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.
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Akaike, H., 1974: A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.
Allen, M. R., and Coauthors, 2006: Quantifying anthropogenic influence on recent near-surface temperature change. Surveys in Geophysics, 27, 491–544.
Bechini, L., S. Bocchi, T. Maggiore, and R. Confalonieri, 2006: Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.). Environmental Modelling and Software, 21, 1042–1054.
Bindraban, P. S., and Coauthors, 2012: Assessing the impact of soil degradation on food production. Current Opinion in Environmental Sustainability, 4, 478–488.
Bloomfield, P., 2000: Fourier Analysis of Time Series: An Introduction. 2nd ed., John Willey, 288pp.
Bond-Lamberty, B., and A. Thomson, 2010: Temperatureassociated increases in the global soil respiration record. Nature, 464, 579–582.
Box, G. E. P., G. M. Jenkins, and G. C. Reinsel, 1994: Time Series Analysis: Forecasting and Control. 3rd ed., Prentice-Hall, 592pp.
Caldiz, D. O., F. J. Gaspari, A. J. Haverkort, and P. C. Struik, 2001: Agro-ecological zoning and potential yield of single or double cropping of potato in Argentina. Agricultural and Forest Meteorology, 109, 311–320.
Chapin III, F. S., J. McFarland, A. D. McGuire, E. S. Euskirchen, R. W. Ruess, and K. Kielland, 2009: The changing global carbon cycle: Linking plant-soil carbon dynamics to global consequences. Journal of Ecology, 97, 840–850.
Chung, J.-Y., Y. Honda, Y.-C. Hong, X.-C. Pan, Y.-L. Guo, and H. Kim, 2009: Ambient temperature and mortality: An international study in four capital cities of East Asia. Science of the Total Environment, 408, 390–396.
Conant, R. T., J. M. Klopatek, and C. C. Klopatek, 2000: Environmental factors controlling soil respiration in three semiarid ecosystems. Soil Science Society of America Journal, 64, 383–390.
Dale, V. H., and M. R. English, 1999: Tools to Aid Environmental Decision Making. Springer-Verlag, 342pp.
de Gooijer, J. G., and R. J. Hyndman, 2006: 25 years of time series forecasting. International Journal of Forecasting, 22, 443–473.
Gorden, A. H., 1991: Global warming as a manifestation of random walk. J. Climate, 4, 589–597.
Grace, J., 2004: Understanding and managing the global carbon cycle. Journal of Ecology, 92, 189–202.
Hansen, J., M. Sato, R. Ruedy, K. Lo, D. W. Lea, and M. Medina-Elizade, 2006: Global temperature change. Proc. National Academy of Sciences USA, 103, 14288–14293.
IPCC, 2001: Detection of climate change and attribution of causes. Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J. T. Houghton et al., Eds., Cambridge University Press, 44pp.
IPCC, 2007: Technical summary. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon et al., Eds., Cambridge University Press, Cambridge, 74pp.
Jones, P. D., and A. Moberg, 2003: Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001. J. Climate, 16, 206–223.
Jones, P. D., M. New, D. E. Parker, S. Martin, and I. G. Rigor, 1999: Surface air temperature and its changes over the past 150 years. Rev. Geophys., 37, 173–199.
Kerr, R. A., 2009: What happened to global warming? Scientists say just wait a bit. Science, 326, 28–29.
Knight, J., and Coauthors, 2009: Do global temperature trends over the last decade falsify climate predictions? Bull. Amer. Meteor. Soc., 90, S22–S23.
Lean, J. L., and D. H. Rind, 2008: How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett., 35, L18701, doi: 10.1029/2008GL034864.
Lean, J. L., and D. H. Rind, 2009: How will earth’s surface temperature change in future decades. Geophys. Res. Lett., 36, L15708, doi: 10.1029/2009GL038932.
Lee, J.-H., and K.-T. Sohn, 2007: Prediction of monthly mean surface air temperature in a region of China. Adv. Atmos. Sci., 24, 503–508, doi: 10.1007/s00376-007-0503-1.
Ljung, G. M., and G. E. P. Box, 1978: On a measure of lack of fit in time series models. Biometrika, 65, 553–564.
