期刊名称：Tellus A: Dynamic Meteorology and Oceanography
电子版ISSN：1600-0870
出版年度：2008
卷号：61
期号：4
页码：550-564
DOI：10.1111/j.1600-0870.2009.00402.x
摘要：A new concept, namely the critical predictable time T c , is introduced to give a more precise description of computed chaotic solutions of non-linear differential equations: it is suggested that computed chaotic solutions are unreliable and doubtable when t > T c . This provides us a strategy to detect reliable solution from a given computed result. In this way, the computational phenomena, such as computational chaos (CC), computational periodicity (CP) and computational prediction uncertainty, which are mainly based on long-term properties of computed time-series, can be completely avoided. Using this concept, the famous conclusion 'accurate long-term prediction of chaos is impossible' should be replaced by a more precise conclusion that 'accurate prediction of chaos beyond the critical predictable time T c is impossible'. So, this concept also provides us a timescale to determine whether or not a particular time is long enough for a given non-linear dynamic system. Besides, the influence of data inaccuracy and various numerical schemes on the critical predictable time is investigated in details by using symbolic computation software as a tool. A reliable chaotic solution of Lorenz equation in a rather large interval 0 ≤ t < 1200 non-dimensional Lorenz time units is obtained for the first time. It is found that the precision of the initial condition and the computed data at each time step, which is mathematically necessary to get such a reliable chaotic solution in such a long time, is so high that it is physically impossible due to the Heisenberg uncertainty principle in quantum physics. This, however, provides us a so-called 'precision paradox of chaos', which suggests that the prediction uncertainty of chaos is physically unavoidable, and that even the macroscopical phenomena might be essentially stochastic and thus could be described by probability more economically.
