Instituto de Física, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo, Uruguay.
Departament de Física, Universitat Politecnica de Catalunya, 08222 Terrassa, Barcelona, Spain.
Sci Rep. 2017 Mar 30;7:45676. doi: 10.1038/srep45676.
Understanding the complex dynamics of the atmosphere is of paramount interest due to its impact in the entire climate system and in human society. Here we focus on identifying, from data, the geographical regions which have similar atmospheric properties. We study surface air temperature (SAT) time series with monthly resolution, recorded at a regular grid covering the Earth surface. We consider two datasets: NCEP CDAS1 and ERA Interim reanalysis. We show that two surprisingly simple measures are able to extract meaningful information: i) the distance between the lagged SAT and the incoming solar radiation and ii) the Shannon entropy of SAT and SAT anomalies. The distance uncovers well-defined spatial patterns formed by regions with similar SAT response to solar forcing while the entropy uncovers regions with similar degree of SAT unpredictability. The entropy analysis also allows identifying regions in which SAT has extreme values. Importantly, we uncover differences between the two datasets which are due to the presence of extreme values in one dataset but not in the other. Our results indicate that the distance and entropy measures can be valuable tools for the study of other climatological variables, for anomaly detection and for performing model inter-comparisons.
理解大气的复杂动力学具有至关重要的意义，因为它会影响整个气候系统和人类社会。在这里，我们专注于从数据中识别出具有相似大气属性的地理区域。我们研究了记录在地球表面规则网格上的具有每月分辨率的地表空气温度（SAT）时间序列。我们考虑了两个数据集：NCEP CDAS1 和 ERA 中期再分析。我们表明，两个非常简单的措施能够提取有意义的信息：i）滞后 SAT 与入射太阳辐射之间的距离，ii）SAT 和 SAT 异常的香农熵。距离揭示了由对太阳强迫具有相似 SAT 响应的区域形成的明确定义的空间模式，而熵揭示了具有相似 SAT 不可预测性程度的区域。熵分析还可以识别 SAT 具有极端值的区域。重要的是，我们揭示了两个数据集之间的差异，这是由于一个数据集中存在极值而另一个数据集中不存在极值。我们的研究结果表明，距离和熵措施可以成为研究其他气候变量、异常检测和进行模型比较的有用工具。
