Stochastic precipitation generation for climate change studies

用于气候变化研究的随机降水生成

• 批准号: RGPIN-2014-05006
• 负责人: Rasmussen, Peter
• 金额: $ 1.75万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611382
• 关键词: Stochastic; precipitation; generation; climate; change

Canada is already experiencing the effect of climate change in the form of shrinking Arctic sea ice, shorter windows for operation of winter roads, and the apparent increase in the frequency of extreme weather events, to name a few examples. This trend is likely to continue into the future, and there is an interest in better understanding the magnitude and the nature of changes. Global climate models (GCMs) are used to predict the response of the Earth system to changing levels of greenhouse gasses. However, GCMs are known to produce biased estimates of precipitation which makes it difficult to use model data as input to hydrologic models and other management models. In practice, it is necessary to post-process GCM precipitation output using statistical methods. The proposed research program addresses the general issue of how precipitation information from climate models (global and regional) can be post-processed in a way that makes the data useful for input to different models. The methods currently in use have various short-comings which, depending on the specific application of the data, could be important. Specific research objectives include the development of new bias-correction techniques that will address some of the short-comings of existing methods and will result in more realistic precipitation scenarios. A second objective will focus on the development of stochastic weather generators that can be used to downscale precipitation to scales of practical interest. The proposed research will extend classical single-site and multi-site weather generators to fully gridded weather generators that can be used in climate change studies of large watersheds. A third objective will address the issue of climate change impact on the distribution of extreme precipitation. The design of urban drainage infrastructure is based on this type of information. Because climate model predictions of extreme precipitation of short duration are known to be unreliable, we will develop procedures to extract relevant information from precipitation information at other time scales using the principle of temporal scaling. The proposed research will address some of the gaps that exist between the climate modelling community and the end users, which include engineers who must design and plan the operation of water-related infrastructure. Climate change will have a large economic impact on countries around the world and Canada is no exception. Water is Canada's most precious natural resource and the proposed research will produce tools that will assist engineers and scientists in the practical use of climate model output. It is expected that this will lead to more sustainable and cost-effective design solutions for Canada's infrastructure.

加拿大已经经历了气候变化的影响，例如北极海冰减少，冬季道路运营的窗口缩短，极端天气事件的频率明显增加等。这一趋势很可能会持续到未来，人们有兴趣更好地了解变化的规模和性质。全球气候模型（GCM）用于预测地球系统对温室气体水平变化的反应。然而，众所周知，大气环流模型会产生有偏差的降水估计，这使得很难使用模型数据作为水文模型和其他管理模型的输入。在实际应用中，有必要对GCM输出的降水数据进行统计后处理。 拟议的研究计划解决了如何从气候模型（全球和区域）的降水信息可以后处理的方式，使数据有用的输入到不同的模型的一般问题。目前使用的方法有各种各样的缺点，根据数据的具体应用，这些缺点可能很重要。具体的研究目标包括开发新的偏差校正技术，以解决现有方法的一些缺点，并产生更现实的降水情景。第二个目标将侧重于开发随机天气发生器，可用于将降水量缩小到实际感兴趣的规模。拟议的研究将把传统的单站点和多站点天气发生器扩展到完全网格化的天气发生器，可用于大流域的气候变化研究。第三个目标是解决气候变化对极端降水分布的影响问题。城市排水基础设施的设计就是基于这种类型的信息。由于气候模式对短时间极端降水的预测是不可靠的，我们将开发程序，利用时间尺度的原则，从其他时间尺度的降水信息中提取相关信息。 拟议的研究将解决气候建模界和最终用户之间存在的一些差距，其中包括必须设计和规划与水有关的基础设施运营的工程师。气候变化将对世界各国产生巨大的经济影响，加拿大也不例外。水是加拿大最宝贵的自然资源，拟议的研究将产生工具，协助工程师和科学家实际使用气候模型输出。预计这将为加拿大的基础设施带来更具可持续性和成本效益的设计解决方案。