Abstract

Abstract. This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options based on modern language formats, and high code quality standards following the best practices for software development.

Highlights

  • The future generations of Earth system model (ESM) experiments will challenge the scientific community with an increasing amount of model results to be analyzed, evaluated, and interpreted

  • This paper aims at providing a general, technical overview of ESMValTool v2.0

  • A new version of ESMValTool has been developed to address the challenges posed by the increasing data volume of simulations produced by Earth system models as contributions to large model intercomparison projects, such as CMIP Phase 6 (CMIP6)

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Summary

Introduction

The future generations of Earth system model (ESM) experiments will challenge the scientific community with an increasing amount of model results to be analyzed, evaluated, and interpreted. Based on the user settings, ESMValCore reads in the input data (models and observations), applies the required preprocessing operations, and writes the output to netCDF files. A large part of the ESMValTool workflow manager and of the interface, handling the communication between the Python core and the multi-language diagnostic packages at a lower level, has been completely rewritten following the most recent coding standards for code syntax, automated testing, and documentation These quality standards are strictly applied to the ESMValCore package, while for the diagnostics more relaxed standards are used to allow a larger community to contribute code to ESMValTool. Additional features, such as the handling of external observational datasets, provenance, and tagging, as well as the automated testing are briefly summarized in Sect. Code standards are somewhat more relaxed, since compliance with all of these standards can be quite challenging and may introduce unnecessary hurdles for scientists contributing their diagnostic code to ESMValTool

New recipe format
Documentation section
Datasets section
Preprocessor section
Diagnostics section
Advanced recipe features
Data preprocessing
Variable derivation
CMOR check and fixes
Level selection and vertical interpolation
Land–sea fraction weighting
Horizontal regridding
Missing value masking
Temporal and spatial subsetting
Detrend
4.10 Multi-model statistics
4.11 Temporal and spatial statistics
4.12 Unit conversion
CMORization of observational datasets
Provenance and tags
Automated testing and coding standards
Performance and scaling tests
Findings
Summary
Full Text
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