The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation

Nicholas R. Cavanaugh, Teddy Allen, Aneesh Subramanian, Brian Mapes, Hyodae Seo, Arthur J. Miller

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

A suite of statistical atmosphere-only linear inverse models of varying complexity are used to hindcast recent MJO events from the Year of Tropical Convection and the Cooperative Indian Ocean Experiment on Intraseasonal Variability/Dynamics of the Madden–Julian Oscillation mission periods, as well as over the 2000–2009 time period. Skill exists for over two weeks, competitive with the skill of some numerical models in both bivariate correlation and root-mean-squared-error scores during both observational mission periods. Skill is higher during mature Madden–Julian Oscillation conditions, as opposed to during growth phases, suggesting that growth dynamics may be more complex or non-linear since they are not as well captured by a linear model. There is little prediction skill gained by including non-leading modes of variability.

Original languageEnglish (US)
Pages (from-to)897-906
Number of pages10
JournalClimate Dynamics
Volume44
Issue number3-4
DOIs
StatePublished - Feb 2014

Keywords

  • Hindcast
  • Linear inverse model
  • Madden–Julian Oscillation
  • Predictability
  • Tropical dynamics

ASJC Scopus subject areas

  • Atmospheric Science

Fingerprint

Dive into the research topics of 'The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation'. Together they form a unique fingerprint.

Cite this