Stochastic modeling of hydroclimatic variability

气候变化将对加拿大社会产生重大影响。全球气候模型(GCM)是用于预测温度和降水等变量变化的主要工具。遗憾的是，当前的GCM分辨率非常低，使得直接的模型输出不适合于许多地方或地区的影响研究。为了提高区域气候变化情景的细节水平，已经提出了各种缩小尺度的技术。该研究计划的总体目标是为气候变化研究的统计缩小尺度技术的发展做出贡献。研究的重点是随机天气生成技术的发展。目前的天气发生器有一定的局限性，包括它们无法保持一个区域内多个地点之间的空间相关性，以及它们无法再现年际变化。此外，对于天气发生器应该如何根据GCM模拟的气候变化进行调节也存在一些问题。该研究计划将解决目前随机天气发生器的缺点，并建议开发一个计算机模型，该模型集成了一系列天气产生技术，用于气候变化研究。这将对加拿大具有理论和实践价值，预计气候变化将对农业和水电等关键部门产生重大影响。

Climate change will have significant impact on the Canadian society. Global climate models (GCMs) are the main tool used to predict changes in variables such as temperature and precipitation. Current GCMs unfortunately have very coarse resolution, making direct model output unsuitable for many local or regional impact studies. Various downscaling techniques have been proposed to increase the level of detail of regional climate change scenarios.The general objective of this research program is to contribute to the development of statistical downscaling techniques for climate change studies. The particular focus of the research is the development of techniques for stochastic weather generation. Current weather generators have certain limitations, including their inability to preserve the spatial correlation between multiple sites in a region and their inability to reproduce interannual variability. In addition, there are also some questions as to how weather generators should be conditioned on GCM simulated climate change.The research program will address the shortcomings of current stochastic weather generators and proposes the development of a computer model that integrates a range of weather generation techniques for use in climate change studies. This will be of both theoretical and practical value to Canada where climate change is expected to have significant impact on key sectors such as agriculture and hydropower.