基于VAR模型的加拿大气候变化预测

南极冰川融化、飓风不断增加、海平面逐渐上涨等现象的出现，使人们意识到全球气候变暖给人类生存带来极大的挑战，对全球气候变化发展趋势的预测是十分有必要的。本文针对全球气候变暖现象，对加拿大具有代表性的4个省份数据进行缺失值填补后分析研究，建立一个考虑太阳辐射强度、二氧化碳含量、土壤含水量、温度、降雨量等因素的向量自回归（VAR）模型。通过对其进行平稳性检验、脉冲响应和方差分析得出具体模型并利用该模型对加拿大气温和降水量进行预测。实验结果表明，未来25年加拿大平均气温将达到15.0410 ℃，平均降水量达到2.0950 mm。

Climate Change Prediction in Canada Based on VAR Model

The melting of Antarctic glaciers, the increasing of hurricanes and the gradual rising of sea level make people aware of the great challenges caused by global warming. So it is necessary to do research on global climate change. Missing data imputation is taken to study the data of four representative provinces in Canada, and a vector autoregressive (VAR) model is established considering the factors of solar radiation intensity, carbon dioxide content, soil water content, temperature, rainfall etc. to study Canada’s climate change. The specific model is established by doing stability test, impulse response and variance analysis, and is used to predict the temperature and precipitation in Canada. The experimental results show that the average temperature in Canada in the next 25 years will reach 15.0410 ℃, and the average precipitation will reach 2.0950 mm.