Predictability of climate tipping focusing on the internal variability of the Earth system

TL;DR

This paper investigates the predictability of climate tipping points by analyzing the role of internal variability and observation accuracy, highlighting the importance of high-quality data for reliable predictions.

Contribution

It introduces an OSSE framework using simplified models to quantify the observation accuracy needed for predicting climate tipping points.

Findings

High signal-to-noise ratio improves prediction accuracy.

Current observation networks may be insufficient for reliable climate tipping predictions.

Assimilating observations of internal variability enhances prediction skill.

Abstract

Prediction of climate tipping is challenging due to the lack of recent observation of actual climate tipping. Despite many previous efforts to accurately predict the existence and timing of climate tippings under specific climate scenarios, the predictability of climate tipping, the necessary conditions under which climate tipping can be predicted, has yet to be explored. In this study, the predictability of climate tipping is analyzed by Observation System Simulation Experiment (OSSE), in which the value of observation for prediction is assessed through the idealized experiment of data assimilation, using a simplified dynamic vegetation model and an Atlantic Meridional Overturning Circulation (AMOC) two box model. We find that the ratio of internal variability to observation error, or signal-to-noise ratio, should be large enough to accurately predict climate tippings. When observation can accurately resolve the internal variability of the system, assimilating these observations into process-based models can effectively improve the skill of predicting climate tippings. Our quantitative estimation of required observation accuracy to predict climate tipping implies that the existing observation network is not always sufficient to accurately project climate tipping.