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Hazards from convective weather pose a serious threat to the contiguous United States (CONUS) every year. Previous studies have examined how future projected changes in climate might impact the frequency and intensity of convective weather using simulations with both convection-permitting regional models and coarser-grid climate and Earth system models. We build on this existing literature by utilizing a large-ensemble of historical and future Earth system model simulations to investigate the time evolution of the forced responses in large-scale convective environments and how those responses might be modulated by the rich spectrum of internal climate variability. Specifically, 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 convective environment over the eastern CONUS during March-June from 1870 to 2100. Results indicate that anthropogenically forced changes include increases in convective available potential energy and atmospheric stability (convective inhibition) throughout this century, while tropospheric vertical wind shear is projected to decrease across much of the CONUS. Internal climate variability on decadal and longer time scales can either significantly enhance or suppress these forced changes. The time evolution of two-dimensional histograms of convective indices suggests that future springtime convective environments over the eastern CONUS may, on average, be supportive of relatively less frequent and shorter-lived, but deeper and more intense convection. 

Abstract Earth system models are powerful tools to simulate the climate response to hypothetical climate intervention strategies, such as stratospheric aerosol injection (SAI). Recent simulations of SAI implement a tool from control theory, called a controller, 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°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 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. 

Abstract Predicting Pacific Decadal Oscillation (PDO) transitions and understanding the associated mechanisms has proven a critical but challenging task in climate science. As a form of decadal variability, the PDO is associated with both large‐scale climate shifts and regional climate predictability. We show that artificial neural networks (ANNs) predict PDO persistence and transitions with lead times of 12 months onward. Using layer‐wise relevance propagation to investigate the ANN predictions, we demonstrate that the ANNs utilize oceanic patterns that have been previously linked to predictable PDO behavior. For PDO transitions, ANNs recognize a build‐up of ocean heat content in the off‐equatorial western Pacific 12–27 months before a transition occurs. The results support the continued use of ANNs in climate studies where explainability tools can assist in mechanistic understanding of the climate system. 

Abstract We show that explainable neural networks can identify regions of oceanic variability that contribute predictability on decadal timescales in a fully coupled Earth‐system model. The neural networks learn to use sea‐surface temperature anomalies to predict future continental surface temperature anomalies. We then use a neural‐network explainability method called layerwise relevance propagation to infer which oceanic patterns lead to accurate predictions made by the neural networks. In particular, regions within the North Atlantic Ocean and North Pacific Ocean lend the most predictability for surface temperature across continental North America. We apply the proposed methodology to decadal variability, although the concept is generalizable to other timescales of predictability. Furthermore, while our approach focuses on predictable patterns of internal variability within climate models, it should be generalizable to observational data as well. Our study contributes to the growing evidence that explainable neural networks are important tools for advancing geoscientific knowledge. 

Abstract Many problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science “toolbox” for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change.