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

合作研究：探索南本格拉上升流系统中一氧化二氮的动力学

• 批准号: 2241433
• 负责人: Annie Bourbonnais
• 金额: $ 14.75万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241433&HistoricalAwards=false
• 关键词: Collaborative; Research; Exploring; dynamics; nitrous

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.

一氧化二氮是一种长寿且强大的温室气体。它也是平流层中臭氧耗竭的驱动器。一氧化二氮的天然来源包括细菌通过称为硝化和反硝化的过程产生。海洋是大气中一氧化二氮的主要来源，已经显示出高生物生产力（如东部边界上升流系统）的区域可产生特别高的氧化氧化物对大气的排放。南部的班格拉上升流系统（SBU）可以说是东部边界上升界最大的上升流系统，但该地区尚无对一氧化二氮的公开测量。另外，SBU中一氧化二氮产生的主要生物学途径的相对重要性尚未得到很好的确定。来自康涅狄格大学和南卡罗来纳大学的科学家团队将调查SBU中的一氧化二氮循环，估计一氧化二氮通量到大气，并探索整个季节和不同位置的一氧化二氮循环的驱动变化。这项研究将提高人们对重要温室气体的理解，以及其循环如何随着海洋状况的响应而变化。拟议工作的资金将赞助康涅狄格大学的一名研究生和南卡罗来纳大学的本科生的培训。它还将为博士后研究员提供在区域建模和机器学习中运用计算技能的机会，为学术界或行业的职业做准备。 The project will contribute to undergraduate education and communication of climate change science to the general public and policymakers through the employment 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 biologic pathways to nitrous oxide production and consumption, and to query regional and large scale SBU中的一氧化二氮动力学强迫。为此，团队将（a）测量季节性调查期间在SBU中收集的样品中的一氧化二氮浓度，测量表面一氧化二氮的连续，并得出对一氧化二氮从复合风速速度的海对空气通量的可靠估计； （b）从测量一氧化二氮和硝酸盐的氮和氧同位素比，并研究一氧化二氮循环的环境相关物，从测量氮和氧同位素比，查询一氧化二氮的产生和消耗途径； （c）从这些测量结果中开发用于一氧化二氮的区域机器学习模型，以研究季节性和年度二氮氧化物动力学的环境驱动因素，从而通过历史数据预测SBU中的一氧化二氮氧化物，并具有源自区域动态海洋模型的水文领域。这项工作将洞悉以宽连续的架子为边界的东部边界上边界上的氧化二氮产生和消耗的途径 - 与从活跃边缘获得的更大的知识相反。 The statistical nitrous oxide models born 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 precious of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.