The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model‐based seasonal‐to‐interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy‐resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∼5 months in the Mid‐Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large‐scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES.

中文翻译：

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美国东北部大陆架底部温度的季节性预测

美国东北架（NES）是一个海洋学动态的海洋生态系统，为世界上一些最有价值的水下渔业提供支持。对NES环境变量，尤其是海底温度的可靠预测可能会导致深海渔业管理的显着改善。但是，当前这一基于气候模型的季节至年际预测在这种大陆架环境中的预测能力有限。在这里，我们使用解析涡旋的海洋再分析数据集，开发了NES底部温度的统计季节预测层次。一个简单的，衰减的局部持续性预测模型可以为大西洋中部约5个月以内和缅因湾约10个月的交货时间提供重要技能，尽管预测技巧因季节而异。考虑来自对流过程的结果，将来自附近或上游（即，极地）区域的温度视为额外的预测因素通常会提高预测技能。大规模的大气和海洋指数，例如墨西哥湾流径指数（GSI）和北大西洋涛动指数，也被作为NES底部温度的预测指标进行了测试。相对于局部持久性，只有从在200 m深度处观察到的温度构造的GSI才能显着提高预测技能。但是，此GSI的预测技巧并不比使用包含附近或上游架子/斜坡温度的模型获得的预测技巧大。根据这些结果，已开发出一种简化的统计模型，可以针对NES的渔业管理进行调整。