Collaborative Research: Developing a Next-Generation Approach to Regional Climate Prediction at High Resolution

人们普遍认识到需要更准确和详细的区域气候预测；工业界、地方政府和社会越来越需要足够的了解和警告，以便能够进行适当的规划和适应，以减少未来的成本和气候多变和变化造成的干扰。该项目正在解决关键的研究和模型开发问题，旨在分三个阶段改进这种区域预测。国家大气研究中心的嵌套区域气候模式正在进一步开发，其中包括海洋和陆地耦合、大气化学以及为社会和行业用户提供的决策支持工具。该模式还构成了下一代跨尺度预测模式(MPAS)的试验台，这是一个具有复杂大气化学成分的下一代区域天气-气候-海洋预测系统。还将开发一些统计和相关的缩减尺度技术，以改进对极端天气系统及其影响的评估。该计划的一个特点是在区域气候、恶劣天气、数值模拟、数据同化、大气化学、工业和社会应用方面的专家之间广泛、跨学科的社区方法和新颖的合作。直接与社会应用和决策工具的开发协调开发新的预测系统和缩小规模的方法，将使它们能够相互影响，互惠互利。因此，该计划既有重大的智力价值，也有社会和科学利益。例如，它将使社会和行业更早地使用正在开发的预测能力，并导致改进的、紧密耦合的预测和决策系统。这将以新的社会方法为特色，例如，长期合同，如多年保险单，加上长期贷款，可以在鼓励投资于具有成本效益的适应措施方面发挥作用，以应对气候变化。

The need for more accurate and detailed regional climate predictions is widely recognized; industry, local government and society increasingly require sufficient understanding and warning to enable proper planning and adaptation to mitigate future costs and disruptions arising from climate variability and change. This project is addressing critical research and model development issues aimed at improving such regional predictions in a three-phase approach. The Nested Regional Climate Model from the National Center for Atmospheric Research is being further developed with the inclusion of ocean and land coupling, atmospheric chemistry, and decision support tools for societal and industry users. This model also forms a test bed for the next generation Model for Prediction Across Scales (MPAS), a next-generation regional weather-climate-ocean prediction system with sophisticated atmospheric chemistry components. A number of statistical and related downscaling techniques will also be developed to enable improved assessment of extreme weather systems and their impacts.Broader Impacts. A feature of the program is the wide, cross-disciplinary community approach and novel collaboration amongst experts in regional climate, severe weather, numerical modeling, data assimilation, atmospheric chemistry, industry, and societal applications. Coordinating the development of the new prediction systems and downscaling approaches directly with societal applications and development of decision tools will enable each to influence the other to their mutual benefit. Thus, the program entails both substantial intellectual merit and societal and scientific benefit. For example, it will enable earlier societal and industry use of the developing predictive capacity and lead to an improved, tightly-coupled predictive and decision system. This will feature novel societal approaches, such as the role that long-term contracts like multi-year insurance policies coupled with long-term loans can play in encouraging investments in cost-effective adaptation measures in the presence of climate change.