Prediction and Abnormality Analysis of Climate Change Based on PCA-ARMA and PCC

Abstract

Climate change, as an important environmental issue, has been widely investigated in recent decades. On the one hand, the climate prediction is an essential part for policy makers to response to the change of climate, which has received many attentions. On the other hand, there is another challenging problem facing us today that some abnormal weathers occur globally, which seems to have relation to climate change, e.g., the global greenhouse effect, but with little existing researches on this relation. Therefore, in this paper, we propose two kinds of climatic and meteorological models based on statistical data: 1) an autoregressive-moving-average (ARMA) prediction model with principal component analysis (PCA) and 2) abnormal analysis model based on Pearson correlation coefficient (PCC). In detail, firstly, we propose the PCA-ARMA prediction model to predict climate change in the next 25 years, including two steps: 1) generation of new components for data reduction by PCA using the past 75 years' data, and 2) prediction based on step 1 by ARMA for next 25 years. Then, we establish another model to find out the relation between climate change and abnormal weathers, e.g., the extreme cold weather, mainly by PCC. The relevant data are collected, and by these two models, we get the corresponding results, which show that our prediction fits well and the abnormal weather is strongly connected with the climate change.