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

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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 (similar to 10-year) environmental planning and decision making.
Keywords
statistical model, time series analysis, polynomial trend, Fourier method, ARIMA, climate change, SURFACE AIR-TEMPERATURE, EAST CENTRAL FLORIDA, PRECIPITATION CHEMISTRY, SOIL RESPIRATION, CLIMATE-CHANGE, CARBON-CYCLE, CHINA, SIMULATION, REGRESSION, RECORD

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Chicago
Ye, Liming, Guixia Yang, Eric Van Ranst, and Huajun Tang. 2013. “Time-series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making.” Advances in Atmospheric Sciences 30 (2): 382–396.
APA
Ye, Liming, Yang, G., Van Ranst, E., & Tang, H. (2013). Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. ADVANCES IN ATMOSPHERIC SCIENCES, 30(2), 382–396.
Vancouver
1.
Ye L, Yang G, Van Ranst E, Tang H. Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. ADVANCES IN ATMOSPHERIC SCIENCES. 2013;30(2):382–96.
MLA
Ye, Liming, Guixia Yang, Eric Van Ranst, et al. “Time-series Modeling and Prediction of Global Monthly Absolute Temperature for Environmental Decision Making.” ADVANCES IN ATMOSPHERIC SCIENCES 30.2 (2013): 382–396. Print.
@article{3130240,
  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 (similar to 10-year) environmental planning and decision making.},
  author       = {Ye, Liming and Yang, Guixia and Van Ranst, Eric and Tang, Huajun},
  issn         = {0256-1530},
  journal      = {ADVANCES IN ATMOSPHERIC SCIENCES},
  keyword      = {statistical model,time series analysis,polynomial trend,Fourier method,ARIMA,climate change,SURFACE AIR-TEMPERATURE,EAST CENTRAL FLORIDA,PRECIPITATION CHEMISTRY,SOIL RESPIRATION,CLIMATE-CHANGE,CARBON-CYCLE,CHINA,SIMULATION,REGRESSION,RECORD},
  language     = {eng},
  number       = {2},
  pages        = {382--396},
  title        = {Time-series modeling and prediction of global monthly absolute temperature for environmental decision making},
  url          = {http://dx.doi.org/10.1007/s00376-012-1252-3},
  volume       = {30},
  year         = {2013},
}

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