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

环绕南极洲的南大洋是深海、冰冻圈和大气可以自由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，对全球气候有着不成比例的巨大影响。然而，在一个复杂的动力系统中，诸如热量、淡水和CO2等活性示踪剂的这种交换对于难以预测的耦合反馈具有相当大的潜力，这种耦合反馈可能会深刻地影响区域和全球气候。例如，由于人类活动驱动的风的变化，深海温水的垂直涌升可能会增加。这将更多的温水带到表面，融化海冰并为系统增加淡水。反过来，淡水驱动地表沃茨更强的分层，并可能减少随后的热量和碳富集沃茨的下降，减少海洋碳/热汇并反馈大气CO2，变暖和风增加。目前，IPCC（政府间气候变化专门委员会）类气候模式不能对南大洋的未来做出一致的预测，这主要是由于这些动力系统的建模方式不同。这个项目将利用新兴的数据分析技术和算法来检查IPCC最先进的气候模式套件，并确定和验证南部海洋-大气-确定未来极地气候预测范围广泛的冰变量目前给未来气候预测带来了不确定性。“气候信息学”是一个令人兴奋的新兴领域（Monteleoni et al. 2012），具有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的巨大潜力。这项工作将在耦合气候模型中将这些技术应用于南大洋气候系统，以发现决定该区域对自然和人为强迫的反应的关键参数，并在此过程中努力了解真实的系统的动态，减少目前气候预测的不确定性。

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.