Predictability at intraseasonal time scale. Issue 16 (24th August 2017) - Krishnamurthy, V.; Sharma, A. S.
Abstract: 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. Key Points: Based on the inherent nonlinear dynamics of climate, a phase space reconstruction model is used to predict the intraseasonal variability The model demonstrates that it is possible to make more accurate predictions of certain aspects of climate at intraseasonal time scale Predictions of the Indian monsoon rainfall by the phase reconstruction model are more accurate than those by an operational forecast system