Incorporating a Probabilistic Climate Emulator Into an Agent-Based Coastal Futures Forecasting System

Leung, Meredith; Ruggiero, Peter; Anderson, Dylan; Méndez, Fernando J.; Rueda, Ana; Bolte, John P.

doi:10.1142/9789811204487_0100

Cited by 1 publication

(4 citation statements)

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“…We analyze the natural variability of coastal flood risk in the San Francisco Bay using TESLA, a statistical emulator of coastal water levels presented in Anderson et al (2019). Past studies have successfully applied the TESLA framework at other tide gauge locations such as San Diego (Anderson et al, 2019(Anderson et al, , 2021, Oregon (Leung et al, 2019) as well as different islands in the Pacific region (Marra et al, 2022). Following that study, we use several different atmospheric and oceanographic data sets for developing synthetic SWL time series, which we summarize here (see Anderson et al, 2019 for more detail).…”

Section: Input Data For Swl Emulatormentioning

confidence: 99%

“…This point was highlighted in Lang et al. (2019), which used simulated sea levels for the German Bight over the past 1,000 yr from an Earth systems model to show that multidecadal to centennial variability in extreme flood events are difficult to capture using conventional approaches, and that long‐term ensemble simulations of sea level are an important tool for quantifying flood risk.…”

Section: Introductionmentioning

confidence: 99%

“…Those same oceanic and atmospheric conditions can influence the likelihood of MSL anomalies and storminess patterns that influence storm surge, suggesting that similar reconstructions can be used to better understand past variability in coastal water levels and potentially pre‐historic flood frequency. While regional numerical studies (Lang et al., 2019) have used paleo data‐informed reanalysis of ocean systems models, to our knowledge paleo proxy data has not yet been utilized directly in statistical and hydrodynamic coastal flood models.…”

Section: Introductionmentioning

confidence: 99%

“…To overcome these limitations, a variety of methods have been forwarded to produce very long‐time series of TWLs that account for interactions between meteorological, oceanographic, and hydrological drivers that may or may not have occurred in the instrumental record. One approach employs hindcasts from coupled climate and ocean system models (Lang et al., 2019; Muis et al., 2016; Vitousek et al., 2017), though these simulations can be very computationally expensive. Other approaches employ statistical methods including Monte Carlo simulations (Serafin & Ruggiero, 2014) and joint probability models (Callaghan et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the Natural Variability of Still Water Levels in the San Francisco Bay Over the Past 500 yr: Implications for Future Coastal Flood Risk

et al. 2023

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Increasing exposure to coastal flood hazards will potentially induce an enormous socio‐economic toll on vulnerable communities. To accurately characterize the hazard, we must consider both natural water level variability and climate change‐induced sea‐level rise. In this study, we develop a paleo‐proxy‐based reconstruction of coastal flood events over the last 500 yr to capture natural water level variability and superimpose that reconstruction onto expected sea‐level rise to explore interannual and multidecadal variability in plausible future coastal flood risk. We first develop reconstructions of leading principal components (PCs) of sea surface temperature anomalies from 1500 CE onwards, using tree‐ring, coral, and sclerosponge chronology‐based El Niño Southern Oscillation reconstructions as predictors in a wavelet autoregression model. These reconstructions of PCs are then used in a stochastic water level emulator to develop ensemble simulations of hourly still water levels (SWLs) in the San Francisco Bay. The emulator accounts for multiple relevant processes, including monthly mean sea level (MMSL) anomalies, storm surge, and tide, all varying at different timescales. Accounting for natural variability in water levels over 1500–2000 CE increases coastal flood risk beyond that suggested by instrumental records alone. When superimposed on 0.22 m of sea‐level rise (approximately the amount experienced over the previous century), the simulations show that while high tides and large storm surges cause the smaller extreme SWLs, the larger extreme SWLs occur during concurrent high MMSL, high tides, and significant storm surges. Our findings thus highlight the need to consider natural water level variability for coastal adaptation and planning.

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