Publications

Journal of Computational and Applied Mathematics

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Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

This study presents the development of a computational framework designed to predict the interaction between permafrost and infrastructure, addressing potential failure modes and mitigation strategies in the context of climate change. The framework, rooted in advanced modeling and simulation (mod/sim) techniques, integrates thermomechanical coupling to account for the complex interplay between heat flow, ice content, and mechanical behavior in permafrost. Existing models fail to fully capture these dynamics, particularly as they relate to the effects of ice saturation on structural integrity. Our innovative Arctic Coastal Erosion (ACE) framework fills this gap by coupling thermal and mechanical models to accurately simulate subsidence and deformation in permafrost environments. We applied the ACE framework to a representative runway, demonstrating its capability to predict settlement due to rising temperatures and subsequent permafrost thaw. This proof-of-concept showcases the potential of the framework to evaluate risks to Arctic infrastructure, which supports over four million people and 70% of existing permafrost-based structures. By simulating various infrastructure types and environmental conditions, our research offers insights into failure mechanisms and evaluates structural solutions to mitigate risk. The anticipated deliverables, including a prototype runway exemplar, position this project as a critical advancement in permafrost infrastructure modeling, with applications in national security and resilience planning.

Climate impacts have broad economic, health, political, and national security ramifications. Societally relevant impacts are typically farther downstream, are the product of multiple interacting processes, and can arise over small regions and timeframes because their sources are short-term and localized. Short-term forcings (as can be seen in volcanic eruptions, climatic tipping points (e.g., the collapse of rainforests or the disappearance of sea ice), or in increasingly plausible climate interventions) fundamentally possess low signal-to-noise and could benefit from accounting for the multiple conditional processes through which a downstream impact arises. Under the Grand Challenge LDRD CLDERA (CLimate impacts: Discovering Etiology thRough pAthways), we have developed tools to enable downstream impact attribution from geographically and temporally localized source forcings in the climate. CLDERA developed methods that can distinguish how a localized source drives the climate system to respond with particular impacts. The how is embodied in pathways – the spatio-temporally evolving chain of physical processes that connects a source to a series of increasingly distant impacts. Novel analytic methods in pursuit of downstream impact attribution were developed and demonstrated on simulations and observations of the 1991 eruption of Mt. Pinatubo in the Philippines. As described within this report we have • developed stratospheric expertise and aerosol modeling capabilities in E3SM, • created original methods to detect and model pathways from source-to-impact, and • advanced climate attribution through novel methods, cases, and approaches. Further, CLDERA developed a tiered verification process consisting of controlled datasets to prototype, verify, and refine the original method development. CLDERA increased Sandia’s footprint in the climate analytics community and developed new climate collaborations whilst also creating a cadre of climate analysts at Sandia. The products from CLDERA have been extensive with a total of 9 journal articles published, 12 articles submitted and under review, and an additional 8 articles in preparation. We have produced 1750 simulated years and developed 9 code-bases. This report details these accomplishments and serves as a summary of the work completed during the CLDERA Grand Challenge.

The Mt. Pinatubo eruption on 15 June 1991 is often associated with surface warming in the subsequent Northern Hemisphere winter. Employing E3SMv2 with prognostic aerosol modifications, we generated an ensemble of simulations initialized on 1 June 1991 to limit the intra-ensemble variability at the time of the eruption and a more traditional ensemble representing the full range of intra-ensemble variability. For each ensemble member we generated a paired counterfactual simulation with the Pinatub forcing removed allowing for isolation of the Pinatubo impact. In general, the limited variability ensemble has greater coherence in the Pinatubo impact across ensemble members which leads to more statistically robust signals compared to the full variability ensemble. Stratospheric warming patterns from Pinatubo were approximately zonally symmetric and confined between 30°S and 50°N. Isolating localized surface temperature impacts was more difficult, but the limited variability simulation did identify a preferential region of cooling between 20°S to 50°N.

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Combinatorial research, the incorporation of multiple domains in a unified research agenda, is a strong contributor to the growing corpus of scientific knowledge and technological advancements worldwide. In 2019, a study team at Sandia National Laboratories (Sandia, the Labs) used a systems approach to understand if and how combinatorial research agendas were playing out at Sandia, one of America’s premiere national security research venues. The study team used the data collection effort described in this report to ground the discussion of the broad social environment and particular organizational environments within which combinatorial research agendas are developed, as described in the full study. The team interviewed twenty-five staff members engaged in combinatorial research at Sandia in New Mexico and California during the months of June – September 2019. Analysis of this corpus of ethnographic data, combined with knowledge drawn from relevant literature, concluded that there is an individual type who would be most likely to engage in combinatoric research, described by both demographic and psychographic components. This type demonstrates both intellectual depth and the curiosity which leads to breadth. The analysis also showed that Sandia as an organization and as perceived by the respondents, set up tension for the combinatorial researcher. While Sandia was generally agnostic towards combinatorial research, that agnostic posture depended on whether the researcher was able to fulfill all her customer obligations – obligations that are structured primarily in transactional relationships with customers with relatively short time horizons. This report concludes with suggestions for additional research in the ethnographic domain.

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