Measuring Global Warming: Global and Hemisphere Mean Temperature Anomalies Predictions Using Sliced Functional Time Series (SFTS) Model

• 作者: Farah Yasmeen
• 来源期刊: 应用科学(英文)
• 学科: 地球科学
• 关键词: TEMPERATURE SERIES HEMISPHERIC TEMPERATURE TEMPERATURE Anomalies GLOBAL Warming Weather Prediction Sliced FUNCTIONAL Time SERIES Outliers
• 年，卷（期）: 2019,（5）
• 页码范围: 316-334
• 页数: 19页
• 分类号: P4

摘要

In this study, the sliced functional time series (SFTS) model is applied to the Global, Northern and Southern temperature anomalies. We obtained the combined land-surface air and sea-surface water temperature from Goddard Institute for Space Studies (GISS), NASA. The data are available for Global mean, Northern Hemisphere mean and Southern Hemisphere means (monthly, quarterly and annual) since 1880 to present (updated through March 2019). We analyze the global surface temperature change, compare alternative analyses, and address the questions about the reality of global warming. We detected the outliers during the last century not only in global temperature series but also in northern and southern hemisphere series. The forecasts for the next twenty years are obtained using SFTS models. These forecasts are compared with ARIMA, Random Walk with drift and Exponential Smoothing State Space (ETS) models. The comparison is made on the basis of root mean square error (RMSE), mean absolute percentage error (MAPE) and the length of prediction intervals.