Abstract
Abstract. Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.
Highlights
BweDeavnyeinnhceariegmahsti,incgwlsayveevipdeerniot dinarnedfor stochastic models that can describe the variability in both cent years that the climate may change due to increased anspace and time at various scales of the environmental conditions
This paper has presented a Bayesian hierarchical spatiotemporal model for 10 m wind speeds over an area in the North Atlantic ocean
A similar model has previously been applied to extreme wave climate over the same area and identified inter alia, increasing trends in the monthly maximum significant wave height
Summary
For the purpose of this study, an area in the mid-latitudes of the North Atlantic ocean was selected for investigation. 290 not explicitly included in the model It is rather the complex I stochastic dependence structures, in space and time at vari- the ous scales, in the wind data itself that have been modelled. It is observed that this line has a negative 265 slope of −0.0008171 m s−1 per month with an intercept of 18.64 m s−1, corresponding to slightly decreasing monthly maximum wind speeds of about 0.44 m s−1 overall throughb0a.s4be4adsmemd/osmdoeovlsde.eralsll. It is interesting 315 to observe the seasonality in the raw wind data as illustrated 3b.y13F.1iMg.aMi3n,amwinhoemdreeoldsthpeelecssippfieacctaiatfiilcolyantaiovneraged data for the first ten years are shown.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
