ESMValTool

The Earth System Model eValuation Tool is a community diagnostics and performance metrics tool for the evaluation of Earth System Models that allows for routine comparison of models and observations.

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What ESMValTool can do for you

  • Facilitates the complex evaluation of ESMs and their simulations submitted to international Model Intercomparison Projects (e.g., CMIP).
  • Standardized model evaluation can be performed against observations, against other models or to compare different versions of the same model.
  • Wide scope: includes many diagnostics and performance metrics covering different aspects of the Earth System (dynamics, radiation, clouds, carbon cycle, chemistry, aerosol, sea-ice, etc.) and their interactions.
  • Well-established analysis: standard recipes reproduce specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature.
  • High flexibility: new diagnostics and more observational data can be easily added.
  • Multi-language support: Python, NCL, R, Julia... other open-source languages are possible.
  • CF/CMOR compliant: data from many different projects can be handled (CMIP, obs4mips, ana4mips, CCMI, CCMVal, AEROCOM, etc.). Routines are provided to CMOR-ize non-compliant data.
  • Integration in modeling workflows: for EMAC, NOAA-GFDL and NEMO, can be easily extended.

The Earth System Model eValuation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth System Models (ESMs) that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected Essential Climate Variables, a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases and soil hydrology-climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard recipes for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The ESMValTool is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the CMIP ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within the Coupled Model Intercomparison Project (CMIP) and at individual modeling centers. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.

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Tags
  • Workflow technologies
  • Big data
  • Visualization
  • Optimized data handling
Programming Language
  • Python
  • R
  • YAML
License
  • Apache-2.0
Source code

Participating organizations

Contributors

  • Bouwe Andela
    Netherlands eScience Center
  • Niels Drost
    Netherlands eScience Center
  • Faruk Diblen
    Netherlands eScience Center
  • Peter Kalverla
    Netherlands eScience Center
  • Stefan Verhoeven
    Netherlands eScience Center
  • Fakhereh Alidoost
    Netherlands eScience Center
  • Jaro Camphuijsen
    Netherlands eScience Center
  • Yifat y Dzigan
    Netherlands eScience Center
  • Inti Pelupessy
    Netherlands eScience Center
  • Jerom Aerts
    Delft University of Technology
  • Stef Smeets
    Netherlands eScience Center
  • Klaus Zimmermann
    Swedish Meteorological and Hydrological Institute
  • Nikolay Koldunov
    Alfred Wegener Institute
  • Lee de Mora
    Plymouth Marine Laboratory
  • Veronika Eyring
    DLR
  • Javier Vegas-Regidor
    Barcelona Supercomputing Center
  • Benjamin Müller
    Ludwig Maximilian University of Munich
  • Björn Brötz
    DLR
  • Mattia Rigi
    DLR
  • Axel Lauer
    DLR
  • Valeriu Predoi
    University of Reading
  • Manuel Schlund
    DLR
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Contact person
Bouwe Andela
Netherlands eScience Center

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