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

TitleThe skill of atmospheric linear inverse models in hindcasting the Madden-Julian Oscillation
Publication TypeJournal Article
Year of Publication2015
AuthorsCavanaugh N.R, Allen T., Subramanian A., Mapes B., Seo H, Miller AJ
JournalClimate Dynamics
Volume44
Pagination897-906
Date Published2015/02
Type of ArticleArticle
ISBN Number0930-7575
Accession NumberWOS:000349406100017
Keywordsenso; extended-range; hemisphere; hindcast; Linear inverse; madden-julian oscillation; model; multivariate mjo index; outgoing longwave radiation; pattern-analysis; predictability; prediction; sea-surface temperatures; statistical forecast model; Tropical dynamics; variability
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.

DOI10.1007/s00382-014-2181-x
Short TitleClim. Dyn.
Student Publication: 
No
sharknado