Collaborative Research: The Bermuda Atlantic Time-Series Study: Sustained Biogeochemical, Ecosystem, and Ocean Change Observations and Linkages in the North Atlantic (Years 31-35).

合作研究：百慕大大西洋时间序列研究：北大西洋持续的生物地球化学、生态系统和海洋变化观测和联系（第 31-35 年）。

• 批准号: 1756054
• 负责人: Michael Lomas
• 金额: $ 61.18万
• 依托单位: Bigelow Laboratory for Ocean Sciences
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1756054&HistoricalAwards=false
• 关键词: Collaborative; Research; Bermuda; Atlantic; Time

Long-term observations over several decades are a powerful tool for investigating ocean physics, biology, and chemistry, and the response of the oceans to environmental change. The Bermuda Atlantic Time-Series Study, known as BATS, has been running continuously since 1988. The research goals of the BATS program are: (1) to improve our understanding of the time-varying components of the ocean carbon cycle and the cycles of related nutrient elements such as nitrogen, phosphorus, and silicon; and, (2) to identify the relevant physical, chemical and ecosystem properties responsible for this variability. In addition, the BATS program has strong and diverse broader impacts, contributing to the field of ocean sciences by providing high quality ocean observations and data for seagoing scientists and modelers, and a framework through which researchers can conceive and test hypotheses. This award will support the operations of the BATS program for five more years.The primary BATS research themes are as follows: (1) Quantify the role of ocean-atmosphere coupling and climate variability on air-sea exchange of CO2, and carbon export to the ocean interior; (2) Document trends and the controls on the interannual to decadal scale variability in carbon and nutrient cycles to their coupling in the surface and deep ocean via the Redfield Ratio paradigm; (3) Quantify the response of planktonic community structure and function, and impact on biogeochemical cycles to variability in surface fluxes and dynamical processes; (4) Facilitate development, calibration and validation of next generation oceanographic sensors, tools and technologies; and, (5) Generate a dataset that can be utilized by empiricists, modelers and students. This research integrates ocean physics, chemistry and biology into a framework for understanding oceanic processes and ocean change in the North Atlantic subtropical gyre. The existing 29 years of BATS data provide robust constraints on seasonal and interannual variability, the response of the Sargasso Sea ecosystem to natural climate variability, and signal detection of potential ocean changes. This project would extend the BATS program through years 31-35 to address a series of ten interlinked questions through integrated research approaches and a multitude of collaborative efforts. In addition to the themes above, and embedded into the ten questions and approaches, the BATS team will focus on, for example, coupling of particle production and biogeochemistry; revisiting the complexities of the biological carbon pump; oxygen decline; and changes in the hydrography, physics, ocean carbon cycle and biogeochemistry of the Sargasso Sea. The highest quality data observation and collection will be maintained and used to address these questions. Importantly, a wide range of collaborations at the BATS site, spanning the physical and biogeochemical disciplines, will aid these broad goals. Strong links to community stakeholders, and close collaboration (including methods intercomparisons and personnel exchanges) with the Hawaii Ocean Time-series are proposed. This work will extend the research findings of the project into educational and training opportunities within and beyond the oceanographic community, including training and mentorship of both undergraduate and graduate students.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.

几十年的长期观测是研究海洋物理、生物和化学以及海洋对环境变化的响应的强大工具。百慕大大西洋时间序列研究，简称BATS，自1988年以来一直在进行。BATS计划的研究目标是：(1)提高我们对海洋碳循环的时间变化成分以及相关营养元素(如氮、磷和硅)循环的理解；以及(2)确定导致这种变化的相关物理、化学和生态系统属性。此外，BATS计划具有强大和多样化的更广泛影响，通过为海洋科学家和建模人员提供高质量的海洋观测和数据，以及研究人员可以构思和测试假设的框架，为海洋科学领域做出贡献。BATS的主要研究主题如下：(1)量化海洋-大气耦合和气候变化对二氧化碳的海-气交换和向海洋内陆输出碳的作用；(2)通过Redfield比率范例记录碳和营养循环的年际和年代际尺度变化的趋势和控制；(3)量化浮游生物群落结构和功能的响应，以及对生物地球化学循环对表层通量和动力过程变化的影响；(4)促进下一代海洋传感器、工具和技术的开发、校准和验证；以及(5)生成可供经验学家、模型师和学生使用的数据集。这项研究将海洋物理、化学和生物学整合到一个框架中，以了解北大西洋副热带环流中的海洋过程和海洋变化。现有的29年BATS数据对季节和年际变化、马尾藻海生态系统对自然气候变化的反应以及对潜在海洋变化的信号检测提供了强有力的约束。该项目将把BATS计划延长到31-35年，通过综合研究方法和多种合作努力解决一系列相互关联的问题。除了上述主题，并嵌入到十个问题和方法中，例如，BATS小组将侧重于粒子生产和生物地球化学的耦合；重新探讨生物碳泵的复杂性；氧气减少；以及马尾藻海的水文、物理、海洋碳循环和生物地球化学的变化。将保持和收集最高质量的数据观察和收集，并用来解决这些问题。重要的是，BATS现场的广泛合作，跨越物理和生物地球化学学科，将有助于实现这些广泛的目标。建议与社区利益攸关方建立强有力的联系，并与夏威夷海洋时间序列密切合作(包括方法相互比较和人员交流)。这项工作将把该项目的研究成果扩展到海洋学社区内外的教育和培训机会，包括对本科生和研究生的培训和指导。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Drawdown of Atmospheric pCO 2 Via Variable Particle Flux Stoichiometry in the Ocean Twilight Zone

通过海洋暮光区可变粒子通量化学计量减少大气 pCO 2

• DOI: 10.1029/2021gl094924
• 发表时间: 2021
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Tanioka, Tatsuro;Matsumoto, Katsumi;Lomas, Michael W.
• 通讯作者: Lomas, Michael W.

Biodiversity and Stoichiometric Plasticity Increase Pico‐Phytoplankton Contributions to Marine Net Primary Productivity and the Biological Pump

• DOI: 10.1029/2023gb007756
• 发表时间: 2023-07
• 期刊: Global Biogeochemical Cycles
• 影响因子: 5.2
• 作者: R. Letscher;J. Moore;A. Martiny;M. Lomas

Size-fractionated biomass and primary productivity of Sargasso Sea phytoplankton

• DOI: 10.1016/j.dsr.2019.103141
• 发表时间: 2020-02
• 作者: B. Cotti-Rausch;M. Lomas;E. M. Lachenmyer;Emily G. Baumann;T. Richardson

Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry

• DOI: 10.1098/rstb.2019.0254
• 发表时间: 2020-05-11
• 期刊: PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
• 影响因子: 6.3
• 作者: Garcia, Catherine A.;Hagstrom, George, I;Martiny, Adam C.
• 通讯作者: Martiny, Adam C.

Machine learning identifies a strong association between warming and reduced primary productivity in an oligotrophic ocean gyre

• DOI: 10.1038/s41598-020-59989-y
• 发表时间: 2020-02-25
• 期刊: SCIENTIFIC REPORTS
• 影响因子: 4.6
• 作者: D'Alelio, Domenico;Rampone, Salvatore;Lomas, Michael W.
• 通讯作者: Lomas, Michael W.