This repo queries and downloads data from CMIP6 models to create machine learning-based emulators. The repo is based on the Pangeo library on how to download data from the Pangeo CMIP6 archive on Google Cloud.
conda env create -f environment.yml
conda activate cesm-em
jupyter notebook code/explore_cesm_data.ipynb
- Notebooks to preprocess and download CMIP6 data:
- explore_cesm_data: Queries, stores, and animates CMIP6 CESM data
- ECS_Gregory_method: Approximate the equilibrium climate sensitivity (ECS) of CMIP6 models via the "Gregory method" using the first 150 years after abrupt quadrupling of CO2 concentrations
- global_mean_surface_temp: Calculates the global mean surface temperature using similar methods to "Gregory method"
- intake_ESM_example: Queries CMIP6 data; experimental and still unstable
- precip_frequency_change: Calculate the distribution of precipitation intensity
- Notebooks to create emulators
- python_models_copy: Tests various deep learning models on CMIP6 data. Still in dev.
- Numpy files containing 3D arrays of lat, lon, time, and variable (instructions on extracting this info below)
- A configuration file,
binder-gallery.yaml, which provides important configuration parameters (see pangeo gallery documentation).
- Explore Data:
- Go to explore_cesm_data
- Query data using desired filters:
- Need only three filters (experiment_id, table_id, variable_id) but can use more if needed
- Default values should be "activity_id=='CMIP' & table_id == 'Amon' & variable_id == 'tas' & experiment_id == 'historical'"
- Scroll down to for loop where data is saved as numpy then stored into a numpy file
- Change numpy file name to desired name
- Change data variables to new variable_id (replace all the "tas" with new variable_id)