The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variabilit...The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variability.To analyze sea ice changes in the Barents Sea,sea ice data from the National Snow and Ice Data Center were utilized.A remarkable decline in sea ice has been witnessed in the northern and eastern regions.This phenomenon has expanded the ice-free operational area for marine structures,highlighting the significance of wave factors.A site within this area was chosen to estimate the wave parameters.The wave data from ERA5 were categorized according to wave energy in each season.Four mixture joint distribution models for the wave height and period were constructed based on the mixture distribution method and copula theory,and environmental contours were developed and compared with the conditional probability method.Despite differences in the design parameter results,the mixture models demonstrate good performance in sample fitting,particularly in the distribution tails.Among these models,the Gaussian copula offers the best fit.展开更多
基金the National Natural Science Foundation of China(No.52171284)the Natural Science Foundation of Shandong Province(No.ZR2023QE016).
文摘The Barents Sea is a marginal sea of the Arctic Ocean and contains substantial hydrocarbon resources.In recent years,the Barents Sea has emerged as one of the Arctic regions with the most pronounced sea ice variability.To analyze sea ice changes in the Barents Sea,sea ice data from the National Snow and Ice Data Center were utilized.A remarkable decline in sea ice has been witnessed in the northern and eastern regions.This phenomenon has expanded the ice-free operational area for marine structures,highlighting the significance of wave factors.A site within this area was chosen to estimate the wave parameters.The wave data from ERA5 were categorized according to wave energy in each season.Four mixture joint distribution models for the wave height and period were constructed based on the mixture distribution method and copula theory,and environmental contours were developed and compared with the conditional probability method.Despite differences in the design parameter results,the mixture models demonstrate good performance in sample fitting,particularly in the distribution tails.Among these models,the Gaussian copula offers the best fit.
