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Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature

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  • Eric Hillebrand

    (CREATES, Department of Economics and Business Economics, Aarhus University, 8000 Aarhus, Denmark)

  • Søren Johansen

    (CREATES, Department of Economics and Business Economics, Aarhus University, 8000 Aarhus, Denmark
    Department of Economics, University of Copenhagen, 1353 Copenhagen K, Denmark)

  • Torben Schmith

    (Danish Metorological Institute, 2100 Copenhagen, Denmark)

Abstract

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880–2001 and 1880–2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.

Suggested Citation

  • Eric Hillebrand & Søren Johansen & Torben Schmith, 2020. "Data Revisions and the Statistical Relation of Global Mean Sea Level and Surface Temperature," Econometrics, MDPI, vol. 8(4), pages 1-19, November.
    • Handle: RePEc:gam:jecnmx:v:8:y:2020:i:4:p:41-:d:438688
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    References listed on IDEAS

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    1. Yoshihide Wada & Min-Hui Lo & Pat J.-F. Yeh & John T. Reager & James S. Famiglietti & Ren-Jie Wu & Yu-Heng Tseng, 2016. "Fate of water pumped from underground and contributions to sea-level rise," Nature Climate Change, Nature, vol. 6(8), pages 777-780, August.
    2. Bruns, Stephan B. & Csereklyei, Zsuzsanna & Stern, David I., 2020. "A multicointegration model of global climate change," Journal of Econometrics, Elsevier, vol. 214(1), pages 175-197.
    3. Hendry, David F., 1995. "Dynamic Econometrics," OUP Catalogue, Oxford University Press, number 9780198283164.
    4. James A. Duffy & David F. Hendry, 2017. "The impact of integrated measurement errors on modeling long-run macroeconomic time series," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 568-587, October.
    5. Carling C. Hay & Eric Morrow & Robert E. Kopp & Jerry X. Mitrovica, 2015. "Probabilistic reanalysis of twentieth-century sea-level rise," Nature, Nature, vol. 517(7535), pages 481-484, January.
    6. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    7. Malte Meinshausen & S. Smith & K. Calvin & J. Daniel & M. Kainuma & J-F. Lamarque & K. Matsumoto & S. Montzka & S. Raper & K. Riahi & A. Thomson & G. Velders & D.P. Vuuren, 2011. "The RCP greenhouse gas concentrations and their extensions from 1765 to 2300," Climatic Change, Springer, vol. 109(1), pages 213-241, November.
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    Cited by:

    1. Rocco Mosconi & Paolo Paruolo, 2022. "A Conversation with Søren Johansen," Econometrics, MDPI, vol. 10(2), pages 1-16, April.

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