Regional Climate Modelling: Probing the strengths and limitations of dynamical downscaling with nested models

区域气候建模：利用嵌套模型探讨动态降尺度的优点和局限性

• 批准号: 42544-2013
• 负责人: Laprise, René
• 金额: $ 4.88万
• 依托单位: Université du Québec à Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=568902
• 关键词: Regional; Climate; Modelling; Probing; strengths

Several sectors of economy, environment and health are sensitive to weather vagaries, climate variations and anticipated changes. A technologically advanced society needs to use the best information to design a coping strategy to reduce vulnerability, based on a quantitative assessment of anticipated impacts. This requires accurate projections of future climate that are obtained with climate simulators issued from research in the physics of climate. Training and academic research in the Physics of climate are therefore the very foundation for climate impacts and adaptation studies. The objective of the proposed research programme is to further our understanding of the strengths and (specially) the limitations of dynamical downscaling with nested Regional Climate Models (RCMs). RCMs' projections need to be driven by boundary conditions (BC) from Global Climate Models (GCMs). But GCMs provide only imperfect large-scale information. The added value of RCMs is thus to resolve fine scales that are absent in GCMs. The limited area of an RCM implies that fine scales must develop entirely within the regional domain, with implications on the resolution jump from the GCM to the RCM and RCM domain size to allow proper spin up, while maintaining control of the RCM simulation. Carefully designed experiments, some following idealised approaches such as the "Big-Brother Experiment" I designed a few years ago and which is now used by several research centres around the world, and other experiments using more realistic and complex context, will be realised to estimate the limits of confidence and the potential and real added value of high-resolution RCM simulations and projections on regional processes.

经济、环境和健康的多个部门对天气变幻莫测、气候变化和预期变化很敏感。技术先进的社会需要基于对预期影响的定量评估，使用最佳信息来设计应对策略，以减少脆弱性。这需要通过气候物理学研究发布的气候模拟器来准确预测未来气候。因此，气候物理学的培训和学术研究是气候影响和适应研究的基础。 拟议研究计划的目标是进一步了解嵌套区域气候模型（RCM）动态降尺度的优势和（特别是）局限性。 RCM 的预测需要由全球气候模型 (GCM) 的边界条件 (BC) 驱动。但 GCM 只能提供不完善的大规模信息。因此，RCM 的附加值是解决 GCM 中不存在的精细尺度问题。 RCM 的有限面积意味着精细尺度必须完全在区域域内发展，这会对从 GCM 到 RCM 的分辨率跳跃和 RCM 域大小产生影响，以允许适当的旋转，同时保持对 RCM 模拟的控制。精心设计的实验，其中一些遵循理想化的方法，例如我几年前设计的“老大哥实验”，现在已被世界各地的多个研究中心使用，以及其他使用更现实和复杂背景的实验，将实现估计置信极限以及高分辨率RCM模拟和区域过程预测的潜在和实际附加值。