Institute for Theoretical Physics, Eötvös Loránd University, Budapest, H-1117, Hungary.
MTA-ELTE Theoretical Physics Research Group, Budapest, H-1117, Hungary.
Sci Rep. 2019 Mar 7;9(1):3896. doi: 10.1038/s41598-019-40451-7.
The intensity of the atmospheric large-scale spreading can be characterized by a measure of chaotic systems, called topological entropy. A pollutant cloud stretches in an exponential manner in time, and in the atmospheric context the topological entropy corresponds to the stretching rate of its length. To explore the plethora of possible climate evolutions, we investigate here pollutant spreading in climate realizations of two climate models to learn what the typical spreading behavior is over a climate change. An overall decrease in the areal mean of the stretching rate is found to be typical in the ensembles of both climate models. This results in larger pollutant concentrations for several geographical regions implying higher environmental risk. A strong correlation is found between the time series of the ensemble mean values of the stretching rate and of the absolute value of the relative vorticity. Here we show that, based on the obtained relationship, the typical intensity of the spreading in an arbitrary climate realization can be estimated by using only the ensemble means of the relative vorticity data of a climate model.
大气大尺度扩散的强度可以用一种称为拓扑熵的混沌系统度量来描述。污染物云在时间上呈指数方式扩散，而在大气环境中，拓扑熵对应于其长度的拉伸速率。为了探索大量可能的气候演变，我们在这里研究了两个气候模型的气候实现中的污染物扩散，以了解在气候变化过程中污染物扩散的典型行为。发现在两个气候模型的集合中，拉伸率的面积平均值总体呈下降趋势是典型的。这导致几个地理区域的污染物浓度增加，意味着更高的环境风险。发现拉伸率和相对涡度的绝对值的集合平均值的时间序列之间存在很强的相关性。在这里，我们表明，基于所获得的关系，可以仅使用气候模型的相对涡度数据的集合平均值来估计任意气候实现中扩散的典型强度。
