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，环境和健康的几个部门对天气变化，气候变化和预期变化敏感。技术先进的社会需要使用最佳信息来设计应对策略，以减少对预期影响的定量评估。这需要对未来气候的准确预测，这些预测是通过气候研究中发出的气候模拟器获得的。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。 拟议的研究计划的目的是进一步了解我们对使用嵌套区域气候模型（RCM）动力学缩放的限制的局限性。 RCMS的预测需要由全球气候模型（GCM）的边界条件（BC）驱动。但是GCM仅提供不完美的大规模信息。因此，RCMS的附加值是为了解决GCM中不存在的精细尺度。 RCM的有限面积意味着细量表必须在区域域内完全发展，这对从GCM到RCM和RCM域大小的分辨率的影响含义，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，一些以下理想化的方法，例如我几年前设计的“大兄弟实验”，现在已被世界各地的几个研究中心使用，而其他使用更现实和复杂的环境的实验将被实现，以估算信心的限制以及高分辨率RCM模拟和对区域过程的潜在和真实的增值。