Abstract
Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests.
Highlights
No location on the earth will have exactly the same climate as another; many do have very similar climatic characteristics, which depend on various factors such as latitude and longitude of the region, temperature, humidity, air pressure, wind, cloudiness, and nearness of sea and vegetation
We have presented a method to find out similarity or dissimilarity between the two pieces of time series data where each series has periodic variations in the data
Analyzing only the recurrence plot (RP) of the two time series is not sufficient to test whether they have similar periodic variations, so we applied cross recurrence plot (CRP) to test the similarity of periodic variations
Summary
No location on the earth will have exactly the same climate as another; many do have very similar climatic characteristics, which depend on various factors such as latitude and longitude of the region, temperature, humidity, air pressure, wind, cloudiness, and nearness of sea and vegetation. Temperature is one of the important factors in climate change. The temperature of a region affects humans and biological and physical systems in all continents [1]. Decisions about temperature change are complex and costly and have longterm implications. We need to understand the quality and provenance of that evidence and to find whether any assumptions have been made in generating it. Understanding temperature change patterns and their periodic variations across time (such as yearly, monthly, and daily changes) and their changes across environmental space is of great significance
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