The study identifies critical slowing down as an early warning signal for abrupt climate change. By analyzing eight ancient abrupt climate shifts, the researchers found that all were preceded by a characteristic slowing down of fluctuations, measured as increased autocorrelation. This slowing down is a hallmark of tipping points, indicating that past abrupt shifts were associated with crossing critical thresholds. The mechanism causing slowing down is inherent to tipping points, suggesting that detecting slowing down can serve as a universal early warning signal for catastrophic change. This is particularly important as tipping points in ecosystems and complex systems are difficult to predict. The study shows that slowing down can be detected by analyzing the autocorrelation of time series data, which increases as a system approaches a tipping point. The results are supported by model simulations, which also show increased slowing down before abrupt transitions. The findings imply that critical slowing down is a universal property of systems approaching a tipping point, providing independent empirical evidence for the existence of tipping points in the climate system. The study highlights the importance of detecting slowing down as a potential indicator of future climate change, especially given the difficulty in predicting tipping points. The results have significant implications for climate science, suggesting that internal feedbacks can drive rapid changes once a critical threshold is reached. The study also acknowledges the challenges in detecting slowing down, including the need for long, high-resolution time series data and proper detrending. Overall, the study provides a promising perspective for predicting future climate change by identifying slowing down as an early warning signal.The study identifies critical slowing down as an early warning signal for abrupt climate change. By analyzing eight ancient abrupt climate shifts, the researchers found that all were preceded by a characteristic slowing down of fluctuations, measured as increased autocorrelation. This slowing down is a hallmark of tipping points, indicating that past abrupt shifts were associated with crossing critical thresholds. The mechanism causing slowing down is inherent to tipping points, suggesting that detecting slowing down can serve as a universal early warning signal for catastrophic change. This is particularly important as tipping points in ecosystems and complex systems are difficult to predict. The study shows that slowing down can be detected by analyzing the autocorrelation of time series data, which increases as a system approaches a tipping point. The results are supported by model simulations, which also show increased slowing down before abrupt transitions. The findings imply that critical slowing down is a universal property of systems approaching a tipping point, providing independent empirical evidence for the existence of tipping points in the climate system. The study highlights the importance of detecting slowing down as a potential indicator of future climate change, especially given the difficulty in predicting tipping points. The results have significant implications for climate science, suggesting that internal feedbacks can drive rapid changes once a critical threshold is reached. The study also acknowledges the challenges in detecting slowing down, including the need for long, high-resolution time series data and proper detrending. Overall, the study provides a promising perspective for predicting future climate change by identifying slowing down as an early warning signal.