Advancing global storm surge modelling using the new ERA5 climate reanalysis

Dullaart, Job; Muis, Sanne; Bloemendaal, Nadia; Aerts, Jeroen C. J. H.

doi:10.1007/s00382-019-05044-0

Cited by 126 publications

(92 citation statements)

References 59 publications

(68 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ERA5 data were released in 2019 and are freely available in Google Earth Engine and the Copernicus Climate Data Store. Studies have shown ERA5 to have satisfactory performance in accurately modeling storm surges ( 141 ), Canadian prairie climate ( 142 ), and global discharge reanalysis ( 143 ) compared to previous generations and other models. ERA5 monthly aggregated precipitation and temperature values were summarized annually and seasonally over the study domain.…”

Section: Methodsmentioning

confidence: 99%

Declining greenness in Arctic-boreal lakes

Kuhn

Butman

2021

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The highest concentration of the world’s lakes are found in Arctic-boreal regions [C. Verpoorter, T. Kutser, D. A. Seekell, L. J. Tranvik, Geophys. Res. Lett. 41, 6396–6402 (2014)], and consequently are undergoing the most rapid warming [J. E. Overland et al., Arctic Report Card (2018)]. However, the ecological response of Arctic-boreal lakes to warming remains highly uncertain. Historical trends in lake color from remote sensing observations can provide insights into changing lake ecology, yet have not been examined at the pan-Arctic scale. Here, we analyze time series of 30-m Landsat growing season composites to quantify trends in lake greenness for >4 × 105 waterbodies in boreal and Arctic western North America. We find lake greenness declined overall by 15% from the first to the last decade of analysis within the 6.3 × 106-km2 study region but with significant spatial variability. Greening declines were more likely to be found in areas also undergoing increases in air temperature and precipitation. These findings support the hypothesis that warming has increased connectivity between lakes and the land surface [A. Bring et al., J. Geophys. Res. Biogeosciences 121, 621–649 (2016)], with implications for lake carbon cycling and energy budgets. Our study provides spatially explicit information linking climate to pan-Arctic lake color changes, a finding that will help target future ecological monitoring in remote yet rapidly changing regions.

show abstract

Section: Methodsmentioning

confidence: 99%

Declining greenness in Arctic-boreal lakes

Kuhn

Butman

2021

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…While data with such a resolution performs reasonably for extratropical storms (Dullaart et al, 2019), it strongly underestimates the intensity of tropical cyclones (Schenkel and Hart, 2012) and associated storm surges (Bloemendaal et al, 2017;Muis et al, 2019). In 2019, the European Centre for Medium-Range Weather Forecasts (ECWMF) released the ERA5 climate reanalysis dataset.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The ERA5 gridded reanalysis data set was used to select the climatological forcing factors, including the pressure at mean sea level (SLP) and the corresponding gradients (GRDslp), the meridional and zonal wind components at 10 m (Uwind and Vwind, respectively), the temperature in 850 hPa (850T), the geopotential heights in 700 hPa (700Z), as well as the total precipitation (TP). The ERA5 reanalysis data set has a spatial resolution of 0.25 × 0.25° and a one‐hour temporal resolution, which is available from 1 January 1979 to present (Dullaart et al., 2020). To link the ERA5 reanalysis data set with the SSL records, the meteorological variables were averaged over the neighboring window of size 3 × 3 centered at each tidal gauging station to eliminate the bias caused by the spatially heterogeneous surface (see Text of the supplementary material for sensitivity analysis of grid sizes).…”

Section: Extreme Storm Surge Observations and Climatological Forcing mentioning

confidence: 99%

Probabilistic Characterization of Extreme Storm Surges Induced by Tropical Cyclones

Zhang

Wang

2021

JGR Atmospheres

View full text Add to dashboard Cite

The overtopping of flood defenses by extreme storm surges during tropical cyclones poses a significant threat to life and property in coastal and estuarine regions. Since little effort has been devoted to investigating the complex interaction of multiple mechanisms causing extreme storm surges, for the first time, we propose a robust data‐driven framework to explicitly uncover the complex interaction and thus to improve the characterization of extreme storm surges induced by tropical cyclones. The framework constructs a probabilistic ensemble of dependence structures of multiple climatological forcing factors that potentially cause extreme storm surges based on a multi‐structure regular vine copula approach. The uncertainty in the vine‐based model structure is explicitly addressed in a Bayesian framework. The climatological forcing factors sensitive to extreme storm surge levels are selected using partial correlations, including 10‐m wind, 850‐mb temperature, 700‐mb geopotential height, precipitation, sea level pressure, and its spatial gradient extracted from the ERA5 reanalysis data. We demonstrate the framework by an in‐depth analysis of the extreme storm surge levels observed over two tidal gauging stations in Hong Kong during 1979–2018. Our findings show that the proposed framework substantially improves the characterization of extreme storm surges by taking into account the joint evolution of multiple mechanisms causing extreme storm surges and underlying uncertainties. Furthermore, the framework not only demonstrates higher skill than previous single‐structure vine‐based models but also can outperform the principal components regression and the random forest regression in terms of characterizing extreme storm surges.

show abstract

Advancing global storm surge modelling using the new ERA5 climate reanalysis

Cited by 126 publications

References 59 publications

Declining greenness in Arctic-boreal lakes

Declining greenness in Arctic-boreal lakes

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

Probabilistic Characterization of Extreme Storm Surges Induced by Tropical Cyclones

Contact Info

Product

Resources

About