Future_Global_EnS_Database: Global Environmental Stratification - Algorithm (Python) Code
The algorithms used to conduct the geospatial analysis for the Future_Global_EnS_Database were coded in Python 3.2 and uses the ArcPy (ArcGIS) package. They are available here for download and include scripts to:
- Calculate Input Variables
- Signature Analysis
- Maximum Likelihood - Classify
- Multimodel Ensemble - Classify
- Multimodel Ensemble Uncertainty
The Future_Global_EnS_Database, contains the results of the global geospatial analysis of projected climate change impacts on the geographic distribution and shifting of bioclimatic strata, and is fully described in the publication submitted to Open Research Europe:
Zomer, R.J., Metzger, M., Xu, J., Trabucco, A. (2025). High Resolution Global Environmental Stratification to Model Shifting Bioclimatic Conditions and Climate Change Impacts on Terrestrial Ecosystems. Open Research Europe. Open Research Europe (In Prep).
The Future_Global_EnS_Database provides a high-resolution (30 arc-seconds) global raster dataset of bioclimatic zones and strata produced using the Environmental Stratification (EnS) methodology. This release includes reconstructions of current and future global environmental stratifications for:
- Two historical periods: 1960–1990 (WorldClim 1.4), 1970–2000 (WorldClim 2.1)
- Two future periods: 2021–2040, 2041–2060
- 22 CMIP6 Earth System Models (ESMs)
- Four emission scenarios (SSPs): SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5
- Two multi-model averaged ensembles:
- Consensus Ensemble (15 ESMs, excluding high ECS models)
- High-Risk Ensemble (5 “hot” models projecting extreme climate outcomes)
The Future_Global_EnS_Database is available from Open Research Europe Repository (Zenodo) at this link:
- Zomer, R.J., Trabucco, A. . v1. (2025). Future_Global_EnS: CMIP6 Global Environmental Stratification. Open Research Europe Repository: Zenodo. DOI:10.5281/zenodo.15099764
Contact: Robert Zomer - r.zomer@cmcc.it