Importance of non-stationary analysis for assessing extreme sea levels under sea level rise

Baldan, Damiano; Coraci, Elisa; Crosato, Franco; Ferla, Maurizio; Bonometto, Andrea; Morucci, Sara

doi:10.5194/egusphere-2022-347

Cited by 1 publication

(1 citation statement)

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“…As a result, inference on future ESLs becomes indeterminate due to large cascading uncertainties. Additionally, when the AF is used, as in past studies that have relied merely on tide gauge observations (Buchanan et al., 2016, 2017; Rasmussen et al., 2018; Tebaldi et al., 2012), the relationship between MSL and extreme events is only implicitly established through an increase in the location parameter equal to a future SLR scenario, while the extreme value distribution that generates ESLs is not explicitly modeled as it is reliant on factors that evolve over time (Baldan et al., 2022; Ceres et al., 2017; Cheng et al., 2014; Ragno et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Coevolution of Extreme Sea Levels and Sea‐Level Rise Under Global Warming

2023

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Design of coastal defense structures like seawalls and breakwaters can no longer be based on stationarity assumption. In many parts of the world, an anticipated sea‐level rise (SLR) due to climate change will constitute present‐day extreme sea levels inappropriate for future coastal flood risk assessments since it will significantly increase their probability of occurrence. Here, we first show that global annual maxima sea levels (AMSLs) have been increasing in magnitude over the last decades, primarily due to a positive shift in mean sea level (MSL). Then, we apply non‐stationary extreme value theory to model the extremal behavior of sea levels with MSL as a covariate and quantify the evolution of AMSLs in the following decades using revised probabilistic sea‐level rise projections. Our analysis reveals that non‐stationary distributions exhibit distinct differences compared to simply considering stationary conditions with a change in location parameter equal to the amount of MSL rise. With the use of non‐stationary distributions, we show that by the year 2050 many locations will experience their present‐day 100‐yr return level as an event with return period less than 15 and 9 years under the moderate (RCP4.5) and high (RCP8.5) representative concentration pathways. Also, we find that by the end of this century almost all locations examined will encounter their current 100‐yr return level on an annual basis, even if CO2 concentration is kept at moderate levels (RCP4.5). Our assessment accounts for large uncertainty by incorporating ambiguities in both SLR projections and non‐stationary extreme value distribution parameters via a Monte Carlo simulation.

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