Climate Classifications

A collection of Python notebooks and scripts for classifying global climate scenarios using different climate classification systems:

• Holdridge Life Zones
• Köppen-Geiger Climate Classification
• Thornthwaite Climate Classification

Overview

This project uses NASA's GDDP-CMIP6 climate data through Google Earth Engine to classify global climates. It includes implementations of multiple classification systems to analyze both historical and future climate scenarios.

Prerequisites

• Python 3.11+
• Google Earth Engine account and authentication
• Required Python packages:

  earthengine-api
  pandas
  matplotlib
  

Project Structure

.
├── Holdridge.ipynb          # Holdridge life zones classification
├── Koppen_Geiger.ipynb      # Köppen-Geiger climate classification
├── Thornthwaite.ipynb      # Thornthwaite climate classification
├── holdridge.py            # Helper functions for Holdridge classification
├── 1a_regional_stats.ipynb # Analysis of regional classification data
└── README.md

Setup

1. Install required packages:

   pip install earthengine-api pandas matplotlib

2. Authenticate with Google Earth Engine:

   import ee
   ee.Authenticate()
   ee.Initialize(project='your-project-name')

Data Sources

• Climate Data: NASA-GDDP CMIP6 dataset via Google Earth Engine
• Elevation Data: USGS GTOPO30 Digital Elevation Model
• Regional Boundaries: USDOS LSIB Simple 2017

Usage

Each classification system is implemented in its own Jupyter notebook:

1. Holdridge Life Zones

   • Uses biotemperature, precipitation, and potential evapotranspiration ratio
   • Includes both altitudinal and latitudinal classifications

2. Köppen-Geiger

   • Uses temperature and precipitation thresholds
   • Classifies into main climate groups and subtypes

3. Thornthwaite

   • Based on potential evapotranspiration
   • Considers moisture and thermal indices

Regional statistics can be analyzed using 1a_regional_stats.ipynb.

Output

The classification results are provided as:

• Earth Engine image layers
• Regional statistics in CSV format
• Visualizations using custom color palettes

Contributing

Feel free to open issues or submit pull requests with improvements.

License

This project is licensed under the GNU General Public License v3.0

Contact

Darri Eythorsson, University of Calgary

[email protected]