Constraining Global Coastal Ocean Methane Emissions to the Atmosphere

该项目将确定沿海海洋环境的全球甲烷排放量，这是大气中最不确定的甲烷自然来源之一。甲烷是一种温室气体，其对未来气候变暖的影响将取决于人类排放和不断变化的自然环境。因此，了解天然甲烷源对大气的基准排放率及其对变化的敏感性是至关重要的。虽然开阔的海洋环境被认为只向大气排放少量的甲烷，但接近海岸线时甲烷的浓度和排放率会大幅增加。沿海海洋甲烷排放在全球范围内具有潜在的重大意义，但由于缺乏准确捕捉沿海分布的观测，仍然高度不确定。此外，沿海地表水排放的甲烷的来源尚不清楚，这限制了我们预测未来排放将如何变化的能力。该项目将确定沿海海洋向大气中甲烷的来源和全球排放率，并建立一个框架来预测气候变暖下的未来排放率。除了这些科学和社会影响外，该项目还将产生强大的教育影响，因为它将为本科生提供体验从想法构思到最终结果发表的整个科学过程的机会。罗彻斯特大学的PI已经建立了一系列课程，从海洋科学的角度指导学生完成这一过程。这个项目将成为下一个课程的重点，参与的学生将是研究的重要贡献者。在以前进行的这种教育推广活动中，本科生有能力追求自己的科学兴趣，并导致大量学生在海洋科学领域追求研究生职业。该项目还将为博士生提供真正独特的体验，使她/他将有机会在海上测量和建模实验室中进行有意义的研究，从而融入两个通常不同的社区。该项目将通过观测和统计建模工作的独特而平等的结合来完成，利用在PI和共同PI实验室中建立的方法，在2.5年的项目期间取得快速进展。总的来说，四个沿海地区的地表甲烷浓度“跨越三个不同的海洋盆地和亚热带至亚极地纬度范围”将使用超快速真空提取方法进行测量，从而获得在以前的数据集中无与伦比的沿海数据覆盖范围。此外，还将测量地表甲烷的放射性碳含量，以确定化石和微生物来源之间的来源指纹，并收集叶绿素、营养物和溶解氧浓度等生物地球化学数据。初始巡航数据（第一年）将用于训练人工神经网络模型，以预测地表甲烷过饱和作为生物地球化学变量的函数，随后的巡航（第二年）将允许在未用于训练的区域进行独立模型验证。在建立了该模型可以在沿海环境之间进行推广的保真度之后，它将被应用于外推甲烷过饱和度图，并在量化其不确定性的同时估计区域和全球尺度的沿海甲烷排放量。总的来说，这项工作将填补我们对天然甲烷预算知识的这些空白，得出迄今为止最可靠的沿海海洋排放估计，并对维持它们的机制有了新的认识。该奖项反映了美国国家科学基金会的法定使命，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。

This project will determine global methane emissions from coastal marine environments, one of the most uncertain natural sources of methane to the atmosphere. Methane is a greenhouse gas whose impact on future climate warming will depend on emissions from both human sources and the changing natural environment. It is therefore critical to understand the baseline emission rates of natural methane sources to the atmosphere as well as their sensitivity to change. While the open ocean environment is thought to emit only minor amounts of methane to the atmosphere, concentrations and emission rates of methane increase substantially approaching coastlines. Coastal ocean methane emissions are potentially significant at the global scale but remain highly uncertain due to a lack of observations that accurately capture coastal distributions. Furthermore, the source of methane emitted from coastal surface waters is not well known, limiting our ability to predict how emissions will change in the future. This project will determine the source and global emission rate of methane from the coastal ocean to the atmosphere, and establish a framework to predict future emission rates in a warming climate. In addition to these scientific and societal impacts, this project will have strong educational impacts as it will provide undergraduate students the opportunity to experience the entire scientific process from idea conception to publication of the final results. A sequence of classes has been established by the PI at the University of Rochester to guide students through this process from an ocean science perspective. This project will serve as the focus for the next iteration of the class sequence, and participating students will be vital contributors to the research. When conducted previously, this educational outreach has empowered undergraduates to pursue their own scientific interests and has led to significant numbers of students pursuing graduate careers in the ocean sciences. This project will also support a Ph.D. student in a truly unique experience whereby she/he will have the opportunity to conduct meaningful research in both sea-going measurement as well as modeling laboratories, and thus integrate into two often disparate communities.This project will be accomplished through a unique and equal combination of observational and statistical modeling work, leveraging methodologies that are well established in the PI and co-PI's laboratories to make rapid progress over the 2.5-year duration of the project. In total, surface methane concentrations in four coastal regions "spanning three different ocean basins and subtropical to subpolar latitude ranges" will be measured using an ultra-fast vacuum extraction method, yielding coastal data coverage that is unparalleled in previous datasets. Additionally, the radiocarbon content of surface methane will be measured to fingerprint its provenance between fossil and microbial sources, and biogeochemical data including chlorophyll, nutrient, and dissolved oxygen concentrations will be collected. Initial cruise data (year 1) will be used to train Artificial Neural Network models to predict surface methane supersaturation as a function of biogeochemical variables, and later cruises (year 2) will allow for independent model validation in regions that were not used for training. Having established the fidelity with which this model can generalize between coastal environments, it will be applied to extrapolate maps of methane supersaturation and estimate regional and global scale coastal methane emissions while quantifying their uncertainty. Overall, this work will close these gaps in our knowledge of the natural methane budget, yielding the most robust estimates to date of coastal ocean emissions and a new understanding of the mechanisms that sustain them.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.