Madsen, B. C., T. Kheoh, C. R. Hinkle, and T. Dreschel, 1992: Precipitation chemistry in east Central Florida from 1978 to 1987. Water, Air and Soil Pollution, 65, 7–21.
Mayhew, P. J., G. B. Jenkins, and T. B. Benton, 2008: A long-term association between global temperature and biodiversity, origination and extinction in the fossil record. Procedings of the Royal Society B, 275, 47–53.
NCDC (National Climate Data Center), cited 2012: Global surface temperature anomalies. [Available online at: http://www.ncdc.noaa.gov/cmbfaq/anomalies.html.]
Nickerson, D. M., and B. C. Madsen, 2005: Nonlinear regression and ARIMA models for precipitation chemistry in East Central Florida from 1978 to 1997. Environmental Pollution, 135, 371–379.
Peñuelas, J., T. Rutishauser, and I. Filella, 2009: Phenology feedbacks on climate change. Science, 324, 887–888.
R Development Core Team, cited 2008: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. [Available online at http://www.R-project.org.]
Rahmstorf, S., A. Cazenave, J. A. Church, J. E. Hansen, R. F. Keeling, D. E. Parker, and R. C. J. Somerville, 2007: Recent climate observations compared to projections. Science, 316, doi: 10.1126/science.1136843.
Romilly, P., 2005: Time series modeling of global mean temperature for managerial decision-making. Journal of Environmental Management, 76, 61–70.
Schwarz, G. E., 1978: Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
Shumway, R. H., and D. S. Stoffer, 2006: Time Series Analysis and Its Applications with R Examples. 2nd ed. Springer, 575pp.
Smith, T. M., and R. W. Reynolds, 2005: A global merged land-air-sea surface temperature reconstruction based on historical observations (1880–1997). J. Climate, 18, 2021–2036.
Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s Historical Merged Land-Ocean Surface Temperature Analysis (1880–2006). J. Climate, 21, 2283–2296.
Steltzer, H., and E. Post, 2009: Seasons and life cycles. Science, 324, 886–887.
Venables, W. N., and B. D. Ripley, 2002: Modern Applied Statistics with S. 4th ed. Springer, 495pp.
Verdoodt, A., E. Van Ranst, and L. Ye, 2004: Daily simulation of potential dry matter production of annual field crops in tropical environments. Agronomy Journal, 96, 1739–1753.
Visser, H., and J. Molenaar, 1995: Trend estimation and regression analysis in climatological time series: an application of structural time series models and the random filter. J. Climate, 8, 969–979.
Wang, S., Q. Ge, F. Wang, X. Wen, and J. Huang, 2010a: Key issues on debating about the global Warming. Advances in Earth Science, 25, 656–665. (in Chinese)
Wang, S., Y. Luo, G. Tang, Z. Zhao, J. Huang, and X. Wen, 2010b: Does the global warming pause in the last decade: 1999–2008? Advances in Climate Change Research, 6, 95–99. (in Chinese)
Ye, L., and E. Van Ranst, 2002: Population carrying capacity and sustainable agricultural use of land resources in Caoxian County (North China). Journal of Sustainable Agriculture, 19, 75–94.
Ye, L., and E. Van Ranst, 2009: Production scenarios and the effect of soil degradation on long-term food security in China. Global Environmental Change, 19, 464–481.
Ye, L., H. Tang, J. Zhu, A. Verdoodt, and E. Van Ranst, 2008: Spatial patterns and effects of soil organic carbon on grain productivity assessment in China. Soil Use and Management, 24, 80–91.
Ye, L., and Coauthors, 2012: Climate change impact on China food security in 2050. Agronomy for Sustainable Development, doi: 10.1007/s13593-012-0102-0.
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Ye, L., Yang, G., Van Ranst, E. et al. Time-series modeling and prediction of global monthly absolute temperature for environmental decision making. Adv. Atmos. Sci. 30, 382–396 (2013). https://doi.org/10.1007/s00376-012-1252-3
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DOI: https://doi.org/10.1007/s00376-012-1252-3