Wind and Wave Extremes over the World Oceans from Very Large Ensembles

TL;DR

This study uses very large ensemble forecasts at +240-h lead time to estimate global extreme wind and wave conditions, providing more accurate and confident return value estimates than reanalysis data.

Contribution

It introduces a novel approach of using large ensemble forecasts at long lead times for direct estimation of extreme marine conditions, improving confidence and reducing bias.

Findings

Ensemble forecasts yield higher return estimates than reanalysis.

Large dataset results in tighter confidence intervals.

Method provides comparable estimates outside tropical cyclone areas.

Abstract

Global return values of marine wind speed and significant wave height are estimated from very large aggregates of archived ensemble forecasts at +240-h lead time. Long lead time ensures that the forecasts represent independent draws from the model climate. Compared with ERA-Interim, a reanalysis, the ensemble yields higher return estimates for both wind speed and significant wave height. Confidence intervals are much tighter due to the large size of the dataset. The period (9 yrs) is short enough to be considered stationary even with climate change. Furthermore, the ensemble is large enough for non-parametric 100-yr return estimates to be made from order statistics. These direct return estimates compare well with extreme value estimates outside areas with tropical cyclones. Like any method employing modeled fields, it is sensitive to tail biases in the numerical model, but we find that the biases are moderate outside areas with tropical cyclones.