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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634104
• 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变幻莫测的天气、气候变化和预期变化很敏感。一个技术先进的社会需要根据对预期影响的定量评估，利用最好的信息来设计减少脆弱性的应对战略。这需要对未来气候进行准确的预估，这些预估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。提出的研究计划的目的是进一步了解嵌套区域气候模式（RCMs）的动力降尺度的优势和（特别是）局限性。RCMs的预估需要由全球气候模式（GCMs）的边界条件（BC）驱动。但gcm只能提供不完美的大规模信息。因此，rcm的附加价值是解决了gcm中没有的精细尺度。RCM的有限面积意味着精细尺度必须完全在区域范围内发展，这意味着从GCM到RCM的分辨率跳跃和RCM域的大小，以允许适当的旋转，同时保持对RCM模拟的控制。将实现精心设计的实验，一些理想化的方法，如几年前我设计的“老大哥实验”，现在被世界各地的几个研究中心使用，以及其他使用更现实和复杂背景的实验，以估计高分辨率RCM模拟和区域过程预测的置信度限制以及潜在和实际附加价值。