Stratospheric aerosol injection (SAI has been proposed as a potential method for mitigating risks and impacts associated with anthropogenic climate change. One such risk is widespread permafrost thaw and associated carbon release. While permafrost has been shown to stabilize under different SAI scenarios, natural variability may lead to a wide range of projected climate futures under SAI. Here we use the 10-member ensemble from the ARISE-SAI-1.5 simulations to assess the spread in projected active layer depth and permafrost temperature across boreal permafrost soils and specifically in four peatland and Yedoma regions. The forced response in active layer depth and permafrost temperature quickly diverge between an SAI and non-SAI world, but individual ensemble members overlap for several years following SAI deployment. Projected permafrost variability may mask the forced response to SAI and make it difficult to detect if and when SAI is stabilizing permafrost in any single realization. We find that it may take more than a decade of SAI deployment to detect the effects of SAI on permafrost temperature and almost 30 years to detect its effects on active layer depth. Not only does natural variability make it more difficult to detect SAI’s influence, it could also affect the likelihood of reaching a permafrost tipping point. In some realizations, SAI fails to prevent a tipping point that is also reached in a non-SAI world. Our results underscore the importance of accounting for natural variability in assessments of SAI’s potential influence on the climate system.

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.

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 from 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.

The week 3-6 averaged winter North Atlantic Oscillation (NAO) predictive skill in a state-of-the-art coupled climate prediction system is attributed to two principle sources: upper and lower boundary conditions linked to the stratosphere and El Niño-Southern Oscillation (ENSO), respectively. A 20-member ensemble of 45-day reforecasts over 1999-2015 is utilized, together with uninitialized simulations with the atmospheric component of the prediction system forced with observed radiative forcing and lower boundary conditions. NAO forecast skill for lead times out to six weeks is higher following extreme stratospheric polar vortex conditions (weak and strong vortex events) compared to neutral states. Enhanced week 3-6 NAO predictive skill for weak vortex events results primarily from stratospheric downward coupling to the troposphere, while enhanced skill for strong vortex events can be partly attributed to lower boundary forcing related to the ENSO phenomenon. Implications for forecast system development and improvement are discussed.

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 interpretable neural networks are important tools for advancing geoscientific knowledge.