DOI: 10.5061/dryad.hqbzkh1rr
title: Continuous abrupt vegetation shifts in the global terrestrial ecosystem

description: Previous studies have primarily focused on single abrupt shifts, yet the
 actual ecosystem will experience continuous abrupt shifts (CAS), including
 Different Directions Shifts (DDS) and Same Directions Shifts (SDS). The
 patterns and drivers of these CAS remain unclear. We examined the patterns
 of the DDS and SDS by two vegetation datasets, then tested climate drivers
 comprising atmospheric temperature (MAT), atmospheric precipitation (MAP),
 soil temperature (ST), and soil water content (SW), and finally hysteresis
 effects were examined with reference to principal drivers. The results
 demonstrate that the DDS and SDS varied across climatic regions.
 The ST, SW, MAT, and MAP were primary drivers of the DDS, while the MAT
 and MAP were primary drivers of the SDS. Furthermore, the existence of
 hysteresis effects were validated via the DDS. This study presents the
 widespread occurrence of the CAS and the divergent roles of climate change
 on the DDS and SDS globally.
description: All the raw data sources are from public websites.  
 The data processing and plotting are done using NCL (NCAR Command
 Language, https://www.ncl.ucar.edu/) and R (https://www.r-project.org/),
 with NCL being the primary tool and R serving as a supplementary
 tool.
description: # Continuous abrupt vegetation shifts in the global terrestrial ecosystem
 [https://doi.org/10.5061/dryad.hqbzkh1rr](https://doi.org/10.5061/dryad.hqbzkh1rr) ## Description of the data and file structure The dataset includes the code, data, and data descriptions for all the figures and tables mentioned in the article, as well as the code, data, and data descriptions for calculating CAS, along with the raw data. Each figure, table, or CAS calculation method is accompanied by a detailed guide in their respective folders. ### Files and variables #### File: Supplementary\_Data.rar **Description:** The dataset contains all of the raw data, as well as all of the data and code for the figures and tables. Each folder has its own README. The missing value for all data is -32767.0. ## Code/software The data were processed using the NCAR command language (NCL, [https://www.ncl.ucar.edu/](https://www.ncl.ucar.edu/)) and R, and reproducing the results requires NCL and R to be installed. The functionality of NCL was migrated into Python in 2019, and the project is called the Geosciences Community Analysis Toolkit (GeoCAT). NCL users can switch directly to Python with little barrier. ## Access information Raw data was derived from the following sources: * NDVI data were from the GIMMS NDVI3g datasets ([https://ecocast.arc.nasa.gov](https://ecocast.arc.nasa.gov)) and the PKU GIMMS NDVI datasets ([https://doi.org/10.5281/zenodo.8253971](https://doi.org/10.5281/zenodo.8253971)). * Vegetation type data were obtained from the GLASS-GLC datasets ([https://doi.org/10.1594/PANGAEA.913496](https://doi.org/10.1594/PANGAEA.913496)) and the ESA CCI Land Cover datasets ([https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview](https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview)) . * Tree cover data were from the VCF5KYR datasets ([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)). * MAT and MAP data were from the CRU datasets ([https://crudata.uea.ac.uk/cru/data/hrg/](https://crudata.uea.ac.uk/cru/data/hrg/)). * ST and SW were from the ESA5 datasets ([https://cds-dev.copernicus-climate.eu/](https://cds-dev.copernicus-climate.eu/)).