4  WorldClim

This manual is still under development and may be subject to change.

This warning will be removed once the manual is finalized.

WorldClim is a project created by Robert J. Hijmans, Stephen E. Fick, and colleagues that provides high-resolution global climate data for spatial mapping and modeling (Fick & Hijmans, 2017). These datasets include historical series downscaled from CRU-TS-4.09, containing data interpolated from thousands of weather stations, as well as future projections derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) models.

In agent-based models, WorldClim data can help simulate the effects of climate change on species distributions, ecosystem dynamics, and human-environment interactions, adding structural realism and complexity to simulations.

4.1 Data Series

LogoClim supports simulation with all three climate data series provided by WorldClim 2.1. Each series is available at multiple spatial resolutions (from 10 minutes (~340 km² at the equator) to 30 seconds (~1 km² at the equator)).

4.1.1 Historical Climate Data

This series includes only 12 monthly data points representing long-term average climate conditions for the period 1970-2000. It provides averages on minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, vapor pressure, elevation, and on bioclimatic variables.

4.1.2 Historical Monthly Weather Data

This series includes 12 monthly data points for each year from 1951 to 2024, based on downscaled data from CRU-TS-4.09, developed by the Climatic Research Unit at the University of East Anglia. It provides monthly averages for minimum temperature, maximum temperature, and total precipitation.

4.1.3 Future Climate Data

This series includes 12 monthly data points from downscaled climate projections derived from CMIP6 models for four future periods: 2021-2040, 2041-2060, 2061-2080, and 2081-2100. The projections cover four SSPs: 126, 245, 370, and 585, with data available for average minimum temperature, average maximum temperature, total precipitation, and bioclimatic variables.

4.2 WorldClim Data with LogoClim

LogoClim relies on raster data to represent climate variables. The datasets are available for download from WorldClim 2.1, but must be converted to ASCII format for compatibility with NetLogo.

To simplify this workflow, we developed two functions in the orbis R package (Vartanian, 2026) to streamline the process of data extraction and processing:

Using these functions effectively requires a basic understanding of the R programming language and its ecosystem. This manual and the package documentations provides instructions for their use. For additional guidance on R, consider exploring Garrett Grolemund’s (2014) Hands-On Programming with R or Hadley Wickham’s (2023) R for Data Science.

If you’re already familiar to WorldClim, you can skip this section and proceed to the the Data Download section.

4.3 WorldClim License

It’s important to note that WorldClim data is freely available only for non-commercial use. If you wish to use the data for commercial purposes, you must obtain permission from the WorldClim team.

Here is a summary of the licensing terms:

The data are freely available for academic use and other non-commercial use.
Redistribution or commercial use is not allowed without prior permission.
Using the data to create maps for publishing of academic research articles is
allowed. Thus you can use the maps you made with WorldClim data for figures in
articles published by PLoS, Springer Nature, Elsevier, MDPI, etc. You are
allowed (but not required) to publish these articles (and the maps they
contain) under an open license such as CC-BY as is the case with PLoS
journals and may be the case with other open access articles.

You can find more about the WorldClim licensing terms here.

4.4 WorldClim Literature

Each dataset is accompanied by literature detailing its creation, methodology, and potential applications. Always refer to the WorldClim website for proper citation guidelines and to access the most current and relevant references for your work.

The main references are the ones below:

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086

Harris, I., Osborn, T. J., Jones, P., & Lister, D. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7(1), 109. https://doi.org/10.1038/s41597-020-0453-3

Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016