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首页> 外文期刊>Journal of marine systems: journal of the European Association of Marine Sciences and Techniques >Mechanism studies of seasonal variability of dissolved oxygen in Mass Bay: A multi-scale FVCOM/UG-RCA application
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Mechanism studies of seasonal variability of dissolved oxygen in Mass Bay: A multi-scale FVCOM/UG-RCA application

机译：马斯湾溶解氧季节变化的机理研究：多尺度FVCOM / UG-RCA应用

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摘要

Long-term (1992-2010) water quality monitoring records reveal that the dissolved oxygen (DO) concentration in Mass Bay exhibits a well-defined seasonal cycle, highest in March-April and lowest in October. This pattern persists in all years with insignificant interannual variability. A multi-domain-nested coupled physical- biogeochemical model was developed and applied to simulate the DO field over the 16-year period 1995-2010. Themodel-computed DO and nitrogen concentrationswere in good agreementwith observations. An EOF analysis of themodeled DO field indicates that DO inMass Bay features bothwell-defined seasonal and spatialmodes. The magnitude and phase of the DOseasonal cycle varymore significantly in the southern bay than in the northern bay. Horizontal advection, which is connected to the western Gulf of Maine coastal currents, plays a dominant role in the DO variability in the northern bay. The southern bay features a well-defined local retention mechanism with a longer residence time. In this region, the DO variation is controlled predominantly by local biogeochemical processes. Since the photosynthetic minus respiration production of DO is always balanced to a large degree by the oxidation of organic matters, reaeration becomes a major driver for the seasonal cycle of DO.
机译：长期（1992-2010年）水质监测记录显示，马斯湾的溶解氧（DO）浓度具有明确的季节周期，最高的3月至4月，最低的10月。这种模式在所有年份中都持续存在，年际变化很小。建立了多域嵌套的物理生物地球化学耦合模型，并将其应用于模拟1995年至2010年这16年间的DO场。模型计算的溶解氧和氮浓度与观测值吻合良好。对模型化DO的EOF分析表明，马斯湾的DO具有既定的季节模式和空间模式。 DOseasonal周期的大小和相位在南部海湾比北部海湾变化更大。连接到缅因州西部沿海海流的水平对流在北部海湾的溶解氧变化中起主要作用。南部海湾具有定义明确的局部保留机制，具有更长的停留时间。在该区域，DO的变化主要由局部生物地球化学过程控制。由于DO的光合负呼吸产生总是通过有机物的氧化而在很大程度上平衡，因此，养分成为DO季节性周期的主要驱动力。

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