LogoClim

Overview

LogoClim is a NetLogo model for simulating
and visualizing global climate conditions. It allows researchers to
integrate high-resolution climate data into agent-based models,
supporting reproducible research in ecology, agriculture, environmental
sciences, and other fields that rely on climate data.

The model utilizes raster data to represent climate variables such as
temperature and precipitation over time. It incorporates historical data
(1951-2024) and future climate projections (2021-2100) derived from
global climate models under various Shared Socioeconomic Pathways
(SSPs,
O’Neill et al.,
2017). All climate
inputs come from WorldClim 2.1, a widely used
source of high-resolution, interpolated climate datasets based on
weather station observations worldwide (Fick & Hijmans,
2017).

See the Logônia model for an
example of how to integrate LogoClim into your model.

If you find this project useful, please consider giving it a star!  

The continuous development of LogoClim depends on community support.
If you find this project useful, and can afford to do so, please
consider becoming a sponsor.  

[!IMPORTANT]
LogoClim is an independent project with no affiliation to WorldClim or its developers. Users should be aware that WorldClim datasets are freely available for academic and other non-commercial use only. Any use of WorldClim data within LogoClim must comply with WorldClim's licensing terms.

How It Works

LogoClim operates on a grid of patches, where each patch represents a
geographical area and stores values for latitude, longitude, and
selected climate variables. During the simulation, patches update their
colors based on the data values. The results can be visualized on a map,
accompanied by plots that display the mean, minimum, maximum, and
standard deviation of the selected variable over time.

Data Series

The model 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)) and can be selected
within the model interface to fit your research needs. More information
about each series can be found in the WorldClim website.

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.

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.

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.

Refer to the Info tab in the model for additional details.

How to Use It

Setup

To get started, ensure you have NetLogo
installed. This model was developed using NetLogo 7.0.0, so it is
recommended to use this version or later.

The model relies on the GIS,
Pathdir,
String, and
Time NetLogo extensions. These
are automatically installed when the model is run for the first time.

Downloading the Model

You can download the latest release of the model from its GitHub
releases page.
For the development version, you can clone or download its GitHub
repository directly.

To run the model, make sure to download all files in the nlogox
folder. Note that climate data from WorldClim is required but not
included in this repository; see the next section for instructions on
obtaining and preparing the data.

Downloading the Data

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 provide
Quarto notebooks in the repository qmd folder
with reproducible pipelines for downloading and processing the data.
These notebooks can be customized to meet specific research needs.

We also provide example datasets for testing and demonstration. These
files are available in the model’s OSF
repository and are ready to use
with LogoClim.

After downloading and processing the files, place them in the data
folder within the model’s directory. Alternatively, you can use the
Select Data Directory button in the model interface to specify the
location of your data files.

We suggest starting with the 10-minute resolution to verify that the
model runs smoothly on your system before trying higher resolutions.

Running the Model

Once everything is set, open the logoclim.nlogox file located in the
nlogox folder to start exploring!

Refer to the Info tab in the model for additional details.

Integrating with Other Models

LogoClim was created to be integrated with other models using
NetLogo’s
LevelSpace
extension. This extension enables parallel execution and data exchange
between models.

See the Logônia model for an
example of how to integrate LogoClim into your model.

How to Cite

[!IMPORTANT]
When using WorldClim data, you must also cite the original data sources. The appropriate citation depends on the specific dataset utilized. Please refer to the WorldClim website for up-to-date citation guidelines and dataset references.

If you use this model in your research, please cite it to acknowledge
the effort invested in its development and maintenance. Your citation
helps support the ongoing improvement of the model.

To cite LogoClim in publications please use the following format:

Vartanian, D., Garcia, L., & Carvalho, A. M. (2025). LogoClim:
WorldClim in NetLogo [Computer software].
https://doi.org/10.17605/OSF.IO/EAPZU

A BibTeX entry for LaTeX users is:

@Misc{vartanian2025,
  title = {LogoClim: WorldClim in NetLogo},
  author = {{Daniel Vartanian} and {Leandro Garcia} and {Aline Martins de Carvalho}},
  year = {2025},
  doi = {10.17605/OSF.IO/EAPZU},
  note = {Computer software}
}

How to Contribute

Contributions are always welcome! Whether you want to report bugs,
suggest new features, or help improve the code or documentation, your
input makes a difference.

Before opening a new issue, please check the issues
tab to see if your
topic has already been reported.

You can also support the development of LogoClim by becoming a
sponsor.

Click here to make a
donation. Please mention LogoClim in your donation message.

License

Copyright (C) 2025 Daniel Vartanian

LogoClim is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.

Acknowledgments

We gratefully acknowledge Stephen E.
Fick, Robert J.
Hijmans, and the entire
WorldClim team for their outstanding work in
creating and maintaining the WorldClim datasets.

We thank the Climatic Research
Unit
at the University of East Anglia and the
United Kingdom’s Met Office for
developing and providing access to the
CRU-TS-4.09
dataset, a vital source of historical climate data.

We also acknowledge the World Climate Research Programme
(WCRP), its Working Group on Coupled
Modelling, and the Coupled Model Intercomparison Project Phase 6
(CMIP6) for coordinating and advancing
global climate model development.

We are grateful to the climate modeling groups for producing and sharing
their model outputs, the Earth System Grid Federation
(ESGF) for archiving and providing access to
the data, and the many funding agencies that support CMIP6 and ESGF.