Theoretical studies of large-scale atmospheric dynamics

我的国家科学研究委员会发现基金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，包括它在复杂的数值模型中的表现，如用于气候和天气预报。这项研究的目的是更全面地了解过去的大气行为，并更可靠地预测未来的行为。这一总体目标是通过一系列不断发展的主题领域来实现的，从基础到应用，涉及数学理论，建模和数据分析，并强调平流层在气候系统中的作用，但不是唯一的强调。多年来，我的核心研究计划的发展受到了我的个人角色的强烈影响，自1992年以来，我担任了一系列支持开发和使用加拿大中层大气模型（cmm）的项目的私人顾问，以及我参与了有关臭氧和气候科学的国际评估，特别是WMO/UNEP臭氧评估。这些活动促使我把精力集中在一个关键问题上，即我们可以在多大程度上相信cmm等模型的预测，这些模型在这些评估中用于指导政策的制定，并将越来越多地用于为气候变化影响和缓解战略提供信息。解决这一问题需要采取多管齐下的方法，其中包括改进模型公式的物理自一致性，用观测结果测试模型，评估模型对外部强迫响应的物理可信度和稳健性，以及理解自然变率，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.