Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation

TL;DR

This paper develops an uncertainty quantification method for critical slowing down analysis in Earth system observations, addressing biases from data gaps and processing, and applies it to Atlantic circulation datasets.

Contribution

It introduces a novel approach to propagate dataset uncertainties into CSD analysis and develop significance tests, improving reliability of stability assessments.

Findings

Dataset properties and gap filling can cause false CSD signals.

CSD indicators in the North Atlantic remain significant after accounting for uncertainties.

The method enhances robustness of CSD detection in observational data.

Abstract

Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.