This Project Pythia Cookbook covers analysis of CESM LENS data publicly available on Amazon S3 (us-west-2 region) using Xarray and Dask
The National Center for Atmospheric Research (NCAR) Community Earth System Model Large Ensemble (CESM LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100. All model runs were subject to the same radiative forcing scenario: historical up to 2005, and RCP8.5 thereafter. RCP8.5 - Representative Concentration Pathway 8.5 - refers to the worst-case scenario considered in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Each of the 40 runs begins from a slightly different initial atmospheric state (created by randomly perturbing temperatures at the level of round-off error). The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains.
The total LENS data volume is ~500 TB, and is traditionally accessible through the NCAR Climate Data Gateway (CDG) for download or via web services. A subset (currently ~70 TB compressed) including the most useful variables is now freely available on AWS S3 thanks to the AWS Public Dataset Program.
There is one notebook in this section that describes how to access the CESM LENS data from AWS using Intake ESM. It includes examples of using an enhanced catalog.
You can either run the notebook using Binder or on your local machine.
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.
If you are interested in running this material locally on your computer, you will need to follow this workflow:
(Replace “cookbook-example” with the title of your cookbooks)
git clone https://github.com/ProjectPythia/cesm-lens-aws-cookbook.git
Move into the
Create and activate your conda environment from the
conda env create -f environment.yml conda activate cla-cookbook-dev
Move into the
notebooksdirectory and start up Jupyterlab
cd notebooks/ jupyter lab