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

Liming Ye UGent, Guixia Yang, Eric Van Ranst UGent and Huajun Tang (2013) ADVANCES IN ATMOSPHERIC SCIENCES. 30(2). p.382-396
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
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
journal title
ADVANCES IN ATMOSPHERIC SCIENCES
Adv. Atmos. Sci.
volume
30
issue
2
pages
382 - 396
Web of Science type
Article
Web of Science id
000314747500011
JCR category
METEOROLOGY & ATMOSPHERIC SCIENCES
JCR impact factor
1.459 (2013)
JCR rank
47/76 (2013)
JCR quartile
3 (2013)
ISSN
0256-1530
DOI
10.1007/s00376-012-1252-3
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
3130240
handle
http://hdl.handle.net/1854/LU-3130240
date created
2013-02-14 09:29:22
date last changed
2017-10-02 12:35:07
@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},
}

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.