NA-CORDEX Data Visualization Cookbook

nightly-build Binder DOI

The NA-CORDEX data archive contains cloud-optimized, Zarr-based output from regional climate models (RCMs) run over a domain covering most of North America using boundary conditions from global climate model (GCM) simulations in the CMIP5 archive. These simulations run from 1950–2100 with a spatial resolution of 0.22°/25km or 0.44°/50km. Data is available for impacts-relevant variables at daily and longer frequencies.

This Project Pythia Cookbook covers how to load and view data summaries from the NA-CORDEX dataset.

  • Complete documentation for the cloud-optimized version of the NA-CORDEX dataset can be found here:

  • More information on the scope and objectives for NA-CORDEX can be found here:


This cookbook provides useful methods for summarizing data values in large climate datasets. It clearly shows where data have extreme values and where data are missing; this can be useful for validating that the data were gathered and stored correctly. While this cookbook is specifically designed to examine any portion of the NA-CORDEX dataset using an intake catalog, the code can be adapted straightforwardly to other datasets that can be loaded via xarray.

The main python packages used are xarray, intake-esm, dask, and matplotlib.


Brian Bonnlander, Seth McGinnis



This cookbook is a single notebook broken into several sections. The first section allows users to tailor the notebook to their chosen computing environment and storage cloud. Other sections contain code for subsetting and plotting data, obtaining Dask workers, and producing summary plots.

Select User Options

Users may choose the value of several boolean switches:

  • Cloud storage provider (Amazon AWS or NCAR),

  • Whether to truncate the data if running on a resource-limited computer,

  • Whether to obtain a Dask cluster from a PBS Scheduler or via the Dask Gateway package.

Note: Using the NCAR cloud storage system requires a login account on an NCAR HPC computer.

Create and Connect to Dask Distributed Cluster

This section shows several possible ways to create and connect to a Dask cluster for processing data in parallel.

The user can choose to obtain workers using a PBS Scheduler, Dask Gateway, or a dask LocalCluster.

Find and Obtain Data Using an Intake Catalog

This section shows how to interact with an Intake-ESM catalog to find, select, and open datasets via xarray.

Functions for Subsetting and Plotting

This section provides python functions for subsetting and plotting data. They rely on the xarray and matplotlib packages, and can be generalized to other climate datasets.

Produce Diagnostic Plots

This section produces the diagnostic plots. It shows how to produce timing information and gives options for saving high-resolution images of the resulting plots.

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing Shift+Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

  1. Clone the repository:

     git clone
  2. Move into the na-cordex-viz-cookbook directory

    cd na-cordex-viz-cookbook
  3. Create and activate your conda environment from the environment.yml file

    conda env create -f environment.yml
    conda activate na-cordex-viz-cookbook

    Note: If building the environment stalls using conda, consider instead installing the mamba package and using mamba env create -f environment.yml.

  4. Move into the notebooks directory and start up Jupyterlab

    cd notebooks/
    jupyter lab