Abstract
Abstract. Understanding future impacts of sea-level rise at the local level is important for mitigating its effects. In particular, quantifying the range of sea-level rise outcomes in a probabilistic way enables coastal planners to better adapt strategies, depending on cost, timing and risk tolerance. For a time horizon of 100 years, frameworks have been developed that provide such projections by relying on sea-level fingerprints where contributions from different processes are sampled at each individual time step and summed up to create probability distributions of sea-level rise for each desired location. While advantageous, this method does not readily allow for including new physics developed in forward models of each component. For example, couplings and feedbacks between ice sheets, ocean circulation and solid-Earth uplift cannot easily be represented in such frameworks. Indeed, the main impediment to inclusion of more forward model physics in probabilistic sea-level frameworks is the availability of dynamically computed sea-level fingerprints that can be directly linked to local mass changes. Here, we demonstrate such an approach within the Ice-sheet and Sea-level System Model (ISSM), where we develop a probabilistic framework that can readily be coupled to forward process models such as those for ice sheets, glacial isostatic adjustment, hydrology and ocean circulation, among others. Through large-scale uncertainty quantification, we demonstrate how this approach enables inclusion of incremental improvements in all forward models and provides fidelity to time-correlated processes. The projection system may readily process input and output quantities that are geodetically consistent with space and terrestrial measurement systems. The approach can also account for numerous improvements in our understanding of sea-level processes.
Highlights
Reliable projections of local sea-level change, together with robust uncertainties, are a key quantity for stakeholders to shape adequate and cost-effective mitigation and adaptation measures to sea-level rise (Kopp et al, 2019)
AR5 supplies several projection components in sea-level rise (SLR) equivalents: the “expansion” term (STR), the “glacier” term, “antnet” and “greennet” for net barystatic contribution from the Antarctica and Greenland ice sheets, which can be converted into an average change rate for HAIS and HGIS, and the “landwater” term for terrestrial water storage (TWS) contribution to SLR
For each of these terms, AR5 supplies the mean projection and the 5 %–95 % confidence interval. We can use this information to calibrate probability density function (PDF) distributions for thickness change rates at each time step, with the mean of each PDF corresponding to the AR5 mean and the standard deviation calibrated from the 5 %–95 % interval
Summary
Reliable projections of local sea-level change, together with robust uncertainties, are a key quantity for stakeholders to shape adequate and cost-effective mitigation and adaptation measures to sea-level rise (Kopp et al, 2019). 2013b; Kopp et al, 2014; Jackson and Jevrejeva, 2016; Kopp et al, 2017; Jevrejeva et al, 2019) These projections are widely used by coastal planners and stakeholders, as is, for example, demonstrated by the impact of Kopp et al (2014, 2017) on assessment reports across the United States (Gornitz et al, 2019; City of Boston, 2016; Kopp et al, 2016; Kaplan et al, 2016; Callahan et al, 2017; Dalton et al, 2017; Griggs et al, 2017; Miller et al, 2018; Boesch et al, 2018). We generally refer to these as KOPP14 (Kopp et al, 2014)) and write n
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