A1 Refereed original research article in a scientific journal
Does temperature contain a stochastic trend? Evaluating conflicting statistical results
Authors: Kaufmann RK, Kauppi H, Stock JH
Publisher: SPRINGER
Publication year: 2010
Journal: Climatic Change
Journal name in source: CLIMATIC CHANGE
Journal acronym: CLIMATIC CHANGE
Number in series: 3-4
Volume: 101
Issue: 3-4
First page : 395
Last page: 405
Number of pages: 11
ISSN: 0165-0009
DOI: https://doi.org/10.1007/s10584-009-9711-2
Abstract
We evaluate the claim by Gay et al. (Clim Change 94:333-349, 2009) that "surface temperature can be better described as a trend stationary process with a one-time permanent shock" than efforts by Kaufmann et al. (Clim Change 77:249-278, 2006) to model surface temperature as a time series that contains a stochastic trend that is imparted by the time series for radiative forcing. We test this claim by comparing the in-sample forecast generated by the trend stationary model with a one-time permanent shock to the in-sample forecast generated by a cointegration/error correction model that is assumed to be stable over the 1870-2000 sample period. Results indicate that the in-sample forecast generated by the cointegration/error correction model is more accurate than the in-sample forecast generated by the trend stationary model with a one-time permanent shock. Furthermore, Monte Carlo simulations of the cointegration/error correction model generate time series for temperature that are consistent with the trend-stationary-with-a-break result generated by Gay et al. (Clim Change 94:333-349, 2009), while the time series for radiative forcing cannot be modeled as trend stationary with a one-time shock. Based on these results, we argue that modeling surface temperature as a time series that shares a stochastic trend with radiative forcing offers the possibility of greater insights regarding the potential causes of climate change and efforts to slow its progression.
We evaluate the claim by Gay et al. (Clim Change 94:333-349, 2009) that "surface temperature can be better described as a trend stationary process with a one-time permanent shock" than efforts by Kaufmann et al. (Clim Change 77:249-278, 2006) to model surface temperature as a time series that contains a stochastic trend that is imparted by the time series for radiative forcing. We test this claim by comparing the in-sample forecast generated by the trend stationary model with a one-time permanent shock to the in-sample forecast generated by a cointegration/error correction model that is assumed to be stable over the 1870-2000 sample period. Results indicate that the in-sample forecast generated by the cointegration/error correction model is more accurate than the in-sample forecast generated by the trend stationary model with a one-time permanent shock. Furthermore, Monte Carlo simulations of the cointegration/error correction model generate time series for temperature that are consistent with the trend-stationary-with-a-break result generated by Gay et al. (Clim Change 94:333-349, 2009), while the time series for radiative forcing cannot be modeled as trend stationary with a one-time shock. Based on these results, we argue that modeling surface temperature as a time series that shares a stochastic trend with radiative forcing offers the possibility of greater insights regarding the potential causes of climate change and efforts to slow its progression.
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