Time-series modeling and prediction of global monthly absolute temperature for environmental decision making

Abstract

A generalized, structural, time series modeling framework was developed to analyze the monthly records of absolute surface temperature, one of the most important environmental parameters, using a deterministicstochastic combined (DSC) approach. Although the development of the framework was based on the characterization of the variation patterns of a global dataset, the methodology could be applied to any monthly absolute temperature record. Deterministic processes were used to characterize the variation patterns of the global trend and the cyclic oscillations of the temperature signal, involving polynomial functions and the Fourier method, respectively, while stochastic processes were employed to account for any remaining patterns in the temperature signal, involving seasonal autoregressive integrated moving average (SARIMA) models. A prediction of the monthly global surface temperature during the second decade of the 21st century using the DSC model shows that the global temperature will likely continue to rise at twice the average rate of the past 150 years. The evaluation of prediction accuracy shows that DSC models perform systematically well against selected models of other authors, suggesting that DSC models, when coupled with other ecoenvironmental models, can be used as a supplemental tool for short-term (∼10-year) environmental planning and decision making.

Cropland soil organic matter content change inNortheast China, 1985-2005

“…• C in the twentieth century, being consistent with the general global trend [41]. The warming has been observed stronger in NEC and weaker in southwest China.…”

Chinese Food Security and Climate Change: Agriculture Futures

“…Climate change will further exacerbate the already-fragile global food production system and the natural resource base. Global surface temperature has increased 0.8°C during the twentieth century; four thirds of this increase occurred in the last three decades (Hansen et al, 2006;Ye et al, 2013a). The acceleration in global warming and its associated changes in precipitation have already affected global agriculture and the food production system in many ways (Godfray et al 2011).…”

Forecasting Temperature Records in PEI, Canada

“…As suggested by Ye et al (2012), univariate time-series models have gained popularity in environmental modeling due to their ability to detect trends in the series and also due to the complexity of mainstream climate models. In addition, the newly developed statistical methods used in this paper to identify seasonal patterns of time series are quite powerful to produce accurate forecasting.…”