4.7 Article

An ensemble approach to quantify global mean sea-level rise over the 20th century from tide gauge reconstructions

Journal

ENVIRONMENTAL RESEARCH LETTERS
Volume 16, Issue 4, Pages -

Publisher

IOP Publishing Ltd
DOI: 10.1088/1748-9326/abdaec

Keywords

sea level rise; climate change observations; tide gauge reconstructions; uncertainty quantification; climate change assessment

Funding

  1. Met Office Hadley Centre Climate Programme - BEIS
  2. Defra, UK
  3. Australian Research Council (ARC) [FT130101532, DP160103130]
  4. ARC Centre of Excellence for Climate Extremes [CE170100023]
  5. Natural Environment Research Council (TICTOC) [NE/P019293/1]
  6. Academy of Finland [322432]
  7. Australian Research Council [FT130101532] Funding Source: Australian Research Council
  8. Academy of Finland (AKA) [322432, 322432] Funding Source: Academy of Finland (AKA)

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The study introduces an ensemble approach for quantifying historical global mean sea-level rise, which provides a conservative estimate of total uncertainty by combining internal and structural uncertainties. Comparisons with past assessments and satellite altimeter data show good agreement with the results. Sensitivity tests demonstrate the robustness of the estimates to variations in reference period and central estimate timeseries, indicating potential applications to other global or regional climate change indicators.
We present an ensemble approach to quantify historical global mean sea-level (GMSL) rise based on tide gauge reconstructions. This approach combines the maximum internal uncertainty across the ensemble with an estimate of structural uncertainty to provide a conservative estimate of the total uncertainty. Comparisons of GMSL rise over the 20th century based on deltas and linear trends (and their respective uncertainties) are consistent with past Intergovernmental Panel on Climate Change assessments and show good agreement with satellite altimeter timeseries. Sensitivity tests show that our estimates of GMSL rise are robust to the choice of reference period and central estimate timeseries. The methods proposed in this study are generic and could be easily applied to other global or regional climate change indicators.

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