Statistical analysis of global surface air temperature and sea level using cointegration methods
AbstractGlobal sea levels are rising which is widely understood as a consequence of thermal expansion and melting of glaciers and land-based ice caps. Due to physically-based models being unable to simulate observed sea level trends, semi-empirical models have been applied as an alternative for projecting of future sea levels. There is in this, however, potential pitfalls due to the trending nature of the time series. We apply a statistical method called cointegration analysis to observed global sea level and surface air temperature, capable of handling such peculiarities. We find a relationship between sea level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. We further find that the warming episode in the 1940s is exceptional in the sense that sea level and warming deviates from the expected relationship. This suggests that this warming episode is mainly due to internal dynamics of the ocean rather than external radiative forcing. On the other hand, the present warming follows the expected relationship, suggesting that it is mainly due to radiative forcing. In a second step, we use the total radiative forcing as an explanatory variable, but unexpectedly find that the sea level does not depend on the forcing. We hypothesize that this is due to a long adjustment time scale of the ocean and show that the number of years of data needed to build statistical models that have the relationship expected from physics exceeds what is currently available by a factor of almost ten.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2011-39.
Date of creation: 14 Oct 2011
Date of revision:
Contact details of provider:
Web page: http://www.econ.au.dk/afn/
Sea level; mean annual temperature; forcing variables; cointegration.;
Other versions of this item:
- Torben Schmith & Søren Johansen & Peter Thejll, 2011. "Statistical analysis of global surface air temperature and sea level using cointegration methods," Discussion Papers 11-26, University of Copenhagen. Department of Economics.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
- Liu, Hui & Rodríguez, Gabriel, 2005.
"Human activities and global warming: a cointegration analysis,"
9939, University Library of Munich, Germany.
- Hui Liu & Gabriel Rodriguez, 2003. "Human Activities and Global Warming: A Cointegration Analysis," Working Papers 0307E, University of Ottawa, Department of Economics.
- Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
- Stern, David I., 2006. "An atmosphere-ocean time series model of global climate change," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1330-1346, November.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.