The pattern in data that do not conform to expected behavior is the definition of anomaly detection, where an anomalous pattern is the non-conformant pattern. It is also known as outliers, abnormalities, exceptions, deviants, or surprises. The data are gathered in a variety of ways, and they may be measurements for entity observations. If the data adopt an unexpected form, it is called an outlier. In general, handling outliers involves two steps: first, if the outlier is not interested in analysis, it will be identified as noise and eliminated from the data set; second, analyzing the outlier will provide valuable information about the system's abnormality, many studies have been established on ACC give the evidence that the abrupt climate change is real, where Several methods were proposed in the literature for the change point detection, a point that massively changed in the data pattern is a change-point so this paper will focus on the abnormality in climate.