期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 108, 期 29, 页码 11766-11771出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1015753108
关键词
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资金
- US Department of Energy [DE-FG02-07ER64439, DE-FG02-02ER63413]
- National Science Foundation [DMS-1049253]
- Direct For Mathematical & Physical Scien [1049253] Funding Source: National Science Foundation
- Division Of Mathematical Sciences [1049253] Funding Source: National Science Foundation
Interannual and interdecadal prediction are major challenges of climate dynamics. In this article we develop a prediction method for climate processes that exhibit low-frequency variability (LFV). The method constructs a nonlinear stochastic model from past observations and estimates a path of the weather noise that drives this model over previous finite-time windows. The method has two steps: (i) select noise samples-or snippets-from the past noise, which have forced the system during short-time intervals that resemble the LFV phase just preceding the currently observed state; and (ii) use these snippets to drive the system from the current state into the future. The method is placed in the framework of pathwise linear-response theory and is then applied to an El Nino-Southern Oscillation (ENSO) model derived by the empirical model reduction (EMR) methodology; this nonlinear model has 40 coupled, slow, and fast variables. The domain of validity of this forecasting procedure depends on the nature of the system's pathwise response; it is shown numerically that the ENSO model's response is linear on interannual time scales. As a result, the method's skill at a 6- to 16-month lead is highly competitive when compared with currently used dynamic and statistic prediction methods for the Nino-3 index and the global sea surface temperature field.
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