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、生物学和化学以及海洋对环境变化的反应的有力工具。百慕大大西洋时间序列研究，简称BATS，自1988年以来一直持续运行。BATS计划的研究目标是：（1）提高我们对海洋碳循环的时变组成部分以及氮、磷、硅等相关营养元素循环的认识； (2) 确定造成这种变化的相关物理、化学和生态系统特性。此外，BATS 计划具有强大而多样的广泛影响，通过为航海科学家和建模者提供高质量的海洋观测和数据，以及研究人员可以构想和检验假设的框架，为海洋科学领域做出贡献。该奖项将支持BATS项目再运行五年。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CO2海气交换和向海洋内部碳输出的作用； (2) 通过雷德菲尔德比率范式记录碳和养分循环的年际至年代际尺度变化与其在表层和深海耦合的趋势和控制； (3) 量化浮游群落结构和功能的响应，以及表面通量和动力过程变化对生物地球化学循环的影响； (4) 促进下一代海洋传感器、工具和技术的开发、校准和验证； (5) 生成可供经验主义者、建模者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的海洋过程和海洋变化。现有 29 年的 BATS 数据对季节性和年际变化、马尾藻海生态系统对自然气候变化的响应以及潜在海洋变化的信号检测提供了强有力的约束。该项目将 BATS 计划延长至 31-35 岁，通过综合研究方法和大量协作努力解决一系列十个相互关联的问题。除了上述主题外，BATS 团队还将重点关注粒子产生与生物地球化学的耦合等十个问题和方法；重新审视生物碳泵的复杂性；氧气下降；以及马尾藻海的水文学、物理、海洋碳循环和生物地球化学的变化。将维持最高质量的数据观察和收集并用于解决这些问题。重要的是，BATS 站点跨越物理和生物地球化学学科的广泛合作将有助于实现这些广泛的目标。建议与社区利益相关者建立牢固的联系，并与夏威夷海洋时间序列密切合作（包括方法比对和人员交流）。这项工作将把该项目的研究成果扩展到海洋学界内外的教育和培训机会，包括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价值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.