NSFGEO-NERC: Using Time-series Field Observations to Constrain an Ocean Iron Model

铁是海洋浮游植物生长的必需营养素。因此，铁在调节海洋初级生产和碳循环方面发挥着关键作用。因此，海洋生物学和化学模型必须考虑到铁，以探讨海洋生产力过去、现在和未来的变化以及海洋在全球碳循环中的作用。在这个涉及美国和英国研究人员的联合项目中，在美国国家科学基金会和自然环境研究理事会（英国）的支持下，百慕大大西洋时间序列研究区域的实地数据将与现有的最先进的海洋生物地球化学模型相结合。通过利用已知的季节尺度的物理，化学和生物变化的最佳可行技术区域，海洋环境提供的最佳可行技术核心数据，和现有的区域物理循环模型，拟议的研究将产生过程相关的信息，是普遍适用于公海。特别是，拟议的研究将侧重于了解海洋水柱中溶解铁的大气输入、生物吸收、再生和清除，这些都是海洋铁循环中的主要不确定因素。该项目将包括对K-12、本科生、研究生和博士后水平的重大教育和培训贡献，以及旨在解释这项研究及其重要性的公共外展工作。由于对驱动溶解铁变异性的机制了解不够，海洋模型模拟铁的能力仍然很粗糙，特别是铁结合配体的参与，胶体和颗粒在表面输入，生物吸收，再生和清除溶解铁在上层海洋。GEOTRACES项目产生的盆地尺度数据为测试和改进模型提供了重要资源，并进一步为我们对海洋铁循环的机械理解提供了重要资源。然而，这样的数据只提供准天气的“快照”，这限制了他们的效用，在隔离和识别的过程中，控制溶解铁在海洋上层。拟议的研究旨在提供这些管理过程的机制洞察相结合的时间序列数据从BATS地区的数值模拟实验。具体而言，将从五次巡航期间收集的水柱样本的化学分析中获得颗粒、溶解、胶体、可溶和配体结合铁物种的垂直（上方2 000米）和横向（数十公里）分布的季节性解析数据，这五次巡航跨越一个完整的年度周期，与每月的最佳可行技术方案巡航共享。这些数据，沿着辅助数据从BATS程序，将被用来测试和通知数值模拟实验，从而获得更好的理解的机制，控制溶解铁在海洋水柱的分布和动态。

Iron is an essential nutrient for the growth of phytoplankton in the oceans. As such, iron plays key roles in regulating marine primary production and the cycling of carbon. It is thus important that models of ocean biology and chemistry consider iron, in order to explore past, present and future variations in marine productivity and the role of the ocean in the global carbon cycle. In this joint project involving researchers in the U.S. and the U.K., supported by both NSF and the Natural Environment Research Council (U.K.), field data from the Bermuda Atlantic Time-series Study (BATS) region will be combined with an established, state-of-the-art ocean biogeochemical model. By leveraging the known seasonal-scale physical, chemical and biological changes in the BATS region, the oceanographic context provided by the BATS core data, and an existing model of the regional physical circulation, the proposed study will yield process-related information that is of general applicability to the open ocean. In particular, the proposed research will focus on understanding the atmospheric input, biological uptake, regeneration and scavenging removal of dissolved iron in the oceanic water column, which have emerged as major uncertainties in the ocean iron cycle. The project will include significant educational and training contributions at the K-12, undergraduate, graduate and postdoctoral levels, as well as public outreach efforts that aim to explain the research and its importance.The ability of ocean models to simulate iron remains crude, owing to an insufficient understanding of the mechanisms that drive variability in dissolved iron, particularly the involvement of iron-binding ligands, colloids and particles in the surface input, biological uptake, regeneration and scavenging of dissolved iron in the upper ocean. Basin-scale data produced by the GEOTRACES program provide an important resource for testing and improving models and, by extension, our mechanistic understanding of the ocean iron cycle. However such data provide only quasi-synoptic 'snapshots', which limits their utility in isolating and identifying the processes that control dissolved iron in the upper ocean. The proposed research aims to provide mechanistic insight into these governing processes by combining time-series data from the BATS region with numerical modeling experiments. Specifically, seasonally resolved data on the vertical (upper 2,000 meters) and lateral (tens of kilometers) distributions of particulate, dissolved, colloidal, soluble and ligand-bound iron species will be obtained from the chemical analysis of water column samples collected during five cruises, spanning a full annual cycle, shared with the monthly BATS program cruises. These data, along with ancillary data from the BATS program, will be used to test and inform numerical modeling experiments, and thus derive an improved understanding of the mechanisms that control the distribution and dynamics of dissolved iron in the oceanic water column.