CMIP7 Evaluation Hackathon: Glossary

ESMValTool provides a reliable interface to analyse and evaluate climate data

A large collection of recipes and diagnostic scripts is already available

ESMValTool is built and maintained by an active community of scientists and developers

ESMValTool recipes work ‘out of the box’ (if input data is available)

There are strong links between the recipe, log file, and output folders

Recipes can easily be modified to re-use existing code for your own use case

There is supported data on Gadi to start with using ESMValTool and the ILAMB

A recipe can work with different preprocessors at the same time.

The setting additional_datasets can be used to add a different dataset.

Variable groups are useful for defining different settings for different variables.

Multiple ensemble members and experiments can be analysed in a single recipe through concatenation.

ESMValTool provides helper functions to interface a Python diagnostic script with preprocessor output.

Existing diagnostics can be used as templates and modified to write new diagnostics.

Helper functions can be imported from esmvaltool.diag_scripts.shared and used in your own diagnostic script.

ESMValTool can be run in a Jupyter Notebook

Access ImageFiles and DataFiles from the recipe run

API can be used as a helper to develop recipes

Preprocessors can be used in a Jupyter Notebook to check the output

Use datasets_to_recipe helper to start making recipes

The ACCESS-NRI ILAMB-Workflow facilitates the configuration of the ILAMB on NCI Gadi.

Users need to set up a run using a configuration file.

The ilamb-tree-generator allows to quickly build a data directory srtucture for the ILAMB.

The ILAMB can take advantage of the multiple CPUs available on Gadi.

The ILAMB-Workflow only support RAW ACCESS data

Running the ILAMB-Workflow on RAW ACCESS data can take some time. Consider if it is appropriate for your work

Only a limited number of CMIP variables are supported