Learning how to work with datasets from climate models can be daunting, even for those with existing technical expertise.
Climate DataLab provides "end-to-end" training on all aspects of the process:
- Understanding the fundamentals of how climate models are set up
- Basics of file formats used for storing climate model output (netCDF)
- Coordinate systems and dealing with 1D vs. 2D latitude and longitude information
- Calendar systems employed by climate models
- Scenarios of future climate change
- Naming schemes for both climate models and model experiments: from scenarios t o MIPs
- Climate model large ensembles
- Model "parameterizations" and inter-model physical differences
👉 Check out our website at www.climate-datalab.org for much more information!
- To increase the usability of climate model output by the broader environmental science community as well as other interested groups
- To foster learning across demographics historically underrepresented in climate science
- To provide transparent, reproducible, and modular code-based workflows for education and research, as well as other applications
Most tutorials are written in Python or R. Some run directly on Binder, while others may require a local setup:
Tip: RStudio can be used for both R and Python code!
Here is a summary of our current repositories, along with the major topics they cover. These are listed in order of increasing complexity, but you can work on them in any order you like!
| Repository | Description | Language |
|---|---|---|
| 🚀 Getting-Started-Tutorials | Onboarding tutorials for installing software and working with climate data. | Python |
| 📈 Time-Series-Plots | Covers the basics of regional averaging (with and without area weighting) and the generation of time series plots. | Python |
| 📉 CMIP6_Trends | Illustrates how to plot time series behavior from climate models; similar to the content of "Time-Series-Plots". | R |
| ☁️ CMIP6_AWS | Covers accessing CMIP6 model data using AWS cloud infrastructure. | Python |
| 🗺️ Map-Plots | Generate publication-quality maps with Python (e.g., Matplotlib, Cartopy). | Python |
| 📊 EnsembleAnalysis | Analyze multi-model ensembles for variability, spread, and agreement. | Python |
| 🗺️ Spatial_Stats | Tutorials on building maps of correlation/linear regression coefficients, using CMIP6 AWS holdings. | Python |
| ✅ Cal-Adapt-diagnostics | Diagnostic tools for evaluating Cal-Adapt downscaled climate model projections. | Python |
We welcome contributions from all backgrounds and experience levels!
Ways to get involved:
- 📥 Fork a repository and open a pull request
- 🐛 Report bugs or request features via GitHub Issues
- ✍️ Share ideas and feedback to help us improve
Climate DataLab is supported by a growing community of educators, scientists, and developers working together to make climate data science more inclusive and effective.