The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation
Author(s)
Cavanaugh, Nicholas R.; Allen, Teddy; Subramanian, Aneesh; Mapes, Brian; Seo, Hyodae; Miller, Arthur J.; ... Show more Show less
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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.
Date issued
2013-11Department
Woods Hole Oceanographic InstitutionJournal
Climate Dynamics
Publisher
Springer Berlin Heidelberg
Citation
Cavanaugh, Nicholas R. et al. “The Skill of Atmospheric Linear Inverse Models in Hindcasting the Madden–Julian Oscillation.” Climate Dynamics 44.3–4 (2015): 897–906.
Version: Author's final manuscript
ISSN
0930-7575
1432-0894