Using statistical learning to build better Earth System Models

使用统计学习建立更好的地球系统模型

• 批准号: RGPIN-2020-04488
• 负责人: Fletcher, Christopher
• 金额: $ 1.75万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717030
• 关键词: Using; statistical; learning; build; better

Climate change caused by emissions of greenhouse gases presents an existential threat to society, industry and ecosystems around the world, and particularly in cold regions like Canada. Most Canadians will experience climate change at local scales, through changes to temperature, wind and rainfall patterns near their regions, cities, lakes and rivers. Climate scientists use sophisticated computer models to make projections of how global climate will respond to increasing greenhouse gas concentrations during the 21st century. However, future projections at the scale of individual Canadian river basins are highly uncertain, because models often disagree on whether future changes in precipitation and runoff will increase, or decrease, water availability. A major cause of the uncertainty is the grid box spacing of the models, known as the spatial resolution, which computational resources limit to about 100 km on each side. Decision-makers such as water managers need reliable river basin-scale projections with much finer grid spacing (around 10 km) to inform and adapt their management practices and infrastructure planning. Therefore, our inability as climate scientists to provide this information presents a major barrier to climate change adaptation in Canada, and beyond. The long-term goal of my research program is to improve the quality and efficiency of climate models to deliver global projections of climate change for Canada at a spatial resolution that is better suited to support decision-making activities. The first objective of the research is to develop and apply novel and efficient computing technologies, including methods based on artificial intelligence, to make it easier for climate scientists to produce climate projections that are useful for decision-makers. A second objective is to apply these high-resolution models to investigate the processes causing the uncertainty in future projections, such as snow accumulation and melt, or how clouds interact with pollution particles and sunlight. This ambitious research program represents a state-of-the-art fusion of modern earth system modelling and artificial intelligence methods, that has not been attempted before within a University environment in Canada. The research program will deliver essential training in climate science, modelling and artificial intelligence to a team of graduate and undergraduate students at the University of Waterloo. Graduates will exit the program with sophisticated and highly-marketable technical skills related to big data that are in high demand across Canada, as government agencies, NGOs and private industry undertake the next phase of evidence-based decision-making for climate change adaptation. The research and training outcomes will deliver new tools and technologies that will directly benefit government labs developing climate models, and all Canadians by improving our nation's capacity to develop resilient solutions to climate change at the local scale.

温室气体排放引起的气候变化对世界各地的社会、工业和生态系统构成了生存威胁，特别是在加拿大等寒冷地区。大多数加拿大人将通过其地区、城市、湖泊和河流附近的温度、风力和降雨模式的变化，经历局部范围的气候变化。气候科学家使用复杂的计算机模型来预测 21 世纪全球气候将如何应对不断增加的温室气体浓度。然而，对加拿大个别河流流域规模的未来预测具有高度不确定性，因为模型常常在降水和径流的未来变化是否会增加或减少可用水量方面存在分歧。不确定性的主要原因是模型的网格盒间距（称为空间分辨率），每侧的计算资源限制为约 100 公里。水管理者等决策者需要可靠的流域规模预测以及更精细的网格间距（约 10 公里），以告知和调整他们的管理实践和基础设施规划。因此，作为气候科学家，我们无法提供这些信息，这对加拿大及其他地区适应气候变化构成了主要障碍。 我的研究计划的长期目标是提高气候模型的质量和效率，以更适合支持决策活动的空间分辨率为加拿大提供全球气候变化预测。该研究的首要目标是开发和应用新颖且高效的计算技术，包括基于人工智能的方法，使气候科学家更容易做出对决策者有用的气候预测。第二个目标是应用这些高分辨率模型来研究导致未来预测不确定性的过程，例如积雪和融化，或者云如何与污染颗粒和阳光相互作用。这项雄心勃勃的研究计划代表了现代地球系统建模和人工智能方法的最先进融合，这在加拿大的大学环境中从未尝试过。 该研究项目将为滑铁卢大学的研究生和本科生团队提供气候科学、建模和人工智能方面的基本培训。随着政府机构、非政府组织和私营企业开展下一阶段适应气候变化的循证决策，毕业生将拥有与大数据相关的复杂且高度市场化的技术技能，这些技能在加拿大各地都有很高的需求。研究和培训成果将提供新的工具和技术，通过提高我们国家在当地范围内开发应对气候变化的弹性解决方案的能力，将直接使开发气候模型的政府实验室和所有加拿大人受益。