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：
1658380
负责人：
Jefferson Moore
金额：
$ 47.07万
依托单位：
University of California-Irvine
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=1658380&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.

通过吸收大气中的二氧化碳，称为浮游植物的微小海洋生物在地球气候中起着至关重要的作用。为了生长，这些浮游植物需要溶于海水中的营养。这些营养素中最稀有，最重要的是铁。即使它是一种持久的生命养分，海洋学家仍然对铁如何进入海洋或如何将其从海水中移出。在过去的几年中，科学家对海水中溶解的铁的数量进行了数千种测量，从深海到北极到热带海洋的环境。他们发现，海水中的铁量因地点而异。这些数据能否告诉我们铁如何进入海洋以及最终如何将其删除？是的。在这个项目中，致力于对海水进行铁测量的科学家将与正在研究铁投入的计算机模型并在海洋中拆除的科学家一起。目的是共同创建一个程序，使我们的计算机模型可以从数据中“学习”，就像人工智能程序一样。该计划将制定一个“最佳估计”，即在哪里和多少铁进入海洋，在海洋中持续多长时间，并最终将其去除。这将使人们更好地了解气候变化将如何影响铁向海洋的传递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来解释这些测量值的科学家之间进行独特的合作，从而使未来的科学家受益。最后，该项目旨在通过有针对性的外展计划来增加少数群体和低收入学生参与STEM（科学，技术，工程和数学）研究。然而，海洋铁循环中的主要通量仍然很差。试图综合我们对FE生物地球化学的理解的海洋模型预测了对海洋的FE输入截然不同，并且通常无法捕获Fe分布的一阶特征。提出的工作旨在使用数据同化（反向）方法来解决这些问题，以“教”广泛使用的生物地球化学元素循环（BEC）模型如何更好地表示FE来源，水槽和循环过程。这将通过在有效的海洋循环反向模型中实现BEC来实现，并扩展其模拟其他示踪剂的循环，这些示踪剂限制了FE循环的独特方面，包括铝，thor，Thorium，氦气和FE同位素。在此框架中，逆模型可以迅速探索Fe循环过程的替代表示，并在大部分在Geotraces计划中大部分的新高质量观察中指导。迄今为止，这项工作将是将这些丰富的数据集合成为船舶FE周期的现实机械模型的最合成的努力。此外，这将导致对海洋铁预算中通量的大小提高共识，以及它们在控制海洋生态系统的FE限制方面的相对重要性，这是积极辩论的领域。它将通过确定仍然受到限制的因素，或在新数据将大大减少剩余的不确定性，并允许在未来的气候变化情景下对FE循环的新预测，最终改善气候变化预测的新数据。这项工作对科学界的更广泛的影响将是开发快速，便携式和灵活的痕量元素循环全球模型，旨在允许非模型测试者检验假设并可视化不同过程对痕量金属分布的影响。这项研究还将支持对研究生的培训，并向当地学区的低收入和少数族裔学生推广。