CAREER: Hydrological Sensitivity Across Timescales

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.