Incorrect computation of Madden-Julian oscillation prediction skill

Details

• Journal Title:
  npj Climate and Atmospheric Science
• Personal Author:
  Suematsu, Tamaki ; Martin, Zane K. ; Barnes, Elizabeth A. ; DeMott, Charlotte A. ; Hagos, Samson ; Ham, Yoo-Geun ; Kim, Daehyun ; Kim, Hyemi ; Koh, Tieh-Yong ; Maloney, Eric D.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; WPO (Weather Program Office)
• Description:
  The Madden–Julian oscillation (MJO) is a major tropical weather system and one of the largest sources of predictability for subseasonal-to-seasonal weather forecasts. Skillful prediction of the MJO has been a highly active area of research due to its large socio-economic impacts. Silini et al., herein S21, developed a machine learning model to predict the MJO, which they claimed to have an MJO prediction skill of 26–27 days over all seasons and 45 days for December–February (DJF) winter. If true, this would make the skill of their model competitive with that of the state-of-the-art dynamical MJO prediction systems at 20–35 days. However, here we show that the MJO prediction was calculated incorrectly in S21, which spuriously increased the performance of their model. Correctly computed skill of their model was substantially lower than that reported in S21; the skill for all seasons drops to 11–12 days and the skill for forecasts initialized during DJF drops to 15 days. Our findings clarify that the S21 machine learning model is not competitive with state-of-the-art numerical weather prediction models in predicting the MJO.
• Source:
  npj Climate and Atmospheric Science, 7(1)
• DOI:
  https://doi.org/10.1038/s41612-024-00687-1
• ISSN:
  2397-3722
• Format:
  PDF
• Publisher:
  Springer Science and Business Media LLC
• Document Type:
  Journal Article
• Funding:
  Grant no. NA19OAR4590151 ; Grant no. NA22OAR4590168 ; Grant no. NA22OAR4310608
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:204c3640869951e95e3ad22f223dbdb74f744e4ad8dfd87a49f85d7bfbaec24b66c80863ca4782a8b2914525e9daa0e88d5a6a2afef399b1098a3968956227be
• Download URL:
  https://repository.library.noaa.gov/view/noaa/70242/noaa_70242_DS1.pdf
• File Type:
  [PDF - 470.57 KB ]