The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.
Global Climate Model tuning (calibration) is a tedious and time-consuming process, with high-dimensional input and output fields. Experts typically tune by iteratively running climate simulations with hand-picked values of tuning parameters. Many, in both the statistical and climate literature, have proposed alternative calibration methods, but most are impractical or difficult to implement. We present a practical, robust, and rigorous calibration approach on the atmosphere-only model of the Department of Energy's Energy Exascale Earth System Model (E3SM) version 2. Our approach can be summarized into two main parts: (a) the training of a surrogate that predicts E3SM output in a fraction of the time compared to running E3SM, and (b) gradient-based parameter optimization. To train the surrogate, we generate a set of designed ensemble runs that span our input parameter space and use polynomial chaos expansions on a reduced output space to fit the E3SM output. We use this surrogate in an optimization scheme to identify values of the input parameters for which our model best matches gridded spatial fields of climate observations. To validate our choice of parameters, we run E3SMv2 with the optimal parameter values and compare prediction results to expertly-tuned simulations across 45 different output fields. This flexible, robust, and automated approach is straightforward to implement, and we demonstrate that the resulting model output matches present day climate observations as well or better than the corresponding output from expert tuned parameter values, while considering high-dimensional output and operating in a fraction of the time.
As the prospect of exceeding global temperature targets set forth in the Paris Agreement becomes more likely, methods of climate intervention are increasingly being explored. With this increased interest there is a need for an assessment process to understand the range of impacts across different scenarios against a set of performance goals in order to support policy decisions. The methodology and tools developed for Performance Assessment (PA) for nuclear waste repositories shares many similarities with the needs and requirements for a framework for climate intervention. Using PA, we outline and test an evaluation framework for climate intervention, called Performance Assessment for Climate Intervention (PACI) with a focus on Stratospheric Aerosol Injection (SAI). We define a set of key technical components for the example PACI framework which include identifying performance goals, the extent of the system, and identifying which features, events, and processes are relevant and impactful to calculating model output for the system given the performance goals. Having identified a set of performance goals, the performance of the system, including uncertainty, can then be evaluated against these goals. Using the Geoengineering Large Ensemble (GLENS) scenario, we develop a set of performance goals for monthly temperature, precipitation, drought index, soil water, solar flux, and surface runoff. The assessment assumes that targets may be framed in the context of risk-risk via a risk ratio, or the ratio of the risk of exceeding the performance goal for the SAI scenario against the risk of exceeding the performance goal for the emissions scenario. From regional responses, across multiple climate variables, it is then possible to assess which pathway carries lower risk relative to the goals. The assessment is not comprehensive but rather a demonstration of the evaluation of an SAI scenario. Future work is needed to develop a more complete assessment that would provide additional simulations to cover parametric and aleatory uncertainty and enable a deeper understanding of impacts, informed scenario selection, and allow further refinements to the approach.
Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol-cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation.