Projection and Analysis of Extreme Wave Climate

Caires, S.; Swail, Val R.; Wang, Xiaolan L.

doi:10.1175/jcli3918.1

Cited by 134 publications

(103 citation statements)

References 16 publications

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“…A number of modelling studies suggest that, in the future, increases in extreme wave height are likely to occur in the mid-latitude oceans (e.g. Wang et al 2004;Caires et al 2006). Woth et al (2006) found that, for the North Sea, a 100-year event could become 10-20 cm higher than today by the 2080s.…”

Section: Expected Change In Storm Surgesmentioning

confidence: 99%

Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen

Hallegatte

Patmore

Mestre

et al. 2009

IOP Conf. Ser.: Earth Environ. Sci.

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This study illustrates a methodology to assess the economic impacts of climate change at a city scale and benefits of adaptation, taking the case of sea level rise and storm surge risk in the city of Copenhagen, capital of Denmark. The approach is a simplified catastrophe risk assessment, to calculate the direct costs of storm surges under scenarios of sea level rise, coupled to an economic input-output (IO) model. The output is a risk assessment of the direct and indirect economic impacts of storm surge under climate change, including, for example, production and job losses and reconstruction duration, and the benefits of investment in upgraded sea defences. The simplified catastrophe risk assessment entails a statistical analysis of storm surge characteristics, geographical-information analysis of population and asset exposure combined with aggregated vulnerability information. For the city of Copenhagen, it is found that in absence of adaptation, sea level rise would significantly increase flood risks. Results call for the introduction of adaptation in long-term urban planning, as one part of a comprehensive strategy to manage the implications of climate change in the city. Mitigation policies can also aid adaptation by limiting the pace of future sea level rise.

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Section: Expected Change In Storm Surgesmentioning

confidence: 99%

Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen

Hallegatte

Patmore

Mestre

et al. 2009

IOP Conf. Ser.: Earth Environ. Sci.

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show abstract

“…Therefore it is difficult to quantitatively assess the relative contribution between more than two factors at a time. Wang et al (2004), Wang and Swail (2006a) as well as Caires et al (2006) used one climate model (the Canadian CGCM2) for different emission scenarios. Wang and Swail (2006b), however, represented the average of three models for the emission scenarios A2 and B2.…”

Section: Uncertainties Related To Wave Climate Projectionsmentioning

confidence: 99%

“…Both seasonal mean SLP anomalies, P, and anomalies of seasonal mean squared SLP gradients, G, have been shown to be good predictors for predicting seasonal SWH statistics (Wang and Swail 2006a, b;Wang et al 2004;Caires et al 2006). The use of predictor anomalies, instead of predictor values, diminishes the effects of 'model climate biases', i.e.…”

Section: Effect Of Downscalingmentioning

confidence: 99%

“…The ERA40 reanalysis (Uppala et al 2005), with global wave reanalysis (e.g. Caires et al 2006) and 2. An ensemble of simulations conducted with the Canadian coupled climate model CGCM2 (Flato and Boer 2001).…”

Section: Effect Of Downscalingmentioning

confidence: 99%

“…Caires et al (2006) used the non-homogeneous Poisson process (NPP) to model extreme values of the 40-year long ECMWF Re-Analysis (ERA-40) data set for significant wave height. The model parameters were expressed as functions of the seasonal mean sea pressure anomaly and seasonal squared sea pressure gradients index.…”

Section: Effect Of Extreme Value Modelmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainties

Bitner‐Gregersen

Eide

Hørte

et al. 2013

SpringerBriefs in Climate Studies

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The oceanographic community has always been concerned with providing environmental models and data which approximate the physics of the ocean in the most accurate way. Industry, on the other hand, needs accurate data and models for design purposes. Although uncertainties of data and models were discussed before the 1980s, they were not systematically quantified. Further development of the reliability methods (Madsen et al. 1986) and their implementation by some parts of the industry in the 1980s has brought much focus onto the uncertainties associated with environmental description. The PROBabilistic Analysis program PROBAN Ò developed by Det Norske Veritas at the end of the 1980s, and continuously improved since then (DNV 2002), is still one of the leading software packages for reliability calculations and is used by academia as well as industry. Reliability methods allow quantification, in a probabilistic way, of the uncertainties in the different parameters that govern structural integrity.

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A weather-type statistical downscaling framework for ocean wave climate

Camus

Ménendez

Méndez

et al. 2014

J. Geophys. Res. Oceans

107

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Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales.

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Projection and Analysis of Extreme Wave Climate

Cited by 134 publications

References 16 publications

Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen

Assessing climate change impacts, sea level rise and storm surge risk in port cities: A case study on Copenhagen

Uncertainties

A weather-type statistical downscaling framework for ocean wave climate

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