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、环境和卫生的若干部门对天气变幻莫测、气候变化和预期的变化很敏感。一个技术先进的社会需要利用最佳信息，根据对预期影响的定量评估，制定一项减少脆弱性的应对战略。这就需要对未来气候进行准确的预测，而预测是通过气候物理学研究中发布的气候模拟器获得的。因此，气候物理学方面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标是进一步了解的优势和（特别是）与嵌套的区域气候模式（RCMs）的动力降尺度的局限性。区域气候模型的预测需要由全球气候模型（GCM）的边界条件（BC）驱动。但是GCM只提供了不完美的大尺度信息。因此，区域气候模型的附加值是解决全球气候模型所不具备的精细尺度问题。有限的区域的RCM意味着，精细尺度必须完全在区域域内发展，与分辨率跳从GCM的RCM和RCM域的大小，以允许适当的旋转，同时保持控制的RCM模拟的影响。精心设计的实验，一些理想化的方法，如我几年前设计的“老大哥实验”，现在被世界各地的几个研究中心使用，和其他实验使用更现实和复杂的背景下，将实现估计的置信度和潜在的极限和真实的附加值的高分辨率区域协调机制模拟和预测的区域进程。