Detecting Changes in Global Extremes Under the GLENS‐SAI Climate Intervention Strategy

Abstract

Significant advances in the scientific understanding of climate change over the past several decades have made it clear that there has been a change in climate that goes beyond the range of natural variability (e.g., Santer, Painter, Mears, et al., 2013, Santer, Painter, Bonfils et al., 2013Bonfils et al., 2020). The culprit is the astonishing rate at which greenhouse gas concentrations have increased in the atmosphere, mostly through the burning of fossil fuels and changes in land use, such as those associated with agriculture and deforestation (IPCC, 2021). Greenhouse gasses are relatively transparent to incoming solar radiation while they absorb and reemit outgoing infrared radiation. The result is that more energy stays in the global climate system, raising not only temperature but also producing many other direct and indirect changes in the climate system, including changes in the frequency and intensity of extreme events (NASEM, 2016).Heat waves, for instance, are exceedingly important to human systems and infrastructure, as well as natural systems. People and ecosystems are adapted to a range of natural weather variations, but it is the extremes of weather and climate that exceed tolerances (e.g., Curtis et al., 2017;Ummenhofer & Meehl, 2017). Widespread changes in temperature extremes have been observed over the last 50 years. In particular, the number of heat waves globally has increased, and there have been widespread increases in the numbers of warm nights.Days with frost as well as cold days and cold nights have become rarer (IPCC, 2021). Changes are also occurring in the amount, intensity, frequency, and type of precipitation in ways that are also consistent with a warming

Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations

“…Despite a much weaker external forcing scenario than was considered in Barnes et al (2022), we find similar results for the accurate detection of temperature and precipitation impacts over global lands areas by the logistic regression model. This occurs within approximately the first decade of SCI initiation.…”

“…We find strikingly similar spatial patterns of higher signal-to-noise between many of the same regions with positive contributions for the logistic regression model. In agreement with Barnes et al (2022), this suggests that the logistic regression model is learning patterns of temperature signals to detect the influence of SCI. Moreover, we note that not all areas of higher positive contributions are associated with higher signal-to-noise, such as for positive contributions across Finally, we repeat this exercise by separately training logistic regression models for the other 8 regions using temperature and precipitation.…”

“…GLENS was simulated with several design simplifications in its implementation of SCI and considered an extreme future greenhouse gas emissions scenario (Representative Concentration Pathway; RCP8.5) . Barnes et al (2022) recently examined the emergence of SCI impacts on climate extremes in GLENS and found that a simple machine learning method could detect whether a global map of extreme precipitation or extreme temperature came from a world under the influence of SCI or RCP8.5 alone in less than two decades. However, the magnitude of the forced climate responses within GLENS may be unrealistic due to the excessive amount of aerosols needed by the end of the 21st century to o↵set warming under RCP8.5 (Burgess et al, 2020;Peters and Hausfather, 2020).…”

“…To address this question, an experiment called the Assessing Responses and Impacts of SCI on the Earth system with SAI (ARISE-SAI-1.5; has recently been conducted using a lower future emissions scenario (Shared Socioeconomic Pathway; SSP2-4.5). In this study, we address the question of the detectability and emergence of climate signals in ARISE-SAI-1.5 by extending the framework of Barnes et al (2022) through several di↵erent ways. First, we design two separate machine learning methods to consider whether we can detect SCI impacts on regional climate, and if so, how long has it been since the initial aerosol injection.…”

Assessing Outcomes in Stratospheric Aerosol Injection Scenarios Shortly After Deployment

“…The noise from internal variability has important implications for three key open problems highlighted by NASEM (2021): detection, monitoring, and social perception of any climate intervention. Machine learning methods have shown promise for rapid detection of the surface climate response to SAI despite the influence of internal variability(Barnes et al, 2022). Improved understanding of the data most useful to detect SAI could help constrain potential observational platforms for longterm monitoring.…”