/pcmdi_metrics

Open-source Python package for Systematic Evaluation of Climate and Earth System Models

Primary LanguageJupyter NotebookBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause





PCMDI Metrics Package (PMP)

latest version Last updated platforms DOI License Formatted with black All Contributors

Conda-forge (NEW, recommended): Download

PCMDI Conda Channel (halted): Download

The PCMDI Metrics Package (PMP) is used to provide "quick-look" objective comparisons of Earth System Models (ESMs) with one another and available observations. Results are produced in the context of all model simulations contributed to CMIP6 and earlier CMIP phases. Among other purposes, this enables modeling groups to evaluate changes during the development cycle in the context of the structural error distribution of the multi-model ensemble. Currently, the comparisons emphasize metrics of large- to global-scale annual cycle, tropical and extra-tropical modes of variability, ENSO, MJO, regional monsoons, high frequency characteristics of simulated precipitation, and cloud feedback.

PCMDI uses the PMP to produce quick-look simulation summaries across generations of CMIP.

The metrics package consists of the following parts:

The package expects model data to be CF-compliant. To successfully use the package some input data "conditioning" may be required. We provide several demo scripts within the package.

Documentation

Getting Started

  • Installation requirements and instructions are available on the Install page

  • Users will need to contact the PMP developers (pcmdi-metrics@llnl.gov) to obtain supporting datasets and get started using the package.

  • An overview for using the package and template scripts are detailed on the Using-the-package page

  • View Demo

Contact

Report Bug

Request Feature

Some installation support for CMIP participating modeling groups is available: pcmdi-metrics@llnl.gov

Acknowledgement

Content in this repository is developed by climate and computer scientists from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) at Lawrence Livermore National Laboratory (LLNL). This work is sponsored by the Regional and Global Model Analysis (RGMA) program, of the Earth and Environmental Systems Sciences Division (EESSD) in the Office of Biological and Environmental Research (BER) within the Department of Energy's Office of Science. The work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Program for Climate Model Diagnosis and Intercomparison  United States Department of Energy  Lawrence Livermore National Laboratory

License

Distributed under the BSD 3-Clause License. See LICENSE for more information.

Release Notes and History

Update summary
v3.3.1 Technical update
v3.3 New metric added: Sea-Ice
v3.2 New metric added: Extremes
v3.1.2 Technical update
v3.1.1 Technical and documentation update
v3.1 New metric added: Precipitation Benchmarking -- distribution bimodality
v3.0.2 Minor patch and more documentation added
v3.0.1 Minor technical patch
v3.0.0 New metric added: Cloud feedback metric by @mzelinka. xCDAT implemented for mean climate metrics
v2.5.1 Technical update
v2.5.0 New metric added: Precipitation Benchmarking -- distribution. Graphics updated
v2.4.0 New metric added: AMO in variability modes
v2.3.2 CMEC interface updates
v2.3.1 Technical update
v2.3 New graphics using archived PMP results
v2.2.2 Technical update
v2.2.1 Minor update
v2.2 New metric implemented: precipitation variability across time scale
v2.1.2 Minor update
v2.1.1 Simplified dependent libraries and CI process
v2.1.0 CMEC driver interfaced added.
v2.0 New capabilities: ENSO metrics, demos, and documentations.
v1.2 Tied to CDAT 8.0. Extensive regression testing added. New metrics: Diurnal cycle and intermittency of precipitation, sample monsoon metrics.
v1.1.2 Now managed through Anaconda, and tied to UV-CDAT 2.10. Weights on bias statistic added. Extensive provenance information incorporated into json files.
v1.1 First public release, emphasizing climatological statistics, with development branches for ENSO and regional monsoon precipitation indices
v1.0 Prototype version of the PMP

Contributors

Thanks goes to these wonderful people (emoji key):

Jiwoo Lee
Jiwoo Lee

💻 📖 👀 ⚠️ 🔬 🤔 🚇
Peter Gleckler
Peter Gleckler

💻 📖 🔬 👀 ⚠️ 🔣 🤔
Ana Ordonez
Ana Ordonez

💻 📖 👀 ⚠️ 🚇
Min-Seop Ahn
Min-Seop Ahn

💻 📖 👀 ⚠️ 🔬
Paul Ullrich
Paul Ullrich

🤔 🔬
Charles Doutriaux
Charles Doutriaux

💻
Karl Taylor
Karl Taylor

🔬 🤔
Paul J. Durack
Paul J. Durack

💻
Mark Zelinka
Mark Zelinka

💻
Celine Bonfils
Celine Bonfils

🔬
Curtis C. Covey
Curtis C. Covey

💻 🔬
Zeshawn Shaheen
Zeshawn Shaheen

💻
Lina Muryanto
Lina Muryanto

🚇
Tom Vo
Tom Vo

🚇
Jason Boutte
Jason Boutte

🚇
Jeffrey Painter
Jeffrey Painter

🔣 🚇 💻
Stephen Po-Chedley
Stephen Po-Chedley

🔣 🚇
Xylar Asay-Davis
Xylar Asay-Davis

🚇
John Krasting
John Krasting

💻 ⚠️
Angeline G Pendergrass
Angeline G Pendergrass

💻 🔬 🤔
Michael Wehner
Michael Wehner

💻 🔬
Daehyun Kim
Daehyun Kim

💻 🔬

This project follows the all-contributors specification.