DOI: 10.1007/s00382-015-2658-2
Scopus记录号: 2-s2.0-84959156967
论文题名: Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data
作者: Székely E. ; Giannakis D. ; Majda A.J.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期: 2017-05-06 起始页码: 1473
结束页码: 1502
语种: 英语
英文关键词: Dimension reduction
; MJO
; Regime predictability
; Tropical intraseasonal oscillations
英文摘要: This work studies the spatiotemporal structure and regime predictability of large-scale intraseasonal oscillations (ISOs) of tropical convection in satellite observations of infrared brightness temperature ((Formula presented.)). Using nonlinear Laplacian spectral analysis (NLSA), a data analysis technique designed to extract intrinsic timescales of dynamical systems, the (Formula presented.) field over the tropical belt (Formula presented.) and the years 1983–2006 (sampled every 3 h at (Formula presented.) resolution) is decomposed into spatiotemporal modes spanning interannual to diurnal timescales. A key advantage of NLSA is that it requires no preprocessing such as bandpass filtering or seasonal partitioning of the input data, enabling simultaneous recovery of the dominant ISOs and other patterns influenced by or influencing ISOs. In particular, the eastward-propagating Madden–Julian oscillation (MJO) and the poleward-propagating boreal summer intraseasonal oscillation (BSISO) naturally emerge as distinct families of modes exhibiting non-Gaussian statistics and strong intermittency. A bimodal ISO index constructed via NLSA is found to have significantly higher discriminating power than what is possible via linear methods. Besides MJO and BSISO, the NLSA spectrum contains a multiscale hierarchy of modes, including the annual cycle and its harmonics, ENSO, and modulated diurnal modes. These modes are used as predictors to quantify regime predictability of the MJO amplitude in (Formula presented.) data through a cluster-based framework. It is found that the most predictable MJO regimes occur before the active-MJO season (November–December), when ENSO has a strong influence on the future statistical behavior of MJO activity. In forecasts initialized during the active-MJO period (February), both ENSO and the current state of MJO are significant predictors, but the predictive information provided by the large-scale convective regimes in (Formula presented.) is found to be smaller than in the early-season forecasts. © 2015, Springer-Verlag Berlin Heidelberg. 资助项目: ONR, Office of Naval Research
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53797
Appears in Collections: 过去全球变化的重建
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作者单位: Courant Institute of Mathematical Sciences, New York University, 251 Mercer St, New York, NY, United States
Recommended Citation:
Székely E.,Giannakis D.,Majda A.J.. Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data[J]. Climate Dynamics,2016-01-01,46(2017-05-06)