Global reconstructed daily surge levels from the 20th Century Reanalysis (1871–2010)

Cid, Alba; Camus, Paula; Castanedo, Sonia; Méndez, Fernando J.; Medina, Raúl

doi:10.1016/j.gloplacha.2016.11.006

Cited by 42 publications

(68 citation statements)

References 21 publications

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“…The statistical method to reconstruct the surge levels is explained in detail in Cid et al (), which was in turn based on a similar approach used by Camus et al () to downscale multivariate wave climate. It consists of fitting a multivariate regression model between daily maximum surge levels (predictand) and the PCs of the daily SLP, GRDslp, and wind (predictor)

s u r g e (x_{i}, t) = a_{i} + b_{1, i} \times P C_{(1)} (x_{i}, t) + b_{2, i} \times P C_{(2)} (x_{i}, t) + \dots + b_{n, i} \times P C_{(n)} (x_{i}, t)

where n is the number of PCs that achieved a statistical improvement of the results and

a_{i}, b_{1, i}, \dots, b_{n, i}

are the coefficients obtained from the regression model.…”

Section: Methodsmentioning

confidence: 99%

“…Wahl and Chambers () used linear regression models to explain the observed multidecadal storm surge variability along the U.S. coast by means of traditional and tailored climate indices. Cid et al () extended the temporal coverage of the global DAC database (Dynamic Atmospheric Correction from AVISO) using tailored indices obtained from atmospheric fields from the 20th Century Reanalysis (Compo et al, ). We are not aware of local or regional studies where statistical models have been used to analyze extreme sea levels in Southeast Asia.…”

Section: Introductionmentioning

confidence: 99%

“…We are not aware of local or regional studies where statistical models have been used to analyze extreme sea levels in Southeast Asia. Hence, our study has the following three main objectives: (1) to apply a similar approach as used in Cid et al () to develop, calibrate, and validate a statistical model to simulate daily and monthly maxima values of storm surge at tide gauge locations in Southeast Asia; (2) to use the model to simulate storm surges for the 147 year period from 1866 to 2012; and (3) to calculate more robust return water levels from the entire simulated data set, and subsets thereof to assess potential changes between different periods.…”

Section: Introductionmentioning

confidence: 99%

“…The main contributor to nontidal residual extremes is the storm surge, even though the importance of the seasonal cycle and the steric component have been addressed in this work. Tides are deterministic and can be predicted, whereas information on storm surges need to be extracted from tide gauge (TG) records, if available, or modeled (numerically or statistically) by taking into account their forcing variables, such as wind stress and sea level pressure (SLP) gradients over the sea surface (e.g., Cid et al, 2016). In either case, high frequency (hourly or daily temporal resolution) and long storm surge records are required to analyze Key Points: A multiple linear regression model is used to reconstruct daily maxima surge levels at Southeast Asia from 1866 to 2012 Return water levels are estimated using a GEV distribution and monthly maxima values The statistical model gives daily surge values of similar or higher quality than a global numerical model…”

Section: Introductionmentioning

confidence: 99%

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Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

et al. 2018

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We present a methodology to reconstruct the daily maximum storm surge levels, obtained from tide gauges, based on the surrounding atmospheric conditions from an atmospheric reanalysis (20th Century Reanalysis—20CR). Tide gauge records in Southeast Asia are relatively short, so this area is often underrepresented in studies based on long observational records, and there are just a few studies that have analyzed storm surge trends, variability or return water levels (RWLs) from numerical models in this area. Here we develop, calibrate, and validate a multivariate linear regression model that relates the storm surge with the principal components of the local atmospheric conditions. This allows us to reconstruct storm surges for the 147 year 20CR period (1866–2012) and therefore to calculate more robust RWLs from the entire simulated data set and subsets thereof. RWLs are obtained by fitting the monthly maxima values to the Generalize Extreme Value (GEV) distribution. We find an increase in the 50 year RWL from the second half of the 19th century to the present unrelated to mean sea level; this increase is less noticeable when comparing only recent periods. Therefore, further research is needed since there is evidence that atmospheric reanalyses can include spurious trends in the late 19th and early 20th. RWLs obtained from the statistical reconstruction are validated against the ones obtained from observations and from a numerical model. Agreements are generally higher when using surge levels from the statistical model, even before its calibration.

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s u r g e (x_{i}, t) = a_{i} + b_{1, i} \times P C_{(1)} (x_{i}, t) + b_{2, i} \times P C_{(2)} (x_{i}, t) + \dots + b_{n, i} \times P C_{(n)} (x_{i}, t)

where n is the number of PCs that achieved a statistical improvement of the results and

a_{i}, b_{1, i}, \dots, b_{n, i}

are the coefficients obtained from the regression model.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

et al. 2018

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“…The purpose of this study is to create a New Zealand daily storm surge database comprised by both a global reconstruction of the storm surge since 1870 and projections until 2,100 based on GCMs. To achieve this aim, following the methodology developed in Cid et al (2017a), a statistical model is used to obtain the relationship between the storm surge from the Dynamic Atmospheric Correction (DAC) database and the sea level pressure (SLP) fields from ERA-Interim (Dee et al, 2011). Once the model is validated, it is applied to both the 20th century Reanalysis (20CR, Compo et al, 2011) to reconstruct the past storm surge, and to the future atmospheric sea level pressure fields from the different GCMs to obtain future projections.…”

Section: Introductionmentioning

confidence: 99%

Historical and future storm surge around New Zealand: From the 19th century to the end of the 21st century

Cagigal

Rueda

Castanedo

et al. 2019

Intl Journal of Climatology

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We developed a new hindcast for storm surge at a 0.25° spatial scale for the whole New Zealand area using a statistical downscaling technique that links the mean local atmospheric conditions with the maximum storm surge levels at a daily scale. After validating the hindcast against sea level instrumental records from 17 tidal gauges around New Zealand, the same technique has been applied to obtain storm surge projections until 2,100 using different global climate models. The global climate models have been previously classified according to their ability to reproduce the past climatology in the studied area and seven models have been selected in order to explore their effect on storm surge projections. For the two representative Concentration Pathways studied, the projections indicate that the storm surge associated with the 50 years return period will increase in magnitude in the Southern areas while it will decrease in the Northern region. Even where a decreasing linear trend over the annual maxima is observed in the future time series, sporadic events of higher magnitude than the historical peaks can be.

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A comparison of two global datasets of extreme sea levels and resulting flood exposure

et al. 2017

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Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of −0.2 m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

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Global reconstructed daily surge levels from the 20th Century Reanalysis (1871–2010)

Cited by 42 publications

References 21 publications

Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

Storm Surge Reconstruction and Return Water Level Estimation in Southeast Asia for the 20th Century

Historical and future storm surge around New Zealand: From the 19th century to the end of the 21st century

A comparison of two global datasets of extreme sea levels and resulting flood exposure

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