CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas)

CAMPUS（结合自主观测和模型来预测和理解陆架海）

• 批准号: NE/R006822/2
• 负责人: Jason Holt
• 金额: $ 23.8万
• 依托单位: NATIONAL OCEANOGRAPHY CENTRE
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR006822%2F2
• 关键词: CAMPUS; Combining; Autonomous; observations; Models

Shelf seas are of major societal importance providing a diverse range of goods (e.g. fisheries, renewable energy, transport) and services (e.g. carbon and nutrient cycling and biodiversity). Managing UK seas to maintain clean, healthy, safe, productive and biologically diverse oceans and seas is a key governmental objective, as evidenced by the obligations to obtain Good Environmental Status (GES) under the UK Marine Strategy Framework, the Convention on Biological Diversity and ratification of the Oslo-Paris Convention (OSPAR) .. The delivery of these obligations requires comprehensive information about the state of our seas which in turn requires a combination of numerical models and observational programs. Computer modelling of marine ecosystems allows us to explore the recent past and predict future states of physical, chemical and biological properties of the sea, and how they vary in 3D space and time. In an analogous manner to the weather forecast, the Met Office runs a marine operational forecast system providing both short term forecast and multi-decadal historical data products. The quality of these forecasts is improved by using data assimilation; the process of predicting the most accurate ocean state using observations to nudge model simulations, producing a combined observation and model product. Marine autonomous vehicles (MAVs) are a rapidly maturing technology and are now routinely deployed both in support of research and as a component of an ocean observing system. When used in conjunction with fixed point observatories, ships of opportunity and satellite remote sensing, the strategic deployment of MAVs offers the prospect of substantial improvement in our observing network. Marine Gliders in particular have the capability to provide depth resolved data sets of high resolution from deployments that can endure several months and cover 100s kms, allowing the collection of sufficient information to be useful for assimilation into models. We will improve the exchange of data between model systems and observational networks to inform an improved strategy for the deployment of the UK's high-cost marine observing capability. In particular we will utilise mathematical and statistical models to develop and test "smart" autonomy - autonomous systems that are enabled to selectively search and monitor explicit features within the marine system. By developing data assimilation techniques to utilise autonomous data, our model systems will be able to better characterise episodic events such as the spring bloom, harmful algal blooms and oxygen depletion, which are currently not well captured and are key to understanding ecosystem variability and therefore quantifying GES.In doing so CAMPUS will provide a step change in the combined use of observation and modelling technologies, delivered through a combination of autonomous technologies (gliders), other observations and shelf-wide numerical models. This will provide improved analysis of key ocean variables, better predictions of episodic events, and 'smart' observing systems in order to improve the evidence base for compliance with European directives and support the UK industrial strategy.

大陆架海具有重大的社会意义，提供各种各样的货物（如渔业、可再生能源、运输）和服务（如碳和营养物循环及生物多样性）。管理联合王国的海洋，以保持清洁、健康、安全、富有生产力和生物多样性的海洋是政府的一个关键目标，根据联合王国海洋战略框架、《生物多样性公约》和批准《奥斯陆-巴黎公约》，有义务获得良好环境状况就是证明。履行这些义务需要关于我们海洋状况的全面信息，而这又需要将数值模型和观测方案结合起来。海洋生态系统的计算机建模使我们能够探索最近的过去，并预测海洋的物理，化学和生物特性的未来状态，以及它们在3D空间和时间中的变化。与天气预报类似，气象局运行着一个海洋业务预报系统，提供短期预报和数十年历史数据产品。通过使用数据同化，这些预报的质量得到了提高;利用观测结果预测最准确的海洋状态，以推动模型模拟，产生综合观测和模型产品的过程。海洋自主飞行器（MAV）是一项迅速成熟的技术，现在已被常规部署，用于支持研究和作为海洋观测系统的一个组成部分。当与固定点观测站、机会船和卫星遥感结合使用时，微型飞行器的战略部署为我们的观测网络提供了重大改进的前景。特别是海洋滑翔机有能力从部署中提供高分辨率的深度分辨数据集，这些数据集可以持续几个月，覆盖100公里，从而收集足够的信息，用于同化成模型。我们将改进模型系统和观测网络之间的数据交换，为部署联合王国高成本海洋观测能力的改进战略提供信息。特别是，我们将利用数学和统计模型来开发和测试“智能”自主性-自主系统能够有选择地搜索和监控海洋系统中的显式特征。通过开发利用自主数据的数据同化技术，我们的模型系统将能够更好地模拟春季水华、有害藻类水华和氧气消耗等偶发事件，这些事件目前还没有很好地捕捉到，是理解生态系统变异性的关键，因此也是量化GES的关键。在这样做的过程中，CAMPUS将为观测和建模技术的结合使用提供一个步骤，通过自主技术（滑翔机）、其他观测和货架范围的数值模型相结合提供。这将提供对关键海洋变量的更好分析，更好地预测偶发事件，以及“智能”观测系统，以改善遵守欧洲指令的证据基础，并支持英国的工业战略。

项目成果

Control of a phytoplankton bloom by wind-driven vertical mixing and light availability

通过风驱动的垂直混合和光照控制浮游植物的繁殖

• DOI: 10.1002/lno.11734
• 发表时间: 2021
• 期刊: Limnology and Oceanography
• 影响因子: 4.5
• 作者: Hopkins J

Improved consistency between the modelling of ocean optics, biogeochemistry and physics, and its impact on the North-West European Shelf seas

提高海洋光学、生物地球化学和物理学建模之间的一致性及其对西北欧陆架海洋的影响

• DOI: 10.1002/essoar.10506737.1
• 发表时间: 2021
• 作者: Skakala J

The effect of uncertain river forcing on the thermohaline properties of the North West European Shelf Seas

• DOI: 10.1016/j.ocemod.2023.102196
• 发表时间: 2023-04
• 期刊: Ocean Modelling
• 影响因子: 3.2
• 作者: S. Zedler;J. Polton;Robert R. King;S. Wakelin

Designing a Large Scale Autonomous Observing Network: A Set Theory Approach

• DOI: 10.3389/fmars.2022.879003
• 发表时间: 2022-06
• 作者: David Byrne;J. Polton;Joseph Ribeiro;L. Fernand;J. Holt

Sensitivity of Shelf Sea Marine Ecosystems to Temporal Resolution of Meteorological Forcing

• DOI: 10.1029/2019jc015922
• 发表时间: 2020-06
• 期刊: Journal of Geophysical Research: Oceans
• 作者: H. Powley;J. Bruggeman;J. Hopkins;Tim Smyth;J. Blackford