Predictability at intraseasonal time scale Geophysical Research Letters

Establishing the predictability of the climate system beyond the weather time scale of about 10 days is essential for extended range prediction. To overcome the limitation imposed by deterministic chaos on long‐range prediction, we exploit the near‐oscillatory behavior of monsoon intraseasonal oscillation (MISO) and other such phenomena in the tropical climate. These are nonlinear oscillations in the time range of 30–60 days. Based on the phase space reconstruction method of nonlinear dynamical systems theory, we have developed a prediction model using the time series of MISO. We demonstrate that the Indian monsoon intraseasonal oscillation can be predicted with more accuracy at extended range. The phase space reconstruction model performs better than the Climate Forecast System of the National Centers for Environmental Prediction in predicting the MISO. Our results show that intraseasonal variability can be modeled as a low‐dimensional dynamical system and demonstrate extended predictability of climate. Grant no. NA14OAR4310160

建立超出约10天天气时间尺度的气候系统可预报性，对于延伸期预报至关重要。为克服确定性混沌对长期预报造成的局限，我们利用热带气候中季风季节内振荡（monsoon intraseasonal oscillation，MISO）及其他同类现象的近振荡特性。这类现象属于30~60天时间尺度的非线性振荡。基于非线性动力学系统理论的相空间重构方法，我们依托MISO的时间序列构建了预报模型。研究证实，印度季风季节内振荡在延伸期尺度上可实现更高精度的预报。在MISO的预报任务中，该相空间重构模型的表现优于美国国家环境预报中心的气候预报系统。本研究结果表明，季节内变率可被建模为低维动力学系统，同时证实了气候系统具备延伸期可预报性。资助编号：NA14OAR4310160