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# Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China [https://doi.org/10.5061/dryad.vdncjsz1z](https://doi.org/10.5061/dryad.vdncjsz1z) This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. ## Description of the data and file structure The predicted annual incidence of national SFTS cases with or without human population reduction under four RCPs under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The value represents the annual incidence, and the unit is 105/year. The Dataset-1 file includes the predicted annual incidence of national SFTS cases with a fixed future human population under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The Dataset-2 file includes the predicted annual incidence of national SFTS cases in the 2030s, 2050s, and 2080s with human population reduction (SSP2) under four RCPs. ## Sharing/Access information Data was derived from the following sources: * https://doi.org/10.1111/gcb.16969 This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.  See the Materials and methods section in the original paper. The code used in the statistical analyses are present in the paper and/or the Supplementary Materials.

Matlab program and data for the paper “An energy consumption rectification method based on Bayesian linear regression and heating degree-days". "simulation model.zip" is the heating house model in Trnsys simulation software. "Example1" and "Example2" is the Matlab program and data in this paper.

Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

Aim: Climate change threatens freshwater faunal diversity. To prioritize areas for conservation, patterns in the distribution of species must be understood. We apply genetic analysis and species distribution models to identify patterns in the distribution of freshwater amphipods around Xinjiang, China, and project the impact of climate change on endemic species. Location: Xinjiang, China. Methods: A time-calibrated tree containing 37 freshwater amphipod molecular samples from Xinjiang is built to calculate phylogenetic diversity, the standardized effect sizes of phylogenetic diversity, weighted endemism, and phylogenetic endemism, in 100 × 100 km grid cells. Niche differentiation among species in an area of high phylogenetic endemism is explored using n-dimensional hypervolumes and principal components analyses. Present-day and projected future suitability of habitat of endemic freshwater amphipod species is described using species distribution models. Results: Areas of high freshwater amphipod diversity occur along the western boundary of Xinjiang; Areas north of Irtysh River, Tian Shan mountains, and the eastern margin of Pamir, have high phylogenetic endemism. Seasonal temperature and average annual water temperature contribute most to niche differentiation between geographically related freshwater species, negatively affect the projected distributions of endemic amphipods, and with continued warming, reduce future range distributions or latitudinal shifts of species. Main Conclusions: High freshwater amphipod phylogenetic endemism occurs in Xinjiang. Environmental factors are responsible for niche differentiation of endemic species. Future climate change will substantially affect the geographic distributions of endemic amphipods. Conservation efforts should be prioritized in areas with highly concentrated phylogenetic endemism. # Diversity of endemic cold-water amphipods threatened by climate warming in northwestern China [https://doi.org/10.5061/dryad.h44j0zpsg](https://doi.org/10.5061/dryad.h44j0zpsg) Datasets for phylogenetic analysis. ## Description of the data and file structure 1.gene\_partition.txt: Used to explain the position of each gene in a tandem sequence. 2.xinjiang\_28S\_COI.fasta: A file of tandem sequence. 3.RAxML\_xinjiang\_tree.tre: A phylogenetic tree from the 52-tip data set. 4.MCMC\_tree.tre: A time-calibrated tree using three calibration points. ##

Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.

This dataset provides the data applied in the case studies of the manuscript "Backcasting the Techno-economic Targets For Constructing Low-carbon Power Systems". Both the modified Garver’s 6-bus and realistic Northwest China power system are presented here, in two excel files respectively. The datasets include detailed information about buses, units, existing corridors, and candidate corridors.Average cost variations and load growth rate over the planning period are also provided.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVImax day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVImax day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero. # Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland The dataset includes all original images used in this study to extract seasonal transition dates and corresponding results. ## Description of the data and file structure Datasets included: (1) The spatial distribution of NDVI values for this study region (168 rows and 166 columns). Each file is named in the form of '' year-month-day''. For example, a file named "2016-05-02'' represents the data for 2nd, May of 2016. The normal NDVI values in each file range from -1 to 1, and NaN represents no valid value. The folder named 'unique_date_NDVI' refers to the spatial distribution of NDVI for all available dates, directly acquired from satellite images. The folder named 'unique_date_NDVI_rm_outlier' refers to the spatial distribution of NDVI after quality correction for each date using the described method. (2) The extracted phenology indicators for each pixel in this study region. Five tables named 'Phe_pixel_XXXX.xlsx' include the extracted seasonal transition dates during 2016–2020, pixel by pixel. There are 9 columns in each table, they are row number and column number (used to describe the specific location of pixel), year, start of spring, middle of spring, end of spring, start of fall, middle of fall, and end of fall. ## Sharing/Access information All functions regarding the extraction of seasonal transition dates can be found here: * All parameters and associated functions regarding the SnowModel can be found here: * All original meteorological data in this study is from: * Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVImax) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.CAS.FGOALS-f3-L' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FGOALS-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.