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Exploring sources of uncertainty in steric sea‐level change estimates
Camargo, C.M.L.; Riva, R.E.M.; Hermans, T.H.J.; Slangen, A.B.A. (2020). Exploring sources of uncertainty in steric sea‐level change estimates. JGR: Oceans 125(10): e2020JC016551.

Bijhorende data:
In: Journal of Geophysical Research-Oceans. AMER GEOPHYSICAL UNION: Washington. ISSN 2169-9275; e-ISSN 2169-9291, meer
Peer reviewed article  

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Author keywords
    Steric sea-level change; global mean sea-level change; regional sea-level change; stochastic noise models; temperature and salinity datasets

Auteurs  Top 
  • Machado Lima de Camargo, C., meer
  • Riva, R.E.M.
  • Hermans, T.H.J., meer
  • Slangen, A.B.A., meer

    Recent studies disagree about the contribution of variations in temperature and salinity of the oceans – steric change – to the observed sea‐level change. This article explores two sources of uncertainty to both global mean and regional steric sea‐level trends. First, we analyse the influence of different temperature and salinity datasets on the estimated steric sea‐level change. Next, we investigate the impact of different stochastic noise models on the estimation of trends and their uncertainties. By varying both the datasets and noise models, the global mean steric sea‐level trend and uncertainty can vary from 0.69–2.40 mm/yr and 0.02–1.56 mm/yr, respectively for 1993‐2017. This range is even larger on regional scales, reaching up to 30 mm/yr. Our results show that a first‐order autoregressive model (AR(1)) is the most appropriate choice to describe the residual behaviour of the ensemble mean of all datasets for the global mean steric sea‐level change over the last 25 years, which consequently leads to the most representative uncertainty. Using the ensemble mean and the AR(1) noise model, we find a global mean steric sea‐level change of 1.36 ± 0.10 mm/yr for 1993‐2017, and 1.08 ± 0.07 mm/yr for 2005‐2015. Regionally, a combination of different noise models is the best descriptor of the steric sea‐level change and its uncertainty. The spatial coherence in the noise model preference indicates clusters that may be best suited to investigate the regional sea‐level budget.

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