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Collaborative research: Combining models and observations to constrain the marine iron cycle

合作研究：结合模型和观测来限制海洋铁循环

基本信息

批准号：
1658436
负责人：
Seth John
金额：
$ 19.8万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1658436&HistoricalAwards=false
关键词：
Collaborative research Combining models observations

项目摘要

Tiny marine organisms called phytoplankton play a critical role in Earth's climate, by absorbing carbon dioxide from the atmosphere. In order to grow, these phytoplankton require nutrients that are dissolved in seawater. One of the rarest and most important of these nutrients is iron. Even though it is a critical life-sustaining nutrient, oceanographers still do not know much about how iron gets into the ocean, or how it is removed from seawater. In the past few years, scientists have made many thousands of measurements of the amount of dissolved iron in seawater, in environments ranging from the deep sea, to the Arctic, to the tropical oceans. They found that the amount of iron in seawater varies dramatically from place to place. Can this data tell us about how iron gets into the ocean, and how it is ultimately removed? Yes. In this project, scientists working on making measurements of iron in seawater will come together with scientists who are working on computer models of iron inputs and removal in the ocean. The goal is to work together to create a program that allows our computer models to "learn" from the data, much like an Artificial Intelligence program. This program will develop a "best estimate" of where and how much iron is coming into the ocean, how long it stays in the ocean, and ultimately how it gets removed. This will lead to a better understanding of how climate change will impact the delivery of iron to the ocean, and how phytoplankton will respond to climate change. With better climate models, society can make more informed decisions about how to respond to climate change. The study will also benefit a future generation of scientists, by training graduate students in a unique collaboration between scientists making seawater measurements, and those using computer models to interpret those measurements. Finally, the project aims to increase the participation of minority and low-income students in STEM (Science, Technology, Engineering, and Mathematics) research, through targeted outreach programs.Iron (Fe) is an important micronutrient for marine phytoplankton that limits primary productivity over much of the ocean; however, the major fluxes in the marine Fe cycle remain poorly quantified. Ocean models that attempt to synthesize our understanding of Fe biogeochemistry predict widely different Fe inputs to the ocean, and are often unable to capture first-order features of the Fe distribution. The proposed work aims to resolve these problems using data assimilation (inverse) methods to "teach" the widely used Biogeochemical Elemental Cycling (BEC) model how to better represent Fe sources, sinks, and cycling processes. This will be achieved by implementing BEC in the efficient Ocean Circulation Inverse Model and expanding it to simulate the cycling of additional tracers that constrain unique aspects of the Fe cycle, including aluminum, thorium, helium and Fe isotopes. In this framework, the inverse model can rapidly explore alternative representations of Fe-cycling processes, guided by new high-quality observations made possible in large part by the GEOTRACES program. The work will be the most concerted effort to date to synthesize these rich datasets into a realistic and mechanistic model of the marine Fe cycle. In addition, it will lead to a stronger consensus on the magnitude of fluxes in the marine Fe budget, and their relative importance in controlling Fe limitation of marine ecosystems, which are areas of active debate. It will guide future observational efforts, by identifying factors that are still poorly constrained, or regions of the ocean where new data will dramatically reduce remaining uncertainties and allow new robust predictions of Fe cycling under future climate change scenarios to be made, ultimately improving climate change predictions. A broader impact of this work on the scientific community will be the development of a fast, portable, and flexible global model of trace element cycling, designed to allow non-modelers to test hypotheses and visualize the effects of different processes on trace metal distributions. The research will also support the training of graduate students, and outreach to low-income and minority students in local school districts.

被称为浮游植物的微小海洋生物通过吸收大气中的二氧化碳在地球气候中起着关键作用。为了生长，这些浮游植物需要溶解在海水中的营养物质。这些营养素中最稀有和最重要的一种是铁。尽管铁是维持生命的重要营养物质，但海洋学家仍然不太了解铁是如何进入海洋的，或者它是如何从海水中去除的。在过去的几年里，科学家们对海水中溶解的铁的含量进行了数千次测量，从深海到北极，再到热带海洋。他们发现，海水中的铁含量因地而异。这些数据能告诉我们铁是如何进入海洋的，以及它最终是如何被移除的吗？是的在这个项目中，致力于测量海水中铁的科学家将与致力于研究海洋中铁输入和去除的计算机模型的科学家一起工作。我们的目标是共同努力创建一个程序，让我们的计算机模型从数据中“学习”，就像人工智能程序一样。该项目将对铁进入海洋的位置和数量、铁在海洋中停留的时间以及铁最终如何被去除进行“最佳估计”。这将有助于更好地了解气候变化如何影响铁向海洋的输送，以及浮游植物如何应对气候变化。有了更好的气候模型，社会可以就如何应对气候变化做出更明智的决定。这项研究还将使未来一代的科学家受益，通过培训研究生进行海水测量的科学家与使用计算机模型解释这些测量结果的科学家之间的独特合作。最后，该项目旨在通过有针对性的推广计划，提高少数民族和低收入学生在STEM（科学、技术、工程和数学）研究中的参与度。铁（Fe）是海洋浮游植物的重要微量营养素，限制了大部分海洋的初级生产力;然而，海洋铁循环中的主要通量仍然很难量化。试图综合我们对Fe地球化学的理解的海洋模型预测了不同的Fe输入到海洋中，并且通常无法捕获Fe分布的一阶特征。拟议的工作旨在解决这些问题，使用数据同化（逆）的方法来“教”广泛使用的生物地球化学元素循环（BEC）模型如何更好地代表铁源，汇和循环过程。这将通过在有效的海洋环流逆模型中实施BEC并将其扩展到模拟限制铁循环独特方面的其他示踪剂的循环来实现，包括铝，钍，氦和铁同位素。在这个框架中，在新的高质量观测的指导下，逆模型可以快速探索铁循环过程的替代表示，这在很大程度上是由GEOTRACES计划实现的。这项工作将是迄今为止最协调一致的努力，将这些丰富的数据集合成为海洋铁循环的现实和机械模型。此外，它将导致一个更强的共识，在海洋铁预算通量的大小，其在控制海洋生态系统的铁限制，这是积极的辩论领域的相对重要性。它将指导未来的观测工作，通过确定仍然缺乏约束的因素，或新数据将大大减少剩余不确定性的海洋区域，并允许在未来气候变化情景下对Fe循环进行新的稳健预测，最终改善气候变化预测。这项工作对科学界的更广泛影响将是开发一个快速，便携式和灵活的微量元素循环全球模型，旨在使非建模者能够测试假设和可视化不同过程对微量金属分布的影响。这项研究还将支持研究生的培训，以及对当地学区低收入和少数民族学生的宣传。