Hazards from convective weather pose a serious threat to the continental United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of severe weather using simulations with both convection-permitting regional models and coarser climate and Earth system models. However, many of these studies have been limited to single representations of the future climate state with little insight into the uncertainty of how the population of convective storms may evolve. To thoroughly explore this aspect, a large ensemble of Earth system model simulations was implemented to investigate how forced responses in large-scale convective environments might be modulated by internal climate variability. Daily data from an ensemble of 50 simulations with the most recent version of the Community Earth System Model was used to examine changes in the severe weather environment over the eastern CONUS during boreal spring from 1870-2100. Results indicate that forced changes in convective environments were small between 1870 and 1990, but throughout the 21st century, convective available potential energy and atmospheric stability (convective inhibition) is projected to increase while 0-6 km vertical wind shear decreases. Internal climate variability can either significantly enhance or suppress these forced changes. The time evolution of bivariate distributions of convective indices illustrates that future springtime convective environments over the eastern CONUS will be characterized by relatively less frequent, less organized, but deeper, more intense convection. Future convective environments will also be less supportive of the most severe convective modes and associated hazards.

Earth system models are a powerful tool to simulate the response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement tools from control theory, called “controllers”, to determine the quantity of aerosol to inject into the stratosphere to reach or maintain specified global temperature targets, such as limiting global warming to 1.5\textdegree C above pre-industrial temperatures. This work explores how internal (unforced) climate variability can impact controller-determined injection amounts using the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) simulations. Since the ARISE-SAI controller determines injection amounts by comparing global annual-mean surface temperature to predetermined temperature targets, internal variability that impacts temperature can impact the total injection amount as well. Using an offline version of the ARISE-SAI controller and data from CESM2 earth system model simulations, we quantify how internal climate variability and volcanic eruptions impact injection amounts. While idealized, this approach allows for the investigation of a large variety of climate states without additional simulations and can be used to attribute controller sensitivities to specific modes of internal variability.

Stratospheric aerosol injection (SAI) has been proposed as a possible complementary solution to limit global warming and its societal consequences. However, the climate impacts of such intervention remain unclear. Here, we introduce an explainable artificial intelligence (XAI) framework to quantify how distinguishable an SAI climate might be from a pre-deployment climate. A suite of neural networks is trained on Earth system model data to learn to distinguish between pre- and post-deployment periods across a variety of climate variables. The network accuracy is analogous to the “climate distinguishability” between the periods, and the corresponding distinctive patterns are identified using XAI methods to gain insights into the emerging signals from SAI. For many variables, the two periods are less distinguishable under SAI than under a no-SAI scenario, suggesting that the specific intervention modeled decelerates future climatic changes. Other climate variables for which the intervention has negligible effect are also highlighted.

A document by Ariel L Morrison. Click on the document to view its contents.

Climate and Earth system models are important tools to assess the benefits and risks of stratospheric aerosol injection (SAI) relative to those associated with anthropogenic climate change. A “controller” algorithm has been used to specify injection amounts of sulfur dioxide in SAI experiments performed with the Community Earth System Model (CESM). The experiments are designed to maintain specific temperature targets, such as limiting global mean temperature to 1.5ºC above the pre-industrial level. However, the influence of natural climate variability on the injection amount has not been extensively documented. Our study reveals that more than 70% of the year-to-year variation in the total injection amount (excluding the long-term trend) in CESM SAI experiments is attributed to the El Niño-Southern Oscillation (ENSO). A simplified statistical model further suggests that the intrinsic, lagged response of the controller to the climate can increase the variance of global mean temperature in the model simulations.

Continued climate warming, together with the overall evaluation and implementation of a range of climate mitigation and adaptation approaches, has prompted increasing research into proposed solar climate intervention (SCI) methods, such as stratospheric aerosol injection (SAI). SAI would use aerosols to reflect a small amount of incoming solar radiation away from Earth to stabilize or reduce future warming due to increasing greenhouse gas concentrations. Research into the possible risks and benefits of SAI relative to the risks from climate change is emerging. There is not yet, however, an adequate understanding of how SAI might impact human and natural systems. For instance, little to no research to date has examined how SAI might impact environmental conditions critical to the formation of severe convective weather over the United States (U.S.). This study uses ensembles of Earth system model simulations of future climate change, with and without hypothetical SAI deployment, to examine possible future changes in thermodynamic and kinematic parameters critical to the formation of severe weather during convectively active seasons over the U.S. Results show that simulated forced changes in thermodynamic parameters are significantly reduced under SAI relative to a no-SAI world, while simulated changes in kinematic parameters are more difficult to distinguish. Also, unforced internal climate variability is likely to significantly modulate the projected forced climate changes over large regions of the U.S.

As anthropogenic activities continue to drive increases in extreme events, the fundamental solution of reducing greenhouse gas emissions remains elusive. Thus, there is growing interest in stratospheric aerosol injection (SAI) to offset some of the most dangerous consequences of climate change. If SAI was deployed at a global scale, it would likely be easy to detect by some metrics. However, the detectability of SAI on extreme events might be more difficult, given the presence of natural climate variability. We examine this question in climate model simulations of SAI. Specifically, we train a logistic regression model to predict whether a map of global extremes came from climate simulations with or without SAI. The timing of accurate predictions is a quantification of the time to detection of SAI impacts. We find that regional changes in extreme temperature and precipitation are robustly detected within 1 and 15 years of initial SAI injection, respectively.