (green grass, dead grass, debris) and underground (0-10cm, 10-20cm, 20-40cm). The data is retained to two decimal places in g/m2. For lack of test data in the "empty", or due to causes such as the sample lost data measurement, data set corresponding cell expressed as a blank.

Based on the observation monthly climatic data collected from 2766 weather observation stations on global during the period from 1981 to 2010, and the climatic scenarios data of SSP1_2.6、SSP1_4.5 and SSP1_8.5 scenarios released by CMIP6, the mean annual biotemperature, average total annual precipitation and potential evapotranspiration ratio on spatial resolution of 0.1º× 0.1º were respectively obtained by operating a high accuracy and speed method of surfacing modeling (HASM) (Yue, 2010, Yue et al., 2016) during all the four periods from 2020 to 2050 per decade. The method for surface modelling of land cover scenarios (SMLCS) has been developed to simulate the scenarios of land cover in Eurasia （Fan et al., 2019, 2020, 2021）. Finally, the scenario dataset of land cover under scenario SSP1_2.6、SSP1_4.5 and SSP1_8.5 were simulated by the SMLCS method from 2020 to 2050. 采用1981-2010年全球2766个气象观测站的观测月气候数据，以及CMIP6发布的SSP1_2.6、SSP1_4.5和SSP1_8.5情景的气候情景数据。通过运行高精度面建模方法（HASM）(Yue, 2010, Yue et al., 2016)，分别获得2020-2050年间每10年的空间分辨率为0.1º×0.1º的平均生物温度数据、多年平均年降水和潜在蒸散比率数据。采用自主研发的土地覆被情景曲面建模（SMLCS）方法（Fan et al., 2019, 2020, 2021），实现了SSP1_2.6、SSP1_4.5和SSP1_8.5情景的2020-2050年间每10年的全球土地覆被变化情景模拟。

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Spatio-temporal variations in extreme drought in China during 1961–2015 Spatio-temporal variations in extreme drought in China during 1961–2015

This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84).

This data set is the experimental data set of maize agroecosystem adaptation to climate change from 2018 to 2021 in Gucheng Station. It mainly contains the interannual and annual variation data of growth period, biomass, leaf area index, photosynthetic physiology, spectral characteristics, soil moisture, grain filling rate and yield of the same variety of maize at different sowing dates. This dataset has great significance for the revision of agrometeorological business service index, the improvement and regional application of agrometeorological simulation model, and the development of agrometeorological applicable technology for the study of maize ecosystem adaptation to climate change. This data set is the experimental data set of maize agroecosystem adaptation to climate change from 2018 to 2021 in Gucheng Station. It mainly contains the interannual and annual variation data of growth period, biomass, leaf area index, photosynthetic physiology, spectral characteristics, soil moisture, grain filling rate and yield of the same variety of maize at different sowing dates. This dataset has great significance for the revision of agrometeorological business service index, the improvement and regional application of agrometeorological simulation model, and the development of agrometeorological applicable technology for the study of maize ecosystem adaptation to climate change.

Studies of historical climate and environment changes in the eastern monsoonal region of China have made great progress in recent years. However, progressively studies indicate that the processes of climate and environment changes during the last 2000 years differed greatly in different regions. This is especially the case among studies based only on single-site records, possibly because of different materials, proxy indicators, dating accuracy, etc. Therefore, it is necessary to conduct comprehensive and integrated analyses of regional climate and environment changes at larger spatial scales. Here, multiple climate proxy records selected from 16 sites in southeastern China (i.e., approximately east of 105° E and south of 30° N) were used to synthesize and reconstruct the temperature and precipitation changes over the past 2000 years, and the possible forcing mechanisms behind these changes were explored. The results indicate that the integrated temperature sequence in the study region is quite comparable to the reconstructions for the entirety of China, eastern China, and the Northern Hemisphere. Several typical characteristic climate periods on centennial timescales, such as the Dark Age Cold Period, the Medieval Warm Period, the Little Ice Age, and the modern warm period of the 20th century, were well revealed in the present reconstruction. The integrated precipitation sequence shows that the dry and wet phases in southeastern China have changed significantly during the past 2000 years. Specifically, precipitation was relatively higher during the Medieval Warm Period, but it was relatively lower during the Little Ice Age (i.e., less precipitation occurred in the former part of the Little Ice Age and more precipitation occurred during the latter part). In general, the synthesized temperature and precipitation changes in southeastern China had relatively diverse hydrothermal combinations during the last 2000 years. However, it is noteworthy that the integrated precipitation sequence in southeastern China is weakly comparable to the sequence in northern China, reflecting great regional differences in historical precipitation changes. Thus, the forcing mechanisms might differ greatly from south to north in the monsoonal region of eastern China. Furthermore, the results suggest that large-scale atmosphere–ocean interactions, volcanic activities, and changes in solar radiation could have had significant effects on the climate and environment changes in southeastern China during the last 2000 years.

