• Energy Research

Predictable Behavior Few internally forced large-scale atmospheric circulation patterns exhibit periodic behavior, and those that do are centered in the tropics. Identifying these periodic processes is important for understanding the dynamics of weather. Thompson and Barnes (p. 641 ) report the discovery of a 20- to 30-day periodicity in the atmospheric circulation in the Southern Hemisphere. The oscillation could potentially drive large-scale climate variability throughout much of the mid-latitude Southern Hemisphere.

Code for exploratory data analysis and ANN architecture for: Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations, Environmental Research Letters. https://doi.org/10.1088/1748-9326/acc81a {"references": ["Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations,\u00a0Environmental Research Letters.\u00a0https://doi.org/10.1088/1748-9326/acc81a"]} If you use this software, please cite it as below.

Abstract Stratospheric aerosol injection is a proposed form of solar climate invention (SCI) that could potentially reduce the amount of future warming from externally-forced climate change. However, more research is needed, as there are significant uncertainties surrounding the possible impacts of SCI, including unforeseen effects on regional climate patterns. In this study, we consider a climate model simulation of the deployment of stratospheric aerosols to maintain the global mean surface temperature at 1.5 ∘C above pre-industrial levels (ARISE-SAI-1.5). Leveraging two different machine learning methods, we evaluate when the effects of SCI would be detectable at regional scales. Specifically, we train a logistic regression model to classify whether an annual mean map of near-surface temperature or total precipitation is from future climate change under the influence of SCI or not. We then design an artificial neural network to predict how many years it has been since the deployment of SCI by inputting the regional maps from the climate intervention scenario. In both detection methods, we use feature attribution methods to spatially understand the forced climate patterns that are important for the machine learning model predictions. The differences in regional temperature signals are detectable in under a decade for most regions in the SCI scenario compared to greenhouse gas warming. However, the influence of SCI on regional precipitation patterns is more difficult to distinguish due to the presence of internal climate variability.

AbstractIt remains difficult to disentangle the relative influences of aerosols and greenhouse gases on regional surface temperature trends in the context of global climate change. To address this issue, we use a new collection of initial‐condition large ensembles from the Community Earth System Model version 1 that are prescribed with different combinations of industrial aerosol and greenhouse gas forcing. To compare the climate response to these external forcings, we adopt an artificial neural network (ANN) architecture from previous work that predicts the year by training on maps of near‐surface temperature. We then utilize layer‐wise relevance propagation (LRP) to visualize the regional temperature signals that are important for the ANN's prediction in each climate model experiment. To mask noise when extracting only the most robust climate patterns from LRP, we introduce a simple uncertainty metric that can be adopted to other explainable artificial intelligence (AI) problems. We find that the North Atlantic, Southern Ocean, and Southeast Asia are key regions of importance for the neural network to make its prediction, especially prior to the early‐21st century. Notably, we also find that the ANN predictions based on maps of observations correlate higher to the actual year after training on the large ensemble experiment with industrial aerosols held fixed to 1920 levels. This work illustrates the sensitivity of regional temperature signals to changes in aerosol forcing in historical simulations. By using explainable AI methods, we have the opportunity to improve our understanding of (non)linear combinations of anthropogenic forcings in state‐of‐the‐art global climate models.

Abstract While achieving net-zero emissions is likely to stabilize the long-term global temperature, the possibility of continued warming and extreme events could cause those efforts to be perceived as a failure. Leveraging decarbonization scenarios from multiple global climate models, we find that much of the world faces >30% probability of decadal warming after net-zero CO2 emissions are achieved, and most areas exhibit >10% probability of exceeding the most extreme hot and wet events of the decarbonization period. Further, substantial fractions of the global population and gross domestic product could experience post-net-zero warming, including hundreds of millions of people and trillions of dollars in the United States, China and India during the decade following net-zero. This likelihood highlights the possibility that some of the most populous, wealthy, and powerful regions may experience climatic conditions that could be perceived – at least in the near-term – to indicate that climate stabilization policies have failed.

AbstractEvaluating historical simulations from global climate models (GCMs) remains an important exercise for better understanding future projections of climate change and variability in rapidly warming regions, such as the Arctic. As an alternative approach for comparing climate models and observations, we set up a machine learning classification task using a shallow artificial neural network (ANN). Specifically, we train an ANN on maps of annual mean near‐surface temperature in the Arctic from a multi‐model large ensemble archive in order to classify which GCM produced each temperature map. After training our ANN on data from the large ensembles, we input annual mean maps of Arctic temperature from observational reanalysis and sort the prediction output according to increasing values of the ANN's confidence for each GCM class. To attempt to understand how the ANN is classifying each temperature map with a GCM, we leverage a feature attribution method from explainable artificial intelligence. By comparing composites from the attribution method for every GCM classification, we find that the ANN is learning regional temperature patterns in the Arctic that are unique to each GCM relative to the multi‐model mean ensemble. In agreement with recent studies, we show that ANNs can be useful tools for extracting regional climate signals in GCMs and observations.

Current global actions to reduce greenhouse gas emissions are very likely to be insufficient to meet the climate targets outlined under the Paris Agreement. This motivates research on possible methods for intervening in the Earth system to minimize climate risk while decarbonization efforts continue. One such hypothetical climate intervention is stratospheric aerosol injection (SAI), where reflective particles would be released into the stratosphere to cool the planet by reducing solar insolation. The climate response to SAI is not well understood, particularly on short-term time horizons frequently used by decision makers and planning practitioners to assess climate information. This knowledge gap limits informed discussion of SAI outside the scientific community. We demonstrate two framings to explore the climate response in the decade after SAI deployment in modeling experiments with parallel SAI and no-SAI simulations. The first framing, which we call a snapshot around deployment, displays change over time within the SAI scenarios and applies to the question “What happens before and after SAI is deployed in the model?” The second framing, the intervention impact, displays the difference between the SAI and no-SAI simulations, corresponding to the question “What is the impact of a given intervention relative to climate change with no intervention?” We apply these framings to annual mean 2-meter temperature, precipitation, and a precipitation extreme in the first two experiments to use large ensembles of Earth system models that comprehensively represent both the SAI injection process and climate response, and connect these results to implications for other climate variables.

Abstract In the coming decades, climate change is expected to dramatically affect communities worldwide, altering the patterns of many ambient exposures and disasters, including extreme temperatures, heat waves, wildfires, droughts, and floods. These exposures, in turn, can affect risks for a variety of human diseases and health outcomes. Climate epidemiology plays an important role in informing policy related to climate change and its threats to public health. Climate epidemiology leverages deep, integrated collaborations between epidemiologists and climate scientists to understand the current and potential future impacts of climate-related exposures on human health. A variety of recent and ongoing developments in climate science are creating new avenues for epidemiologic contributions. Here, we discuss the contributions of climate epidemiology and describe some key current research directions, including research to better characterize uncertainty in climate health projections. We end by outlining 3 developing areas of climate science that are creating opportunities for high-impact epidemiologic advances in the near future: 1) climate attribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions.