Published February 19, 2024 | Version v1
Model Open

Extended nonlinear Recharge Oscillator (XRO) model for "Explainable El Niño predictability from climate mode interactions"

Authors/Creators

  • 1. University of Hawai'i at Mānoa

Description

This repository include the XRO model source code for the paper "Explainable El Niño predictability from climate mode interactions" (Zhao et al. 2024, Nature). For the latest version of the XRO code, visit https://github.com/senclimate/XRO.

Description

The XRO is an eXtended nonlinear Recharge Oscillator model for El Niño-Southern Oscillation (ENSO) and other modes of variability in the global oceans. It builds on the legacies of the Hasselmann stochastic climate model capturing upper ocean memory in SST variability, and the recharge oscillator model for the oscillatory core dynamics of ENSO. It constitutes a parsimonious representation of the climate system in a reduced variable and parameter space that still captures the essential dynamics of interconnected global climate variability. 

For the detailed formulation of XRO model, please refer to Methods section of our paper Zhao et al. (2024).

Files

code/

  • XRO.py  XRO model code written in python
  • XRO_Cookbook.ipynb   examples demonstrate how to use XRO and reproduce the analyses presented in Zhao et al. (2024). 
  • XRO_Cookbook.pdf   printed version of XRO_Cookbook.ipynb

data/

  • indices_oras5.nc Sample data for XRO state vectors (Nino34, WWV, NPMM, SPMM, IOB, IOD, SIOD, TNA, ATL3, SASD time series) from detrended ORAS5 reanalysis from 1979-01 to 2023-10 

LICENSE  XRO model code license

README.md   XRO model readme file

XRO_logo.png XRO model logo picture

Source

This XRO model code is hosted at https://github.com/senclimate/XRO. We have designed XRO to be user-friendly, aiming to be a valuable tool not only for research but also for operational forecasting and as an educational resource in the classroom. We hope that XRO proves to be both a practical and accessible tool that enhances your research and teaching experiences. If you encounter problems in running XRO or have questions, please feel free to contact Sen Zhao (zhaos@hawaii.edu) or create issues Here.
 

References

Kindly requested to cite our paper and the code if use the XRO model in your published works.

Zhao, S., Jin, F.-F., Stuecker, M. F., Thompson, P. R., Kug, J.-S., McPhaden, M. J., Cane, M.A., Wittenberg, A.T., Cai, W. (2024). Explainable El Niño predictability from climate mode interactions. Nature. 630(8018), 891-898 https://doi.org/10.1038/s41586-024-07534-6

Zhao, S. (2024). Extended nonlinear Recharge Oscillator (XRO) model for "Explainable El Niño predictability from climate mode interactions". Zenodo. https://doi.org/10.5281/zenodo.10681114

Files

XRO.zip

Files (2.1 MB)

Name Size Download all
md5:46b688485c7229e99ee0f9af685c548f
2.1 MB Preview Download

Additional details

Related works

Is published in
Journal article: 10.1038/s41586-024-07534-6 (DOI)

Dates

Submitted
2024-02-19

Software

Repository URL
https://github.com/senclimate/XRO
Programming language
Python
Development Status
Active