Collaborative Research: Climate Diagnostics and Predictions Using Nonlinear Empirical Models

Elnser, James B.佛罗里达州立大学标题：使用非线性经验模型的气候诊断和预测摘要：这个项目的主要目标是开发和测试用于气候诊断和气候预测的非线性统计模型。基于人工神经网络和局部逼近模型的方法是最近在确定性混沌和并行分布式处理等新兴领域发展起来的。目标是在太平洋和大西洋盆地的季节至年际时间尺度上建立熟练的海表温度（SST）异常模式，并通过预测加强对所涉及过程的了解。季节至年际时间尺度上的气候变率与ENSO密切相关。最近的研究表明，ENSO主要是一个太平洋信号，可以被认为是一个低维混沌系统。此外，大西洋地区在大气和海洋资料中也有主要的气候变化。预测ENSO和大西洋振荡将尝试应用用于预测混沌系统行为的非线性统计建模技术。几个非线性统计模型的性能将通过早在1950年的数据集的后投实验来验证和优化。绝对验证实验将采用TOGA季节到年际预测项目工作组（T-POP）建立的1985-1990年ENSO海温事件的后验。所开发的算法，特别是为连接机所开发的算法，是以新的建模技术为基础的，并将随时提供给气象和海洋学界。

ATM-9310715 Elnser, James B. Florida State University Title: Climate Diagnostics and Predictions Using Nonlinear Empirical Models ABSTRACT This project's main objectives are to develop and test nonlinear statistical models for climate diagnostics and for climate predictions. The methodologies to be applied, based on artificial neural networks and local-approximation models, have been developed recently in the emerging fields of deterministic chaos and parallel distributed processing. The goal is to achieve skillful models of sea surface temperature (SST) anomalies on seasonal to interannual time scales for both the Pacific and Atlantic basins and, through prediction, to enhance understanding of the processes involved. Climate variability on the seasonal to interannual time scale is strongly related to ENSO. Recent research suggests that ENSO, primarily a Pacific signal, can be considered as a low-dimensional chaotic system. In addition, the Atlantic region also has major climate variations shown in atmospheric and oceanic data. Predictions of both ENSO and Atlantic oscillations will be attempted by applying the nonlinear statistical modeling techniques developed for the prediction of chaotic system behavior. The performance of several nonlinear statistical models will be validated and optimized through hindcast experiments with data sets from as early as 1950. The absolute verification experiments will be hindcasts of ENSO SST events in the period of 1985-1990, as established by the Working Group of the TOGA Program on Seasonal to Interannual Prediction (T-POP). The algorithms developed, particularly those for the Connection Machine, are based on new modeling techniques and will be made readily available to the meteorological and oceanographic communities.