Uncertainty in Sea Level Rise Projections Due to the Dependence Between Contributors

Abstract

Sea level rises at an accelerating pace threatening coastal communities all over the world. In this context sea level projections are key tools to help risk mitigation and adaptation. Projections are often made using models of the main contributors to sea level rise (e.g., thermal expansion, glaciers, and ice sheets). To obtain the total sea level these contributions are added; therefore, the uncertainty of total sea level depends on the correlation between the uncertainties of the contributors. This fact is important to understand the differences in the uncertainty of sea level projections from different methods. Using two process‐based models to project sea level for the 21st century, we show how to model the correlation structure and its time dependence. In these models the correlation primarily arises from uncertainty of future global mean surface temperature that correlates with almost all contributors. Assuming that sea level contributors are independent of each other, an assumption made in many sea level projections underestimates the uncertainty in sea level projections. As a result, high‐end low probability events that are important for decision making are underestimated. The uncertainty in the strength of the dependence between contributors is also explored. New dependence relations between the uncertainty of dynamical processes and surface mass balance in glaciers and ice sheets are introduced in our model. Total sea level uncertainty is found to be as sensitive to the dependence between contributors as to uncertainty in certain individual contributors like thermal expansion and Greenland ice sheet.

Adaptation to uncertain sea-level rise; how uncertainty in Antarctic mass-loss impacts the coastal adaptation strategy of the Netherlands

“…A Monte Carlo method is used to propagate the uncertainty between individual sea level contributors and the total sea level [66]. The correlation between contributors is modeled through their dependence on global mean surface temperature [67].…”

Framework for High‐End Estimates of Sea Level Rise for Stakeholder Applications

“…Katsman et al (2011) produced a plausible high-end scenario for the Dutch Delta Commission for their adaptation program in 2100 and 2200. However, their method was very different to H++ as they summed the uncertainties in the components quadratically, producing a smaller rise (Le Bars, 2018) but moreover used current observed rates of dynamical ice mass loss as their starting point, essentially ruling out the possibility that new emerging dynamical processes like Marine Ice Cliff Instability will dominate the Antarctic contribution to 2100. The strength was the constraint posed by the observations of ice mass loss.…”

Probabilistic Sea Level Projections at the Coast by 2100

“…In some studies, the individual PDFs of each term are considered independent. This assumption could result in underestimation of the upper tail of the projections (see Le Bars et al 2018 for a review of dependence and correlation schemes used so far in the area of probabilistic sea level rise projections). In Church et al (2013a), the sum of these components, with their uncertainties, provided an estimate of the median and 90% range (5th-95th percentiles) of sea level change under each representative concentration pathway (1)…”

“…Furthermore, they need to make assumptions about the dependencies between the different contributions of sea level rise. These choices can have a strong impact on the final probabilistic sea level projections produced for time periods after 2050 (Church et al 2013a, Kopp et al 2017Le Bars, 2018), as well as on the coastal impacts to be expected.…”