SC5.14

EDI
Earth System Model Evaluation with ESMValTool in the Jupyter notebook
Co-organized by BG9/CL6/ESSI1
Convener: Bouwe Andela | Co-conveners: Fakhereh AlidoostECSECS, Carsten Ehbrecht, Peter C. KalverlaECSECS, Klaus Zimmermann

This Short Course is aimed at researchers in climate-related domains, who have an interest in working with climate data. We will introduce the ESMValTool, a Python project developed to facilitate the analysis of climate data through so-called recipes. An ESMValTool recipe specifies which input data will be used, which preprocessor functions will be applied, and which analytics should be computed. As such, it enables readable and reproducible workflows. The tool takes care of finding, downloading, and preparing data for analysis. It includes a suite of preprocessing functions for commonly used operations on the input data, such as regridding or computation of various statistics, as well as a large collection of established analytics.

In this course, we will run some of the available example recipes using ESMValTool’s convenient Jupyter notebook interface. You will learn how to customize the examples, in order to get started with implementing your own analysis. A number of core developers of ESMValTool will be present to answer any and all questions you may have.

The ESMValTool has been designed to analyze the data produced by Earth System Models participating in the Coupled Model Intercomparison Project (CMIP), but it also supports commonly used observational and re-analysis climate datasets, such as ERA5. Version 2 of the ESMValTool has been specifically developed to target the increased data volume and complexity of CMIP Phase 6 (CMIP6) datasets. ESMValTool comes with a large number of well-established analytics, such as those in Chapter 9 of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) (Flato et al., 2013) and has been extensively used in preparing the figures of the Sixth Assessment Report (AR6). In this way, the evaluation of model results can be made more efficient, thereby enabling scientists to focus on developing more innovative methods of analysis rather than constantly having to "reinvent the wheel".