Environmental Research and Teaching Institute, École Normale Supérieure, F-75230 Paris Cedex 05, France.
Proc Natl Acad Sci U S A. 2011 Jul 19;108(29):11766-71. doi: 10.1073/pnas.1015753108. Epub 2011 Jul 5.
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 Niño-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 Niño-3 index and the global sea surface temperature field.
年际和年代际预测是气候动力学的主要挑战。在本文中，我们开发了一种用于具有低频可变性 (LFV) 的气候过程的预测方法。该方法从过去的观测中构建一个非线性随机模型，并估计驱动该模型的“天气”噪声的路径，该噪声在过去的有限时间窗口中强制该模型。该方法有两个步骤：（i）从过去的噪声中选择噪声样本（或“片段”），这些样本在类似于当前观测状态之前的 LFV 阶段的短时间间隔内迫使系统；（ii）使用这些片段从当前状态驱动系统进入未来。该方法被置于路径线性响应理论的框架中，然后应用于通过经验模型约简 (EMR) 方法推导出的厄尔尼诺-南方涛动 (ENSO) 模型；该非线性模型具有 40 个耦合的、缓慢的和快速的变量。该预测程序的有效域取决于系统路径响应的性质；数值表明，ENSO 模型的响应在年际时间尺度上是线性的。因此，与目前用于尼诺-3 指数和全球海面温度场的动态和统计预测方法相比，该方法在 6 至 16 个月的领先时间内具有很高的竞争力。
