Influence of El Niño‐Southern Oscillation on Global Coastal Flooding

Muis, Sanne; Haigh, Ivan D.; Nobre, Gabriela Guimarães; Aerts, Jeroen C. J. H.; Ward, Philip J.

doi:10.1029/2018ef000909

Cited by 47 publications

(33 citation statements)

References 77 publications

(121 reference statements)

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“…It is unfeasible to save the time series at all 5 million grid cells, as this would have generated huge amounts of data. In previous studies with GTSM (Muis et al, 2016(Muis et al, , 2018, the output locations were based on the DIVA coastal segmentation, which included 16,611 locations (Vafeidis et al, 2008). For the CoDEC project, we develop a new set of output locations (Figure 1) based on the following procedure.…”

Section: Simulations and Data Processingmentioning

confidence: 99%

“…At the same time, the absolute biases are also less than 0.1 m. This indicates a low variability in extremes in some regions, which may be due to few storms and a small tidal range, which make it difficult to capture the variability correctly. Moreover, in some regions steric processes, which are not included in the GTSM, may add to the sea level variability (Muis et al, 2018).…”

Section: Evaluation Of Codec-era5 On the Basis Of Annual Maximamentioning

confidence: 99%

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A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections

et al. 2020

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The world's coastal areas are increasingly at risk of coastal flooding due to sea-level rise (SLR). We present a novel global dataset of extreme sea levels, the Coastal Dataset for the Evaluation of Climate Impact (CoDEC), which can be used to accurately map the impact of climate change on coastal regions around the world. The third generation Global Tide and Surge Model (GTSM), with a coastal resolution of 2.5 km (1.25 km in Europe), was used to simulate extreme sea levels for the ERA5 climate reanalysis from 1979 to 2017, as well as for future climate scenarios from 2040 to 2100. The validation against observed sea levels demonstrated a good performance, and the annual maxima had a mean bias (MB) of-0.04 m, which is 50% lower than the MB of the previous GTSR dataset. By the end of the century (2071-2100), it is projected that the 1 in 10-year water levels will have increased 0.34 m on average for RCP4.5, while some locations may experience increases of up to 0.5 m. The change in return levels is largely driven by SLR, although at some locations changes in storms surges and interaction with tides amplify the impact of SLR with changes up to 0.2 m. By presenting an application of the CoDEC dataset to the city of Copenhagen, we demonstrate how climate impact indicators derived from simulation can contribute to an understanding of climate impact on a local scale. Moreover, the CoDEC output locations are designed to be used as boundary conditions for regional models, and we envisage that they will be used for dynamic downscaling.

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Section: Simulations and Data Processingmentioning

confidence: 99%

Section: Evaluation Of Codec-era5 On the Basis Of Annual Maximamentioning

confidence: 99%

A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections

et al. 2020

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“…The increase in the length of the climate record from 41 to 70 years, allows to further enhance our understanding of decadal to interannual sea level variability (Woodworth et al 2019). Extending the length of the extreme sea level reanalysis would contribute to more accurate assessments of the influence of natural climate variability such as the El Niño-Southern Oscillation on extreme sea levels (Marcos et al 2017;Muis et al 2018). In addition, trends related to climate change could be investigated with more confidence (Cid et al 2016).…”

Section: Potential Use Of Era5mentioning

confidence: 99%

“…Numerical models that accurately simulate TWLs along the world's coastline are essential for operational forecasting (Verlaan et al 2015) and hindcasting of extreme TWL events (Powell et al 2010). Moreover, at longer timescales, such models can help to prioritize adaptation efforts by assessing which regions may see an increase in flooding frequency due to climate change (Vousdoukas et al 2018), and identify the drivers of decadal to interannual sea level variability (Muis et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Advancing global storm surge modelling using the new ERA5 climate reanalysis

et al. 2019

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This study examines the implications of recent advances in global climate modelling for simulating storm surges. Following the ERA-Interim (0.75° × 0.75°) global climate reanalysis, in 2018 the European Centre for Medium-range Weather Forecasts released its successor, the ERA5 (0.25° × 0.25°) reanalysis. Using the Global Tide and Surge Model, we analyse eight historical storm surge events driven by tropical-and extra-tropical cyclones. For these events we extract wind fields from the two reanalysis datasets and compare these against satellite-based wind field observations from the Advanced SCATterometer. The root mean squared errors in tropical cyclone wind speed reduce by 58% in ERA5, compared to ERA-Interim, indicating that the mean sea-level pressure and corresponding strong 10-m winds in tropical cyclones greatly improved from ERA-Interim to ERA5. For four of the eight historical events we validate the modelled storm surge heights with tide gauge observations. For Hurricane Irma, the modelled surge height increases from 0.88 m with ERA-Interim to 2.68 m with ERA5, compared to an observed surge height of 2.64 m. We also examine how future advances in climate modelling can potentially further improve global storm surge modelling by comparing the results for ERA-Interim and ERA5 against the operational Integrated Forecasting System (0.125° × 0.125°). We find that a further increase in model resolution results in a better representation of the wind fields and associated storm surges, especially for small size tropical cyclones. Overall, our results show that recent advances in global climate modelling have the potential to increase the accuracy of early-warning systems and coastal flood hazard assessments at the global scale.

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“…The impact of ENSO on rainfall variability has been the topic of many studies (Risbey et al 2009, Verdon-Kidd and Kiem 2009, Cai et al 2012, Evans and Boyer-Souchet 2012 Muis et al 2018). However, there have been no studies on the climatic influence of the dependence between extreme rainfall and surge, which can subsequently influence the risk of compound floods (Wu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Impact of ENSO on dependence between extreme rainfall and storm surge

Leonard

2019

Environ. Res. Lett.

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Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena-ENSO-on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate modelthe Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.

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Influence of El Niño‐Southern Oscillation on Global Coastal Flooding

Cited by 47 publications

References 77 publications

A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections

A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections

Advancing global storm surge modelling using the new ERA5 climate reanalysis

Impact of ENSO on dependence between extreme rainfall and storm surge

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