Collaborative Research: Exploring the dynamics of nitrous oxide in the Southern Benguela Upwelling System

一氧化二氮是一种长期存在的强大温室气体。它也是平流层臭氧消耗的一个驱动因素。一氧化二氮的天然来源包括细菌通过硝化和反硝化过程产生的。海洋是向大气排放一氧化二氮的主要来源，生物生产力高的地区，如东部边界上升流系统，向大气排放的一氧化二氮特别多。南部本格拉上升流系统（SBUS）可以说是最富有成效的东部边界上升流系统，但没有公布的测量一氧化二氮从这个地区。此外，在SBUS中一氧化二氮产生的主要生物途径的相对重要性还没有很好地确定。来自康涅狄格大学和南卡罗来纳州大学的一组科学家将调查SBUS中的一氧化二氮循环，估计一氧化二氮向大气的通量，并探索是什么驱动了一氧化二氮在不同季节和不同地点的循环变化。这项研究将提高对一种重要的温室气体的理解，以及它的循环如何随着海洋条件的变化而变化。拟议工作的资金将资助康涅狄格大学的一名研究生和南卡罗来纳州大学的一名本科生的培训。它还将为博士后研究员提供在区域建模和机器学习中应用计算技能的机会，为学术界或工业界的职业生涯做好准备。该项目将通过将研究方法纳入康涅狄格大学的本科服务学习课程，为本科教育和向公众和决策者传播气候变化科学做出贡献。这项工作旨在提供一氧化二氮向大气的区域通量估计，检查季节动态，评估一氧化二氮生产和消费的生物途径，并查询区域和大尺度的一氧化二氮动力学在SBUS强迫。为此，该小组将（a）测量在季节性调查期间在SBUS收集的样本中的一氧化二氮浓度，持续测量地表一氧化二氮，并根据一致风速得出一氧化二氮从海洋到空气的通量的可靠估计;（B）从一氧化二氮和硝酸盐的氮和氧同位素比率的测量来查询一氧化二氮的产生和消耗途径，研究一氧化二氮循环的环境相关因素;（c）根据这些测量数据建立一氧化二氮区域机器学习模型，以研究季节性和年际一氧化二氮动态的环境驱动因素，利用历史数据和区域海洋动态模型得出的水文场预测天基信息系统中的一氧化二氮。这项工作将深入了解一氧化二氮的生产和消费的途径，在东部边界上升流系统的边界广阔的大陆架-对比从活动的边缘获得的知识更大的身体。基于机器学习的一氧化二氮统计模型最终将用于从SBUS的气候预测中预测一氧化二氮，并预测一氧化二氮对全球海洋脱氧的区域响应。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Nitrous oxide is a long-lived and powerful greenhouse gas. It is also a driver of ozone depletion in the stratosphere. Natural sources of nitrous oxide include production by bacteria through processes called nitrification and denitrification. The ocean is a major source of nitrous oxide to the atmosphere, and areas of high biological productivity like eastern boundary upwelling systems have been shown to generate particularly high nitrous oxide emissions to the atmosphere. The Southern Benguela Upwelling System (SBUS) is arguably the most productive eastern boundary upwelling system, yet there are no published measurements of nitrous oxide from this region. In addition, the relative importance of the major biological pathways of nitrous oxide production in the SBUS is not well established. A team of scientists from the University of Connecticut and the University of South Carolina will investigate nitrous oxide cycling in the SBUS, estimate nitrous oxide fluxes to the atmosphere, and explore what drives changes in nitrous oxide cycling across seasons and in different locations. This study will improve understanding of an important greenhouse gas and how its cycling might change in response to changing ocean conditions. Funding from the proposed work will sponsor the training of a graduate student at the University of Connecticut and an undergraduate student at the University of South Carolina. It will also provide a postdoctoral fellow the opportunity to apply computational skills in regional modeling and machine learning, offering preparation for a career in academia or industry. The project will contribute to undergraduate education and communication of climate change science to the general public and policymakers through the incorporation of the study methodology into an undergraduate Service Learning course at the University of Connecticut.This work seeks to provide estimates of the regional flux of nitrous oxide to the atmosphere, to examine seasonal dynamics, to assess the biological pathways to nitrous oxide production and consumption, and to query regional and large scale forcings on nitrous oxide dynamics in the SBUS. To this end, the team will (a) measure nitrous oxide concentrations in samples collected in the SBUS during seasonal surveys, measure surface nitrous oxide continuously underway, and derive robust estimates of the sea-to-air flux of nitrous oxide from coincident windspeed; (b) query nitrous oxide production and consumption pathways from measurements of the nitrogen and oxygen isotope ratios of nitrous oxide and nitrate, and investigate environmental correlates of nitrous oxide cycling; (c) develop regional machine learning models for nitrous oxide from these measurements to investigate environmental drivers of seasonal and inter-annual nitrous oxide dynamics, predicting nitrous oxide in the SBUS with historical data and with hydrographic fields derived from a regional dynamical ocean model. The work will give insights into pathways of nitrous oxide production and consumption in an eastern boundary upwelling system bounded by a broad continental shelf – contrasting the greater body of knowledge obtained from active margins. The statistical nitrous oxide models borne of machine learning will ultimately serve to predict nitrous oxide from climate projections of the SBUS, and to anticipate the regional response of nitrous oxide to global ocean de-oxygenation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.