Feb 12, 2024

Data for Exploring the Relative Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability

Cookies must be enabled.

Version: 1.0

Here we explore the relative contribution of the Madden-Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherently interpretable neural network applied to pre-industrial control runs of the Community Earth System Model version 2. We find that this interpretable network generally favors the state of ENSO, rather than the MJO, to make correct predictions on a range of subseasonal lead times and predictand averaging windows. Moreover, the predictability of positive circulation anomalies over the North Pacific is comparatively lower than that of their negative counterparts, especially evident when the ENSO state is important. However, when ENSO is in a neutral state, our findings indicate that the MJO provides some predictive information, particularly for positive anomalies. We identify three distinct evolutions of these MJO states, offering fresh insights into opportune forecasting windows for MJO teleconnections.

Download Data and Documentation
38 Files, 1.02 TB Total Size

Individual Files - View, select, and download individual files from this Dataset.

Zip File - Download a ZIP file containing all files.

Python script - Download all files via Python 3, preferred for all operating systems.

Wget shell script - Download all files using Wget, preferred for Linux.

Curl shell script - Download all files via Curl, preferred for MacOS.

Temporal Range
0100 to 0400
Spatial Resolution
1.0 degree
Related Links
Github - Code Repository
GCMD Science Keywords
  • Climate Indicators > Atmospheric/Ocean Indicators > Teleconnections > El Nino Southern Oscillation (Enso)
  • Climate Indicators > Atmospheric/Ocean Indicators > Teleconnections > Madden-Julian Oscillation
GCMD Platform Types
  • Other > Models > Cesm > Ncar Community Earth System Model
File Media Types
  • application/x-netcdf
Support Contact
Will Chapman
UCAR/NCAR - Climate and Global Dynamics Laboratory

Data Curator
GDEX Curator

The work conducted by Kirsten Mayer is supported by the U.S. Department of Energy, Office of Science, Office of Biological & Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program under Award Number DE-SC0022070 and National Science Foundation (NSF) IA 1947282. Will Chapman received M²LInES research funding by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program. Further, this material is based upon work supported by the U.S. National Science Foundation under Grant No. AGS - #2230301 (NSF SOARS). Additionally, this work was supported by the U.S. National Science Foundation National Center for Atmospheric Research (NSF NCAR), which is a major facility sponsored by the NSF under Cooperative Agreement No. 1852977.
Legal Constraints
Creative Commons Attribution 4.0 International License.
Access Constraints
Full Metadata
ISO19139 XML
Version History

Latitude Range
90.0° N to 90.0° S
Longitude Range
180.0° W to 180.0° E
Will Chapman
Kirsten Mayer

Suggested Citation
Will Chapman, Kirsten Mayer. (2024). Data for Exploring the Relative Contribution of the MJO and ENSO to Midlatitude Subseasonal Predictability. Version 1.0. UCAR/NCAR - GDEX. https://doi.org/10.5065/f21v-ya03. Accessed 24 Apr 2024.
Download Volume
247.36 MB Total