Managing the risks of climate tipping points
- Prof. Klaus Keller
The coupled natural-human systems can react abruptly to anthropogenic forcings (sometimes referred to as tipping-point responses). The nonlinearity, abruptness, and hysteresis of the response poses nontrivial challenges to analyze and manage the coupled natural-human systems. This session reviews frameworks (i) to analyze past and project potential future tipping point responses and (ii) to support decisions to navigate synergies and trade-offs driven by tipping point responses.
Data assimilation for geophysical fluid systems
- Prof. Yoonsang Lee
Numerical weather prediction integrates observational data to enhance the accuracy of forecasts for geophysical fluid systems. Developing a reliable and precise prediction method requires two key components: (i) high-fidelity prediction models and (ii) scalable approaches to data assimilation. This lecture will address these challenges through the framework of stochastic processes, applied to both the prediction models and the assimilation techniques. Specifically, we will explore the concept of high-fidelity models within the context of Bayesian inference. Interestingly, the traditional numerical analysis perspective on high accuracy can be misleading in the Bayesian framework. Highly accurate prediction models can overemphasize inherent biases in the model, leading to overly confident predictions. Instead, we will demonstrate how stochastic modeling offers the statistical flexibility needed to account for noisy and incomplete observational data. The lecture is designed to be accessible to students with some background in probability, stochastic processes, and basic numerical linear algebra.
Constraining uncertainty in the human impacts of climate change
- Prof. Justin Mankin
How will climate change affect people and the things they value? Drawing on examples from violent conflict, economic growth, and water resources, I highlight my research to inform society's management of climate risks, with implications for everything from drought monitoring to climate liability. My work looks retrospectively, documenting the impacts that have already unfolded, and prospectively, helping to anticipate the ones to come. Across all of this work, I discuss my effort to (1) meaningfully connect geophysical changes with human consequences, (2) quantify, attribute, and constrain uncertainty, especially given structural data inequities, and (3) inform model design and analysis choices to ensure that scientific answers about our present and future are sound, transparent, reproducible, useful, and just. Collectively, my research and that of my group demonstrates the importance of science that spans both fundamental and applied questions of climate impacts to inform adaptations and prepare society for a warmer world.
Earth to Mars: Climate change in periglacial systems -
Prof. Marisa Palucis
Planetary geomorphology is a field founded in field-, lab-, and remote sensing-based observations, and we ask questions such as: 1) how is environmental change on rocky bodies recorded in their landscapes? 2) how does terrestrial climate change affect rates and mechanisms of sediment transport? and 3) how can we apply what we learn on Earth to other planets, such as Mars? These major lines of scientific inquiry are important for determining if places like Mars ever supported life and how life on our own planet is affected by climate change In this lecture, we will discuss 1) how we can decipher climate records from sedimentary deposits on Earth and Mars; 2) the climate and environment of early Mars and why the Arctic is a good analog; and 3) the ways in which we are developing new theories for icy extraterrestrial sediment transport processes.
Soil organic matter stores over three times as much carbon as the atmosphere. As our climate warms, microbial decomposition of this organic matter can speed up, which may reduce the amount of carbon stored in soils and increase the amount of CO2 respired by microbes into the atmosphere where it acts as a greenhouse gas. This process could be a reinforcing feedback to climate change. Despite concerns about increased decomposition, soil is simultaneously receiving much attention as a potential climate change mitigation strategy. Some scientists have posited that by managing soil differently, we could remove CO2 from the atmosphere at a rate equivalent to annual fossil fuel emissions. In this session, we will discuss 1) how soil carbon is responding to climate change; 2) how soil carbon responds to management on agricultural land; and 3) models that aim to project the response of soil carbon to future global change.
Climate change and global agricultural production: Impacts and adaptation strategies -
Prof. Jonathan Winter
Global food demand is expected increase 70% by 2050 due to population growth and increased meat consumption; rising competition for land, water, and energy will necessitate improved agricultural practices to minimize environmental effects; and climate change threatens to disrupt crop production globally and disproportionately reduce food security in regions that already have high undernourishment. This session will focus on (1) climate change impacts on global agricultural production of staple crops, (2) adaptation strategies at local to global scales, and (3) the potential to increase agricultural production through irrigation.
Nonparametric modeling and analysis of geophysical flows (and other complex systems) using quantum-inspired approaches to nonlinear dynamics -
Prof. Joanna Slawinska
This talk will provide an overview of recent research conducted by our group in the Department of Mathematics at Dartmouth College, focusing on data-driven operator-theoretic approaches to modeling nonlinear dynamical systems. A central theme of this work is the development of Koopman-based frameworks, enriched with kernel methods and delay-coordinate embeddings, for analyzing and forecasting complex spatiotemporal behavior.
The group has developed empirical methods to approximate infinite-dimensional evolution operators with finite-dimensional matrix representations, enabling spectral decomposition and interpretable modeling in a learned observable space. Extensions to vector-valued observables allow for the analysis of multivariate and coupled systems, facilitating the extraction of dominant spatiotemporal patterns without relying on intrusive solvers or governing equations.
A more recent direction introduces a quantum-inspired formulation, where evolving system states are represented by data-driven density operators, and observables evolve under Koopman dynamics analogous to Heisenberg evolution. This formulation supports nonparametric data assimilation and incorporates uncertainty quantification in a natural way.
Our framework has proven particularly well suited for partially observed systems and those where first-principles models are unavailable, such as in climate and atmospheric dynamics. It also offers tools for constructing subgrid-scale parameterizations and closure models by learning the influence of unresolved dynamics directly from data.
The group is currently exploring quantum-like computational platforms for implementing these methods, leveraging their operator structure to align with emerging quantum architectures for scalable dynamical modeling.
In closing, we will reflect on the relevance and promise of these approaches in geoscientific applications—including climate science—as well as their potential across a broader range of complex systems studied by our group.
Weather, Cimate, Trade, and Pestilence -
Prof. Matt Ayres
Global change involves climate, land use, and biotic invasions. Globalization and land use change are drivers of increased greenhouse gas emissions and therefore climate change. At the same time, climate change affects globalization and land use (e.g., new shipping routes and newly possible plant production systems).
Climate change, globalization, and land use all contribute to the movement of biota around the world. Some of the new species, frequently insects or fungi, become highly consequential pests that can devastate crops, disrupt economies, harm humans, and alter global trade.
Limiting the unintentional introduction of plant pests is the most immediately soluble step in mitigating global change. The majority of plant pest introductions are via solid wood packing material and movement of live plants ("plants for planting"). There are sensible, and relatively inexpensive, proposals for federal action to manage these pathways of pest movement into the United States. The same principles would work elsewhere. The science of ecology has a growing capacity to understand how climate affects the potential distribution limits of species, and to predict which particular species of potential pests pose the greatest risk if introduced.
