Abstract
AbstractDifferent strategies have been proposed in previous studies for monitoring the Atlantic meridional overturning circulation (AMOC). As well as arrays to directly monitor the AMOC strength, various fingerprints have been suggested to represent an aspect of the AMOC based on properties such as temperature and density. The additional value of fingerprints potentially includes the ability to detect a change earlier than a change in the AMOC itself, the ability to extend a time series back into the past, and the ability to detect crossing a threshold. In this study we select metrics that have been proposed as fingerprints in previous studies and evaluate their ability to detect AMOC changes in a number of scenarios (internal variability, weakening from increased greenhouse gases, weakening from hosing and hysteresis) in the eddy-permitting coupled climate model HadGEM3-GC2. We find that the metrics that perform best are the temperature metrics based on large-scale differences, the large-scale meridional density gradient, and the vertical density difference in the Labrador Sea. The best metric for monitoring the AMOC depends somewhat on the processes driving the change. Hence the best strategy would be to consider multiple fingerprints to provide early detection of all likely AMOC changes.
Highlights
The Atlantic meridional overturning circulation (AMOC) transports large amounts of heat in the Atlantic significantly influencing the climate
In particular for studying variability, we examine the potential of the metrics to be fingerprints of both the AMOC and Atlantic Ocean heat transport (AOHT)
There are some differences between metrics, but it is difficult to assess whether those differences are significant or due to internal variability without having ensembles for each forcing scenario
Summary
The Atlantic meridional overturning circulation (AMOC) transports large amounts of heat in the Atlantic significantly influencing the climate. We will use an eddy-permitting coupled climate model to test candidate metrics for detecting AMOC change in different scenarios: internal variability, changes from an AMOC weakening (forced by an increase in greenhouse gases or addition of freshwater), and the recovery after a weakening. In particular for studying variability, we examine the potential of the metrics to be fingerprints of both the AMOC and AOHT. Some of the experiments used here have been found to have a threshold beyond which the AMOC does not recover when freshwater is added (Jackson and Wood 2018a), we will not be covering the use of the time series statistics to indicate the approach of a threshold.
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