Department of Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.
Department of Ocean Sciences, Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, Florida, USA.
Sci Rep. 2017 Aug 1;7(1):7021. doi: 10.1038/s41598-017-07177-w.
We construct a Markov-chain representation of the surface-ocean Lagrangian dynamics in a region occupied by the Gulf of Mexico (GoM) and adjacent portions of the Caribbean Sea and North Atlantic using satellite-tracked drifter trajectory data, the largest collection so far considered. From the analysis of the eigenvectors of the transition matrix associated with the chain, we identify almost-invariant attracting sets and their basins of attraction. With this information we decompose the GoM's geography into weakly dynamically interacting provinces, which constrain the connectivity between distant locations within the GoM. Offshore oil exploration, oil spill contingency planning, and fish larval connectivity assessment are among the many activities that can benefit from the dynamical information carried in the geography constructed here.
我们利用卫星跟踪漂流轨迹数据，构建了一个墨西哥湾（GoM）及其毗邻的加勒比海和北大西洋部分地区的海洋表面拉格朗日动力学的马尔可夫链表示。这是迄今为止最大的数据集。通过分析与链相关的转移矩阵的特征向量，我们确定了几乎不变的吸引集及其吸引域。利用这些信息，我们将 GoM 的地理区域分解为弱动态相互作用的省份，从而限制了 GoM 内部远距离地点之间的连通性。近海石油勘探、溢油应急规划和鱼类幼虫连通性评估等许多活动都可以从这里构建的地理信息中受益。
