Collaborative Research: Changing Seasonality of the Arctic: Alteration of Production Cycles and Trophic Linkages in Response to Changes in Sea Ice and Upper Ocean Physics

合作研究：北极季节性的变化：生产周期和营养联系的变化响应海冰和上层海洋物理的变化

• 批准号: 0901926
• 负责人: Robert Campbell
• 金额: $ 10.85万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901926&HistoricalAwards=false
• 关键词: Collaborative; Research; Changing; Seasonality; Arctic; Collaborative; Research; Changing; Seasonality; Arctic

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Significant seasonal changes in arctic sea ice have been observed in recent years, characterized by unprecedented summer melt-back because of the lengthening of the summer melt season with earlier melt and later freeze onsets. As summer sea ice extent shrinks to record low levels, the peripheral seas of the Arctic Ocean are exposed much earlier to atmospheric surface heat flux, resulting in longer and warmer summers with more oceanic heat absorption. The changing seasonality in the arctic ice/ocean system will alter the timing, magnitude, duration, and pattern of marine production cycles, by disrupting key trophic linkages and feedbacks in planktonic food webs. These alterations will have consequences on the structure of arctic marine ecosystems with reverberations through the entire arctic system up to and including humans. It is, thus, important to understand the changes in the patterns of seasonality in the arctic physical and biological system. The PIs propose to investigate future changes in the seasonal linkages and interactions among arctic sea ice, the water column, and the marine production cycles and trophic structure as an integrated system. Their investigation will target the following seasonality hypotheses:1) Changes in the timing of spring sea ice retreat will cause a mismatch in the timing of primary and secondary production such that the plankton community size structure is altered. That in turn will modify the utilization of primary production and the availability of large zooplankton prey for upper trophic levels (e.g., whales, fish).2) Closer coupling of phytoplankton and zooplankton production cycles will result in a reduction in the magnitude of the spring bloom over productive shelves, greater utilization of primary production by water column consumers and a reduction in its export to the benthos.3) A longer growing season, which might include a secondary production peak later in summer/early fall, will permit the colonization of the Arctic Ocean by subarctic species that presently are unable to persist there because of the short duration of the growing season. These hypotheses will be explored by synthesizing model estimates and satellite and field observations. The PIs will use the existing coupled pan-Arctic Biology/Ice/Ocean Modeling and Assimilation System (BIOMAS), together with observations, to strengthen their understanding of the integrated arctic system of sea ice, ocean, and the marine production cycles. A series of model hindcasts, sensitivity studies, and future scenario simulations will be conducted to identify seasonally sensitive linkages and to test their hypotheses

该奖项是根据2009年的《美国回收与再投资法》（Priblaw 111-5）资助的。近年来已经观察到了北极海冰的季节性变化，其特征是夏季融化的夏季融化季节的延长，夏季融化的季节越来越长。随着夏季冰的范围缩小到记录低水平，北极海洋的外围海洋较早暴露于大气表面热通量，从而导致更长，更温暖的夏季，并吸收更多的海洋热量。北极冰/海洋系统中的季节性不断变化，将通过破坏浮游食物网中的关键营养联系和反馈来改变海洋生产周期的时机，幅度，持续时间和模式。这些改变将对北极海洋生态系统的结构产生影响，并在整个北极系统中进行回响，直到包括人类。因此，重要的是要了解北极物理和生物系统中季节性模式的变化。 PI提议研究北极海冰，水柱以及海洋生产周期和营养结构作为综合系统的季节性联系和相互作用的未来变化。他们的调查将针对以下季节性假设：1）春季海冰静修时机的变化会导致初级和二级生产时机的不匹配，从而改变了浮游生物社区大小的结构。反过来，这将修改主要产量的利用和上层营养水平的大型浮游动物猎物（例如鲸鱼，鱼类）.2）更紧密地耦合浮游植物和浮游动物的生产周期将导致春季范围的范围更大，从而使生产列的量度更大，而越来越多的生产量会导致较大的生产量的量化，从而逐渐置于较大的消费列中。 3）一个更长的生长季节，其中可能包括夏季/秋季初期的二级生产高峰，它将允许亚北极物种通过北极海洋殖民，由于生长季节的持续时间很短，目前无法持续存在。这些假设将通过合成模型估计以及卫星和现场观测来探讨这些假设。 PIS将使用现有的偶联泛氧化生物学/冰/海洋建模和同化系统（BIOMAS）以及观察结果，以增强他们对海冰，海洋和海洋生产周期综合北极系统的理解。将进行一系列模型后广播，灵敏度研究和未来的情景模拟，以识别季节性敏感的联系并检验其假设