Impact of oceanic mesoscale eddies on the productivity of the western Bay of Bengal: contribution of new EO data and machine learning

海洋中尺度涡旋对孟加拉湾西部生产力的影响：新的地球观测数据和机器学习的贡献

• 批准号: 2886218
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886218
• 关键词: Impact; oceanic; mesoscale; eddies; productivity

Background and objectives: Oceanic mesoscale variability and eddies (scales from ~10 to 100 km) play a key role in regulating regional and global physical and biogeochemical processes, including heat transport and mixing of nutrients. These mesoscale eddies affect the phytoplankton productivity, hence marine species, and local populations dependent on them. It has been recently found that tropical oceans mesoscale variability is decreasing overall. The tropical north Indian Ocean and the western Bay of Bengal, in particular, is an eddyrich region which experiences prominent phytoplankton blooms. Previous works on the mechanisms of the productivity in the western Bay of Bengal have mainly focused on tropical cyclones, which impact the supply of nutrients to the surface waters. The presence of eddies and their contribution to biological productivity enhancement in the Bay have been examined for some cyclone events. However, the mesoscale eddies' variability in the presence of strong stratification and the effect of changes in wind stress forcing on mesoscale processes and primary productivity, remain to be explored in detail. Additionally, eddy presence and influence on the regional productivity has to-date only been investigated using the conventional satellite data or few sparse in-situ data, and no specific eddy detection method was applied, nor improved coastal satellite products used. The aim of this research is to carry out a comprehensive study of the causes and consequences of such variability using Earth Observation (EO) in synergy with numerical model outputs and machine learning. There is considerable flexibility in the direction that the research may ultimately take. The PhD will explore the following key research questions:1) How has the eddy field varied over the past decades in the western part of the Bay of Bengal?2) How this variability is affected by changes in wind forcing and how does it affect the regional productivity?3) How are eddies and their contribution to productivity changing between years and seasons? This will include examining the effects of the El-Niño Southern Oscillation and the Indian Ocean DipoleMethodology: This PhD will investigate the applicability of unsupervised machine learning techniques to a set of historical and new EO data and numerical model outputs to unravel the impact of mesoscale eddies and wind on the regional ecosystem productivity. The nonlinear interactions of the mesoscale features make unsupervised Machine Learning (ML) methods well suited to objectively determine the eddies spatiotemporal variation. The main ML methods that will be explored are Self Organizing Maps, K-means clustering, and variational autoencoders. Both historical and new satellite datasets will be exploited. These will cover highresolution satellite ocean colour derived chlorophyll-a data and Sea Surface Temperature (SST), winds, altimetry derived Sea Surface Height (SSH) and currents. New satellite SSH altimeter observations can better capture oceanic mesoscale processes, such as data from SWOT which will be launched in December 2022. Additionally, the recent Sentinel 3A&3B satellites, that carry higher along-track resolution synthetic aperture radar (SAR) altimeters, also provide improved data close to the coast. Argo float measurements, which provide physical and biological parameters at different depths, will also be used to understand the biological andhydrographic properties of the western Bay of Bengal. The new and historical EO data will be compared to output from a high resolution ocean model (NEMO) that includes biogeochemical processes (MEDUSA), covering the satellite data period. Using environmental factors inferred from numerical model outputs, additional physical and biological parameters (e.g., mixed layer depth (MLD), subsurface chlorophyll) will be available which can help further exploration of the variability changes.

背景和目标：海洋中尺度的变异性和涡流（从〜10到100 km）在调节区域和全球物理和生物地球化学过程中起关键作用，包括养分和养分的混合。这些中尺度的涡流会影响浮游植物的生产力，因此会影响海洋物种和依赖于它们的当地人群。最近发现，热带海洋中尺度的变异性总体上正在降低。尤其是北印度洋的热带北洋和孟加拉西部湾是一个埃德里奇地区，经历了著名的浮游植物血液。以前关于孟加拉西部生产率机制的工作主要集中在热带气旋上，这影响了向地表水的养分供应。已经检查了一些旋风事件的涡流及其对海湾生物生产力的贡献。然而，在存在强分层的情况下，中尺度涡流的变异性以及风应力强迫对中尺度过程和主要生产率的影响的影响，尚待详细探讨。此外，仅使用常规卫星数据或少量稀疏的原位数据研究了涡流对区域生产力的影响和影响，并且没有应用特定的涡流检测方法，也没有改善所使用的沿海卫星产品。这项研究的目的是对使用数值模型输出和机器学习的协同作用进行这种可变性的原因和后果进行全面研究。研究最终可能采取的方向具有相当大的灵活性。博士将探讨以下关键研究问题：1）在过去的几十年中，涡流领域在孟加拉湾西部有何不同？2）这种可变性如何受风强迫的变化影响，以及它如何影响区域生产力？3）EDDIE及其对几年和季节之间的生产力变化及其对生产力的贡献如何？这将包括检查El-NiñoSouthern振荡和印度洋二脑外径学的影响：该博士将调查无监督的机器学习技术对一组历史和新的EO数据的适用性，以及一组新的EO数据以及数值模型输出，以揭示中尺度涡流和风向对区域生态系统生产的影响。中尺度特征的非线性相互作用使无监督的机器学习（ML）方法非常适合客观地确定涡流的空间时间变化。将要探索的主要ML方法是自组织地图，K-均值聚类和变异自动编码器。将探索历史和新卫星数据集。这些将涵盖高分辨率的卫星海洋颜色衍生的叶绿素-A数据和海面温度（SST），风，高度学衍生的海面高度（SSH）和电流。新的卫星SSH高度计观测可以更好地捕获海洋中尺度的过程，例如SWOT的数据，该过程将于2022年12月启动。此外，最近的Sentinel 3A和3B卫星卫星，这些卫星及其及较高的沿轨道分辨率的分辨率合成孔径雷达（SAR）Altimeters，还提供了改进的数据数据，还提供了附近海岸的改进数据。 Argo Float测量值在不同深度提供物理和生物学参数，也将用于了解孟加拉湾西部湾的生物学和水文特性。将新的和历史的EO数据与包括生物地球化学过程（MEDUSA）的高分辨率海洋模型（NEMO）的输出进行比较，涵盖了卫星数据周期。使用数值模型输出推断出的环境因素，将提供其他物理和生物学参数（例如混合层深度（MLD），地下叶绿素），这可以帮助进一步探索变异性变化。