Even as the IPCC warns humanity that our “window of opportunity to secure a livable and sustainable future for all” is rapidly closing, new climate models driven by machine learning suggest our chances of halting global average temperature increases of more than 1.5°C are slim.
Using neural networks, Stanford researchers Noah S. Diffenbaugh and Elizabeth A. Barnes have pinpointed 2033-2035 as the years when the Paris Agreement is most likely to be exceeded. More ominously, they write, “our results suggest a higher likelihood of reaching 2°C in the Low scenario than indicated in some previous assessments,” which will in turn lead to catastrophic and potentially irreversible impacts, including pushing nearly half the planet into chronic water scarcity.
The authors’ sobering conclusion has profound implications for both climate modeling and global adaptation efforts, says Climate Alpha chief scientific officer Michael Ferrari. “This study supports the idea that the 2° scenario by mid-century may be the best-case scenario, and that climate models have actually been a little conservative regarding the timing and severity of potential impacts of climate change. It’s also a compelling use case on how to utilize artificial neural networks towards better assessing and refining climate model output.”
Climate Alpha’s own AI-powered platform combines standard climate models with socio-economic variables to model climate impacts under different scenarios, offering investors, public officials, and homeowners concrete risk-adjusted valuations. We believe tools such as our will be essential in guiding decision-makers on how, where, and when to invest in urgently needed climate adaptation efforts.
“Mitigation efforts are crucial,” Ferrari adds, “but we also need to be more responsible about identifying and addressing the adaptation side of the equation.”
Visit www.climatealpha.ai to learn more about our approach to AI and how we can help you invest in the most resilient geographies of a rapidly warming world.
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