全球温度场信息熵的时空特征分析

本文基于信息熵理论定义气象要素信息熵,并运用其分析全球温度场在不同时空尺度上偏离气候态(1971—2000)的不确定性. 研究结果表明:1)温度场气候态信息熵(CE)具有明显的纬向分布特征,总体表现为温度场CE由低纬度地区向中高纬度地区递增,且海陆差异显著,可以较好地区分各个气候带;其垂直变化,在低纬度地区表现为随高度的升高而增加,但在中高纬度地区则以300hPa为界呈准对称分布,在此高度之上其值随高度升高而增加,之下则相反,这一特征在高纬度地区更为明显.2)温度场月信息熵(ME)的季节性差异显著,总体表 关键词： 信息熵 温度场 可预测性

Spatiotemporal analysis of information entropy of the global temperature

Based on the concept of entropy in information theory, the entropy of meteorological elements is determined and used to analyze the uncertainty of the global temperature field anomaly from the climate state (1971—2000) on different time and spatial scales. It is found that the temperature climate entropy (CE) possesses a zonal distribution, increases from tropics to mid-high latitudes and has an obvious difference between the ocean region and the continent, thereby being able to distinguish the climatic zones properly. The temperature CE in low-mid troposphere increases with altitude increasing, while in extratropical the situation retains above 300 hPa but below 300 hPa the situation is reversed, and this feature is more obvious in high latituderegions. On the whole, the temperature monthly entropy (ME) is obviously dependent on season change: it is smallest in summer and largest in winter. Besides, there exists a distinguishable interdecadal period. Different monthly ME values from low atmosphere to high atmosphere each have an obvious five -to-ten year quasi-period oscillation. All the spatiotemporal characteristics and their relationships with annual temperature range verify the usefulness of the entropy in meteorology, and it is an effective method to measure the uncertainty of the meteorological elements.