CAREER: Hydrological Sensitivity Across Timescales

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。随着温室气体浓度继续上升，地球表面将继续变暖，预计降雨量将发生重大变化。预测降雨将如何变化对于制定适应和缓解计划非常重要。目前，对长期降雨变化的预测主要依赖于气候模式的模拟。然而，模式在降雨变化的许多方面存在分歧，这极大地破坏了降雨预测的有效性。由于观测时间通常太短，无法用来直接推断长期降雨变化，限制模式预测一直是一个巨大的挑战。幸运的是，有大量的短期（例如，年或月）降雨变化的观测资料。这些观测使科学家能够研究降雨的机制，其中一些机制在短期和长期时间尺度上都起作用。研究人员已经确定了长期降雨变化的几个方面，这些变化从根本上与降雨对短期地表温度变化的反应有关。基于这种关系，他将利用观测到的短期降雨变化来评估气候模型的长期降雨预测。这项工作将使人们更好地了解降雨预测中的不确定性，并将确定观测约束可用于改进模式的关键过程。该方法随后将应用于一个最先进的全球气候模式，在该模式中，已确定的观测约束将用于改进模式参数，并最终改进其对未来降雨变化的预测。通过了解降雨变化的机制和预测不确定性的原因，该项目将确定需要观测验证的关键过程，以改进模式预测。这种改进最终将有助于社会更好地应对气候变化。这个项目的教育部分包括气候模型在研究生、本科和高中阶段的教学和应用。具体而言，研究者将(1)为气候科学研究生创建一个实践气候建模课程，(2)设计一个可纳入高中科学课程的气候科学和建模模块，以及(3)通过NSF本科生暑期研究经验项目培训本科生进行和分析气候模型实验。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).As greenhouse gas concentrations continue to rise, the Earth’s surface will continue to warm, and the amount of rainfall is expected to change substantially. Predicting how rainfall will change is of great importance for preparing adaptation and mitigation plans. Currently, predictions of long-term rainfall changes rely predominantly on simulations of climate models. However, models disagree on many aspects of rainfall changes, which greatly undermines the usefulness of rainfall predictions. Because observations are generally too short to be used to directly infer long-term rainfall changes, constraining model predictions has been a great challenge. Fortunately, there are abundant observations of short-term (for example, annual or monthly) rainfall variations. These observations allow scientists to study mechanisms of rain, some of which operate at both short and long timescales. The investigator has identified several aspects of long-term rainfall changes that are fundamentally tied to how rainfall responds to short-term surface temperature variations. Based on such relationships, he will evaluate long-term rainfall predictions from climate models by using observed short-term rainfall variations. This work will yield a better understanding of the uncertainty in rainfall predictions and will identify key processes of which observational constraints are available to improve models. This method will then be applied to a state-of-the-art global climate model, where the identified observational constraints will be used to improve model parameters and ultimately, its prediction of future rainfall changes.By understanding mechanisms of rainfall changes and causes of prediction uncertainties, this project will identify key processes that require observational validation to improve model predictions. Such an improvement will ultimately help society to cope better with climate variations. The educational component of this project involves the teaching and application of climate models at graduate, undergraduate and high school levels. Specifically, the investigator will (1) create a hands-on a climate modeling course for graduate students in climate science, (2) design a climate science and modeling module that can be incorporated into high school science curriculum, and (3) train undergraduate students to conduct and analyze climate model experiments via the summer NSF Research Experiences for Undergraduates Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.