Untangling the web: Using machine learning to understand climate critical dynamics in the Southern Ocean

理清网络：利用机器学习了解南大洋的气候关键动态

• 批准号: 2302274
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2302274
• 关键词: Untangling; web; Using; machine; learning

The Southern Ocean surrounding Antarctica is the principle region where the deep ocean, cryosphere and atmosphere may freely exchange properties with one another. This is the major pathway for heat, carbon and nutrients into the ocean interior and has a disproportionately large impact on global climate. However, such exchanges of active tracers such as heat, freshwater and CO2 within a complex dynamical system presents considerable potential for difficult-to-predict coupled feedbacks that may profoundly influence both regional and global climates. For example the vertical upwelling of warm water from the deep ocean may be increased due to anthropogenically driven wind changes. This brings more warm water to the surface, melting sea ice and adding freshwater to the system. In turn the freshwater drives stronger stratification of surface waters and may reduce subsequent downwelling of heat and carbon enriched waters, reducing the ocean carbon/heat sink and feeding back atmospheric CO2, warming and wind increases. Presently IPCC (Intergovernmental Panel on Climate Change) class climate models do not produce coherent future projections for the Southern Ocean, largely due to differences in how such dynamical systems are modelled. This represents a major source of uncertainty for global predictions of surface warming and sea level rise and needs to be addressed to improve regional and global climate forecasting.This project will utilise emerging data analysis techniques and algorithms to examine the IPCC suite of state-of-the-art climate models and identify and characterise the key dynamical relationships between southern ocean-atmosphere-ice variables that set the wide range of future polar climate projections currently introducing uncertainty into future climate projections. The field of 'climate informatics' is an exciting and newly emerging one (Monteleoni et al. 2012), with great untapped potential for applying recent advances in machine learning and data science to the large and multi-dimensional datasets that climate data and models represent. This work will apply such techniques to the Southern Ocean climate system in coupled climate models to discover the key parameters governing the response of the region to both natural and anthropogenic forcing, and in doing so work to understand the dynamics of the real system and reduce the present uncertainty in climate projections.

南极洲周围的南大洋是深海、冰冻圈和大气可以自由相互交换性质的主要区域。这是热量、碳和营养物质进入海洋内部的主要途径，对全球气候产生不成比例的巨大影响。然而，复杂动力系统中热量、淡水和二氧化碳等活性示踪剂的这种交换呈现出巨大的潜力，难以预测的耦合反馈可能会对区域和全球气候产生深远的影响。例如，由于人为驱动的风的变化，来自深海的暖水的垂直上升流可能会增加。这会将更多的温水带到表面，融化海冰并向系统添加淡水。反过来，淡水会推动地表水更强烈的分层，并可能减少随后富含热量和碳的水域的下降，从而减少海洋碳/热汇并反馈大气中的二氧化碳、变暖和风的增加。目前，IPCC（政府间气候变化专门委员会）级别的气候模型无法对南大洋的未来做出一致的预测，这主要是由于此类动力系统建模方式的差异。这是全球地表变暖和海平面上升预测的不确定性的一个主要来源，需要加以解决，以改善区域和全球气候预测。该项目将利用新兴的数据分析技术和算法来检查IPCC最先进的气候模型套件，并识别和描述南大洋-大气-冰变量之间的关键动态关系，这些变量设定了目前引入的广泛的未来极地气候预测 未来气候预测的不确定性。 “气候信息学”领域是一个令人兴奋的新兴领域（Monteleoni et al. 2012），在将机器学习和数据科学的最新进展应用于气候数据和模型所代表的大型多维数据集方面具有巨大的未开发潜力。这项工作将把这些技术应用于耦合气候模型中的南大洋气候系统，以发现控制该地区对自然和人为强迫响应的关键参数，并在此过程中了解真实系统的动态并减少目前气候预测的不确定性。