Evaluation of a CMIP5 derived dynamical global wind wave climate model ensemble

Hemer, Mark; Trenham, Claire

doi:10.1016/j.ocemod.2015.10.009

Cited by 108 publications

(91 citation statements)

References 44 publications

(75 reference statements)

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“…In this case, almost all the GCMs project a positive change, giving a consistent variation compared with the results at the southern Australia. Similar consistency is expected regarding T p changes, with mean changes close to 0.25 s. Wave direction changes reach values around 4° anticlockwise near latitudes 40°–45°S, obtaining a similar change spatial variation as in Hemer and Trenham [].…”

Section: Regional Climate Projectionsmentioning

confidence: 99%

Statistical wave climate projections for coastal impact assessments

et al. 2017

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Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi‐supervised weather‐typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

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Section: Regional Climate Projectionsmentioning

confidence: 99%

Statistical wave climate projections for coastal impact assessments

et al. 2017

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“…They find an agreed projected decrease in annual mean significant wave height (SWH) over 25.8% of the global ocean area (including the North Atlantic) which is greater during Boreal winter (January–March) than austral winter. Hemer and Trenham () show that high performance of GCMs for standard climate variables does not imply high performance for GCM‐forced wave simulations. Camus et al (), using weather typing and statistical downscaling, informed by a historical data set of wave data, showed how this method could be used to provide high‐resolution coastal impact assessment, including additional variables such as wave period and direction as well as SWH.…”

Section: Introductionmentioning

confidence: 99%

Future Wave Conditions of Europe, in Response to High‐End Climate Change Scenarios

Bricheno

Wolf

2018

JGR Oceans

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Changes in future North Atlantic storminess will impact upon wave conditions along the European coasts, with implications for coastal erosion, overtopping, and flood risk. In this study we make a detailed analysis of historic and future wave conditions around the European Atlantic coast, making projections out to the year 2100 under Representative Concentration Pathways 4.5 and 8.5 future emissions scenarios. A decrease in mean significant wave height of the order 0.2 m is projected across most of the European coast. Increases in the annual maximum and 99th percentile wave height as large as 0.5–1 m are observed in some areas but with a more complex spatial pattern. An increase in waves to the north of Scotland is also observed, mainly caused by a reduction in sea ice. We generate a set of coastal wave projections at around 10‐km resolution around continental Europe, Ireland, and the British Isles. Widening of the probability density function (PDF) is observed, suggesting an increased intensity of rare high wave events in the future. The emergent signal of a reduced mean wave height is statistically robust, while the future changes in extreme waves have a wider confidence interval. An assessment of different extreme waves metrics reveals different climate change response at very high percentiles; thus, care should be taken when assessing future changes in rare wave events.

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“…From all these hazards, marine storms cause most of the damage to non-seismic coasts. This situation may eventually be aggravated as a consequence of Climate-Change, which affects the intensity and frequency of extreme wave-conditions (Wang et al, 2015;Hemer and Trenham, 2016).…”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical modelling of future marine storms

Lin-Ye¹,

Garcı́a-León²,

Gràcia³

et al. 2017

Applied Ocean Research

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Extreme events, such as wave-storms, need to be characterized for coastal infrastructure design purposes. Such description should contain information on both the univariate behaviour and the joint-dependence of storm-variables. These two aspects have been here addressed through generalized Pareto distributions and hierarchical Archimedean copulas. A non-stationary model has been used to highlight the relationship between these extreme events and non-stationary climate. It has been applied to a Representative Concentration Pathway 8.5 Climate-Change scenario, for a fetch-limited environment (Catalan Coast). In the non-stationary model, all considered variables decrease in time, except for storm-duration at the northern part of the Catalan Coast. The joint distribution of storm variables presents cyclical fluctuations, with a stronger influence of climate dynamics than of climate itself.Peer ReviewedPostprint (author's final draft

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Evaluation of a CMIP5 derived dynamical global wind wave climate model ensemble

Cited by 108 publications

References 44 publications

Statistical wave climate projections for coastal impact assessments

Statistical wave climate projections for coastal impact assessments

Future Wave Conditions of Europe, in Response to High‐End Climate Change Scenarios

Multivariate statistical modelling of future marine storms

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