Theoretical studies of large-scale atmospheric dynamics

我的NSERC Discovery Grant研究计划的总体目标是加强大尺度大气动力学的理论基础，包括它在用于气候和天气预测等复杂数值模型中的表现。这项研究的目的是更全面地了解过去的大气行为，并对未来的行为做出更可靠的预测。这一总体目标是通过一系列不断演变的主题领域来实现的，从基础领域到应用领域，涉及数学理论、建模和数据分析，重点强调平流层在气候系统中的作用。多年来，我的核心研究计划的演变受到了我自1992年以来作为私人情报员的角色的强烈影响，这些项目支持开发和使用加拿大中层大气模式(CMAM)，以及我大量参与了有关臭氧和气候科学的国际评估，特别是世界气象组织/环境署的臭氧评估。这些活动促使我将我的努力集中在一个关键问题上，即我们在多大程度上可以信任CMAM等模型的预测，这些模型被用于这些评估，以指导政策的制定，并将越来越多地被用于为气候变化影响和缓解战略提供信息。解决这个问题需要多管齐下的方法，包括改善模型制定的物理自我一致性，用观测测试模型，评估模型对外部强迫的物理可信度和稳健性，以及了解自然变化，包括空间和时间上的非局部耦合。

The overall goal of my NSERC Discovery Grant research program is to strengthen the theoretical basis of large-scale atmospheric dynamics, including its representation in sophisticated numerical models such as are used for climate and weather prediction. The purpose of this research is a more complete understanding of past atmospheric behaviour and more reliable predictions of future behaviour. This overall goal is achieved through an evolving suite of topic areas, ranging from the fundamental to the applied and involving mathematical theory, modelling, and data analysis, with a strong but not exclusive emphasis on the role of the stratosphere in the climate system. The evolution of my core research program over the years has been strongly influenced by my role as P.I., since 1992, of a series of projects supporting the development and use of the Canadian Middle Atmosphere Model (CMAM), as well as by my significant involvement in international assessments concerning the science of ozone and climate, especially the WMO/UNEP Ozone Assessments. These activities have motivated me to focus my efforts on the critical question of how well we can trust the predictions of models such as CMAM, which are used in these assessments to guide development of policy and will increasingly be used to inform climate-change impacts and mitigation strategies. Addressing that question requires a multi-pronged approach which includes improving the physical self-consistency of the model formulation, testing the model with observations, assessing the physical credibility and robustness of the model response to external forcings, and understanding natural variability including nonlocal couplings in both space and time.