Predictability of Weather and Climate: Predictability of seasonal climate variations: a pedagogical review
Predictability of Weather and Climate: Predictability of seasonal climate variations: a pedagogical review
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天气和气候的可预测性：季节性气候变化的可预测性：教学回顾
DOI：
10.1017/cbo9780511617652.013
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发表时间：
2006
影响因子：
5.5
通讯作者：
J. Kinter
中科院分区：
文献类型：
--
作者：
J. Shukla;J. Kinter
众所周知，大尺度大气环流的日常变化在两周之后是不可预测的。与大规模环流模式相关的小规模降雨模式甚至可能在几天后就无法预测。然而，某些大气和海洋变量的时空平均值在几个月到几个季节内是可以预测的。这一章给出了一个教学审查的想法和结果，导致我们目前的理解和季节性气候变化的可预测性的地位。我们首先回顾了目前的状态的理解的限制天气的可预测性。我们采用洛伦茨对天气可预报性的经典定义，即两个几乎相同的初始条件下的预报之间的差异在统计意义上与两个随机选择的大气状态之间的差异一样大的范围。根据可预报性的定义，可预报性的上限取决于最大可能误差的饱和值，而最大可能误差又取决于气候方差。Lorenz提出了一个简单的概念模型，其中天气预报技能的上限由三个基本量描述：初始误差的大小，误差的增长率和误差的饱和值。这个简单的模式能够解释数值天气预报技术的季节、区域和半球变化的现状。例如，冬天更
It is well known that the day-to-day changes in the large-scale atmospheric circulation are not predictable beyond two weeks. The small-scale rainfall patterns associated with the large-scale circulation patterns may not be predictable beyond even a few days. However, the space–time averages of certain atmospheric and oceanic variables are predictable for months to seasons. This chapter gives a pedagogical review of the ideas and the results that have led to our current understanding and the status of the predictability of seasonal climate variations. We first review the current status of the understanding of the limits of the predictability of weather. We adopt Lorenz’classical definition of the predictability of weather as the range at which the difference between forecasts from two nearly identical initial conditions is as large in a statistical sense as the difference between two randomly chosen atmospheric states. With this definition of predictability, it is implied that the upper limit of predictability depends on the saturation value of the maximum possible error, which, in turn, is determined by the climatological variance. Lorenz provided a simple conceptual model in which the upper limit of weather prediction skill is described by three fundamental quantities: the size of the initial error, the growth rate of the error and the saturation value of the error. This simple model is able to explain the current status of the seasonal, regional and hemispheric variations of numerical weather prediction (NWP) skill. For example, winter is more