As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region. As a basic properties of forest vegetation, forest succession law is the basis of understanding forest community, managing forest and utilizing forest rationally. Typical evergreen broad-leaved forest is a zonal vegetation in the subtropical area of east China. The existing vegetation is mostly in different secondary succession stages due to human and natural disturbance. Plant species composition is an important indicator of the long-term terrestrial ecosystem observation of National Ecosystem Research Network of China (CNERN). It affects the biogeochemical cycle, productivity, carbon sequestration, biodiversity and ecosystem services of forest ecosystems. According to CNERN monitoring standards, Zhejiang Tiantong Forest Ecosystem National Observation and Research Station finished three investigations at three succession plots and established a dataset on species composition during 2008 and 2017. The dataset included species name, abundance, mean diameter and biomass of woody plants in the plot. The species composition database provides critical data for in-depth studies of forest species diversity, structure and function under succession or environment change, and can support forest management and ecosystem service evaluation in this region.

The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation. The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation.

Subtropical forests are the largest forest type in China, and play an important role in regulating global climate change and maintaining atmospheric carbon balance. The carbon emitted by soil through respiration is the main source of atmospheric CO2, so slight changes in soil carbon pool will also significantly affect the concentration of atmospheric CO2. Therefore, at present, the size and dynamic change characteristics of soil carbon storage in subtropical forests are widely concern.In this study, soil respiration was estimated of subtropical evergreen broad-leaved forest ecosystem at Ailao Mountain by using a multichannel automated chamber system for soil respiration measurement method. Ailao Mountain Station for Subtropical Forest Ecosystem Studies is a national field station and a basic observation station of the Chinese Ecosystem Research Network. We collected soil respiration data of subtropical evergreen broad-leaved forest in Ailao Mountain from 2010 to 2014 based on a multichannel automated chamber system for soil respiration measurement platform. Including soil temperature 5cm (TS), soil moisture content 10cm (SWC) and soil respiration data (RS), divided into three types of daily, monthly and annual scale files. This dataset is of great significance for revealing the effects of climate change on soil ecological processes in subtropical evergreen broad-leaved forests, correctly assessing soil organic carbon emissions and forest ecosystem management, and provides empirical and theoretical basis for further research on the effects of global changes such as climate warming on soil respiration components, especially soil organic carbon emissions.  Subtropical forests are the largest forest type in China, and play an important role in regulating global climate change and maintaining atmospheric carbon balance. The carbon emitted by soil through respiration is the main source of atmospheric CO2, so slight changes in soil carbon pool will also significantly affect the concentration of atmospheric CO2. Therefore, at present, the size and dynamic change characteristics of soil carbon storage in subtropical forests are widely concern.In this study, soil respiration was estimated of subtropical evergreen broad-leaved forest ecosystem at Ailao Mountain by using a multichannel automated chamber system for soil respiration measurement method. Ailao Mountain Station for Subtropical Forest Ecosystem Studies is a national field station and a basic observation station of the Chinese Ecosystem Research Network. We collected soil respiration data of subtropical evergreen broad-leaved forest in Ailao Mountain from 2010 to 2014 based on a multichannel automated chamber system for soil respiration measurement platform. Including soil temperature 5cm (TS), soil moisture content 10cm (SWC) and soil respiration data (RS), divided into three types of daily, monthly and annual scale files. This dataset is of great significance for revealing the effects of climate change on soil ecological processes in subtropical evergreen broad-leaved forests, correctly assessing soil organic carbon emissions and forest ecosystem management, and provides empirical and theoretical basis for further research on the effects of global changes such as climate warming on soil respiration components, especially soil organic carbon emissions.