Northern North Atlantic sea level in CMIP5 climate models - evaluation of mean state, variability and trends against altimetric observations
Richter, K.; Nilsen, J.E.Ø.; Raj, R.P.; Bethke, I.; Johannessen, J.A.; Slangen, A.B.A.; Marzeion, B. (2017). Northern North Atlantic sea level in CMIP5 climate models - evaluation of mean state, variability and trends against altimetric observations. J. Clim. 30(23): 9383-9398. https://dx.doi.org/10.1175/jcli-d-17-0310.1
The northern North Atlantic comprises a dynamically complex area with distinct topographic features, making it challenging to model oceanic features with global climate models. As climate models form the basis for assessment reports of future regional sea level rise, model evaluation is important. In this study, the representation of regional sea level in this area is evaluated in 18 climate models that contributed to the Coupled Model Intercomparison Project Phase 5.Modeled regional dynamic height is compared to observations from an altimetry-based record over the period 1993–2012 in terms of mean dynamic topography, interannual variability, and linear trend patterns. As models are expected to reproduce the location and magnitude but not the timing of internal variability, the observations are compared to the full 150-yr historical simulations using 20-yr time slices. This approach allows to examine modeled natural variability versus observed changes and to assess whether a forced signal is detectable over the 20-yr record or whether the observed changes can be explained by internal variability.The models perform well with respect to mean dynamic topography. However, model performances degrade when interannual variability and linear trend patterns are considered. The modeled region-wide average steric and dynamic sea level rise is larger than estimated from observations and the marked observed increase in the subpolar gyre is not consistent with a forced response but rather a result of internal variability. Using a simple weighting scheme, it is shown that the results can be used to reduce uncertainties in sea level projections.
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