AI Climate Tipping-Point Simulator (ACTS)

As the climate continues to become more unstable, the ability to predict when major shifts, or tipping points, in our climate system may occur is essential. Since their occurrence in climate models is challenged by the fact that they depend on a number of physical processes that are governed by poorly constrained parameters [1,2], predicting these shifts is computationally challenging (if not impossible) using traditional numerical methods. We propose to develop an artificial intelligence (AI) climate tipping point digit twin for climate tipping point and cascade discovery using a deep learning generative approach, to be integrated into the NASA Advanced Information Systems Technology (AIST) Earth Systems Digital Twin (ESDT). The core innovations of this project are: (a) the development of large foundational models trained on global circulation models, NASA-generated observations, and data assimilating model output that will enable the dynamic generation of surrogate (reduced) models, (b) a generative adversarial tipping point method, based on our previous work [1,2] built to discover tipping points and cascades across surrogate climate models, (c) a learned causal model that identifies factors with strong influences on tips, and that works across models to enable cascading experiments and intervention exploration, and (d) a neuro-symbolic Large Language Model (LLM) interface, based on our previous work [73], that enables asking "what-if" scientific questions. When combined, these innovations will provide a general machinery for studying the triggers that could lead to climate tipping point occurrences, and further anticipate how tips within one system will impact other tipping points. The capacity to ask "what-if" questions will enable both "what-if" scenarios for individual climate tipping points as well as cascades across climate tipping points, and will further include support for questions on the impact of climate interventions. A key component of this innovation will be learning the causal paths in ways that are explainable, i.e. showing how parameters influence the path that led to a tipping and providing an explainability model related to how conclusions were formed from responses to "what-if" questions. To show the power of the AI simulator, this work will focus on a set of interwoven climate tipping points as exemplars of the general machinery: the Meridional overturning circulation collapse, the Amazon dieback, and the West African monsoon/Sahel rainfall. These have been selected to enable the study of cascading effects, while also demonstrating that the foundation model and surrogate model generator can dynamically enable new discoveries across a variety of Earth systems. The chosen climate tipping points are chosen to be aligned with NASA's Earth science activities as described in the most recent Decadal Survey [74], and will allow for studies related to cascading behavior across oceans and land. Intervention-related questions, such as - Would increasing carbon storage make a sizable difference in slowing the Amazon deforestation? -- could be studied using the proposed AI simulator. This will be a invaluable tool for scientists as more geo-engineering and regional climate interventions are explored in the wild without much regard for consequential effects [75].

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

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