The Annals of Applied Statistics

Statistical downscaling for future extreme wave heights in the North Sea

Ross Towe, Emma Eastoe, Jonathan Tawn, and Philip Jonathan

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For safe offshore operations, accurate knowledge of the extreme oceanographic conditions is required. We develop a multi-step statistical downscaling algorithm using data from a low resolution global climate model (GCM) and local-scale hindcast data to make predictions of the extreme wave climate in the next 50-year period at locations in the North Sea. The GCM is unable to produce wave data accurately so instead we use its 3-hourly wind speed and direction data. By exploiting the relationships between wind characteristics and wave heights, a downscaling approach is developed to relate the large and local-scale data sets, and hence future changes in wind characteristics can be translated into changes in extreme wave distributions. We assess the performance of the methods using within sample testing and apply the method to derive future design levels over the northern North Sea.

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Ann. Appl. Stat., Volume 11, Number 4 (2017), 2375-2403.

Received: October 2016
Revised: May 2017
First available in Project Euclid: 28 December 2017

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Climate change covariate modelling environmental modelling extreme value theory sea waves statistical downscaling


Towe, Ross; Eastoe, Emma; Tawn, Jonathan; Jonathan, Philip. Statistical downscaling for future extreme wave heights in the North Sea. Ann. Appl. Stat. 11 (2017), no. 4, 2375--2403. doi:10.1214/17-AOAS1084.

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