| DOI: | 10.1175/JCLI-D-16-0563.1
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| Scopus记录号: | 2-s2.0-85021074554
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| 论文题名: | Investigating nonlinear dependence between climate fields |
| 作者: | Fischer M.J.
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| 刊名: | Journal of Climate
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| ISSN: | 8948755
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| 出版年: | 2017
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| 卷: | 30, 期:14 | | 起始页码: | 5547
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| 结束页码: | 5562
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| 语种: | 英语
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| Scopus关键词: | Earth atmosphere
; Neural networks
; Oceanography
; Sea ice
; Empirical Orthogonal Function
; Non-linear model
; Oscillations
; Statistical techniques
; Tropical variability
; Orthogonal functions
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| 英文摘要: | The Earth's ice-ocean-atmosphere system exhibits nonlinear responses, such as the difference in the magnitude of the atmospheric response to positive or negative ocean or sea ice anomalies. Two classes of methods that have previously been used to investigate the nonlinear dependence between climate fields are kernel methods and neural network methods. In this paper, a third methodology is introduced: gradient-based kernel dimension reduction. Gradient-based kernel methods are an extension of conventional kernel methods, but gradient-based methods focus on the directional derivatives of the regression surface between two fields. Specifically, a new gradient-based method is developed here: gradient kernel canonical correlation analysis (gKCCA). In gKCCA, the canonical directions maximize the directional derivatives between the predictor field and the response field, while the canonical components of the response field maximize the correlation with a nonlinear augmentation of the predictor canonical components. Gradient-based kernel methods have several advantages: their components can be directly related to the original fields (unlike in conventional kernel methods), and the projection vectors are determined by analytical solution (unlike in neural networks). Here gKCCA is applied to the question of nonlinear coupling between high-frequency (2-3 years) and low-frequency (4-6 years) modes in the Pacific Ocean. The leading gKCCA subspace shows a significant nonlinear coupling between the low-pass and high-pass fields. The paper also shows that the results of gKCCA are robust to different levels of noise and different kernel functions. © 2017 American Meteorological Society. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/48858
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| Appears in Collections: | 气候变化与战略
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作者单位: | Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW, Australia
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| Recommended Citation: | |
Fischer M.J.. Investigating nonlinear dependence between climate fields[J]. Journal of Climate,2017-01-01,30(14)
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