The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation
Name
382_2014_Article_2181.pdf
Size
2.28 MB
Format
Adobe PDF
Checksum (MD5)
4d6f9e16d8fec0229a4a5a6e7dc0c447
Author(s)
Cavanaugh, Nicholas R.
Allen, Teddy
Subramanian, Aneesh
Mapes, Brian
Seo, Hyodae
Miller, Arthur J.
Date Issued
November 2013
Journal
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
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.
MIT Department
Woods Hole Oceanographic Institution
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s00382-014-2181-x
