Observation-Consistent Nonlinear Ice Interactions in an Efficient Earth Systems Model and their Implications for Sea-Level Projections

Sea level rise is a major result of climate change that threatens coastal communities and has the potential to incur annual costs by 2100 of $11-95 billion in flood damages alone, assuming a global mean sea level rise of 25-123 cm (Hinkel et al. 2014). Projecting sea level rise as temperatures rise is therefore crucial for policy and decision-making.

The two methods currently used to project future sea level change are process-based modelling and semi-empirical modelling. Process-based models rely on combining outputs from coupled atmosphere/ocean models for each component of sea level rise. Semi-empirical models calculate sea level as an integrated response to either warming or radiative forcing, using parameters constrained from past observations.

Historically, there is little agreement in sea-level projections between these two methods (Orlić and Pasarić, 2013). One potential source of the discrepancies is uncertainty in land ice response to warming; although nonlinearities exist within processes affecting this response, most existing semi-empirical models treat the relationship between warming and ice-melt as linear.

Non-linear processes in sea level rise may have not yet affected the observational record (such as tipping points as future warming crosses some threshold) or may have already occurred (such as non-linear effects that apply across all levels of warming, or for which the threshold has been passed). Here, we examine the effect on semi-empirical projections of sea level rise of nonlinearities that have already affected the observed sea level record, by adding a nonlinear term to the relationship between warming and the rate of sea level rise within a large ensemble of historically constrained efficient earth systems model simulations.

Projections reach a median sea level rise of 0.47m by 2100 following SSP245, and 0.77m by 2100 following SSP585. Preliminary results suggest that nonlinear interactions in each ensemble member can be sublinear, superlinear or 0, with a mainly symmetrical distribution – although there are high-end, low-probability superlinear interactions up to 3x greater than low-end sublinear. Thus, we find that observation-consistent nonlinear interactions in the model configuration lead to insignificant differences in sea level rise by 2300 over the entire ensemble. However, it is key to note that nonlinear interactions that have not yet occurred but that may occur in the future, are not considered – these will lead to an increased projection of sea level rise by 2300 if not earlier (e.g. DeConto and Pollard, 2016).

References

• Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl. Acad. Sci. U. S. A. 111, 3292–3297 (2014).
• Kopp, R. E. et al. Probabilistic 21st and 22nd century sea‐level projections at a global network of tide‐gauge sites. Earth’s Future. 2, 383–406 (2014).
• Jevrejeva, S., Moore, J. C. & Grinsted, A. How will sea level respond to changes in natural and anthropogenic forcings by 2100? Geophys. Res. Lett. 37, 1–5 (2010).
• Orlić, M. & Pasarić, Z. Semi-empirical versus process-based sea-level projections for the twenty-first century. Nat. Clim. Chang. 3, 735–738 (2013).