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/R006822/1
• 负责人: Jason Holt
• 金额: $ 47.34万
• 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: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FR006822%2F1
• 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.

陆架海具有重要的社会重要性，提供多种商品（例如渔业、可再生能源、运输）和服务（例如碳和养分循环以及生物多样性）。管理英国海洋以维持清洁、健康、安全、富有生产力和生物多样性的海洋是政府的一项关键目标，英国海洋战略框架、生物多样性公约和批准《奥斯陆-巴黎公约》（OSPAR）..履行这些义务需要有关我们海洋状况的全面信息，而这反过来又需要数字模型和观测计划的结合。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 空间和时间上的变化。与天气预报类似，英国气象局运行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。通过使用数据同化可以提高这些预测的质量；使用观测来推动模型模拟来预测最准确的海洋状态的过程，产生观测和模型的组合产品。海洋自主航行器 (MAV) 是一项快速成熟的技术，目前已被常规部署以支持研究并作为海洋观测系统的组成部分。当与定点观测站、机会船和卫星遥感结合使用时，微型飞行器的战略部署有望大幅改善我们的观测网络。海洋滑翔机尤其能够通过部署提供高分辨率的深度解析数据集，这些数据集可以持续数月并覆盖数百公里，从而可以收集足够的信息以用于同化到模型中。我们将改善模型系统和观测网络之间的数据交换，为部署英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”自治，即能够有选择地搜索和监控海洋系统内的显式特征的自治系统。通过开发数据同化技术来利用自主数据，我们的模型系统将能够更好地描述诸如春季水华、有害藻华和氧气消耗等偶发事件，这些事件目前尚未得到很好的捕获，但对于了解生态系统变异性并因此量化这些事件至关重要。 GES.在此过程中，CAMPUS 将通过自主技术（滑翔机）、其他观测和全大陆架数值模型的组合，在观测和建模技术的组合使用方面带来重大变革。这将改进对关键海洋变量的分析、更好地预测偶发事件和“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

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

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