A scalable probabilistic emulation and uncertainty quantification tool for Earth-system models

We propose to develop a new, fully automated toolbox for uncertainty quantification in Earth-system models, to provide insight into the largest and most critical information gaps in Earth Sciences and to identify where potential future observations would be most valuable. We will achieve this goal by building a probabilistic emulator that is able to learn the non-Gaussian distribution of spatio-temporal fields from a small number of nature runs from Earth-system models, allowing users to, for example, discover and examine nonlinear dependence structures. In a significant step toward an Earth system digital twin (ESDT), the learned distribution can be a function of covariates (e.g., emissions scenarios), which allows interpolation between observed covariate values and running extensive what-if scenarios. Our proposed software tool is a crucial component in societal decision-making and in numerous NASA applications and missions, including studying climate projections, efficient observing system simulation experiments (OSSEs), uncertainty-quantification efforts for existing missions, and "what-if" investigations for potential future observing systems. The project will consist of three broad goals. As the first goal, the team will develop statistical methodology for efficient estimation of and simulation from the probability distribution of multiple geophysical fields of interest. The approach, based on Bayesian transport maps, learns the spatio-temporal and multivariate dependence structure from a small- to moderate-sized ensemble of runs from an Earth-system model. The second goal is to implement the methodology in a user-friendly open-source software-package with documented examples. The third goal is to demonstrate use of the toolbox in assessing the probability distribution of important hydrological variables, as represented in Earth system models. In order to characterize the uncertainty in spatio-temporal dependence that besets so many variables in the Earth sciences, we will specifically examine precipitation, snow water equivalent, and runoff in CMIP6 simulations to demonstrate how powerful a tool like this can be for the future NASA applications. Our technology provides flexibility to identify impactful characteristics of the water cycle under current conditions and its response to climate change, including nonlinear changes in time and under multiple emission scenarios. Further, the investigation will provide insight into hydrological processes and associated scales that are particularly uncertain, providing motivation for future observing needs for terrestrial hydrology, including upcoming missions like Surface Water and Ocean Topography (SWOT) and the mass change (MC) designated observable. The proposed project addresses the advanced and emerging technology (AET) topic area in the AIST-21 solicitation. It will provide technologies and tools for use in ESDT. Specifically, the project will "enable running large permutations of what-if scenarios using large amounts of data and high-resolution and high-fidelity models" and comprises "statistical methodologies that optimize the computational efficiency of such what-if investigations."

Advance Earth system science knowledge through the Identification, develop, and demonstrate innovative information systems technologies

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