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.
Highlights
Historical time series of annual global surface temperature and global sea level are subject to frequent revisions
Long-term sea level projections obtained by using the RCP4.5 and RCP6 temperature scenarios for the models proposed in Rahmstorf (2007); Vermeer and Rahmstorf (2009) and using simulations from the estimated temperature model for Grassi et al (2013) show differences due to data updates by the year 2100 of 19 cm for the Grassi et al model, 34 cm for the Rahmstorf (2007) model, and 40 cm for the Vermeer and Rahmstorf (2009) model
We have studied the influence of revisions of global mean temperature and global mean sea level data on the estimated statistical relation between the two series
Summary
Historical time series of annual global surface temperature and global sea level are subject to frequent revisions. Econometrics 2020, 8, 41 at establishing statistical relationships between time series of the global sea level and associated time series of drivers, such as surface temperature They are a pragmatic response to the current limitations of process-based models to project future sea level rise (Grinsted et al 2010). This motivates the statistical study of the relationship between global sea level and surface temperature.
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