A Data-Driven Exploration: Providing Early Feedback on Socioeconomic Mitigation Strategies for Climate Change
Abstract
The current rate of climate change is unprecedented in millennia and represents one of humanity’s most significantissues for the 21st century, with serious consequences for both civilization and the environment if no action is taken.To reduce the future impact of changes, coordinated international efforts such as those outlined in the Paris Agree-ment are required. However, mitigation and adaptation efforts should not only be extensive, but also be precise inorder to align international socioeconomic development with temperature targets as well as other societal and envi-ronmental sustainability goals. In order to inform about the future climate, extensive efforts through institutions suchas the Intergovernmental Panel on Climate Change (IPCC) have resulted in extensive scenario-based research withEarth system models (ESMs). This work forms the knowledge basis for information provided to the worlds decisionmakers.Currently, established methods for near-term feedback on mitigation efforts are mainly based on the global surfaceair temperature (GSAT) variable alone. By these methods, a clear separation of socioeconomic pathways does notemerge before 20 to 30 years after emission separation due to the internal variability of the climate system. Here, weuse a machine learning approach to create a separation of the climatic response from the socioeconomic developmentpathways based on ESM output data. This policy feedback strategy has not been described previously. Using 40realizations of ACCESS-ESM1.5 under SSP1-2.6 and SSP5-8.5, where emission starts to differ in 2015, we estimatethat a classification accuracy above 80 % is attainable by the appropriate feature-set/model combinations as early as2026 based on the mean accuracy across 50 random states. However, the uncertainty of estimated accuracy is greatlyreduced towards 2030–2040, indicating that real-world applications are not yet attainable. Our findings suggest thatclassification models trained on ESM-forecasts have the potential to become a powerful tool for providing early feedbackon how the climate system responds to mitigation efforts.