M2: Computational Climate Science

How can we analyze gridded climate datasets with spatial and temporal attributes? How is climate variability measured and modeled?

The second module of our open climate-science curriculum focuses on preparing learners to work with gridded climate datasets. At the end of this module, you should be able to:

• Learn what indices are available for meteorological drought, soil moisture drought, atmospheric water demand, and soil water balance.
• Efficiently load and analyze big climate datasets, including long climate data records.
• Calculate a drought index.

Contents

1. Managing Software Dependencies
2. Working with Gridded Climate Data
3. Climate and Drought Indices
4. Processing Long Climate Data Records Concurrently
5. Creating a Reproducible Climate Analysis

Getting Started

See our installation guide here.

You can run the notebooks in this repository using Github Codespaces or as a VSCode Dev Container. Once your container is running, launch Jupyter Notebook by:

# Create your own password when prompted
jupyter server password

# Then, launch Jupyter Notebook; enter your password when prompted
jupyter notebook

The Python libraries required for the exercises can be installed using the pip package manager:

pip install xarray netcdf4 dask

Learning Outcomes

• Uses meaningful but brief filenames and folder names. Uses one of the following strategies: Timestamps, Process hierarchy, or Filename metadata. (CC1.3)
• Uses a package manager to install and manage software dependencies. (CC1.10)
• Knows how to navigate a file system using both a graphical user interface (GUI) and a command-line interface (CLI). (CC1.4)
• Records relationships between code, results, and metadata. (CC1.5)
• Understands machine representations of numeric data types. (CC2.1)
• Can debug a computational workflow, either by deduction, print statements, breakpoints, or an interactive debugger. (CC2.7)

Climate Datasets Used

• Terrestrial water storage anomalies from the NASA GRACE and GRACE-FO missions
• Monthly precipitation estimates for Africa from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset
• Monthly potential evapotranspiration data from TerraClimate
• Daily mean, minimum, and maximum air temperatures from the NASA Global Modeling and Assimilation Office's MERRA-2 re-analysis dataset

Acknowledgements

This curriculum was enabled by a grant from NASA's Transition to Open Science (TOPS) Training program (80NSSC23K0864), part of NASA's TOPS Program