Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Project Information
• Funding

Samples were collected from the small boat R/V Connell, operated by UCSB. Gases dissolved in seawater were extracted following the procedures outlined in Sparrow and Kessler (2017) and Joung et al (2019). As detailed in these prior publications, surface waters (~ 1 m below sea surface) were pumped through a suction hose with a discharge pump and were passed through multiple filters for removing particles. This water was then continuously passed through a gas-permeable membrane to vacuum extract the dissolved gases in the seawater.

Following these at-sea sample collection procedures, the extracted gas samples were purified and analyzed following vacuum line procedures previously detailed in Sparrow and Kessler (2017).

Please see the Sparrow and Kessler (2017) for detailed descriptions and validation tests for these procedures.

Dissolved methane concentration measurements were conducted using a headspace equilibration technique previously described in Weinstein et al. (2016) and Leonte et al (2017).

BCO-DMO Processing:
- modified parameter names;
- converted date and time (local) into ISO 8601 UTC format.

NSF Award Abstract:
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.