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

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.

DOI

https://doi.org/10.5065/f21v-ya03

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
wchapman@ucar.edu

Data Curator

GDEX Curator
UCAR/NCAR - GDEX
gdex@ucar.edu

Credit

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.

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Version History

1.0